@article {pmid40295498,
year = {2025},
author = {Fang, Z and Sims, CR},
title = {Humans learn generalizable representations through efficient coding.},
journal = {Nature communications},
volume = {16},
number = {1},
pages = {3989},
pmid = {40295498},
issn = {2041-1723},
support = {2024M761999//China Postdoctoral Science Foundation/ ; },
abstract = {Reinforcement learning theory explains human behavior as driven by the goal of maximizing reward. Conventional approaches, however, offer limited insights into how people generalize from past experiences to new situations. Here, we propose refining the classical reinforcement learning framework by incorporating an efficient coding principle, which emphasizes maximizing reward using the simplest necessary representations. This refined framework predicts that intelligent agents, constrained by simpler representations, will inevitably: 1) distill environmental stimuli into fewer, abstract internal states, and 2) detect and utilize rewarding environmental features. Consequently, complex stimuli are mapped to compact representations, forming the foundation for generalization. We tested this idea in two experiments that examined human generalization. Our findings reveal that while conventional models fall short in generalization, models incorporating efficient coding achieve human-level performance. We argue that the classical RL objective, augmented with efficient coding, represents a more comprehensive computational framework for understanding human behavior in both learning and generalization.},
}

@article {pmid40294568,
year = {2025},
author = {Pan, L and Wang, K and Huang, Y and Sun, X and Meng, J and Yi, W and Xu, M and Jung, TP and Ming, D},
title = {Enhancing motor imagery EEG classification with a Riemannian geometry-based spatial filtering (RSF) method.},
journal = {Neural networks : the official journal of the International Neural Network Society},
volume = {188},
number = {},
pages = {107511},
doi = {10.1016/j.neunet.2025.107511},
pmid = {40294568},
issn = {1879-2782},
abstract = {Motor imagery (MI) refers to the mental simulation of movements without physical execution, and it can be captured using electroencephalography (EEG). This area has garnered significant research interest due to its substantial potential in brain-computer interface (BCI) applications, especially for individuals with physical disabilities. However, accurate classification of MI EEG signals remains a major challenge due to their non-stationary nature, low signal-to-noise ratio, and sensitivity to both external and physiological noise. Traditional classification methods, such as common spatial pattern (CSP), often assume that the data is stationary and Gaussian, which limits their applicability in real-world scenarios where these assumptions do not hold. These challenges highlight the need for more robust methods to improve classification accuracy in MI-BCI systems. To address these issues, this study introduces a Riemannian geometry-based spatial filtering (RSF) method that projects EEG signals into a lower-dimensional subspace, maximizing the Riemannian distance between covariance matrices from different classes. By leveraging the inherent geometric properties of EEG data, RSF enhances the discriminative power of the features while maintaining robustness against noise. The performance of RSF was evaluated in combination with ten commonly used MI decoding algorithms, including CSP with linear discriminant analysis (CSP-LDA), Filter Bank CSP (FBCSP), Minimum Distance to Riemannian Mean (MDM), Tangent Space Mapping (TSM), EEGNet, ShallowConvNet (sCNN), DeepConvNet (dCNN), FBCNet, Graph-CSPNet, and LMDA-Net, using six publicly available MI-BCI datasets. The results demonstrate that RSF significantly improves classification accuracy and reduces computational time, particularly for deep learning models with high computational complexity. These findings underscore the potential of RSF as an effective spatial filtering approach for MI EEG classification, providing new insights and opportunities for the development of robust MI-BCI systems. The code for this research is available at https://github.com/PLC-TJU/RSF.},
}

@article {pmid40292964,
year = {2025},
author = {Kasawala, E and Mouli, S},
title = {Dual-Mode Visual System for Brain-Computer Interfaces: Integrating SSVEP and P300 Responses.},
journal = {Sensors (Basel, Switzerland)},
volume = {25},
number = {6},
pages = {},
doi = {10.3390/s25061802},
pmid = {40292964},
issn = {1424-8220},
mesh = {*Brain-Computer Interfaces ; Humans ; *Evoked Potentials, Visual/physiology ; *Event-Related Potentials, P300/physiology ; Electroencephalography/methods ; Algorithms ; Photic Stimulation ; Male ; Adult ; Signal Processing, Computer-Assisted ; Female ; Young Adult ; },
abstract = {In brain-computer interface (BCI) systems, steady-state visual-evoked potentials (SSVEP) and P300 responses have achieved widespread implementation owing to their superior information transfer rates (ITR) and minimal training requirements. These neurophysiological signals have exhibited robust efficacy and versatility in external device control, demonstrating enhanced precision and scalability. However, conventional implementations predominantly utilise liquid crystal display (LCD)-based visual stimulation paradigms, which present limitations in practical deployment scenarios. This investigation presents the development and evaluation of a novel light-emitting diode (LED)-based dual stimulation apparatus designed to enhance SSVEP classification accuracy through the integration of both SSVEP and P300 paradigms. The system employs four distinct frequencies-7 Hz, 8 Hz, 9 Hz, and 10 Hz-corresponding to forward, backward, right, and left directional controls, respectively. Oscilloscopic verification confirmed the precision of these stimulation frequencies. Real-time feature extraction was accomplished through the concurrent analysis of maximum Fast Fourier Transform (FFT) amplitude and P300 peak detection to ascertain user intent. Directional control was determined by the frequency exhibiting maximal amplitude characteristics. The visual stimulation hardware demonstrated minimal frequency deviation, with error differentials ranging from 0.15% to 0.20% across all frequencies. The implemented signal processing algorithm successfully discriminated between all four stimulus frequencies whilst correlating them with their respective P300 event markers. Classification accuracy was evaluated based on correct task intention recognition. The proposed hybrid system achieved a mean classification accuracy of 86.25%, coupled with an average ITR of 42.08 bits per minute (bpm). These performance metrics notably exceed the conventional 70% accuracy threshold typically employed in BCI system evaluation protocols.},
}

*Brain-Computer Interfaces
Humans
*Evoked Potentials, Visual/physiology
*Event-Related Potentials, P300/physiology
Electroencephalography/methods
Algorithms
Photic Stimulation
Male
Adult
Signal Processing, Computer-Assisted
Female
Young Adult

@article {pmid40292808,
year = {2025},
author = {Gao, D and Wang, Y and Fu, P and Qiu, J and Li, H},
title = {Modeling and Parameter Analysis of Basic Single Channel Neuron Mass Model for SSVEP.},
journal = {Sensors (Basel, Switzerland)},
volume = {25},
number = {6},
pages = {},
doi = {10.3390/s25061706},
pmid = {40292808},
issn = {1424-8220},
mesh = {*Evoked Potentials, Visual/physiology ; Humans ; Brain-Computer Interfaces ; *Neurons/physiology ; *Models, Neurological ; Visual Cortex/physiology ; Electroencephalography ; Photic Stimulation ; },
abstract = {While steady-state visual evoked potentials (SSVEPs) are widely used in brain-computer interfaces (BCIs) due to their robustness to rhythmic visual stimuli, their generation mechanisms remain poorly understood. Challenges such as experimental complexity, inter-subject variability, and limited physiological interpretability hinder the development of efficient BCI systems. This study employed a single-channel neural mass model (NMM) of V1 cortical dynamics to investigate the biophysical underpinnings of SSVEP generation. By systematically varying synaptic gain, time constants, and external input parameters, we simulated δ/α/γ band oscillations and analyzed their generation principles. The model demonstrates that synaptic gain controls oscillation amplitude and harmonic content, and time constants determine signal decay kinetics and frequency precision, while input variance modulates harmonic stability. Our results reveal how V1 circuitry generates frequency-locked SSVEP responses through excitatory-inhibitory interactions and dynamic filtering mechanisms. This computational framework successfully reproduces fundamental SSVEP characteristics without requiring multi-subject experimental data, offering new insights into the physiological basis of SSVEP-based brain-computer interfaces.},
}

*Evoked Potentials, Visual/physiology
Humans
Brain-Computer Interfaces
*Neurons/physiology
*Models, Neurological
Visual Cortex/physiology
Electroencephalography
Photic Stimulation

@article {pmid40292646,
year = {2025},
author = {Jeon, Y and Kim, M and Song, KH},
title = {Development of Hydrogels Fabricated via Stereolithography for Bioengineering Applications.},
journal = {Polymers},
volume = {17},
number = {6},
pages = {},
doi = {10.3390/polym17060765},
pmid = {40292646},
issn = {2073-4360},
support = {Incheon National University (International Cooperative) Research Grant in 2020//Incheon National University/ ; },
abstract = {The architectures of hydrogels fabricated with stereolithography (SLA) 3D printing systems have played various roles in bioengineering applications. Typically, the SLA systems successively illuminated light to a layer of photo-crosslinkable hydrogel precursors for the fabrication of hydrogels. These SLA systems can be classified into point-scanning types and digital micromirror device (DMD) types. The point-scanning types form layers of hydrogels by scanning the precursors with a focused light, while DMD types illuminate 2D light patterns to the precursors to form each hydrogel layer at once. Overall, SLA systems were cost-effective and allowed the fabrication of hydrogels with good shape fidelity and uniform mechanical properties. As a result, hydrogel constructs fabricated with the SLA 3D printing systems were used to regenerate tissues and develop lab-on-a-chip devices and native tissue-like models.},
}

@article {pmid40292025,
year = {2025},
author = {Pawlak, WA and Howard, N},
title = {Neuromorphic algorithms for brain implants: a review.},
journal = {Frontiers in neuroscience},
volume = {19},
number = {},
pages = {1570104},
pmid = {40292025},
issn = {1662-4548},
abstract = {Neuromorphic computing technologies are about to change modern computing, yet most work thus far has emphasized hardware development. This review focuses on the latest progress in algorithmic advances specifically for potential use in brain implants. We discuss current algorithms and emerging neurocomputational models that, when implemented on neuromorphic hardware, could match or surpass traditional methods in efficiency. Our aim is to inspire the creation and deployment of models that not only enhance computational performance for implants but also serve broader fields like medical diagnostics and robotics inspiring next generations of neural implants.},
}

@article {pmid40290410,
year = {2025},
author = {Ng, JY},
title = {Exploring the intersection of brain-computer interfaces and traditional, complementary, and integrative medicine.},
journal = {Integrative medicine research},
volume = {14},
number = {2},
pages = {101142},
pmid = {40290410},
issn = {2213-4220},
abstract = {Brain-computer interfaces (BCIs) represent a transformative innovation in healthcare, enabling direct communication between the brain and external devices. This educational article explores the potential intersection of BCIs and traditional, complementary, and integrative medicine (TCIM). BCIs have shown promise in enhancing mind-body practices such as meditation, while their integration with energy-based therapies may offer novel insights and measurable outcomes. Emerging advancements, including artificial intelligence-enhanced BCIs, hold potential for improving personalization and expanding the therapeutic efficacy of TCIM interventions. Despite these opportunities, integrating BCIs with TCIM presents considerable ethical, cultural, and practical challenges. Concerns related to informed consent, cultural sensitivity, data privacy, accessibility, and regulatory frameworks must be addressed to ensure responsible implementation. Interdisciplinary collaboration among relevant stakeholders, including TCIM and conventional practitioners, researchers, and policymakers among other relevant stakeholders is crucial for developing integrative healthcare models that balance innovation with patient safety and respect for diverse healing traditions. Future directions include expanding evidence bases to validate TCIM practices through BCI-enhanced research, fostering equitable access to neurotechnological advancements, and promoting global ethical guidelines to navigate complex sociocultural dynamics. BCIs have the potential to revolutionize TCIM, offering novel solutions for complex health challenges and fostering a more inclusive, integrative approach to healthcare, provided that they are utilized responsibly and ethically.},
}

@article {pmid40289727,
year = {2025},
author = {Lin, X and Zhang, X and Chen, J and Liu, J},
title = {Material Selection and Device Design of Scalable Flexible Brain-Computer Interfaces: A Balance Between Electrical and Mechanical Performance.},
journal = {Advanced materials (Deerfield Beach, Fla.)},
volume = {},
number = {},
pages = {e2413938},
doi = {10.1002/adma.202413938},
pmid = {40289727},
issn = {1521-4095},
support = {DMR-2011754//Directorate for Engineering/ ; },
abstract = {Brain-computer interfaces (BCIs) hold the potential to revolutionize brain function restoration, enhance human capability, and advance our understanding of cognitive mechanisms by directly linking neural signals with hardware. However, the mechanical mismatch between conventional rigid BCIs and soft brain tissue limits long-term interface stability. Next-generation BCIs must achieve long-term biocompatibility while maintaining high performance, enabling the integration of millions of sensors within tissue-level flexible and soft, stable neural interfaces. Lithographic fabrication techniques provide scalable thin-film flexible electronics, but traditional electronic materials often fail to meet the unique requirements of BCIs. This review examines the selection of materials and device design for flexible BCIs, starting with an analysis of intrinsic material properties-Young's modulus, electrical conductivity and dielectric constant. It then explores the integration of material selection with electrode design to optimize electrical circuits and assess key mechanical factors. Next, the correlation between electrical and mechanical performance is analyzed to guide material selection and device design. Finally, recent advances in neural probes are reviewed, highlighting improvements in signal quality, recording stability, and scalability. This review focuses on scalable, lithography-based BCIs, aiming to identify optimal materials and designs for long-term, reliable neural recordings.},
}

@article {pmid40289349,
year = {2025},
author = {Pitt, KM and Boster, JB},
title = {Identifying P300 brain-computer interface training strategies for AAC in children: a focus group study.},
journal = {Augmentative and alternative communication (Baltimore, Md. : 1985)},
volume = {},
number = {},
pages = {1-10},
doi = {10.1080/07434618.2025.2495912},
pmid = {40289349},
issn = {1477-3848},
abstract = {The integration of Brain-Computer Interface (BCI) technology into Augmentative and Alternative Communication (AAC) systems introduces new complexities in training, particularly for children with diverse cognitive, sensory, motor, and linguistic abilities. Effective AAC training is crucial for enabling individuals to achieve personal goals and enhance social participation. This study aimed to explore potential training strategies for children using P300 based BCI-AAC systems through focus group discussions with experts in AAC and BCI technologies. Participants identified six key themes for effective training: (1) Scaffolding-developing adaptive systems tailored to each child's developmental level, including preteaching, visual display adaptations, and gamification; (2) Verbal Instructions-emphasizing the use of clear, simple language and spoken prompts; (3) Feedback-incorporating immediate feedback and biofeedback methods to reinforce learning; (4) Positioning-ensuring proper trunk stability and addressing electrode placement; (5) Modeling and Physical Supports-using physical cues and demonstrating BCI-AAC use; and (6) Considerations for Visual Impairment-accommodating cortical visual impairment (CVI) with suitable stimuli and environmental adjustments. These insights offer an initial foundation for identifying P300 BCI-AAC training strategies for children. Further systematic research with end users, support networks, and professionals is needed to validate, refine, and expand interventions that support diverse communication needs.},
}

@article {pmid40289107,
year = {2025},
author = {Zhi, Y and Guo, Y and Li, S and He, X and Wei, H and Laster, K and Wu, Q and Zhao, D and Xie, J and Ruan, S and Lemoine, NR and Li, H and Dong, Z and Liu, K},
title = {FBL promotes hepatocellular carcinoma tumorigenesis and progression by recruiting YY1 to enhance CAD gene expression.},
journal = {Cell death & disease},
volume = {16},
number = {1},
pages = {348},
pmid = {40289107},
issn = {2041-4889},
abstract = {Hepatocellular carcinoma (HCC) is the third leading cause of cancer-related death worldwide. Accumulating evidence suggests that epigenetic dysregulation contributes to the initiation and progression of HCC. We aimed to investigate key epigenetic regulators that contribute to tumorigenesis and progression, providing a theoretical basis for targeted therapy for HCC. We performed a comprehensive epigenetic analysis of differentially expressed genes in LIHC from the TCGA database. We identified fibrillarin (FBL), an rRNA 2'-O-methyltransferase, as an essential contributor to HCC. A series of in vitro and in vivo biological experiments were performed to investigate the potential mechanisms of FBL. FBL knockdown suppressed the proliferation of HCC cells. In vivo studies using cell-derived xenograft (CDX), patient-derived xenograft (PDX), and diethylnitrosamine (DEN)-induced HCC models in Fbl liver-specific knockout mice demonstrated the critical role of FBL in HCC carcinogenesis and progression. Mechanistically, FBL regulates the expression of CAD in HCC cells by recruiting YY1 to the CAD promoter region. We also revealed that fludarabine phosphate is a novel inhibitor of FBL and can inhibit HCC growth in vitro and in vivo. The antitumor activity of lenvatinib has been shown to be synergistically enhanced by fludarabine phosphate. Our study highlights the cancer-promoting role of the FBL-YY1-CAD axis in HCC and identifies fludarabine phosphate as a novel inhibitor of FBL. A schematic diagram depicting the FBL-YY1-CAD signaling pathway and its regulatory role in HCC progression.},
}

@article {pmid40288968,
year = {2025},
author = {Pan, L and Sun, X and Wang, K and Cao, Y and Xu, M and Ming, D},
title = {[Cross-session motor imagery-electroencephalography decoding with Riemannian spatial filtering and domain adaptation].},
journal = {Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi},
volume = {42},
number = {2},
pages = {272-279},
doi = {10.7507/1001-5515.202411035},
pmid = {40288968},
issn = {1001-5515},
abstract = {Motor imagery (MI) is a mental process that can be recognized by electroencephalography (EEG) without actual movement. It has significant research value and application potential in the field of brain-computer interface (BCI) technology. To address the challenges posed by the non-stationary nature and low signal-to-noise ratio of MI-EEG signals, this study proposed a Riemannian spatial filtering and domain adaptation (RSFDA) method for improving the accuracy and efficiency of cross-session MI-BCI classification tasks. The approach addressed the issue of inconsistent data distribution between source and target domains through a multi-module collaborative framework, which enhanced the generalization capability of cross-session MI-EEG classification models. Comparative experiments were conducted on three public datasets to evaluate RSFDA against eight existing methods in terms of classification accuracy and computational efficiency. The experimental results demonstrated that RSFDA achieved an average classification accuracy of 79.37%, outperforming the state-of-the-art deep learning method Tensor-CSPNet (76.46%) by 2.91% (P < 0.01). Furthermore, the proposed method showed significantly lower computational costs, requiring only approximately 3 minutes of average training time compared to Tensor-CSPNet's 25 minutes, representing a reduction of 22 minutes. These findings indicate that the RSFDA method demonstrates superior performance in cross-session MI-EEG classification tasks by effectively balancing accuracy and efficiency. However, its applicability in complex transfer learning scenarios remains to be further investigated.},
}

@article {pmid40288455,
year = {2025},
author = {Tang, Y and Tang, Z and Zhou, Y and Luo, Y and Wen, X and Yang, Z and Jiang, T and Luo, N},
title = {A systematic review of resting-state functional-MRI studies in the diagnosis, comorbidity and treatment of postpartum depression.},
journal = {Journal of affective disorders},
volume = {},
number = {},
pages = {},
doi = {10.1016/j.jad.2025.04.142},
pmid = {40288455},
issn = {1573-2517},
abstract = {BACKGROUND: Postpartum depression (PPD) is a common and serious mental health problem that affects many new mothers and their families worldwide. In recent years, there has been an increasing number of studies using magnetic resonance techniques (MRI), particularly functional MRI (fMRI), to explore the neuroimaging biomarker of this disease.

METHODS: PubMed database was used to search for English literature focusing on resting-state fMRI and PPD published up to June 2024.

RESULTS: After screening, 17 studies were finally identified, among which all 17 studies reported abnormal regions or connectivity compared to health controls (HC), 4 studies reported results considering the differences between PPD and PPD with anxiety (PPD-A), and 2 studies reported biomarkers for the treatment of PPD. The existing studies indicate that PPD is characterized by functional impairments in multiple brain regions, especially the medial prefrontal cortex (MPFC), precentral gyrus and cerebellum. Abnormal functional connectivity has been widely reported in the dorsomedial prefrontal cortex (dmPFC), anterior cingulate cortex (ACC) and the orbitofrontal cortex (OFC). However, none of the four comorbidity studies identified overlapping discriminative biomarkers between PPD and PPD-A. Additionally, the two treatment-related studies consistently reported functional improvements in the amygdala after effective treatment.

CONCLUSION: The affected brain regions were highly overlapped with major depressive disorder (MDD), suggesting that PPD may be categorized as a potential subtype of MDD. Considering the negative effects of medication on PPD, future efforts should focus on developing non-pharmacological therapies, such as transcranial magnetic stimulation (TMS) and acupuncture, to support women with PPD in overcoming this unique and important phase.},
}

@article {pmid40288311,
year = {2025},
author = {Li, L and Jiang, C},
title = {Electrodeposited coatings for neural electrodes: A review.},
journal = {Biosensors & bioelectronics},
volume = {282},
number = {},
pages = {117492},
doi = {10.1016/j.bios.2025.117492},
pmid = {40288311},
issn = {1873-4235},
abstract = {Neural electrodes play a pivotal role in ensuring safe stimulation and high-quality recording for various bioelectronics such as neuromodulation devices and brain-computer interfaces. With the miniaturization of electrodes and the increasing demand for multi-functionality, the incorporation of coating materials via electrodeposition to enhance electrodes performance emerges as a highly effective strategy. These coatings not only substantially improve the stimulation and recording performance of electrodes but also introduce additional functionalities. This review began by outlining the application scenarios and critical requirements of neural electrodes. It then delved into the deposition principles and key influencing factors. Furthermore, the advancements in the electrochemical performance and adhesion stability of these coatings were reviewed. Ultimately, the latest innovative works in the electrodeposited coating applications were highlighted, and future perspectives were summarized.},
}

@article {pmid40287824,
year = {2025},
author = {Kumar, R and Waisberg, E and Ong, J and Lee, AG},
title = {The potential power of neuralink - how brain-machine interfaces can revolutionize medicine.},
journal = {Expert review of medical devices},
volume = {},
number = {},
pages = {},
doi = {10.1080/17434440.2025.2498457},
pmid = {40287824},
issn = {1745-2422},
}

@article {pmid40287725,
year = {2025},
author = {Zhang, X and Xie, L and Liu, W and Liang, S and Huang, L and Wang, M and Tian, L and Zhang, L and Liang, Z and Li, H and Huang, G},
title = {Exoskeleton-guided passive movement elicits standardized EEG patterns for generalizable BCIs in stroke rehabilitation.},
journal = {Journal of neuroengineering and rehabilitation},
volume = {22},
number = {1},
pages = {97},
pmid = {40287725},
issn = {1743-0003},
support = {62201356//National Natural Science Foundation of China/ ; 62276169//National Natural Science Foundation of China/ ; 62271326//National Natural Science Foundation of China/ ; 2023SHIBS0003//Shenzhen-Hong Kong Institute of Brain Science-Shenzhen Fundamental Research Institutions/ ; JCYJ20210324134401004//Shenzhen Science and Technology Innovation Program/ ; JCYJ20241202124222027//Shenzhen Science and Technology Innovation Program/ ; C2401028//Shenzhen Medical Research Foundation/ ; },
abstract = {BACKGROUND: Brain-computer interfaces (BCIs) hold significant potential for post-stroke motor recovery, yet active movement-based BCIs face limitations in generalization due to inter-subject variability. This study investigates passive movement-based BCIs, driven by exoskeleton-guided rehabilitation, to address these challenges by evaluating electroencephalogram (EEG) responses and algorithmic generalization in both healthy subjects and stroke patients.

METHODS: EEG signals were recorded from 20 healthy subjects and 10 stroke patients during voluntary and passive hand movements. Time and time-frequency domain analyses were performed to examine the event-related potential (ERP), event-related desynchronization (ERD), and synchronization (ERS) patterns. The performance of two BCI algorithms, Common Spatial Patterns (CSP) and EEGNet, was evaluated in both within-subject and cross-subject decoding tasks.

RESULTS: Time-domain and time-frequency analyses revealed that passive movements elicited stronger, more consistent ERPs in healthy subjects, particularly in bilateral motor cortices (contralateral: - 7.29 ± 4.51  μV; ipsilateral: - 4.33 ± 3.69  μV). Stroke patients exhibited impaired mu/beta ERD/ERS in the affected hemisphere during voluntary movements but demonstrated EEG patterns during passive movements resembling those of healthy subjects. Machine learning evaluation highlighted EEGNet's superior performance, achieving 84.19% accuracy in classifying affected vs. unaffected movements in patients, surpassing healthy subject left-right discrimination (58.38%). Cross-subject decoding further validated passive movement efficacy, with EEGNet attaining 86.00% (healthy) and 72.63% (stroke) accuracy, outperforming traditional CSP methods.

CONCLUSIONS: These findings underscore that passive movement elicits consistent neural responses, thereby enhancing the generalizability of decoding algorithms for stroke patients. By integrating exoskeleton-evoked proprioceptive feedback, this paradigm reduces inter-subject variability and improves clinical feasibility. Future work should explore the application of exoskeletons in the combination of active and passive movement for stroke rehabilitation.},
}

@article {pmid40281719,
year = {2025},
author = {Wan, X and Liu, Z and Yao, Y and Wan Hasan, WZ and Liu, T and Duan, D and Xie, X and Wen, D},
title = {Data Uncertainty (DU)-Former: An Episodic Memory Electroencephalography Classification Model for Pre- and Post-Training Assessment.},
journal = {Bioengineering (Basel, Switzerland)},
volume = {12},
number = {4},
pages = {},
doi = {10.3390/bioengineering12040359},
pmid = {40281719},
issn = {2306-5354},
support = {62206014//National Natural Science Foundation of China/ ; 62276022//National Natural Science Foundation of China/ ; 2023YFF1203702//National Key Research and Development Program of China/ ; },
abstract = {Episodic memory training plays a crucial role in cognitive enhancement, particularly in addressing age-related memory decline and cognitive disorders. Accurately assessing the effectiveness of such training requires reliable methods to capture changes in memory function. Electroencephalography (EEG) offers an objective way of evaluating neural activity before and after training. However, EEG classification in episodic memory assessment remains challenging due to the variability in brain responses, individual differences, and the complex temporal-spatial dynamics of neural signals. Traditional EEG classification methods, such as Support Vector Machines (SVMs) and Convolutional Neural Networks (CNNs), face limitations when applied to episodic memory training assessment, struggling to extract meaningful features and handle the inherent uncertainty in EEG signals. To address these issues, this paper introduces DU-former, which improves feature extraction and enhances the model's robustness against noise. Specifically, data uncertainty (DU) explicitly handles data uncertainty by modeling input features as Gaussian distributions within the reparameterization module. One branch predicts the mean through convolution and normalization, while the other estimates the variance via average pooling and normalization. These values are then used for Gaussian reparameterization, enabling the model to learn more robust feature representations. This approach allows the model to remain stable when dealing with complex or noisy data. To validate the method, an episodic memory training experiment was designed with 17 participants who underwent 28 days of training. Behavioral data showed a significant reduction in task completion time. Object recognition accuracy also improved, as indicated by the higher proportion of correctly identified target items in the episodic memory testing game. Furthermore, EEG data collected before and after the training were used to evaluate the DU-former's performance, demonstrating significant improvements in classification accuracy. This paper contributes by introducing uncertainty learning and proposing a more efficient and robust method for EEG signal classification, demonstrating superior performance in episodic memory assessment.},
}

@article {pmid40281692,
year = {2025},
author = {Acuña Luna, KP and Hernandez-Rios, ER and Valencia, V and Trenado, C and Peñaloza, C},
title = {Deep Learning-Enhanced Motor Training: A Hybrid VR and Exoskeleton System for Cognitive-Motor Rehabilitation.},
journal = {Bioengineering (Basel, Switzerland)},
volume = {12},
number = {4},
pages = {},
doi = {10.3390/bioengineering12040331},
pmid = {40281692},
issn = {2306-5354},
abstract = {This research explored the integration of the real-time machine learning classification of motor imagery data with a brain-machine interface, leveraging prefabricated exoskeletons and an EEG headset integrated with virtual reality (VR). By combining these technologies, the study aimed to develop practical and scalable therapeutic applications for rehabilitation and daily motor training. The project showcased an optimized system designed to assess and train cognitive-motor functions in elderly individuals. Key innovations included a motor imagery EEG acquisition protocol for data classification and a machine learning framework leveraging deep learning with a wavelet packet transform for feature extraction. Comparative analyses were conducted with traditional models such as Support Vector Machines (SVMs), Convolutional Neural Networks (CNNs), and Long Short-Term Memory (LSTM) networks. The performance was further enhanced through a random hyperparameter search, optimizing feature extraction and learning parameters to achieve high classification accuracy (89.23%). A novel VR fishing game was developed to dynamically respond to EEG outputs, enabling the performance of interactive motor imagery tasks in coordination with upper limb exoskeleton arms. While clinical testing is ongoing, the system demonstrates potential for increasing ERD/ERS polarization rates in alpha and beta waves among elderly users after several weeks of training. This integrated approach offers a tangible step forward in creating effective, user-friendly solutions for motor function rehabilitation.},
}

@article {pmid40281628,
year = {2025},
author = {Atkinson, C and Lombardi, L and Lang, M and Keesey, R and Hawthorn, R and Seitz, Z and Leuthardt, EC and Brunner, P and Seáñez, I},
title = {Development and evaluation of a non-invasive brain-spine interface using transcutaneous spinal cord stimulation.},
journal = {Journal of neuroengineering and rehabilitation},
volume = {22},
number = {1},
pages = {95},
pmid = {40281628},
issn = {1743-0003},
support = {K01-NS127936/NS/NINDS NIH HHS/United States ; K01-NS127936/NS/NINDS NIH HHS/United States ; K01-NS127936/NS/NINDS NIH HHS/United States ; K01-NS127936/NS/NINDS NIH HHS/United States ; K01-NS127936/NS/NINDS NIH HHS/United States ; K01-NS127936/NS/NINDS NIH HHS/United States ; K01-NS127936/NS/NINDS NIH HHS/United States ; K12-HD073945//National Institute of Child Health and Human Development/ ; K12-HD073945//National Institute of Child Health and Human Development/ ; K12-HD073945//National Institute of Child Health and Human Development/ ; K12-HD073945//National Institute of Child Health and Human Development/ ; K12-HD073945//National Institute of Child Health and Human Development/ ; U24-NS109103/NH/NIH HHS/United States ; U24-NS109103/NH/NIH HHS/United States ; P41-EB018783/EB/NIBIB NIH HHS/United States ; P41-EB018783/EB/NIBIB NIH HHS/United States ; },
mesh = {Humans ; Male ; Female ; Adult ; Electroencephalography ; *Spinal Cord Stimulation/methods ; *Brain-Computer Interfaces ; Middle Aged ; *Spinal Cord Injuries/rehabilitation/physiopathology ; Movement/physiology ; Young Adult ; *Transcutaneous Electric Nerve Stimulation/methods ; Sensorimotor Cortex/physiology ; Discriminant Analysis ; },
abstract = {Motor rehabilitation is a therapeutic process to facilitate functional recovery in people with spinal cord injury (SCI). However, its efficacy is limited to areas with remaining sensorimotor function. Spinal cord stimulation (SCS) creates a temporary prosthetic effect that may allow further rehabilitation-induced recovery in individuals without remaining sensorimotor function, thereby extending the therapeutic reach of motor rehabilitation to individuals with more severe injuries. In this work, we report our first steps in developing a non-invasive brain-spine interface (BSI) based on electroencephalography (EEG) and transcutaneous spinal cord stimulation (tSCS). The objective of this study was to identify EEG-based neural correlates of lower limb movement in the sensorimotor cortex of unimpaired individuals (N = 17) and to quantify the performance of a linear discriminant analysis (LDA) decoder in detecting movement onset from these neural correlates. Our results show that initiation of knee extension was associated with event-related desynchronization in the central-medial cortical regions at frequency bands between 4 and 44 Hz. Our neural decoder using µ (8-12 Hz), low β (16-20 Hz), and high β (24-28 Hz) frequency bands achieved an average area under the curve (AUC) of 0.83 ± 0.06 s.d. (n = 7) during a cued movement task offline. Generalization to imagery and uncued movement tasks served as positive controls to verify robustness against movement artifacts and cue-related confounds, respectively. With the addition of real-time decoder-modulated tSCS, the neural decoder performed with an average AUC of 0.81 ± 0.05 s.d. (n = 9) on cued movement and 0.68 ± 0.12 s.d. (n = 9) on uncued movement. Our results suggest that the decrease in decoder performance in uncued movement may be due to differences in underlying cortical strategies between conditions. Furthermore, we explore alternative applications of the BSI system by testing neural decoders trained on uncued movement and imagery tasks. By developing a non-invasive BSI, tSCS can be timed to be delivered only during voluntary effort, which may have implications for improving rehabilitation.},
}

Humans
Male
Female
Adult
Electroencephalography
*Spinal Cord Stimulation/methods
*Brain-Computer Interfaces
Middle Aged
*Spinal Cord Injuries/rehabilitation/physiopathology
Movement/physiology
Young Adult
*Transcutaneous Electric Nerve Stimulation/methods
Sensorimotor Cortex/physiology
Discriminant Analysis

@article {pmid40280929,
year = {2025},
author = {Bai, Y and Tang, Q and Zhao, R and Liu, H and Zhang, S and Guo, M and Guo, M and Wang, J and Wang, C and Xing, M and Ni, G and Ming, D},
title = {TMNRED, A Chinese Language EEG Dataset for Fuzzy Semantic Target Identification in Natural Reading Environments.},
journal = {Scientific data},
volume = {12},
number = {1},
pages = {701},
pmid = {40280929},
issn = {2052-4463},
mesh = {Humans ; *Electroencephalography ; *Reading ; *Semantics ; China ; *Language ; *Natural Language Processing ; East Asian People ; },
abstract = {Semantic understanding is central to advanced cognitive functions, and the mechanisms by which the brain processes language information are still being explored. Existing EEG datasets often lack natural reading data specific to Chinese, limiting research on Chinese semantic decoding and natural language processing. This study aims to construct a Chinese natural reading EEG dataset, TMNRED, for semantic target identification in natural reading environments. TMNRED was collected from 30 participants reading sentences sourced from public internet resources and media reports. Each participant underwent 400-450 trials in a single day, resulting in a dataset with over 10 hours of continuous EEG data and more than 4000 trials. This dataset provides valuable physiological data for studying Chinese semantics and developing more accurate Chinese natural language processing models.},
}

Humans
*Electroencephalography
*Reading
*Semantics
China
*Language
*Natural Language Processing
East Asian People

@article {pmid40280532,
year = {2025},
author = {Gazerani, P},
title = {The neuroplastic brain: current breakthroughs and emerging frontiers.},
journal = {Brain research},
volume = {},
number = {},
pages = {149643},
doi = {10.1016/j.brainres.2025.149643},
pmid = {40280532},
issn = {1872-6240},
abstract = {Neuroplasticity, the brain's capacity to reorganize itself by forming new neural connections, is central to modern neuroscience. Once believed to occur only during early development, research now shows that plasticity continues throughout the lifespan, supporting learning, memory, and recovery from injury or disease. Substantial progress has been made in understanding the mechanisms underlying neuroplasticity and their therapeutic applications. This overview article examines synaptic plasticity, structural remodeling, neurogenesis, and functional reorganization, highlighting both adaptive (beneficial) and maladaptive (harmful) processes across different life stages. Recent strategies to harness neuroplasticity, ranging from pharmacological agents and lifestyle interventions to cutting-edge technologies like brain-computer interfaces (BCIs) and targeted neuromodulation are evaluated in light of current empirical evidence. Contradictory findings in the literature are addressed, and methodological limitations that hamper widespread clinical adoption are discussed. The ethical and societal implications of deploying novel neuroplasticity-based interventions, including issues of equitable access, data privacy, and the blurred line between treatment and enhancement, are then explored in a structured manner. By integrating mechanistic insights, empirical data, and ethical considerations, the aim is to provide a comprehensive and balanced perspective for researchers, clinicians, and policymakers working to optimize brain health across diverse populations.},
}

@article {pmid40280369,
year = {2025},
author = {Zhang, Y and Gao, Y and Zhou, J and Zhang, Z and Feng, M and Liu, Y},
title = {Advances in Brain-Computer Interface Controlled Functional Electrical Stimulation for Upper Limb Recovery After Stroke.},
journal = {Brain research bulletin},
volume = {},
number = {},
pages = {111354},
doi = {10.1016/j.brainresbull.2025.111354},
pmid = {40280369},
issn = {1873-2747},
abstract = {Stroke often results in varying degrees of functional impairment, significantly affecting patients' quality of daily life. In recent years, brain-computer interface-controlled functional electrical stimulation has offered new therapeutic approaches for post-stroke rehabilitation. This paper reviews the application of BCI-FES in the recovery of upper limb function after stroke and explores its underlying mechanisms. By analyzing relevant studies, the aim is to provide a theoretical basis for rehabilitating upper limb function post-stroke, promote BCI-FES, and offer guidance for future clinical practice.},
}

@article {pmid40280291,
year = {2025},
author = {Han, MJ and Oh, Y and Ann, Y and Kang, S and Baeg, E and Hong, SJ and Sohn, H and Kim, SG},
title = {Whole-brain effective connectivity of the sensorimotor system using 7T fMRI with electrical microstimulation in non-human primates.},
journal = {Progress in neurobiology},
volume = {},
number = {},
pages = {102760},
doi = {10.1016/j.pneurobio.2025.102760},
pmid = {40280291},
issn = {1873-5118},
abstract = {The sensorimotor system is a crucial interface between the brain and the environment, and it is endowed with multiple computational mechanisms that enable efficient behaviors. For example, predictive processing via an efference copy of a motor command has been proposed as one of the key computations used to compensate for the sensory consequence of movement. However, the neural pathways underlying this process remain unclear, particularly regarding whether the M1-to-S1 pathway plays a dominant role in predictive processing and how its influence compares to that of other pathways. In this study, we present a causally inferable input-output map of the sensorimotor effective connectivity that we made by combining ultrahigh-field functional MRI, electrical microstimulation of the S1/M1 cortex, and dynamic causal modeling for the whole sensorimotor network in anesthetized primates. We investigated how motor signals from M1 are transmitted to S1 at the circuit level, either via direct cortico-cortical projections or indirectly via subcortical structures such as the thalamus. Across different stimulation conditions, we observed a robust asymmetric connectivity from M1 to S1 that was also the most prominent output from M1. In the thalamus, we identified distinct activations: M1 stimulation showed connections to the anterior part of ventral thalamic nuclei, whereas S1 was linked to the more posterior regions of the ventral thalamic nuclei. These findings suggest that the cortico-cortical projection from M1 to S1, rather than the cortico-thalamic loop, plays a dominant role in transmitting movement-related information. Together, our detailed dissection of the sensorimotor circuitry underscores the importance of M1-to-S1 connectivity in sensorimotor coordination.},
}

@article {pmid40280150,
year = {2025},
author = {Singer-Clark, T and Hou, X and Card, NS and Wairagkar, M and Iacobacci, C and Peracha, H and Hochberg, LR and Stavisky, S and Brandman, D},
title = {Speech motor cortex enables BCI cursor control and click.},
journal = {Journal of neural engineering},
volume = {},
number = {},
pages = {},
doi = {10.1088/1741-2552/add0e5},
pmid = {40280150},
issn = {1741-2552},
abstract = {Decoding neural activity from ventral (speech) motor cortex is known to enable high-performance speech brain-computer interface (BCI) control. It was previously unknown whether this brain area could also enable computer control via neural cursor and click, as is typically associated with dorsal (arm and hand) motor cortex. Approach. We recruited a clinical trial participant with ALS and implanted intracortical microelectrode arrays in ventral precentral gyrus (vPCG), which the participant used to operate a speech BCI in a prior study. We developed a cursor BCI driven by the participant's vPCG neural activity, and evaluated performance on a series of target selection tasks. Main results. The reported vPCG cursor BCI enabled rapidly-calibrating (40 seconds), accurate (2.90 bits per second) cursor control and click. The participant also used the BCI to control his own personal computer independently. Significance. These results suggest that placing electrodes in vPCG to optimize for speech decoding may also be a viable strategy for building a multi-modal BCI which enables both speech-based communication and computer control via cursor and click. (BrainGate2 ClinicalTrials.gov ID NCT00912041).},
}

@article {pmid40280132,
year = {2025},
author = {Zhou, N and Chen, J and Hu, M and Wen, N and Cai, W and Li, P and Zhao, L and Meng, Y and Zhao, D and Yang, X and Liu, S and Huang, F and Zhao, C and Feng, X and Jiang, Z and Xie, E and Pan, H and Cen, Z and Chen, X and Luo, W and Tang, B and Min, J and Wang, F and Yang, J and Xu, H},
title = {SLC7A11 is an unconventional H[+] transporter in lysosomes.},
journal = {Cell},
volume = {},
number = {},
pages = {},
doi = {10.1016/j.cell.2025.04.004},
pmid = {40280132},
issn = {1097-4172},
abstract = {Lysosomes maintain an acidic pH of 4.5-5.0, optimal for macromolecular degradation. Whereas proton influx is produced by a V-type H[+] ATPase, proton efflux is mediated by a fast H[+] leak through TMEM175 channels, as well as an unidentified slow pathway. A candidate screen on an orphan lysosome membrane protein (OLMP) library enabled us to discover that SLC7A11, the protein target of the ferroptosis-inducing compound erastin, mediates a slow lysosomal H[+] leak through downward flux of cystine and glutamate, two H[+] equivalents with uniquely large but opposite concentration gradients across lysosomal membranes. SLC7A11 deficiency or inhibition caused lysosomal over-acidification, reduced degradation, accumulation of storage materials, and ferroptosis, as well as facilitated α-synuclein aggregation in neurons. Correction of abnormal lysosomal acidity restored lysosome homeostasis and prevented ferroptosis. These studies have revealed an unconventional H[+] transport conduit that is integral to lysosomal flux of protonatable metabolites to regulate lysosome function, ferroptosis, and Parkinson's disease (PD) pathology.},
}

@article {pmid40280131,
year = {2025},
author = {Xin, Q and Wang, J and Zheng, J and Tan, Y and Jia, X and Ni, Z and Xu, Z and Feng, J and Wu, Z and Li, Y and Li, XM and Ma, H and Hu, H},
title = {Neuron-astrocyte coupling in lateral habenula mediates depressive-like behaviors.},
journal = {Cell},
volume = {},
number = {},
pages = {},
doi = {10.1016/j.cell.2025.04.010},
pmid = {40280131},
issn = {1097-4172},
abstract = {The lateral habenula (LHb) neurons and astrocytes have been strongly implicated in depression etiology, but it was not clear how the two dynamically interact during depression onset. Here, using multi-brain-region calcium photometry recording in freely moving mice, we discover that stress induces a most rapid astrocytic calcium rise and a bimodal neuronal response in the LHb. LHb astrocytic calcium requires the α1A-adrenergic receptor and depends on a recurrent neural network between the LHb and locus coeruleus (LC). Through the gliotransmitter glutamate and ATP/adenosine, LHb astrocytes mediate the second-wave LHb neuronal activation and norepinephrine (NE) release. Activation or inhibition of LHb astrocytic calcium signaling facilitates or prevents stress-induced depressive-like behaviors, respectively. These results identify a stress-induced positive feedback loop in the LHb-LC axis, with astrocytes being a critical signaling relay. The identification of this prominent neuron-glia interaction may shed light on stress management and depression prevention.},
}

@article {pmid40280000,
year = {2025},
author = {Jiang, Y and Zhou, C and Zhao, J and Ren, X and Wang, Q and Ni, P and Li, T},
title = {Generation and Characterization of a Human-Derived iPSC line from a female child with First-Episode of sporadic schizophrenia.},
journal = {Stem cell research},
volume = {86},
number = {},
pages = {103713},
doi = {10.1016/j.scr.2025.103713},
pmid = {40280000},
issn = {1876-7753},
abstract = {Schizophrenia is a highly heritable neurodevelopmental disorder. In this study, peripheral blood mononuclear cells (PBMCs) were obtained from a female child diagnosed with first-episode of sporadic schizophrenia. Induced pluripotent stem cells (iPSCs) were generated by introducing the reprogramming factors OCT4, SOX2, NANOG, LIN28, c-MYC, KLF4, and SV40LT. The iPSC line was confirmed through karyotyping and the expression of key pluripotency markers. These cells demonstrated the ability to differentiate into all three germ layers in vivo.},
}

@article {pmid40279233,
year = {2025},
author = {Aung, HW and Jiao Li, J and An, Y and Su, SW},
title = {A Real-Time Framework for EEG Signal Decoding With Graph Neural Networks and Reinforcement Learning.},
journal = {IEEE transactions on neural networks and learning systems},
volume = {PP},
number = {},
pages = {},
doi = {10.1109/TNNLS.2025.3558171},
pmid = {40279233},
issn = {2162-2388},
abstract = {Brain-computer interfaces (BCIs) rely on accurately decoding electroencephalography (EEG) motor imagery (MI) signals for effective device control. Graph neural networks (GNNs) outperform convolutional neural networks (CNNs) in this regard, by leveraging the spatial relationships between EEG electrodes through adjacency matrices. The EEG graph lottery ticket framework, EEG_GLT-Net, featuring the state-of-the-art (SOTA) EEG_GLT adjacency matrix method, has notably enhanced EEG MI signal classification, evidenced by an average accuracy of 83.95% across 20 subjects on the PhysioNet dataset. This significantly exceeds the 76.10% accuracy rate achieved using the Pearson correlation coefficient (PCC) method in the same framework. In this research, we advance the field by applying a reinforcement learning (RL) approach to the classification of EEG MI signals. Our innovative method empowers the RL agent, enabling not only the classification of EEG MI data points with higher accuracy but effective identification of EEG MI data points that are less distinct. We present the EEG_RL-Net, an enhancement of the EEG_GLT-Net framework, which incorporates the trained EEG_GCN Block from EEG_GLT-Net at an adjacency matrix density of 13.39% alongside the RL-centric dueling deep Q network (Dueling DQN) block. The EEG_RL-Net model showcases exceptional classification performance, achieving an unprecedented average accuracy of 96.40% across 20 subjects within 25 ms. This model illustrates the transformative effect of the RL in EEG MI time point classification.},
}

@article {pmid40277624,
year = {2025},
author = {Zhang, W and Tang, X and Dang, X and Wang, M},
title = {A Capsule Decision Neural Network Based on Transfer Learning for EEG Signal Classification.},
journal = {Biomimetics (Basel, Switzerland)},
volume = {10},
number = {4},
pages = {},
doi = {10.3390/biomimetics10040225},
pmid = {40277624},
issn = {2313-7673},
abstract = {Transfer learning is the act of using the data or knowledge in a problem to help solve different but related problems. In a brain computer interface (BCI), it is important to deal with individual differences between topics and/or tasks. A kind of capsule decision neural network (CDNN) based on transfer learning is proposed. In order to solve the problem of feature distortion caused by EEG feature extraction algorithm, a deep capsule decision network was constructed. The architecture includes multiple primary capsules to form a hidden layer, and the connection between the advanced capsule and the primary capsule is determined by the neural decision routing algorithm. Unlike the dynamic routing algorithm that iteratively calculates the similarity between primary capsules and advanced capsules, the neural decision network computes the relationship between each capsule in the deep and shallow hidden layers in a probabilistic manner. At the same time, the distribution of the EEG covariance matrix is aligned in Riemann space, and the regional adaptive method is further introduced to improve the independent decoding ability of the capsule decision neural network for the subject's EEG signals. Experiments on two motor imagery EEG datasets show that CDNN outperforms several of the most advanced transfer learning methods.},
}

@article {pmid40277574,
year = {2025},
author = {Jiao, P and Jia, Q and Li, S and Shan, J and Xu, W and Wang, Y and Liu, Y and Wang, M and Song, Y and Zhang, Y and Yu, Y and Wang, M and Cai, X},
title = {Distinct Neural Activities in Hippocampal Subregions Revealed Using a High-Performance Wireless Microsystem with PtNPs/PEDOT:PSS-Enhanced Microelectrode Arrays.},
journal = {Biosensors},
volume = {15},
number = {4},
pages = {},
doi = {10.3390/bios15040262},
pmid = {40277574},
issn = {2079-6374},
support = {2022YFC2402501//the National Key Research and Development Program of China/ ; 2022YFC2402503//the National Key Research and Development Program of China/ ; 2022YFB3205602//the National Key Research and Development Program of China/ ; T2293730//the National Natural Science Foundation of China/ ; T2293731//the National Natural Science Foundation of China/ ; 61960206012//the National Natural Science Foundation of China/ ; 62121003//the National Natural Science Foundation of China/ ; 62333020//the National Natural Science Foundation of China/ ; 62171434//the National Natural Science Foundation of China/ ; 2021ZD02016030//Major Program of Scientific and Technical Innovation 2030/ ; PTYQ2024BJ0009//the Scientific Instrument Developing Project of the Chinese Academy of Sciences/ ; },
mesh = {Animals ; Microelectrodes ; *Wireless Technology ; Rats ; *Hippocampus/physiology ; Platinum/chemistry ; Polymers/chemistry ; Polystyrenes/chemistry ; Metal Nanoparticles/chemistry ; Bridged Bicyclo Compounds, Heterocyclic/chemistry ; *Biosensing Techniques ; Rats, Sprague-Dawley ; Male ; },
abstract = {Wireless microsystems for neural signal recording have emerged as a solution to overcome the limitations of tethered systems, which restrict the mobility of subjects and introduce noise interference. However, existing microsystems often face data throughput, signal processing, and long-distance wireless transmission challenges. This study presents a high-performance wireless microsystem capable of 32-channel, 30 kHz real-time recording, featuring Field Programmable Gate Array (FPGA)-based signal processing to reduce transmission load. The microsystem is integrated with platinum nanoparticles/poly (3,4-ethylenedioxythiophene) polystyrene sulfonate-enhanced microelectrode arrays for improved signal quality. A custom NeuroWireless platform was developed for seamless data reception and storage. Experimental validation in rats demonstrated the microsystem's ability to detect spikes and local field potentials from the hippocampal CA1 and CA2 subregions. Comparative analysis of the neural signals revealed distinct activity patterns between these subregions. The wireless microsystem achieves high accuracy and throughput over distances up to 30 m, demonstrating its resilience and potential for neuroscience research. This work provides a compact, adaptable solution for multi-channel neural signal detection and offers a foundation for future applications in brain-computer interfaces.},
}

Animals
Microelectrodes
*Wireless Technology
Rats
*Hippocampus/physiology
Platinum/chemistry
Polymers/chemistry
Polystyrenes/chemistry
Metal Nanoparticles/chemistry
Bridged Bicyclo Compounds, Heterocyclic/chemistry
*Biosensing Techniques
Rats, Sprague-Dawley
Male

@article {pmid40277527,
year = {2025},
author = {Tavakolidakhrabadi, A and Stark, M and Küenzi, A and Carrara, S and Bessire, C},
title = {Optimized Microfluidic Biosensor for Sensitive C-Reactive Protein Detection.},
journal = {Biosensors},
volume = {15},
number = {4},
pages = {},
doi = {10.3390/bios15040214},
pmid = {40277527},
issn = {2079-6374},
support = {52116.1 IP-LS//Innosuisse - Swiss Innovation Agency/ ; },
mesh = {*C-Reactive Protein/analysis ; *Biosensing Techniques ; Metal Nanoparticles/chemistry ; Gold/chemistry ; Immunoassay ; Humans ; Point-of-Care Testing ; Microfluidics ; },
abstract = {Lateral flow immunoassays (LFIAs) were integrated into microfluidic chips and tested to enhance point-of-care testing (POCT), with the aim of improving sensitivity and expanding the range of CRP detection. The microfluidic approach improves upon traditional methods by precisely controlling fluid speed, thus enhancing sensitivity and accuracy in CRP measurements. The microfluidic approach also enables a one-step detection system, eliminating the need for buffer solution steps and reducing the nitrocellulose (NC) pad area to just the detection test line. This approach minimizes the non-specific binding of conjugated antibodies to unwanted areas of the NC pad, eliminating the need to block those areas, which enhances the sensitivity of detection. The gold nanoparticle method detects CRP in the high-sensitivity range of 1-10 μg/mL, which is suitable for chronic disease monitoring. To broaden the CRP detection range, including infection levels beyond 10 μg/mL, fluorescent labels were introduced, extending the measuring range from 1 to 70 μg/mL. Experimental results demonstrate that integrating microfluidic technology significantly enhances operational efficiency by precisely controlling the flow rate and optimizing the mixing efficiency while reducing fabrication resources by eliminating the need for separate pads, making these methods suitable for resource-limited settings. Microfluidics also provides greater control over fluid dynamics compared to traditional LFIA methods, which contributes to enhanced detection sensitivity even with lower sample volumes and no buffer solution, helping to enhance the usability of POCT. These findings highlight the potential to develop accessible, accurate, and cost-effective diagnostic tools essential for timely medical interventions at the POC.},
}

*C-Reactive Protein/analysis
*Biosensing Techniques
Metal Nanoparticles/chemistry
Gold/chemistry
Immunoassay
Humans
Point-of-Care Testing
Microfluidics

@article {pmid40275590,
year = {2025},
author = {Haghani Dogahe, M and Mahan, MA and Zhang, M and Bashiri Aliabadi, S and Rouhafza, A and Karimzadhagh, S and Feizkhah, A and Monsef, A and Habibi Roudkenar, M},
title = {Advancing Prosthetic Hand Capabilities Through Biomimicry and Neural Interfaces.},
journal = {Neurorehabilitation and neural repair},
volume = {},
number = {},
pages = {15459683251331593},
doi = {10.1177/15459683251331593},
pmid = {40275590},
issn = {1552-6844},
abstract = {Background and ObjectivesProsthetic hand development is undergoing a transformative phase, blending biomimicry and neural interface technologies to redefine functionality and sensory feedback. This article explores the symbiotic relationship between biomimetic design principles and neural interface technology (NIT) in advancing prosthetic hand capabilities.MethodsDrawing inspiration from biological systems, researchers aim to replicate the intricate movements and capabilities of the human hand through innovative prosthetic designs. Central to this endeavor is NIT, facilitating seamless communication between artificial devices and the human nervous system. Recent advances in fabrication methods have propelled brain-computer interfaces, enabling precise control of prosthetic hands by decoding neural activity.ResultsAnatomical complexities of the human hand underscore the importance of understanding biomechanics, neuroanatomy, and control mechanisms for crafting effective prosthetic solutions. Furthermore, achieving the goal of a fully functional cyborg hand necessitates a multidisciplinary approach and biomimetic design to replicate the body's inherent capabilities. By incorporating the expertise of clinicians, tissue engineers, bioengineers, electronic and data scientists, the next generation of the implantable devices is not only anatomically and biomechanically accurate but also offer intuitive control, sensory feedback, and proprioception, thereby pushing the boundaries of current prosthetic technology.ConclusionBy integrating machine learning algorithms, biomechatronic principles, and advanced surgical techniques, prosthetic hands can achieve real-time control while restoring tactile sensation and proprioception. This manuscript contributes novel approaches to prosthetic hand development, with potential implications for enhancing the functionality, durability, and safety of the prosthetic limb.},
}

@article {pmid40274133,
year = {2025},
author = {Meng, L and Wang, D and Ma, J and Shi, Y and Zhao, H and Wang, Y and Shi, Q and Zhu, X and Ming, D},
title = {Unraveling Parkinson's disease motor subtypes: A deep learning approach based on spatiotemporal dynamics of EEG microstates.},
journal = {Neurobiology of disease},
volume = {},
number = {},
pages = {106915},
doi = {10.1016/j.nbd.2025.106915},
pmid = {40274133},
issn = {1095-953X},
abstract = {BACKGROUND: Despite prior studies on early-stage PD brain connectivity and temporal patterns, differences between tremor-dominant (TD) and postural instability/gait difficulty (PIGD) motor subtypes, remain poorly understood. Our study aims to understand the contribution of altered brain network dynamics to heterogeneous motor phenotypes in Parkinson's disease (PD) for improving personalized treatment.

METHODS: Electroencephalography (EEG) microstate dynamics were firstly used to capture spatiotemporal brain network changes. A deep learning model was developed to classify PD motor subtypes where spatial variability and electrode location data were incorporated into the analysis.

RESULTS: Compared to healthy individuals, both PD-TD and PD-PIGD patients showed increased local segregation of brain regions. The PD-PIGD subtype had more severe and extensive disorganization in microstate A dynamics, suggesting greater disruption in auditory and motor-related networks. Incorporating spatial information significantly improved the accuracy of subtype classification, with an AUC of 0.972, indicating that EEG microstate dynamic spatial patterns reflect distinct PD motor pathologies. The increased spatial variability in the PD-PIGD group was more closely associated with motor impairments.

CONCLUSIONS: This study presents a novel framework for differentiating PD motor subtypes and emphasizes dynamic brain network features as potential markers for understanding motor symptom variability in PD, which may contribute to the development of personalized treatment strategies.

TRIAL REGISTRATION: ChiCTR2300067657.},
}

@article {pmid40273947,
year = {2025},
author = {Ahmadi, H and Mesin, L},
title = {Universal semantic feature extraction from EEG signals: A task-independent framework.},
journal = {Journal of neural engineering},
volume = {},
number = {},
pages = {},
doi = {10.1088/1741-2552/add08f},
pmid = {40273947},
issn = {1741-2552},
abstract = {Extracting universal, task-independent semantic features from electroencephalography (EEG) signals remains an open challenge. Traditional approaches are often task-specific, limiting their generalization across different EEG paradigms. This study aims to develop a robust, unsupervised framework for learning high-level, task-independent neural representations. Approach: We propose a novel framework integrating convolutional neural networks (CNNs), AutoEncoders, and Transformers to extract both low-level spatiotemporal patterns and high-level semantic features from EEG signals. The model is trained in an unsupervised manner to ensure adaptability across diverse EEG paradigms, including motor imagery (MI), steady-state visually evoked potentials (SSVEP), and event-related potentials (ERP, specifically P300). Extensive analyses, including clustering, correlation, and ablation studies, are conducted to validate the quality and interpretability of the extracted features. Main Results: Our method achieves state-of-the-art performance, with average classification accuracies of 83.50% and 84.84% on MI datasets (BCICIV 2a and BCICIV 2b), 98.41% and 99.66% on SSVEP datasets (Lee2019-SSVEP and Nakanishi2015), and an average AUC of 91.80% across eight ERP datasets. t-SNE and clustering analyses reveal that the extracted features exhibit enhanced separability and structure compared to raw EEG data. Correlation studies confirm the framework's ability to balance universal and subject-specific features, while ablation results highlight the near-optimality of the selected model configuration. Significance: This work establishes a universal framework for task-independent semantic feature extraction from EEG signals, bridging the gap between conventional feature engineering and modern deep learning (DL) methods. By providing robust, generalizable representations across diverse EEG paradigms, this approach lays the foundation for advanced brain-computer interface (BCI) appli- cations, cross-task EEG analysis, and future developments in semantic EEG processing.},
}

@article {pmid40271395,
year = {2025},
author = {Han, CH and Kim, SU and Lim, KS and Jung, YJ and Lee, S and Kim, SH and Hwang, HJ},
title = {Evaluation of a silicone-based flexible dry electrode for measuring human bioelectrical signals.},
journal = {Biomedical engineering letters},
volume = {15},
number = {3},
pages = {563-574},
doi = {10.1007/s13534-025-00471-x},
pmid = {40271395},
issn = {2093-985X},
abstract = {The development of conductive polymer-based dry electrodes with high conductivity is promising for practical applications in daily life due to their biocompatibility, flexibility, lightweight, and comfort. The objective of this study is to demonstrate the feasibility of using a novel silicone-based dry electrode for measuring various bioelectrical signals.The silicone-based electrode, manufactured using an optimized polymer matrix, combines high conductivity with flexibility, ensuring superior wearability and reliable bioelectrical signal monitoring. To evaluate its performance, we compared its impedance and flexibility with those of a commercial electrode. Additionally, its compatibility for measuring biological signals was assessed through performance comparisons across various bioelectrical signals. Fourteen healthy participants performed three experimental paradigms: (1) eyes closed and open to measure alpha electroencephalography (EEG) as well as resting-state electrocardiography (ECG), (2) eye blinking to measure electrooculography (EOG), and (3) wrist movement to measure electromyography (EMG). All bioelectrical signals were measured simultaneously using both the silicone-based dry electrode and a commercial dry electrode. The performance of the silicone-based dry electrode was evaluated by comparing the signal quality of both electrodes. The silicone-based dry electrode exhibited lower electrical impedance (39.43 kΩ on average, p = 0.0058) and greater flexibility (Young's modulus: silicone 1.51 ± 0.10 MPa vs. commercial 2.46 ± 0.38 MPa) compared to the commercial dry electrode. Overall, there were minimal differences in signal quality between the two electrodes: i) EEG (α power SNR: silicone 1.39 ± 0.34 vs. commercial 1.36 ± 0.29), ii) ECG (R-peak recall: 99.20 ± 2.50%, correlation coefficient: 0.96 ± 0.08), iii) EOG (eye blink recall: 100.00%, correlation coefficient: 0.98 ± 0.03), and iv) EMG (no significant difference in SNR values). These findings indicate that the developed electrode not only ensures superior flexibility but also maintains compatible electrical properties for measuring various bioelectrical signals.},
}

@article {pmid40270987,
year = {2025},
author = {Jiang, Z and Hu, K and Qu, J and Bian, Z and Yu, D and Zhou, J},
title = {Recognition of MI-EEG signals using extended-LSR-based inductive transfer learning.},
journal = {Frontiers in neuroinformatics},
volume = {19},
number = {},
pages = {1559335},
doi = {10.3389/fninf.2025.1559335},
pmid = {40270987},
issn = {1662-5196},
abstract = {INTRODUCTION: Motor imagery electroencephalographic (MI-EEG) signal recognition is used in various brain-computer interface (BCI) systems. In most existing BCI systems, this identification relies on classification algorithms. However, generally, a large amount of subject-specific labeled training data is required to reliably calibrate the classification algorithm for each new subject. To address this challenge, an effective strategy is to integrate transfer learning into the construction of intelligent models, allowing knowledge to be transferred from the source domain to enhance the performance of models trained in the target domain. Although transfer learning has been implemented in EEG signal recognition, many existing methods are designed specifically for certain intelligent models, limiting their application and generalization.

METHODS: To broaden application and generalization, an extended-LSR-based inductive transfer learning method is proposed to facilitate transfer learning across various classical intelligent models, including neural networks, Takagi-SugenoKang (TSK) fuzzy systems, and kernel methods.

RESULTS AND DISCUSSION: The proposed method not only promotes the transfer of valuable knowledge from the source domain to improve learning performance in the target domain when target domain training data are insufficient but also enhances application and generalization by incorporating multiple classic base models. The experimental results demonstrate the effectiveness of the proposed method in MI-EEG signal recognition.},
}

@article {pmid40270567,
year = {2025},
author = {Hammond, L and Rowley, D and Tuck, C and Floreani, ED and Wieler, A and Kim, VS and Bahari, H and Andersen, J and Kirton, A and Kinney-Lang, E},
title = {BCI move: exploring pediatric BCI-controlled power mobility.},
journal = {Frontiers in human neuroscience},
volume = {19},
number = {},
pages = {1456692},
doi = {10.3389/fnhum.2025.1456692},
pmid = {40270567},
issn = {1662-5161},
abstract = {INTRODUCTION: Children and young people (CYP) with severe physical disabilities often experience barriers to independent mobility, placing them at risk for developmental impairments and restricting their independence and participation. Pilot work suggests that brain-computer interface (BCIs) could enable powered mobility control for children with motor disabilities. We explored how severely disabled CYP could use BCI to achieve individualized, functional power mobility goals and acquire power mobility skills. We also explored the practicality of pediatric BCI-enabled power mobility.

METHODS: Nine CYP aged 7-17 years with severe physical disabilities and their caregivers participated in up to 12 BCI-enabled power mobility training sessions focused on a personalized power mobility goal. Goal achievement was assessed using the Canadian Occupational Performance Measure (COPM) and Goal Attainment Scaling (GAS). The Assessment for Learning Powered Mobility (ALP) was used to measure session-by-session power mobility skill acquisition. BCI set-up and calibration metrics, perceived workload, and participant engagement were also reported.

RESULTS: Significant improvements in COPM performance (Z = -2.869, adjusted p = 0.012) and satisfaction scores (Z = -2.809, adjusted p = 0.015) and GAS T scores (Z = -2.805, p = 0.005) were observed following the intervention. ALP scores displayed a small but significant increase over time (R [2] = 0.07-0.19; adjusted p = <0.001-0.039), with 7/9 participants achieving increased overall ALP scores following the intervention. Setup and calibration times were practical although calibration consistency was highly variable. Participants reported moderate workload with no significant change over time (R [2] = 0.00-0.13; adjusted p = 0.006-1.000), although there was a trend towards increased frustration over time(R [2] = 0.13; adjusted p = 0.006).

DISCUSSION: Participants were highly engaged throughout the intervention. BCI-enabled power mobility appears to help CYP with severe physical disabilities achieve personalized power mobility goals and acquire power mobility skills. BCI-enabled power mobility training also appears to be practical, but BCI performance optimization and skill acquisition may be needed to translate this technology into clinical use.},
}

@article {pmid39711704,
year = {2025},
author = {Deng, G and Niu, M and Luo, Y and Rao, S and Xie, J and Yu, Z and Liu, W and Zhao, S and Pan, G and Li, X and Deng, W and Guo, W and Li, T and Jiang, H},
title = {A Unified Flexible Large Polysomnography Model for Sleep Staging and Mental Disorder Diagnosis.},
journal = {medRxiv : the preprint server for health sciences},
volume = {},
number = {},
pages = {},
pmid = {39711704},
abstract = {Sleep quality is vital to human health, yet automated sleep staging faces challenges in cross-center generalization due to data scarcity and domain gaps. Traditional scoring is labor-intensive, while deep learning models often fail to generalize across datasets. Here, we present LPSGM, a unified and flexible large polysomnography (PSG) model designed to enhance cross-center generalization in sleep staging and enable fine-tuning for disease diagnosis. Trained on 220,500 hours of PSG data from 16 public datasets, LPSGM integrates domain-adaptive learning and supports variable-channel configurations, achieving performance comparable to models trained directly on target-center data. In a prospective clinical study, LPSGM matches expert-level accuracy with lower variability. When fine-tuned, it attains 88.01% accuracy in narcolepsy detection and 100% in depression detection. These results establish LPSGM as a scalable, plug-and-play solution for automated PSG analysis, bridging the gap between sleep staging and clinical deployment.},
}

@article {pmid40269846,
year = {2025},
author = {Mansour, S and Giles, J and Nair, KPS and Marshall, R and Ali, A and Arvaneh, M},
title = {A clinical trial evaluating feasibility and acceptability of a brain-computer interface for telerehabilitation in patients with stroke.},
journal = {Journal of neuroengineering and rehabilitation},
volume = {22},
number = {1},
pages = {91},
pmid = {40269846},
issn = {1743-0003},
support = {MC-PC-19051//UK Medical Research Council/ ; },
abstract = {BACKGROUND: We have created a groundbreaking telerehabilitation system known as Tele BCI-FES. This innovative system merges brain-computer interface (BCI) and functional electrical stimulation (FES) technologies to rehabilitate upper limb function following a stroke. Our system pioneers the concept of allowing patients to undergo BCI therapy from the comfort of their homes, while ensuring supervised therapy and real-time adjustment capabilities. In this paper, we introduce our single-arm clinical trial, which evaluates the feasibility and acceptance of this proposed system as a telerehabilitation solution for upper extremity recovery in stroke survivors.

METHOD: The study involved eight chronic patients with stroke and their caregivers who were recruited to attend nine home-based Tele BCI-FES sessions (three sessions per week) while receiving remote support from the research team. The primary outcomes of this study were recruitment and retention rates, as well as participants perception on the adoption of technology. The secondary outcomes involved assessing improvements in upper extremity function using the Fugl-Meyer Assessment for Upper Extremity (FMA_UE) and the Leeds Arm Spasticity Impact Scale.

RESULTS: Seven chronic patients with stroke completed the home-based Tele BCI-FES sessions, with high retention (87.5%) and recruitment rates (86.7%). Although participants provided mixed feedback on setup ease, they found the system progressively easier to use, and the setup process became more efficient with continued sessions. Participants suggested modifications to enhance user experience. Following the intervention, a significant increase in FMA_UE scores was observed, with an average improvement of 3.83 points (p = 0.032). The observed improvement of 3.83 points in the FMA-UE score approaches the reported Minimal clinically important difference of 4.25 points for patients with chronic stroke.

CONCLUSION: This study serves as a proof of concept, showcasing the feasibility and acceptability of the proposed Tele BCI-FES system for rehabilitating the upper extremities of stroke survivors. While some participants demonstrated significant improvements in FMA-UE scores, these findings are not generalizable, as they were derived from a small-scale feasibility study. The results should be interpreted cautiously within the study's specific context. Additionally, the intervention was not compared to other therapeutic approaches, limiting conclusions regarding its relative effectiveness. To further validate the efficacy of the proposed Tele BCI-FES system, it is essential to conduct additional research with larger sample sizes and extended rehabilitation sessions. Moreover, future studies should include comparisons with other therapeutic approaches to better evaluate the relative effectiveness of this intervention. Trial registration This clinical study is registered at clinicaltrials.gov https://clinicaltrials.gov/study/NCT05215522 under the study identifier (NCT05215522) and registered with the ISRCTN registry https://doi.org/10.1186/ISRCTN42991002 (ISRCTN42991002).},
}

@article {pmid40264507,
year = {2025},
author = {Decker, J and Daeglau, M and Zich, C and Kranczioch, C},
title = {Nature documentaries vs. quiet rest: no evidence for an impact on event-related desynchronization during motor imagery and neurofeedback.},
journal = {Frontiers in human neuroscience},
volume = {19},
number = {},
pages = {1539172},
pmid = {40264507},
issn = {1662-5161},
abstract = {Motor imagery (MI) in combination with neurofeedback (NF) has emerged as a promising approach in motor neurorehabilitation, facilitating brain activity modulation and promoting motor learning. Although MI-NF has been demonstrated to enhance motor performance and cortical plasticity, its efficacy varies considerably across individuals. Various context factors have been identified as influencing neurophysiological outcomes in motor execution and MI, however, their specific impact on event-related desynchronization (ERD), a key neurophysiological marker in NF, remains insufficiently understood. Previous research suggested that declarative interference following MI-NF may serve as a context factor hindering the progression of ERD. Yet, no significant changes in ERD within the mu and beta (8-30 Hz) frequency bands were observed across blocks in either a declarative interference or a control condition. This raises the question of whether the absence of ERD modulation could be attributed to the break task that was common to both declarative interference and control condition: watching nature documentaries immediately after MI blocks. To investigate this, we conducted a follow-up study replicating the original methodology while collecting new data. We compared NF-MI-ERD between groups with and without nature documentaries as a post-MI condition. Participants completed three sessions of kinesthetic MI-NF training involving a finger-tapping task over two consecutive days, with quiet rest as the post-MI condition (group quiet rest). 64-channel EEG data were analyzed from 17 healthy participants (8 females, 18-35 years, M and SD: 25.2 ± 4.2 years). Data were compared to a previously recorded dataset (group documentaries), in which 17 participants (10 females, 23-32 years, M and SD: 25.8 ± 2.5 years) watched nature documentaries after MI blocks. The results showed no significant main effects for blocks or group, though a session-by-group interaction was observed. Post-hoc tests, however, did not reveal significant differences in ERD development between the groups across individual blocks. These findings do not provide evidence that nature documentaries used as a post-MI condition negatively affect across-block development of NF-MI-ERD. This study highlights the importance of exploring additional context factors in MI-NF training to better understand their influence on ERD development.},
}

@article {pmid40263649,
year = {2025},
author = {Li, X and Dai, P and Yuan, Y},
title = {[Perioperative safety assessment and complications follow-up of simultaneous bilateral cochlear implantation in young infants].},
journal = {Lin chuang er bi yan hou tou jing wai ke za zhi = Journal of clinical otorhinolaryngology head and neck surgery},
volume = {39},
number = {5},
pages = {413-418;424},
doi = {10.13201/j.issn.2096-7993.2025.05.004},
pmid = {40263649},
issn = {2096-7993},
mesh = {Humans ; *Cochlear Implantation/adverse effects/methods ; Infant ; *Postoperative Complications ; *Hearing Loss, Sensorineural/surgery ; Follow-Up Studies ; Male ; Perioperative Period ; Female ; Cochlear Implants ; },
abstract = {Objective:To evaluate the perioperative safety and long-term complications of simultaneous bilateral cochlear implantation（BCI） in young infants, providing reference data for clinical BCI in young children. Methods:Seventy-four infants aged 6-23 months with congenital severe to profound sensorineural hearing loss who were candidates for cochlear implantation at the Department of Otolaryngology, Chinese PLA General Hospital between August 2018 and August 2019 were consecutively enrolled. Parents made the decision to implant either unilaterally or bilaterally. Participants were divided into unilateral cochlear implantation（UCI） group（before and after 12 months of age） and simultaneous BCI group（before and after 12 months of age）. Safety indicators, including perioperative risk variables, complications, and other postoperative adverse events were monitored, with complications followed up for 5-6 years. Comparisons were made between the BCI and UCI, as well as between implantation before and after 12 months of age regarding perioperative safety and long-term complications. Results:A total of 40 BCI patients（23 before 12 months, 17 after 12 months） and 34 UCI patients（20 before 12 months, 14 after 12 months） were included in the study. Regarding perioperative risk variables, the BCI group showed significantly longer anesthesia duration, operative time, and greater blood loss compared to the UCI group, though less than twice that of the UCI group; no anesthetic complications occurred in either group; and there was no significant difference in postoperative hospital stay between the groups. Regarding surgical complications during the 5-year follow-up period, the BCI group experienced 7 complications（2 major, 5 minor）, while the UCI group had 7 complications（1 major, 6 minor）, with no statistical differences between groups. Regarding other postoperative adverse events, the BCI group demonstrated significantly higher total adverse event rates than the UCI group（80.0% vs 38.2%）, with higher rates of moderate to severe anemia（60.0% vs 20.6%） and lower mean hemoglobin levels[（92.35±12.14） g/L vs（102.39±13.09） g/L]. No significant differences were found in postoperative fever rates（50.0% vs 52.9%） or C-reactive protein levels between groups. Within the BCI group, patients implanted before 12 months indicated notably higher rates of total adverse events（91.3% vs 64.7%）, high fever（26.1% vs 0）, and moderate to severe anemia（78.3% vs 35.3%） compared to those implanted after 12 months. Conclusion:Simultaneous BCI in young children under 2 years of age demonstrates controllable overall risks. Compared to UCI, while it shows no increase in anesthetic or surgical complications, it presents higher perioperative risks and adverse event rates, especially in patients implanted before 12 months of age, warranting special attention from medical staff.},
}

Humans
*Cochlear Implantation/adverse effects/methods
Infant
*Postoperative Complications
*Hearing Loss, Sensorineural/surgery
Follow-Up Studies
Male
Perioperative Period
Female
Cochlear Implants

@article {pmid40262425,
year = {2025},
author = {Bao, X and Feng, X and Huang, H and Li, M and Chen, D and Wang, Z and Li, J and Huang, Q and Cai, Y and Li, Y},
title = {Day-night hyperarousal in tinnitus patients.},
journal = {Sleep medicine},
volume = {131},
number = {},
pages = {106519},
doi = {10.1016/j.sleep.2025.106519},
pmid = {40262425},
issn = {1878-5506},
abstract = {Tinnitus, which affects 12-30 % of the population, is associated with sleep disturbances and daytime dysfunction, yet the neural mechanisms that link wake-up states remain unclear. This study investigated electroencephalographic (EEG) characteristics of 51 tinnitus patients and 51 controls across wakefulness (eyes-open, eyes-closed, mental arithmetic) and sleep stages (N1, N2, N3, REM) to clarify day-night pathological mechanisms. The key findings showed persistent hyperarousal in tinnitus: wakefulness revealed enhanced gamma power (30-45 Hz) in eyes-closed and task states, while sleep demonstrated elevated gamma/beta power across all stages accompanied by reduced delta/theta power in deep sleep (N2/N3).). An analysis of sleep structure indicates impaired stability in maintaining the N2 stage among tinnitus patients, corroborating a reduction in N3 duration and an increased proportion of the N2 stage. From the wake states to the sleep stages, group × state interactions for the delta/theta power suggest an impaired state regulation capacity in tinnitus patients. Correlation clustering further revealed aberrant integration of wake-related gamma/beta activity into non-rapid eye movement sleep, indicating neuroplastic overgeneralization of wake hyperarousal into sleep. These results extend the so-called loss-of-inhibition theory to sleep, proposing that deficient low-frequency oscillations fail to suppress hyperarousal, impairing sleep-dependent neuroplasticity, and perpetuating daytime symptoms. Furthermore, this study establishes sleep as a critical therapeutic target to interrupt the 24-h dysfunctional cycle of tinnitus.},
}

@article {pmid40262392,
year = {2025},
author = {Li, X and Dong, X and Wang, J and Mao, H and Tu, X and Li, W and He, J and Li, Q and Zhang, P},
title = {Deep transfer learning-based decoder calibration for intracortical brain-machine interfaces.},
journal = {Computers in biology and medicine},
volume = {192},
number = {Pt A},
pages = {110231},
doi = {10.1016/j.compbiomed.2025.110231},
pmid = {40262392},
issn = {1879-0534},
abstract = {Intracortical brain-machine interfaces (iBMIs) aim to establish a communication path between the brain and external devices. However, in the daily use of iBMIs, the non-stationarity of recorded neural signals necessitates frequent recalibration of the iBMI decoder to maintain decoding performance, which requires collecting and labeling a large amount of new data. To address this challenge and minimize the time needed for decoder recalibration, we proposed an active learning domain adversarial neural network (AL-DANN). This model leveraged a substantial volume of historical data alongside a small amount of current data (four samples per category) to calibrate the decoder. By incorporating domain adversarial and active learning strategies, the model effectively transferred knowledge from historical data to new data, reducing the demand for new samples. We validated the proposed method using neural signals recorded from three monkeys performing different movements in a classification task or a regression task. The results showed that the AL-DANN outperformed existing state-of-the-art methods. Impressively, it required only four new samples per category for decoder recalibration, leading to a recalibration time reduction of over 80 %. To our knowledge, this is the first study to incorporate deep transfer learning into iBMI decoder calibration, highlighting the significant potential of applying deep learning technologies in iBMIs.},
}

@article {pmid40262075,
year = {2025},
author = {Shao, WW and Shao, Q and Xu, HH and Qiao, GJ and Wang, RX and Ma, ZY and Meng, WW and Yang, ZB and Zang, YL and Li, XH},
title = {Repetitive training enhances the pattern recognition capability of cultured neural networks.},
journal = {PLoS computational biology},
volume = {21},
number = {4},
pages = {e1013043},
doi = {10.1371/journal.pcbi.1013043},
pmid = {40262075},
issn = {1553-7358},
abstract = {Cultured neural networks in vitro have demonstrated the biocomputing capability to recognize patterns. However, the underlying mechanisms behind information processing and pattern recognition remain less understood. Here, we developed an in vitro neural network integrated with microelectrode arrays (MEAs) to explore the network's classification capability and elucidate the mechanisms underlying this classification. After applying different stimulation patterns using MEAs, the network exhibited structural alterations and distinct electrical responses that recognized various stimulation patterns. Alongside the reshaping of network structures, repeated training increased recognition accuracy for each stimulation pattern. Additionally, it was reported for the first time that spontaneous networks after stimulation are more closely related to the structures of evoked networks. This work provides new insights into the structural changes underlying information processing and contributes to our understanding of how cultured neural networks respond to different patterns.},
}

@article {pmid40261790,
year = {2025},
author = {Luo, J and Liu, Q and Tai, P and Li, G and Li, Y},
title = {A Multi-level Integrated EEG-Channel Selection Method Based on the Lateralization Index.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {PP},
number = {},
pages = {},
doi = {10.1109/TNSRE.2025.3563416},
pmid = {40261790},
issn = {1558-0210},
abstract = {The importance of optimizing channel selection for portable brain-computer interface (BCI) technology is increasingly recognized. Effective channel selection reduces computational load and enhances user experience by making BCI systems more comfortable and easier to use. A significant challenge lies in reducing the number of electrodes without compromising decoding accuracy. Although some methods have been proposed in previous studies, these often increase computational load and overlook the importance of channel selection across different subjects. Therefore, we propose a novel Multi-level Integrated EEG-Channel Selection method based on the Lateralization Index (MLI-ECS-LI). This method leverages the lateralization index in selecting important channels and can achieve the channel selection for the cross-tasks and the cross-subjects scenarios. To evaluate the effectiveness of the proposed method, the time and frequency domain features from selected channels were extracted. Three widely used classifiers, Least Squares Support Vector Machine (LSSVM), Random Forest (RF), and Support Vector Machine (SVM) were used to classify movement types based on these features. Compared to the conventional condition (C1-C6), the average decoding accuracies across 21 healthy subjects demonstrated an improved performance of 6.6%, 4.9%, 6.9% (LSSVM); 3.8%, 2.8%, 4.5%(RF); and 7.6%, 5.6%, 9.2%(SVM) via using the channels selected from the conditions of the single task, the cross-tasks, and the cross-subjects scenarios, respectively. These results demonstrated the potential of the proposed method in improving the utility of the portable Motor Imagery Brain-Computer Interface (MI-BCI) and effectiveness in practical applications.},
}

@article {pmid40260641,
year = {2025},
author = {Xu, Y and Li, YL and Yu, G and Ou, Z and Yao, S and Li, Y and Huang, Y and Chen, J and Ding, Q},
title = {Effect of Brain Computer Interface Training on Frontoparietal Network Function for Young People: A Functional Near-Infrared Spectroscopy Study.},
journal = {CNS neuroscience & therapeutics},
volume = {31},
number = {4},
pages = {e70400},
pmid = {40260641},
issn = {1755-5949},
support = {2024A04J3082//Guangzhou Science and Technology Program/ ; A2024500//Guangdong Medical Research Foundation/ ; 82102678//National Natural Science Foundation of China/ ; },
mesh = {Humans ; Spectroscopy, Near-Infrared ; Male ; Female ; *Parietal Lobe/physiology/diagnostic imaging ; *Brain-Computer Interfaces ; Young Adult ; *Frontal Lobe/physiology/diagnostic imaging ; Adult ; Attention/physiology ; *Nerve Net/physiology/diagnostic imaging ; Executive Function/physiology ; },
abstract = {AIMS: Inattention in young people is one of the main reasons for their declining learning ability. Frontoparietal networks (FPNs) are associated with attention and executive function. Brain computer interface (BCI) training has been applied in neurorehabilitation, but there is a lack of research on its application to cognition. This study aimed to investigate the effect of BCI on the attention network in healthy young adults.

METHODS: Twenty-seven healthy people performed BCI training for 5 consecutive days. An attention network test (ANT) was performed at baseline and immediately after the fifth day of training and included simultaneous functional near-infrared spectroscopy recording.

RESULTS: BCI performance improved significantly after BCI training (p = 0.005). The efficiencies of the alerting and executive control networks were enhanced after BCI training (p = 0.032 and 0.003, respectively). The functional connectivity in the bilateral prefrontal cortices and the right posterior parietal cortex increased significantly after BCI training (p < 0.05).

CONCLUSION: Our findings suggested that repetitive BCI training could improve attention and induce lasting neuroplastic changes in FPNs. It might be a promising rehabilitative strategy for clinical populations with attention deficits. The right PPC may also be an effective target for neuromodulation in diseases with attention deficits.},
}

Humans
Spectroscopy, Near-Infrared
Male
Female
*Parietal Lobe/physiology/diagnostic imaging
*Brain-Computer Interfaces
Young Adult
*Frontal Lobe/physiology/diagnostic imaging
Adult
Attention/physiology
*Nerve Net/physiology/diagnostic imaging
Executive Function/physiology

@article {pmid40260139,
year = {2025},
author = {Zhang, T and Wang, N and Chai, X and He, Q and Cao, T and Yuan, L and Lan, Q and Yang, Y and Zhao, J},
title = {Evaluation of pressure-induced pain in patients with disorders of consciousness based on functional near infrared spectroscopy.},
journal = {Frontiers in neurology},
volume = {16},
number = {},
pages = {1542691},
pmid = {40260139},
issn = {1664-2295},
abstract = {OBJECTIVE: This study aimed to investigate the brain's hemodynamic responses (HRO) and functional connectivity in patients with disorders of consciousness (DoC) in response to acute pressure pain stimulation using near-infrared spectroscopy (NIRS).

METHODS: Patients diagnosed with DoC underwent pressure stimulation while brain activity was measured using NIRS. Changes in oxygenated hemoglobin (HbO) and deoxygenated hemoglobin (HbR) concentrations were monitored across several regions of interest (ROIs), including the primary somatosensory cortex (PSC), primary motor cortex (PMC), dorsolateral prefrontal cortex (dPFC), somatosensory association cortex (SAC), temporal gyrus (TG), and frontopolar area (FPA). Functional connectivity was assessed during pre-stimulation, stimulation, and post-stimulation phases.

RESULTS: No significant changes in HbO or HbR concentrations were observed during the stimulation vs. baseline or stimulation vs. post-stimulation comparisons, indicating minimal activation of the targeted brain regions in response to the pressure stimulus. However, functional connectivity between key regions, particularly the PSC, PMC, and dPFC, showed significant enhancement during the stimulation phase (r > 0.9, p < 0.001), suggesting greater coordination among sensory, motor, and cognitive regions. These changes in connectivity were not accompanied by significant activation in pain-related brain areas.

CONCLUSION: Although pain-induced brain activation was minimal in patients with DoC, enhanced functional connectivity during pain stimulation suggests that the brain continues to process pain information through coordinated activity between regions. The findings highlight the importance of assessing functional connectivity as a potential method for evaluating pain processing in patients with DoC.},
}

@article {pmid40257892,
year = {2025},
author = {Hashemi, M and Depannemaecker, D and Saggio, M and Triebkorn, P and Rabuffo, G and Fousek, J and Ziaeemehr, A and Sip, V and Athanasiadis, A and Breyton, M and Woodman, M and Wang, H and Petkoski, S and Sorrentino, P and Jirsa, V},
title = {Principles and Operation of Virtual Brain Twins.},
journal = {IEEE reviews in biomedical engineering},
volume = {PP},
number = {},
pages = {},
doi = {10.1109/RBME.2025.3562951},
pmid = {40257892},
issn = {1941-1189},
abstract = {Current clinical methods often overlook individual variability by relying on population-wide trials, while mechanismbased trials remain underutilized in neuroscience due to the brain's complexity. This situation may change through the use of a Virtual Brain Twin (VBT), which is a personalized digital replica of an individual's brain, integrating structural and functional brain data into advanced computational models and inference algorithms. By bridging the gap between molecular mechanisms, whole-brain dynamics, and imaging data, VBTs enhance the understanding of (patho)physiological mechanisms, advancing insights into both healthy and disordered brain function. Central to VBT is the network modeling that couples mesoscopic representation of neuronal activity through white matter connectivity, enabling the simulation of brain dynamics at a network level. This transformative approach provides interpretable predictive capabilities, supporting clinicians in personalizing treatments and optimizing interventions. This Review outlines the key components of VBT development, covering the conceptual, mathematical, technical, and clinical aspects. We describe the stages of VBT construction-from anatomical coupling and modeling to simulation and Bayesian inference-and demonstrate their applications in resting-state, healthy aging, multiple sclerosis, and epilepsy. Finally, we discuss potential extensions to other neurological disorders, such as Parkinson's disease, and explore future applications in consciousness research and brain-computer interfaces, paving the way for advancements in personalized medicine and brainmachine integration.},
}

@article {pmid40257874,
year = {2025},
author = {Li, S and Liu, G and Feng, F and Chang, Z and Li, W and Duan, F},
title = {An Interventional Brain-Computer Interface for Long-Term EEG Collection and Motion Classification of a Quadruped Mammal.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {PP},
number = {},
pages = {},
doi = {10.1109/TNSRE.2025.3562922},
pmid = {40257874},
issn = {1558-0210},
abstract = {Brain-computer interfaces (BCI) acquire electroencephalogram (EEG) signals to effectively address postoperative motor dysfunction in stroke patients by discerning their motor intentions during significant movements. Traditionally, noninvasive BCIs have been constrained by limitations in their usage environments; whereas, invasive BCIs damage neural permanently. Therefore, we proposed a novel interventional BCI, in which electrodes are implanted along the veins into the brain to acquire intracerebral EEG signals without an open craniotomy. We collect EEG signals from the primary motor cortex in the superior sagittal sinus of sheep during three different significant movements: laying down; standing; and walking. The first three month data are used to train the neural network, and The fourth month of data were used to validate. The deep learning model achieved an 86% accuracy rate in classifying motion states in validation. Furthermore, the results of the power spectral density (PSD) show that the signal power in the main frequency band did not decrease over a period of five months, which demonstrates that the interventional BCI has the ability to effectively capture EEG signals over long periods of time.},
}

@article {pmid40257872,
year = {2025},
author = {Huang, S and Liu, Y and Wang, Z and Wu, W and Guo, J and Xu, W and Ming, D},
title = {Enhanced Brain Functional Interaction Following BCI-guided Supernumerary Robotic Finger Training Based on Sixth-Finger Motor Imagery.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {PP},
number = {},
pages = {},
doi = {10.1109/TNSRE.2025.3562700},
pmid = {40257872},
issn = {1558-0210},
abstract = {Supernumerary robotic finger (SRF) has shown unique advantages in the field of motor augmentation and rehabilitation, while the development of brain computer interface (BCI) technology has provided the possibility for direct control of SRF. However, the neuroplasticity effects of BCI-actuated SRF (BCI-SRF) training based on the "six finger" motor imagery paradigm are still unclear. This study recruited 20 healthy right-handed participants and randomly assigned them to either a BCI-SRF training group or a sham SRF training group. During the testing phase before and after 4 weeks of training, all participants were tested for SRF-finger opposition sequence behavior, resting state fMRI (rs-fMRI), and task-based fMRI (tb-fMRI). The results showed that compared with the Sham group, the BCI-SRF group improved the accuracy rate of the SRF-finger opposition sequence by 132%. The activation analysis of tb-fMRI before and after training revealed a significant increase in left middle frontal gyrus only in the BCI-SRF group. In addition, the BCI-SRF group showed an increase in FC between the right primary motor cortex and left cerebellum inferior lobe, as well as between the left middle frontal gyrus and the right precuneus lobe after training, while there was no significant change in the Sham group. In addition, only the BCI-SRF group showed a significant increase in clustering coefficients after training. Moreover, the increase in the clustering coefficients of the two groups is positively correlated with the improvement of the accuracy of the SRF-finger opposition sequences. These results indicate that the integration of BCI and SRF significantly regulates the functional interaction between motor learning and cognitive imagery brain regions, enhances the integration and processing ability of brain networks for local information, and improves human-machine interaction behavioral performance.},
}

@article {pmid40254808,
year = {2025},
author = {Wang, H and Wang, X},
title = {Exploring the Role of Psychedelics in Modulating Ego and Treating Neuropsychiatric Disorders.},
journal = {ACS chemical neuroscience},
volume = {},
number = {},
pages = {},
doi = {10.1021/acschemneuro.5c00247},
pmid = {40254808},
issn = {1948-7193},
abstract = {This viewpoint explores the therapeutic potential of psychedelics in treating neuropsychiatric disorders, particularly through the modulation of brain entropy and the experience of ego dissolution. Psychedelics disrupt rigid neural patterns, facilitating enhanced connectivity and fostering profound emotional breakthroughs that may alleviate symptoms of disorders like depression, anxiety, PTSD, and addiction. Despite their promising potential, the clinical application of psychedelics presents significant challenges, including the need for careful patient screening, managing adverse experiences, and addressing ethical considerations, all of which are essential for their safe integration into therapy.},
}

@article {pmid40254540,
year = {2025},
author = {Fu, Q and Tong, L and Zhang, H and Xu, H},
title = {Multimodal Imaging Diagnosis of Apical Ventricular Aneurysm With Thrombosis Resulting From Blunt Myocardial Injury: A Case Report.},
journal = {Journal of clinical ultrasound : JCU},
volume = {},
number = {},
pages = {},
doi = {10.1002/jcu.24026},
pmid = {40254540},
issn = {1097-0096},
support = {20210101260JC//the Science and Technology Development Program of the Jilin Province/ ; },
abstract = {This article presents the case of a male patient who sustained blunt myocardial injury following a traffic accident. A series of diagnostic imaging procedures were conducted on the patient, including electrocardiography, echocardiography, computed tomography angiography, and cardiac magnetic resonance imaging, which demonstrated edema in a portion of the myocardium and the formation of a ventricular aneurysm with thrombus in the left ventricular apex. After 6 months and 1 year, echocardiography demonstrated no detection of thrombus, but the apical left ventricular aneurysm was not significantly different from the anterior film, leading to a final clinical diagnosis of blunt cardiac injury (BCI).},
}

@article {pmid40253420,
year = {2025},
author = {Wang, B and Zhang, X and Zhang, L and Kong, XZ},
title = {A naturalistic fMRI dataset in response to public speaking.},
journal = {Scientific data},
volume = {12},
number = {1},
pages = {659},
pmid = {40253420},
issn = {2052-4463},
support = {32171031//National Natural Science Foundation of China (National Science Foundation of China)/ ; },
mesh = {Humans ; *Magnetic Resonance Imaging ; Female ; Male ; *Speech ; Young Adult ; Adult ; *Brain/physiology ; *Communication ; },
abstract = {Public speaking serves as a powerful tool for informing, inspiring, persuading, motivating, or entertaining an audience. While some speeches effectively engage audience and disseminate knowledge, others fail to resonate. This dataset presents functional magnetic resonance imaging (fMRI) data from 31 participants (14 females; age: 22.29 ± 2.84 years) who viewed two informative speeches with varying effectiveness, selected from YiXi talks (similar to TED Talks), and matched in length and topic. A total of 22 participants (10 females; age: 22.64 ± 2.77 years) who completed the full task were included in the validation analyses. A comprehensive validation process, involving behavioral data analysis and head motion assessment, confirmed the quality of the fMRI dataset. While previous analyses have used inter-subject correlation to examine neural synchronization during the reception of informative public speaking, this dataset can be utilized for a variety of analyses to further elucidate the neural mechanisms underlying audience engagement and effective communication.},
}

Humans
*Magnetic Resonance Imaging
Female
Male
*Speech
Young Adult
Adult
*Brain/physiology
*Communication

@article {pmid40253415,
year = {2025},
author = {He, T and Wei, M and Wang, R and Wang, R and Du, S and Cai, S and Tao, W and Li, H},
title = {VocalMind: A Stereotactic EEG Dataset for Vocalized, Mimed, and Imagined Speech in Tonal Language.},
journal = {Scientific data},
volume = {12},
number = {1},
pages = {657},
pmid = {40253415},
issn = {2052-4463},
support = {62271432//National Natural Science Foundation of China (National Science Foundation of China)/ ; },
mesh = {Humans ; *Electroencephalography ; *Speech ; *Language ; Brain-Computer Interfaces ; },
abstract = {Speech BCIs based on implanted electrodes hold significant promise for enhancing spoken communication through high temporal resolution and invasive neural sensing. Despite the potential, acquiring such data is challenging due to its invasive nature, and publicly available datasets, particularly for tonal languages, are limited. In this study, we introduce VocalMind, a stereotactic electroencephalography (sEEG) dataset focused on Mandarin Chinese, a tonal language. This dataset includes sEEG-speech parallel recordings from three distinct speech modes, namely vocalized speech, mimed speech, and imagined speech, at both word and sentence levels, totaling over one hour of intracranial neural recordings related to speech production. This paper also presents a baseline model as the reference model for future studies, at the same time, ensuring the integrity of the dataset. The diversity of tasks and the substantial data volume provide a valuable resource for developing advanced algorithms for speech decoding, thereby advancing BCI research for spoken communication.},
}

Humans
*Electroencephalography
*Speech
*Language
Brain-Computer Interfaces

@article {pmid40253381,
year = {2025},
author = {Xue, S and Jin, B and Jiang, J and Guo, L and Zhou, J and Wang, C and Liu, J},
title = {A multi-subject and multi-session EEG dataset for modelling human visual object recognition.},
journal = {Scientific data},
volume = {12},
number = {1},
pages = {663},
pmid = {40253381},
issn = {2052-4463},
support = {U21B2043, 62206279//National Natural Science Foundation of China (National Science Foundation of China)/ ; U21B2043, 62206279//National Natural Science Foundation of China (National Science Foundation of China)/ ; U21B2043, 62206279//National Natural Science Foundation of China (National Science Foundation of China)/ ; U21B2043, 62206279//National Natural Science Foundation of China (National Science Foundation of China)/ ; U21B2043, 62206279//National Natural Science Foundation of China (National Science Foundation of China)/ ; },
mesh = {Humans ; *Electroencephalography ; Brain-Computer Interfaces ; Machine Learning ; *Visual Perception ; *Pattern Recognition, Visual ; Algorithms ; },
abstract = {We share a multi-subject and multi-session (MSS) dataset with 122-channel electroencephalographic (EEG) signals collected from 32 human participants. The data was obtained during serial visual presentation experiments in two paradigms. Dataset of first paradigm consists of around 800,000 trials presenting stimulus sequences at 5 Hz. Dataset of second paradigm comprises around 40,000 trials displaying each image for 1 second. Each participant completed between 1 to 5 sessions on different days, and each session lasted for approximately 1.5 hours of EEG recording. The stimulus set used in the experiments included 10,000 images, with 500 images per class, manually selected from PASCAL and ImageNet image databases. The MSS dataset can be useful for various studies, including but not limited to (1) exploring the characteristics of EEG visual response, (2) comparing the differences in EEG response of different visual paradigms, and (3) designing machine learning algorithms for cross-subject and cross-session brain-computer interfaces (BCIs) using EEG data from multiple subjects and sessions.},
}

Humans
*Electroencephalography
Brain-Computer Interfaces
Machine Learning
*Visual Perception
*Pattern Recognition, Visual
Algorithms

@article {pmid40252874,
year = {2025},
author = {Ye, Z and Lv, C and Zhou, H and Bao, Y and Hong, T and He, Q and Hu, Y},
title = {Neural substrates of attack event prediction in video games: the role of ventral posterior cingulate cortex and theory of mind network.},
journal = {NeuroImage},
volume = {},
number = {},
pages = {121228},
doi = {10.1016/j.neuroimage.2025.121228},
pmid = {40252874},
issn = {1095-9572},
abstract = {Action anticipation, the ability to observe actions and predict the intent of others, plays a crucial role in social interaction and fields such as electronic sports. However, the neural mechanisms underlying the inference of purpose from action observation remain unclear. In this study, we conducted an fMRI experiment using video game combat scenarios to investigate the neural correlates of action anticipation and its relationship with task performance. The results showed that the higher level of ability to infer the purpose from action observation during experiment associates with higher level of proficiency in real world electric gaming competition. The action anticipation task activates visual streams, fronto-parietal network, and the ventral posterior cingulate cortex (vPCC), a key hub in the theory of mind network. The strength of vPCC activation during action anticipation, but not movement direction judgment, was positively correlated with gaming proficiency. Finite impulse response analysis revealed distinct dynamic response profiles in the vPCC compared to other theory of mind regions. These findings suggest that theory of mind ability may be an important factor influencing individual competitive performance, with the vPCC serving as a core neural substrate for inferring purpose from action observation.},
}

@article {pmid40250541,
year = {2025},
author = {Liu, Y and Wang, M and Rao, H},
title = {Common Neural Activations of Creativity and Exploration: A Meta-analysis of Task-based fMRI Studies.},
journal = {Neuroscience and biobehavioral reviews},
volume = {},
number = {},
pages = {106158},
doi = {10.1016/j.neubiorev.2025.106158},
pmid = {40250541},
issn = {1873-7528},
abstract = {Creativity is a common, complex, and multifaceted cognitive activity with significant implications for technological progress, social development, and human survival. Understanding the neurocognitive mechanisms underlying creative thought is essential for fostering individual creativity. While previous studies have demonstrated that exploratory behavior positively influences creative performance, few studies investigated the relationship between creativity and exploration at the neural level. To address this gap, we conducted a quantitative meta-analysis comprising 80 creativity experiments (1,850 subjects) and 23 exploration experiments (646 subjects) to examine potential shared neural activations between creativity and exploration. Furthermore, we analyzed the neural similarities and differences among three forms of creative thinking-divergent thinking (DT), convergent thinking (CT), and artistic creativity-and their relationship with exploration. The conjunction analysis of creativity and exploration revealed significant activations in the bilateral IFJ and left preSMA. Further conjunction analyses revealed that both CT and artistic creativity exhibited common neural activations with exploration, with CT co-activating the left IFJ and artistic creativity co-activating both the right IFJ and left preSMA, while DT did not. Additionally, the conjunction analyses across the three forms of creativity did not identify shared neural activations. Further functional decoding analyses of the overlapping brain regions associated with CT and exploration, as well as artistic creativity and exploration, revealed correlations with inhibitory control mechanisms. These results enhance our understanding of the role of exploration in the creative thinking process and provide valuable insights for developing strategies to foster innovative thinking.},
}

@article {pmid40249697,
year = {2025},
author = {Cao, B and Tsai, CL and Zhou, N and Do, T and Lin, CT},
title = {A Novel 3D Paradigm for Target Expansion of Augmented Reality SSVEP.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {PP},
number = {},
pages = {},
doi = {10.1109/TNSRE.2025.3562217},
pmid = {40249697},
issn = {1558-0210},
abstract = {Steady-State Visual Evoked Potentials (SSVEP) have proven to be practical in Brain-Computer Interfaces (BCI), particularly when integrated with augmented reality (AR) for real-world application. However, unlike conventional computer screen-based SSVEP (CS-SSVEP), which benefits from stable experimental environments, AR-based SSVEP (AR-SSVEP) systems are susceptible to the interference of real-world environment and device instability. Particularly, the performance of AR-SSVEP significantly declines as the target frequency increases. Therefore, our study introduced a 3D paradigm that combines flicker frequency with rotation patterns as stimuli, enabling expansion of target sets without additional frequencies. In the proposed design, in addition to the conventional frequency-based SSVEP feature, bio-marker elicited by visual perception of rotation was investigated. An experimental comparison between this novel 3D paradigm and a traditional 2D approach, which increases targets by adding frequencies, reveals significant advantages. The 12-class 3D paradigm achieved an accuracy of 76.5% and an information transfer rate (ITR) of 70.42 bits/min using 1-second EEG segments. In contrast, the 2D paradigm exhibited a lower performance with 72.07% accuracy and 62.28 bits/min ITR. The result underscores the 3D paradigm's superiority in enhancing the practical applications of SSVEP-based BCIs in AR settings, especially with shorter time windows, by effectively expanding target recognition without compromising system efficiency.},
}

@article {pmid40247883,
year = {2025},
author = {Uszko, JM and Schroeder, JC and Eichhorn, SJ and Patil, AJ and Hall, SR},
title = {Morphological control of cuprate superconductors using sea sponges as templates.},
journal = {RSC advances},
volume = {15},
number = {14},
pages = {11189-11193},
pmid = {40247883},
issn = {2046-2069},
abstract = {Functional porous superconducting sponges, consisting of YBa2Cu3O6+δ (YBCO) and Bi2Sr2CaCu2O8+δ (BSCCO), were created by biotemplating with natural sea sponges. Naturally occurring calcium in the spongin fibers was utilized to dope YBCO and to form BSCCO without adding any external calcium source. The sample morphology was confirmed with scanning electron microscopy, and the sample composition was confirmed with energy-dispersive X-ray spectroscopy, powder electron diffraction and high-resolution transmission electron microscopy. The YBCO sponge exhibited a critical temperature (T c) of approximately 70 K, and the BSCCO sponge showed a T c of 77 K. This proof-of-concept study demonstrates the feasibility of using sea sponges as a greener, more sustainable template for superconductor synthesis.},
}

@article {pmid40247859,
year = {2025},
author = {Kuo, YT and Wang, HL and Chen, BW and Wang, CF and Lo, YC and Lin, SH and Chen, PC and Chen, YY},
title = {Degradation-aware neural imputation: Advancing decoding stability in brain machine interfaces.},
journal = {APL bioengineering},
volume = {9},
number = {2},
pages = {026106},
pmid = {40247859},
issn = {2473-2877},
abstract = {Neural signal degradation poses a significant challenge in maintaining stable performance when decoding motor tasks using multiunit activity (MUA) and local field potential (LFP) signals in the implantable brain machine interface (iBMI) applications. Effective methods for imputing degraded or missing signals are essential to restore neural signal integrity, thereby improving decoding accuracy and system robustness over long-term recordings with fluctuating signal quality. This study introduces a confidence-weighted Bayesian linear regression (CW-BLR) approach to impute neural signals affected by degradation, enhancing the robustness and consistency of decoding. The performance of CW-BLR was compared to traditional methods-mean imputation (Mean-imp) and Gaussian-mixture-model-based expectation-maximization (GMM-EM)-using a kernel-sliced inverse regression (kSIR) decoder to evaluate decoding outcomes. Four Wistar rats were trained to perform a forelimb-reaching task while neural activity (MUA and LFPs) was recorded over 27 days. CW-BLR imputed signals degraded during days 8-27. Decoding performance was evaluated using kSIR and compared with Mean-imp and GMM-EM. CW-BLR demonstrated superior performance by effectively preserving both temporal and spatial dependencies within the neural signals. CW-BLR-imputed data significantly improved decoding accuracy over traditional imputation methods, with the kSIR decoder showing consistently higher performance, particularly in maintaining signal quality from the degraded period. CW-BLR offers a robust and effective imputation framework for iBMI applications, addressing signal degradation challenges and maintaining accurate decoding over prolonged recordings. By utilizing confidence-based quality metrics, CW-BLR surpasses traditional methods, providing stable neural decoding across fluctuating signal quality scenarios.},
}

@article {pmid40246195,
year = {2025},
author = {Isis Yonza, AK and Tao, L and Zhang, X and Postnov, D and Kucharz, K and Lind, B and Asiminas, A and Han, A and Sonego, V and Kim, K and Cai, C},
title = {Spatially and temporally mismatched blood flow and neuronal activity by high-intensity intracortical microstimulation.},
journal = {Brain stimulation},
volume = {18},
number = {3},
pages = {885-896},
doi = {10.1016/j.brs.2025.04.015},
pmid = {40246195},
issn = {1876-4754},
abstract = {INTRODUCTION: Intracortial microstimulation (ICMS) is widely used in neuroprosthetic brain-machine interfacing, particularly in restoring lost sensory and motor functions. Spiking neuronal activity requires increased cerebral blood flow to meet local metabolic demands, a process conventionally denoted as neurovascular coupling (NVC). However, it is unknown precisely how and to what extent ICMS elicits NVC and how the neuronal and blood flow responses to ICMS correlate. Suboptimal NVC by ICMS may compromise oxygen and energy delivery to the activated neurons thus impair neuroprosthetic functionality.

MATERIAL AND METHOD: We used wide-field imaging (WFI), laser speckle imaging (LSI) and two-photon microscopy (TPM) to study living, transgenic mice expressing calcium (Ca[2+]) fluorescent indicators in either neurons or vascular mural cells (VMC), as well as to measure vascular inner lumen diameters.

RESULT: By testing a range of stimulation amplitudes and examining cortical tissue responses at different distances from the stimulating electrode tip, we found that high stimulation intensities (≥50 μA) elicited a spatial and temporal neurovascular decoupling in regions most adjacent to electrode tip (<200 μm), with significantly delayed onset times of blood flow responses to ICMS and compromised maximum blood flow increases. We attribute these effects respectively to delayed Ca[2+] signalling and decreased Ca[2+] sensitivity in VMCs.

CONCLUSION: Our study offers new insights into ICMS-associated neuronal and vascular physiology with potentially critical implications towards the optimal design of ICMS in neuroprosthetic therapies: low intensities preserve NVC; high intensities disrupt NVC responses and potentially precipitate blood supply deficits.},
}

@article {pmid40246009,
year = {2025},
author = {Ren, J and Wang, Y and Wang, Y and Zhang, Y and Xing, M and Deng, S and Tong, S and Wang, L and Zheng, C and Yang, J and Ni, G and Ming, D},
title = {Dynamic changes of hippocampal dendritic spines in Alzheimer's disease mice among the different stages.},
journal = {Experimental neurology},
volume = {},
number = {},
pages = {115266},
doi = {10.1016/j.expneurol.2025.115266},
pmid = {40246009},
issn = {1090-2430},
abstract = {Alzheimer's disease (AD) is characterized by the accumulation of amyloid-β (Aβ) peptides and a progressive decline in cognitive function. Hippocampus as a crucial brain area for learning and memory, is also adversely affected by AD's pathology. The accumulation of Aβ is often associated with the loss of dendritic spines of the hippocampus. However, the dynamic alterations in dendritic spines throughout AD progression are not fully understood. To investigate it, we conducted in-vivo imaging in two mouse models representing the early and late stages of AD pathology: young mice injected with Aβ1-42 oligomers and APP/PS1 transgenic mice. In the early-stage AD model, imaging was conducted at third- and fifth- weeks post-injection. In the late-stage AD model, a four-month imaging began at 14 months old. The imaging results showed spine elimination in both models. Notably, acute Aβ exposure was linked to heightened spine loss on secondary dendrites, while in the late stage the primary effect was on tertiary dendrites. Concurrently, with the metabolism of Aβ, cognition recovered to some extent by five weeks post Aβ1-42 exposure. These findings suggested that dendritic spine plasticity was impaired during the development of AD, as evidenced by increasing spine loss at different levels. However, the cognitive recovery observed in early-stage AD model mice may indicate a compensatory structural reorganization, highlighting the potential of early intervention to mitigate disease progression. Our results provide novel insights into the neurotoxic effects of Aβ1-42 and may contribute to the development of therapeutic strategies for AD.},
}

@article {pmid40245876,
year = {2025},
author = {Zhao, D and Dong, G and Pei, W and Gao, X and Wang, Y},
title = {Comparisons of stimulus paradigms for SSVEP-based brain-computer interfaces.},
journal = {Journal of neural engineering},
volume = {22},
number = {2},
pages = {},
doi = {10.1088/1741-2552/adce32},
pmid = {40245876},
issn = {1741-2552},
abstract = {Objective.With the recent development of visual evoked potential (VEP) based brain-computer interfaces (BCIs), the stimulus paradigm has been continuously innovated, in which the pursuit of higher BCI performance and better user experience has become indispensable.Approach.To optimize the stimulus paradigm, a 12-target online BCI system was designed in this study by adopting flicker for steady-state VEPs, Newton's ring for steady-state motion VEP, and frame rate based video stimulus, respectively. The signal characteristics of VEP, classification accuracy, and user experience of the three stimulus paradigms were quantitatively evaluated and compared.Main results.The online information transfer rates for the three stimulus paradigms were 53.77 bits min[-1], 51.41 ± 3.55 bits min[-1], and 52.07 ± 3.09 bits min[-1], respectively. The video stimulus had a significantly better user experience, while the flicker stimulus showed the worst.Significance.These results demonstrate the advantage of the proposed video stimulus paradigm and have significant theoretical and applied implications for developing VEP-based BCI systems.},
}

@article {pmid40245253,
year = {2025},
author = {Singh, K and Lin, CC and Huang, WH and Lei, WL and Chiueh, H and Wang, YH and Chang, PH and Lin, RZ and Huang, WC},
title = {Ultrabioconformal, Self-Healable, and Antioxidized Polydopamine-Inspired Nanowire Hydrogels Enable Resolving Power in Forehead and Ear Electroencephalograms for Brain Function Assessment.},
journal = {ACS applied materials & interfaces},
volume = {},
number = {},
pages = {},
doi = {10.1021/acsami.4c23013},
pmid = {40245253},
issn = {1944-8252},
abstract = {Continuous brain function monitoring by high-performance electroencephalogram (EEG) suggests a high impact for advancing precision personalized medication of neurodevelopmental or neurodegenerative disorders. Forehead and ear EEGs are nonhairy recording strategies that allow the recording of brain activity using only a few electrodes. However, they require well-designed electrodes that are easy and comfortable to carry while simultaneously performing durable high-quality EEG acquisition. Herein, we propose a new ultrabiocompliant EEG sensor that enables seamless contact to surfaces of both earhole and forehead, while permitting prolonged and high-quality EEG signal identification. Bioinspired polydopamine/platinum-silver nanowires, called PDA-Ag@Pt NWs, are synthesized with noticeable performances in electrical conductivity, antioxidation ability, cytocompatibility, and adhesion. PDA-Ag@Pt NWs can promote synchronic gelation and interlinks within polydopamine-polyacrylamide (PDA-PAM) hydrogels, in turn leading to the one-step formation of a nanowire/hydrogel matrix, called PDA-PAM/NW, as an electrode patch in the presence of adhesive and self-healing capabilities. Combined with a self-designed signal processor, a portable electrophysiological signal recording system was realized. The PDA-PAM/NW electrode patch outperformed commercial electrodes in terms of reliability and resolution for electrocardiography (ECG), electromyography (EMG), and electroencephalography (EEG) recording. In addition, through brain cognitive assessment by frontal- and ear-EEG recording, the ultrathin design and comfortable adhesion of PDA-PAM/NW electrodes make participants comfortable over time, subsequently providing the identification of the brain activity in high resolution. This work underscores the potential of the ultrabiocompliant and durable patch in the development of comfy, long-lasting, and high-performance wearable brain-machine interfaces for the revolution in neuroscience.},
}

@article {pmid40245060,
year = {2025},
author = {Akhter, J and Nazeer, H and Naseer, N and Naeem, R and Kallu, KD and Lee, J and Ko, SY},
title = {Improved performance of fNIRS-BCI by stacking of deep learning-derived frequency domain features.},
journal = {PloS one},
volume = {20},
number = {4},
pages = {e0314447},
pmid = {40245060},
issn = {1932-6203},
mesh = {Humans ; Spectroscopy, Near-Infrared/methods ; *Deep Learning ; *Brain-Computer Interfaces ; Male ; Adult ; Algorithms ; Female ; Neural Networks, Computer ; Young Adult ; Hand Strength/physiology ; },
abstract = {The functional near-infrared spectroscopy-based brain-computer interface (fNIRS-BCI) systems recognize patterns in brain signals and generate control commands, thereby enabling individuals with motor disabilities to regain autonomy. In this study hand gripping data is acquired using fNIRS neuroimaging system, preprocessing is performed using nirsLAB and features extraction is performed using deep learning (DL) Algorithms. For feature extraction and classification stack and fft methods are proposed. Convolutional neural networks (CNN), long short-term memory (LSTM), and bidirectional long-short-term memory (Bi-LSTM) are employed to extract features. The stack method classifies these features using a stack model and the fft method enhances features by applying fast Fourier transformation which is followed by classification using a stack model. The proposed methods are applied to fNIRS signals from twenty participants engaged in a two-class hand-gripping motor activity. The classification performance of the proposed methods is compared with conventional CNN, LSTM, and Bi-LSTM algorithms and one another. The proposed fft and stack methods yield 90.11% and 87.00% classification accuracies respectively, which are significantly higher than those achieved by CNN (85.16%), LSTM (79.46%), and Bi-LSTM (81.88%) conventional algorithms. The results show that the proposed stack and fft methods can be effectively used for the classification of the two and three-class problems in fNIRS-BCI applications.},
}

Humans
Spectroscopy, Near-Infrared/methods
*Deep Learning
*Brain-Computer Interfaces
Male
Adult
Algorithms
Female
Neural Networks, Computer
Young Adult
Hand Strength/physiology

@article {pmid40244939,
year = {2025},
author = {Noel, JP and Bockbrader, M and Bertoni, T and Colachis, S and Solca, M and Orepic, P and Ganzer, PD and Haggard, P and Rezai, A and Blanke, O and Serino, A},
title = {Neuronal responses in the human primary motor cortex coincide with the subjective onset of movement intention in brain-machine interface-mediated actions.},
journal = {PLoS biology},
volume = {23},
number = {4},
pages = {e3003118},
pmid = {40244939},
issn = {1545-7885},
support = {K99 NS128075/NS/NINDS NIH HHS/United States ; R00 NS128075/NS/NINDS NIH HHS/United States ; },
mesh = {Humans ; *Brain-Computer Interfaces ; *Motor Cortex/physiology ; *Intention ; Movement/physiology ; Male ; Adult ; Female ; *Neurons/physiology ; Electric Stimulation ; Quadriplegia/physiopathology ; },
abstract = {Self-initiated behavior is accompanied by the experience of intending our actions. Here, we leverage the unique opportunity to examine the full intentional chain-from intention to action to environmental effects-in a tetraplegic person outfitted with a primary motor cortex (M1) brain-machine interface (BMI) generating real hand movements via neuromuscular electrical stimulation (NMES). This combined BMI-NMES approach allowed us to selectively manipulate each element of the intentional chain (intention, action, effect) while probing subjective experience and performing extra-cellular recordings in human M1. Behaviorally, we reveal a novel form of intentional binding: motor intentions are reflected in a perceived temporal attraction between the onset of intentions and that of actions. Neurally, we demonstrate that evoked spiking activity in M1 largely coincides in time with the onset of the experience of intention and that M1 spike counts and the onset of subjective intention may co-vary on a trial-by-trial basis. Further, population-level dynamics, as indexed by a decoder instantiating movement, reflect intention-action temporal binding. The results fill a significant knowledge gap by relating human spiking activity in M1 with the onset of subjective intention and complement prior human intracranial work examining pre-motor and parietal areas.},
}

Humans
*Brain-Computer Interfaces
*Motor Cortex/physiology
*Intention
Movement/physiology
Male
Adult
Female
*Neurons/physiology
Electric Stimulation
Quadriplegia/physiopathology

@article {pmid40242802,
year = {2025},
author = {Iosif, R and Skrbinšek, T and Erős, N and Konec, M and Boljte, B and Jan, M and Promberger-Fürpass, B},
title = {Wolf Population Size and Composition in One of Europe's Strongholds, the Romanian Carpathians.},
journal = {Ecology and evolution},
volume = {15},
number = {4},
pages = {e71200},
pmid = {40242802},
issn = {2045-7758},
abstract = {Strategies of coexistence with large carnivores should integrate scientific evidence, population monitoring providing an opportunity for advancing outdated management paradigms. We estimated wolf population density and social dynamics across a 1400 km[2] area in a data-poor region of the Romanian Carpathians. Across three consecutive years (2017-2018 until 2019-2020), we collected and genotyped 505 noninvasive DNA wolf samples (scat, hair and urine) to identify individuals, reconstruct pedigrees, and check for the presence of hybridization with domestic dogs. We identified 27 males, 20 females, and one F1 wolf-dog hybrid male. We delineated six wolf packs, with pack size varying between two and seven individuals, and documented yearly changes in pack composition. Using a spatial capture-recapture approach, we estimated population density at 2.35 wolves/100 km[2] (95% BCI = 1.68-3.03) and population abundance at 70 individuals (95% BCI = 49-89). Noninvasive DNA data collection coupled with spatial capture-recapture has the potential to inform on wolf population size and dynamics at broader spatial scales, across different sampling areas representative of the diverse Carpathian landscapes, and across different levels of human impact, supporting wildlife decision making in one of Europe's main strongholds for large carnivores.},
}

@article {pmid40242584,
year = {2025},
author = {Hu, S and Lin, C and Wang, H and Wang, X},
title = {Psychedelics and Eating Disorders: Exploring the Therapeutic Potential for Anorexia Nervosa and Beyond.},
journal = {ACS pharmacology & translational science},
volume = {8},
number = {4},
pages = {910-916},
pmid = {40242584},
issn = {2575-9108},
abstract = {Anorexia nervosa (AN) is a severe psychiatric disorder characterized by extreme food restriction, an intense fear of weight gain, and a distorted body image, leading to significant morbidity and mortality. Conventional treatments such as cognitive-behavioral therapy (CBT) and pharmacotherapy often prove inadequate, especially in severe cases, highlighting the need for novel therapeutic approaches. Recent research into psychedelics, such as psilocybin and 3,4-methylenedioxymethamphetamine (MDMA), offers promising avenues for treating anorexia nervosa by targeting its neurobiological and psychological underpinnings. These psychedelics disrupt maladaptive neural circuits, enhance cognitive flexibility, and facilitate emotional processing, offering potential relief for patients unresponsive to traditional therapies. Early studies have shown positive outcomes with psychedelics, including reductions in anorexia nervosa symptoms and improvements in psychological well-being. However, further research is needed to establish their long-term safety, efficacy, and integration into clinical practice. Addressing the legal, ethical, and safety challenges will be crucial in determining whether psychedelics can transform the treatment landscape for anorexia nervosa and other eating disorders.},
}

@article {pmid40242456,
year = {2025},
author = {Yan, W and Luo, Q and Du, C},
title = {Channel component correlation analysis for multi-channel EEG feature component extraction.},
journal = {Frontiers in neuroscience},
volume = {19},
number = {},
pages = {1522964},
pmid = {40242456},
issn = {1662-4548},
abstract = {INTRODUCTION: Electroencephalogram (EEG) analysis has shown significant research value for brain disease diagnosis, neuromodulation and brain-computer interface (BCI) application. The analysis and processing of EEG signals is complex since EEG are nonstationary, nonlinear, and often contaminated by intense background noise. Principal component analysis (PCA) and independent component analysis (ICA), as the commonly used methods for multi-dimensional signal feature component extraction, still have some limitations in terms of performance and calculation.

METHODS: In this study, channel component correlation analysis (CCCA) method was proposed to extract feature components of multi-channel EEG. Firstly, empirical wavelet transform (EWT) decomposed each channel signal into different frequency bands, and reconstructed them into a multi-dimensional signal. Then the objective optimization function was constructed by maximizing the covariance between multi-dimensional signals. Finally the feature components of multi-channel EEG were extracted using the calculated weight coefficient.

RESULTS: The results showed that the CCCA method could find the most relevant frequency band between multi-channel EEG. Compared with PCA and ICA methods, CCCA could extract the common components of multi-channel EEG more effectively, which is of great significance for the accurate analysis of EEG.

DISCUSSION: The CCCA method proposed in this study has shown excellent performance in the feature component extraction of multi-channel EEG and could be considered for practical engineering applications.},
}

@article {pmid40241786,
year = {2025},
author = {Hernández-Gloria, JJ and Jaramillo-Gonzalez, A and Savić, AM and Mrachacz-Kersting, N},
title = {Toward brain-computer interface speller with movement-related cortical potentials as control signals.},
journal = {Frontiers in human neuroscience},
volume = {19},
number = {},
pages = {1539081},
pmid = {40241786},
issn = {1662-5161},
abstract = {Brain Computer Interface spellers offer a promising alternative for individuals with Amyotrophic Lateral Sclerosis (ALS) by facilitating communication without relying on muscle activity. This study assessed the feasibility of using movement related cortical potentials (MRCPs) as a control signal for a Brain-Computer Interface speller in an offline setting. Unlike motor imagery-based BCIs, this study focused on executed movements. Fifteen healthy subjects performed three spelling tasks that involved choosing specific letters displayed on a computer screen by performing a ballistic dorsiflexion of the dominant foot. Electroencephalographic signals were recorded from 10 sites centered around Cz. Three conditions were tested to evaluate MRCP performance under varying task demands: a control condition using repeated selections of the letter "O" to isolate movement-related brain activity; a phrase spelling condition with structured text ("HELLO IM FINE") to simulate a meaningful spelling task with moderate cognitive load; and a random condition using a randomized sequence of letters to introduce higher task complexity by removing linguistic or semantic context. The success rate, defined as the presence of an MRCP, was manually determined. It was approximately 69% for both the control and phrase conditions, with a slight decrease in the random condition, likely due to increased task complexity. Significant differences in MRCP features were observed between conditions with Laplacian filtering, whereas no significant differences were found in single-site Cz recordings. These results contribute to the development of MRCP-based BCI spellers by demonstrating their feasibility in a spelling task. However, further research is required to implement and validate real-time applications.},
}

@article {pmid40241515,
year = {2025},
author = {Jin, F and Li, M and Yang, L and Yang, L and Shang, Z},
title = {Exploring the value learning in pigeons: The role of dual pathways in the basal ganglia and synaptic plasticity.},
journal = {The Journal of experimental biology},
volume = {},
number = {},
pages = {},
doi = {10.1242/jeb.249507},
pmid = {40241515},
issn = {1477-9145},
support = {62301496//National Natural Science Foundation of China/ ; GZC20232447//National Postdoctoral Researcher Program/ ; 252102210008//Key Scientific and Technological Projects of Henan Province/ ; },
abstract = {Understanding value learning in animals is a key focus in cognitive neuroscience. Current models used in research are often simple, and while more complex models have been proposed, it remains unclear which assumptions align with actual value learning strategies of animals. This study investigated the computational mechanisms behind value learning in pigeons using a free-choice task. Three models were constructed based on different assumptions about the role of the basal ganglia's dual pathways and synaptic plasticity in value computation, followed by model comparison and neural correlation analysis. Among the three models tested, the dual-pathway reinforcement learning model with Hebbian rules most closely matched the pigeons' behavior. Furthermore, the striatal gamma band connectivity showed the highest correlation with the values estimated by this model. Additionally, enhanced beta band connectivity in the nidopallium caudolaterale supported value learning. This study provides valuable insights into reinforcement learning mechanisms in non-human animals.},
}

@article {pmid40240152,
year = {2025},
author = {Ma, YN and Karako, K and Song, P and Hu, X and Xia, Y},
title = {Integrative neurorehabilitation using brain-computer interface: From motor function to mental health after stroke.},
journal = {Bioscience trends},
volume = {},
number = {},
pages = {},
doi = {10.5582/bst.2025.01109},
pmid = {40240152},
issn = {1881-7823},
abstract = {Stroke remains a leading cause of mortality and long-term disability worldwide, frequently resulting in impairments in motor control, cognition, and emotional regulation. Conventional rehabilitation approaches, while partially effective, often lack individualization and yield suboptimal outcomes. In recent years, brain-computer interface (BCI) technology has emerged as a promising neurorehabilitation tool by decoding neural signals and providing real-time feedback to enhance neuroplasticity. This review systematically explores the use of BCI systems in post-stroke rehabilitation, focusing on three core domains: motor function, cognitive capacity, and emotional regulation. This review outlines the neurophysiological principles underpinning BCI-based motor rehabilitation, including neurofeedback training, Hebbian plasticity, and multimodal feedback strategies. It then examines recent advances in upper limb and gait recovery using BCI integrated with functional electrical stimulation (FES), robotics, and virtual reality (VR). Moreover, it highlights BCI's potential in cognitive and language rehabilitation through EEG-based neurofeedback and the integration of artificial intelligence (AI) and immersive VR environments. In addition, it discusses the role of BCI in monitoring and regulating post-stroke emotional disorders via closed-loop systems. While promising, BCI technologies face challenges related to signal accuracy, device portability, and clinical validation. Future research should prioritize multimodal integration, AI-driven personalization, and large-scale randomized trials to establish long-term efficacy. This review underscores BCI's transformative potential in delivering intelligent, personalized, and cross-domain rehabilitation solutions for stroke survivors.},
}

@article {pmid40239679,
year = {2025},
author = {Faes, A and Calvo Merino, E and Branco, MP and Van Hoylandt, A and Keirse, E and Theys, T and Ramsey, NF and Van Hulle, MM},
title = {Decoding sign language finger flexions from high-density electrocorticography using graph-optimized block term tensor regression.},
journal = {Journal of neural engineering},
volume = {22},
number = {2},
pages = {},
doi = {10.1088/1741-2552/adcd9e},
pmid = {40239679},
issn = {1741-2552},
mesh = {Humans ; *Electrocorticography/methods ; *Sign Language ; *Fingers/physiology ; Male ; Gestures ; Female ; Adult ; Regression Analysis ; Middle Aged ; Movement/physiology ; },
abstract = {Objective.A novel method is introduced to regress over the sign language finger movements from human electrocorticography (ECoG) recordings.Approach.The proposed graph-optimized block-term tensor regression (Go-BTTR) method consists of two components: a deflation-based regression model that sequentially Tucker-decomposes multiway ECoG data into a series of blocks, and a causal graph process (CGP) that accounts for the complex relationship between finger movements when expressing sign language gestures. Prior to each regression block, CGP is applied to decide which fingers should be kept separate or grouped and should therefore be referred to BTTR or its extended version eBTTR, respectively.Main results.Two ECoG datasets were used, one recorded in five patients expressing four hand gestures of the American sign language alphabet, and another in two patients expressing all gestures of the Flemish sign language alphabet. As Go-BTTR combines fingers in a flexible way, it can better account for the nonlinear relationship ECoG exhibits when expressing hand gestures, including unintentional finger co-activations. This is reflected by the superior joint finger trajectory predictions compared to eBTTR, and predictions that are on par with BTTR in single-finger scenarios. For the American sign language alphabet (Utrecht dataset), the average correlation across all fingers for all subjects was 0.73 for Go-BTTR, 0.719 for eBTTR and 0.70 for BTTR. For the Flemish sign language alphabet (Leuven dataset), the average correlation across all fingers for all subjects was 0.37 for Go-BTTR, 0.34 for eBTTR and 0.33 for BTTR.Significance.Our findings show that Go-BTTR is capable of decoding complex hand gestures taken from the sign language alphabet. Go-BTTR also demonstrates computational efficiency, providing a notable benefit when intracranial electrodes are inserted during a patient's pre-surgical evaluation. This efficiency helps reduce the time required for developing and testing a brain-computer interface solution.},
}

Humans
*Electrocorticography/methods
*Sign Language
*Fingers/physiology
Male
Gestures
Female
Adult
Regression Analysis
Middle Aged
Movement/physiology

@article {pmid40238867,
year = {2025},
author = {Li, X and Deng, Z and Zhang, W and Zhou, W and Liu, X and Quan, H and Li, J and Li, P and Li, Y and Hu, C and Li, F and Niu, L and Tian, Z and Meng, L and Zheng, H},
title = {Oscillating microbubble array-based metamaterials (OMAMs) for rapid isolation of high-purity exosomes.},
journal = {Science advances},
volume = {11},
number = {16},
pages = {eadu8915},
pmid = {40238867},
issn = {2375-2548},
mesh = {*Microbubbles ; *Exosomes/metabolism/chemistry ; Humans ; Acoustics ; },
abstract = {Exosomes secreted by cells hold substantial potential for disease diagnosis and treatment. However, the rapid isolation of high-purity exosomes and their subpopulations from biofluids (e.g., undiluted whole blood) remains challenging. This study presents oscillating microbubble array-based metamaterials (OMAMs) for enabling the rapid isolation of high-purity exosomes and their subpopulations from biofluids without labeling or preprocessing. Particularly, leveraging acoustically excited microbubble oscillation, OMAMs can generate numerous acoustofluidic traps for filtering in-fluid micro/nanoparticles, thus allowing for removing bioparticles larger than exosomes to obtain high-purity (93%) exosomes from undiluted whole blood in ~3 minutes. Moreover, exosome subpopulations in different size ranges can be isolated by tuning the microbubble oscillation amplitude. Additionally, as each oscillating microbubble functions as an ultradeep subwavelength (~λ/186) acoustic amplifier and a nonlinear source, OMAMs can generate high-resolution complex acoustic energy patterns and tune the patterns by activating different-sized microbubbles at their distinct resonance frequencies.},
}

*Microbubbles
*Exosomes/metabolism/chemistry
Humans
Acoustics

@article {pmid40236895,
year = {2025},
author = {Wen, D and Xing, Y and Yao, Y and Liang, G and Xing, Y and Jung, TP and Yu, H and Xie, X and Wan, X and Liu, T and Duan, D and Li, D and Zhou, Y},
title = {Transforming long-term adjunctive therapy for cognitive impairment: the role of multimodal self-adaptive digital medicine.},
journal = {Frontiers in neurology},
volume = {16},
number = {},
pages = {1571817},
pmid = {40236895},
issn = {1664-2295},
}

@article {pmid40236742,
year = {2025},
author = {Wei, B and Cheng, S and Feng, Y},
title = {Neural personal information and its legal protection: evidence from China.},
journal = {Journal of law and the biosciences},
volume = {12},
number = {1},
pages = {lsaf006},
doi = {10.1093/jlb/lsaf006},
pmid = {40236742},
issn = {2053-9711},
abstract = {The rapid advancements in neuroscience highlight the pressing need to safeguard neural personal information (NPI). China has achieved significant progress in brain-computer interface technology and its clinical applications. Considering the intrinsic vulnerability of NPI and the paucity of legal scrutiny concerning NPI breaches, a thorough assessment of the adequacy of China's personal information protection legislation is essential. This analysis contends that NPI should be classified as sensitive personal information. The absence of bespoke provisions for NPI in current legislation underscores persistent challenges in its protection. To address these gaps, this work proposes the establishment of a concentric-circle hard-soft law continuum to support a hybrid governance model for NPI, rooted in fundamental human rights principles.},
}

@article {pmid40236412,
year = {2025},
author = {Jude, JJ and Levi-Aharoni, H and Acosta, AJ and Allcroft, SB and Nicolas, C and Lacayo, BE and Card, NS and Wairagkar, M and Brandman, DM and Stavisky, SD and Willett, FR and Williams, ZM and Simeral, JD and Hochberg, LR and Rubin, DB},
title = {An intuitive, bimanual, high-throughput QWERTY touch typing neuroprosthesis for people with tetraplegia.},
journal = {medRxiv : the preprint server for health sciences},
volume = {},
number = {},
pages = {},
doi = {10.1101/2025.04.01.25324990},
pmid = {40236412},
abstract = {Recognizing keyboard typing as a familiar, high information rate communication paradigm, we developed an intracortical brain computer interface (iBCI) typing neuroprosthesis providing bimanual QWERTY keyboard functionality for people with paralysis. Typing with this iBCI involves only attempted finger movements, which are decoded accurately with as few as 30 calibration sentences. Sentence decoding is improved using a 5-gram language model. This typing neuroprosthesis performed well for two iBCI clinical trial participants with tetraplegia - one with ALS and one with spinal cord injury. Typing speed is user-regulated, reaching 110 characters per minute, resulting in 22 words per minute with a word error rate of 1.6%. This resembles able-bodied typing accuracy and provides higher throughput than current state-of-the-art hand motor iBCI decoding. In summary, a typing neuroprosthesis decoding finger movements, provides an intuitive, familiar, and easy-to-learn paradigm for individuals with impaired communication due to paralysis.},
}

@article {pmid40236074,
year = {2025},
author = {Busch, EL and Fincke, EC and Lajoie, G and Krishnaswamy, S and Turk-Browne, NB},
title = {Accelerated learning of a noninvasive human brain-computer interface via manifold geometry.},
journal = {bioRxiv : the preprint server for biology},
volume = {},
number = {},
pages = {},
doi = {10.1101/2025.03.29.646109},
pmid = {40236074},
issn = {2692-8205},
abstract = {Brain-computer interfaces (BCIs) promise to restore and enhance a wide range of human capabilities. However, a barrier to the adoption of BCIs is how long it can take users to learn to control them. We hypothesized that human BCI learning could be accelerated by leveraging the naturally occurring geometric structure of brain activity, or its intrinsic manifold, extracted using a data-diffusion process. We trained participants on a noninvasive BCI that allowed them to gain real-time control of an avatar in a virtual reality game by modulating functional magnetic resonance imaging (fMRI) activity in brain regions that support spatial navigation. We then perturbed the mapping between fMRI activity patterns and the movement of the avatar to test our manifold hypothesis. When the new mapping respected the intrinsic manifold, participants succeeded in regaining control of the BCI by aligning their brain activity within the manifold. When the new mapping did not respect the intrinsic manifold, participants could not learn to control the avatar again. These findings show that the manifold geometry of brain activity constrains human learning of a complex cognitive task in higher-order brain regions. Manifold geometry may be a critical ingredient for unlocking the potential of future human neurotechnologies.},
}

@article {pmid40235786,
year = {2025},
author = {Wang, LP and Yang, C and Fu, JY and Zhang, XY and Shen, XM and Shi, NL and Wu, HL and Gao, XR},
title = {A preliminary study of steady-state visually-evoked potential-based non-invasive brain-computer interface technology as a communication aid for patients with amyotrophic lateral sclerosis.},
journal = {Quantitative imaging in medicine and surgery},
volume = {15},
number = {4},
pages = {3469-3479},
pmid = {40235786},
issn = {2223-4292},
abstract = {BACKGROUND: Amyotrophic lateral sclerosis (ALS) is a progressive neurodegenerative disease that affects motor neurons, leading to severe disability and ultimately death. Communication difficulties are common in ALS patients as the disease progresses; thus, alternative communication aids need to be explored. This study sought to examine the use and effect of steady-state visually-evoked potential (SSVEP)-based non-invasive brain-computer interface (BCI) technology as a communication aid for patients with ALS and to examine possible influencing factors.

METHODS: In total, 12 patients with ALS were selected, and a 40-character target selection was performed using SSVEP-based non-invasive BCI technology. The patients were presented with specific visual stimuli, and nine-lead electroencephalogram (EEG) signals in the occipital region were acquired when the patients were looking at the target. Using the feature recognition analysis method, the final output was the characters recognized by the patients. The basic clinical data of the patients (e.g., age, gender, course of disease, affected area, and ALS functional scale score) were collected, and the BCI accuracy rate, information transmission rate, and average SSVEP recognition time were calculated.

RESULTS: The results revealed that the recognition efficiency of the ALS patients varied. The accuracy potential increased as the stimulus duration extended, highlighting the possibility for improvement via further optimization. The results also showed that the experimental design schedules typically used for healthy individuals may not be entirely suitable for ALS patients, which presents an exciting opportunity to tailor future studies to better meet the unique needs of ASL patients. Further, the results revealed the necessity of using customized experimental schedules in future studies, which could lead to more relevant and effective data collection for ALS patients.

CONCLUSIONS: The study found that SSVEP-based non-invasive BCI technology has promising potential as a communication aid for ALS patients. While further algorithm optimization and comprehensive studies with larger sample sizes are necessary, the initial findings are encouraging, and could lead to the development of more effective communication solutions that are specifically tailored to address the challenges faced by ALS patients.},
}

@article {pmid40234729,
year = {2025},
author = {Webster, P},
title = {Can AI-powered brain-computer interfaces boost human intelligence?.},
journal = {Nature medicine},
volume = {31},
number = {4},
pages = {1045-1047},
doi = {10.1038/s41591-025-03641-7},
pmid = {40234729},
issn = {1546-170X},
}

@article {pmid40234486,
year = {2025},
author = {Zhao, W and Zhang, B and Zhou, H and Wei, D and Huang, C and Lan, Q},
title = {Multi-scale convolutional transformer network for motor imagery brain-computer interface.},
journal = {Scientific reports},
volume = {15},
number = {1},
pages = {12935},
pmid = {40234486},
issn = {2045-2322},
support = {3502Z202374054//Natural Science Foundation of Xiamen, China/ ; 2023J01785//Natural Science Foundation of Fujian Province of China/ ; JAT191153 and JAT201045//Education and Scientific Research Project for Young and Middle-aged Teachers of Fujian Province of China/ ; CKZ24016//Jimei University Chengyi College Provincial and Ministerial-Level Scientific Research Cultivation Project/ ; CKZ24016//Jimei University Chengyi College Provincial and Ministerial-Level Scientific Research Cultivation Project/ ; },
mesh = {*Brain-Computer Interfaces ; Humans ; Electroencephalography/methods ; *Neural Networks, Computer ; *Imagination/physiology ; *Brain/physiology ; Signal Processing, Computer-Assisted ; },
abstract = {Brain-computer interface (BCI) systems allow users to communicate with external devices by translating neural signals into real-time commands. Convolutional neural networks (CNNs) have been effectively utilized for decoding motor imagery electroencephalography (MI-EEG) signals in BCIs. However, traditional CNN-based methods face challenges such as individual variability in EEG signals and the limited receptive fields of CNNs. This study presents the Multi-Scale Convolutional Transformer (MSCFormer) model that integrates multiple CNN branches for multi-scale feature extraction and a Transformer module to capture global dependencies, followed by a fully connected layer for classification. The multi-branch multi-scale CNN structure effectively addresses individual variability in EEG signals, enhancing the model's generalization capabilities, while the Transformer encoder strengthens global feature integration and improves decoding performance. Extensive experiments on the BCI IV-2a and IV-2b datasets show that MSCFormer achieves average accuracies of 82.95% (BCI IV-2a) and 88.00% (BCI IV-2b), with kappa values of 0.7726 and 0.7599 in five-fold cross-validation, surpassing several state-of-the-art methods. These results highlight MSCFormer's robustness and accuracy, underscoring its potential in EEG-based BCI applications. The code has been released in https://github.com/snailpt/MSCFormer .},
}

*Brain-Computer Interfaces
Humans
Electroencephalography/methods
*Neural Networks, Computer
*Imagination/physiology
*Brain/physiology
Signal Processing, Computer-Assisted

@article {pmid40234393,
year = {2025},
author = {Bertoni, T and Noel, JP and Bockbrader, M and Foglia, C and Colachis, S and Orset, B and Evans, N and Herbelin, B and Rezai, A and Panzeri, S and Becchio, C and Blanke, O and Serino, A},
title = {Pre-movement sensorimotor oscillations shape the sense of agency by gating cortical connectivity.},
journal = {Nature communications},
volume = {16},
number = {1},
pages = {3594},
pmid = {40234393},
issn = {2041-1723},
support = {163951//Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung (Swiss National Science Foundation)/ ; },
mesh = {Humans ; *Motor Cortex/physiology ; Male ; Adult ; Female ; Electroencephalography ; Young Adult ; Movement/physiology ; Brain-Computer Interfaces ; Alpha Rhythm/physiology ; Hand/physiology ; Sense of Agency ; },
abstract = {Our sense of agency, the subjective experience of controlling our actions, is a crucial component of self-awareness and motor control. It is thought to originate from the comparison between intentions and actions across broad cortical networks. However, the underlying neural mechanisms are still not fully understood. We hypothesized that oscillations in the theta-alpha range, thought to orchestrate long-range neural connectivity, may mediate sensorimotor comparisons. To test this, we manipulated the relation between intentions and actions in a tetraplegic user of a brain machine interface (BMI), decoding primary motor cortex (M1) activity to restore hand functionality. We found that the pre-movement phase of low-alpha oscillations in M1 predicted the participant's agency judgements. Further, using EEG-BMI in healthy participants, we found that pre-movement alpha oscillations in M1 and supplementary motor area (SMA) correlated with agency ratings, and with changes in their functional connectivity with parietal, temporal and prefrontal areas. These findings argue for phase-driven gating as a key mechanism for sensorimotor integration and sense of agency.},
}

Humans
*Motor Cortex/physiology
Male
Adult
Female
Electroencephalography
Young Adult
Movement/physiology
Brain-Computer Interfaces
Alpha Rhythm/physiology
Hand/physiology
Sense of Agency

@article {pmid40232894,
year = {2025},
author = {Wang, D and Wei, Q},
title = {SMANet: A Model Combining SincNet, Multi-branch Spatial-Temporal CNN and Attention Mechanism for Motor Imagery BCI.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {PP},
number = {},
pages = {},
doi = {10.1109/TNSRE.2025.3560993},
pmid = {40232894},
issn = {1558-0210},
abstract = {Building a brain-computer interface (BCI) based on motor imagery (MI) requires accurately decoding MI tasks, which poses a significant challenge due to individual discrepancy among subjects and low signal-to-noise ratio of EEG signals. We propose an end-to-end deep learning model, Sinc-multibranch-attention network (SMANet), which combines a SincNet, a multibranch spatial-temporal convolutional neural network (MBSTCNN), and an attention mechanism for MI-BCI classification. Firstly, Sinc convolution is utilized as a band-pass filter bank for data filtering; Second, pointwise convolution facilitates the effective integration of feature information across different frequency ranges, thereby enhancing the overall feature expression capability; Next, the resulting data are fed into the MBSTCNN to learn a deep feature representation. Thereafter, the outputs of the MBSTCNN are concatenated and then passed through an efficient channel attention (ECA) module to enhance local channel feature extraction and calibrate feature mapping. Ultimately, the feature maps yielded by ECA are classified using a fully connected layer. This model SMANet enhances discriminative features through a multi-objective optimization scheme that integrates cross-entropy loss and central loss. The experimental outcomes reveal that our model attains an average accuracy of 80.21% on the four-class MI dataset (BCI Competition IV 2a), 84.02% on the two-class MI dataset (BCI Competition IV 2b), and 72.70% on the two-class MI dataset (OpenBMI). These results are superior to those of the current state-of-the-art methods. The SMANet is capable to effectively decoding the spatial-spectral-temporal information of EEG signals, thereby enhancing the performance of MI-BCIs.},
}

@article {pmid40231563,
year = {2025},
author = {Park, K and Hong, J and Shin, H and Choi, J and Xu, D and Lee, J and Ryu, J and Kim, S and Jeong, H and Choe, J and Yang, S and Yang, S and Ahn, JH},
title = {2D Material-Based Injectable Sensor for Minimally-Invasive Cerebral Blood Flow Monitoring.},
journal = {Small (Weinheim an der Bergstrasse, Germany)},
volume = {},
number = {},
pages = {e2501744},
doi = {10.1002/smll.202501744},
pmid = {40231563},
issn = {1613-6829},
support = {20012355//Ministry of Trade, Industry and Energy/ ; },
abstract = {Monitoring cerebral blood flow is an important method for diagnosing and treating brain diseases. Thermal transport caused by blood flow provides valuable information for detecting abnormalities in blood flow. Here, a minimally invasive, injectable blood flow sensor is reported, consisting of a flexible, graphene-based thin film heater and MoS2-based temperature sensor array integrated on a mesh-structured polymer substrate. Upon injection through a small skull hole in the skull, the device unfolds and achieves conformal contact on the cortical surface, aligning with the target vessel. By measuring temperature variations in response to the heater activation, the injectable sensor continuously monitors blood flow changes in the underlying vessel. This approach offers a new potential for cerebral blood flow sensing via minimally invasive implantation.},
}

@article {pmid40230710,
year = {2025},
author = {Finney, JN and Levy, IR and Chandrasekaran, S and Collinger, JL and Boninger, ML and Gaunt, RA and Helm, ER and Fisher, LE},
title = {Techniques to mitigate lead migration for percutaneous trials of cervical spinal cord stimulation.},
journal = {Frontiers in surgery},
volume = {12},
number = {},
pages = {1458572},
pmid = {40230710},
issn = {2296-875X},
abstract = {INTRODUCTION: Epidural spinal cord stimulation (SCS) is a clinical neuromodulation technique that is commonly used to treat neuropathic pain, with patients typically undergoing a one-week percutaneous trial phase before permanent implantation. Traditional SCS involves stimulation of the thoracic spinal cord, but there has been substantial recent interest in cervical SCS to treat upper extremity pain, restore sensation from the missing hand after amputation, or restore motor function to paretic limbs in people with stroke and spinal cord injury. Because of the additional mobility of the neck, as compared to the trunk, lead migration can be a major challenge for cervical SCS, especially during the percutaneous trial phase. The objective of this study was to optimize the implantation procedure of cervical SCS leads to minimize lead migration and increase lead stability during SCS trials.

METHODS: In this study, four subjects underwent percutaneous placement of three SCS leads targeting the cervical spinal segments as part of a clinical trial aiming to restore sensation for people with upper-limb amputation. The leads were maintained for up to 29 days and weekly x-ray imaging was used to measure rostrocaudal and mediolateral lead migration based on bony landmarks.

RESULTS AND DISCUSSION: Lead migration was primarily confined to the rostrocaudal axis with the most migration occurring during the first week. Iterative improvements to the implantation procedure were implemented to increase lead stability across subjects. There was a decrease in lead migration for patients who had more rostral placement of the SCS leads. The average migration from the day of lead implant to lead removal was 29.7 mm for Subject 1 (lead placement ranging from T3-T4 to T1-T2), 41.9 mm for Subject 2 (T2-T3 to C7-T1), 1.9 mm for Subject 3 (T1-T2 to C7-T1), and 16.6 mm for Subject 4 (T1-T2 to C7-T1). We found that initial placement of spinal cord stimulator leads in the cervical epidural space as rostral as possible was critical to minimizing subsequent rostrocaudal lead migration.},
}

@article {pmid40228689,
year = {2025},
author = {Hesam-Shariati, N and Alexander, L and Stapleton, F and Newton-John, T and Lin, CT and Zahara, P and Chen, K and Restrepo, S and Skinner, IW and McAuley, JH and Moseley, GL and Jensen, MP and Gustin, SM},
title = {The Effect of an EEG Neurofeedback Intervention for Corneal Neuropathic Pain: A Single-Case Experimental Design with Multiple Baselines.},
journal = {The journal of pain},
volume = {},
number = {},
pages = {105394},
doi = {10.1016/j.jpain.2025.105394},
pmid = {40228689},
issn = {1528-8447},
abstract = {Corneal neuropathic pain is a complex condition, rarely responsive to current treatments. This trial investigated the potential effect of a novel home-based self-directed EEG neurofeedback intervention on corneal neuropathic pain using a multiple-baseline single-case experimental design. Four Participants completed a predetermined baseline of 7, 10, 14, and 17 days, randomly assigned to each participant, followed by 20 intervention sessions over four weeks. Two one-week follow-ups occurred immediately and five weeks post-intervention during which participants were encouraged to practice mental strategies. Daily pain severity and pain interference observations were the outcome measures, while anxiety, depression, or sleep problems were the generalisation measures. The results showed a medium effect on pain severity and interference across participants when comparing baseline to five-week post-intervention according to Tau-U effect sizes. At the individual level, both Tau-U and NAP effect sizes indicated significant reductions in pain severity and interference for three participants when comparing baseline to five-week post-intervention. However, the reductions indicated by the visual inspection were not considered clinically meaningful. This single-case experimental design study raises the possibility that the intervention may improve pain severity and interference for some individuals, variability in outcomes highlights the need for future research to better understand individual responses and optimize the intervention effect. REGISTRATION: Australian New Zealand Clinical Trial Registry ACTRN12623000173695 PERSPECTIVE: This trial demonstrates the potential of EEG neurofeedback to reduce pain severity and interference in individuals with corneal neuropathic pain. It also highlights user preferences for technology-based interventions, emphasizing ease of use, accessibility, and self-administration to enhance adherence, especially for individuals with limited mobility or restricted healthcare access.},
}

@article {pmid40228398,
year = {2025},
author = {Grigoryan, KA and Mueller, K and Wagner, M and Masri, D and Pine, KJ and Villringer, A and Sehm, B},
title = {Short-term BCI intervention enhances functional brain connectivity associated with motor performance in chronic stroke.},
journal = {NeuroImage. Clinical},
volume = {46},
number = {},
pages = {103772},
doi = {10.1016/j.nicl.2025.103772},
pmid = {40228398},
issn = {2213-1582},
abstract = {BACKGROUND: Evidence suggests that brain-computer interface (BCI)-based rehabilitation strategies show promise in overcoming the limited recovery potential in the chronic phase of stroke. However, the specific mechanisms driving motor function improvements are not fully understood.

OBJECTIVE: We aimed at elucidating the potential functional brain connectivity changes induced by BCI training in participants with chronic stroke.

METHODS: A longitudinal crossover design was employed with two groups of participants over the span of 4 weeks to allow for within-subject (n = 21) and cross-group comparisons. Group 1 (n = 11) underwent a 6-day motor imagery-based BCI training during the second week, whereas Group 2 (n = 10) received the same training during the third week. Before and after each week, both groups underwent resting state functional MRI scans (4 for Group 1 and 5 for Group 2) to establish a baseline and monitor the effects of BCI training.

RESULTS: Following BCI training, an increased functional connectivity was observed between the medial prefrontal cortex of the default mode network (DMN) and motor-related areas, including the premotor cortex, superior parietal cortex, SMA, and precuneus. Moreover, these changes were correlated with the increased motor function as confirmed with upper-extremity Fugl-Meyer assessment scores, measured before and after the training.

CONCLUSIONS: Our findings suggest that BCI training can enhance brain connectivity, underlying the observed improvements in motor function. They provide a basis for developing novel rehabilitation approaches using non-invasive brain stimulation for targeting functionally relevant brain regions, thereby augmenting BCI-induced neuroplasticity and enhancing motor recovery.},
}

@article {pmid40227907,
year = {2025},
author = {Zhong, Y and Wang, Y and Farina, D and Yao, L},
title = {A Closed-Loop Tactile Stimulation Training Protocol for Motor Imagery-Based BCI: Boosting BCI Performance for BCI-Deficiency Users.},
journal = {IEEE transactions on bio-medical engineering},
volume = {PP},
number = {},
pages = {},
doi = {10.1109/TBME.2025.3560713},
pmid = {40227907},
issn = {1558-2531},
abstract = {BACKGROUND: Brain-computer interfaces (BCIs) enable users to control and communicate with the external environment. However, a significant challenge in BCI research is the occurrence of "BCI-illiteracy" or "BCI-deficiency", where a notable percentage of users (estimated at 15 to 30%) are unable to achieve successful BCI control. For those users, they are struggling to generate stable and distinguishable brain activity patterns, which are essential for BCI control. Existing neurofeedback training protocols, often rely on the trial-and-error process, which is time-consuming and inefficient, particularly for these low-performing users.

METHODS: To address this issue, we propose a closed-loop tactile stimulation training protocol, in which tactile stimulation training is incorporated within the closed neurofeedback loop, providing users with explicit guidance on how to correctly perform MI tasks. When a subject performs an incorrect MI trial, tactile-assisted MI training is provided to guide the user toward the correct brain state, while no training is given during correct performance.

RESULTS: The results from our study demonstrated that the proposed training protocol significantly enhances BCI decoding performance, with an improvement of 16.9%. Moreover, the BCI-deficiency rate was reduced by 61.5%. Further analysis revealed that the training process also led to enhanced motor imagery-related cortical activation.

CONCLUSION: The proposed training protocol significantly improved BCI decoding performance, enabling previously BCI-deficient users to surpass the 70% control threshold.

SIGNIFICANCE: This study demonstrates the effectiveness of closed-loop tactile-assisted training in enhancing BCI accessibility and efficiency, paving the way for more inclusive neurofeedback-based BCI training strategies.},
}

@article {pmid40227903,
year = {2025},
author = {Mai, X and Meng, J and Ding, Y and Zhu, X and Guan, C},
title = {SRRNet: Unseen SSVEP Response Regression From Stimulus for Cross-Stimulus Transfer in SSVEP-BCIs.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {33},
number = {},
pages = {1460-1472},
doi = {10.1109/TNSRE.2025.3560434},
pmid = {40227903},
issn = {1558-0210},
mesh = {*Brain-Computer Interfaces ; Humans ; *Evoked Potentials, Visual/physiology ; Male ; Adult ; Electroencephalography/methods ; Female ; Algorithms ; Young Adult ; Photic Stimulation ; Regression Analysis ; Calibration ; Neural Networks, Computer ; Signal Processing, Computer-Assisted ; },
abstract = {The prolonged calibration time required by steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCIs) poses a significant challenge to real-life applications. Cross-stimulus transfer emerges as a promising solution, wherein a model trained on a subset of classes (seen classes) can predict both seen and unseen classes. Existing approaches extracted common components from SSVEP templates of seen classes to construct templates for unseen classes; however, they are limited by the class-specific activities and noise contained in these components, leading to imprecise templates that degrade classification performance. To address this issue, this study proposed an SSVEP Response Regression Network (SRRNet), which learned the regression mapping between sine-cosine reference signals and SSVEP templates using seen class data. This network reconstructed SSVEP templates for unseen classes utilizing their corresponding sine-cosine signals. Additionally, an SSVEP template regressing and spatial filtering (SRSF) framework was introduced, where both test data and SSVEP templates were projected by task-related component analysis (TRCA) spatial filters, and correlations were computed for target prediction. Comparative evaluations on two public datasets revealed that our method significantly outperformed state-of-the-art methods, elevating the information transfer rate (ITR) from 173.33 bits/min to 203.79 bits/min. By effectively modeling the regression from sine-cosine reference signals to SSVEP templates, SRRNet can construct SSVEP templates for unseen classes without training samples from those classes. By integrating regressed SSVEP templates with spatial filtering-based methods, our method enhances cross-stimulus transfer performance in SSVEP-BCIs, thus advancing their practical applicability. The code is available at https://github.com/MaiXiming/SRRNet.},
}

*Brain-Computer Interfaces
Humans
*Evoked Potentials, Visual/physiology
Male
Adult
Electroencephalography/methods
Female
Algorithms
Young Adult
Photic Stimulation
Regression Analysis
Calibration
Neural Networks, Computer
Signal Processing, Computer-Assisted

@article {pmid40227525,
year = {2025},
author = {Yan, L and Liu, Z and Wang, J and Yu, L},
title = {Integrating Hard Silicon for High-Performance Soft Electronics via Geometry Engineering.},
journal = {Nano-micro letters},
volume = {17},
number = {1},
pages = {218},
pmid = {40227525},
issn = {2150-5551},
abstract = {Soft electronics, which are designed to function under mechanical deformation (such as bending, stretching, and folding), have become essential in applications like wearable electronics, artificial skin, and brain-machine interfaces. Crystalline silicon is one of the most mature and reliable materials for high-performance electronics; however, its intrinsic brittleness and rigidity pose challenges for integrating it into soft electronics. Recent research has focused on overcoming these limitations by utilizing structural design techniques to impart flexibility and stretchability to Si-based materials, such as transforming them into thin nanomembranes or nanowires. This review summarizes key strategies in geometry engineering for integrating crystalline silicon into soft electronics, from the use of hard silicon islands to creating out-of-plane foldable silicon nanofilms on flexible substrates, and ultimately to shaping silicon nanowires using vapor-liquid-solid or in-plane solid-liquid-solid techniques. We explore the latest developments in Si-based soft electronic devices, with applications in sensors, nanoprobes, robotics, and brain-machine interfaces. Finally, the paper discusses the current challenges in the field and outlines future research directions to enable the widespread adoption of silicon-based flexible electronics.},
}

@article {pmid40226198,
year = {2025},
author = {Nirabi, A and Rahman, FA and Habaebi, MH and Sidek, KA and Yusoff, S},
title = {Cognitive load assessment through EEG: A dataset from arithmetic and Stroop tasks.},
journal = {Data in brief},
volume = {60},
number = {},
pages = {111477},
pmid = {40226198},
issn = {2352-3409},
abstract = {This study introduces a thoughtfully curated dataset comprising electroencephalogram (EEG) recordings designed to unravel mental stress patterns through the perspective of cognitive load. The dataset incorporates EEG signals obtained from 15 subjects, with a gender distribution of 8 females and 7 males, and a mean age of 21.5 years [1]. Recordings were collected during the subjects' engagement in diverse tasks, including the Stroop color-word test and arithmetic problem-solving tasks. The recordings are categorized into four classes representing varying levels of induced mental stress: normal, low, mid, and high. Each task was performed for a duration of 10-20 s, and three trials were conducted for comprehensive data collection. Employing an OpenBCI device with an 8-channel Cyton board, the EEG captures intricate responses of the frontal lobe to cognitive challenges posed by the Stroop and Arithmetic Tests, recorded at a sampling rate of 250 Hz. The proposed dataset serves as a valuable resource for advancing research in the realm of brain-computer interfaces and offers insights into identifying EEG patterns associated with stress. The proposed dataset serves as a valuable resource for researchers, offering insights into identifying EEG patterns that correlate with different stress states. By providing a solid foundation for the development of algorithms capable of detecting and classifying stress levels, the dataset supports innovations in non-invasive monitoring tools and contributes to personalized healthcare solutions that can adapt to the cognitive states of users. This study's foundation is crucial for advancing stress classification research, with significant implications for cognitive function and well-being.},
}

@article {pmid40225841,
year = {2025},
author = {Kashou, N},
title = {Editorial: New horizons in stroke management.},
journal = {Frontiers in human neuroscience},
volume = {19},
number = {},
pages = {1587791},
doi = {10.3389/fnhum.2025.1587791},
pmid = {40225841},
issn = {1662-5161},
}

@article {pmid40225573,
year = {2025},
author = {Bai, R and Jia, Y and Wang, B and Wang, Z and Han, G and Liang, L and Chen, L and Ming, Y and Zhu, G and Hsu, YC and Zhao, P and Zhang, Y and Liu, Z and Liu, C and Li, Z and Liu, Y},
title = {In vivo spatiotemporal mapping of proliferation activity in gliomas via water-exchange dynamic contrast-enhanced MRI.},
journal = {Theranostics},
volume = {15},
number = {10},
pages = {4693-4707},
pmid = {40225573},
issn = {1838-7640},
mesh = {*Glioma/diagnostic imaging/pathology ; *Magnetic Resonance Imaging/methods ; Animals ; Humans ; *Cell Proliferation ; Temozolomide/pharmacology ; Cell Line, Tumor ; *Contrast Media ; Aquaporin 4/metabolism ; *Brain Neoplasms/diagnostic imaging/pathology ; *Water/metabolism ; Mice ; },
abstract = {Proliferation activity mapping is crucial for the guidance of first biopsy and treatment evaluation of gliomas due to the highly heterogenous nature of glioma tumor. Here we propose and demonstrate an ease-of-use way of in vivo spatiotemporal mapping of proliferation activity by simply tracking transmembrane water dynamics with magnetic resonance imaging (MRI). Specifically, we demonstrated that proliferation activity can accelerate the transmembrane water transport in glioma cells. Method: The transmembrane water-efflux rate (k io) measured by water-exchange dynamic contrast-enhanced (DCE) MRI. Immunofluorescence, immunohistochemistry, and immunocytochemistry staining were used to validate results obtained from the in vivo imaging studies. Results: In glioma cell cultures, k io precisely followed the dynamic changes of proliferation activity in growth cycles and response to temozolomide (TMZ) treatment. In both animal glioma model and human glioma, k io linearly and strongly correlated with the spatial heterogeneity of intra-tumoral proliferation activity. More importantly, proliferation activity predicted by the single MRI parameter k io is much more accurate than those predicted by state-of-the-art methods using multimodal standard MRIs and advanced machine learning. Upregulated aquaporin 4 (AQP4) expression were observed in most proliferating glioma cells and the knockout of AQP4 could largely slow down proliferation activity, suggesting AQP4 is the potential molecule connecting MRI-k io with proliferation activity. Conclusion: This study provides an ease-of-use, accurate, and non-invasive imaging method for the spatiotemporal monitoring of proliferation activity in glioma.},
}

*Glioma/diagnostic imaging/pathology
*Magnetic Resonance Imaging/methods
Animals
Humans
*Cell Proliferation
Temozolomide/pharmacology
Cell Line, Tumor
*Contrast Media
Aquaporin 4/metabolism
*Brain Neoplasms/diagnostic imaging/pathology
*Water/metabolism
Mice

@article {pmid40223771,
year = {2025},
author = {Kapur, A and Van Til, M and Daignault-Newton, S and Seibel, C and Nagpal, S and Ippolito, GM and Smith, AL and Lucioni, A and Lee, U and Suskind, A and Anger, J and Chung, D and Reynolds, WS and Cameron, A and Tenggardjaja, C and Padmanabhan, P and Brucker, BM and , },
title = {Association Between Urodynamic Findings and Urinary Retention After Onabotulinumtoxin A for Idiopathic Overactive Bladder.},
journal = {Neurourology and urodynamics},
volume = {},
number = {},
pages = {},
doi = {10.1002/nau.70050},
pmid = {40223771},
issn = {1520-6777},
support = {//This secondary analysis did not receive any external sources of funding. Funding for the primary analysis which utilized the same original data set as the current study was Society of Urodynamics, Female Pelvic Medicine and Urogenital Reconstruction Foundation (SUFU); National Institutes of Health, Grant/Award Number: UL1TR002240./ ; },
abstract = {INTRODUCTION: Onabotulinumtoxin A (BTX-A) is a minimally invasive therapy for idiopathic overactive bladder (iOAB). Incomplete bladder emptying is a known risk of the procedure, with an overall rate as high as 20% in male and female patients. Risk factors for incomplete bladder emptying after BTX-A have been reported in the literature, but are widely variable amongst studies and therefore patients at increased risk of this adverse effect cannot easily be identified by clinicians. The aim of this study was to evaluate whether pre-procedure urodynamics (UDS) findings are associated with incomplete bladder emptying after intradetrusor BTX-A injection for iOAB.

METHODS: Data were analyzed from the SUFU Research Network (SURN) multi-institutional retrospective database. Men and women undergoing first-time injection of 100 units BTX-A for iOAB in 2016 were included. Subjects were excluded if they did not have record of pre-procedure and post-procedure (within 1 month) post-void residual volume (PVR). The primary outcome was incidence of urinary retention within 1 month after BTX-A, defined as PVR > 300 mL and/or initiation of self-catheterization or indwelling catheter. We assessed the association of pre-procedure UDS parameters with urinary retention using Wilcoxon rank tests, Fisher's exact test, and chi-squared tests.

RESULTS: A total of 167 subjects (141 women, 26 men) were included. Ninety-nine subjects (59%) had urodynamic data. Thirty-seven subjects (22%) had urinary retention within 1 month of BTX-A. There were no significant differences in age, gender, race, or body mass index between the retention and non-retention groups. There was no statistically significant difference in median Qmax between those who did and did not have postprocedure retention (10.0 vs. 14.3 mL/s respectively, p = 0.06). Mean PVR at the start of UDS was not statistically significant when comparing the retention and non-retention groups (22.5 vs. 10.0 mL respectively, p = 0.70). Bladder outlet obstruction index (BOOI), bladder contractility index (BCI), and presence of detrusor overactivity (DO) were not found to be associated with posttreatment retention.

CONCLUSION: This retrospective multi-institutional cohort study revealed that of patients who receive UDS before BTX-A, there are no significant UDS parameters or baseline demographic factors associated with incomplete bladder emptying after intradetrusor BTX-A injections for iOAB. Future studies that focus on better defining objective evidence-based predictors of incomplete emptying after BTX are needed to optimize patient perception of efficacy and satisfaction with this therapy.},
}

@article {pmid40223534,
year = {2025},
author = {Jung, M and Abu Shihada, J and Decke, S and Koschinski, L and Graff, PS and Maruri Pazmino, S and Höllig, A and Koch, H and Musall, S and Offenhäusser, A and Rincón Montes, V},
title = {Flexible 3D Kirigami Probes for In Vitro and In Vivo Neural Applications.},
journal = {Advanced materials (Deerfield Beach, Fla.)},
volume = {},
number = {},
pages = {e2418524},
doi = {10.1002/adma.202418524},
pmid = {40223534},
issn = {1521-4095},
support = {VH-NG-1611//Helmholtz Association/ ; GRK2610//Deutsche Forschungsgemeinschaft (DFG, German Research Foundation)/ ; 424556709//Deutsche Forschungsgemeinschaft (DFG, German Research Foundation)/ ; GRK2416//Deutsche Forschungsgemeinschaft (DFG, German Research Foundation)/ ; 368482240//Deutsche Forschungsgemeinschaft (DFG, German Research Foundation)/ ; },
abstract = {3D microelectrode arrays (MEAs) are gaining popularity as brain-machine interfaces and platforms for studying electrophysiological activity. Interactions with neural tissue depend on the electrochemical, mechanical, and spatial features of the recording platform. While planar or protruding 2D MEAs are limited in their ability to capture neural activity across layers, existing 3D platforms still require advancements in manufacturing scalability, spatial resolution, and tissue integration. In this work, a customizable, scalable, and straightforward approach to fabricate flexible 3D kirigami MEAs containing both surface and penetrating electrodes, designed to interact with the 3D space of neural tissue, is presented. These novel probes feature up to 512 electrodes distributed across 128 shanks in a single flexible device, with shank heights reaching up to 1 mm. The 3D kirigami MEAs are successfully deployed in several neural applications, both in vitro and in vivo, and identified spatially dependent electrophysiological activity patterns. Flexible 3D kirigami MEAs are therefore a powerful tool for large-scale electrical sampling of complex neural tissues while improving tissue integration and offering enhanced capabilities for analyzing neural disorders and disease models where high spatial resolution is required.},
}

@article {pmid40223097,
year = {2025},
author = {Wu, Y and Liu, Y and Yang, Y and Yao, MS and Yang, W and Shi, X and Yang, L and Li, D and Liu, Y and Yin, S and Lei, C and Zhang, M and Gee, JC and Yang, X and Wei, W and Gu, S},
title = {A concept-based interpretable model for the diagnosis of choroid neoplasias using multimodal data.},
journal = {Nature communications},
volume = {16},
number = {1},
pages = {3504},
pmid = {40223097},
issn = {2041-1723},
support = {62236009//National Science Foundation of China | Key Programme/ ; },
mesh = {Humans ; *Choroid Neoplasms/diagnosis/diagnostic imaging ; *Melanoma/diagnosis/diagnostic imaging ; Machine Learning ; Artificial Intelligence ; Female ; Uveal Melanoma ; Male ; *Uveal Neoplasms/diagnostic imaging/diagnosis ; Hemangioma/diagnosis/diagnostic imaging ; Middle Aged ; Diagnosis, Differential ; Multimodal Imaging/methods ; Adult ; },
abstract = {Diagnosing rare diseases remains a critical challenge in clinical practice, often requiring specialist expertise. Despite the promising potential of machine learning, the scarcity of data on rare diseases and the need for interpretable, reliable artificial intelligence (AI) models complicates development. This study introduces a multimodal concept-based interpretable model tailored to distinguish uveal melanoma (0.4-0.6 per million in Asians) from hemangioma and metastatic carcinoma following the clinical practice. We collected a comprehensive dataset on Asians to date on choroid neoplasm imaging with radiological reports, encompassing over 750 patients from 2013 to 2019. Our model integrates domain expert insights from radiological reports and differentiates between three types of choroidal tumors, achieving an F1 score of 0.91. This performance not only matches senior ophthalmologists but also improves the diagnostic accuracy of less experienced clinicians by 42%. The results underscore the potential of interpretable AI to enhance rare disease diagnosis and pave the way for future advancements in medical AI.},
}

Humans
*Choroid Neoplasms/diagnosis/diagnostic imaging
*Melanoma/diagnosis/diagnostic imaging
Machine Learning
Artificial Intelligence
Female
Uveal Melanoma
Male
*Uveal Neoplasms/diagnostic imaging/diagnosis
Hemangioma/diagnosis/diagnostic imaging
Middle Aged
Diagnosis, Differential
Multimodal Imaging/methods
Adult

@article {pmid40223012,
year = {2025},
author = {Lorente-Piera, J and Manrique-Huarte, R and Picciafuoco, S and Lima, JP and Calavia, D and Serra, V and Manrique, M},
title = {Optimization of surgical interventions in auditory rehabilitation for chronic otitis media: comparative between passive middle ear implants, bone conduction implants, and active middle ear systems.},
journal = {European archives of oto-rhino-laryngology : official journal of the European Federation of Oto-Rhino-Laryngological Societies (EUFOS) : affiliated with the German Society for Oto-Rhino-Laryngology - Head and Neck Surgery},
volume = {},
number = {},
pages = {},
pmid = {40223012},
issn = {1434-4726},
abstract = {INTRODUCTION: In otology consultations, patients with chronic otitis media (COM) often present as candidates for various hearing rehabilitation options. Selecting the most suitable approach requires careful consideration of patient preferences and expectations, the risk of disease progression, and the integrity of the bone conduction pathway. This study aims to evaluate and compare postoperative hearing outcomes in COM patients undergoing tympanoplasty (with or without passive middle ear implants), bone conduction systems (BCI), or active middle ear implants (AMEI). The objective is to assess the effectiveness of each surgical approach in hearing rehabilitation, considering the type and severity of hearing loss as well as the duration of the disease.

METHODS: Retrospective data analysis in a tertiary referral center studying average PTA across six different frequencies, speech perception at 65 dB, influence of Eustachian tube dysfunction, reintervention rate and adverse effects, and the influence of disease duration on functional outcomes via linear regression analysis.

RESULTS: 116 patients underwent surgery due to COM between 1998 and 2024. With a slight female predominance (54.31%). AMEIs and bone conduction devices provided the highest amplification in terms of PTA and speech discrimination, with a lower reintervention rate when comparing both groups with passive middle ear implants, OR in BCI group of 0.30 (0.10; 0.89, p = 0.030), OR in VSB group of 0.15 (0.04; 0.56, p = 0.005). It was also observed that a longer evolution time could be associated with greater auditory gain, with a p-value = 0.033.

CONCLUSIONS: The selection of each treatment option primarily depends on bone conduction thresholds, along with surgical risk, patient preferences, and MRI compatibility. In our study, AMEIs demonstrated the highest functional gain in terms of speech discrimination and frequency-specific amplification, followed by BCI. These findings support the use of implantable hearing solutions as effective alternatives for auditory rehabilitation in COM patients.},
}

@article {pmid40222332,
year = {2025},
author = {Choi, JY and Kim, YJ and Shin, JS and Choi, E and Kim, Y and Kim, MG and Kim, YT and Park, BS and Kim, JK and Kim, JG},
title = {Integrative metabolic profiling of hypothalamus and skeletal muscle in a mouse model of cancer cachexia.},
journal = {Biochemical and biophysical research communications},
volume = {763},
number = {},
pages = {151766},
doi = {10.1016/j.bbrc.2025.151766},
pmid = {40222332},
issn = {1090-2104},
abstract = {Cancer cachexia is a multifactorial metabolic syndrome characterized by progressive weight loss, muscle wasting, and systemic inflammation. Despite its clinical significance, the underlying mechanisms linking central and peripheral metabolic changes remain incompletely understood. In this study, we employed a murine model of cancer cachexia induced by intraperitoneal injection of Lewis lung carcinoma (LLC1) cells to investigate tissue-specific metabolic adaptations. Cachectic mice exhibited reduced food intake, body weight loss, impaired thermoregulation, and decreased energy expenditure. Metabolomic profiling of serum, skeletal muscle, and hypothalamus revealed distinct metabolic shifts, with increased fatty acid and ketone body utilization and altered amino acid metabolism. Notably, hypothalamic metabolite changes diverged from peripheral tissues, showing decreased neurotransmitter-related metabolites and enhanced lipid-based energy signatures. Gene expression analysis further confirmed upregulation of glycolysis- and lipid oxidation-related genes in both hypothalamus and muscle. These findings highlight coordinated yet compartmentalized metabolic remodeling in cancer cachexia and suggest that hypothalamic adaptations may play a central role in the systemic energy imbalance associated with cachexia progression.},
}

@article {pmid40221457,
year = {2025},
author = {Rybář, M and Poli, R and Daly, I},
title = {Simultaneous EEG and fNIRS recordings for semantic decoding of imagined animals and tools.},
journal = {Scientific data},
volume = {12},
number = {1},
pages = {613},
pmid = {40221457},
issn = {2052-4463},
mesh = {Humans ; *Electroencephalography ; *Brain-Computer Interfaces ; Spectroscopy, Near-Infrared ; *Semantics ; *Imagination ; Animals ; Male ; Female ; Adult ; Brain/physiology ; },
abstract = {Semantic neural decoding aims to identify which semantic concepts an individual focuses on at a given moment based on recordings of their brain activity. We investigated the feasibility of semantic neural decoding to develop a new type of brain-computer interface (BCI) that allows direct communication of semantic concepts, bypassing the character-by-character spelling used in current BCI systems. We provide data from our study to differentiate between two semantic categories of animals and tools during a silent naming task and three intuitive sensory-based imagery tasks using visual, auditory, and tactile perception. Participants were instructed to visualize an object (animal or tool) in their minds, imagine the sounds produced by the object, and imagine the feeling of touching the object. Simultaneous electroencephalography (EEG) and near-infrared spectroscopy (fNIRS) signals were recorded from 12 participants. Additionally, EEG signals were recorded from 7 other participants in a follow-up experiment focusing solely on the auditory imagery task. These datasets can serve as a valuable resource for researchers investigating semantic neural decoding, brain-computer interfaces, and mental imagery.},
}

Humans
*Electroencephalography
*Brain-Computer Interfaces
Spectroscopy, Near-Infrared
*Semantics
*Imagination
Animals
Male
Female
Adult
Brain/physiology

@article {pmid40219565,
year = {2025},
author = {Liu, N and Man, L and He, F and Huang, G and Zhai, J},
title = {[Correlation between urination intermittences and urodynamic parameters in benign prostatic hyperplasia patients].},
journal = {Beijing da xue xue bao. Yi xue ban = Journal of Peking University. Health sciences},
volume = {57},
number = {2},
pages = {328-333},
pmid = {40219565},
issn = {1671-167X},
mesh = {*Prostatic Hyperplasia/complications/physiopathology ; Humans ; Male ; *Urodynamics ; *Urination ; Retrospective Studies ; Middle Aged ; Aged ; Aged, 80 and over ; *Urinary Bladder/physiopathology ; *Urination Disorders/etiology ; },
abstract = {OBJECTIVE: To explore the impact factors and the clinical significance of the urination intermittences in benign prostatic hyperplasia (BPH) patients.

METHODS: A retrospective study was performed in BPH patients who underwent urodynamic studies in Beijing Jishuitan Hospital form January 2016 to June 2021. The patients were aged 45 to 84 years with a median age of 63 years, and all the patients had no previous history of neurological disease and had no positive findings in neurological examinations. All the patients had free uroflometry followed by urethral catheterization and urodynamic tests. The voiding work of bladder was calculated using the detrusor power curve method, and the voiding power of bladder and the voiding energy consumption were also calculated. The frequency of urination intermittences generated in uroflometry was also recorded and the patients were divided into different groups according to it. The detrusor pressure at maximal flow rate (PdetQmax), the maximal flow rate (Qmax), the bladder contractile index (BCI), the bladder outlet obstruction index (BOOI), the voiding work, the voiding power, and the voiding energy consumption were compared among the different groups. Multiva-riate analyses associated with presence of urination intermittences were performed using step-wise Logistic regressions.

RESULTS: There were 272 patients included in this study, of whom, 179 had no urination intermittence (group A), 46 had urination intermittence for only one time (group B), 22 had urination intermittence for two times (group C), and 25 had urination intermittence for three times and more (group D). The BCI were 113.4±28.2, 101.0±30.2, 83.3±30.2, 81.0±30.5 in groups A, B, C, and D, respectively; The voiding power were (29.2±14.8) mW, (16.4±9.6) mW, (14.5±7.1) mW, (8.5±5.0) mW in groups A, B, C, and D, respectively, and the differences were significant (P < 0.05). The BOOI were 41.6±29.3, 46.4±31.0, 41.4±29.0, 42.7±22.8 in groups A, B, C, and D, respectively; The voiding energy consumption were (5.41±2.21) J/L, (4.83±2.31) J/L, (5.02±2.54) J/L, (4.39±2.03) J/L in groups A, B, C, and D, respectively, and the differences were insignificant (P>0.05). Among the patients, 179 cases were negative in presence of urination intermittences and 93 cases were positive. Step-wise Logistic regression analysis showed that bladder power (OR=0.814, 95%CI: 0.765-0.866, P < 0.001), BCI (OR=1.023, 95%CI: 1.008-1.038, P=0.003), and bladder work (OR=2.232, 95%CI: 1.191-4.184, P=0.012) were independent risk factors for urination intermittences in the BPH patients.

CONCLUSION: The presence of urination intermittences in the BPH patients was mainly influenced by bladder contractile functions, and was irrelevant to the degree of bladder outlet obstruction. The increase of frequency of urination intermittences seemed to be a sign of the decrease of the bladder contractile functions in the BPH patients.},
}

*Prostatic Hyperplasia/complications/physiopathology
Humans
Male
*Urodynamics
*Urination
Retrospective Studies
Middle Aged
Aged
Aged, 80 and over
*Urinary Bladder/physiopathology
*Urination Disorders/etiology

@article {pmid40218833,
year = {2025},
author = {Ranjbar Koleibi, E and Lemaire, W and Koua, K and Benhouria, M and Bostani, R and Serri Mazandarani, M and Gauthier, LP and Besrour, M and Ménard, J and Majdoub, M and Gosselin, B and Roy, S and Fontaine, R},
title = {Design and Implementation of a Low-Power Biopotential Amplifier in 28 nm CMOS Technology with a Compact Die-Area of 2500 μm[2] and an Ultra-High Input Impedance.},
journal = {Sensors (Basel, Switzerland)},
volume = {25},
number = {7},
pages = {},
pmid = {40218833},
issn = {1424-8220},
abstract = {Neural signal recording demands compact, low-power, high-performance amplifiers, to enable large-scale, multi-channel electrode arrays. This work presents a bioamplifier optimized for action potential detection, designed using TSMC 28 nm HPC CMOS technology. The amplifier integrates an active low-pass filter, eliminating bulky DC-blocking capacitors and significantly reducing the size and power consumption. It achieved a high input impedance of 105.5 GΩ, ensuring minimal signal attenuation. Simulation and measurement results demonstrated a mid-band gain of 58 dB, a -3 dB bandwidth of 7 kHz, and an input-referred noise of 11.1 μVrms, corresponding to a noise efficiency factor (NEF) of 8.4. The design occupies a compact area of 2500 μm2, making it smaller than previous implementations for similar applications. Additionally, it operates with an ultra-low power consumption of 3.4 μW from a 1.2 V supply, yielding a power efficiency factor (PEF) of 85 and an area efficiency factor of 0.21. These features make the proposed amplifier well suited for multi-site in-skull neural recording systems, addressing critical constraints regarding miniaturization and power efficiency.},
}