@article {pmid40819020,
year = {2025},
author = {Shao, X and Chung, RS and Cavaleri, JM and Del Campo-Vera, RM and Parra, M and Sundaram, S and Zhang, S and Surabhi, A and McGinn, RJ and Liu, CY and Kellis, SS and Lee, B},
title = {Directional hand movement can be classified from insular cortex SEEG signals using recurrent neural networks and high-gamma band features.},
journal = {Scientific reports},
volume = {15},
number = {1},
pages = {29993},
pmid = {40819020},
issn = {2045-2322},
abstract = {Motor BCIs, with the help of Artificial Intelligence (AI) and machine learning, have shown promise in decoding neural signals for restoring motor function. Structures beyond motor cortex have provided additional sources for movement signals. New evidence points to the role of the insula in motor control, specifically directional hand-movements. In this study, we applied AI and machine learning techniques to decode directional hand-movements from high-gamma band (70-200 Hz) activity in the insular cortex. Seven participants with medication-resistant epilepsy underwent stereo electroencephalographic (SEEG) implantation of depth electrodes for seizure monitoring in the insula. SEEG data were sampled throughout a cued motor task involving three conditions: left-hand movement, right-hand movement, or no movement. Neural signal processing focused on high-gamma band activity. Demixed Principal Component Analysis (dPCA) was used for dimension reduction (d = 10) and feature extraction from the time-frequency analysis. For movement classification, we implemented a bidirectional Long Short-Term Memory (LSTM) architecture with a single layer, utilizing the capacity to process temporal sequences in forward and back directions for optimal decoding of movement direction. Our findings revealed robust directional-specific high-gamma modulation within the insular cortex during motor execution. Temporal decomposition through dPCA demonstrated distinct spatiotemporal patterns of high-gamma activity across movement conditions. Subsequently, LSTM networks successfully decoded these condition-specific neural signatures, achieving a classification accuracy of 72.6% ± 13.0% (mean ± SD), which significantly exceeded chance-level performance of 33.3% (p < 0.0001, n = 16 sessions). Furthermore, we identified a strong negative correlation between temporal distance of training-testing sessions and decoding performance (r = -0.868, p < 0.0001), indicating temporal difference of the neural representations. Our study highlights the potential role of deep brain structures, such as the insula, in conditional movement discrimination. We demonstrate that LSTM networks and high-gamma band analysis can advance the understanding of neural mechanisms underlying movement. These insights may pave the way for improvements in SEEG-based BCI.},
}

@article {pmid40818100,
year = {2025},
author = {Wei, W and Li, C and Li, W and Jiang, M and Zhang, X and Xing, L and Qian, Z and Jin, X},
title = {Study of a non-water-cooled microwave ablation needle based on a vacuum needle rod to achieve carbonization-free operation: design, simulation, and experimental research.},
journal = {Minimally invasive therapy & allied technologies : MITAT : official journal of the Society for Minimally Invasive Therapy},
volume = {},
number = {},
pages = {1-12},
doi = {10.1080/13645706.2025.2543894},
pmid = {40818100},
issn = {1365-2931},
abstract = {BACKGROUND: At present, the microwave ablation needle used in clinic needs to add water circulation in the needle rod to reduce the rod temperature. However, the water circulation will take away a lot of heat during the ablation process, which requires increasing the ablation dose to achieve the expected thermal coagulation target volume. This undoubtedly increases the risk of carbonization.

METHODS: A design scheme of non-water-cooled microwave ablation needle based on double-layer vacuum structure was proposed. Two types of non-water-cooled microwave ablation needles with long and short needles were designed, and the ablation simulation was carried out by establishing the finite element simulation model.

RESULTS: Simulation and experimental results indicate that, at 20 W power, the long-needle vacuum tube ablation needle can create a carbonization-free solidification zone with a length of 34 mm after 180 s of ablation, whereas the short-needle vacuum tube ablation needle requires 300 s to form a similar zone with a length of 30 mm. Additionally, the axial ratio of the solidification zone created by the long-needle vacuum tube ablation needle exceeds that of the short-needle one. Consequently, the long-needle vacuum tube ablation needle is more apt for creating a larger solidification zone with minimal carbonization, while also achieving a more spherical shape.By comparing the ablation effects of long needle vacuum tube ablation needle and ky-2450b1 under low power,It is verified that the vacuum tube non-water-cooled ablation needle can ensure the effective ablation volume and non carbonization ablation under low-power and short-time ablation, which provides an important technical scheme for clinical optimization of the therapeutic effect of microwave ablation.

CONCLUSIONS: The LPH-W-F-MWA is more adept at creating a larger coagulation zone with minimal carbonization, achieving a more spherical shape to a greater extent. This ensures both an effective ablation volume and char-free ablation, offering a crucial technical solution for optimizing the therapeutic effect of MWA in clinical settings.},
}

@article {pmid40817330,
year = {2025},
author = {Jiang, L and Genon, S and Ye, J and Zhu, Y and Wang, G and He, R and Valdes-Sosa, PA and Wan, F and Yao, D and Eickhoff, SB and Dong, D and Li, F and Xu, P},
title = {Gene transcription, neurotransmitter, and neurocognition signatures of brain structural-functional coupling variability.},
journal = {Nature communications},
volume = {16},
number = {1},
pages = {7623},
pmid = {40817330},
issn = {2041-1723},
abstract = {The relationship between brain structure and function, known as structural-functional coupling (SFC), is highly dynamic. However, the temporal variability of this relationship, referring to the fluctuating extent to which functional profiles interact with anatomy over time, remains poorly elucidated. Here, we propose a framework to quantify SFC temporal variability and determine its neurocognitive map, genetic architecture, and neurochemical basis in 1206 healthy human participants. Results reveal regional heterogeneity in SFC variability and a composite emotion dimension co-varying with variability patterns involving the dorsal attention, somatomotor, and visual networks. The transcriptomic signatures of SFC variability are enriched in synapse- and cell cycle-related biological processes and implicated in emotion-related disorders. Moreover, regional densities of serotonin, glutamate, γ-aminobutyric acid, and opioid systems are predictive of SFC variability across the cortex. Collectively, SFC variability mapping provides a biologically plausible framework for understanding how SFC fluctuates over time to support macroscale neurocognitive specialization.},
}

@article {pmid40816538,
year = {2025},
author = {Kong, K and Wang, J and Li, M and Zhang, T and Qi, E and Zhao, Q},
title = {Action sequence guidance with exposure trajectory technology improves performance of motor imagery-based brain-computer interface.},
journal = {Journal of neuroscience methods},
volume = {},
number = {},
pages = {110553},
doi = {10.1016/j.jneumeth.2025.110553},
pmid = {40816538},
issn = {1872-678X},
abstract = {BACKGROUND: The paradigms greatly influence the performance of motor imagery (MI)-based brain-computer interfaces (BCI) by guiding subjects to imagine. How to make the guidance clear and intuitive is important for MI-BCI to improve performance.

NEW METHODS: This study proposes a novel MI-BCI paradigm based on action sequence (AS) guidance, which visualizes and choreographs sequential actions to support motor imagery. In a drawing task, the action exposure trajectory technique presents a gray nib at the starting point of the next stroke while the current stroke is being drawn, highlighting the order and details of the movement. Ten subjects participated in offline and online experiments under both AS and traditional MI conditions. EEG activation regarding multiple frequencis and periods, and MI-BCI performance are evaluated.

RESULTS: The AS paradigm evokes more significant ERD/ERS features, and improves offline and online BCI accuracies and information transfer rates to 85.69%, 78.77%, and 15.60 bits/min, which are 8.37%, 7.95%, and 7.13 bits/min higher than the traditional paradigm. In addition, the subjects are demonstrated more comfortable subjective feelings.

The AS paradigm offers clearer and more intuitive guidance, enhances EEG feature activation, and significantly improves MI-BCI performance in both offline and online experiments.

CONCLUSIONS: Dynamic action sequences action with exposure trajectory technique could enhance the subject's brian activation by offering richer content and more intuitive guidance, providing a new way for prompting BCI performance.},
}

@article {pmid40816265,
year = {2025},
author = {Kunz, EM and Abramovich Krasa, B and Kamdar, F and Avansino, DT and Hahn, N and Yoon, S and Singh, A and Nason-Tomaszewski, SR and Card, NS and Jude, JJ and Jacques, BG and Bechefsky, PH and Iacobacci, C and Hochberg, LR and Rubin, DB and Williams, ZM and Brandman, DM and Stavisky, SD and AuYong, N and Pandarinath, C and Druckmann, S and Henderson, JM and Willett, FR},
title = {Inner speech in motor cortex and implications for speech neuroprostheses.},
journal = {Cell},
volume = {},
number = {},
pages = {},
doi = {10.1016/j.cell.2025.06.015},
pmid = {40816265},
issn = {1097-4172},
abstract = {Speech brain-computer interfaces (BCIs) show promise in restoring communication to people with paralysis but have also prompted discussions regarding their potential to decode private inner speech. Separately, inner speech may be a way to bypass the current approach of requiring speech BCI users to physically attempt speech, which is fatiguing and can slow communication. Using multi-unit recordings from four participants, we found that inner speech is robustly represented in the motor cortex and that imagined sentences can be decoded in real time. The representation of inner speech was highly correlated with attempted speech, though we also identified a neural "motor-intent" dimension that differentiates the two. We investigated the possibility of decoding private inner speech and found that some aspects of free-form inner speech could be decoded during sequence recall and counting tasks. Finally, we demonstrate high-fidelity strategies that prevent speech BCIs from unintentionally decoding private inner speech.},
}

@article {pmid40815626,
year = {2025},
author = {Padrão, N and Gregoricchio, S and Eickhoff, N and Dong, J and Luzietti, L and Bossi, D and Severson, TM and Siefert, J and Calcinotto, A and Buluwela, L and Donaldson Collier, M and Ali, S and Young, L and Theurillat, JP and Varešlija, D and Zwart, W},
title = {TRIM24 as a therapeutic target in endocrine treatment-resistant breast cancer.},
journal = {Proceedings of the National Academy of Sciences of the United States of America},
volume = {122},
number = {33},
pages = {e2507571122},
doi = {10.1073/pnas.2507571122},
pmid = {40815626},
issn = {1091-6490},
support = {813599//EC | ERC | HORIZON EUROPE European Research Council (ERC)/ ; 813599//EC | ERC | HORIZON EUROPE European Research Council (ERC)/ ; 813599//EC | ERC | HORIZON EUROPE European Research Council (ERC)/ ; 9171640//KWF Kankerbestrijding (DCS)/ ; 016.156.401//ZonMw (Netherlands Organisation for Health Research and Development)/ ; 2014MayPR234//Breast Cancer Now (BCN)/ ; C37/A18784//Cancer Research UK (CRUK)/ ; 20/FFP-P/8597//Research Ireland/ ; 23/SPP/11783//Research Ireland/ ; 2019AugSF1310//Breast Cancer Now (BCN)/ ; 18239A01//Breast Cancer Ireland (BCI)/ ; 19/FFP/6443//Research Ireland/ ; 23/SPP/11783//Research Ireland/ ; 2021JulyPCC1460//Breast Cancer Now (BCN)/ ; },
abstract = {While Estrogen receptor alpha (ERα)+ breast cancer treatment is considered effective, resistance to endocrine therapy is common. Since ERα is still the main driver in most therapy-resistant tumors, alternative therapeutic strategies are needed to disrupt ERα transcriptional activity. In this work, we position TRIM24 as a therapeutic target in endocrine resistance, given its role as a key component of the ERα transcriptional complex. TRIM24 interacts with ERα and other well-known ERα cofactors to facilitate ERα chromatin interactions and allows for maintenance of active histone marks including H3K23ac and H3K27ac. Consequently, genetic perturbation of TRIM24 abrogates ERα-driven transcriptional programs and reduces tumor cell proliferation capacity. Using a recently developed degrader targeting TRIM24, ERα-driven transcriptional output and growth were blocked, effectively treating not only endocrine-responsive cell lines but also drug-resistant derivatives thereof as well as cell line models bearing activating ESR1 point mutations. Finally, using human tumor-derived organoid models, we could show the efficacy of TRIM24 degrader in the endocrine-responsive and -resistant setting. Overall, our study positions TRIM24 as a central component for the integrity and activity of the ERα transcriptional complex, with degradation-mediated perturbation of TRIM24 as a promising therapeutic avenue in the treatment of primary and endocrine resistance breast cancer.},
}

@article {pmid40815349,
year = {2025},
author = {Li, Z and Li, M and Yang, Y},
title = {Motor imagery decoding network with multisubject dynamic transfer.},
journal = {Brain informatics},
volume = {12},
number = {1},
pages = {20},
pmid = {40815349},
issn = {2198-4018},
support = {Nos. 62173010//National Natural Science Foundation of China/ ; },
abstract = {Brain computer interface (BCI) provides a promising and intelligent rehabilitation method for motor function, and it is crucial to acquire the patient's movement intentions accurately through decoding motor imagery EEG (MI-EEG) . Because of the inter-individual heterogeneity, the decoding model should demonstrate dynamic adaptation abilities.Domain adaptation (DA) is effective to enhance model generalization by reducing the inherent distribution difference among subjects. However, the existing DA methods usually mix the multiple source domains into a new domain, the resulting multi-source domain conflict may cause negative transfer. In this paper, we propose a multi-source dynamic conditional domain adaptation network (MSDCDA). First, a multi-channel attention block is employed in the feature extractor to focus on the channels relevant to the corresponding MI task. Subsequently, the shallow spatial-temporal features are extracted using a spatial-temporal convolution block. And a dynamic residual block is applied in the feature extractor to dynamically adapt specific features of each subject to alleviate conflicts among multiple source domains since each domain is viewed as a distribution of electroencephalogram (EEG) signals. Furthermore, we employ the Margin Disparity Discrepancy (MDD) as the metric to achieve conditional distribution domain adaptation between the source and target domains through adversarial learning with an auxiliary classifier. MSDCDA achieved accuracies of 78.55 % and 85.08 % on Datasets IIa and IIb of BCI Competition IV, respectively. Our experimental results demonstrate that MSDCDA can effectively address multi-source domain conflicts and significantly enhance the decoding performance of target subjects. This study positively facilitates the application of BCI based on motor function rehabilitation.},
}

@article {pmid40813381,
year = {2025},
author = {Hendry, MF and Cruz-Garza, JG and Delgado-Jiménez, EA and Lima-Carmona, YE and Aguilar-Herrera, AJ and Ramírez-Moreno, MA and Ravindran, AS and Paek, AY and Smith, M and Kan, J and Fors, M and Alam, A and Liu, R and Noble, A and Contreras-Vidal, JL},
title = {Mobile Brain-Body Imaging and Visual Data of Theatrical Actors During Rehearsal and Performance.},
journal = {Scientific data},
volume = {12},
number = {1},
pages = {1421},
pmid = {40813381},
issn = {2052-4463},
support = {1757949//National Science Foundation (NSF)/ ; 2137255//National Science Foundation (NSF)/ ; 2412731//National Science Foundation (NSF)/ ; },
abstract = {This longitudinal Mobile Brain-Body Imaging dataset was acquired during six rehearsal sessions and three public performances of a scene from a play with highly emotional components. Three student actor dyads (N=6), one theatre director (N=1) and three audience members (N=3) participated in this study. The MoBI data recorded includes mobile electroencephalography, electrooculography, blood volume pulse, heart rate, body temperature, electrodermal activity, triaxial arm and head acceleration. The visual data includes five streams of video. This article describes the experimental setup, the multi-modal data streams acquired using a hyperscanning methodology, and an assessment of the data quality.},
}

@article {pmid40813218,
year = {2025},
author = {Xie, J and Xu, G and Yang, Z and Su, H and Zhang, S},
title = {Modeling multiscale time-frequency complex networks on Riemannian manifolds for motor imagery BCI classification with graph convolutional networks.},
journal = {ISA transactions},
volume = {},
number = {},
pages = {},
doi = {10.1016/j.isatra.2025.07.058},
pmid = {40813218},
issn = {1879-2022},
abstract = {Motor imagery brain-computer interface (MI-BCI) classification faces challenges such as low decoding accuracy and difficulty in capturing the spatiotemporal dynamics of EEG signals. The use of Riemannian geometry classifiers for this task has become one of the most popular classification methods. However, current Riemannian geometry classifiers typically compute the covariance matrix over a period of time to capture spatial features, neglecting the multiscale characteristics of EEG signals in both time and frequency, which limits their classification performance. To address these issues, this study proposes a novel framework. Specifically, we introduce graph convolutional network (GCN) on Riemannian geometry (GR) to process multiscale networks, using virtual nodes to capture global topological features and integrating spatial features across time and frequency domains. This method significantly enhances the feature extraction capability of Riemannian geometry classifiers. The proposed method was validated on three public datasets, with average classification accuracies of 91.87 % ± 7.33 %, 87.96 % ± 7.6 %, and 82.50 % ± 7.74 %, respectively. Ablation experiments show that, compared to traditional single-scale methods, the average classification accuracy improved by 9.85 %, highlighting the effectiveness and versatility of the proposed method. This research provides a new perspective for multiscale EEG signal analysis and advances the development of motor imagery BCI classification technology.},
}

@article {pmid40811166,
year = {2025},
author = {Zhang, X and Zheng, W and Li, Z and Yang, Y and Liu, W and Cai, H and Zhu, J and Liu, J and Hu, B and Dong, Q},
title = {Constraint-Driven Causal Representation Learning for Vigilance Robust Estimation in Brain-Computer Interface.},
journal = {IEEE transactions on neural networks and learning systems},
volume = {PP},
number = {},
pages = {},
doi = {10.1109/TNNLS.2025.3594434},
pmid = {40811166},
issn = {2162-2388},
abstract = {Vigilance estimation is a critical task within the field of brain-computer interfaces, extensively applied in monitoring and optimizing user states during human-machine interaction using electroencephalography (EEG). However, most existing vigilance prediction frameworks are prone to spurious correlations stemming from inherent biases in collected data. These biases involve relevant but vigilance-independent information, which may lack robustness when applied to different data distributions, i.e., out-of-distribution (OOD) scenarios. The core idea of this study is to learn constraints that capture causal information from the input based on the assumed underlying data generating process. Leveraging the disentanglement and invariance principles behind the assumptions, we propose a constraint-driven causal representation learning (CCRL) to identify and separate spurious latent variables from biased training data for generalized vigilance estimation. The CCRL training process consists of two phases: self-supervised pretraining and constraint-driven causal information disentanglement. In the first phase, based on the masked autoencoder (MAE) architecture, unlabeled training data are used for reconstructing pretext tasks to capture the comprehensive and intrinsic contextual information from EEG data, which provides a powerful input for downstream disentanglement learning. In the second phase, we propose a novel disentanglement strategy to learn spurious-free latent representations causally related to the vigilance state driven by adversarial and invariance constraints. Comprehensive validation experiments conducted on two well-known public datasets demonstrate the effectiveness and superiority of the proposed framework. In general, this work has promising implications for addressing OOD challenges in vigilance estimation.},
}

@article {pmid40810162,
year = {2025},
author = {Zhang, M and Zhai, H and Yang, L and Li, H and Wang, X},
title = {The Medial Prefrontal Cortex Modulates Psychedelic-like Effects of Psilocin.},
journal = {ACS pharmacology & translational science},
volume = {8},
number = {8},
pages = {2767-2776},
pmid = {40810162},
issn = {2575-9108},
abstract = {Recent advancements in the study of psilocybin and its active metabolite psilocin have highlighted their unique psychedelic properties and potential therapeutic applications, particularly in the rapid and sustained treatment of depression. However, the potent acute psychedelic effects of psilocybin necessitate a deeper understanding of the neural mechanisms underlying its action. In this study, we investigated the psilocin-induced neural activity in male mice using c-Fos immunofluorescent labeling and identified brain regions associated with psychedelic-like activity. Among the medial prefrontal cortex (mPFC), orbitofrontal cortex (OFC), interstitial nucleus of the posterior limb of the anterior commissure (IPAC), and dorsomedial striatum (DMS), only the mPFC was specifically associated with the head twitch response (HTR), a hallmark of psychedelic-like behavior. A picomolar dose of psilocin in the mPFC was sufficient to induce significant HTR, suggesting that c-Fos-positive neurons in this region modulate psychedelic-like activity. To validate this hypothesis, optogenetic activation of these neurons significantly increased spontaneous HTR in TRAP2 mice, whereas acute inhibition suppressed drug-induced HTR. These findings establish the mPFC as a critical regulator of psilocin-induced psychedelic-like activity and provide valuable insights for enhancing the clinical safety and therapeutic application of psychedelics.},
}

@article {pmid40807942,
year = {2025},
author = {Chen, J and Yang, C and Wei, R and Hua, C and Mu, D and Sun, F},
title = {Steady-State Visual-Evoked-Potential-Driven Quadrotor Control Using a Deep Residual CNN for Short-Time Signal Classification.},
journal = {Sensors (Basel, Switzerland)},
volume = {25},
number = {15},
pages = {},
pmid = {40807942},
issn = {1424-8220},
support = {BX2021157//Post Doctoral Innovative Talent Support Program under Grants/ ; 62103221//National Natural Science Foundation of China under Grant/ ; },
abstract = {In this paper, we study the classification problem of short-time-window steady-state visual evoked potentials (SSVEPs) and propose a novel deep convolutional network named EEGResNet based on the idea of residual connection to further improve the classification performance. Since the frequency-domain features extracted from short-time-window signals are difficult to distinguish, the EEGResNet starts from the filter bank (FB)-based feature extraction module in the time domain. The FB designed in this paper is composed of four sixth-order Butterworth filters with different bandpass ranges, and the four bandwidths are 19-50 Hz, 14-38 Hz, 9-26 Hz, and 3-14 Hz, respectively. Then, the extracted four feature tensors with the same shape are directly aggregated together. Furthermore, the aggregated features are further learned by a six-layer convolutional neural network with residual connections. Finally, the network output is generated through an adaptive fully connected layer. To prove the effectiveness and superiority of our designed EEGResNet, necessary experiments and comparisons are conducted over two large public datasets. To further verify the application potential of the trained network, a virtual simulation of brain computer interface (BCI) based quadrotor control is presented through V-REP.},
}

@article {pmid40807891,
year = {2025},
author = {Huang, Y and Cao, L and Chen, Y and Wang, T},
title = {Optimization of Dynamic SSVEP Paradigms for Practical Application: Low-Fatigue Design with Coordinated Trajectory and Speed Modulation and Gaming Validation.},
journal = {Sensors (Basel, Switzerland)},
volume = {25},
number = {15},
pages = {},
pmid = {40807891},
issn = {1424-8220},
abstract = {Steady-state visual evoked potential (SSVEP) paradigms are widely used in brain-computer interface (BCI) systems due to their reliability and fast response. However, traditional static stimuli may reduce user comfort and engagement during prolonged use. This study proposes a dynamic stimulation paradigm combining periodic motion trajectories with speed control. Using four frequencies (6, 8.57, 10, 12 Hz) and three waveform patterns (sinusoidal, square, sawtooth), speed was modulated at 1/5, 1/10, and 1/20 of each frequency's base rate. An offline experiment with 17 subjects showed that the low-speed sinusoidal and sawtooth trajectories matched the static accuracy (85.84% and 83.82%) while reducing cognitive workload by 22%. An online experiment with 12 subjects participating in a fruit-slicing game confirmed its practicality, achieving recognition accuracies above 82% and a System Usability Scale score of 75.96. These results indicate that coordinated trajectory and speed modulation preserves SSVEP signal quality and enhances user experience, offering a promising approach for fatigue-resistant, user-friendly BCI application.},
}

@article {pmid40807821,
year = {2025},
author = {Aziz, MZ and Yu, X and Guo, X and He, X and Huang, B and Fan, Z},
title = {BCINetV1: Integrating Temporal and Spectral Focus Through a Novel Convolutional Attention Architecture for MI EEG Decoding.},
journal = {Sensors (Basel, Switzerland)},
volume = {25},
number = {15},
pages = {},
pmid = {40807821},
issn = {1424-8220},
support = {2025A1515011449//Natural Science Foundation of Guangdong Province/ ; 20240001053007//Aviation Science Foundation Project/ ; 62220106006//National Natural Science Foundation of China/ ; },
abstract = {Motor imagery (MI) electroencephalograms (EEGs) are pivotal cortical potentials reflecting cortical activity during imagined motor actions, widely leveraged for brain-computer interface (BCI) system development. However, effectively decoding these MI EEG signals is often overshadowed by flawed methods in signal processing, deep learning methods that are clinically unexplained, and highly inconsistent performance across different datasets. We propose BCINetV1, a new framework for MI EEG decoding to address the aforementioned challenges. The BCINetV1 utilizes three innovative components: a temporal convolution-based attention block (T-CAB) and a spectral convolution-based attention block (S-CAB), both driven by a new convolutional self-attention (ConvSAT) mechanism to identify key non-stationary temporal and spectral patterns in the EEG signals. Lastly, a squeeze-and-excitation block (SEB) intelligently combines those identified tempo-spectral features for accurate, stable, and contextually aware MI EEG classification. Evaluated upon four diverse datasets containing 69 participants, BCINetV1 consistently achieved the highest average accuracies of 98.6% (Dataset 1), 96.6% (Dataset 2), 96.9% (Dataset 3), and 98.4% (Dataset 4). This research demonstrates that BCINetV1 is computationally efficient, extracts clinically vital markers, effectively handles the non-stationarity of EEG data, and shows a clear advantage over existing methods, marking a significant step forward for practical BCI applications.},
}

@article {pmid40807788,
year = {2025},
author = {Siribunyaphat, N and Tohkhwan, N and Punsawad, Y},
title = {Investigation of Personalized Visual Stimuli via Checkerboard Patterns Using Flickering Circles for SSVEP-Based BCI System.},
journal = {Sensors (Basel, Switzerland)},
volume = {25},
number = {15},
pages = {},
pmid = {40807788},
issn = {1424-8220},
support = {WU67260//Research and Innovation Institute of Excellence, Walailak University/ ; },
abstract = {In this study, we conducted two steady-state visual evoked potential (SSVEP) studies to develop a practical brain-computer interface (BCI) system for communication and control applications. The first study introduces a novel visual stimulus paradigm that combines checkerboard patterns with flickering circles configured in single-, double-, and triple-layer forms. We tested three flickering frequency conditions: a single fundamental frequency, a combination of the fundamental frequency and its harmonics, and a combination of two fundamental frequencies. The second study utilizes personalized visual stimuli to enhance SSVEP responses. SSVEP detection was performed using power spectral density (PSD) analysis by employing Welch's method and relative PSD to extract SSVEP features. Commands classification was carried out using a proposed decision rule-based algorithm. The results were compared with those of a conventional checkerboard pattern with flickering squares. The experimental findings indicate that single-layer flickering circle patterns exhibit comparable or improved performance when compared with the conventional stimuli, particularly when customized for individual users. Conversely, the multilayer patterns tended to increase visual fatigue. Furthermore, individualized stimuli achieved a classification accuracy of 90.2% in real-time SSVEP-based BCI systems for six-command generation tasks. The personalized visual stimuli can enhance user experience and system performance, thereby supporting the development of a practical SSVEP-based BCI system.},
}

@article {pmid40807738,
year = {2025},
author = {Leerskov, KS and Spaich, EG and Jochumsen, MR and Andreasen Struijk, LNS},
title = {Design and Demonstration of a Hybrid FES-BCI-Based Robotic Neurorehabilitation System for Lower Limbs.},
journal = {Sensors (Basel, Switzerland)},
volume = {25},
number = {15},
pages = {},
pmid = {40807738},
issn = {1424-8220},
support = {A33234//Hartmann Fonden/ ; },
abstract = {BACKGROUND: There are only a few available options for early rehabilitation of severely impaired individuals who must remain bedbound, as most exercise paradigms focus on out-of-bed exercises. To enable these individuals to exercise, we developed a novel hybrid rehabilitation system combining a brain-computer interface (BCI), functional electrical stimulation (FES), and a robotic device.

METHODS: The BCI assessed the presence of a movement-related cortical potential (MRCP) and triggered the administration of FES to produce movement of the lower limb. The exercise trajectory was supported by the robotic device. To demonstrate the system, an experiment was conducted in an out-of-lab setting by ten able-bodied participants. During exercise, the performance of the BCI was assessed, and the participants evaluated the system using the NASA Task Load Index, Intrinsic Motivation Inventory, and by answering a few subjective questions.

RESULTS: The BCI reached a true positive rate of 62.6 ± 9.2% and, on average, predicted the movement initiation 595 ± 129 ms prior to the MRCP peak negativity. All questionnaires showed favorable outcomes for the use of the system.

CONCLUSIONS: The developed system was usable by all participants, but its clinical feasibility is uncertain due to the total time required for setting up the system.},
}

@article {pmid40803174,
year = {2025},
author = {Kamali, S and Baroni, F and Varona, P},
title = {Mu and beta power effects of fast response trait double dissociate during precue and movement execution in the sensorimotor cortex.},
journal = {Computers in biology and medicine},
volume = {196},
number = {Pt C},
pages = {110874},
doi = {10.1016/j.compbiomed.2025.110874},
pmid = {40803174},
issn = {1879-0534},
abstract = {A better understanding of the neural and muscular mechanisms underlying motor responses is essential for advancing neurorehabilitation protocols, brain-computer interfaces (BCI), feature engineering for biosignal classification algorithms, and identifying biomarkers of disease and performance enhancement strategies. In this study, we examined the neuromuscular dynamics of healthy individuals during a sequential finger-pinching task, focusing on the relationships between cortical oscillations and muscle activity in simultaneous electroencephalography (EEG) and electromyography (EMG) recordings. We contrasted two pairs of subsets of the dataset based on the latency of EMG onset: an across-subjects trait-based comparison and a within-subjects state-based comparison. Trait-based analyses showed that fast responders had higher baseline beta power, indicating stronger motor inhibition and efficient resetting of motor networks, and greater mu desynchronization during movement, reflecting enhanced motor cortex activation. Visual association areas also displayed more pronounced changes in different phases of the task in subjects with lower latency. Fast responders exhibited lower baseline EMG activity and stronger EMG power during movement initiation, showing effective motor inhibition and rapid muscle activation. State-based analyses revealed no significant EEG differences between fast and slow trials, while EMG differences were only detected after movement onset. These results highlight that fast response trait is related to electrophysiological differences at specific frequency bands and task phases, offering insights for enhancing motor function in rehabilitation, biomarker identification and BCI applications.},
}

@article {pmid40801596,
year = {2025},
author = {Henderson, FC and Tuchman, K},
title = {Angiogenic Cell Precursors and Neural Cell Precursors in Service to the Brain-Computer Interface.},
journal = {Cells},
volume = {14},
number = {15},
pages = {},
pmid = {40801596},
issn = {2073-4409},
abstract = {The application of artificial intelligence through the brain-computer interface (BCI) is proving to be one of the great advances in neuroscience today. The development of surface electrodes over the cortex and very fine electrodes that can be stereotactically implanted in the brain have moved the science forward to the extent that paralyzed people can play chess and blind people can read letters. However, the introduction of foreign bodies into deeper parts of the central nervous system results in foreign body reaction, scarring, apoptosis, and decreased signaling. Implanted electrodes activate microglia, causing the release of inflammatory factors, the recruitment of systemic inflammatory cells to the site of injury, and ultimately glial scarring and the encapsulation of the electrode. Recordings historically fail between 6 months and 1 year; the longest BCI in use has been 7 years. This article proposes a biomolecular strategy provided by angiogenic cell precursors (ACPs) and nerve cell precursors (NCPs), administered intrathecally. This combination of cells is anticipated to sustain and promote learning across the BCI. Together, through the downstream activation of neurotrophic factors, they may exert a salutary immunomodulatory suppression of inflammation, anti-apoptosis, homeostasis, angiogenesis, differentiation, synaptogenesis, neuritogenesis, and learning-associated plasticity.},
}

@article {pmid40800758,
year = {2025},
author = {Kostorz, K and Nguyen, T and Pan, Y and Melinscak, F and Steyrl, D and Hu, Y and Sorger, B and Hoehl, S and Scharnowski, F},
title = {Investigating short windows of interbrain synchrony: A step toward fNIRS-based hyperfeedback.},
journal = {Imaging neuroscience (Cambridge, Mass.)},
volume = {3},
number = {},
pages = {},
pmid = {40800758},
issn = {2837-6056},
abstract = {Social interaction is of fundamental importance to humans. Prior research has highlighted the link between interbrain synchrony and positive outcomes in human social interaction. Neurofeedback is an established method to train one's brain activity and might offer a possibility to increase interbrain synchrony, too. Consequently, it would be advantageous to determine the feasibility of creating a neurofeedback system for enhancing interbrain synchrony to benefit human interaction. One vital step toward developing a neurofeedback setup is to determine whether the target metric can be determined in relatively short time windows. In this study, we investigated whether the most widely employed metric for interbrain synchrony, wavelet transform coherence, can be assessed accurately in short time windows using functional near-infrared spectroscopy (fNIRS), which is recognized for its mobility and ecological suitability for interactive research. To this end, we have undertaken a comprehensive approach where we created artificial data of different noise levels of a dyadic interaction and re-processed two human-interaction datasets. For both artificial and in vivo data, we computed short windows of interbrain synchrony of varying size and assessed significance at each window size. Our findings indicate that relatively short windows of wavelet transform coherence of integration durations of about 1 minute are feasible. This would align well with the methodology of an intermittent neurofeedback procedure. Our investigation lays a foundational step toward an fNIRS-based system to measure interbrain synchrony in real time and provide participants with information about their interbrain synchrony. This advancement is crucial for the future development of a neurofeedback training system tailored to enhance interbrain synchrony to potentially benefit human interaction.},
}

@article {pmid40800536,
year = {2024},
author = {Papadopoulos, S and Darmet, L and Szul, MJ and Congedo, M and Bonaiuto, JJ and Mattout, J},
title = {Surfing beta burst waveforms to improve motor imagery-based BCI.},
journal = {Imaging neuroscience (Cambridge, Mass.)},
volume = {2},
number = {},
pages = {},
pmid = {40800536},
issn = {2837-6056},
abstract = {Our understanding of motor-related, macroscale brain processes has been significantly shaped by the description of the event-related desynchronization (ERD) and synchronization (ERS) phenomena in the mu and beta frequency bands prior to, during, and following movement. The demonstration of reproducible, spatially- and band-limited signal power changes has, consequently, attracted the interest of non-invasive brain-computer interface (BCI) research for a long time. BCIs often rely on motor imagery (MI) experimental paradigms that are expected to generate brain signal modulations analogous to movement-related ERD and ERS. However, a number of recent neuroscience studies has questioned the nature of these phenomena. Beta band activity has been shown to occur, on a single-trial level, in short, transient, and heterogeneous events termed bursts rather than sustained oscillations. In a previous study, we established that an analysis of hand MI binary classification tasks based on beta bursts can be superior to beta power in terms of classification score. In this article, we elaborate on this idea, proposing a signal processing algorithm that is comparable to- and compatible with state-of-the-art techniques. Our pipeline filters brain recordings by convolving them with kernels extracted from beta bursts and then applies spatial filtering before classification. This data-driven filtering allowed for a simple and efficient analysis of signals from multiple sensors, thus being suitable for online applications. By adopting a time-resolved decoding approach, we explored MI dynamics and showed the specificity of the new classification features. In accordance with previous results, beta bursts improved classification performance compared to beta band power, while often increasing information transfer rate compared to state-of-the-art approaches.},
}

@article {pmid40798628,
year = {2025},
author = {Zhao, SJ and Yin, ZY and Yu, SB and Wang, W and Yu, HZ and Li, WH and Tao, C},
title = {Block-based compressive imaging with a swin transformer.},
journal = {Optics express},
volume = {33},
number = {5},
pages = {9587-9603},
doi = {10.1364/OE.546585},
pmid = {40798628},
issn = {1094-4087},
abstract = {Block-based compressive imaging (BCI) is based on the compressive sensing principle, which uses a spatial light modulator and a low-resolution detector to perform parallel high-speed sampling, followed by super-resolution algorithm to reconstruct target image. When compared with traditional compressive imaging, BCI reduces the computational effort but introduces block artifacts. This paper proposes a data-driven deep neural network based on the swin transformer called SwinBCI, which introduces the local attention and shifted window mechanisms to improve the target image reconstruction quality. By using the dataset to train the model to obtain priori knowledge and performing graphics processing unit-accelerated computation, the computation time is greatly reduced to realize real-time BCI. We achieve better reconstruction performances with cake cutting-Hadamard matrix sampling than with Bernoulli matrix sampling. Comparison with three other classical compressed sensing reconstruction methods on four common image datasets and images acquired experimentally using the actual BCI system show that SwinBCI achieves faster high-quality reconstruction at each sampling rate.},
}

@article {pmid40797316,
year = {2025},
author = {Tang, A and Jiang, H and Li, J and Chen, Y and Zhang, J and Wang, D and Hu, S and Lai, J},
title = {Gut microbiota links to cognitive impairment in bipolar disorder via modulating synaptic plasticity.},
journal = {BMC medicine},
volume = {23},
number = {1},
pages = {470},
pmid = {40797316},
issn = {1741-7015},
support = {82201676//National Natural Science Foundation of China/ ; 82471542//National Natural Science Foundation of China/ ; No. JNL-2023001B//Research Project of Jinan Microecological Biomedicine Shandong Laboratory/ ; 2023YFC2506200//National Key Research and Development Program of China/ ; 2021C03107//Zhejiang Provincial Key Research and Development Program/ ; 2023ZFJH01-01//Fundamental Research Funds for the Central Universities/ ; 2024ZFJH01-01//Fundamental Research Funds for the Central Universities/ ; No. 2021R52016//Leading Talent of Scientific and Technological Innovation - "Ten Thousand Talents Program" of Zhejiang Province/ ; 2022KTZ004//Chinese Medical Education Association/ ; },
abstract = {BACKGROUND: Cognitive impairment is an intractable clinical manifestation of bipolar disorder (BD), but its underlying mechanisms remain largely unexplored. Preliminary evidence suggests that gut microbiota can potentially influence cognitive function by modulating synaptic plasticity. Herein, we characterized the gut microbial structure in BD patients with and without cognitive impairment and explored its influence on neuroplasticity in mice.

METHODS: The gut structure of microbiota in BD without cognitive impairment (BD-nCI) patients, BD with cognitive impairment (BD-CI) patients, and healthy controls (HCs) were characterized, and the correlation between specific bacterial genera and clinical parameters was determined. ABX-treated C57 BL/J male mice were transplanted with fecal microbiota from BD-nCI, BD-CI patients or HCs and subjected to behavioral testing. The change of gut microbiota in recipient mice and its influence on the dendritic complexity and synaptic plasticity of prefrontal neurons were examined. Finally, microbiota supplementation from healthy individuals in the BD-CI mice was performed to further determine the role of gut microbiota.

RESULTS: 16S-ribosomal RNA gene sequencing reveals that gut microbial diversity and composition are significantly different among BD-nCI patients, BD-CI patients, and HCs. The Spearman correlation analysis suggested that glucose metabolism-related bacteria, such as Prevotella, Faecalibacterium, and Roseburia, were correlated with cognitive impairment test scores, and inflammation-related bacteria, such as Lachnoclostridium and Bacteroides, were correlated with depressive severity. Fecal microbiota transplantation resulted in depression-like behavior, impaired working memory and object recognition memory in BD-CI recipient mice. Compared with BD-nCI mice, BD-CI mice exhibited more severely impaired object recognition memory, along with greater reductions in dendritic complexity and synaptic plasticity. Supplementation of gut microbiota from healthy individuals partially reversed emotional and cognitive phenotypes and neuronal plasticity in BD-CI mice.

CONCLUSIONS: This study first characterized the gut microbiota in BD-CI patients and highlighted the potential role of gut microbiota in BD-related cognitive deficits by modulating neuronal plasticity in mice model.},
}

@article {pmid40797003,
year = {2025},
author = {Lo, BWY and Fukuda, H},
title = {Advances in Ischemic Stroke Treatment: Current and Future Therapies.},
journal = {Neurology and therapy},
volume = {},
number = {},
pages = {},
pmid = {40797003},
issn = {2193-8253},
abstract = {This review summarizes current concepts in our understanding of stroke anatomy, pathophysiology of cerebral hypoperfusion, and collateral circulation. It also provides an evidence-based update in stroke trials and treatments assessed using PRISMA guidelines. Intravenous thrombolysis, endovascular thrombectomy for anterior circulation strokes, blood pressure control after endovascular thrombectomy, and medical management principles are discussed. Endovascular thrombectomy and medical therapy improves functional independence at 90 days in anterior circulation strokes even in late windows up to 24 h post symptom onset regardless of infarct core size. Intensive systolic blood pressure control acutely post thrombectomy is associated with harm and worse outcomes. This review also provides an evidence-based update on neurorehabilitation strategies with emerging interventions such as brain-computer interface and robotics having the potential to maximize neuroplasticity for potential improvement and recovery post stroke.},
}

@article {pmid40796752,
year = {2025},
author = {Li, D and Zalesky, A and Wang, Y and Wang, H and Ma, L and Cheng, L and Banaschewski, T and Barker, GJ and Bokde, ALW and Brühl, R and Desrivières, S and Flor, H and Garavan, H and Gowland, P and Grigis, A and Heinz, A and Lemaître, H and Martinot, JL and Martinot, MP and Artiges, E and Nees, F and Orfanos, DP and Poustka, L and Smolka, MN and Vaidya, N and Walter, H and Whelan, R and Schumann, G and Jia, T and Chu, C and Fan, L and , },
title = {Mapping the coupling between tract reachability and cortical geometry of the human brain.},
journal = {Nature communications},
volume = {16},
number = {1},
pages = {7489},
pmid = {40796752},
issn = {2041-1723},
support = {R01 DA049238/DA/NIDA NIH HHS/United States ; R56 AG058854/AG/NIA NIH HHS/United States ; U54 EB020403/EB/NIBIB NIH HHS/United States ; U54 MH091657/MH/NIMH NIH HHS/United States ; },
abstract = {The study of cortical geometry and connectivity is prevalent in human brain research. However, these two aspects of brain structure are usually examined separately, leaving the essential connections between the brain's folding patterns and white matter connectivity unexplored. In this study, we aim to elucidate the fundamental links between cortical geometry and white matter tract connectivity. We develop the concept of tract-geometry coupling (TGC) by optimizing the alignment between tract connectivity to the cortex and multiscale cortical geometry. We confirm in two independent datasets that cortical geometry reliably characterizes tract reachability, and that TGC demonstrates high test-retest reliability and individual-specificity. Interestingly, low-frequency TGC is more heritable and behaviorally informative. Finally, we find that TGC can reproduce task-evoked cortical activation patterns and exhibits non-uniform maturation during youth. Collectively, our study provides an approach to mapping cortical geometry-connectivity coupling, highlighting how these two aspects jointly shape the connected brain.},
}

@article {pmid40796392,
year = {2025},
author = {Kim, J and Hong, SK and Lee, A and Kumar, SN and Suchi, M and Park, JI},
title = {Activity-Dependent Effects of ERK1/2 on Hepatic Ischemia-Reperfusion Injury.},
journal = {Transplantation proceedings},
volume = {},
number = {},
pages = {},
doi = {10.1016/j.transproceed.2025.07.005},
pmid = {40796392},
issn = {1873-2623},
abstract = {BACKGROUND: Liver transplantation remains the only cure for end-stage liver disease, but ischemia-reperfusion injury (IRI) limits graft availability. Although extracellular signal-regulated kinase (ERK1/2) signaling is involved in cellular responses to IRI, its precise role in hepatic IRI remains unclear. We investigated the role of ERK1/2 in hepatic IRI by modulating its activity using small-molecule chemical inhibitors.

METHODS: ERK1/2 activation was monitored at different phases of hepatic IRI using a rat model in which liver ischemia was induced with varying reperfusion times. ERK1/2 activity was modulated in this model by administering different doses of trametinib (MEK1/2 inhibitor) and BCI (DUSP1/6 inhibitor). Liver injury was evaluated through histological assessment, serum markers, and molecular analysis of cell death pathways.

RESULTS: ERK1/2 activity increased early in the reperfusion phase and gradually decreased over 6 hours thereafter. Inhibiting the ERK1/2 activity increase using trametinib (0.3 mg/kg) as well as inhibiting its decreases using BCI (7.5 mg/kg) worsened the liver injury. However, the injury was reduced upon titrating ERK1/2 activity to a moderately increased level by BCI and trametinib coadministration. The reduced liver injury was accompanied by decreased expression of ferroptosis markers.

CONCLUSIONS: Our data demonstrate that ERK1/2 activity is required for hepatic cells to tolerate IRI. Our results suggest that modulation of ERK1/2 activity using existing drugs may be a potential therapeutic strategy for mitigating hepatic IRI.},
}

@article {pmid40795874,
year = {2025},
author = {Li, J and Le, T and Fan, C and Chen, M and Shlizerman, E},
title = {Brain-to-text decoding with context-aware neural representations and large language models.},
journal = {Journal of neural engineering},
volume = {},
number = {},
pages = {},
doi = {10.1088/1741-2552/adfab1},
pmid = {40795874},
issn = {1741-2552},
abstract = {Decoding attempted speech from neural activity offers a promising avenue for restoring communication abilities in individuals with speech impairments. Previous studies have focused on mapping neural activity to text using phonemes as the intermediate target. While successful, decoding neural activity directly to phonemes ignores the context dependent nature of the neural activity-to-phoneme mapping in the brain, leading to suboptimal decoding performance. In this work, we propose the use of diphone - an acoustic representation that captures the transitions between two phonemes - as the context-aware modeling target. We integrate diphones into existing phoneme decoding frameworks through a novel divide-and-conquer strategy in which we model the phoneme distribution by marginalizing over the diphone distribution. Our approach effectively leverages the enhanced context-aware representation of diphones while preserving the manageable class size of phonemes, a key factor in simplifying the subsequent phoneme-to-text conversion task. We demonstrate the effectiveness of our approach on the Brain-to-Text 2024 benchmark, where it achieves state-of-the-art Phoneme Error Rate (PER) of 15.34% compared to 16.62% PER of monophone-based decoding. When coupled with finetuned Large Language Models (LLMs), our method yields a Word Error Rate (WER) of 5.77%, significantly outperforming the 8.93% WER of the leading method in the benchmark.},
}

@article {pmid40795479,
year = {2025},
author = {Albahri, AS and Hamid, RA and Alqaysi, ME and Al-Qaysi, ZT and Albahri, OS and Alamoodi, AH and Homod, RZ and Deveci, M and Sharaf, IM},
title = {Trust and explainability in robotic hand control via adversarial multiple machine learning models with EEG sensor data fusion: A fuzzy decision-making solution.},
journal = {Computers in biology and medicine},
volume = {196},
number = {Pt C},
pages = {110922},
doi = {10.1016/j.compbiomed.2025.110922},
pmid = {40795479},
issn = {1879-0534},
abstract = {In the field of brain‒computer interfaces (BCIs), developing a reliable machine learning (ML) model for real-time robotic hand control systems based on motor imagery (MI) brain signals requires substantial research. For this purpose, a set of ML models has been developed and tested to identify robust models via MI sensor data fusion under both nonadversarial and adversarial attack conditions. This paper addresses numerous essential areas, including the development of ML models for electroencephalography (EEG) MI signal datasets, with a focus on proper preprocessing and evaluation under both nonadversarial and adversarial attack conditions. Three phases make up the process. In the first phase, raw MI-EEG datasets from the Graz University BCI competition are identified and preprocessed. The preprocessing encompasses six key stages: EEG-MI signal filtering, segmentation, time‒frequency domain feature extraction, merging and labeling, normalization (resulting in Dataset I), and feature fusion (resulting in Dataset II). In the second phase, both datasets are used to develop nine different ML methods and are evaluated via nine performance metrics. These models are trained and tested against adversarial and nonadversarial scenarios. In the third phase, the fuzzy decision by opinion score method (FDOSM) and the multiperspective decision matrix (MPDM) are combined to benchmark the ML models via the fuzzy multicriteria decision-making (MCDM) approach. The random forest (RF) model achieved the best overall performance, with the lowest FDOSM scores: 0.18241 for Dataset I and 0.21636 for Dataset II. A lower FDOSM score means better results across all evaluation criteria. To further assess the developed methodology, the RF model was tested on Dataset III, comprising EEG data from four participants collected via the EMOTIV EPOC. The mean classification accuracy achieved by the RF model was 83 % with standard preprocessing, and it improved to 86 % with the application of feature fusion techniques. Additionally, this study employed the local interpretability model-agnostic explanation (LIME) method to provide an understanding of the RF model's behavior and enhance the interpretability of the results in the context of individual predictions.},
}

@article {pmid40794110,
year = {2025},
author = {Loss, J and von Sommoggy Und Erdödy, J and Rüter, J and Helten, J and Germelmann, CC and Tittlbach, S},
title = {[Using behavioral and cultural insights to promote physical activity among university students-the "Smart Moving" project].},
journal = {Bundesgesundheitsblatt, Gesundheitsforschung, Gesundheitsschutz},
volume = {},
number = {},
pages = {},
pmid = {40794110},
issn = {1437-1588},
abstract = {BACKGROUND: Physical inactivity is widespread at universities. To promote physical activity among students, it is important to understand their needs. Behavioral and cultural insights (BCIs) help to identify barriers to physical activity and to develop appropriate interventions. The aim of "Smart Moving" was to use BCIs to implement measures to promote physical activity in two universities.

METHOD: "Smart Moving" was carried out at the universities of Bayreuth and Regensburg between 2018 and 2021. The project was implemented in four steps: (1) the target behavior was defined as students being physically active on campus; (2) knowledge about physical activity behavior was gained using a standardized survey of students, photo voice, and expert interviews; (3) a planning group at each university developed and implemented measures to promote physical activity; and (4) acceptance and short-term effects of selected measures were evaluated in short surveys.

RESULTS: University students spent an average of 34 h per week sitting during their stay on campus. Factors influencing physical activity were assigned to the following categories: capability (cognitive/physical ability), opportunity (physical/social environment), and motivation. These included, for example, a lack of knowledge about access, poor accessibility of exercise opportunities, the prevailing norm that learning involves sitting, and shame when exercising in front of others. Various approaches to promote physical activity were developed: movement breaks in lectures, activating desk furniture with sitting/standing options, movement instructions in the outdoor area, and motivational interventions for exercise. The measures were well received by students.

DISCUSSION: The BCI data helped implement needs-based physical activity promotion at universities. Further studies are needed to investigate the long-term effects on physical activity behavior.},
}

@article {pmid40791388,
year = {2025},
author = {Nuñez Ponasso, G and Drumm, DA and Oppermann, H and Wang, A and Noetscher, GM and Maess, B and Knösche, TR and Makaroff, SN and Haueisen, J},
title = {High-Resolution EEG Source Reconstruction from PCA-Corrected BEM-FMM Reciprocal Basis Funcions: A Study with Visual Evoked Potentials from Intermittent Photic Stimulation.},
journal = {bioRxiv : the preprint server for biology},
volume = {},
number = {},
pages = {},
doi = {10.1101/2025.07.11.664246},
pmid = {40791388},
issn = {2692-8205},
abstract = {Modern automated human head segmentations can generate high-resolution computational meshes involving many non-nested tissues. However, most source reconstruction software is limited to 3 -4 nested layers of low resolution and a small number of dipolar sources∼ 10, 000. Recently, we introduced modeling techniques for source reconstruction of magnetoencephalographic (MEG) signals using the reciprocal approach and the boundary element fast multipole method (BEM-FMM). The technique of BEM-FMM can process both nested and non-nested models with as many as 4 million surface elements. In this paper, we present an analogue technique for source reconstruction of electroencephalographic (EEG) signals based on cortical global basis functions. The present work uses Helmholtz reciprocity to relate the reciprocally-generated lead-field matrices to their direct counterpart, while resolving the issue of possible biases toward the reference electrode. Our methodology is tested with experimental EEG data collected from a cohort of 12, young and healthy, volunteers subjected to intermittent photic stimulation (IPS). Our novel high-resolution source reconstruction models can have impact on mental health screening as well as brain-computer inter-faces.},
}

@article {pmid40791170,
year = {2025},
author = {Han, S and Pasquini, D and Sorieul, M and Boratto, MH and Gatecliff, L and Dickson, A and Jang, S and Davy, S and Malliaras, GG and Chen, Y},
title = {Implantable Ion-Selective Organic Electrochemical Transistors Enable Continuous, Long-Term, and In Vivo Plant Monitoring.},
journal = {Advanced science (Weinheim, Baden-Wurttemberg, Germany)},
volume = {},
number = {},
pages = {e04283},
doi = {10.1002/advs.202504283},
pmid = {40791170},
issn = {2198-3844},
support = {NE/T012293/1//Natural Environment Research Council/ ; CSG-FRI12101//Royal Society Te Apārangi/ ; C04X2202//Ministry of Business, Innovation and Employment/ ; C04X1703//Ministry of Business, Innovation and Employment/ ; RS-2024-00452677//National Research Foundation of Korea/ ; RS-2024-00399300//National Research Foundation of Korea/ ; },
abstract = {The development of plant-specific biosensors holds the potential to uncover new insights into plant physiology and advance precision agriculture. Current sensing platforms mainly focus on broad plant phenotypes (e.g., elongation and hydration) and local environmental monitoring (e.g., temperature and moisture). Here, an ion-selective organic electrochemical transistor (IS-OECT) is introduced that enables real-time monitoring of variations in potassium ion concentration within the xylem of pine trees. This work demonstrates that the high sensitivity of the IS-OECT enables the detection of subtle variations in potassium ion concentrations in the xylem sap of living trees, and the high stability of the sensor allows for in vivo measurements over five weeks. Furthermore, the implantable sensors are fabricated using processes that are compatible with low-cost manufacturing (i.e., lithography-free). This sensing technology, therefore, has great potential to be a game-changer in precision forestry and could extend to precision agriculture and horticulture practices.},
}

@article {pmid40789435,
year = {2025},
author = {Tozzi, A and Jaušovec, K},
title = {Takens' theorem to assess EEG traces: Regional variations in brain dynamics.},
journal = {Neuroscience letters},
volume = {865},
number = {},
pages = {138352},
doi = {10.1016/j.neulet.2025.138352},
pmid = {40789435},
issn = {1872-7972},
abstract = {Takens' theorem (TT) proves that the behaviour of a dynamical system can be effectively reconstructed within a multidimensional phase space. This offers a comprehensive framework for examining temporal dependencies, dimensional complexity and predictability of time series data. We applied TT to investigate the physiological regional differences in EEG brain dynamics of healthy subjects, focusing on three key channels: FP1 (frontal region), C3 (sensorimotor region), and O1 (occipital region). We provided a detailed reconstruction of phase spaces for each EEG channel using time-delay embedding. The reconstructed trajectories were quantified through measures of trajectory spread and average distance, offering insights into the temporal structure of brain activity that traditional linear methods struggle to capture. Variability and complexity were found to differ across the three regions, revealing notable regional variations. FP1 trajectories exhibited broader spreads, reflecting the dynamic complexity of frontal brain activity associated with higher cognitive functions. C3, involved in sensorimotor integration, displayed moderate variability, reflecting its functional role in coordinating sensory inputs and motor outputs. O1, responsible for visual processing, showed constrained and stable trajectories, consistent with repetitive and structured visual dynamics. These findings align with the functional specialization of different cortical areas, suggesting that the frontal, sensorimotor and occipital regions operate with autonomous temporal structures and nonlinear properties. This distinction may have significant implications for advancing our understanding of normal brain function and enhancing the development of brain-computer interfaces. In sum, we demonstrated the utility of TT in revealing regional variations in EEG traces, underscoring the value of nonlinear dynamics.},
}

@article {pmid40786597,
year = {2024},
author = {Chen, ZS},
title = {Emerging Brain-to-Content Technologies from Generative AI and Deep Representation Learning.},
journal = {IEEE signal processing magazine},
volume = {41},
number = {6},
pages = {94-104},
pmid = {40786597},
issn = {1053-5888},
support = {R01 NS121776/NS/NINDS NIH HHS/United States ; R01 MH118928/MH/NIMH NIH HHS/United States ; P50 MH132642/MH/NIMH NIH HHS/United States ; RF1 DA056394/DA/NIDA NIH HHS/United States ; R01 MH139352/MH/NIMH NIH HHS/United States ; },
abstract = {Rapid advances in generative artificial intelligence (AI) and deep representation learning have revolutionized numerous engineering applications in signal processing, computer vision, speech recognition and translation, and natural language processing due to amazingly powerful representation power (e.g., [1,2]). Generative AI-empowered tools, such as ChatGPT and Sora, have fundamentally changed the landscape of human-computer communications research. One emerging application along this line is to link the brain to the computer (i.e., brain-computer interface or BCI) and to develop paradigm-shift brain-to-content technologies. This BCI system upgrade (i.e., BCI 2.0) is empowered by generative AI and deep learning ("new engine") and large amounts of data ("gas"). In this article, we will revisit the old song sung in a new tune, highlight some state-of-the-art progresses, and briefly discuss the future outlook.},
}

@article {pmid40786542,
year = {2025},
author = {Zulfiqar, AA},
title = {Hypervitaminemia B12 in the Elderly: A Forgotten Marker of Serious Underlying Diseases.},
journal = {European journal of case reports in internal medicine},
volume = {12},
number = {8},
pages = {005553},
pmid = {40786542},
issn = {2284-2594},
abstract = {UNLABELLED: Hypervitaminemia B12, long neglected in clinical practice, is a biological anomaly whose pathological significance remains largely underestimated, particularly in the elderly. While medical attention has historically focused on vitamin B12 deficiency, several recent studies suggest that elevated levels of this vitamin may reveal serious underlying pathologies, such as solid neoplasia, haematological malignancies, chronic liver disease or renal failure. We report the case of a 91-year-old man hospitalized for asthenia, anorexia and altered general condition, in whom vitamin B12 assay revealed major hypervitaminemia (1318 pg/ml). The work-up revealed hepatic cirrhosis of alcoholic origin, complicated by hepatocellular carcinoma which was metastatic from the outset. This case illustrates the potential prognostic value of vitamin B12 dosage, particularly when coupled with C-reactive protein (BCI index), a high value (> 40,000) of which is associated with short-term mortality in patients with advanced cancer. Beyond hepatopathy, hypervitaminemia B12 is associated in the literature with increased haptocorrin release in myeloproliferative syndromes, excess transcobalamins in renal failure, or paradoxical elevation in certain inflammatory diseases. This biological marker, which is easy to obtain, could therefore become part of standardized geriatric assessment, particularly in oncogeriatrics, in order to guide diagnostic and prognostic strategy. The systematic inclusion of vitamin B12 assays in the general assessment of elderly patients, particularly in oncology settings, deserves to be reassessed.

LEARNING POINTS: Hypervitaminemia B12 is an often overlooked but potentially significant marker of serious underlying pathologies-including solid neoplasms, liver disease, renal failure, and hematologic malignancies-especially in elderly patients.The B12 × C-reactive protein (CRP) index, easily obtainable from routine labs, may serve as a prognostic tool in oncology, with values over 40,000 being strongly associated with short-term mortality in advanced cancers.Routine screening for vitamin B12 levels in geriatric assessments should consider both deficiency and excess, with hypervitaminemia prompting systematic diagnostic evaluation to uncover latent or advanced disease.},
}

@article {pmid40783421,
year = {2025},
author = {Hashemi, SI and Cheron, G and Demolin, D and Cebolla, AM},
title = {EEG oscillations and related brain generators of phonation phases in long utterances.},
journal = {Scientific reports},
volume = {15},
number = {1},
pages = {29150},
pmid = {40783421},
issn = {2045-2322},
support = {ANR-20-CE23-0008//Agence Nationale de la Recherche/ ; },
mesh = {Humans ; Male ; Female ; *Electroencephalography ; Adult ; *Phonation/physiology ; *Brain/physiology ; Young Adult ; Electromyography ; *Speech/physiology ; },
abstract = {While the role of brain rhythms in respiratory and speech motor control has been mainly explored during brief utterances, the specific involvement of brain rhythms in the transition of regulating subglottic pressure phases which are concomitant to specific muscle activation during prolonged phonation remains unexplored. This study investigates whether power spectral variations of the electroencephalogram brain rhythms are related specifically to prolonged phonation phases. High-density EEG and surface EMG were recorded in nineteen healthy participants while they repeatedly produced the syllable [pa] without taking a new breath, until reaching respiratory exhaustion. Aerodynamic, acoustic, and electrophysiological signals were analyzed to detect the brain areas involved in different phases of prolonged phonation. Each phase was defined by successive thoracic and abdominal muscle activity maintaining estimated subglottic pressure. The results showed significant changes in power spectrum, with desynchronization and synchronization in delta, theta, low-alpha, and high-alpha bands during transitions among the phases. Brain source analysis estimated that the first phase (P1), associated with vocal initiation and elastic rib cage recoil, involved frontal regions, suggesting a key role in voluntary phonation preparation. Subsequent phases (P2, P3, P4) showed multiband dynamics, engaging motor and premotor cortices, anterior cingulate, sensorimotor regions, thalamus, and cerebellum, indicating progressive adaptation and fine-tuning of respiratory and articulatory muscle control. Additionally, the involvement of temporal and insular regions in delta rhythm suggests a role in maintaining phonetic representation and preventing spontaneous verbal transformations. These findings provide new insights into the mechanisms and brain regions involved in prolonged phonation. These findings pave the way for applications in vocal brain-machine interfaces, clinical biofeedback for respiratory and vocal disorders, and the development of more ecologically valid paradigms in speech neuroscience.},
}

Humans
Male
Female
*Electroencephalography
Adult
*Phonation/physiology
*Brain/physiology
Young Adult
Electromyography
*Speech/physiology

@article {pmid40783082,
year = {2025},
author = {Wu, K and Gao, L and Feng, Z and Kakkos, I and Li, C and Sun, Y},
title = {Multimodal Brain Network Analysis Reveals Divergent Dysconnectivity Patterns During Mental Fatigue: A Concurrent EEG-fMRI Study.},
journal = {Brain research bulletin},
volume = {},
number = {},
pages = {111505},
doi = {10.1016/j.brainresbull.2025.111505},
pmid = {40783082},
issn = {1873-2747},
abstract = {For the apparent importance of mental fatigue in neuroergonomics, continuous efforts have been made to reveal the underlying neural mechanisms. Using concurrent EEG-fMRI network analysis, this work aims to reveal fatigue-related brain network reorganization. Specifically, multimodal neuroimaging data were acquired from 35 healthy participants during a 15-min sustained attention task (i.e., psychomotor vigilance task). A monotonically decreasing pattern of behavioral performance was revealed where the first and last 3-min windows were determined as the most vigilant and fatigued states. Multimodal brain network architectures within these two states were then quantitatively compared. We found that EEG and fMRI networks exhibited divergent yet interrelated reorganizations. Specifically, MF-related deficiency in parallel information transmission was revealed in multiple EEG frequency bands, yet only local efficiency was altered in fMRI network. Moreover, a convergent decrease of nodal efficiency mainly resided in the default mode network was found in both EEG and fMRI networks, indicating a decline in cognitive control capacity during mental fatigue. Overall, by integrating multimodal EEG-fMRI network analyses, this work provides novel insights into the dynamic neural adaptations to mental fatigue, enhancing our understanding of the underlying neural mechanisms.},
}

@article {pmid40780413,
year = {2025},
author = {Qiu, SJ and Zhang, YL and Gong, WB and Ding, YH and Wu, JW and Wang, ZX and Yao, HW},
title = {BCI inhibits MKP3 by targeting the kinase-binding domain and disrupting ERK2 interaction.},
journal = {The Journal of biological chemistry},
volume = {},
number = {},
pages = {110570},
doi = {10.1016/j.jbc.2025.110570},
pmid = {40780413},
issn = {1083-351X},
abstract = {Mitogen-activated protein kinase phosphatase 3 (MKP3), also known as dual-specificity phosphatase 6 (DUSP6), is a critical regulator of ERK signaling, and its dysregulation is implicated in diseases such as cancer. The small-molecule inhibitor BCI ((E)-2-benzylidene-3-(cyclohexylamino)-2,3-dihydro-1H-inden-1-one) has been reported to inhibit MKP3, thereby enhancing ERK signaling and promoting selective cytotoxicity in cancer cells. However, the molecular mechanism underlying BCI-mediated MKP3 inhibition remains unclear. In this research, we characterized the interaction between BCI and MKP3 using NMR titration, microscale thermophoresis (MST), enzymatic assays, and AlphaFold 3 (AF3) modeling. Our results demon-strate that BCI selectively binds to the kinase-binding domain (KBD) of MKP3, rather than its catalytic domain (CD), thereby disrupting the MKP3-ERK2 interaction and impairing MKP3 activation. Enzymatic assays further reveal that BCI significantly reduces ERK2-mediated MKP3 activity without directly interfering with substrate binding at the active site. AF3 structural modeling suggests that BCI binding induces local conformational changes, notably an outward shift of the α4-helix, which exposes a hydrophobic pocket essential for BCI accommodation. Moreover, BCI exhibits differential bind-ing affinities across the MKP family, showing significant interactions with the KBDs of MKPX and MKP5, but markedly weaker or negligible binding to those of MKP1, MKP2, and MKP4. Together, these findings uncover a novel KBD-targeting mechanism of MKP3 inhibition by BCI and highlight the potential of selectively modulating MAPK phosphatases through allosteric disruption of kinase-phosphatase interactions. This strategy may offer a new avenue for the design and optimization of targeted phosphatase inhibitors.},
}

@article {pmid40774731,
year = {2025},
author = {Liang, R and Gao, J and Liu, X and Li, X and Chang, H and Yang, R and Yang, J and Ming, D},
title = {Regulatory measures for mitigating physical and mental health impacts in aerospace environment: A systematic review.},
journal = {Life sciences in space research},
volume = {46},
number = {},
pages = {106-114},
doi = {10.1016/j.lssr.2025.04.003},
pmid = {40774731},
issn = {2214-5532},
mesh = {Humans ; *Space Flight ; *Mental Health ; *Astronauts/psychology ; *Aerospace Medicine ; Weightlessness/adverse effects ; Exercise ; },
abstract = {Long-term spaceflight poses significant challenges to astronauts' physical and mental health, resulting in physiological issues such as osteoporosis, muscle atrophy, and cardiovascular dysfunction, as well as psychological problems like depression, anxiety, social withdrawal, and cognitive decline. As the duration of space missions continues to increase, the above challenges cannot be ignored. Therefore, identifying effective regulatory measures is essential. This article provides a concise review of the latest domestic and international research on strategies to mitigate physiological and psychological risks in aerospace environment. Including coping strategies for musculoskeletal, cardiovascular, and psychological problems, such as exercise, physical stimulation, psychotherapy, and medication, especially traditional Chinese medicine, which need to be further explored and applied. Its ultimate goal is to offer insights for ensuring the safe execution of space missions by astronauts and advancing the field of space medicine.},
}

Humans
*Space Flight
*Mental Health
*Astronauts/psychology
*Aerospace Medicine
Weightlessness/adverse effects
Exercise

@article {pmid40774087,
year = {2025},
author = {Constant, M and Mandal, A and Asanowicz, D and Panek, B and Kotlewska, I and Yamaguchi, M and Gillmeister, H and Kerzel, D and Luque, D and Molinero, S and Vázquez-Millán, A and Pesciarelli, F and Borelli, E and Ramzaoui, H and Beck, M and Somon, B and Desantis, A and Castellanos, MC and Martín-Arévalo, E and Manini, G and Capizzi, M and Gokce, A and Özer, D and Soyman, E and Yılmaz, E and Eayrs, JO and London, RE and Steendam, T and Frings, C and Pastötter, B and Szaszkó, B and Baess, P and Ayatollahi, S and León Montoya, GA and Wetzel, N and Widmann, A and Cao, L and Low, X and Costa, TL and Chelazzi, L and Monachesi, B and Kamp, SM and Knopf, L and Itier, RJ and Meixner, J and Jost, K and Botes, A and Braddock, C and Li, D and Nowacka, A and Quenault, M and Scanzi, D and Torrance, T and Corballis, PM and Laera, G and Kliegel, M and Welke, D and Mushtaq, F and Pavlov, YG and Liesefeld, HR},
title = {A multilab investigation into the N2pc as an indicator of attentional selectivity: Direct replication of Eimer (1996)☆,☆☆,☆☆☆,☆☆☆☆,☆☆☆☆☆.},
journal = {Cortex; a journal devoted to the study of the nervous system and behavior},
volume = {190},
number = {},
pages = {304-341},
doi = {10.1016/j.cortex.2025.05.014},
pmid = {40774087},
issn = {1973-8102},
abstract = {The N2pc is widely employed as an electrophysiological marker of an attention allocation. This interpretation was largely driven by the observation of an N2pc elicited by an isolated relevant target object, which was reported as Experiment 2 in Eimer (1996). All subsequent refined interpretations of the N2pc had to take this crucial finding into account. Despite its central role for neurocognitive attention research, there have been no direct replications and only few conceptual replications of this seminal work. Within the context of #EEGManyLabs, an international community-driven effort to replicate the most influential EEG studies ever published, the present study was selected due to its strong impact on the study of selective attention. We revisit the idea of the N2pc being an indicator of attentional selectivity by delivering a high powered direct replication of Eimer's work through analysis of 779 datasets acquired from 22 labs across 14 countries. Our results robustly replicate the N2pc to form stimuli, but a direct replication of the N2pc to color stimuli technically failed. We believe that this pattern not only sheds further light on the functional significance of the N2pc as an electrophysiological marker of attentional selectivity, but also highlights a methodological problem with selecting analysis windows a priori. By contrast, the consistency of observed ERP patterns across labs and analysis pipelines is stunning, and this consistency is preserved even in datasets that were rejected for (ocular) artifacts, attesting to the robustness of the ERP technique and the feasibility of large-scale multilab EEG (replication) studies.},
}

@article {pmid40773224,
year = {2025},
author = {Zhang, K and Chen, G and Choi, SH},
title = {Converging technologies: vagus nerve stimulation and brain-computer interfaces as catalysts for advancing post-stroke aphasia rehabilitation.},
journal = {International journal of surgery (London, England)},
volume = {},
number = {},
pages = {},
doi = {10.1097/JS9.0000000000003148},
pmid = {40773224},
issn = {1743-9159},
}

@article {pmid40772250,
year = {2025},
author = {Wilkins, RB and Coffin, T and Pham, M and Klein, E and Marathe, M},
title = {Mind the gap: bridging ethical considerations and regulatory oversight in implantable BCI human subjects research.},
journal = {Frontiers in human neuroscience},
volume = {19},
number = {},
pages = {1633627},
pmid = {40772250},
issn = {1662-5161},
abstract = {The advent of Brain-Computer Interface (BCI) technology brings groundbreaking advancements in medical science but also raises important ethical considerations. This manuscript explores the ethical dimensions of implantable BCIs (iBCIs), focusing on the central role of Institutional Review Boards (IRBs) in the United States, in safeguarding participant rights and welfare. As federally mandated bodies, IRBs ensure that informed consent is obtained ethically, emphasizing participant autonomy, preventing undue coercion, while supporting clear and practical communication of risks and benefits. As part of this discussion, this paper touches on the ethical challenges surrounding the enrollment of participants with impaired consent capacity and the long-term implications of implanted brain devices. Additionally, this work underscores the critical importance of robust cybersecurity measures to prevent data breaches and unauthorized manipulation of brain activity. By examining risk assessments, data management practices, and the need for external cybersecurity expertise, this work offers a comprehensive framework for IRB review of iBCI research. This perspective aims to guide ethical iBCI research and protect human subjects in this rapidly evolving field.},
}

@article {pmid40772248,
year = {2025},
author = {Mohamed, MA and Giles, J and AlSaleh, M and Arvaneh, M},
title = {Associations between pre-cue parietal alpha oscillations and event related desynchronization in motor imagery-based brain-computer interface.},
journal = {Frontiers in human neuroscience},
volume = {19},
number = {},
pages = {1625127},
pmid = {40772248},
issn = {1662-5161},
abstract = {INTRODUCTION: Motor Imagery based brain-computer interfaces (MI-BCIs) offer a promising avenue for controlling external devices via neural signals generated through imagined movements. Despite their potential, the performance of MI-BCIs remains highly variable across users and sessions, presenting a barrier to broader adoption.

METHODS: This study explores the influence of pre-cue parietal alpha power on the quality of the event-related desynchronization (ERD) responses, a critical indicator of MI processes. Analyzing data from 102 sessions involving 77 participants.

RESULTS: We identified a robust significant correlation between pre-cue parietal alpha power and ERD magnitude, indicating that elevated pre-cue parietal alpha power is associated with enhanced ERD responses. Additionally, we observed a significant positive relationship between pre-cue parietal alpha power and MI-BCI classification accuracy, highlighting the potential relevance of this neurophysiological metric for BCI performance.

DISCUSSION: Our findings suggest that pre-cue parietal alpha power can serve as a potential marker for optimizing MI-BCI systems. Integrating this marker into individualized training protocols can potentially enhance MI-BCI systems' consistency, and overall accuracy.},
}

@article {pmid40770162,
year = {2025},
author = {Oikonomidou, O and Beresford, MJ and Galve-Calvo, E and Woeckel, A and Parikh, RC and Hitchens, A and Chen, C and Doan, J and Li, B and Ansquer, VD and Frugier, G and Jimenez, MI and Davis, KL and Broughton, EI},
title = {Real-world clinical outcomes associated with first-line palbociclib and aromatase inhibitor therapy among patients with HR+/HER2- advanced breast cancer in Europe.},
journal = {Breast cancer research and treatment},
volume = {},
number = {},
pages = {},
pmid = {40770162},
issn = {1573-7217},
abstract = {PURPOSE: Cyclin-dependent kinase 4/6 inhibitors (CDK4/6is) combined with endocrine therapy is the recommended first-line (1L) treatment for hormone receptor-positive and human epidermal growth factor receptor 2-negative (HR+/HER2-) advanced breast cancer (ABC). Real-world evidence (RWE) describing 1L CDK4/6i regimens and associated clinical outcomes in Europe is limited. The study objective was to describe clinical characteristics, tumor response, and survival outcomes in patients with HR+/HER2- ABC.

METHODS: This retrospective, observational cohort study used data from 52 treatment centers in the UK, Spain, and Germany and included patients who initiated 1L palbociclib + aromatase inhibitor (AI) therapy for ABC between 2016 and 2020. Primary endpoints were real-world progression-free survival (rwPFS) and overall survival (OS).

RESULTS: Data were abstracted from 856 patients. During treatment, complete response, partial response, or stable disease was achieved for 86.1% of patients in Spain, 80.7% in the UK, and 79.0% in Germany, while complete or partial response was achieved for 43.8% of patients in Spain, 34.9% in the UK, and 16.9% in Germany. Median rwPFS during treatment was 28.1 months for patients in Spain, 33.9 months in the UK, and 48.1 months in Germany. Median OS was 51.3 months (95% CI 46.6-NE) in the UK, 65.2 months (95% CI 65.2-NE) in Germany, and not reached in Spain.

CONCLUSION: This RWE supports the clinical effectiveness of 1L palbociclib + AI in routine clinical practice in European countries-consistent with the efficacy observed in clinical trials-and further supports the implementation of palbociclib-based regimens as frontline treatment of HR+/HER2- ABC.},
}

@article {pmid40769403,
year = {2025},
author = {Key, B and Brown, DJ},
title = {How pain fools everyone: An inference to the best explanation.},
journal = {Neuroscience and biobehavioral reviews},
volume = {177},
number = {},
pages = {106317},
doi = {10.1016/j.neubiorev.2025.106317},
pmid = {40769403},
issn = {1873-7528},
abstract = {There is a commonly held assumption that feelings such as pain are causes of behaviour. We say we withdrew our hand from the hotplate because it hurt or that we flinched at the needle because it stung. The causal role of pain is widely implicated in theories of learning and decision-making. But what if this commonsense idea that feelings cause behaviour is just wrong? To date, there is no known mechanism for how subjectively experienced pain directly modulates neural activity and it is hard to see how there could be. There is no known mechanism by which pain could directly gate ion channels. On this basis, we contend that the real cause of behaviour is neural activity and that feelings of pain have no known causal role. This raises the question of whether pain has any function at all-i.e., whether it has causal powers or is merely epiphenomenal. Epiphenomenalism faces the intractable problem of explaining how such an attention-consuming feeling as pain could be epiphenomenal and yet still have survived evolutionary selection. In response, we infer from the available neuroscientific evidence that the best explanation is that pain has a novel, non-causal function and that decisions to act are instead caused by an internal decoding process involving threshold detection of accumulated evidence of pain rather than by pain per se. Because pain is necessarily implicated in the best explanation of subsequent decision-making, we do not conclude that pain is epiphenomenal or functionless even if it has no causal influence over decisions or subsequent actions. On this view, pain functions to mark neural pathways that are the causes of behaviour as salient, serving as a ground but not a cause of subsequent decision-making and action. This perspective has far-reaching implications for diverse fields including neuropsychiatry, biopsychosocial modelling, robotics, and brain-computer interfaces.},
}

@article {pmid40769034,
year = {2025},
author = {Guggisberg, AG and Siebner, HR and Lundell, H and Madsen, MAJ and Madsen, KH and Wiggermann, V and Mégevand, P and Proix, T and Dalal, SS and Grouiller, F and Vulliémoz, S and Ušćumlić, M and Marchesotti, S},
title = {Emergent technologies in clinical neurophysiology to study the central nervous system: IFCN handbook chapter.},
journal = {Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology},
volume = {178},
number = {},
pages = {2110942},
doi = {10.1016/j.clinph.2025.2110942},
pmid = {40769034},
issn = {1872-8952},
abstract = {This chapter reviews recent breakthroughs in neurophysiological brain mapping, focusing on EEG, MEG, and MRI technologies and their integration with stimulation techniques. High-density and portable EEG systems now allow more precise, user-friendly, and mobile recordings. Machine learning enhances biomarker detection and diagnostic power, particularly in epilepsy, cognitive disorders, and sleep pathology. MEG has become more versatile with the development of wearable optically pumped magnetometers (OPMs), enabling recordings during natural movement and broadening clinical access. Intracranial EEG (iEEG) remains central in epilepsy surgery and neuroscience research, with innovations in seizure forecasting and high-resolution speech decoding via microelectrode arrays and Neuropixels probes. Structural and functional MRI have advanced through ultra-high field imaging, quantitative tissue characterization, and connectomics, while functional MRS (fMRS) enables real-time tracking of neurochemical changes. Crucially, these mapping tools increasingly converge with brain stimulation-TMS, TES, focused ultrasound, and deep brain stimulation-to enable real-time, individualized modulation of brain networks. Simultaneous EEG-fMRI and artifical intelligence-driven brain-computer interfaces further enhance precision interventions. Together, these technologies are transforming clinical neurophysiology, offering new insights into brain function and advancing personalized neuromodulation therapies for neurological and psychiatric disorders.},
}

@article {pmid40768522,
year = {2025},
author = {Singh, G and Chharia, A and Upadhyay, R and Kumar, V and Longo, L},
title = {PyNoetic: A modular python framework for no-code development of EEG brain-computer interfaces.},
journal = {PloS one},
volume = {20},
number = {8},
pages = {e0327791},
pmid = {40768522},
issn = {1932-6203},
mesh = {*Brain-Computer Interfaces ; *Electroencephalography/methods ; Humans ; *Software ; Algorithms ; *Brain/physiology ; Signal Processing, Computer-Assisted ; },
abstract = {Electroencephalography (EEG)-based Brain-Computer Interfaces (BCIs) have emerged as a transformative technology with applications spanning robotics, virtual reality, medicine, and rehabilitation. However, existing BCI frameworks face several limitations, including a lack of stage-wise flexibility essential for experimental research, steep learning curves for researchers without programming expertise, elevated costs due to reliance on proprietary software, and a lack of all-inclusive features leading to the use of multiple external tools affecting research outcomes. To address these challenges, we present PyNoetic, a modular BCI framework designed to cater to the diverse needs of BCI research. PyNoetic is one of the very few frameworks in Python that encompasses the entire BCI design pipeline, from stimulus presentation and data acquisition to channel selection, filtering, feature extraction, artifact removal, and finally simulation and visualization. Notably, PyNoetic introduces an intuitive and end-to-end GUI coupled with a unique pick-and-place configurable flowchart for no-code BCI design, making it accessible to researchers with minimal programming experience. For advanced users, it facilitates the seamless integration of custom functionalities and novel algorithms with minimal coding, ensuring adaptability at each design stage. PyNoetic also includes a rich array of analytical tools such as machine learning models, brain-connectivity indices, systematic testing functionalities via simulation, and evaluation methods of novel paradigms. PyNoetic's strengths lie in its versatility for both offline and real-time BCI development, which streamlines the design process, allowing researchers to focus on more intricate aspects of BCI development and thus accelerate their research endeavors.},
}

*Brain-Computer Interfaces
*Electroencephalography/methods
Humans
*Software
Algorithms
*Brain/physiology
Signal Processing, Computer-Assisted

@article {pmid40768143,
year = {2025},
author = {Gallien, Y and Broussouloux, S and Demesmaeker, A and Fouillet, A and Mertens, C and Chin, F and Cassourret, G and Caserio-Schonemann, C and du Roscoät, E and Le Strat, Y and , },
title = {Outcomes and Cost-Benefit of a National Suicide Reattempt Prevention Program.},
journal = {JAMA network open},
volume = {8},
number = {8},
pages = {e2525671},
pmid = {40768143},
issn = {2574-3805},
mesh = {Humans ; Female ; Male ; Cost-Benefit Analysis ; Adult ; Retrospective Studies ; Middle Aged ; France/epidemiology ; *Suicide Prevention ; *Suicide, Attempted/statistics & numerical data ; },
abstract = {IMPORTANCE: Suicide attempts (SA) are a major public health concern and a preventable cause of premature death with a significant societal cost. Suicide reattempt (SR) rates are high in the postdischarge period for an SA. Brief contact interventions (BCIs) aim to prevent SR by recontacting patients after discharge through crisis cards, calls, letters, or messages. A nationwide BCI was deployed in 6 French regions between 2015 and 2017.

OBJECTIVE: To assess the outcomes and the cost benefit of the program in reducing SR risk within 12 months after discharge.

Retrospective multicenter cohort study using nationwide data from the French health insurance database and emergency department surveillance system. Patients exposed to the program between 2015 and 2017 were matched 1:1 with unexposed patients based on age, sex, history of SA, and diagnosis codes using propensity scores and followed up for 12 months. Survival and cost-benefit analyses were conducted in [month to month] 2022.

EXPOSURE: Participation in the program, including structured follow-up using crisis cards, telephone calls, and/or postcards for up to 6 months after discharge.

MAIN OUTCOMES AND MEASURES: The primary outcome was time to first SR or suicide-related death within 12 months. The secondary outcome was the number of SRs and cost savings.

RESULTS: Among 23 146 individuals, 14 504 (62.6%) were female, 12 244 (52.9%) had no history of SA, and the mean (SD) age was 39 (17) years. Exposure to the program was associated with a lower risk of SR (adjusted hazard ratio [aHR], 0.62; 95% CI, 0.59-0.67). This association was consistent regardless of patients' history of SAs (aHR, 0.63; 95% CI, 0.57-0.71 for those without prior attempts; aHR, 0.61; 95% CI, 0.56-0.66 for those with prior attempts) and appeared greater among female participants (aHR, 0.59; 95% CI, 0.54-0.68) than male participants (aHR, 0.68; 95% CI, 0.61-0.76). The program yielded a return on investment of €2.06 (95% CI, €1.58-€2.50) per euro spent.

CONCLUSION AND RELEVANCE: In this cohort study, exposure to the program was associated with a reduced risk of SR and favorable economic outcomes.},
}

Humans
Female
Male
Cost-Benefit Analysis
Adult
Retrospective Studies
Middle Aged
France/epidemiology
*Suicide Prevention
*Suicide, Attempted/statistics & numerical data

@article {pmid40763175,
year = {2025},
author = {Voola, M and Vignali, L and Mojallal, H and Bogdanov, C and Távora-Vieira, D},
title = {Using Cortical Auditory Evoked Potentials in Active Middle Ear and Bone Conduction Implant Users: An Objective Method to Optimize the Fitting.},
journal = {Otology & neurotology : official publication of the American Otological Society, American Neurotology Society [and] European Academy of Otology and Neurotology},
volume = {},
number = {},
pages = {},
doi = {10.1097/MAO.0000000000004581},
pmid = {40763175},
issn = {1537-4505},
abstract = {OBJECTIVE: The study aimed to investigate whether cortical auditory evoked potential (CAEP) measures could be used to optimize active middle ear implant (aMEI) and bone conduction implant (BCI) fitting, with the goal of improving hearing outcomes in adults.

DESIGN: CAEPs were measured in response to LING sounds /OO/, /AH/, and /SH/ presented in sound field. If CAEP responses were recorded for all sounds, no map adjustments were performed. If a CAEP response was absent for one or more sounds, map parameters were optimized until a CAEP response could be induced. Functional outcomes were measured as pre- vs postoptimization adaptive speech-in-noise results. Subjective feedback was also collected.

RESULTS: Of the 15 participants, one was excluded from the study, three did not need optimization, nine were successfully optimized using CAEP measurements, and two could not be optimized. Comparison of CAEP morphology showed significant differences pre- vs postoptimization for middle- and high-frequency sounds (i.e., /AH/ and /SH/). Speech-in-noise testing revealed significant improvements pre- vs postoptimization, and participants were generally satisfied with the overall procedure.

CONCLUSION: These findings demonstrated that middle- and high-frequency tokens could be successfully optimized using CAEPs, resulting in significant improvements in hearing performance. Our results support the use of CAEPs for the optimization of aMEI and BCI adult users' fitting.},
}

@article {pmid40761593,
year = {2025},
author = {Ding, S and Wang, K and Jiang, W and Xu, C and Bo, H and Ma, L and Li, H},
title = {DGAT: a dynamic graph attention neural network framework for EEG emotion recognition.},
journal = {Frontiers in psychiatry},
volume = {16},
number = {},
pages = {1633860},
pmid = {40761593},
issn = {1664-0640},
abstract = {INTRODUCTION: Emotion recognition based on electroencephalogram (EEG) signals has shown increasing application potential in fields such as brain-computer interfaces and affective computing. However, current graph neural network models rely on predefined fixed adjacency matrices during training, which imposes certain limitations on the model's adaptability and feature expressiveness.

METHODS: In this study, we propose a novel EEG emotion recognition framework known as the Dynamic Graph Attention Network (DGAT). This framework dynamically learns the relationships between different channels by utilizing dynamic adjacency matrices and a multi-head attention mechanism, allowing for the parallel computation of multiple attention heads. This approach reduces reliance on specific adjacency structures while enabling the model to learn information in different subspaces, significantly improving the accuracy of emotion recognition from EEG signals.

RESULTS: Experiments conducted on the EEG emotion datasets SEED and DEAP demonstrate that DGAT achieves higher emotion classification accuracy in both subject-dependent and subject-independent scenarios compared to other models. These results indicate that the proposed model effectively captures dynamic changes, thereby enhancing the accuracy and practicality of emotion recognition.

DISCUSSION: DGAT holds significant academic and practical value in the analysis of emotional EEG signals and applications related to other physiological signal data.},
}

@article {pmid40761318,
year = {2025},
author = {Xiao, H and Huang, C and Wu, Y and Wang, JJ and Wang, H},
title = {Establishing a social behavior paradigm for female mice.},
journal = {Frontiers in neuroscience},
volume = {19},
number = {},
pages = {1630491},
pmid = {40761318},
issn = {1662-4548},
abstract = {INTRODUCTION: Social behavior assessment in female mice has been historically challenged by inconsistent results from the classic three-chamber test, which reliably detects social preferences in males but fails to capture female specific social dynamics.

METHODS: We developed a modified three-chamber paradigm by replacing standard social stimuli with familiar cagemates (co-housed for 2 weeks, 1 week or 24 hours) to better assess sociability and novelty preference in female mice.

RESULTS: In the sociability phase, female mice showed a significant preference for interacting with cagemates compared to empty chambers. Crucially, during the social preference phase, test females demonstrated robust novelty seeking behavior, spending significantly more time exploring novel conspecifics compared to 2-week cagemates or 1-week cagemates. This preference trended similarly, though non significantly, with 24-hour cagemates. Notably, our paradigm enhanced social preference indices without altering total interaction time, confirming its specificity for detecting novelty driven exploration.

DISCUSSION: These findings overcome the limitations of traditional paradigms and establish a validated framework for studying female social behavior, with critical implications for modeling neurodevelopmental disorders like autism spectrum disorder (ASD) in female preclinical research.},
}

@article {pmid40761312,
year = {2025},
author = {Chen, Y and Xu, R and Lau, AT and He, X and Chen, W and Wang, X and Cichocki, A and Jin, J},
title = {Leveraging low-frequency components for enhanced high-frequency steady-state visual evoked potential based brain computer interface in fast calibration scenario.},
journal = {Cognitive neurodynamics},
volume = {19},
number = {1},
pages = {124},
pmid = {40761312},
issn = {1871-4080},
abstract = {High-frequency steady-state visual evoked potential-based brain-computer interface (SSVEP-BCI) systems offer improved user comfort but suffer from reduced performance compared to their low-frequency counterparts, limiting their practical application. To address this issue, we propose a transfer learning-based method that leverages low-frequency SSVEP data to enhance high-frequency SSVEP performance. A filtering mechanism is designed to extract informative components from low-frequency signals, and the least squares algorithm is employed to generate high-quality synthetic high-frequency data. Experiments conducted on two public datasets using TDCA, eTRCA, and advanced TRCA-based algorithms demonstrate significant performance improvements. Our approach requires only two calibration trials, achieving 9.03% and 14.49% accuracy increases for eTRCA and TDCA in Dataset 1, and 13.91% and 14.53% improvements in Dataset 2, all within 1.5 s. Moreover, our approach effectively addresses the issue of single calibration data for high-frequency SSVEP-BCI systems. These results support the feasibility of fast calibration and improved performance in real-world high-frequency BCI applications.},
}

@article {pmid40760397,
year = {2025},
author = {Mondal, S and Nag, A},
title = {A computational eye state classification model using EEG signal based on data mining techniques: comparative analysis.},
journal = {Physical and engineering sciences in medicine},
volume = {},
number = {},
pages = {},
doi = {10.1007/s13246-025-01619-w},
pmid = {40760397},
issn = {2662-4737},
abstract = {Artificial Intelligence has shown great promise in healthcare, particularly in non-invasive diagnostics using bio signals. This study focuses on classifying eye states (open or closed) using Electroencephalogram (EEG) signals captured via a 14-electrode neuroheadset, recorded through a Brain-Computer Interface (BCI). A publicly available dataset comprising 14,980 instances was used, where each sample represents EEG signals corresponding to eye activity. Fourteen classical machine learning (ML) models were evaluated using a tenfold cross-validation approach. The preprocessing pipeline involved removing outliers using the Z-score method, addressing class imbalance with SMOTETomek, and applying a bandpass filter to reduce signal noise. Significant EEG features were selected using a two-sample independent t-test (p < 0.05), ensuring only statistically relevant electrodes were retained. Additionally, the Common Spatial Pattern (CSP) method was used for feature extraction to enhance class separability by maximizing variance differences between eye states. Experimental results demonstrate that several classifiers achieved strong performance, with accuracy above 90%. The k-Nearest Neighbours classifier yielded the highest accuracy of 97.92% with CSP, and 97.75% without CSP. The application of CSP also enhanced the performance of Multi-Layer Perceptron and Support Vector Machine, reaching accuracies of 95.30% and 93.93%, respectively. The results affirm that integrating statistical validation, signal processing, and ML techniques can enable accurate and efficient EEG-based eye state classification, with practical implications for real-time BCI systems and offering a lightweight solution for real-time healthcare wearable applications healthcare applications.},
}

@article {pmid40759633,
year = {2025},
author = {Chen, C and Wu, J and Qin, C and Qiu, Y and Jiang, N and Wang, Q and Liu, M and Jiang, D and Yuan, Q and Wei, X and Zhuang, L and Wang, P},
title = {Planar-electroporated cell biosensor for investigating potential therapeutic effects of ectopic bitter receptors.},
journal = {Microsystems & nanoengineering},
volume = {11},
number = {1},
pages = {147},
pmid = {40759633},
issn = {2055-7434},
support = {32201082//National Natural Science Foundation of China (National Science Foundation of China)/ ; 62301481//National Natural Science Foundation of China (National Science Foundation of China)/ ; },
abstract = {Bitter receptors were initially identified within the gustatory system. In recent years, bitter receptors have been found in various non-gustatory tissues, including the cardiovascular system, where they participate in diverse physiological processes. To investigate the electrophysiological and potential therapeutic implications of bitter receptors, we have developed a highly sensitive, multifunctional planar-electroporated cell biosensor (PECB) for high-throughput evaluation of the effects of bitter substances on cardiomyocytes. The PECB demonstrated the capability for high-throughput, stable, and reproducible detection of intracellular action potentials (IAPs). In comparison to conventional biosensors that utilize extracellular action potentials (EAPs) for data analysis, the IAPs recorded by the PECB provided high-resolution insights into action potentials, characterized by increased amplitudes and an enhanced signal-to-noise ratio (SNR). The PECB successfully monitored IAPs induced by the activation of bitter receptors by using three bitter substances: diphenidol, denatonium benzoate, and arbutin in cardiomyocytes. To further assess the drug development ability of our PECB, we established an in vitro long QT syndrome (LQTS) model to investigate the potential therapeutic effects of arbutin. The results indicated that arbutin altered the electrophysiological properties of cardiomyocytes and significantly shortened the repolarization time in the LQTS model. Moreover, it demonstrated its potential mechanistic pathway by activating bitter receptors to modulate cardiac ion channel activities. The developed PECB provides an effective platform for high-throughput screening of substrates of bitter receptors for the treatment of heart disease, presenting new opportunities for the development of antiarrhythmic therapies.},
}

@article {pmid40757371,
year = {2025},
author = {Landsmeer, LPL and Hua, E and Abunahla, H and Siddiqi, MA and Ishihara, R and De Zeeuw, CI and Hamdioui, S and Strydis, C},
title = {Efficient implementation of the Hodgkin-Huxley potassium channel via a single volatile memristor.},
journal = {Frontiers in neuroscience},
volume = {19},
number = {},
pages = {1569397},
pmid = {40757371},
issn = {1662-4548},
abstract = {INTRODUCTION: In 2012, potassium and sodium ion channels in Hodgkin-Huxley-based brain models were shown to exhibit memristive behavior. This positioned memristors as strong candidates for implementing biologically accurate artificial neurons. Memristor-based brain simulations offer advantages in energy efficiency, scalability, and compactness, benefiting fields such as soft robotics, embedded systems, and neuroprosthetics.

METHODS: Previous approaches used current-controlled Mott memristors, which poorly matched the voltage-controlled nature of ion channels. This study employs volatile, oxide-based memristors that leverage electric-field-driven oxygen-vacancy migration to emulate voltage-dependent channel behavior. We selected candidate WOx and NbOx memristors and modeled their dynamics to verify performance as Hodgkin-Huxley potassium channels.

RESULTS: The device exhibits sigmoidal gating and voltage-dependent time constants consistent with the theoretical model. By scaling the passive circuitry around the memristors, we show that they capture the essential mechanisms of potassium ion-channels, although spike height is reduced due to strong non-linear voltage-dependence. Still, by cascading multiple compartments, typical spike propagation is retained.

DISCUSSION: This is the first demonstration of a voltage-controlled memristor replicating the Hodgkin-Huxley potassium channel, validating its potential for more efficient brain simulation hardware.},
}

@article {pmid40754610,
year = {2025},
author = {Alghamdi, AM and Ashraf, MU and Bahaddad, AA and Almarhabi, KA and Al Shehri, WA and Daraz, A},
title = {Cross-subject EEG signals-based emotion recognition using contrastive learning.},
journal = {Scientific reports},
volume = {15},
number = {1},
pages = {28295},
pmid = {40754610},
issn = {2045-2322},
support = {UJ-24-SUCH-1247//University of Jeddah/ ; },
mesh = {Humans ; *Electroencephalography/methods ; *Emotions/physiology ; *Brain-Computer Interfaces ; *Brain/physiology ; Algorithms ; Signal Processing, Computer-Assisted ; Adult ; Male ; *Machine Learning ; Female ; },
abstract = {Electroencephalography (EEG) signals based emotion brain computer interface (BCI) is a significant field in the domain of affective computing where EEG signals are the cause of reliable and objective applications. Despite these advancements, significant challenges persist, including individual differences in EEG signals across subjects during emotion recognition. To cope this challenge, current study introduces a cutting-edge cross subject contrastive learning (CSCL) scheme for EEG signals representation of brain region. The proposed scheme addresses the generalisation across subjects directly, which is a primary challenge in EEG signals-based emotions recognition. The proposed CSCL scheme captures the complex patterns effectively by employing emotions and stimulus contrastive losses within hyperbolic space. CSCL is designed primarily to learn representations that can effectively distinguish signals originating from different brain regions. Further, we evaluate the significance of our proposed CSCL scheme on five different datasets, including SEED, CEED, FACED and MPED, and obtain 97.70%, 96.26%, 65.98%, and 51.30% respectively. The experimental results show that our proposed CSCL scheme demonstrates strong effectiveness while addressing the challenges related to cross subject variability and label noise in the EEG-based emotion recognition system.},
}

Humans
*Electroencephalography/methods
*Emotions/physiology
*Brain-Computer Interfaces
*Brain/physiology
Algorithms
Signal Processing, Computer-Assisted
Adult
Male
*Machine Learning
Female

@article {pmid40754454,
year = {2025},
author = {Ikegaya, Y},
title = {Semantics of Brain-Machine Hybrids.},
journal = {Biological & pharmaceutical bulletin},
volume = {48},
number = {8},
pages = {1150-1164},
doi = {10.1248/bpb.b25-00285},
pmid = {40754454},
issn = {1347-5215},
mesh = {*Brain-Computer Interfaces ; Humans ; Semantics ; *Brain/physiology ; Animals ; Electroencephalography ; },
abstract = {Brain-machine interfaces, also known as brain-computer interfaces, represent a rapidly advancing field at the intersection of neuroscience and technology, enabling direct communication pathways between the brain and external devices. This review charts the historical evolution of brain-machine interfaces, from fundamental discoveries such as electroencephalography and volitional single-neuron control to sophisticated decoding of neural population activity for real-time control of robotics and sensory reconstruction. Clinical breakthroughs lead to unprecedented success in restoring motor function after paralysis through brain-spine interfaces, enabling high-speed communication through thought-to-text/speech systems, providing sensory feedback for prosthetics, and implementing closed-loop neuromodulation for the treatment of neurological disorders such as epilepsy and depression. Beyond therapeutic applications, brain-machine interfaces drive innovation in neurotech art (neuroart) and entertainment (neurogames), allowing neural activity to directly generate music, visual art, and interactive experiences. In addition, the potential for human augmentation is expanding, with technologies that enhance physical strength, sensory perception, and cognitive abilities. These converging advances challenge fundamental concepts of human identity and suggest that brain-machine interfaces may enable humanity to transcend inherent biological limitations, potentially ushering in an era of technologically guided evolution.},
}

*Brain-Computer Interfaces
Humans
Semantics
*Brain/physiology
Animals
Electroencephalography

@article {pmid40754053,
year = {2025},
author = {Zhao, X and Xu, R and Zhang, Y and Lau, AT and Xu, R and Wang, X and Cichocki, A and Jin, J},
title = {A novel paradigm based on radar-like scanning for directional recognition in event-related potentials based brain-computer interfaces.},
journal = {Journal of neuroscience methods},
volume = {423},
number = {},
pages = {110546},
doi = {10.1016/j.jneumeth.2025.110546},
pmid = {40754053},
issn = {1872-678X},
abstract = {BACKGROUND: Event-related potentials (ERPs) based brain-computer interface (BCI) systems have shown significant potential for directional control applications. Existing paradigms are constrained by the limited scalability of directional commands that demand interface reconfiguration for varying target numbers.

NEW METHOD: We propose a novel radar-like scanning (RS) paradigm for 32-directional recognition tasks to address these limitations. This paradigm continuously scans through directions using a sector-shaped visual stimulus, naturally evoking ERP responses without discrete directional indicators. During the online experiments, an early-stopping strategy is employed to enhance efficiency. Additionally, this study analyzes subjects' directional recognition performance using EEGNet under three sector rotation periods. Thirteen subjects participated in the experiments.

RESULTS: The grand-averaged ERP amplitudes exhibited a stronger negative deflection in the parietal, occipital, and temporoparietal regions. The results demonstrated that, with a 2 s rotation period and early-stopping strategy, the best subject achieved an accuracy of 87.50 % with a mean absolute angle error of 1.64°. When the directional error tolerance was set to 11.25°, the subject-averaged accuracy reached 91.83 % under the same conditions. Longer rotation periods led to better subject-averaged recognition performance. When the rotation period was short (1 s), targets close to the scanning center were challenging to recognize.

Compared with others, the RS paradigm enables more fine-grained directional target recognition and is unaffected by the target numbers.

CONCLUSIONS: The proposed paradigm demonstrates significant potential for applications in ERP-BCI systems.},
}

@article {pmid40749591,
year = {2025},
author = {Elliss, H and Kevill, JL and Proctor, K and Farkas, K and Bailey, O and Shuttleworth, J and Jones, DL and Kasprzyk-Hordern, B},
title = {Flow-driven biomarker movement in gravitational sewers for wastewater-based epidemiology and public health monitoring.},
journal = {Water research},
volume = {287},
number = {Pt A},
pages = {124269},
doi = {10.1016/j.watres.2025.124269},
pmid = {40749591},
issn = {1879-2448},
abstract = {The movement of biological (genetic viral, fungal or bacterial) and chemical indicators (BCIs) within sewer networks is critical to wastewater-based epidemiology (WBE) enabling accurate calculation of chemical and pathogen loads within a community. These quantified BCIs, which include genetic material from pathogens as well as pharmaceuticals, from a range of classes, serve as proxies for community-wide health and behaviour patterns. However, a critical knowledge gap exists in understanding how different BCIs move within complex sewer systems, which could lead to misinterpretation of community-level data. This study aims to address this gap by investigating the transport behaviour of 5 common BCIs (carbamazepine, metoprolol, naproxen, venlafaxine and PMMoV) in a real-world gravitational sewer network. In addition, we also spiked the wastewater with deuterated caffeine-d9, allowing discrimination from native caffeine present in the network and therefore, enabling an accurate assessment of recovery due to no public use. Our results revealed that all targets travelled with limited dispersion throughout the first stage of the gravitational sewer, 0.8 km after introduction into the network. It was observed that carbamazepine (logD = 2.8 at sewer pH), exhibited more dispersion throughout the remaining 2.3 km of the gravitational system, showing a broader, more asymmetric trace with increased tailing, which potentially indicates sorption to the solid phase, impacting its movement through the network. All other chemical targets had similar movement patterns, indicating a lower tendency to bind to the solid phase (logD < 1, at average sewer pH). Loads were calculated using dye-predicted flow rates and normalized to caffeine-d9. Carbamazepine loads were under-predicted by 74 %, attributed to losses to the solid phase throughout the sewer system. Conversely, metoprolol, naproxen, and venlafaxine loads were over-predicted (146 %, 32 %, and 129 %, respectively), likely due to additional public inputs. Our results demonstrate that more hydrophilic chemicals move throughout the sewer network with limited dispersion while hydrophobic compounds may experience significant losses. These findings have important implications for the accurate interpretation of WBE data, future BCI tracing studies and the selection of appropriate chemical markers for community health monitoring.},
}

@article {pmid40748806,
year = {2025},
author = {Yuan, X and Zhang, Y and Rolfe, P},
title = {IIMCNet: Intra- and Inter-modality Correlation Network for Hybrid EEG-fNIRS Brain-Computer Interface.},
journal = {IEEE journal of biomedical and health informatics},
volume = {PP},
number = {},
pages = {},
doi = {10.1109/JBHI.2025.3594203},
pmid = {40748806},
issn = {2168-2208},
abstract = {Hybrid Brain-Computer Interface (BCI) enhances accuracy and reliability by leveraging the complementary information provided by multi-modality signal fusion. EEG-fNIRS, a fusion of electroencephalogram (EEG) and functional near-infrared spectroscopy (fNIRS), have emerged as the suitable techniques for real-world BCI applications due to their portability and economic viability. Existing methods typically focus on the high-level feature representation with late-fusion or early-fusion strategies during the recognition tasks. However, they usually overlook the joint feature extraction of both intra-modality and inter-modality, which is crucial for optimizing BCI performance. In this study, we introduce an Intra- and Inter-modality Correlation Network (IIMCNet) to integrate both the inherent features derived from individual modalities: EEG, deoxygenated hemoglobin (HbR), and oxygenated hemoglobin (HbO), as well as the cross-modality features between EEG-HbR, EEG-HbO, and HbR-HbO data. The intra-modality correlation features are generated using a late fusion method (Intra-net), which combines the uni-modality features extracted by E-Net and f-Net. Concurrently, the inter-modality correlation features are extracted using an early fusion method (Inter-net). Inter-net is consist of three dilated convolution-based C-Nets that focus on neurovascular coupling across modalities. Finally, three intra-modality features, three inter-modality features, and the concatenate hybrid feature are fed into deep supervision module to enhance robustness and accuracy. Experiment results demonstrate the IIMCNet exhibits superior performance compared to methods that rely solely on either intra-modality or inter-modality correlation networks. Furthermore, IIMCNet outperforms other state-of-the-art methods in motor imagery and mental arithmetic tasks, respectively. (The code is available at: github.com/Y-xiaoyang/IIMCNet).},
}

@article {pmid40748802,
year = {2025},
author = {Wang, J and Wang, Z and Xu, T and Li, A and Si, Y and Zhou, T and Zhao, X and Hu, H},
title = {Enhancing the Reliability of Affective Brain-Computer Interfaces by Using Specifically Designed Confidence Estimator.},
journal = {IEEE journal of biomedical and health informatics},
volume = {PP},
number = {},
pages = {},
doi = {10.1109/JBHI.2025.3594219},
pmid = {40748802},
issn = {2168-2208},
abstract = {Recent years, the diverse applications of electroencephalography (EEG) - based affective brain-computer interfaces (aBCIs) are being extensively explored. However, due to adverse factors like noise and physiological variability, the recognition capability of aBCIs can unforeseeably suffer abrupt declines. Since the timing of these aBCI failures is unknown, placing trust in aBCIs without scrutiny can lead to undesirable consequences. To alleviate this issue, we propose an algorithm for estimating the reliability of aBCI (primarily Graph Convolutional Network), synchronously delivering a probabilistic confidence score upon aBCI decision completion, thereby reflecting the aBCI's real-time recognition capabilities. Methodologically, we use the Maximum Softmax Probability (MSP) from EEG recognition networks as confidence scores and leverage the Scaling Operator to calibrate them. Then, the Projection Operator is employed to address confidence estimation biases caused by noise and subject variability. For the numerical concentration of MSP, we provide fresh insights into its causes and propose corresponding solutions. The derivation of the estimator from the Maximum Entropy Principle is also substantiated for robust theoretical underpinnings. Finally, we confirm theoretically that the estimator does not compromise BCI performance. In experiments conducted on public datasets SEED and SEED-IV, the proposed algorithm demonstrates superior performance in estimating aBCIs reliability compared to other benchmarks, and commendable adaptability to new subjects. This research has the potential to lead to more trustworthy aBCIs and advance their broader application in complex real-world scenarios.},
}

@article {pmid40746978,
year = {2025},
author = {Enemark, C},
title = {Loyal Wingmen, Artificial Intelligence, and Cognitive Enhancement: A Warning against Cyborg-Drone Warfare.},
journal = {Journal of military ethics},
volume = {24},
number = {1},
pages = {4-20},
pmid = {40746978},
issn = {1502-7589},
abstract = {Some states are planning to acquire armed drones that incorporate artificial intelligence (AI) and fly alongside inhabited aircraft. The use of drones according to this "Loyal Wingman" concept is an example of tactical human-machine teaming, and it could be militarily advantageous in future aerial warfare. Incorporating AI into the operation of a weapon system's critical functions (selecting and engaging targets) nevertheless carries an ethical risk: that a human will be unable to exercise adequate control over the use of force and unable to take responsibility for any injustice caused. To reduce this risk, one potential approach is to pursue "meaningful human control" over armed and AI-enabled drones by increasing their human supervisors' cognitive capacity. The use of brain-computer interfaces (BCIs) to achieve such an increase might be beneficial from the perspective of military ethics if it enabled faster human interventions to prevent unjust, AI-associated harms. However, as this article shows, that benefit would be outweighed by the ethical downsides of waging cyborg-drone warfare: the undermining of pilots' hors de combat noncombatant status and of human moral agency in the use of force.},
}

@article {pmid40746851,
year = {2025},
author = {Aziz, NA and Ng, K and Alifrangis, C and Tran, B and Conduit, C and Liow, E and Ackerman, C and Georgescu, R and Jamal, T and Relton, C and Mayer, E and Nicol, D and Cazzaniga, W and Huddart, R and Reid, A and Shamash, J and Rajan, P},
title = {Therapy de-escalation for testicular cancer (THERATEST): A multi-centre observational cohort feasibility study of de-escalation therapies for good prognosis stage II germ cell tumours.},
journal = {BJUI compass},
volume = {6},
number = {8},
pages = {e70057},
pmid = {40746851},
issn = {2688-4526},
abstract = {BACKGROUND: Standard of care (SOC) treatments for International Germ Cell Cancer Collaborative Group (IGCCCG) good prognosis stage II germ cell tumours (GCT) involve primary orchidectomy followed by combination chemotherapy for both seminoma and non-seminomatous germ cell tumours (NSGCT). Alternatively, external beam radiotherapy may be used for seminoma and retroperitoneal lymph node dissection (RPLND) for NSGCT. While these treatments achieve high cure rates, they are associated with significant toxicities. De-escalation strategies including three cycles of Carboplatin AUC10 or robotic RPLND with or without adjuvant chemotherapy have demonstrated potential to reduce treatment-related toxicity in stage II seminoma while preserving oncological efficacy. However, these approaches are not widely adopted due to limited prospective comparative trials.

STUDY DESIGN: The THERATEST trial is a prospective multicentre observational feasibility study evaluating participants receiving SOC treatments for good prognosis stage II seminoma and NSGCT or de-escalated treatments for stage II seminoma.

ENDPOINTS: The primary endpoints are to assess feasibility of recruitment and retention. Secondary endpoints include assessing health-related quality of life (HRQOL), sexual function and satisfaction, progression-free survival (PFS), overall survival (OS) and safety and treatment-related complications.

PATIENTS AND METHODS: Thirty participants with good prognosis stage II seminoma or NSGCTs will be recruited over 18 months into two cohorts: de-escalation arm and SOC arm. The de-escalation cohort will receive either Carboplatin AUC10 or robotic RPLND with or without adjuvant therapy depending on institutional SOC. Participants who decline or are ineligible for de-escalation will receive SOC treatment: combination chemotherapy or radiotherapy for seminoma and combination chemotherapy for NSGCT. All participants will be followed for two years post-treatment or until withdrawal. Data collection includes recruitment and retention rates, disease status, surgical outcomes, adverse events and patient-reported outcomes using validated questionnaire: EORTC QLQ-TC26, EORTC QLQ-C30, Brief Male Sexual Function Inventory (BMSFI) and additional enquiries on anejaculation.

COORDINATING CENTRE: THERATEST Trial Coordinator, Centre for Experimental Cancer Medicine, Barts Cancer Institute, Queen Mary University of London, Old Anatomy Building, Charterhouse Square, London, EC1M 6BQ|T: 0207882 8497|E: bci-theratest@qmul.ac.uk.

TRIAL REGISTRATION NUMBER: ISRCTN61007118.},
}

@article {pmid40746199,
year = {2025},
author = {Madhavan, S},
title = {Harnessing Neuroplasticity: The Role of Priming in Enhancing Post Stroke Motor Function.},
journal = {Restorative neurology and neuroscience},
volume = {},
number = {},
pages = {9226028251358162},
doi = {10.1177/09226028251358162},
pmid = {40746199},
issn = {1878-3627},
abstract = {Stroke remains a leading cause of disability worldwide, highlighting the need for innovative neurorehabilitation strategies to enhance recovery. Recent advancements emphasize neuroplasticity-the brain's ability to reorganize and form new connections-through targeted interventions. Among these, cortical priming has emerged as a promising approach to enhance neuroplasticity and improve motor recovery post-stroke by modulating brain excitability for optimal motor learning. This review explores the role of cortical priming in stroke rehabilitation, highlighting its ability to enhance neural excitability and plasticity in motor-related brain regions. Various priming techniques, including non-invasive brain stimulation (rTMS, tDCS), deep brain stimulation (DBS), vagus nerve stimulation (VNS), brain-computer interfaces (BCIs), movement-based priming, aerobic exercise, and sensory stimulation, are examined. Despite promising findings, challenges remain in optimizing protocols and addressing individual variability. Future directions focus on biomarker-driven rehabilitation, personalized strategies, and large-scale trials to integrate cortical priming into clinical practice.},
}

@article {pmid40745321,
year = {2025},
author = {Chen, J and Liu, Q and Chen, G and Cai, G and Jiang, J and Yang, X and Tan, C and Zhang, C and Xu, G and Lan, Y},
title = {iTBS on RDLPFC improves performance of motor imagery: a brain-computer interface study combining EEG and fNIRS.},
journal = {Journal of neuroengineering and rehabilitation},
volume = {22},
number = {1},
pages = {172},
pmid = {40745321},
issn = {1743-0003},
support = {82072548//National Science Foundation of China/ ; 82472619//National Science Foundation of China/ ; 2022YFC2009700//Natural Key Research and Development Program of China/ ; 202206010197 and 202201020378//Guangzhou Municipal Science and Technology Program/ ; },
mesh = {Humans ; *Brain-Computer Interfaces ; Male ; Female ; Spectroscopy, Near-Infrared ; Electroencephalography/methods ; *Imagination/physiology ; Adult ; Young Adult ; *Transcranial Magnetic Stimulation/methods ; *Dorsolateral Prefrontal Cortex/physiology ; Neuronal Plasticity/physiology ; Psychomotor Performance/physiology ; },
abstract = {BACKGROUND: Some individuals using brain-computer interfaces (BCIs) exhibit ineffective control during motor imagery-based BCI (MI-BCI) training. MI-BCI performance correlates with the activation in the frontoparietal attention network, premotor-parietal network, and supplementary motor area (SMA). This study aimed to enhance motor imagery ability and MI-BCI performance by modulating the excitability of the right dorsolateral prefrontal cortex (RDLPFC) through intermittent theta-burst stimulation (iTBS), inducing neuroplastic changes.

METHODS: Fifty-two healthy right-handed participants were randomly assigned to either the iTBS or sham group. They undertook two MI-BCI training sessions, with electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) used to assess acute neuroplasticity changes. The intervention was administered between sessions. Corticospinal excitability and motor imagery vividness were assessed using single-pulse transcranial magnetic stimulation (spTMS) and the Kinesthetic and Visual Imagery Questionnaire-20 (KVIQ-20) before and following the trial.

RESULTS: The iTBS group significantly improved motor state percentage (MSP). Significant µ event-related desynchronization (µ-ERD) was observed at the F4 electrode in the iTBS group. Functional connectivity (FC) analyses revealed decreased connectivity among several electrodes during the post-intervention period. The hemodynamic response function (HRF) indicated significant activation in the right PMC and SMA, with reduced FC among motor areas. No significant differences in MEP, CSP, and KVIQ-20 scores were found between groups.

CONCLUSION: iTBS targeting the RDLPFC may improve MI-BCI training performance and address the "BCI inefficiency" problem. RDLPFC stimulation induced changes in FC of brain regions associated with motor imagery and increased the activation of motor areas, suggesting that the RDLPFC could be a promising target for enhancing motor imagery and optimizing BCI systems.},
}

Humans
*Brain-Computer Interfaces
Male
Female
Spectroscopy, Near-Infrared
Electroencephalography/methods
*Imagination/physiology
Adult
Young Adult
*Transcranial Magnetic Stimulation/methods
*Dorsolateral Prefrontal Cortex/physiology
Neuronal Plasticity/physiology
Psychomotor Performance/physiology

@article {pmid40745252,
year = {2025},
author = {Ke, Y and Han, Y and Liu, P and Ming, D},
title = {Dataset of binocularly coded steady-state visual evoked potentials recorded with an augmented reality headset.},
journal = {Scientific data},
volume = {12},
number = {1},
pages = {1338},
pmid = {40745252},
issn = {2052-4463},
support = {62276184 and 81925020//National Natural Science Foundation of China (National Science Foundation of China)/ ; },
mesh = {Humans ; *Evoked Potentials, Visual ; *Brain-Computer Interfaces ; Electroencephalography ; *Augmented Reality ; Vision, Binocular ; },
abstract = {Steady-state visually evoked potential (SSVEP)-based brain-computer interfaces (BCIs) have shown significant promise for practical applications. The integration of SSVEP-BCIs with head-mounted augmented-reality (AR) displays is expected to foster wearable, portable systems; nevertheless, empirical resources for such configurations are scarce, especially for paradigms employing innovative stimulation paradigms. Here we present a curated SSVEP dataset recorded with a binocular AR headset that independently modulates the visual input to each eye and a lightweight electroencephalography recorder. Beyond the conventional binocular-congruent single-frequency stimulation adopted in AR-SSVEP studies, the dataset systematically explores binocular-incongruent dual-frequency encoding whereby the two lenses render flickers with distinct frequencies and/or phases. We report comparative analyses of SSVEP characteristics and BCI performance under congruent versus incongruent protocols, and delineate the influence of inter-ocular frequency and phase disparities. The results substantiate the feasibility of wearable AR-SSVEP-BCIs and highlight binocular-incongruent dual-frequency stimulation as a compelling strategy for improving target separability. The dataset should accelerate research on portable SSVEP-BCIs, novel encoding schemes, and the neural mechanisms of binocular vision.},
}

Humans
*Evoked Potentials, Visual
*Brain-Computer Interfaces
Electroencephalography
*Augmented Reality
Vision, Binocular

@article {pmid40744250,
year = {2025},
author = {Li, Q and Ping, A and Feng, Y and Xu, B and Zhang, B and Roe, AW and Gao, L and Li, X},
title = {Mesoscale functional connectivity of amygdala to the auditory and prefrontal cortex of macaque monkeys revealed by INS-fMRI.},
journal = {NeuroImage},
volume = {318},
number = {},
pages = {121406},
doi = {10.1016/j.neuroimage.2025.121406},
pmid = {40744250},
issn = {1095-9572},
abstract = {Mammals rely heavily on their auditory system to perceive environmental threats, socially communicate, and care for the young. As an extension of the multiple sensory system including the auditory system, the amygdala evaluates the emotional salience of acoustic stimuli, and mediates its impact on sensory, cognitive, and physiological aspects of emotional processing via the lateral amygdala (LA), basal amygdala (BA), and central amygdala (CeA) nuclei of the amygdala in acoustic domain. However, the functional connections of LA, BA, and CeA with the auditory cortex (AC) and the prefrontal cortex (PFC) remain unclear, particularly at the mesoscale level. Here we employed a novel method called INS-fMRI (Infrared Neural Stimulation combined with high-resolution functional magnetic resonance imaging) in Macaque monkeys, this method permits stimulation of multiple sites within single animals in vivo, so that the relative organization of auditory networks can be studied. We found that: (1) Focal INS stimulation of the amygdala elicited robust and reliable responses in both the AC and the PFC; (2) Amygdala stimulation mainly activated ipsilateral AC and PFC; (3) The stimulation of the amygdala mainly activated the secondary AC, and the dorsolateral PFC; (4) The connection between the amygdala and the cortex is mainly mediated by neurons in LA and BA connection area. Our study further revealed the functional connectivity among the amygdala subnucleus, the auditory cortex and the prefrontal cortex, and will shed light on the research for processing biologically meaningful complex sounds.},
}

@article {pmid40744238,
year = {2025},
author = {Wen, Z and Yang, D and Yang, Y and Hu, J and Parviainen, A and Chen, X and Li, Q and VanDeusen, E and Ma, J and Tay, F},
title = {The path to biotechnological singularity: Current breakthroughs and outlook.},
journal = {Biotechnology advances},
volume = {84},
number = {},
pages = {108667},
doi = {10.1016/j.biotechadv.2025.108667},
pmid = {40744238},
issn = {1873-1899},
abstract = {Fueled by rapid advances in gene editing, synthetic biology, artificial intelligence, regenerative medicine, and brain-computer interfaces, biotechnology is approaching a transformative era often referred to as biotechnological singularity. CRISPR-based gene editing has revolutionized genetic engineering, enabling precise modifications for treating hereditary diseases and cancer. Synthetic biology facilitates sustainable biomaterial production and innovative therapeutic applications. Artificial intelligence accelerates drug discovery, enhances diagnostic accuracy, and personalizes treatment through deep learning models. Driven by stem cell research, regenerative medicine offers promising avenues for reversing aging and treating degenerative diseases. Brain-computer interfaces merge human cognition with technology, enabling direct neural control of prosthetics and expanding human-machine interactions. These breakthroughs, however, raise ethical, regulatory, and societal concerns, including equitable access, biosecurity risks, and the implications of human enhancement. The convergence of biological and computational technologies challenges traditional boundaries, necessitating comprehensive governance frameworks. By embracing responsible innovation, society can harness these advancements for transformative health interventions, environmental sustainability, and extended longevity. The realization of biotechnological singularity depends on interdisciplinary collaboration among scientists, policymakers, and the public to ensure that progress aligns with the well-being of humanity and ethical considerations.},
}

@article {pmid40743699,
year = {2025},
author = {Amiri, G and Shalchyan, V},
title = {Decoding muscle activity via CNN-LSTM from 3D spatiotemporal EEG.},
journal = {Computer methods and programs in biomedicine},
volume = {271},
number = {},
pages = {108983},
doi = {10.1016/j.cmpb.2025.108983},
pmid = {40743699},
issn = {1872-7565},
abstract = {OBJECTIVE: Reconstructing muscle activity from electromyogram (EMG) data using non-invasive electroencephalogram (EEG) signals could lead to significant advancements in brain-computer interfaces (BCIs). However, extracting muscle-related signals from EEG poses considerable challenges due to the mixed nature of signals captured by EEG sensors from various cortical regions.

APPROACH: This study introduces a new method for estimating muscle activity from non-invasive EEG signals while participants performed the grasp and lift (GAL) task. Envelopes of the delta, theta, alpha, beta, and gamma frequency bands were chosen as EEG features for the decoding models, computed similarly to muscle activity (EMG envelopes). These were converted into three-dimensional spatiotemporal matrices based on EEG electrode locations. A deep learning model, combining convolutional neural networks (CNN) for spatial and long short-term memory (LSTM) network for temporal EEG information extraction, was applied. This model was compared with two linear and nonlinear decoding methods: multivariate linear regression (mLR) and multilayer perceptron (MLP).

MAIN RESULTS: The average ± standard deviation of the normalized root mean square error (nRMSE), coefficient of determination (R²), and correlation coefficient (CC) between the estimated and actual muscle activity of two muscles in five participants were 0.21 ± 0.05, 0.54 ± 0.17, and 0.76 ± 0.10, respectively. The CNN-LSTM model outperformed both mLR and MLP approaches (p-value < 0.016), with higher frequencies proving more effective for decoding.

SIGNIFICANCE: The proposed model effectively captures nonlinear relationships between brain and muscle activities, indicating its potential to enhance the accuracy and reliability of non-invasive BCIs.},
}

@article {pmid40742862,
year = {2025},
author = {Heo, D and Kim, SP},
title = {Freeing P300-Based Brain-Computer Interfaces From Daily Recalibration by Extracting Daily Common ERPs.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {33},
number = {},
pages = {2977-2987},
doi = {10.1109/TNSRE.2025.3594341},
pmid = {40742862},
issn = {1558-0210},
mesh = {*Brain-Computer Interfaces ; Humans ; *Event-Related Potentials, P300/physiology ; Male ; Algorithms ; Electroencephalography/methods ; Adult ; Female ; Young Adult ; Calibration ; Reproducibility of Results ; },
abstract = {When people use brain-computer interfaces (BCIs) based on event-related potentials (ERPs) over different days, they often need to repeatedly calibrate BCIs every day using ERPs acquired on the same day. This cumbersome recalibration procedure would make it difficult to use BCIs daily. We aim to address the daily recalibration issue by examining across-day variations of the BCI performance and proposing a method to avoid daily recalibration. To this end, we implemented a P300-based BCI system designed to control a home appliance over five days. We first examined how the BCI performance varied across days with or without daily recalibration. On each day, the BCIs were tested using recalibration-based and recalibration-free decoders (RB and RF), with an RB or an RF decoder being built on the training data on each day or those on the first day, respectively. Using the RF decoder resulted in lower BCI performance on subsequent days compared to the RB decoder. Then, we developed a method to extract daily common ERP patterns from observed ERP signals using the sparse dictionary learning algorithm. We applied this method to the RF decoder and retested the BCI performance over days. Using the proposed method improved the RF decoder performance on subsequent days; the performance was closer to the level of the RB decoder compared to the original RF decoder. The method may provide a novel approach to addressing the daily-recalibration issue for P300-based BCIs, which is essential to implementing BCIs into daily life.},
}

*Brain-Computer Interfaces
Humans
*Event-Related Potentials, P300/physiology
Male
Algorithms
Electroencephalography/methods
Adult
Female
Young Adult
Calibration
Reproducibility of Results

@article {pmid40741299,
year = {2025},
author = {Si, Y and Sun, Y and Wu, K and Gao, L and Wang, S and Xu, M and Qi, X},
title = {Effects of ASMR on mental fatigue recovery revealed by EEG power and brain network analysis.},
journal = {Frontiers in human neuroscience},
volume = {19},
number = {},
pages = {1619424},
pmid = {40741299},
issn = {1662-5161},
abstract = {INTRODUCTION: Mental fatigue, resulting from prolonged cognitive tasks or sleep deprivation, significantly impacts safety and performance, particularly in high-risk environments. However, effective intervention methods are limited, highlighting the urgent need for new approaches to alleviate mental fatigue. This study explores the effectiveness of Autonomous Sensory Meridian Response (ASMR) as a novel intervention for alleviating mental fatigue.

METHODS: A within-subject design was employed in this work, where 28 healthy young subjects (M/F = 17/11, age = 21.82 ± 0.37 years) were requested to perform a continuous 30 min sustained attention task (named No-Break session) and a 30 min task with a 4-min mid-task ASMR break (named ASMR-Break session) at a counterbalanced order. The immediate effect and general effect of ASMR were quantitatively assessed on behavioral performance and EEG characteristics.

RESULTS: Behaviorally, only significant immediate effect was revealed as showing in reduced reaction time. Further interrogation of brain dynamics showed complex patterns of spatio-spectrum alterations and an interaction in small-world metric in theta band. Specifically, the ASMR intervention prevented an increase in small-worldness, and the correlation between changes in small-worldness and reaction times diminished after the intervention.

DISCUSSION: In sum, this preliminary investigation provides insight into ASMR's neural mechanisms and suggests it may help attenuate fatigue. Further research in larger, more diverse samples will be necessary to confirm its utility for mental fatigue management in real-world settings.},
}

@article {pmid40741298,
year = {2025},
author = {Zhu, T and Tang, H and Jiang, L and Li, Y and Li, S and Wu, Z},
title = {A study of motor imagery EEG classification based on feature fusion and attentional mechanisms.},
journal = {Frontiers in human neuroscience},
volume = {19},
number = {},
pages = {1611229},
pmid = {40741298},
issn = {1662-5161},
abstract = {INTRODUCTION: Motor imagery EEG-based action recognition is an emerging field arising from the intersection of brain science and information science, which has promising applications in the fields of neurorehabilitation and human-computer collaboration. However, existing methods face challenges including the low signal-to-noise ratio of EEG signals, inter-subject variability, and model overfitting.

METHODS: We propose HA-FuseNet, an end-to-end motor imagery action classification network. This model integrates feature fusion and attention mechanisms to classify left hand, right hand, foot, and tongue movements. Its innovations include: (1) multi-scale dense connectivity, (2) hybrid attention mechanism, (3) global self-attention module, and (4) lightweight design for reduced computational overhead.

RESULTS: On BCI Competition IV Dataset 2A, HA-FuseNet achieved 77.89% average within-subject accuracy (8.42% higher than EEGNet) and 68.53% cross-subject accuracy.

CONCLUSION: The model demonstrates robustness to spatial resolution variations and individual differences, effectively mitigating key challenges in motor imagery EEG classification.},
}

@article {pmid40741296,
year = {2025},
author = {Degirmenci, M and Yuce, YK and Perc, M and Isler, Y},
title = {Classification of finger movements through optimal EEG channel and feature selection.},
journal = {Frontiers in human neuroscience},
volume = {19},
number = {},
pages = {1633910},
pmid = {40741296},
issn = {1662-5161},
abstract = {INTRODUCTION: Electrencephalography (EEG)-based brain-computer interfaces (BCIs) have become popular as EEG is accepted as the simplest and non-invasive neuroimaging modality to record the brain's electrical activity. In the current BCI research context, apart from predicting extremity movements, recent BCI studies have been interested in accurately predicting finger movements of the same hand using different pattern recognition methods over EEG data collected based on motor imagery (MI), through which a mental image of the desired action is generated when a person ideally simulates or imagines carrying out a certain motor task. Although several pattern recognition methods have already been recommended in literature, majority of the studies focusing on classifying five finger movements, were based on study designs that neglected or excluded the idle state of brain (i.e., no mental task state) during which brain does not carry out any MI task. This study design may result in an increasing number of false positives and a significant decrease in the prediction rates and classification performance. Moreover, recent studies have focused on improving prediction performance using complex feature extraction and machine learning algorithms while ignoring comprehensive EEG channels and feature investigation in the prediction of finger movements from EEGs. Therefore, the objectives of this study are threefold: (i) to develop a more viable and practical system to predict the movements of five fingers and the no mental task (NoMT) state from EEG signals (ii) to analyze the effects of the statistical-significance based feature selection method over four different feature domains (nonlinear domain, time-domain, frequency-domain and time-frequency domain) and their combinations, and (iii) to test these feature sets with different and prominent classifiers.

METHODS: In this study, our major goal is not to explore the best machine algorithm performance, but to investigate the best EEG channels and features that can be used in the classification of finger movements. Hence, the comprehensive analysis of the effectiveness of EEG channels and features is performed utilizing a statistically significant feature distribution over 19 EEG channels for each feature set independently. A bulky dataset of electroencephalographic MI for EEG-based BCIs is used in this study. A total of 1102 EEG features supplied from different feature domains have been investigated. Subsequently, these features were tested with eight well-known classifiers, comprising Decision tree, Discriminant analysis, Naive Bayes, Support vector machine, k-nearest neighbor, Ensemble learning, Neural networks, and Kernel approximation.

RESULTS: For subject-dependent analysis, the maximum accuracy of 59.17% was obtained using the EEG features that were selected the most (including (i) energy and variance of five frequency bands in frequency-domain feature set, (ii) all feature types in time-domain, time-frequency domain, and nonlinear domain feature sets) and all EEG channels by the Support vector machine algorithm. For subject-independent analysis, the maximum accuracy of 39.30% was obtained using the mostly selected EEG features (which are (i) all feature types excluding the waveform length, average amplitude change value, absolute difference in standard deviation, and slope-change value feature types in time-domain feature set, (ii) the energy and variance values of all frequency bands except gamma frequency band in frequency-domain feature set, (iii) the entropy value of five frequency bands in time-frequency-domain feature set, and (iv) SD 2 and SD 1/SD 2 values where lag = 1 in nonlinear feature set) and EEG channels (which are (i) some definite EEG channels including 2nd, 3rd, 7th, 11th, 13th, 14th, and 15th channels in time-frequency-domain feature set and (ii) all EEG channels in time-domain, frequency-domain, and nonlinear feature sets) by the Support vector machine classifier.

DISCUSSION: Experimental results demonstrate that despite the high-class number, the proposed approach obtained a modest yet considerable advancement in finger movement prediction when the results are compared to the results of similar studies. Additionally, for almost all feature sets, the statistical significance-based feature reduction method improves the prediction performance in the most of classifiers, contributing elaborate EEG channel and feature analysis. Nonetheless, in this study, we used an EEG dataset recorded from only 13 healthy subjects; therefore, a dataset covering more subjects is necessary to reach a more general conclusion.},
}

@article {pmid40740060,
year = {2025},
author = {Kumar, A and Kumar, A},
title = {BiLSTM-Based Human Emotion Classification Using EEG Signal.},
journal = {Clinical EEG and neuroscience},
volume = {},
number = {},
pages = {15500594251364017},
doi = {10.1177/15500594251364017},
pmid = {40740060},
issn = {2169-5202},
abstract = {Emotion recognition using electroencephalography (EEG) signals has garnered significant attention due to its applications in affective computing, human-computer interaction, and healthcare. This study employs a Bidirectional Long Short-Term Memory (BiLSTM) network to classify emotions using EEG data from four well-established datasets: SEED, SEED-IV, SEED-V, and DEAP. By leveraging the temporal dependencies inherent in EEG signals, the BiLSTM model demonstrates robust learning of emotional states. The model achieved notable classification accuracies, with 92.30% for SEED, 99.98% for SEED-IV, 99.97% for SEED-V, and 88.33% for DEAP, showcasing its effectiveness across datasets with varying class distributions. The superior performance on SEED-IV and SEED-V underscores the BiLSTM's capability to capture bidirectional temporal information, which is crucial for emotion recognition tasks. Moreover, this work highlights the importance of utilizing diverse datasets to validate the generalizability of EEG-based emotion recognition models. The integration of both dimensional and discrete emotion models in the study demonstrates the framework's flexibility in addressing various emotion representation paradigms. Future directions include optimizing the framework for real-world applications, such as wearable EEG devices, and exploring transfer learning techniques to enhance cross-subject and cross-cultural adaptability. Overall, this study advances EEG-based emotion recognition methodologies, establishing a robust foundation for integrating affective computing into various domains and paving the way for real-time, reliable emotion recognition systems.},
}

@article {pmid40739107,
year = {2025},
author = {Jia, O and Tan, Q and Zhang, S and Jia, K and Gong, M},
title = {The precision of attention selection during reward learning influences the mechanisms of value-driven attention.},
journal = {NPJ science of learning},
volume = {10},
number = {1},
pages = {49},
pmid = {40739107},
issn = {2056-7936},
support = {2023-PT310-01//Non-profit Central Research Institute Fund of Chinese Academy of Medical Sciences/ ; 32371087//National Natural Science Foundation of China/ ; 32300855//National Natural Science Foundation of China/ ; 226-2024-00118//Fundamental Research Funds for the Central University/ ; 2021ZD0200409//National Science and Technology Innovation 2030-Major Project/ ; },
abstract = {Reward-predictive items capture attention even when task-irrelevant. While value-driven attention typically generalizes to stimuli sharing critical reward-associated features (e.g., red), recent evidence suggests an alternative generalization mechanism based on feature relationships (e.g., redder). Here, we investigated whether relational coding of reward-associated features operates across different learning contexts by manipulating search mode and target-distractor similarity. Results showed that singleton search training induced value-driven relational attention regardless of target-distractor similarity (Experiments 1a-1b). In contrast, feature search training produced value-driven relational attention only when targets and distractors were dissimilar, but not when they were similar (Experiments 2a-2c). These findings indicate that coarse selection training (singleton search or feature search among dissimilar items) promotes relational coding of reward-associated features, while fine selection (feature search among similar items) engages precise feature coding. The precision of target selection during reward learning thus critically determines value-driven attentional mechanisms.},
}

@article {pmid40737169,
year = {2025},
author = {Xiong, D and Hu, L and Jin, J and Ding, Y and Tan, C and Zhang, J and Tian, Y},
title = {Interpretable Cross-Modal Alignment Network for EEG Visual Decoding With Algorithm Unrolling.},
journal = {IEEE transactions on neural networks and learning systems},
volume = {PP},
number = {},
pages = {},
doi = {10.1109/TNNLS.2025.3592646},
pmid = {40737169},
issn = {2162-2388},
abstract = {Accurate decoding in electroencephalography (EEG) technology, particularly for rapid visual stimuli, remains challenging due to the low signal-to-noise ratio (SNR). Additionally, existing neural networks struggle with issues related to generalization and interpretability. This article proposes a cross-modal aligned network, E2IVAE, which leverages shared information from multiple modalities for self-supervised alignment of EEG to images for extracting visual perceptual information and features a novel EEG encoder, ISTANet, based on algorithm unrolling. This network framework significantly enhances the accuracy and stability of EEG decoding for object recognition in novel classes while reducing the extensive neural data typically required for training neural decoders. The proposed ISTANet employs algorithm unrolling to transform the multilayer sparse coding algorithm into an end-to-end format, extracting features from noisy EEG signals while incorporating the interpretability of traditional machine learning. The experimental results demonstrate that our method achieves SOTA top-1 accuracy of 62.39% and top-5 accuracy of 88.98% on a comprehensive rapid serial visual presentation (RSVP) dataset for public comparison in a 200-class zero-shot neural decoding task. Additionally, ISTANet enables visualization and analysis of multiscale atom features and overall reconstruction features, exploring biological plausibility across temporal, spatial, and spectral dimensions. On another more challenging RSVP large-scale dataset, the proposed framework also achieves significantly above chance-level performance, proving its robustness and generalization. This research provides critical insights into neural decoding and brain-computer interfaces (BCIs) within the fields of cognitive science and artificial intelligence.},
}

@article {pmid40735361,
year = {2025},
author = {Zhang, Q and Zhang, C and Ji, H and Chen, J and Wang, X and Zhang, T and Liu, P and Wang, Z and Xu, Y},
title = {Ethical governance of clinical research on the brain-computer interface for mental disorders: a modified Delphi study.},
journal = {General psychiatry},
volume = {38},
number = {4},
pages = {e101755},
pmid = {40735361},
issn = {2517-729X},
abstract = {BACKGROUND: Clinical brain-computer interface (BCI) for mental disorders is an emerging interdisciplinary research field, posing new ethical concerns and challenges, yet lacking practical ethical governance guidelines for stakeholders and the entire community.

AIMS: This study aims to establish a multidisciplinary consensus of principles for ethical governance of clinical BCI research for mental disorders and offer practical ethical guidance to stakeholders involved.

METHODS: A systematic literature review, symposium and roundtable discussions, and a pre-Delphi (round 0) survey were conducted to form the questionnaire for the three-round modified Delphi study. Two rounds of surveys, followed by a third round of independent interviews of 25 experts from BCI-related research domains, were involved. We conducted quantitative analysis of responses and agreements among experts to reveal the consensus and differences regarding the ethical governance of mental BCI research from a multidisciplinary perspective.

RESULTS: The Delphi panel emphasised important concerns of ethical review practices and ethical principles within the BCI context, identified qualified and highly influential institutions and personnel in conducting and advancing clinical BCI research, and recognised prioritised aspects in the risk-benefit evaluation. Experts expressed diverse opinions on specific ethical concerns, including concerns about invasive technology, its impact on humanity and potential social consequences. Agreement was reached that the practices of ethical governance of clinical BCI for mental disorders should focus on patient voluntariness, autonomy, long-term effects and related assessments of BCI interventions, as well as privacy protection, transparent reporting and ensuring that the research is conducted in qualified institutions with strong data security.

CONCLUSIONS: Ethical governance of clinical research on BCI for mental disorders should include interdisciplinary experts to balance various needs and incorporate the expertise of different stakeholders to avoid serious ethical issues. It requires scientifically grounded approaches, continuous monitoring and interdisciplinary collaboration to ensure evidence-based policies, comprehensive risk assessments and transparency, thereby promoting responsible innovations and protecting patient rights and well-being.},
}

@article {pmid40735214,
year = {2025},
author = {Ran, J and Xu, J and Luo, D and Li, T and Xu, J},
title = {Problematic internet use and aggression in Chinese middle school students: mediation effect of reality social connectedness.},
journal = {Frontiers in public health},
volume = {13},
number = {},
pages = {1587400},
pmid = {40735214},
issn = {2296-2565},
mesh = {Humans ; *Aggression/psychology ; China/epidemiology ; Male ; Female ; Cross-Sectional Studies ; *Students/psychology/statistics & numerical data ; Adolescent ; Surveys and Questionnaires ; *Internet Addiction Disorder/psychology/epidemiology ; *Internet Use/statistics & numerical data ; East Asian People ; },
abstract = {INTRODUCTION: Problematic internet use (PIU) has become a prevalent concern worldwide and is associated with increased aggression. However, the underlying effect of PIU on aggression remains unclear. In this study, we aimed to investigate the potential influence of reality social connectedness (RSC) on the relationship between PIU and aggression.

METHODS: We used cross-sectional data from a large survey conducted among middle school students in four provinces of China between September 2022 and March 2023. PIU, RSC, and aggression were assessed using Young's 20-item Internet Addiction Test (IAT-20), the modified Social Connectedness Scale-Revised (SCS-R), and the Buss-Perry Aggression Questionnaire (BPAQ), respectively.

RESULTS: We found that students who experienced PIU had significantly higher scores on the BPAQ, which reflects the aggression levels, compared to students without PIU. Specifically, all four dimensions of aggression-verbal aggression, physical aggression, hostility, and anger-were elevated in the PIU group. Additionally, RSC was significantly reduced among individuals with PIU. Notably, RSC significantly mediated the relationship between PIU and aggression, accounting for 18.89% of the total effect. Among the four dimensions of aggression, the mediating effect of RSC was strongest for hostility, followed by anger and physical aggression, with the weakest observed for verbal aggression.

DISCUSSION: RSC significantly mediated the relationship between PIU and aggression, suggesting that reduced RSC partially explains how PIU exacerbates aggression. This result highlights the importance of fostering RSC as a strategy to reduce aggression related to PIU.},
}

Humans
*Aggression/psychology
China/epidemiology
Male
Female
Cross-Sectional Studies
*Students/psychology/statistics & numerical data
Adolescent
Surveys and Questionnaires
*Internet Addiction Disorder/psychology/epidemiology
*Internet Use/statistics & numerical data
East Asian People

@article {pmid40734822,
year = {2025},
author = {Liu, ZY and Zhang, L and Wang, ZD and Huang, ZQ and Li, MC and Lu, Y and Hu, JP and Chen, QL and Chen, XY},
title = {Magnetic resonance imaging for spinocerebellar ataxia: a bibliometric analysis based on web of science.},
journal = {Frontiers in neurology},
volume = {16},
number = {},
pages = {1512800},
pmid = {40734822},
issn = {1664-2295},
abstract = {The objective of this study was to review the history of magnetic resonance imaging (MRI) research on spinocerebellar ataxia (SCA) over the last 16 years. We conducted a comprehensive bibliometric analysis of relevant scientific literature that explores the use of MRI in studying SCA using CiteSpace. A total of 761 scientific manuscripts, published between January 2009 and March 2025 and available in the Web of Science (WoS) database, were included in this analysis. A total of 197 out of 761 articles were analyzed using CiteSpace to determine the number and centrality of publications, countries, institutions, journals, authors, cited references, and keywords related to MRI and SCA. Overall, the number of publications that use MRI to study SCA has gradually increased over the years. The United States, China, Italy, Germany, and Brazil are at the forefront in this research field; a total of 420 authors from 317 research institutions in these nations have published articles in neuroscience-related journals. Among the most cited publications are an article by Rezende et al. on brain structural damage in SCA3 patients and an review by Klockgether et al. on spinocerebellar ataxia. The keyword "spinocerebellar ataxia" has the highest frequency of occurrence. However, "feature" may become a research hotspot in the coming years based on the analysis of the keyword's citation burst. The findings of this bibliometric study provide a summary of the last 16 years of SCA research using MRI technology. More importantly, the present study identifies current trends and future research hotspots in the field, helping researchers to identify new and unexplored research areas.},
}

@article {pmid40731219,
year = {2025},
author = {Motiwala, A and Soldado-Magraner, J and Batista, AP and Smith, MA and Yu, BM},
title = {Brain-computer interfaces as a causal probe for scientific inquiry.},
journal = {Trends in cognitive sciences},
volume = {},
number = {},
pages = {},
pmid = {40731219},
issn = {1879-307X},
support = {R01 MH118929/MH/NIMH NIH HHS/United States ; R01 NS105318/NS/NINDS NIH HHS/United States ; R01 NS129584/NS/NINDS NIH HHS/United States ; },
abstract = {Establishing causal relationships between neural activity and brain function requires experimental perturbations of neural activity. Many existing perturbation methods modify activity by directly applying external signals to the brain. We review an alternative approach where brain-computer interfaces (BCIs) leverage volitional control of neural activity to manipulate and causally perturb it. We highlight the potential of BCIs to manipulate neural activity in ways that are flexible, accurate, and adhere to intrinsic biophysical and network-level constraints to investigate the consequences of configuring neural population activity in specified ways. We discuss the advantages and disadvantages of using BCIs as a perturbation tool compared with other perturbation methods and how BCIs can expand the scope of questions that can be addressed about brain function.},
}

@article {pmid40731189,
year = {2025},
author = {Zhao, X and Yu, J and Xu, B and Xu, Z and Lei, X and Han, S and Luo, S and Zhang, C and Peng, G and Li, J and Yu, J and Ling, Y and Fan, Z and Mo, W and Yang, Y and Zhang, J},
title = {Gut-derived bacterial vesicles carrying lipopolysaccharide promote microglia-mediated synaptic pruning.},
journal = {Alzheimer's & dementia : the journal of the Alzheimer's Association},
volume = {21},
number = {8},
pages = {e70331},
pmid = {40731189},
issn = {1552-5279},
support = {82020108012//National Natural Science Foundation of China/ ; 82371250//National Natural Science Foundation of China/ ; 2024C03098//Key Research and Development Program of Zhejiang Province/ ; 2024SSYS0018//Key Research and Development Program of Zhejiang Province/ ; LZ23H090002//Natural Science Foundation of Zhejiang Province/ ; LY24H090006//Natural Science Foundation of Zhejiang Province/ ; //Innovative Institute of Basic Medical Science of Zhejiang University/ ; },
mesh = {*Lipopolysaccharides/metabolism ; *Microglia/metabolism ; Humans ; *Gastrointestinal Microbiome/physiology ; Animals ; Mice ; *Extracellular Vesicles/metabolism ; *Alzheimer Disease/metabolism ; *Neuronal Plasticity/physiology ; Blood-Brain Barrier/metabolism ; Male ; Female ; Brain/metabolism ; },
abstract = {INTRODUCTION: Growing evidence links gut microbiota (GM) to Alzheimer's disease (AD). Elevated lipopolysaccharide (LPS) levels, a Gram-negative bacteria component, are found in AD brains, but how LPS breaches the blood-brain barrier (BBB) remains unclear. Hypotheses suggest that bacteria-derived extracellular vesicles (bEVs) may transport LPS across the BBB.

METHODS: bEVs were extracted from human and mouse feces and blood, and LPS levels were measured. In vivo imaging and immunofluorescence confirmed the transport of blood LPS-carrying bEVs across the BBB. The role of these bEVs in microglia was investigated both in vivo and in vitro.

RESULTS: Elevated LPS-containing bEVs were detected in the plasma of AD patients compared to healthy individuals. These bEVs activated microglial Piezo1, consequently precipitating an excessive synaptic pruning process mediated by the C1q-C3 complement pathway.

DISCUSSION: These findings illuminate the complex interplay between the gut microbiota, bEVs, neuroinflammation, and synaptic plasticity - a key early event in AD - offering insights for potential therapeutic interventions.

HIGHLIGHTS: GM-derived bEVs can traverse the BBB. LPS was necessary for bEVs' penetration into the brain, and bEVs might be closely related to AD progression. bEVs mediated microglial activation and synaptic pruning via C1q-C3 complement pathway. Microglia Piezo1 was involved in bEV-induced excessive synaptic pruning.},
}

*Lipopolysaccharides/metabolism
*Microglia/metabolism
Humans
*Gastrointestinal Microbiome/physiology
Animals
Mice
*Extracellular Vesicles/metabolism
*Alzheimer Disease/metabolism
*Neuronal Plasticity/physiology
Blood-Brain Barrier/metabolism
Male
Female
Brain/metabolism

@article {pmid40730254,
year = {2025},
author = {Alouani, Z and Gannour, OE and Saleh, S and El-Ibrahimi, A and Daanouni, O and Cherradi, B and Bouattane, O},
title = {A novel contrastive Dual-Branch Network (CDB-Net) for robust EEG-Based Alzheimer's disease diagnosis.},
journal = {Brain research},
volume = {1865},
number = {},
pages = {149863},
doi = {10.1016/j.brainres.2025.149863},
pmid = {40730254},
issn = {1872-6240},
abstract = {Alzheimer's Disease (AD) is neurodegenerative disorder that causes cognitive decline, memory loss, confusion, and changes in behavior. Early and accurate detection is important for timely intervention, current diagnostic methods can be slow, expensive, and have limited sensitivity. Electroencephalography (EEG) offers a simple and non-invasive way to measure brain activity, and it has shown promise in supporting AD diagnosis. However, EEG signals are often affected by noise-such as muscle movement, blinking, or electrical interference-which can make it harder for models to give reliable results. To address these challenges, we introduce CDB-Net (Contrastive Dual-Branch Network), a deep learning model built to improve the accuracy and robustness of EEG-based AD classification. The model uses two parallel branches: one processes clean EEG data, while the other processes a noisy version of the same data. By training these branches together using contrastive learning, the model learns to focus on features that stay consistent even when the signal is distorted by noise. A classification head is trained jointly using cross-entropy loss for downstream diagnosis. We tested our method on a public EEG dataset and found that CDB-Net achieved 97.92% accuracy on clean data and 83.41% accuracy even under adversarial attacks (FGSM), outperforming traditional machine learning classifiers and deep learning baselines models. These results highlight the effectiveness of contrastive dual-branch learning in enhancing model generalization and robustness, positioning CDB-Net as a promising tool for reliable EEG-based clinical decision support in the context of Alzheimer's Disease diagnosis.},
}

@article {pmid40728869,
year = {2025},
author = {Kawaguchi, N and Koyano, K and Morita, H and Pengiran Mohamad Fadly, DNRAC and Shinabe, Y and Noguchi, Y and Arioka, M and Nakao, Y and Ozaki, M and Nakamura, S and Kondo, S and Konishi, Y and Kuboi, T and Okada, H and Yasuda, S and Itoh, S and Murao, K and Kusaka, T},
title = {Quantitative effects of bilirubin photoisomers on the measurement of direct bilirubin by the enzymatic bilirubin oxidase method.},
journal = {Annals of clinical biochemistry},
volume = {},
number = {},
pages = {45632251367245},
doi = {10.1177/00045632251367245},
pmid = {40728869},
issn = {1758-1001},
abstract = {BACKGROUND: Bilirubin photoisomers, generated during phototherapy or through inadvertent light exposure, may interfere with the measurement of direct bilirubin (DB) using the bilirubin oxidase method. This interference is particularly relevant in neonates, who physiologically exhibit elevated levels of unconjugated bilirubin.

METHODS: Residual serum samples from 30 neonates were irradiated under controlled conditions to selectively produce bilirubin configurational isomers (BCIs) and structural isomers (BSIs). DB and total bilirubin (TB) values were measured before and after irradiation using the bilirubin oxidase method. The concentrations of BCIs and BSIs were quantified using high-performance liquid chromatography (HPLC). Linear and multiple regression analyses were performed to evaluate the extent to which these photoisomers contributed to the DB values.

RESULTS: Following irradiation, DB values significantly increased in correlation with BCI and BSI concentrations. Approximately 11% of BCI and 32% of BSI were quantified as DB using the bilirubin oxidase method. These findings were consistent across both individual and multiple regression models.

CONCLUSIONS: Bilirubin photoisomers can significantly elevate DB values measured by the bilirubin oxidase method, leading to a potential overestimation of conjugated bilirubin. In neonatal clinical practice, careful interpretation of DB values is warranted, particularly under conditions involving light exposure. Accurate sample handling and an awareness of photoisomer interference are essential for reliable assessment of hyperbilirubinemia in newborns.},
}

@article {pmid40727297,
year = {2025},
author = {Ebrahimibasabi, S and Golshahi, M and Shahraki, N and Tamjid Shabestari, D and Sajjadi, M and Hashemi, S and Borchert, A and Baker, I and Khalifehzadeh, L and Arami, H},
title = {Designing parylene coating for implantable brain-machine interfaces.},
journal = {RSC advances},
volume = {15},
number = {33},
pages = {26660-26672},
pmid = {40727297},
issn = {2046-2069},
abstract = {Parylene is widely recognized as an effective candidate for encapsulating implantable bioelectronics due to its outstanding chemical stability, conformity and biocompatibility. However, its weak adhesion to inorganic substrates remains a significant challenge. Here, we explored various pre- and post-deposition treatments to enhance adhesion and stability of parylene coating for implantable brain-machine interfaces (BMIs). We utilized 0%, 0.5%, 1%, and 1.5% (v/v) 3-(trimethoxysilyl)propyl-methacrylate as an adhesion promoter for substrate treatment prior to deposition. Deposited samples were subsequently subjected to post-heat treatments at various temperatures. Samples were exposed to an in vitro accelerated aging bath at 87 °C for 7 days to assess their post-implantation durability. Cytotoxicity and in vivo biocompatibility were also investigated to further evaluate biocompatibility and encapsulation efficiency of parylene coatings on commonly used rigid and flexible bioelectronic substrates. The emergence of carboxyl groups in FTIR and chlorine abstraction in EDS analyses, indicated that the as-deposited samples were degraded during aging. The chemical stability of these coatings was improved in heat-treated samples due to their higher crystallinity. Additionally, delamination and microcrack initiation/growth reduced due to post-heat treatments. We found the optimal heat treatment temperature to be 150 °C; any increase beyond this compromised coating quality by increasing delamination and defect formation. Increasing the concentration of adhesion promoter enhanced coating adhesion to the substrates in both as-deposited samples and the ones heat-treated at 150 °C. In contrast, the adhesion strength decreased when heat-treatment was performed at higher temperatures, even when the concentration of adhesion promoter was increased. Numerical analysis was used to assess the effect of parylene coating on the electrical performance of a typical implantable, wirelessly powered model device. The results demonstrated that the presence of the parylene layer not only preserved the wireless coupling between this device and the pickup probe, but also enhanced it. In addition to these favourable physiochemical improvements, parylene also promoted general in vivo brain compatibility and cell viability of the devices. This study revealed the synergistic effects of pre- and post-deposition treatments and systematically optimized adhesion and stability of parylene coatings for implantable BMIs for the first time.},
}

@article {pmid40722830,
year = {2025},
author = {Ga, YJ and Go, YY and Yeh, JY},
title = {Small Interfering RNAs Targeting VP4, VP3, 2B, or 3A Coding Regions of Enterovirus A71 Inhibit Viral Replication In Vitro.},
journal = {Biomedicines},
volume = {13},
number = {7},
pages = {},
pmid = {40722830},
issn = {2227-9059},
support = {2019//Incheon National University/ ; },
abstract = {Background: Enterovirus A71 (EV-A71) is considered as the primary causative agent of hand, foot, and mouth disease (HFMD) in young children, leading to severe neurological complications and contributing to substantial mortalities in recent HFMD outbreaks across Asia. Despite this, there is currently no effective antiviral treatment available for EV-A71. RNA interference (RNAi) is a powerful mechanism of post-transcriptional gene regulation that utilizes small interfering RNA (siRNA) to target and degrade specific RNA sequences. Objectives: The aim of this study was to design various siRNAs targeting EV-A71 genomic regions and evaluate the RNAi efficacy against a novel, previously genetically uncharacterized EV-A71 strain. Methods: A novel EV-A71 strain was first sequenced to design target-specific siRNAs. The viral titers, viral protein expression, cytopathic effects, and cell viability of EV-A71-infected HeLa cells were examined to evaluate the specific viral inhibition by the siRNAs. Results: A substantial reduction in viral titers and viral protein synthesis was observed in EV-A71-infected HeLa cells treated with specific siRNAs targeting the VP4, VP3, 2B, and 3A genes. siRNAs delayed cytopathic effects and increased cell viability of EV-A71-infected HeLa cells. Nonspecific interferon induction caused by siRNAs was not observed in this study. In contrast, replication of coxsackievirus B3, another important member of the Enterovirus genus, remained unaffected. Conclusions: Overall, the findings demonstrate that RNAi targeting genomic regions of EV-A71 VP4, VP3, 2B, or 3A could become a potential strategy for controlling EV-A71 infection, and this promising result can be integrated into future anti-EV-A71 therapy developments.},
}

@article {pmid40722467,
year = {2025},
author = {Zhan, H and Li, X and Song, X and Lv, Z and Li, P},
title = {MCTGNet: A Multi-Scale Convolution and Hybrid Attention Network for Robust Motor Imagery EEG Decoding.},
journal = {Bioengineering (Basel, Switzerland)},
volume = {12},
number = {7},
pages = {},
pmid = {40722467},
issn = {2306-5354},
support = {No. 2108085MF207//Anhui Natural Science Foundation/ ; No. 2024AH050054//Natural Science Research Project of Anhui Educational Committee under Grant/ ; No. 2208085J05//Distinguished Youth Foundation of Anhui Scientific Committee/ ; No. 62476004//National Natural Science Foundation of China (NSFC)/ ; },
abstract = {Motor imagery (MI) EEG decoding is a key application in brain-computer interface (BCI) research. In cross-session scenarios, the generalization and robustness of decoding models are particularly challenging due to the complex nonlinear dynamics of MI-EEG signals in both temporal and frequency domains, as well as distributional shifts across different recording sessions. While multi-scale feature extraction is a promising approach for generalized and robust MI decoding, conventional classifiers (e.g., multilayer perceptrons) struggle to perform accurate classification when confronted with high-order, nonstationary feature distributions, which have become a major bottleneck for improving decoding performance. To address this issue, we propose an end-to-end decoding framework, MCTGNet, whose core idea is to formulate the classification process as a high-order function approximation task that jointly models both task labels and feature structures. By introducing a group rational Kolmogorov-Arnold Network (GR-KAN), the system enhances generalization and robustness under cross-session conditions. Experiments on the BCI Competition IV 2a and 2b datasets demonstrate that MCTGNet achieves average classification accuracies of 88.93% and 91.42%, respectively, outperforming state-of-the-art methods by 3.32% and 1.83%.},
}

@article {pmid40722306,
year = {2025},
author = {Deniz, SM and Ademoglu, A and Duru, AD and Demiralp, T},
title = {Application of Graph-Theoretic Methods Using ERP Components and Wavelet Coherence on Emotional and Cognitive EEG Data.},
journal = {Brain sciences},
volume = {15},
number = {7},
pages = {},
pmid = {40722306},
issn = {2076-3425},
abstract = {Background/Objectives: Emotion and cognition, two essential components of human mental processes, have traditionally been studied independently. The exploration of emotion and cognition is fundamental for gaining an understanding of human mental functioning. Despite the availability of various methods to measure and evaluate emotional states and cognitive processes, physiological measurements are considered to be one of the most reliable methods due to their objective approach. In particular, electroencephalography (EEG) provides unique insight into emotional and cognitive activity through the analysis of event-related potentials (ERPs). In this study, we discriminated pleasant/unpleasant emotional moods and low/high cognitive states using graph-theoretic features extracted from spatio-temporal components. Methods: Emotional data were collected at the Physiology Department of Istanbul Medical Faculty at Istanbul University, whereas cognitive data were obtained from the DepositOnce repository of Technische Universität Berlin. Wavelet coherence values for the N100, N200, and P300 single-trial ERP components in the delta, theta, alpha, and beta frequency bands were investigated individually. Then, graph-theoretic analyses were performed using wavelet coherence-based connectivity maps. Global and local graph metrics such as energy efficiency, strength, transitivity, characteristic path length, and clustering coefficient were used as features for classification using support vector machines (SVMs), k-nearest neighbor(K-NN), and linear discriminant analysis (LDA). Results: The results show that both pleasant/unpleasant emotional moods and low/high cognitive states can be discriminated, with average accuracies of up to 92% and 89%, respectively. Conclusions: Graph-theoretic metrics based on wavelet coherence of ERP components in the delta band with the SVM algorithm allow for the discrimination of emotional and cognitive states with high accuracy.},
}

@article {pmid40722299,
year = {2025},
author = {Zhang, Z and Lu, G},
title = {Multimodal Knowledge Distillation for Emotion Recognition.},
journal = {Brain sciences},
volume = {15},
number = {7},
pages = {},
pmid = {40722299},
issn = {2076-3425},
abstract = {Multimodal emotion recognition has emerged as a prominent field in affective computing, offering superior performance compared to single-modality methods. Among various physiological signals, EEG signals and EOG data are highly valued for their complementary strengths in emotion recognition. However, the practical application of EEG-based approaches is often hindered by high costs and operational complexity, making EOG a more feasible alternative in real-world scenarios. To address this limitation, this study introduces a novel framework for multimodal knowledge distillation, designed to improve the practicality of emotion decoding while maintaining high accuracy, with the framework including a multimodal fusion module to extract and integrate interactive and heterogeneous features, and a unimodal student model structurally aligned with the multimodal teacher model for better knowledge alignment. The framework combines EEG and EOG signals into a unified model and distills the fused multimodal features into a simplified EOG-only model. To facilitate efficient knowledge transfer, the approach incorporates a dynamic feedback mechanism that adjusts the guidance provided by the multimodal model to the unimodal model during the distillation process based on performance metrics. The proposed method was comprehensively evaluated on two datasets based on EEG and EOG signals. The accuracy of the valence and arousal of the proposed model in the DEAP dataset are 70.38% and 60.41%, respectively. The accuracy of valence and arousal in the BJTU-Emotion dataset are 61.31% and 60.31%, respectively. The proposed method achieves state-of-the-art classification performance compared to the baseline method, with statistically significant improvements confirmed by paired t-tests (p < 0.05), and the framework effectively transfers knowledge from multimodal models to unimodal EOG models, enhancing the practicality of emotion recognition while maintaining high accuracy, thus expanding the applicability of emotion recognition in real-world scenarios.},
}

@article {pmid40722278,
year = {2025},
author = {Yazıcı, M and Ulutaş, M and Okuyan, M},
title = {Effect of EEG Electrode Numbers on Source Estimation in Motor Imagery.},
journal = {Brain sciences},
volume = {15},
number = {7},
pages = {},
pmid = {40722278},
issn = {2076-3425},
abstract = {The electroencephalogram (EEG) is one of the most popular neurophysiological methods in neuroscience. Scalp EEG measurements are obtained using various numbers of channels for both clinical and research applications. This pilot study explores the effect of EEG channel count on motor imagery classification using source analysis in brain-computer interface (BCI) applications. Different channel configurations are employed to evaluate classification performance. This study focuses on mu band signals, which are sensitive to motor imagery-related EEG changes. Common spatial patterns are utilized as a spatiotemporal filter to extract signal components relevant to the right hand and right foot extremities. Classification accuracies are obtained using configurations with 19, 30, 61, and 118 electrodes to determine the optimal number of electrodes in motor imagery studies. Experiments are conducted on the BCI Competition III Dataset Iva. The 19-channel configuration yields lower classification accuracy when compared to the others. The results from 118 channels are better than those from 19 channels but not as good as those from 30 and 61 channels. The best results are achieved when 61 channels are utilized. The average accuracy values are 83.63% with 19 channels, increasing to 84.70% with 30 channels, 84.73% with 61 channels, and decreasing to 83.95% when 118 channels are used.},
}

@article {pmid40721281,
year = {2025},
author = {Ognard, J and Douri, D and El Hajj, G and Ghozy, S and Rohleder, M and Gentric, JC and Kadirvel, R and Kallmes, DF and Brinjikji, W},
title = {Future is Ven(o)us: A 5-year narrative update on the venous route for therapeutics in Neurointervention.},
journal = {AJNR. American journal of neuroradiology},
volume = {},
number = {},
pages = {},
doi = {10.3174/ajnr.A8942},
pmid = {40721281},
issn = {1936-959X},
abstract = {Over the past five years, transvenous (TV) techniques have rapidly expanded the neurointerventional landscape, offering new diagnostic and therapeutic strategies for a range of cerebrovascular conditions. This narrative review synthesizes contemporary evidence and technical advances across multiple venous applications, including TV embolization for arteriovenous malformations and dural fistulas, treatment of cerebrospinal fluid-venous fistulas, and venous sinus stenting for pulsatile tinnitus, intracranial hypertension, and skull-base leaks. Recent data underscore high efficacy rates and favorable safety profiles in carefully selected patients, often matching or surpassing traditional arterial approaches. Innovations such as fetal vein of Galen embolization, vein-targeted brain-computer interface implantation, and endovascular cerebrospinal fluid shunting exemplify the therapeutic versatility of venous access. However, procedural challenges, such as venous anatomy, access, and embolic control, require meticulous planning and advanced skillsets. Trials like TATAM and DIVE-IIN are and will shape evidence-based indications for TV therapy. With expanding indications and growing operator expertise, the venous route is evolving from a niche adjunct into a cornerstone of neurovascular care.ABBREVIATIONS: bAVM(s)＝ brain arteriovenous malformation(s); CVF(s)＝ cerebrospinal fluid-venous fistula(s); CVT＝ cerebral venous thrombosis; DAVF(s)＝ dural arteriovenous fistula(s); EVT＝ endovascular therapy; EVOH＝ ethylene-vinyl alcohol copolymer; IIH＝ idiopathic intracranial hypertension; JR-NET3＝ Japanese Registry of NeuroEndovascular Therapy; PT＝ pulsatile tinnitus; RPCT＝ retrograde pressure-cooker technique; SIH＝ spontaneous intracranial hypotension; sCSFL＝ spontaneous cerebrospinal fluid leak; SSWA＝ sigmoid sinus wallabnormality/abnormalities; TV＝ transvenous; TVE＝ transvenous embolization; VSS＝ venous sinus stenting.},
}

@article {pmid40720979,
year = {2025},
author = {Silva, AB and Liu, JR and Anderson, VR and Kurtz-Miott, CM and Hallinan, IP and Littlejohn, KT and Brosler, SC and Tu-Chan, A and Ganguly, K and Moses, DA and Chang, EF},
title = {Implications of shared motor and perceptual activations on the sensorimotor cortex for neuroprosthetic decoding.},
journal = {Journal of neural engineering},
volume = {22},
number = {4},
pages = {},
pmid = {40720979},
issn = {1741-2552},
support = {F30 DC021872/DC/NIDCD NIH HHS/United States ; U01 DC018671/DC/NIDCD NIH HHS/United States ; },
mesh = {Adult ; Female ; Humans ; Male ; Middle Aged ; *Brain-Computer Interfaces ; *Electrocorticography/methods ; Motor Cortex/physiology ; *Neural Prostheses ; *Sensorimotor Cortex/physiology ; *Speech Perception/physiology ; Clinical Trials as Topic ; },
abstract = {Objective.Neuroprostheses can restore communicative ability to people with paralysis by decoding intended speech motor movements from the sensorimotor cortex (SMC). However, overlapping neural populations in the SMC are also engaged in visual and auditory perceptual processing. The nature of these shared motor and perceptual activations and their potential to interfere with decoding are particularly relevant questions for speech neuroprostheses, as reading and listening are essential daily functions.Approach.In two participants with vocal-tract paralysis and anarthria (ClinicalTrials.gov; NCT03698149), we developed an online electrocorticography (ECoG) based speech-decoding system that maintained accuracy and specificity to intended speech, even during common daily tasks like reading and listening. Offline, we studied the spectrotemporal characteristics and spatial distribution of reading, listening, and attempted-speech responses across our participants' ECoG arrays.Main results.Across participants, the speech-decoding system had zero false-positive activations during 63.2 min of attempted speech and perceptual tasks, maintaining accuracy and specificity to volitional speech attempts. Offline, though we observed shared neural populations that responded to attempted speech, listening, and reading, we found they leveraged different neural representations with differentiable spectrotemporal responses. Shared populations localized to the middle precentral gyrus and may have a distinct role in speech-motor planning.Significance.Potential neuroprosthesis users strongly desire reliable systems that will retain specificity to volitional speech attempts during daily use. These results demonstrate a decoding framework for speech neuroprostheses that maintains this specificity and further our understanding of shared perceptual and motor activity on the SMC.},
}

Adult
Female
Humans
Male
Middle Aged
*Brain-Computer Interfaces
*Electrocorticography/methods
Motor Cortex/physiology
*Neural Prostheses
*Sensorimotor Cortex/physiology
*Speech Perception/physiology
Clinical Trials as Topic

@article {pmid40720264,
year = {2025},
author = {Cobilean, V and Mavikumbure, HS and Drake, D and Stuart, M and Manic, M},
title = {Investigating Membership Inference Attacks against CNN Models for BCI Systems.},
journal = {IEEE journal of biomedical and health informatics},
volume = {PP},
number = {},
pages = {},
doi = {10.1109/JBHI.2025.3593443},
pmid = {40720264},
issn = {2168-2208},
abstract = {As Deep Learning (DL) algorithms become more widely adopted in healthcare applications, there is a greater emphasis on understanding and addressing potential privacy risks associated with these models. The purpose of this study is to investigate the privacy vulnerabilities of the Convolutional Neural Network (CNN) classifiers for Electroencephalogram (EEG) data in the Brain-Computer Interfaces (BCIs). Specifically, it focuses on the Membership Inference Attack (MIA), which seeks to determine if data from an individual were used in model training. The novelty of this work lies in its empirical analysis of MIA, specifically by addressing two key challenges that are less common in other domains: 1) datasets that are heterogeneous and 2) spatial-temporal design choices. Motivated by these challenges, we investigate the susceptibility to MIA based on: 1) specifics of the training dataset (number of participants, demographics), and 2) specifics of the CNN (such as architecture, regularization). Our experiments revealed that an adversary with limited knowledge of the model and its training process can compromise the privacy of training participants, noting that the same attack is not effective against deep learning models trained on image and tabular datasets. Some of our findings are: 1) training on diverse participant datasets improves the privacy of the most participants but increases risks of memorization and vulnerabilities for underrepresented groups; 2) regularization is less effective in defending against the MIA for EEG data CNN classifiers when compared to other types of input data; 3) depth and width of model architecture has no impact on membership attack effectiveness. We hope that the presented insights will assist future researchers develop more privacy-aware deep learning based BCI systems.},
}

@article {pmid40720262,
year = {2025},
author = {Wang, K and Liu, Y and Tian, F and Yi, W and Zhang, Y and Jung, TP and Xu, M and Ming, D},
title = {Adaptive Neurofeedback Training Using a Virtual Reality Game Enhances Motor Imagery Performance in Brain-Computer Interfaces.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {33},
number = {},
pages = {2956-2966},
doi = {10.1109/TNSRE.2025.3592988},
pmid = {40720262},
issn = {1558-0210},
mesh = {Humans ; *Brain-Computer Interfaces ; *Neurofeedback/methods ; Male ; Female ; Electroencephalography ; Adult ; *Imagination/physiology ; Young Adult ; *Virtual Reality ; *Video Games ; Psychomotor Performance/physiology ; Healthy Volunteers ; Hand/physiology ; Sensorimotor Cortex/physiology ; Algorithms ; },
abstract = {Neurofeedback training (NFT) has been widely used in motor rehabilitation. However, NFT combined with motor imagery-based brain-computer interface (MI-BCI) faces challenges such as mental fatigue and non-personalized training strategies. Therefore, we proposed an adaptive NFT based on a VR game that simulates real-life motor tasks to improve training efficiency. We conducted a detailed comparative analysis of the efficiency of the VR-based NFT and traditional Graz-based NFT. Forty-eight healthy subjects were randomly assigned to five groups and underwent various NFT protocols. Among them, the subjects in the four experimental groups were required to perform the NFT three times over five days, including virtual or real scenarios, as well as unilateral or bilateral hands training. We evaluated training effects by analyzing EEG features and classification performance, while online recognition duration served as the primary measure for assessing the adaptive NFT strategy. EEG analysis showed that VR-based NFT significantly enhanced the Event-related desynchronization (ERD) activations in the sensorimotor cortices over five days. The VR-based NFT group achieved a classification accuracy of 81.85%, representing a 10.14% improvement from baseline, which exceeded the 6.43% increase observed in the Graz-based NFT group. Furthermore, implementing the adaptive NFT strategy reduced the mean task duration by over 30% compared to the fixed-time training protocol. The results demonstrated that the adaptive MI-BCI-based NFT in a VR game achieves superior training outcomes while reducing training duration. These findings suggest the promising potential for applying MI-BCI NFT with VR games in motor rehabilitation following a stroke.},
}

Humans
*Brain-Computer Interfaces
*Neurofeedback/methods
Male
Female
Electroencephalography
Adult
*Imagination/physiology
Young Adult
*Virtual Reality
*Video Games
Psychomotor Performance/physiology
Healthy Volunteers
Hand/physiology
Sensorimotor Cortex/physiology
Algorithms

@article {pmid40719991,
year = {2025},
author = {Luo, X and Dong, J and Li, T},
title = {The Role of CCL11-CCR3 Induced Mitochondrial Dysfunction and Oxidative Stress in Cognitive Impairment in Early-onset Schizophrenia: Insights from Preclinical Studies.},
journal = {Inflammation},
volume = {},
number = {},
pages = {},
pmid = {40719991},
issn = {1573-2576},
support = {81920108018//National Nature Science Foundation of China Key Project/ ; },
abstract = {Abnormal cytokine expression has been implicated as a potential contributor to neurodegeneration. This study aimed to investigate the plasma cytokine profiles in patients with early-onset schizophrenia (SCZ) and to explore the molecular mechanisms underlying the role of the key cytokine CCL11 in contributing to cognitive impairment. Plasma concentrations of 44 cytokines were quantified in individuals with SCZ. The effects of CCL11 on mitochondrial function were examined in vitro using primary hippocampal neurons. An in vivo model was subsequently developed by administering CCL11 into the lateral ventricle. The impact of the CCL11-CCR3 signaling pathway on mitochondrial function, oxidative stress, and cognitive function within the hippocampus was assessed using a combination of behavioral testing, molecular biology experiments, transcriptomic analysis, and non-targeted metabolomics. In individuals with SCZ, CCL11 and IL-13 levels were notably higher than in controls. In vitro, CCL11 exposure caused mitochondrial dysfunction and increased reactive oxygen species in hippocampal neurons. In vivo, CCL11-treated mice showed cognitive deficits, mitochondrial fission, and neuroinflammation in the hippocampus. Comprehensive integration of transcriptomic and metabolomic data revealed that CCL11 significantly disrupted the Glucokinase/Glucose-6-phosphate metabolism pathway, coinciding with elevated metabolites indicative of oxidative damage. Finally, downregulation of the CCR3 receptor in the hippocampus mitigated CCL11-induced oxidative stress, mitochondrial dysfunction, and cognitive impairment. CCL11 causes cytotoxicity in neurons by increasing oxidative stress and mitochondrial dysfunction. In a mouse model, knockout of the CCR3 receptor alleviates CCL11-induced cognitive impairment, mitochondrial dysfunction, and oxidative stress.},
}

@article {pmid40719383,
year = {2025},
author = {Greenbaum, D},
title = {Enhancing the Warfighter: Ethical, Legal, and Strategic Implications of Brain-Machine Interface-Enabled Military Exoskeletons.},
journal = {AJOB neuroscience},
volume = {},
number = {},
pages = {1-26},
doi = {10.1080/21507740.2025.2530952},
pmid = {40719383},
issn = {2150-7759},
abstract = {The integration of brain-machine interfaces (BMIs) with military exoskeletons represents a significant development in human-machine interaction, raising complex ethical, legal, and strategic challenges. Unlike conventional human enhancement technologies, BMI-exoskeleton systems translate neural intent directly into mechanical movement, generating new concerns regarding agency, accountability, long-term health outcomes, and the governance of neuroadaptive changes. This paper offers a structured interdisciplinary analysis, developing taxonomies of current technologies, tracing the historical trajectory of military exoskeleton development, and critically assessing the emerging convergence between exoskeletal augmentation and neural interface systems. We argue that BMI-exoskeletons constitute a distinct category of augmentation that blurs traditional boundaries between operator and tool, requiring governance frameworks attentive to both operational effectiveness and the ethical implications for individual service members, military institutions, and broader society. Drawing on research in engineering, neuroscience, military studies, and bioethics, we outline a comprehensive ethical-legal framework designed to guide the entire lifecycle of human enhancement-from recruitment and informed consent processes through active service, operational deployment, and post-discharge reintegration. Particular attention is given to autonomy, cybersecurity vulnerabilities, distributive justice, gender equity, and the risks associated with de-enhancement and neuroplastic adaptation. Recognizing the preliminary and rapidly evolving nature of empirical evidence in this domain, we emphasize the need for anticipatory, adaptive policy approaches that safeguard the dignity, rights, and long-term welfare of enhanced warfighters while ensuring that technological innovation proceeds with responsible, ethically-informed oversight.},
}

@article {pmid40719065,
year = {2025},
author = {Xu, G and Wang, Z and Xu, K and Zhu, J and Zhang, J and Wang, Y and Hao, Y},
title = {Decoding Handwriting Trajectories from Intracortical Brain Signals for Brain-to-Text Communication.},
journal = {Advanced science (Weinheim, Baden-Wurttemberg, Germany)},
volume = {},
number = {},
pages = {e05492},
doi = {10.1002/advs.202505492},
pmid = {40719065},
issn = {2198-3844},
abstract = {The potential to decode handwriting trajectories from brain signals has yet to be fully explored in clinical brain-computer interfaces (BCIs). Here, intracortical neural signals are recorded from a paralyzed individual during attempted handwriting of complex characters. An innovative decoding framework is introduced to address both shape and temporal distortions between neural activity and movement, effectively resolving the misalignment issue commonly encountered in clinical BCIs due to the lack of accurate movement labels. The results demonstrated the reconstruction of highly accurate and human-recognizable handwriting trajectories, significantly outperforming conventional methods. Furthermore, the new framework enabled effective multi-day data fusion, leading to additional improvements in trajectory quality. By employing a dynamic time warping approach to translate trajectories into text, a recognition rate up to 91.1% is achieved within a 1000-character database. Additionally, the framework is applied to reconstruct single-trial trajectories of English letters using a previously published dataset, achieving similarly high recognition rates. Collectively, these findings present a novel BCI decoding scheme capable of accurately reconstructing handwriting trajectories, demonstrating its applicability to both alphabetic and logographic brain-to-text translation. This approach has the potential to revolutionize communication for individuals with motor impairments by enabling accurate brain-to-text translation across diverse languages.},
}

@article {pmid40718756,
year = {2025},
author = {Kim, R and Liu, Y and Zhang, J and Xie, C and Luan, L},
title = {Towards Precise Synthetic Neural Codes: High-dimensional Stimulation with Flexible Electrodes.},
journal = {Npj flexible electronics},
volume = {9},
number = {1},
pages = {},
pmid = {40718756},
issn = {2397-4621},
support = {R01 EY036094/EY/NEI NIH HHS/United States ; R01 NS102917/NS/NINDS NIH HHS/United States ; U01 NS115588/NS/NINDS NIH HHS/United States ; U01 NS131086/NS/NINDS NIH HHS/United States ; },
abstract = {Neural representations arise from the spatiotemporally structured activity of neuron populations, inherently residing in high-dimensional spaces. Writing specific information into the central nervous system requires precisely manipulating neural states within this framework. However, current neuromodulation methods lack the precision to fully address this complexity, presenting a significant challenge for advancing effective bidirectional interfaces. In this perspective, we advocate for high-dimensional stimulation as a systematic approach capable of approximating the high dimensionality of natural neural code for brain-machine interface applications. We outline key technological requirements on resolution, coverage, and safety, review recent advances in critical application areas, and highlight the promise of flexible electrode technology in enabling a transformative leap towards precise synthetic neural codes.},
}

@article {pmid40718596,
year = {2025},
author = {Chang, L and Yang, B and Zhang, J and Li, T and Feng, J and Xu, W},
title = {DSTA-Net: dynamic spatio-temporal feature augmentation network for motor imagery classification.},
journal = {Cognitive neurodynamics},
volume = {19},
number = {1},
pages = {118},
pmid = {40718596},
issn = {1871-4080},
abstract = {Accurate decoding and strong feature interpretability of Motor Imagery (MI) are expected to drive MI applications in stroke rehabilitation. However, the inherent nonstationarity and high intra-class variability of MI-EEG pose significant challenges in extracting reliable spatio-temporal features. We proposed the Dynamic Spatio-Temporal Feature Augmentation Network (DSTA-Net), which combines DSTA and the Spatio-Temporal Convolution (STC) modules. In DSTA module, multi-scale temporal convolutional kernels tailored to the α and β frequency bands of MI neurophysiological characteristics, while raw EEG serve as a baseline feature layer to retain original information. Next, Grouped Spatial Convolutions extract multi-level spatial features, combined with weight constraints to prevent overfitting. Spatial convolution kernels map EEG channel information into a new spatial domain, enabling further feature extraction through dimensional transformation. And STC module further extracts features and conducts classification. We evaluated DSTA-Net on three public datasets and applied it to a self-collected stroke dataset. In tenfold cross-validation, DSTA-Net achieved average accuracy improvements of 6.29% (p < 0.01), 3.05% (p < 0.01), 5.26% (p < 0.01), and 2.25% over the ShallowConvNet on the BCI-IV-2a, OpenBMI, CASIA, and stroke dataset, respectively. In hold-out validation, DSTA-Net achieved average accuracy improvements of 3.99% (p < 0.01) and 4.2% (p < 0.01) over the ShallowConvNet on the OpenBMI and CASIA datasets, respectively. Finally, we applied DeepLIFT, Common Spatial Pattern, and t-SNE to analyze the contributions of individual EEG channels, extract spatial patterns, and visualize features. The superiority of DSTA-Net offers new insights for further research and application in MI. The code is available in https://github.com/CL-Cloud-BCI/DSTANet-code.},
}

@article {pmid40718569,
year = {2025},
author = {Lopez Blanco, C and Tyler, WJ},
title = {The vagus nerve: a cornerstone for mental health and performance optimization in recreation and elite sports.},
journal = {Frontiers in psychology},
volume = {16},
number = {},
pages = {1639866},
pmid = {40718569},
issn = {1664-1078},
abstract = {Decades of physiological and psychological research into human performance and wellness have established a critical role for vagus nerve signaling in peak physical and cognitive performance. We outline models and perspectives that have emerged through neuroscience and psychophysiology studies to elucidate how the vagus nerve governs human performance through its influence on central nervous system functions and autonomic nervous system activity. These functions include the monitoring and regulation of cardio-respiratory activity, emotional responses, inflammation and physical recovery, cognitive control, stress resilience, and team cohesion. We briefly review some useful interventions such as transcutaneous auricular vagus nerve stimulation, heart-rate variability biofeedback, and controlled breathing as accessible tools for enhancing vagal tone, improving executive functioning under pressure, and mitigating fatigue and burnout. We describe how these approaches and their biological underpinnings are rooted by psychological models like the Yerkes-Dodson law and Polyvagal theory to contextualize their effects on athletic performance. These perspectives suppor recent shifts in sports science toward integrating vagal-centered approaches as scalable, evidence-based strategies that can enhance human performance and wellness.},
}

@article {pmid40717726,
year = {2025},
author = {Otarbay, Z and Kyzyrkanov, A},
title = {SVM-enhanced attention mechanisms for motor imagery EEG classification in brain-computer interfaces.},
journal = {Frontiers in neuroscience},
volume = {19},
number = {},
pages = {1622847},
pmid = {40717726},
issn = {1662-4548},
abstract = {Brain-Computer Interfaces (BCIs) leverage brain signals to facilitate communication and control, particularly benefiting individuals with motor impairments. Motor imagery (MI)-based BCIs, utilizing non-invasive electroencephalography (EEG), face challenges due to high signal variability, noise, and class overlap. Deep learning architectures, such as CNNs and LSTMs, have improved EEG classification but still struggle to fully capture discriminative features for overlapping motor imagery classes. This study introduces a hybrid deep neural architecture that integrates Convolutional Neural Networks, Long Short-Term Memory networks, and a novel SVM-enhanced attention mechanism. The proposed method embeds the margin maximization objective of Support Vector Machines directly into the self-attention computation to improve interclass separability during feature learning. We evaluate our model on four benchmark datasets: Physionet, Weibo, BCI Competition IV 2a, and 2b, using a Leave-One-Subject-Out (LOSO) protocol to ensure robustness and generalizability. Results demonstrate consistent improvements in classification accuracy, F1-score, and sensitivity compared to conventional attention mechanisms and baseline CNN-LSTM models. Additionally, the model significantly reduces computational cost, supporting real-time BCI applications. Our findings highlight the potential of SVM-enhanced attention to improve EEG decoding performance by enforcing feature relevance and geometric class separability simultaneously.},
}

@article {pmid40715543,
year = {2025},
author = {Xu, H and Huang, Q and Song, P and Chen, Y and Li, Q and Zhai, Y and Du, X and Ye, H and Bao, X and Mehmood, I and Tanigawa, H and Niu, W and Tu, Z and Chen, P and Zhang, T and Zhang, L and Zhao, X and Zhang, L and Wen, W and Cao, L and Yu, X},
title = {EEG neural indicator of temporal integration in the human auditory brain with clinical implications.},
journal = {Communications biology},
volume = {8},
number = {1},
pages = {1109},
pmid = {40715543},
issn = {2399-3642},
support = {32171044//National Natural Science Foundation of China (National Science Foundation of China)/ ; 32100827//National Natural Science Foundation of China (National Science Foundation of China)/ ; 32271078//National Natural Science Foundation of China (National Science Foundation of China)/ ; LGF22H170006//Natural Science Foundation of Zhejiang Province (Zhejiang Provincial Natural Science Foundation)/ ; },
mesh = {Humans ; *Electroencephalography/methods ; Male ; Female ; Adult ; *Auditory Perception/physiology ; Acoustic Stimulation ; Young Adult ; Middle Aged ; *Auditory Cortex/physiology ; *Brain/physiology ; Evoked Potentials, Auditory ; },
abstract = {Temporal integration, the process by which the auditory system combines sound information over a certain period to form a coherent auditory experience, is essential for auditory perception, yet its neural mechanisms remain underexplored. We use a "transitional click train" paradigm, which concatenates two click trains with slightly differing inter-click intervals (ICIs), to investigate temporal integration in the human brain. Using a 64-channel electroencephalogram (EEG), we recorded responses from healthy participants exposed to regular and irregular transitional click trains and conducted change detection tasks. Regular transitional click trains elicited significant change responses in the human brain, indicative of temporal integration, whereas irregular trains did not. These neural responses were modulated by length, contrast, and regularity of ICIs. Behavioral data mirrored EEG findings, showing enhanced detection for regular conditions compared to irregular conditions and pure tones. Furthermore, variations in change responses were associated with decision-making processes. Temporal continuity was critical, as introducing gaps between click trains diminished both behavioral and neural responses. In clinical assessments, 22 coma patients exhibited diminished or absent change responses, effectively distinguishing them from healthy individuals. Our findings identify distinct neural markers of temporal integration and highlight the potential of transitional click trains for clinical diagnostics.},
}

Humans
*Electroencephalography/methods
Male
Female
Adult
*Auditory Perception/physiology
Acoustic Stimulation
Young Adult
Middle Aged
*Auditory Cortex/physiology
*Brain/physiology
Evoked Potentials, Auditory

@article {pmid40715225,
year = {2025},
author = {Das, A and Singh, S and Kim, J and Ahanger, TA and Pise, AA},
title = {Enhanced EEG signal classification in brain computer interfaces using hybrid deep learning models.},
journal = {Scientific reports},
volume = {15},
number = {1},
pages = {27161},
pmid = {40715225},
issn = {2045-2322},
support = {No.RS-2022-00155857//Institute of Information & communications Technology Planning & Evaluation (IITP) grant funded by the Korea government (MSIT) (No.RS-2022-00155857, Artificial Intelligence Convergence Innovation Human Resources Development (Chungnam National University). Also supported part by Woosong university research fund 2024./ ; No.RS-2022-00155857//Institute of Information & communications Technology Planning & Evaluation (IITP) grant funded by the Korea government (MSIT) (No.RS-2022-00155857, Artificial Intelligence Convergence Innovation Human Resources Development (Chungnam National University). Also supported part by Woosong university research fund 2024./ ; },
mesh = {*Brain-Computer Interfaces ; Humans ; *Electroencephalography/methods ; *Deep Learning ; *Brain/physiology ; Neural Networks, Computer ; Support Vector Machine ; Bayes Theorem ; Signal Processing, Computer-Assisted ; },
abstract = {Brain-computer interfaces (BCIs) establish a communication pathway between the human brain and external devices by decoding neural signals. This study focuses on enhancing the classification of Motor Imagery (MI) within BCI systems by leveraging advanced machine learning and deep learning techniques. The accurate classification of electroencephalogram (EEG) data is crucial for enhancing BCI performance. The BCI architecture processes electroencephalography signals through three critical stages: data pre-processing, feature extraction, and classification. The research evaluates the performance of five traditional machine learning classifiers- K-Nearest Neighbors (KNN), Support Vector Classifier (SVC), Logistic Regression (LR), Random Forest (RF), and Naive Bayes (NB)-using the "PhysioNet EEG Motor Movement/Imagery Dataset". This dataset encompasses EEG data from various motor tasks, including both actual and imagined movements. Among the traditional classifiers, Random Forest achieved the highest accuracy of 91%, underscoring its efficacy in motor imagery classification within BCI systems. In addition to conventional approaches, the study also explores deep learning techniques, with Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks yielding accuracies of 88.18% and 16.13%, respectively. However, the proposed hybrid model, which synergistically combines CNN and LSTM, significantly surpasses both traditional machine learning and individual deep learning methods, achieving an exceptional accuracy of 96.06%. This substantial improvement highlights the potential of hybrid deep learning models to advance the state of the art in BCI systems, offering a more robust and precise approach to motor imagery classification.},
}

*Brain-Computer Interfaces
Humans
*Electroencephalography/methods
*Deep Learning
*Brain/physiology
Neural Networks, Computer
Support Vector Machine
Bayes Theorem
Signal Processing, Computer-Assisted