@article {pmid40588007,
year = {2025},
author = {Mahoney, TB and Grayden, DB and John, SE},
title = {Sub-scalp EEG for sensorimotor brain-computer interface.},
journal = {Journal of neural engineering},
volume = {},
number = {},
pages = {},
doi = {10.1088/1741-2552/ade9f1},
pmid = {40588007},
issn = {1741-2552},
abstract = {To establish sub-scalp electroencephalography (EEG) as a viable option for brain-computer interface (BCI) applications, particularly for chronic use, by demonstrating its effectiveness in recording and classifying sensorimotor neural activity. Approach: Two experiments were conducted in this study. The first aim was to demonstrate the high spatial resolution of sub-scalp EEG through analysis of somatosensory evoked potentials in sheep models. The second focused on the practical application of sub-scalp EEG, classifying motor execution using data collected during a sheep behavioural experiment. Main Results: We successfully demonstrated the recording of sensorimotor rhythms using sub-scalp EEG in sheep models. Important spatial, temporal, and spectral features of these signals were identified, and we were able to classify motor execution with above-chance performance. These results are comparable to previous work that investigated signal quality and motor execution classification using ECoG and endovascular arrays in sheep models. Significance: These results suggest that sub-scalp EEG may provide signal quality that approaches that of more invasive neural recording methods such as ECoG and endovascular arrays, and support the use of sub-scalp EEG for chronic BCI applications.},
}

@article {pmid40587936,
year = {2025},
author = {Vadivelan D, S and Sethuramalingam, P},
title = {A hybrid approach for EEG motor imagery classification using adaptive margin disparity and knowledge transfer in convolutional neural networks.},
journal = {Computers in biology and medicine},
volume = {195},
number = {},
pages = {110675},
doi = {10.1016/j.compbiomed.2025.110675},
pmid = {40587936},
issn = {1879-0534},
abstract = {- Motor Imagery (MI) using Electroencephalography (EEG) is essential in Brain-Computer Interface (BCI) technology, enabling interaction with external devices by interpreting brain signals. Recent advancements in Convolutional Neural Networks (CNNs) have significantly improved EEG classification tasks; however, traditional CNN-based methods rely on fixed convolution modes and kernel sizes, limiting their ability to capture diverse temporal and spatial features from one-dimensional EEG-MI signals. This paper introduces the Adaptive Margin Disparity with Knowledge Transfer 2D Model (AMD-KT2D), a novel framework designed to enhance EEG-MI classification. The process begins by transforming EEG-MI signals into 2D time-frequency representations using the Optimized Short-Time Fourier Transform (OptSTFT), which optimizes windowing functions and time-frequency resolution to preserve dynamic temporal and spatial features. The AMD-KT2D framework integrates a guide-learner architecture where Improved ResNet50 (IResNet50), pre-trained on a large-scale dataset, extracts high-level spatial-temporal features, while a Customized 2D Convolutional Neural Network (C2DCNN) captures multi-scale features. To ensure feature alignment and knowledge transfer, the Adaptive Margin Disparity Discrepancy (AMDD) loss function minimizes domain disparity, facilitating multi-scale feature learning in C2DCNN. The optimized learner model then classifies EEG-MI images into left and right-hand movement motor imagery classes. Experimental results on the real-world EEG-MI dataset collected using the Emotiv Epoc Flex system demonstrated that AMD-KT2D achieved a classification accuracy of 96.75 % for subject-dependent and 92.17 % for subject-independent, showcasing its effectiveness in leveraging domain adaptation, knowledge transfer, and multi-scale feature learning for advanced EEG-based BCI applications.},
}

@article {pmid40587626,
year = {2025},
author = {Li, Z and Huang, Z and Li, J and Tang, Y and Li, J and Ding, X},
title = {Shear-Aligned Flexible Polarized Fluorescent Antennas for Wearable Visible Light Communications.},
journal = {ACS applied materials & interfaces},
volume = {},
number = {},
pages = {},
doi = {10.1021/acsami.5c06121},
pmid = {40587626},
issn = {1944-8252},
abstract = {Wearable visible light communication systems face fundamental limitations in dense multi-input multioutput configurations due to signal crosstalk between channels. Here, we demonstrate shear-aligned flexible polarized fluorescent antennas (FPFAs) fabricated through a scalable thermally assisted brush-coating induction (BCI) process. By systematically investigating the synergistic effects of ″coffee-ring″ phenomena and shear forces on halloysite nanotube alignment, we reveal the underlying physical mechanism enabling the formation of highly ordered structures with an orientation degree of 0.89. We encapsulate these structures in a sandwich configuration that maintains polarization performance while exhibiting mechanical stability, with parallel fracture strength 4.25 times higher than conventional designs. When integrated with quantum dot fluorescent conversion layers, these FPFAs achieve a 4.95-fold improvement in signal-to-noise ratio (SNR) compared to traditional receivers across wide viewing angles, even under extreme bending conditions. The resulting wearable communication system maintains 85.1% transmission accuracy at distances up to 9 m under ambient lighting, a 935% improvement over conventional approaches, with superior resilience to environmental disturbances including rain and fog. This work establishes an effective strategy for polarization multiplexing in wearable optical communications, with applications spanning healthcare monitoring, secure communications, and augmented reality interfaces in dynamic environments.},
}

@article {pmid40586134,
year = {2025},
author = {Zhang, Q and Liu, B and Wang, Z and Zhou, J and Yang, X and Zhou, Q and Zhao, Y and Li, S and Zhou, J and Wang, C},
title = {Training-Free Regulation of Grasping by Intracortical Tactile Feedback Designed via S1-M1 Communication.},
journal = {Advanced science (Weinheim, Baden-Wurttemberg, Germany)},
volume = {},
number = {},
pages = {e03011},
doi = {10.1002/advs.202503011},
pmid = {40586134},
issn = {2198-3844},
support = {2021ZD0201600//STI 2030-Major Projects, Ministry of Science and Technology of the People's Republic of China/ ; 2021ZD0201604//STI 2030-Major Projects, Ministry of Science and Technology of the People's Republic of China/ ; 82327810//National Major Scientific Instruments and Equipments Development Project of National Natural Science Foundation of China/ ; },
abstract = {Tactile feedback is essential for grip force control when operating a neuroprosthesis. Due to limited knowledge of cortical sensorimotor coordination, artificial feedback is mostly counterintuitive, requiring training to be associated with grasping behaviors. The current study investigates sensorimotor communication by recording neural activities from the primary sensory cortex (S1) and the primary motor cortex (M1) while macaques grasp targets of various textures and loads. Intracortical micro-stimulation is also delivered to S1 to validate the intervention of sensorimotor communication in grasping. The findings identify an S1→M1 functional pathway through which tactile information is transferred. The pathway is shared by both natural and artificial neural propagations. Moreover, it is demonstrated that sensory and motor decoding of neural activities in M1, as well as the actual grip force, are modulated by stimulation designed via S1→M1 communication, without prior training. The work provides a biomimetic strategy to design intuitive haptic feedback for brain-machine interfaces utilizing the S1→M1 pathway.},
}

@article {pmid40585760,
year = {2025},
author = {Zheng, Q and Wu, Y and Zhu, J and Feng, K and Bai, Y and Li, G and Ni, G},
title = {Applications and Challenges of Auditory Brain-Computer Interfaces in Objective Auditory Assessments for Pediatric Cochlear Implants.},
journal = {Exploration (Beijing, China)},
volume = {5},
number = {3},
pages = {20240078},
pmid = {40585760},
issn = {2766-2098},
abstract = {Cochlear implants (CI) are the premier intervention for individuals with severe to profound hearing impairment. Worldwide, an estimated 600,000 individuals have enhanced their hearing through cochlear implantation, with nearly half being children. The evaluations after implantation are crucial for appropriate clinical interventions and care. Current clinical practice lacks methods to assess the recovery of advanced auditory functions in cochlear-implanted children. Yet, recent advancements in electroencephalographic (EEG) techniques show promise in accurately evaluating auditory rehabilitation in this demographic. This review elucidates the evolution of brain-computer interface (BCI) technology for auditory assessment, focusing primarily on its application in pediatric cochlear implant recipients. Emphasis is placed on promising clinical biomarkers for auditory rehabilitation and the neural adaptability accompanying cortical adjustments after implantation. Additionally, we discuss emerging challenges and prospects in applying BCI technology to these children.},
}

@article {pmid40584823,
year = {2025},
author = {Jiang, M and Pan, X and Wang, X and Luo, Q},
title = {Will the embedded semantic radicals be activated when recognizing Chinese phonograms?.},
journal = {Frontiers in human neuroscience},
volume = {19},
number = {},
pages = {1550536},
pmid = {40584823},
issn = {1662-5161},
abstract = {INTRODUCTION: A majority of Chinese characters are phonograms composed of phonetic and semantic radicals that serve different functions. While radical processing in character recognition has drawn significant interest, there is inconsistency regarding the semantic activation of embedded semantic radicals, and little is known about the duration of such sub-lexical semantic activation.

METHODS: Using a priming character decision task and a between-subjects design, this study examined whether semantic radicals embedded in SP phonograms (semantic radicals on the left and phonetic radicals on the right) can be automatically activated and how long such activation persists. We manipulated semantic relatedness between embedded radicals and target characters, prime frequency, and stimulus onset asynchronies (SOAs).

RESULTS: Facilitatory effects were observed on targets preceded by low-frequency primes at an SOA of 500 ms. No significant priming effects were found at SOAs of 100 ms or 1000 ms, regardless of prime frequency.

DISCUSSION: These findings suggest that sub-lexical semantic activation can occur and remain robust at 500 ms but may dissipate before 1000 ms. The study contributes valuable evidence for the automaticity and time course of embedded semantic radical processing in Chinese phonogram recognition, thereby enhancing our understanding of sub-lexical semantic processing in logographic writing systemse.},
}

@article {pmid40584523,
year = {2025},
author = {Shen, Y and Jiang, L and Lai, J and Hu, J and Liang, F and Zhang, X and Ma, F},
title = {A comprehensive review of rehabilitation approaches for traumatic brain injury: efficacy and outcomes.},
journal = {Frontiers in neurology},
volume = {16},
number = {},
pages = {1608645},
pmid = {40584523},
issn = {1664-2295},
abstract = {Traumatic Brain Injury (TBI), particularly in moderate-to-severe cases, remains a leading cause of long-term disability worldwide, affecting over 64 million individuals annually. Its complex and multifactorial nature demands an integrated, multidisciplinary rehabilitation approach to address the diverse physical, cognitive, behavioral, and psychosocial impairments that follow injury. We conducted a structured literature search using PubMed, Scopus, and Web of Science databases for suitable studies. This comprehensive review critically examines key rehabilitation strategies for TBI, including neuropsychological assessments, cognitive and neuroplasticity-based interventions, psychosocial support, and community reintegration through occupational therapy. The review emphasizes emerging technological innovations such as virtual reality, robotics, brain-computer interfaces, and tele-rehabilitation, which are expanding access to care and enhancing recovery outcomes. Furthermore, it also explores regenerative approaches, such as stem cell therapies and nanotechnology, highlighting their future potential in neurorehabilitation. Special attention is given to the importance of rigorous outcome evaluation, including standardized functional measures, neuropsychological testing, and advanced statistical methodologies to assess treatment efficacy and clinical significance. Patient-centered care is emphasized as a core element-rehabilitation plans are tailored to each individual's cognitive profile, functional needs, and life goals. Studies show this approach leads to better outcomes in executive functioning, emotional wellbeing, and community reintegration. It identifies gaps in current research, such as the lack of longitudinal studies, predictors of individualized treatment success, cost-benefit evaluations, and strategies to manage comorbidities like PTSD. Thus, combining conventional and technology-assisted rehabilitation-guided by patient-centered strategies-can enhance recovery in moderate-to-severe TBI. Future research should focus on long-term effectiveness, cost-efficiency, and scalable personalized care models.},
}

@article {pmid40584436,
year = {2025},
author = {Zamani, S and Sadeghi, J and Kamalabadi-Farahani, M and Aghayan, SN and Arabpour, Z and Djalilian, AR and Salehi, M},
title = {Comparison of cellular, mechanical, and optical properties of different polymers for corneal tissue engineering.},
journal = {Iranian journal of basic medical sciences},
volume = {28},
number = {8},
pages = {1082-1099},
doi = {10.22038/ijbms.2025.85468.18477},
pmid = {40584436},
issn = {2008-3866},
abstract = {OBJECTIVES: The invention of corneal tissue engineering is essential for vision due to the lack of effective treatments and donated corneas. Finding the right polymer is crucial for reducing inflammation, ensuring biocompatibility, and mimicking natural cornea properties.

MATERIALS AND METHODS: In this study, solvent casting and physical crosslinking (freeze-thaw cycles) were used to fabricate polymeric scaffolds of Polyvinyl alcohol, alginate, gelatin, carboxymethyl chitosan, carboxymethyl cellulose, polyacrylic acid, polyvinyl pyrrolidone, and their combinations. The mechanical evaluation of scaffolds for tension and suture ability was conducted. Biodegradability, swelling, water vapor, bacterial permeability, anti-inflammatory properties, blood compatibility, Blood Clotting Index (BCI), pH alterations, and cell compatibility with human Mesenchymal Stem cells (MSCs) were investigated with MTT. The hydrophilicity of the samples and the ability to adhere to surfaces were also compared with the contact angle and adhesive test, respectively. Finally, quantitative and qualitative analysis was used to check the transparency of the samples.

RESULTS: The mechanical strength of polyvinyl alcohol and polyvinyl pyrrolidone samples was highest, showing good suture ability. All samples had blood compatibility below 5% and cell compatibility above 75%. Polyvinyl alcohol was the most transparent at around 93%. Carboxymethyl chitosan effectively inhibited bacterial permeability, while its anti-inflammatory potential showed no significant difference.

CONCLUSION: This study aims to choose the best polymer composition for corneal tissue engineering. The selection depends on the study's goals, like mechanical strength or transparency. Comparing polymers across different dimensions provides better insight for polymer selection.},
}

@article {pmid40584269,
year = {2025},
author = {Ji, D and Yu, H and Xiao, X and Huang, Y and Zhou, X and Xu, M and Jung, TP and Ming, D},
title = {A user-friendly BCI encoding by high frequency single-frequency-SDMA SSaVEF using MEG.},
journal = {Cognitive neurodynamics},
volume = {19},
number = {1},
pages = {101},
doi = {10.1007/s11571-025-10279-1},
pmid = {40584269},
issn = {1871-4080},
abstract = {Magnetoencephalography (MEG) delivers high spatial resolution and superior detection performance for high-frequency signals compared to Electroencephalography (EEG). Therefore, researchers can leverage MEG for high-frequency steady-state asymmetric visual evoked potential (SSaVEP). Current SSaVEP encoding typically uses low-frequency stimulation with relatively large stimulus areas, hindering the applicability of this encoding method in user-friendly brain-computer interface (BCI) systems. This study introduces an ultra critical flicker frequency (ultra-CFF) single-frequency-SDMA steady-state asymmetric visual evoked field (SSaVEF) encoding powered by MEG and presents an eight-command SSaVEF-BCI system. The BCI system features a 60 Hz SSVEF visual stimulus landmark and eight visual targets spaced 45° apart. Ten participants took part in the offline experiments, during which data from 41 channels in the occipital region were collected. This study analyzed the spatiotemporal characteristics, frequency-space characteristics, signal-to-noise ratio, and other features of the SSaVEF signals. We also evaluated the system's performance using the multi-DCPM algorithm. Using the multi-DCPM algorithm, the system achieved an impressive average classification accuracy of 81.65% with 4-s length data. With a data length of 1 s, the system achieved an average Information Transfer Rate (ITR) of 32.05 bits/min, with the highest individual ITR reached an astonishing 64.45 bits/min. This study represents the exploration of a high-frequency spatial encoding SSVEF-BCI system based on MEG. The results demonstrate MEG's feasibility and potential of applying MEG in such BCI systems, providing both theoretical and practical value for the further development and implementation of future BCI systems.},
}

@article {pmid40584164,
year = {2025},
author = {Sharma, MK and Chaudhary, S and Shenoy, S},
title = {Development and testing of range of motion driven motor unit recruitment device for knee rehabilitation: A randomized controlled trial.},
journal = {MethodsX},
volume = {14},
number = {},
pages = {103382},
doi = {10.1016/j.mex.2025.103382},
pmid = {40584164},
issn = {2215-0161},
abstract = {Existing research on neuromuscular electrical stimulation (NMES) identifies two primary control approaches: therapist-operated systems and participant-controlled systems. Therapist-operated NMES devices typically employ switches and potentiometers for control, whereas participant-controlled systems offer diverse input methods, including switches, buttons, joysticks, electromyography electrodes, voice-activated commands, and sip-and-puff devices. A critical limitation of current NMES technology lies in its failure to mimic the body's natural muscle recruitment process during electrical stimulation, resulting in premature fatigue and diminished user engagement. A particularly significant drawback is the absence of joint range-of-motion dependency observed during voluntary movements and active involvement of participant. This limitation prevents precise control over spatial and temporal parameters, such as modulating motor unit recruitment relative to joint position, during neuromuscular rehabilitation. Furthermore, existing devices cannot accurately reproduce the co-contraction dynamics and reciprocal activation patterns seen in synergistic, agonist, and antagonist muscle groups during natural movement. Addressing these challenges requires developing innovative NMES technology capable of activating the neuromuscular system while replicating natural voluntary recruitment patterns. Such advancements would not only improve muscle strengthening outcomes but also enhance participant adherence through more effective cortical and peripheral neuromuscular engagement.•Development of neuromuscular electrical stimulation (NMES) device to replicate natural neuromuscular activation patterns through bio-inspired stimulation protocols.•Engineered to mitigate existing limitations of conventional NMES systems, optimizing therapeutic applications for neuromuscular re-education and functional recovery.•Integrates muscle synergy principles, enabling synchronized synergistic, agonist and antagonist activation for enhanced cortical and peripheral neuromuscular engagement and optimize functional rehabilitation outcomes.•Advances rehabilitation strategies by combining dual focus on muscular reconditioning and neural adaptation for holistic recovery.•Demonstrates potential to amplify strength gains while fostering neuroplasticity, supporting long-term functional recovery in neuromuscular rehabilitation.},
}

@article {pmid40581689,
year = {2025},
author = {Olza, A and Soto, D and Santana, R},
title = {Domain Adaptation-enhanced searchlight: enabling classification of brain states from visual perception to mental imagery.},
journal = {Brain informatics},
volume = {12},
number = {1},
pages = {17},
pmid = {40581689},
issn = {2198-4018},
support = {IT1504-22//IKUR strategy/ ; IT1504-22//IKUR strategy/ ; IT1504-22//IKUR strategy/ ; KK-2023/00090//Elkartek/ ; KK-2023/00090//Elkartek/ ; PID2019-105494GB-I00//Project grant/ ; PID2019-105494GB-I00//Project grant/ ; PID2019-105494GB-I00//Project grant/ ; PID2022-137442NB-I00//BERC by Spanish Ministry of Science and Innovation/ ; PID2022-137442NB-I00//BERC by Spanish Ministry of Science and Innovation/ ; CEX2020-001010-S//Severo Ochoa programme/ ; CEX2020-001010-S//Severo Ochoa programme/ ; },
abstract = {In cognitive neuroscience and brain-computer interface research, accurately predicting imagined stimuli is crucial. This study investigates the effectiveness of Domain Adaptation (DA) in enhancing imagery prediction using primarily visual data from fMRI scans of 18 subjects. Initially, we train a baseline model on visual stimuli to predict imagined stimuli, utilizing data from 14 brain regions. We then develop several models to improve imagery prediction, comparing different DA methods. Our results demonstrate that DA significantly enhances imagery prediction in binary classification on our dataset, as well as in multiclass classification on a publicly available dataset. We then conduct a DA-enhanced searchlight analysis, followed by permutation-based statistical tests to identify brain regions where imagery decoding is consistently above chance across subjects. Our DA-enhanced searchlight predicts imagery contents in a highly distributed set of brain regions, including the visual cortex and the frontoparietal cortex, thereby outperforming standard cross-domain classification methods. The complete code and data for this paper have been made openly available for the use of the scientific community.},
}

@article {pmid40581220,
year = {2025},
author = {Alsamri, J and Alamgeer, M and Alamri, MZ and Ghaleb, M and Asklany, SA and Almansour, H and Alsafari, S and Alghamdi, EA},
title = {Longitudinal EEG-Based Assessment of Neuroplasticity and Adaptive Responses to Transcranial Focused Ultrasound Stimulation.},
journal = {Journal of neuroscience methods},
volume = {},
number = {},
pages = {110521},
doi = {10.1016/j.jneumeth.2025.110521},
pmid = {40581220},
issn = {1872-678X},
abstract = {BACKGROUND: An emerging non-invasive neuromodulation technique named Transcranial-focused ultrasound stimulation (tFUS) offered several advantages than the conventional methods in terms of high spatial precision and penetration depth. In neurological disorders, this emerging method have gained a lot of attention, because of has the potential for therapeutic modulation of brain activity. Then, lack of standardized, Real-Time (RT) assessment protocols will result in unclear comprehehnsion regarding the way the repeated tFUS applications may impacts the neuroplasticity and adaptive brain responses in a long-term. Here, the short-term and long-term neuroplastic modifications were effectively identified by the the longitudinal integration of EEG biomarkers with tFUS stimulation sessions. An adaptive modulation strategies customized for individual neural responses are also facilitated by this hypothesis.

NEW METHODS: To integrate the tFUS with high-resolution electroencephalogram (EEG) monitoring in many sessions, Integrated Longitudinal Evaluation Protocol (ILEP) model was suggested in this study. To extract amplitude, latency, spectral dynamics, and connectivity features from evoked potentials, pre-, during-, and post-stimulation EEG signals were identified by the protocol. Then, for monitoring neuroadaptive trajectories over time, the intrgration of the statistical modeling and neural network (NN)-based pattern recognition was employed, and it will assist in analysing those features. For the purpose of differentiating the short-term oscillatory effects from long-term neuroplastic shifts, the following ways will helps in processing the EEG signals: time-frequency decomposition, event-related potential (ERP) analysis, and machine learning (ML) classifiers. Here, the subject-specific response patterns and temporal evolution of brain dynamics were effectively detected by the application of the Deep learning (DL) models.

RESULTS ANALYSIS: After the tFUS, both the short-term and long-term modifications in brain activity were effectively detected by the application of ILEP, and it was demonstrated by the outcomes of the simulation and empirical data. Here, the location-specific, session-dependent EEG modifications are consistent with the adaptive neuroplastic processes, and it was revealed by the outcomes of the simulation. Then, accurate neuroadaptive signals were separated from noise and temporary conditions, and it was facilitated by the potential of the model.

A dynamic, session-over-session monitoring of brain responses was facilitated by the ILEP model. But static images was offered by those conventional methods. With an integration of closed-loop feedback and advanced neural modelling, the suggested model executes better than the conventional methods. This suggested model also facilitates in offering a customized neuromodulation therapies.

CONCLUSION: For monitoring the neuroplastic modifications induced by tFUS,this suggested ILEP model becomes an effective, sacalable. So, this suggested model facilitates an adaptive assessment model for that tracking, and it was demonstrated in this study. The future, RT, closed-loop neuromodulation systems in therapeutic and cognitive enhancement contexts may get benefits from the integration of EEG feedback mechanisms in the suggested model.},
}

@article {pmid40579374,
year = {2025},
author = {Yang, C and Zhang, L and Liu, J and Li, K and Li, S and Yang, Z and Bishop, JR and Deng, W and Yao, L and Lui, S and Gong, Q},
title = {More Severe Brain Network Hierarchy Disorganization in Treatment-Naive Deficit Compared to Non-deficit Schizophrenia and Underlying Neurotransmitter Associations.},
journal = {Schizophrenia bulletin},
volume = {},
number = {},
pages = {},
doi = {10.1093/schbul/sbae231},
pmid = {40579374},
issn = {1745-1701},
support = {82102007//National Natural Science Foundation of China/ ; 82120108014//National Natural Science Foundation of China/ ; 82071908//National Natural Science Foundation of China/ ; 82202110//National Natural Science Foundation of China/ ; 2022YFC2009901//National Key Research and Development Program of China/ ; 2022YFC2009900//National Key Research and Development Program of China/ ; 2021JDTD0002//Sichuan Science and Technology Program/ ; 2022-YF09-00062-SN//Chengdu Science and Technology Office, major technology application demonstration project/ ; 2022-GH03-00017-HZ//Chengdu Science and Technology Office, major technology application demonstration project/ ; ZYGD23003//West China Hospital, Sichuan University/ ; ZYAI24010//West China Hospital, Sichuan University/ ; ZYGX2022YGRH008//Fundamental Research Funds for the Central Universities/ ; GZB20240493//Postdoctoral Fellowship Program of CPSF/ ; T2019069//Humboldt Foundation Friedrich Wilhelm Bessel Research Award and Chang Jiang Scholars/ ; },
abstract = {BACKGROUND AND HYPOTHESIS: Deficit schizophrenia (DS) represents a distinct entity characterized by primary and enduring negative symptoms, yet the neurobiological differences between DS and non-DS (NDS) remain undetermined. Using a gradient-based approach, we hypothesize that DS and NDS will exhibit convergent and divergent brain functional hierarchy patterns, each with a specific underlying neurotransmitter architecture.

STUDY DESIGN: Resting-state functional magnetic resonance imaging images were acquired from 44 treatment-naive DS, 55 treatment-naive NDS, and 60 matched healthy controls (HCs). Gradient metrics were calculated using the BrainSpace toolbox. The spatial correlation between gradient abnormalities in DS or NDS and density maps of 10 neurotransmitters derived by the JuSpace toolbox was analyzed to link the neuroimaging to underlying neurotransmitter information.

STUDY RESULTS: Both DS and NDS exhibited compressed gradient patterns compared to HC, suggesting reduced network differentiation, with more severe disorganization in DS. The ventral attention network was associated with depression symptoms in DS, whereas the visual network was related to total, general, and paranoid symptom scores in NDS. Moreover, spatial correlation of neurotransmitter analysis revealed that the gradient alterations of DS were primarily related to the serotonergic system while those of NDS were predominantly associated with the dopamine system.

CONCLUSIONS: The study suggests that independent from the potential effects of antipsychotic medication, DS and NDS are characterized by different neuropathology in brain hierarchy patterns, potentially linked to neurochemical metabolic distinction. Our findings support the hypothesis that DS is a distinct subtype versus NDS from neurodevelopmental perspective.},
}

@article {pmid40578761,
year = {2025},
author = {Brands, R and Fuchs, L and Seyffer, JM and Bajcinca, N and Bartsch, J and Peuker, UA and Schmidt, V and Thommes, M},
title = {Penetration depth and effective sample size characterization of UV/Vis radiation into pharmaceutical tablets.},
journal = {Journal of pharmaceutical sciences},
volume = {},
number = {},
pages = {103889},
doi = {10.1016/j.xphs.2025.103889},
pmid = {40578761},
issn = {1520-6017},
abstract = {The pharmaceutical industry is moving from off-line to real-time release testing (RTRT) to enhance quality while reducing costs. UV/Vis spectroscopy has emerged as a promising tool for RTRT given its simplicity, sensitivity and cost-effectiveness. Nevertheless, the effective sample size must be characterized in relation to the penetration depth to justify its representativeness and suitability for RTRT. In this study, bilayer tablets were produced using a hydraulic tablet press. The lower layer contained titanium dioxide and microcrystalline cellulose (MCC), while the upper layer consisted of MCC, lactose or a combination with theophylline. The thickness of the upper layer was stepwise increased. Spectra from 224 to 820 nm were recorded with an orthogonally aligned UV/Vis probe. Thereby, the experimental penetration depth reached up to 0.4 mm, while the Kubelka-Munk model yielded a theoretical maximum penetration depth of 1.38 mm. Based on these values, the effective sample sizes were determined. Considering a parabolic penetration profile, the maximum volume was 2.01 mm[3]. The results indicated a wavelength and particle size dependency. Micro-CT analysis confirmed the even distribution of the API in the tablets proving the sufficiency of the UV/Vis sample size. Consequently, UV/Vis spectroscopy is a reliable alternative for RTRT in tableting.},
}

@article {pmid40578508,
year = {2025},
author = {Metin, S and Altan, H and Tercan, E and Dedeoglu, BG and Gurdal, H},
title = {DUSP1 protein's impact on breast cancer: Anticancer response and sensitivity to cisplatin.},
journal = {Biochimica et biophysica acta. Gene regulatory mechanisms},
volume = {},
number = {},
pages = {195103},
doi = {10.1016/j.bbagrm.2025.195103},
pmid = {40578508},
issn = {1876-4320},
abstract = {Dual-Specificity Phosphatase 1 (DUSP1) modulates the activity of members of the Mitogen-Activated Protein Kinase (MAPK) family, including p38, JNK, and ERK1/2, which affects various cellular functions in cancer. Moreover, DUSP1 is known to influence the outcomes of cancer chemotherapy. This study aimed to reduce DUSP1 protein expression using CRISPR/Cas9 and siRNA and assess its effects on cell proliferation, migration, and tumor growth potential in triple-negative breast cancer (TNBC) cells. We examined the expression levels of p38, JNK, and ERK1/2, along with their phosphorylated forms, and investigated DUSP1's influence to cisplatin sensitivity. Our findings revealed that the downregulation of DUSP1 expression inhibited the proliferation, migration, and tumor growth potential of TNBC cells. Additionally, BCI, an inhibitor of DUSP1/6, demonstrated anti-proliferative effects on these cells. Decreasing the expression of DUSP1 increased the phosphorylation ratio of p38 and JNK, but not ERK1/2. Moreover, the anticancer response induced by cisplatin was enhanced by reducing DUSP1 expression or by treating the cells with BCI. Notably, cisplatin treatment increased p38 phosphorylation, which was significantly augmented by reduced DUSP1 expression. We also demonstrated that the DUSP1 inhibition-induced anticancer response in these cells predominantly relied on p38 activity. These findings contribute to a better understanding of the role of DUSP1 in breast cancer and offer insights into potential therapeutic strategies targeting DUSP1 to enhance the efficacy of cisplatin treatment. Our study highlights that decreased DUSP1 protein expression and activity mediates an anticancer response and increases the sensitivity of MDA-MB231 cells to cisplatin by regulating p38.},
}

@article {pmid40578406,
year = {2025},
author = {Cao, Y and Chen, Z and Yin, Y and Kang, X and Zhang, Y and Xu, Z and Yang, X and Yang, B and He, Q and Yan, H and Luo, P},
title = {Autophagy-dependent hepatocyte apoptosis mediates gilteritinib-induced hepatotoxicity.},
journal = {Toxicology letters},
volume = {410},
number = {},
pages = {189-196},
doi = {10.1016/j.toxlet.2025.06.018},
pmid = {40578406},
issn = {1879-3169},
abstract = {Gilteritinib, a dual FLT3/AXL inhibitor, is clinically effective for relapsed/refractory FLT3-mutated acute myeloid leukemia (AML) but is limited by severe hepatotoxicity. This study investigates the molecular mechanisms underlying gilteritinib-induced liver injury, focusing on the interplay between autophagy and apoptosis. In vitro and in vivo models, including human hepatocyte HL-7702 cells and C57BL/6 J mice, were employed. Gilteritinib treatment significantly upregulated autophagy markers (LC3-II) and induced autophagosome formation, as confirmed by western blot, TEM, and mCherry-GFP-LC3 reporter assays. Concurrently, apoptosis markers (cleaved-PARP, cleaved-Caspase3, Annexin V/PI staining) increased dose- and time-dependently. Pharmacological inhibition of autophagy with autophagy inhibitor 3-methyladenine (3-MA, 5 mM) or gene silence of Atg7 attenuated apoptosis, mitochondrial membrane potential loss, and ROS overproduction, while autophagy induction by Torin1 (100 nM) exacerbated hepatocyte death. In vivo, gilteritinib-treated mice exhibited elevated serum alanine aminotransferase (ALT), aspartate aminotransferase (AST), and lactate dehydrogenase (LDH) levels, alongside histopathological damage, all of which were mitigated in Atg7-deficient mice. These findings demonstrate that gilteritinib triggers excessive autophagy, which drives hepatocyte apoptosis and liver injury. Targeting autophagy pathways, represents a potential therapeutic strategy to alleviate gilteritinib-induced hepatotoxicity, enabling safer clinical use of this vital AML therapy. This study elucidates a critical autophagy-apoptosis axis in drug-induced liver injury and provides actionable insights for managing adverse effects of targeted cancer therapies.},
}

@article {pmid40578388,
year = {2025},
author = {Lin, Z and Jiang, X and Dai, C and Jia, F},
title = {Towards real time efficient and robust ECoG decoding for mobile brain-computer interface.},
journal = {Journal of neural engineering},
volume = {},
number = {},
pages = {},
doi = {10.1088/1741-2552/ade917},
pmid = {40578388},
issn = {1741-2552},
abstract = {OBJECTIVE: Decoding locomotion-related brain activities from electrocorticographic (ECoG) signals is essential in brain-computer interfaces (BCIs). Most previous ECoG decoders are computationally demanding and sensitive to noises/outliers. Mobile and robust BCIs are particularly important for physically disabled patients to restore motor ability in outdoor scenarios, where the processing pipeline should be implemented efficiently using constrained computation resources. In this work, we aim to explore the optimal pipeline for mobile BCI decoding.

APPROACH: We comprehensively evaluated the trade-off between the decoding precision, computational efficiency and robustness of diverse decoding algorithms on a combined ECoG dataset of 12 subjects conducting individual finger movement, including partial-least-square and their N-way variants, Bayesian ridge regression, least absolute shrinkage and selection operator, support vector regression, neural networks with diverse architectures, and random forest (RF). We further explored the feature optimization technique for selected models by using their inherent model explainability. We also compared the decoding performance of updatable algorithms when the data is split into multiple batches and used sequentially.

MAIN RESULTS: The RF model, not valued by previous studies, can achieve the best trade-off between precision and efficiency, achieving an average Pearson's correlation coefficient (r) of 0.466 with only 0.5K FLOPs per inference and a model size of 900KiB. We also demonstrate the inherent superior robustness of RF model on corrupted ECoG electrodes, with a > 2× decoding precision on noisy signals compared with all state-of-the-art deep neural networks. The optimized RF processing pipeline was deployed on a STM32-based embedded platform with only a 15.2 ms computation delay.

SIGNIFICANCE: In this study, we systematically explored the performance and efficiency of ECoG decoding algorithms in finger movement analysis. The proposed decoding pipeline is implemented on a compact embedded platform to achieve low-latency, power-efficient real-time decoding. This research accelerates the translation of mobile BCI into real-life practices.},
}

@article {pmid40578216,
year = {2025},
author = {Zhang, H and Wang, H and An, J and Zheng, S and Wu, D},
title = {A lightweight spiking neural network for EEG-based motor imagery classification.},
journal = {Neural networks : the official journal of the International Neural Network Society},
volume = {191},
number = {},
pages = {107741},
doi = {10.1016/j.neunet.2025.107741},
pmid = {40578216},
issn = {1879-2782},
abstract = {Spiking neural networks (SNNs) aim to simulate the human brain neural network, using sparse spike event streams for effective and energy-efficient spatio-temporal signal processing. This paper proposes a lightweight SNN model for electroencephalogram (EEG) based motor imagery (MI) classification, a classical brain-computer interface paradigm. The model has three desirable characteristics: (1) it has a brain-inspired architecture; (2) it is energy efficient; and, (3) it is dataset agnostic. Within-subject and cross-subject experiments on three public datasets demonstrated the superiority of our SNN model over four classical convolutional neural network based models in EEG based MI classification.},
}

@article {pmid40576544,
year = {2025},
author = {Wu, Y and Bao, K and Liang, J and Li, Z and Shi, Y and Tang, R and Xu, K and Wei, M and Chen, Z and Jian, J and Luo, Y and Tang, Y and Deng, Q and Dai, H and Sun, C and Zhang, W and Lin, H and Zhang, K and Li, L},
title = {Photonic Interfaces: an Innovative Wearable Sensing Solution for Continuous Monitoring of Human Motion and Physiological Signals.},
journal = {Small methods},
volume = {},
number = {},
pages = {e2500727},
doi = {10.1002/smtd.202500727},
pmid = {40576544},
issn = {2366-9608},
support = {10300000H062401/001//Science and Technology Support Plan for Youth Innovation of Colleges and Universities of Shandong Province of China/ ; 2024SDXHDX0005//"Pioneer" and "Leading Goose" Key Research and Development Program of Zhejiang Province/ ; 12104375//National Natural Science Foundation of China/ ; 62175202//National Natural Science Foundation of China/ ; 2024C03150//Key Research and Development Program of Zhejiang Province/ ; 2023GD003/110500Y0022303//Key Project of Westlake Institute for Optoelectronics/ ; },
abstract = {Flexible integrated photonic sensors are gaining prominence in intelligent wearable sensing due to their compact size, exceptional sensitivity, rapid response, robust immunity to electromagnetic interference, and the capability to enable parallel sensing through optical multiplexing. However, integrating these sensors for practical applications, such as monitoring human motions and physiological activities together, remains a significant challenge. Herein, it is presented an innovative fully packaged integrated photonic wearable sensor, which features a delicately designed flexible necklace-shaped microring resonator (MRR), along with a pair of grating couplers (GCs) coupled to a fiber array (FA). The necklace-shaped MRR is engineered to minimize waveguide sidewall-induced scattering loss, with a measured intrinsic quality factor (Qint) of 1.68 × 10[5], ensuring highly sensitive and precise signal monitoring. GCs and FA enhance the seamless wearability of devices while maintaining superior sensitivity to monitor various human motions and physiological signs. These are further classified signals using machine learning algorithms, achieving an accuracy rate of 97%. This integrated photonic wearable sensor shows promise for human-machine interfaces, touch-responsive wearable monitors, and artificial skin, especially in environments susceptible to electromagnetic interference, such as intensive care units (ICUs) and spacecraft. This work significantly advances the field of smart wearable technology.},
}

@article {pmid40575493,
year = {2025},
author = {Mehta, D},
title = {Brain-Computer Interface tool use and the Contemplation Conundrum: a blueprint of mental action, agency, and control.},
journal = {Oxford open neuroscience},
volume = {4},
number = {},
pages = {kvaf002},
pmid = {40575493},
issn = {2753-149X},
abstract = {This paper approaches the role of intentional action in brain-computer interface (BCI) tool use to allow for an ethical discourse regarding the development and usage of neurotechnology. The exploration of mental actions and user control in BCI tool use brings us closer to understanding the philosophical underpinnings of intentions and agency for BCI-mediated actions. The author presents that under some theories of intentional action, certain BCI-mediated overt movements qualify as both voluntary and unintentional. This plausibly magnifies the ethical considerations surrounding BCI tool use. This problem is referred by the author as the contemplation conundrum. Thus, the paper proposes research scope for the neural correlates of intention formation and the neural correlates of imagination aimed at clarifying implementational control and safeguarding privacy of thought in BCI tool use.},
}

@article {pmid40574626,
year = {2025},
author = {Kaszás, A and Meszéna, D and Fiáth, R and Slézia, A and Ulbert, I and Katona, G},
title = {Capturing the Electrical Activity of all Cortical Neurons: Are Solutions Within Reach?.},
journal = {Advanced science (Weinheim, Baden-Wurttemberg, Germany)},
volume = {},
number = {},
pages = {e06225},
doi = {10.1002/advs.202506225},
pmid = {40574626},
issn = {2198-3844},
support = {TKP2021-EGA-42//Thematic Programme of Excellence/ ; NAP2022-I-2/2022//Hungarian Brain Research Program/ ; RRF-2.3.1-21-2022-00015//Pharmaceutical Research and Development Laboratory Project/ ; HUN-REN-HAZAHIVO-2023//Hungarian Research Network/ ; KSZF-174/2023//Hungarian Research Network/ ; 2019-2.1.7-ERA-NET-2021-00023//ERA-NET/ ; //Bolyai János Scholarship of the Hungarian Academy of Sciences/ ; 150574//National Research, Development and Innovation Office/ ; PD143582//National Research, Development and Innovation Office/ ; },
abstract = {Recent advancements in high-density implantable intracortical electrode technology have significantly improved neural interfaces for both research and clinical applications. However, a significant challenge persists: scaling up these devices to achieve recording of nearly all single-unit activity across large brain volumes. This critical review explores recent progress in neural electrode design, focusing on the challenges of achieving scalable solutions for this ambitious goal. The physical and technical constraints of both rigid and flexible probes are addressed, highlighting the limitations imposed by shank stiffness, mechanical tissue damage, and foreign body response. It is identified that the physics of inserting the electrodes into the brain tissue poses a fundamental constraint, which inherently restricts achievable electrode density. Biohybrid strategies, integrating biological and synthetic components, have shown promise, but they have yet to overcome the major challenges necessary to achieve a scalable functional interface. It is concluded that, given the current limitations of available techniques, there is a pressing need to explore fundamentally novel approaches to realize the vision of recording the electrical activity of every cortical neuron within the brain.},
}

@article {pmid40573719,
year = {2025},
author = {Safarov, F and Kutlimuratov, A and Khojamuratova, U and Abdusalomov, A and Cho, YI},
title = {Enhanced AlexNet with Gabor and Local Binary Pattern Features for Improved Facial Emotion Recognition.},
journal = {Sensors (Basel, Switzerland)},
volume = {25},
number = {12},
pages = {},
doi = {10.3390/s25123832},
pmid = {40573719},
issn = {1424-8220},
support = {20022362//Korean Agency for Technology and Standard under Ministry of Trade, Industry and Energy in 2024/ ; 2410003714//Establishment of standardization basis for BCI and AI Interoperability/ ; },
mesh = {Humans ; *Emotions/physiology ; *Facial Recognition/physiology ; *Facial Expression ; Algorithms ; Deep Learning ; *Pattern Recognition, Automated/methods ; Face/physiology ; *Automated Facial Recognition/methods ; Neural Networks, Computer ; Convolutional Neural Networks ; },
abstract = {Facial emotion recognition (FER) is vital for improving human-machine interactions, serving as the foundation for AI systems that integrate cognitive and emotional intelligence. This helps bridge the gap between mechanical processes and human emotions, enhancing machine engagement with humans. Considering the constraints of low hardware specifications often encountered in real-world applications, this study leverages recent advances in deep learning to propose an enhanced model for FER. The model effectively utilizes texture information from faces through Gabor and Local Binary Pattern (LBP) feature extraction techniques. By integrating these features into a specially modified AlexNet architecture, our approach not only classifies facial emotions more accurately but also demonstrates significant improvements in performance and adaptability under various operational conditions. To validate the effectiveness of our proposed model, we conducted evaluations using the FER2013 and RAF-DB benchmark datasets, where it achieved impressive accuracies of 98.10% and 93.34% for the two datasets, with standard deviations of 1.63% and 3.62%, respectively. On the FER-2013 dataset, the model attained a precision of 98.2%, a recall of 97.9%, and an F1-score of 98.0%. Meanwhile, for the other dataset, it achieved a precision of 93.54%, a recall of 93.12%, and an F1-score of 93.34%. These results underscore the model's robustness and its capability to deliver high-precision emotion recognition, making it an ideal solution for deployment in environments where hardware limitations are a critical concern.},
}

Humans
*Emotions/physiology
*Facial Recognition/physiology
*Facial Expression
Algorithms
Deep Learning
*Pattern Recognition, Automated/methods
Face/physiology
*Automated Facial Recognition/methods
Neural Networks, Computer
Convolutional Neural Networks

@article {pmid40573479,
year = {2025},
author = {Sasatake, Y and Matsushita, K},
title = {P300 ERP System Utilizing Wireless Visual Stimulus Presentation Devices.},
journal = {Sensors (Basel, Switzerland)},
volume = {25},
number = {12},
pages = {},
doi = {10.3390/s25123592},
pmid = {40573479},
issn = {1424-8220},
support = {JPMJSP2125//JST SPRING/ ; none//THERS/ ; },
mesh = {Humans ; *Event-Related Potentials, P300/physiology ; *Wireless Technology/instrumentation ; Brain-Computer Interfaces ; Male ; Adult ; *Photic Stimulation/methods ; Electroencephalography/methods ; Female ; Young Adult ; },
abstract = {The P300 event-related potential, evoked by attending to specific sensory stimuli, is utilized in non-invasive brain-computer interface (BCI) systems and is considered the only interface through which individuals with complete paralysis can operate devices based on their intention. Conventionally, visual stimuli used to elicit P300 have been presented using displays; however, placing a display directly in front of the user obstructs the field of view and prevents the user from perceiving their surrounding environment. Moreover, every time the user changes posture, the display must be repositioned accordingly, increasing the burden on caregivers. To address these issues, we propose a novel system that employs wirelessly controllable LED visual stimulus presentation devices distributed throughout the surrounding environment, rather than relying on traditional displays. The primary challenge in the proposed system is the communication delay associated with wireless control, which introduces errors in the timing of stimulus presentation-an essential factor for accurate P300 analysis. Therefore, it is necessary to evaluate how such delays affect P300 detection accuracy. The second challenge lies in the variability of visual stimulus strength due to differences in viewing distance caused by the spatial distribution of stimulus devices. This also requires the validation of its impact on P300 detection. In Experiment 1, we evaluated system performance in terms of wireless communication delay and confirmed an average delay of 352.1 ± 30.9 ms. In Experiment 2, we conducted P300 elicitation experiments using the wireless visual stimulus presentation device under conditions that allowed the precise measurement of stimulus presentation timing. We compared P300 waveforms across three conditions: (1) using the exact measured stimulus timing, (2) using the stimulus timing with a fixed compensation of 350 ms for the wireless delay, and (3) using the stimulus timing with both the 350 ms fixed delay compensation and an additional pseudo-random error value generated based on a normal distribution. The results demonstrated the effectiveness of the proposed delay compensation method in preserving P300 waveform integrity. In Experiment 3, a system performance verification test was conducted on 21 participants using a wireless visual presentation device. As a result, statistically significant differences (p < 0.01) in amplitude between target and non-target stimuli, along with medium or greater effect sizes (Cohen's d: 0.49-0.61), were observed under all conditions with an averaging count of 10 or more. Notably, the P300 detection accuracy reached 85% with 40 averaging trials and 100% with 100 trials. These findings demonstrate that the system can function as a P300 speller and be utilized as an interface equivalent to conventional display-based methods.},
}

Humans
*Event-Related Potentials, P300/physiology
*Wireless Technology/instrumentation
Brain-Computer Interfaces
Male
Adult
*Photic Stimulation/methods
Electroencephalography/methods
Female
Young Adult

@article {pmid40571414,
year = {2025},
author = {Yang, L and Li, M and Yang, L and Wang, Z and Shang, Z},
title = {Hippocampal LFP responses during pigeon homing flight in outdoors.},
journal = {The Journal of neuroscience : the official journal of the Society for Neuroscience},
volume = {},
number = {},
pages = {},
doi = {10.1523/JNEUROSCI.0185-25.2025},
pmid = {40571414},
issn = {1529-2401},
abstract = {The hippocampal formation (HF) plays a key role in avian spatial navigation. Previous studies suggest that the HF may serve different functions at various stages in pigeons' long-distance outdoor homing flight. However, it remains unclear whether the HF exhibits specific neural responses during these stages. In this study, we employed a wearable bimodal data recording system to simultaneously capture flight trajectories and hippocampal local field potential (LFP) signals of pigeons (either sex) during outdoor homing navigation. Our results revealed significant differences in hippocampal neural responses across the initial decision-making (DM) and en route navigation (ER) stages. Specifically, elevated LFP power in theta (4-12 Hz) and beta (12-30 Hz) bands was detected during the DM stage compared to the ER stage, while the high gamma (60-120 Hz) band exhibited the opposite pattern. In addition, we examined typical theta-beta phase-amplitude coupling (PAC) during the ER stage. Additionally, stage-specific hippocampal responses remained consistent across release sites. Notably, the difference in hippocampal responses across stages diminished along with the accumulation of homing experience. These results offer new insights into the role of the avian HF in homing flight navigation and suggest parallels between avian and mammalian hippocampal mechanisms in spatial learning.Significance Statement It remains unclear whether the hippocampal formation (HF) exhibits specific neural responses during various stages in the long-distance outdoor navigation of pigeons. By recording hippocampal local field potentials (LFPs) and positional data during natural outdoor flights, we reveal distinct neural response patterns that differentiate between initial decision-making and sustained navigation stages. We detected band-specific power and coupling responses between different navigation stages, consistent across multiple release sites. Additionally, we found that the LFP responses differences across stages gradually diminish along with the accumulation of the homing experience. Our study offers new insights into the role of the avian HF in outdoor homing flight.},
}

@article {pmid40567082,
year = {2025},
author = {Lachkar, S and Ibrahimi, A and Boualaoui, I and Sayegh, HE and Nouini, Y},
title = {Botulinum toxin A in idiopathic overactive bladder: a narrative review of 5410 cases.},
journal = {The Canadian journal of urology},
volume = {32},
number = {3},
pages = {145-165},
pmid = {40567082},
issn = {1488-5581},
mesh = {Humans ; *Urinary Bladder, Overactive/drug therapy ; *Botulinum Toxins, Type A/therapeutic use/adverse effects ; *Neuromuscular Agents/therapeutic use/adverse effects ; Treatment Outcome ; },
abstract = {INTRODUCTION: When conservative treatments fail, botulinum toxin A (BoNT-A) is an option for refractory idiopathic overactive bladder (OAB). This review evaluates the efficacy, safety, and predictive factors for BoNT-A in this situation.

MATERIALS AND METHODS: A literature search up to January 2025 was performed using PubMed, Google Scholar, and Embase to assess efficacy, safety, and predictors of adverse events (AE) related to BoNT-A. The risk of bias was assessed using the Risk of Bias 2 (RoB 2) tool for randomized studies and the Critical Appraisal Skills Programme (CASP) checklist for cohort studies. The quality of the review was evaluated based on the Oxford criteria, following the Strengthening the Assessment of Narrative Review Articles (SANRA) guidelines, and by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines for systematic reviews.

RESULTS: 31 studies were included, involving 5410 patients. BoNT-A improves OAB symptoms even after reinjections. Higher doses do not enhance efficacy but increase AE. AE includes high post-void residual (PVR), clean intermittent self-catheterization (CISC), and Urinary Tract Infection (UTI). Predictors of CISC include age, male gender, hysterectomy, ≥3 vaginal deliveries, mixed incontinence, prior mid-urethral sling (MUS), high PVR, low Pressure at Pdet at First Micturition (PIP1) in women, low Bladder Compliance Index (BCI) in men, and high Bladder Outlet Obstruction Index (BOOI). Diabetes and heart failure increase PVR. UTIs are more frequent in women and men with benign prostatic hyperplasia, with CISC increasing the risk fivefold. Severe complications are rare. Predictors of poor response include male gender, high BOOI, low urinary flow, and diabetes.

DISCUSSION: BoNT-A is effective for OAB, especially for incontinence. AE is dose-dependent and limits treatment adherence. Their link with poor response remains unclear.

CONCLUSION: BoNT-A effectively treats refractory idiopathic OAB, improving symptoms and quality of life with repeated injections.},
}

Humans
*Urinary Bladder, Overactive/drug therapy
*Botulinum Toxins, Type A/therapeutic use/adverse effects
*Neuromuscular Agents/therapeutic use/adverse effects
Treatment Outcome

@article {pmid40566931,
year = {2025},
author = {Lin, K and Chen, J and Pan, J and Wang, R and Wu, S and Wen, W and Li, Y and Wang, L and Yuan, F},
title = {Electro-Acupuncture to Treat Disorder of Consciousness (AcuDoc): Study Protocol for a Randomized Sham-Controlled Trial.},
journal = {Brain and behavior},
volume = {15},
number = {6},
pages = {e70637},
pmid = {40566931},
issn = {2162-3279},
support = {//Health Commission of Guangzhou City/ ; //NATCM's Project of High-level Construction of Key TCM Disciplines/ ; //Guangzhou Municipal Science and Technology Bureau/ ; //2023A04J0473/ ; },
mesh = {Humans ; *Electroacupuncture/methods ; Adult ; *Brain Injuries, Traumatic/complications/therapy/physiopathology ; *Consciousness Disorders/therapy/etiology/physiopathology ; Female ; Male ; Young Adult ; Middle Aged ; Randomized Controlled Trials as Topic ; Electroencephalography ; },
abstract = {BACKGROUND: Treatment of disorders of consciousness (DOC) remains a clinical challenge. Electroacupuncture (EA) was shown to have the potential to promote the recovery of consciousness. This trial aims to explore the therapeutic effects and mechanisms of EA in patients with DOC due to traumatic brain injury (TBI) through a multimodal approach.

METHODS: A total of 50 adult patients with DOC due to TBI and 25 healthy subjects will be enrolled in the study. Patients enrolled in the study will be assigned to the EA group or the sham-EA group through stratified randomization. All patients receive behavioral assessments (CRS-R and brain-computer interface), neurophysiological evaluations (EEG, somatosensory evoked potentials, brainstem auditory evoked potentials), and neuroimaging evaluations (rs-fMRI, amide proton transfer, intravoxel incoherent motion, neurite orientation dispersion and density imaging) before and after the 14-day EA or sham-EA treatment. Each healthy subject will receive a set of neurophysiological and neuroimaging examinations but no treatments. The practitioner administering EA and sham-EA is the only one aware of the grouping results. In the sham-EA group, sham-acupoints, sham-acupuncture, and sham-wire are utilized. The primary outcome measurement is the change in CRS-R score after 14 days of treatment compared with the baseline CRS-R score.

DISCUSSION: The AcuDoc trial will be the first randomized sham-controlled study to investigate the clinical benefits of EA in patients with DOC. This trial will elucidate the role of EA in the treatment of DOC due to TBI and provide evidence of its therapeutic mechanisms.},
}

Humans
*Electroacupuncture/methods
Adult
*Brain Injuries, Traumatic/complications/therapy/physiopathology
*Consciousness Disorders/therapy/etiology/physiopathology
Female
Male
Young Adult
Middle Aged
Randomized Controlled Trials as Topic
Electroencephalography

@article {pmid40566772,
year = {2025},
author = {Zhang, T and Chen, J and Lu, Y and Xu, D and Yan, S and Ouyang, Z},
title = {[The analysis of invention patents in the field of artificial intelligent medical devices].},
journal = {Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi},
volume = {42},
number = {3},
pages = {504-511},
doi = {10.7507/1001-5515.202407044},
pmid = {40566772},
issn = {1001-5515},
mesh = {*Artificial Intelligence ; *Patents as Topic ; Humans ; *Inventions ; China ; Brain-Computer Interfaces ; Telemedicine ; *Equipment and Supplies ; Robotics ; Algorithms ; },
abstract = {The emergence of new-generation artificial intelligence technology has brought numerous innovations to the healthcare field, including telemedicine and intelligent care. However, the artificial intelligent medical device sector still faces significant challenges, such as data privacy protection and algorithm reliability. This study, based on invention patent analysis, revealed the technological innovation trends in the field of artificial intelligent medical devices from aspects such as patent application time trends, hot topics, regional distribution, and innovation players. The results showed that global invention patent applications had remained active, with technological innovations primarily focused on medical image processing, physiological signal processing, surgical robots, brain-computer interfaces, and intelligent physiological parameter monitoring technologies. The United States and China led the world in the number of invention patent applications. Major international medical device giants, such as Philips, Siemens, General Electric, and Medtronic, were at the forefront of global technological innovation, with significant advantages in patent application volumes and international market presence. Chinese universities and research institutes, such as Zhejiang University, Tianjin University, and the Shenzhen Institute of Advanced Technology, had demonstrated notable technological innovation, with a relatively high number of patent applications. However, their overseas market expansion remained limited. This study provides a comprehensive overview of the technological innovation trends in the artificial intelligent medical device field and offers valuable information support for industry development from an informatics perspective.},
}

*Artificial Intelligence
*Patents as Topic
Humans
*Inventions
China
Brain-Computer Interfaces
Telemedicine
*Equipment and Supplies
Robotics
Algorithms

@article {pmid40566769,
year = {2025},
author = {Wu, H and Chen, S and Jia, J},
title = {[Research progress on brain mechanism of brain-computer interface technology in the upper limb motor function rehabilitation in stroke patients].},
journal = {Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi},
volume = {42},
number = {3},
pages = {480-487},
doi = {10.7507/1001-5515.202404015},
pmid = {40566769},
issn = {1001-5515},
mesh = {Humans ; *Brain-Computer Interfaces ; *Stroke Rehabilitation ; *Upper Extremity/physiopathology ; *Brain/physiopathology ; Electroencephalography ; Stroke/physiopathology ; },
abstract = {Stroke causes abnormality of brain physiological function and limb motor function. Brain-computer interface (BCI) connects the patient's active consciousness to an external device, so as to enhance limb motor function. Previous studies have preliminarily confirmed the efficacy of BCI rehabilitation training in improving upper limb motor function after stroke, but the brain mechanism behind it is still unclear. This paper aims to review on the brain mechanism of upper limb motor dysfunction in stroke patients and the improvement of brain function in those receiving BCI training, aiming to further explore the brain mechanism of BCI in promoting the rehabilitation of upper limb motor function after stroke. The results of this study show that in the fields of imaging and electrophysiology, abnormal activity and connectivity have been found in stroke patients. And BCI training for stroke patients can improve their upper limb motor function by increasing the activity and connectivity of one hemisphere of the brain and restoring the balance between the bilateral hemispheres of the brain. This article summarizes the brain mechanism of BCI in promoting the rehabilitation of upper limb motor function in stroke in both imaging and electrophysiology, and provides a reference for the clinical application and scientific research of BCI in stroke rehabilitation in the future.},
}

Humans
*Brain-Computer Interfaces
*Stroke Rehabilitation
*Upper Extremity/physiopathology
*Brain/physiopathology
Electroencephalography
Stroke/physiopathology

@article {pmid40566768,
year = {2025},
author = {Liu, X and Yang, B and Gan, A and Zhang, J},
title = {[Study on speech imagery electroencephalography decoding of Chinese words based on the CAM-Net model].},
journal = {Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi},
volume = {42},
number = {3},
pages = {473-479},
doi = {10.7507/1001-5515.202503048},
pmid = {40566768},
issn = {1001-5515},
mesh = {Humans ; *Electroencephalography/methods ; *Brain-Computer Interfaces ; *Neural Networks, Computer ; *Speech/physiology ; Algorithms ; Male ; Adult ; Imagination ; },
abstract = {Speech imagery is an emerging brain-computer interface (BCI) paradigm with potential to provide effective communication for individuals with speech impairments. This study designed a Chinese speech imagery paradigm using three clinically relevant words-"Help me", "Sit up" and "Turn over"-and collected electroencephalography (EEG) data from 15 healthy subjects. Based on the data, a Channel Attention Multi-Scale Convolutional Neural Network (CAM-Net) decoding algorithm was proposed, which combined multi-scale temporal convolutions with asymmetric spatial convolutions to extract multidimensional EEG features, and incorporated a channel attention mechanism along with a bidirectional long short-term memory network to perform channel weighting and capture temporal dependencies. Experimental results showed that CAM-Net achieved a classification accuracy of 48.54% in the three-class task, outperforming baseline models such as EEGNet and Deep ConvNet, and reached a highest accuracy of 64.17% in the binary classification between "Sit up" and "Turn over". This work provides a promising approach for future Chinese speech imagery BCI research and applications.},
}

Humans
*Electroencephalography/methods
*Brain-Computer Interfaces
*Neural Networks, Computer
*Speech/physiology
Algorithms
Male
Adult
Imagination

@article {pmid40566767,
year = {2025},
author = {Li, X and Cao, X and Wang, J and Zhu, W and Huang, Y and Wan, F and Hu, Y},
title = {[Performance evaluation of a wearable steady-state visual evoked potential based brain-computer interface in real-life scenario].},
journal = {Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi},
volume = {42},
number = {3},
pages = {464-472},
doi = {10.7507/1001-5515.202310069},
pmid = {40566767},
issn = {1001-5515},
mesh = {*Brain-Computer Interfaces ; Humans ; *Evoked Potentials, Visual/physiology ; *Electroencephalography ; *Wearable Electronic Devices ; Algorithms ; Signal Processing, Computer-Assisted ; Adult ; Male ; },
abstract = {Brain-computer interface (BCI) has high application value in the field of healthcare. However, in practical clinical applications, convenience and system performance should be considered in the use of BCI. Wearable BCIs are generally with high convenience, but their performance in real-life scenario needs to be evaluated. This study proposed a wearable steady-state visual evoked potential (SSVEP)-based BCI system equipped with a small-sized electroencephalogram (EEG) collector and a high-performance training-free decoding algorithm. Ten healthy subjects participated in the test of BCI system under simplified experimental preparation. The results showed that the average classification accuracy of this BCI was 94.10% for 40 targets, and there was no significant difference compared to the dataset collected under the laboratory condition. The system achieved a maximum information transfer rate (ITR) of 115.25 bit/min with 8-channel signal and 98.49 bit/min with 4-channel signal, indicating that the 4-channel solution can be used as an option for the few-channel BCI. Overall, this wearable SSVEP-BCI can achieve good performance in real-life scenario, which helps to promote BCI technology in clinical practice.},
}

*Brain-Computer Interfaces
Humans
*Evoked Potentials, Visual/physiology
*Electroencephalography
*Wearable Electronic Devices
Algorithms
Signal Processing, Computer-Assisted
Adult
Male

@article {pmid40566766,
year = {2025},
author = {Zhu, Y and Ji, Z and Li, S and Wang, H and Fu, Y and Wang, H},
title = {[A portable steady-state visual evoked potential brain-computer interface system for smart healthcare].},
journal = {Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi},
volume = {42},
number = {3},
pages = {455-463},
doi = {10.7507/1001-5515.202412051},
pmid = {40566766},
issn = {1001-5515},
mesh = {*Brain-Computer Interfaces ; Humans ; *Evoked Potentials, Visual/physiology ; *Electroencephalography ; Signal Processing, Computer-Assisted ; Software ; Adult ; Male ; },
abstract = {This paper realized a portable brain-computer interface (BCI) system tailored for smart healthcare. Through the decoding of steady-state visual evoked potential (SSVEP), this system can rapidly and accurately identify the intentions of subjects, thereby meeting the practical demands of daily medical scenarios. Firstly, an SSVEP stimulation interface and an electroencephalogram (EEG) signal acquisition software were designed, which enable the system to execute multi-target and multi-task operations while also incorporating data visualization functionality. Secondly, the EEG signals recorded from the occipital region were decomposed into eight sub-frequency bands using filter bank canonical correlation analysis (FBCCA). Subsequently, the similarity between each sub-band signal and the reference signals was computed to achieve efficient SSVEP decoding. Finally, 15 subjects were recruited to participate in the online evaluation of the system. The experimental results indicated that in real-world scenarios, the system achieved an average accuracy of 85.19% in identifying the intentions of the subjects, and an information transfer rate (ITR) of 37.52 bit/min. This system was awarded third prize in the Visual BCI Innovation Application Development competition at the 2024 World Robot Contest, validating its effectiveness. In conclusion, this study has developed a portable, multifunctional SSVEP online decoding system, providing an effective approach for human-computer interaction in smart healthcare.},
}

*Brain-Computer Interfaces
Humans
*Evoked Potentials, Visual/physiology
*Electroencephalography
Signal Processing, Computer-Assisted
Software
Adult
Male

@article {pmid40566765,
year = {2025},
author = {Chai, X and Wang, N and Song, J and Yang, Y},
title = {[Detection of motor intention in patients with consciousness disorder based on electroencephalogram and functional near infrared spectroscopy combined with motor brain-computer interface paradigm].},
journal = {Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi},
volume = {42},
number = {3},
pages = {447-454},
doi = {10.7507/1001-5515.202502027},
pmid = {40566765},
issn = {1001-5515},
mesh = {Humans ; *Brain-Computer Interfaces ; Spectroscopy, Near-Infrared/methods ; *Electroencephalography/methods ; *Consciousness Disorders/physiopathology/diagnosis ; Male ; Movement ; Adult ; Female ; Intention ; Persistent Vegetative State/physiopathology/diagnosis ; },
abstract = {Clinical grading diagnosis of disorder of consciousness (DOC) patients relies on behavioral assessment, which has certain limitations. Combining multi-modal technologies and brain-computer interface (BCI) paradigms can assist in identifying patients with minimally conscious state (MCS) and vegetative state (VS). This study collected electroencephalogram (EEG) and functional near-infrared spectroscopy (fNIRS) signals under motor BCI paradigms from 14 DOC patients, who were divided into two groups based on clinical scores: 7 in the MCS group and 7 in the VS group. We calculated event-related desynchronization (ERD) and motor decoding accuracy to analyze the effectiveness of motor BCI paradigms in detecting consciousness states. The results showed that the classification accuracies for left-hand and right-hand movement tasks using EEG were 93.28% and 76.19% for the MCS and VS groups, respectively; the classification precisions using fNIRS were 53.72% and 49.11% for these groups. When combining EEG and fNIRS features, the classification accuracies for left-hand and right-hand movement tasks in the MCS and VS groups were 95.56% and 87.38%, respectively. Although there was no statistically significant difference in motor decoding accuracy between the two groups, significant differences in ERD were observed between different consciousness states during left-hand movement tasks (P < 0.001). This study demonstrates that motor BCI paradigms can assist in assessing the level of consciousness, with EEG being more sensitive for evaluating residual motor intention intensity. Moreover, the ERD feature of motor intention intensity is more sensitive than BCI classification accuracy.},
}

Humans
*Brain-Computer Interfaces
Spectroscopy, Near-Infrared/methods
*Electroencephalography/methods
*Consciousness Disorders/physiopathology/diagnosis
Male
Movement
Adult
Female
Intention
Persistent Vegetative State/physiopathology/diagnosis

@article {pmid40566764,
year = {2025},
author = {Pan, J and Zhang, Z and Zhang, Y and Wang, F and Xiao, J},
title = {[Brain-computer interface technology and its applications for patients with disorders of consciousness].},
journal = {Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi},
volume = {42},
number = {3},
pages = {438-446},
doi = {10.7507/1001-5515.202410061},
pmid = {40566764},
issn = {1001-5515},
mesh = {Humans ; *Brain-Computer Interfaces ; *Consciousness Disorders/diagnosis/rehabilitation/physiopathology ; Electroencephalography ; Brain/physiopathology ; Consciousness ; },
abstract = {With the continuous advancement of neuroimaging technologies, clinical research has discovered the phenomenon of cognitive-motor dissociation in patients with disorders of consciousness (DoC). This groundbreaking finding has provided new impetus for the development and application of brain-computer interface (BCI) in clinic. Currently, BCI has been widely applied in DoC patients as an important tool for assessing and assisting behaviorally unresponsive individuals. This paper reviews the current applications of BCI in DoC patients, focusing four main aspects including consciousness detection, auxiliary diagnosis, prognosis assessment, and rehabilitation treatment. It also provides an in-depth analysis of representative key techniques and experimental outcomes in each aspect, which include BCI paradigm designs, brain signal decoding method, and feedback mechanisms. Furthermore, the paper offers recommendations for BCI design tailored to DoC patients and discusses future directions for research and clinical practice in this field.},
}

Humans
*Brain-Computer Interfaces
*Consciousness Disorders/diagnosis/rehabilitation/physiopathology
Electroencephalography
Brain/physiopathology
Consciousness

@article {pmid40566763,
year = {2025},
author = {Pan, H and Ding, P and Wang, F and Li, T and Zhao, L and Nan, W and Gong, A and Fu, Y},
title = {[Evaluation methods for the rehabilitation efficacy of bidirectional closed-loop motor imagery brain-computer interface active rehabilitation training systems].},
journal = {Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi},
volume = {42},
number = {3},
pages = {431-437},
doi = {10.7507/1001-5515.202407097},
pmid = {40566763},
issn = {1001-5515},
mesh = {*Brain-Computer Interfaces ; Humans ; *Imagination/physiology ; *Imagery, Psychotherapy/methods ; },
abstract = {The bidirectional closed-loop motor imagery brain-computer interface (MI-BCI) is an emerging method for active rehabilitation training of motor dysfunction, extensively tested in both laboratory and clinical settings. However, no standardized method for evaluating its rehabilitation efficacy has been established, and relevant literature remains limited. To facilitate the clinical translation of bidirectional closed-loop MI-BCI, this article first introduced its fundamental principles, reviewed the rehabilitation training cycle and methods for evaluating rehabilitation efficacy, and summarized approaches for evaluating system usability, user satisfaction and usage. Finally, the challenges associated with evaluating the rehabilitation efficacy of bidirectional closed-loop MI-BCI were discussed, aiming to promote its broader adoption and standardization in clinical practice.},
}

*Brain-Computer Interfaces
Humans
*Imagination/physiology
*Imagery, Psychotherapy/methods

@article {pmid40566538,
year = {2025},
author = {Pilipović, K and Janković, T and Rajič Bumber, J and Belančić, A and Mršić-Pelčić, J},
title = {Traumatic Brain Injury: Novel Experimental Approaches and Treatment Possibilities.},
journal = {Life (Basel, Switzerland)},
volume = {15},
number = {6},
pages = {},
doi = {10.3390/life15060884},
pmid = {40566538},
issn = {2075-1729},
support = {UIP-2017-05-9517//Croatian Science Foundation/ ; uniri-iskusni-biomed-23-56//University of Rijeka/ ; uniri-mladi-biomed-23-38//University of Rijeka/ ; uniri-iskusni-biomed-23-82//University of Rijeka/ ; },
abstract = {Traumatic brain injury (TBI) remains a critical global health issue with limited effective treatments. Traditional care of TBI patients focuses on stabilization and symptom management without regenerating damaged brain tissue. In this review, we analyze the current state of treatment of TBI, with focus on novel therapeutic approaches aimed at reducing secondary brain injury and promoting recovery. There are few innovative strategies that break away from the traditional, biological target-focused treatment approaches. Precision medicine includes personalized treatments based on biomarkers, genetics, advanced imaging, and artificial intelligence tools for prognosis and monitoring. Stem cell therapies are used to repair tissue, regulate immune responses, and support neural regeneration, with ongoing development in gene-enhanced approaches. Nanomedicine uses nanomaterials for targeted drug delivery, neuroprotection, and diagnostics by crossing the blood-brain barrier. Brain-machine interfaces enable brain-device communication to restore lost motor or neurological functions, while virtual rehabilitation and neuromodulation use virtual and augmented reality as well as brain stimulation techniques to improve rehabilitation outcomes. While these approaches show great potential, most are still in development and require more clinical testing to confirm safety and effectiveness. The future of TBI therapy looks promising, with innovative strategies likely to transform care.},
}

@article {pmid40564460,
year = {2025},
author = {Liu, Z and Fan, K and Gu, Q and Ruan, Y},
title = {Channel-Dependent Multilayer EEG Time-Frequency Representations Combined with Transfer Learning-Based Deep CNN Framework for Few-Channel MI EEG Classification.},
journal = {Bioengineering (Basel, Switzerland)},
volume = {12},
number = {6},
pages = {},
doi = {10.3390/bioengineering12060645},
pmid = {40564460},
issn = {2306-5354},
abstract = {The study of electroencephalogram (EEG) signals is crucial for understanding brain function and has extensive applications in clinical diagnosis, neuroscience, and brain-computer interface technology. This paper addresses the challenge of recognizing motor imagery EEG signals with few channels, which is essential for portable and real-time applications. A novel framework is proposed that applies a continuous wavelet transform to convert time-domain EEG signals into two-dimensional time-frequency representations. These images are then concatenated into channel-dependent multilayer EEG time-frequency representations (CDML-EEG-TFR), incorporating multidimensional information of time, frequency, and channels, allowing for a more comprehensive and enriched brain representation under the constraint of few channels. By adopting a deep convolutional neural network with EfficientNet as the backbone and utilizing pre-trained weights from natural image datasets for transfer learning, the framework can simultaneously learn temporal, spatial, and channel features embedded in the CDML-EEG-TFR. Moreover, the transfer learning strategy effectively addresses the issue of data sparsity in the context of a few channels. Our approach enhances the classification accuracy of motor imagery EEG signals in few-channel scenarios. Experimental results on the BCI Competition IV 2b dataset show a significant improvement in classification accuracy, reaching 80.21%. This study highlights the potential of CDML-EEG-TFR and the EfficientNet-based transfer learning strategy in few-channel EEG signal classification, laying a foundation for practical applications and further research in medical and sports fields.},
}

@article {pmid40564444,
year = {2025},
author = {Garcia-Palencia, O and Fernandez, J and Shim, V and Kasabov, NK and Wang, A and The Alzheimer's Disease Neuroimaging Initiative, },
title = {Spiking Neural Networks for Multimodal Neuroimaging: A Comprehensive Review of Current Trends and the NeuCube Brain-Inspired Architecture.},
journal = {Bioengineering (Basel, Switzerland)},
volume = {12},
number = {6},
pages = {},
doi = {10.3390/bioengineering12060628},
pmid = {40564444},
issn = {2306-5354},
support = {Project 22-UOA-120, 23-UOA-055-CSG//Health Research Council of New Zealand and Royal Society Catalyst/ ; 23-UOA-055-CSG//University of Auckland/ ; },
abstract = {Artificial intelligence (AI) is revolutionising neuroimaging by enabling automated analysis, predictive analytics, and the discovery of biomarkers for neurological disorders. However, traditional artificial neural networks (ANNs) face challenges in processing spatiotemporal neuroimaging data due to their limited temporal memory and high computational demands. Spiking neural networks (SNNs), inspired by the brain's biological processes, offer a promising alternative. SNNs use discrete spikes for event-driven communication, making them energy-efficient and well suited for the real-time processing of dynamic brain data. Among SNN architectures, NeuCube stands out as a powerful framework for analysing spatiotemporal neuroimaging data. It employs a 3D brain-like structure to model neural activity, enabling personalised modelling, disease classification, and biomarker discovery. This paper explores the advantages of SNNs and NeuCube for multimodal neuroimaging analysis, including their ability to handle complex spatiotemporal patterns, adapt to evolving data, and provide interpretable insights. We discuss applications in disease diagnosis, brain-computer interfaces, and predictive modelling, as well as challenges such as training complexity, data encoding, and hardware limitations. Finally, we highlight future directions, including hybrid ANN-SNN models, neuromorphic hardware, and personalised medicine. Our contributions in this work are as follows: (i) we give a comprehensive review of an SNN applied to neuroimaging analysis; (ii) we present current software and hardware platforms, which have been studied in neuroscience; (iii) we provide a detailed comparison of performance and timing of SNN software simulators with a curated ADNI and other datasets; (iv) we provide a roadmap to select a hardware/software platform based on specific cases; and (v) finally, we highlight a project where NeuCube has been successfully used in neuroscience. The paper concludes with discussions of challenges and future perspectives.},
}

@article {pmid40564430,
year = {2025},
author = {Darvishi, H and Mohammadi, A and Maghami, MH and Sadeghi, M and Sawan, M},
title = {EEG-Driven Arm Movement Decoding: Combining Connectivity and Amplitude Features for Enhanced Brain-Computer Interface Performance.},
journal = {Bioengineering (Basel, Switzerland)},
volume = {12},
number = {6},
pages = {},
doi = {10.3390/bioengineering12060614},
pmid = {40564430},
issn = {2306-5354},
abstract = {Brain-computer interfaces (BCIs) translate electroencephalography (EEG) signals into control commands, offering potential solutions for motor-impaired individuals. While traditional BCI studies often focus solely on amplitude variations or inter-channel connectivity, movement-related brain activity is inherently dynamic, involving interactions across regions and frequency bands. We propose that combining amplitude-based (filter bank common spatial patterns, FBCSP) and phase-based connectivity features (phase-locking value, PLV) improves decoding accuracy. EEG signals from ten healthy subjects were recorded during arm movements, with electromyography (EMG) as ground truth. After preprocessing (resampling, normalization, bandpass filtering), FBCSP and multi-lag PLV features were fused, and the ReliefF algorithm selected the most informative subset. A feedforward neural network achieved average metrics of: Pearson correlation 0.829 ± 0.077, R-squared value 0.675 ± 0.126, and root mean square error (RMSE) 0.579 ± 0.098 in predicting EMG amplitudes indicative of arm movement angles. Analysis highlighted contributions from both FBCSP and PLV, particularly in the 4-8 Hz and 24-28 Hz bands. This fusion approach, augmented by data-driven feature selection, significantly enhances movement decoding accuracy, advancing robust neuroprosthetic control systems.},
}

@article {pmid40563754,
year = {2025},
author = {Gkintoni, E and Vassilopoulos, SP and Nikolaou, G and Vantarakis, A},
title = {Neurotechnological Approaches to Cognitive Rehabilitation in Mild Cognitive Impairment: A Systematic Review of Neuromodulation, EEG, Virtual Reality, and Emerging AI Applications.},
journal = {Brain sciences},
volume = {15},
number = {6},
pages = {},
doi = {10.3390/brainsci15060582},
pmid = {40563754},
issn = {2076-3425},
abstract = {Background/Objectives: Mild Cognitive Impairment (MCI) represents a clinical syndrome characterized by cognitive decline greater than expected for an individual's age and education level but not severe enough to significantly interfere with daily activities, with variable trajectories that may remain stable, progress to dementia, or occasionally revert to normal cognition. This systematic review examines neurotechnological approaches to cognitive rehabilitation in MCI populations, including neuromodulation, electroencephalography (EEG), virtual reality (VR), cognitive training, physical exercise, and artificial intelligence (AI) applications. Methods: A systematic review following PRISMA guidelines was conducted on 34 empirical studies published between 2014 and 2024. Studies were identified through comprehensive database searches and included if they employed neurotechnological interventions targeting cognitive outcomes in individuals with MCI. Results: Evidence indicates promising outcomes across multiple intervention types. Neuromodulation techniques showed beneficial effects on memory and executive function. EEG analyses identified characteristic neurophysiological markers of MCI with potential for early detection and monitoring. Virtual reality enhanced assessment sensitivity and rehabilitation engagement through ecologically valid environments. Cognitive training demonstrated the most excellent efficacy with multi-domain, adaptive approaches. Physical exercise interventions yielded improvements through multiple neurobiological pathways. Emerging AI applications showed potential for personalized assessment and intervention through predictive modeling and adaptive algorithms. Conclusions: Neurotechnological approaches offer promising avenues for MCI rehabilitation, with the most substantial evidence for integrated interventions targeting multiple mechanisms. Neurophysiological monitoring provides valuable biomarkers for diagnosis and treatment response. Future research should focus on more extensive clinical trials, standardized protocols, and accessible implementation models to translate these technological advances into clinical practice.},
}

@article {pmid40563743,
year = {2025},
author = {Mróz, K and Jonak, K},
title = {Preliminary Electroencephalography-Based Assessment of Anxiety Using Machine Learning: A Pilot Study.},
journal = {Brain sciences},
volume = {15},
number = {6},
pages = {},
doi = {10.3390/brainsci15060571},
pmid = {40563743},
issn = {2076-3425},
support = {FD-20/II-3/999//Lublin University of Technology/ ; },
abstract = {Background: Recent advancements in machine learning (ML) have significantly influenced the analysis of brain signals, particularly electroencephalography (EEG), enhancing the detection of complex neural patterns. ML enables large-scale data processing, offering novel opportunities for diagnosing and treating mental disorders. However, challenges such as data variability, noise, and model interpretability remain significant. This study reviews the current limitations of EEG-based anxiety detection and explores the potential of advanced AI models, including transformers and VAE-D2GAN, to improve diagnostic accuracy and real-time monitoring. Methods: The paper presents the application of ML algorithms, with a focus on convolutional neural networks (CNN) and recurrent neural networks (RNN), in identifying biomarkers of anxiety disorders and predicting therapy responses. Additionally, it discusses the role of brain-computer interfaces (BCIs) in assisting individuals with disabilities by enabling device control through brain activity. Results: Experimental EEG research on BCI applications was conducted, focusing on motor imagery-based brain activity. Findings indicate that successive training sessions improve signal classification accuracy, emphasizing the need for personalized and adaptive EEG analysis methods. Challenges in BCI usability and technological constraints in EEG processing are also addressed. Conclusions: By integrating ML with EEG analysis, this study highlights the potential for future healthcare applications, including neurorehabilitation, anxiety disorder therapy, and predictive clinical models. Future research should focus on optimizing ML algorithms, enhancing personalization, and addressing ethical concerns related to patient privacy.},
}

@article {pmid40563723,
year = {2025},
author = {Fodor, MA and Cantürk, A and Heisenberg, G and Volosyak, I},
title = {Streamlining cVEP Paradigms: Effects of a Minimized Electrode Montage on Brain-Computer Interface Performance.},
journal = {Brain sciences},
volume = {15},
number = {6},
pages = {},
doi = {10.3390/brainsci15060549},
pmid = {40563723},
issn = {2076-3425},
support = {101118964//This project has received funding from the European Union's research and innovation programme under the Marie Skłodowska-Curie grant agreement No 101118964./ ; },
abstract = {(1) Background: Brain-computer interfaces (BCIs) enable direct communication between the brain and external devices using electroencephalography (EEG) signals, offering potential applications in assistive technology and neurorehabilitation. Code-modulated visual evoked potential (cVEP)-based BCIs employ code-pattern-based stimulation to evoke neural responses, which can then be classified to infer user intent. While increasing the number of EEG electrodes across the visual cortex enhances classification accuracy, it simultaneously reduces user comfort and increases setup complexity, duration, and hardware costs. (2) Methods: This online BCI study, involving thirty-eight able-bodied participants, investigated how reducing the electrode count from 16 to 6 affected performance. Three experimental conditions were tested: a baseline 16-electrode configuration, a reduced 6-electrode setup without retraining, and a reduced 6-electrode setup with retraining. (3) Results: Our results indicate that, on average, performance declines with fewer electrodes; nonetheless, retraining restored near-baseline mean Information Transfer Rate (ITR) and accuracy for those participants for whom the system remained functional. The results reveal that for a substantial number of participants, the classification pipeline fails after electrode removal, highlighting individual differences in the cVEP response characteristics or inherent limitations of the classification approach. (4) Conclusions: Ultimately, this suggests that minimal cVEP-BCI electrode setups capable of reliably functioning across all users might only be feasible through other, more flexible classification methods that can account for individual differences. These findings aim to serve as a guideline for what is currently achievable with this common cVEP paradigm and to highlight where future research should focus in order to move closer to a practical and user-friendly system.},
}

@article {pmid40562060,
year = {2025},
author = {Xu, F and Liu, Y and Li, Y and Zhang, C and Han, Z and He, T and Xiao, X and Chao, F and Leng, J and Xu, M},
title = {Research on coding and decoding algorithm of binocular brain-controlled unmanned vehicle.},
journal = {Journal of neural engineering},
volume = {},
number = {},
pages = {},
doi = {10.1088/1741-2552/ade829},
pmid = {40562060},
issn = {1741-2552},
abstract = {With the rapid development of Brain-Computer Interface (BCI) technology, Steady-State Visual Evoked Potential (SSVEP) has emerged as an effective method for high-efficiency information transmission. However, traditional single-frequency stimulation methods face limitations in command set scalability and visual comfort. To address these issues, we propose a novel binocular SSVEP stimulation paradigm for brain-controlled unmanned vehicles. This system uses a checkerboard and phase encoding for stimulus presentation, encoding a single target with two frequencies to expand the command set. The frequencies are set between 30-35 Hz to enhance visual comfort. By leveraging polarized light technology, each eye receives distinct frequencies, suppressing intermodulation components and reducing the stimulated area for each eye. We also introduce an improved Filter Bank Dual-frequency Task-Discriminant Component Analysis (FBD-TDCA) algorithm. Experimental results show that, in a 15-command simulation, only six frequencies successfully encoded all commands, achieving comparable performance to traditional single-frequency paradigms. A 12-target brain-controlled unmanned vehicle online simulation with 12 participants further validated the proposed paradigm and algorithm. In the binocular stimulation paradigm, the average Information Transfer Rate (ITR) reached 154.67±19.69 bits/min in online experiments, with offline training yielding an ITR of 170.7±31.2 bits/min. This novel stimulation paradigm not only supports large-scale target sets for BCI systems but also improves visual comfort, offering stability and feasibility for practical brain-controlled applications. .},
}

@article {pmid40561510,
year = {2025},
author = {Almanna, MA and Elkaim, LM and Alvi, MA and Levett, JJ and Li, B and Mamdani, M and Al-Omran, M and Alotaibi, NM},
title = {Public Perception of the Brain-Computer Interface Based on a Decade of Data on X: Mixed Methods Study.},
journal = {JMIR formative research},
volume = {9},
number = {},
pages = {e60859},
doi = {10.2196/60859},
pmid = {40561510},
issn = {2561-326X},
mesh = {Humans ; *Brain-Computer Interfaces/psychology ; *Social Media/statistics & numerical data ; Natural Language Processing ; *Public Opinion ; Male ; Emotions ; Female ; Adult ; },
abstract = {BACKGROUND: Given the recent evolution and achievements in brain-computer interface (BCI) technologies, understanding public perception and sentiments toward such novel technologies is important for guiding their communication strategies in marketing and education.

OBJECTIVE: This study aims to explore the public perception of BCI technology by examining posts on X (formerly known as Twitter) using natural language processing (NLP) methods.

METHODS: A mixed methods study was conducted on BCI-related posts from January 2010 to December 2021. The dataset included 65,340 posts from 38,962 unique users. This dataset was subject to a detailed NLP analysis including VADER, TextBlob, and NRCLex libraries, focusing on quantifying the sentiment (positive, neutral, and negative), the degree of subjectivity, and the range of emotions expressed in the posts. The temporal dynamics of sentiments were examined using the Mann-Kendall trend test to identify significant trends or shifts in public interest over time, based on monthly incidence. We used the Sentiment.ai tool to infer users' demographics by matching predefined attributes in users' profile biographies to certain demographic groups. We used the BERTopic tool for semantic understanding of discussions related to BCI.

RESULTS: The analysis showed a significant rise in BCI discussions in 2017, coinciding with Elon Musk's announcement of Neuralink. Sentiment analysis revealed that 59.38% (38,804/65,340) of posts were neutral, 32.75% (21,404/65,340) were positive, and 7.85% (5132/65,340) were negative. The average polarity score demonstrated a generally positive trend over the course of the study (Mann-Kendall Statistic=0.266; τ=0.266; P<.001). Most posts were objective (50,847/65,340, 77.81%), with a smaller proportion being subjective (14,393/65,340, 22.02%). Biographic analysis showed that the "broadcasting" group contributed the most to BCI discussions (17,803/58,030, 30.67%), while the "scientific" group, contributing 27.58% (n=16,005), had the highest overall engagement metrics. The emotional analysis identified anticipation (score = 10,802/52,618, 20.52%), trust (score=9244/52,618, 17.56%), and fear (score=7344/52,618, 13.95%) as the most prominent emotions in BCI discussions. Key topics included Neuralink and Elon Musk, practical applications of BCIs, and the potential for gamification.

CONCLUSIONS: This NLP-assisted study provides a decade-long analysis of public perception of BCI technology based on data from X. Overall, sentiments were neutral yet cautiously apprehensive, with anticipation, trust, and fear as the dominant emotions. The presence of fear underscores the need to address ethical concerns, particularly around data privacy, safety, and transparency. Transparent communication and ethical considerations are essential for building public trust and reducing apprehension. Influential figures and positive clinical outcomes, such as advancements in neuroprosthetics, could enhance favorable perceptions. The gamification of BCI, particularly in gaming and entertainment, also offers potential for wider public engagement and adoption. However, public perceptions on X may differ from other platforms, affecting the broader interpretation of results. Despite these limitations, the findings provide valuable insights for guiding future BCI developments, policy making, and communication strategies.},
}

Humans
*Brain-Computer Interfaces/psychology
*Social Media/statistics & numerical data
Natural Language Processing
*Public Opinion
Male
Emotions
Female
Adult

@article {pmid40561478,
year = {2025},
author = {Chen, CS and Chang, SH and Liu, CW and Pan, TM},
title = {Exploring the Potential of Electroencephalography Signal-Based Image Generation Using Diffusion Models: Integrative Framework Combining Mixed Methods and Multimodal Analysis.},
journal = {JMIR medical informatics},
volume = {13},
number = {},
pages = {e72027},
doi = {10.2196/72027},
pmid = {40561478},
issn = {2291-9694},
mesh = {*Electroencephalography/methods ; Humans ; *Signal Processing, Computer-Assisted ; *Brain/physiology/diagnostic imaging ; *Image Processing, Computer-Assisted/methods ; Adult ; },
abstract = {BACKGROUND: Electroencephalography (EEG) has been widely used to measure brain activity, but its potential to generate accurate images from neural signals remains a challenge. Most EEG-decoding research has focused on tasks such as motor imagery, emotion recognition, and brain wave classification, which involve EEG signal analysis and classification. Some studies have explored the correlation between EEG and images, primarily focusing on EEG-image pair classification or transformation. However, EEG-based image generation remains underexplored.

OBJECTIVE: The primary goal of this study was to extend EEG-based classification to image generation, addressing the limitations of previous methods and unlocking the full potential of EEG for image synthesis. To achieve more meaningful EEG-to-image generation, we developed a novel framework, Neural-Cognitive Multimodal EEG-Informed Image (NECOMIMI), which was specifically designed to generate images directly from EEG signals.

METHODS: We developed a 2-stage NECOMIMI method, which integrated the novel Neural Encoding Representation Vectorizer (NERV) EEG encoder that we designed with a diffusion-based generative model. The Category-Based Assessment Table (CAT) score was introduced to evaluate the semantic quality of EEG-generated images. In addition, the ThingsEEG dataset was used to validate and benchmark the CAT score, providing a standardized measure for assessing EEG-to-image generation performance.

RESULTS: The NERV EEG encoder achieved state-of-the-art performance in several zero-shot classification tasks, with an average accuracy of 94.8% (SD 1.7%) in the 2-way task and 86.8% (SD 3.4%) in the 4-way task, outperforming models such as Natural Image Contrast EEG, Multimodal Similarity-Keeping Contrastive Learning, and Adaptive Thinking Mapper ShallowNet. This highlighted its superiority as a feature extraction tool for EEG signals. In a 1-stage image generation framework, EEG embeddings often resulted in abstract or generalized images such as landscapes instead of specific objects. Our proposed 2-stage NECOMIMI architecture effectively extracted semantic information from noisy EEG signals, showing its ability to capture and represent underlying concepts derived from brain wave activity. We further conducted a perturbation study to test whether the model overly depended on visual cortex EEG signals for scene-based image generation. The perturbation of visual cortex EEG channels led to a notable increase in Fréchet inception distance scores, suggesting that our model relied heavily on posterior brain signals to generate semantically coherent images.

CONCLUSIONS: NECOMIMI demonstrated the potential of EEG-to-image generation, revealing the challenges of translating noisy EEG data into accurate visual representations. The novel NERV EEG encoder for multimodal contrastive learning reached state-of-the-art performance both on n-way zero-shot and EEG-informed image generation. The introduction of the CAT score provided a new evaluation metric, paving the way for future research to refine generative models. In addition, this study highlighted the significant clinical potential of EEG-to-image generation, particularly in enhancing brain-machine interface systems and improving quality of life for individuals with motor impairments.},
}

*Electroencephalography/methods
Humans
*Signal Processing, Computer-Assisted
*Brain/physiology/diagnostic imaging
*Image Processing, Computer-Assisted/methods
Adult

@article {pmid40554057,
year = {2025},
author = {Park, S and Ha, J and Kim, L},
title = {Improving single-trial detection of error-related potentials by considering the effect of heartbeat-evoked potentials in a motor imagery-based brain-computer interface.},
journal = {Computers in biology and medicine},
volume = {195},
number = {},
pages = {110563},
doi = {10.1016/j.compbiomed.2025.110563},
pmid = {40554057},
issn = {1879-0534},
abstract = {OBJECTIVE: This study aimed to determine the effect of heartbeat-evoked potentials (HEPs) on changes in the error-related potential (ErrP) epoch and classification performance in single trials, specifically distinguishing between correct and error conditions in a three-class motor imagery-based brain-computer interface.

METHODS: Eleven individuals participated in this study, with 10 participants assigned to the offline group and 10 to the real-time group. The experiment consisted of 360 motor imagery trials, involving both correct and erroneous feedback. The ErrP trial was categorized into three conditions based on whether the heartbeat was within the ErrP epoch time window or not: (1) including heartbeat trials (ErrPIHB), (2) excluding heartbeat trials (ErrPEHB), and (3) total trials (ErrPT).

RESULTS: A small negativity was observed at approximately 200 ms, followed by a subsequent positivity at approximately 300 ms. The prominent amplitudes at approximately 200 and 300 ms in the ErrPEHB condition notably differed from those in the ErrPT and ErrPIHB conditions, showing the highest classification accuracy. In the offline experiment dataset of 10 participants, the ErrPEHB condition demonstrated the highest classification accuracy (0.89). This was higher by 0.07 and 0.11 compared to the ErrPT (0.82) and ErrPIHB (0.78) conditions, respectively. For real-time analysis, the novel ErrP paradigm proposed in this study achieved a classification accuracy of 0.89 for 10 participants, a 0.05 increase compared with that of the conventional ErrP paradigm.

CONCLUSION AND SIGNIFICANCE: These findings can contribute to obtaining pure or clear ErrP epochs associated with the target response and significantly improve classification performance.},
}

@article {pmid40553977,
year = {2025},
author = {Jiang, H and Qi, H and Tang, A and Hu, S and Lai, J},
title = {Start the Engine of Neuroregeneration: A Mechanistic and Strategic Overview of Direct Astrocyte-to-Neuron Reprogramming.},
journal = {Ageing research reviews},
volume = {},
number = {},
pages = {102808},
doi = {10.1016/j.arr.2025.102808},
pmid = {40553977},
issn = {1872-9649},
abstract = {The decline of adult neurogenesis and neuronal function during aging underlies the onset and progression of neurodegenerative diseases such as Alzheimer's disease. Conventional therapies, including neurotransmitter modulators and antibodies targeting pathogenic proteins, offer only symptomatic improvement. As the most abundant glial cells in the brain, astrocytes outnumber neurons nearly fivefold. However, their proliferative and transdifferentiation potential renders them ideal candidates for in situ neuronal replacement. Direct astrocyte-to-neuron reprogramming offers a promising regenerative approach to restore damaged neural circuits. Herein, we propose a "car start-up" model to conceptualize this process, emphasizing the need to inhibit non-neuronal fate pathways (release the handbrake), suppress transcriptional repressors (release the footbrake), and activate neuron-specific gene expression (step on the gas). Additionally, overcoming metabolic barriers in the cytoplasm is essential for successful lineage conversion. Viral or non-viral vectors deliver reprogramming factors, while small molecules serve as metabolic and epigenetic fuel to boost efficiency. In summary, we review the current evidence supporting direct astrocyte-to-neuron reprogramming as a viable regenerative strategy in the aging brain. We also highlight the conceptual "car start-up" model as a useful framework to dissect the molecular logic of lineage conversion and emphasize its promising therapeutic potential for combating neurodegenerative diseases.},
}

@article {pmid40553738,
year = {2025},
author = {Zhang, HG and Wang, JF and Jialin, A and Zhao, XY and Wang, C and Deng, W},
title = {Relationship between multimorbidity burden and depressive symptoms in older Chinese adults: A prospective 10-year cohort study.},
journal = {Journal of affective disorders},
volume = {},
number = {},
pages = {119714},
doi = {10.1016/j.jad.2025.119714},
pmid = {40553738},
issn = {1573-2517},
abstract = {BACKGROUND: Recent research indicates that multimorbidity clusters due to common mechanisms and risk factors, leading to different effects on the development of depressive symptoms (DS) in older populations. This study innovatively examined the association of both the number and specific patterns of multimorbidity with DS.

METHODS: A total of 1988 participants aged 60 years and older were selected from the China Health and Retirement Longitudinal Study (CHARLS) and monitored for DS between June 2011 and September 2020. Twelve chronic conditions were assessed through self-reports. DS was evaluated using the 10-item Center for Epidemiological Studies Depression Scale (CESD-10). Latent class analysis (LCA) was used to identify multimorbidity patterns, and Cox proportional hazards regression models examined the associations of specific diseases, multimorbidity count and multimorbidity patterns with DS.

RESULTS: During the 9.17-year follow-up period, 986 cases of DS were identified. Hypertension (adjusted hazard ratio [HR] = 1.21, 95 % confidence interval [CI] = 1.05-1.39), stroke (HR = 1.77, 95%CI = 1.20-2.63), stomach or other digestive disease (HR = 1.28, 95%CI = 1.11-1.48), arthritis or rheumatism (HR = 1.41, 95%CI = 1.24-1.60), chronic lung diseases (HR = 1.25, 95%CI = 1.03-1.52) and kidney disease (HR = 1.38, 95%CI = 1.07-1.78) were significantly associated with increased DS risk. Each additional chronic condition increased the DS hazard by 13 % (adjusted HR = 1.13, 95 % CI = 1.08-1.18). Four multimorbidity patterns were identified by LCA, with the digestion/arthritis pattern (HR = 1.47, 95 % CI = 1.22-1.77) and respiratory pattern (HR = 1.47, 95 % CI = 1.07-2.04) showing higher DS risk compared to the relatively healthy group.

CONCLUSION: The number and patterns of multimorbidity are significantly associated with heightened DS risk in older populations. Older adults in complex health conditions, particularly those with digestion/arthritis and respiratory multimorbidity patterns, should receive closer mental health monitoring.},
}

@article {pmid40551292,
year = {2025},
author = {Chu, J and Yao, J and Li, Z and Li, J and Zhang, Y and Liu, C and He, H and Li, B and Wei, H},
title = {Brain tissue electrical conductivity as a promising biomarker for dementia assessment using MRI.},
journal = {Alzheimer's & dementia : the journal of the Alzheimer's Association},
volume = {21},
number = {6},
pages = {e70270},
pmid = {40551292},
issn = {1552-5279},
support = {2024YFC2421100//National Key Research and Development Program of China/ ; //National Natural Science Foundation of China/ ; //62471296, 82271441, 62071299, 82372036, 82001342/ ; 23TS1400200//Shanghai Science and Technology Development Funds/ ; STAR 20220103 YG2023LC02//SJTU Trans-med Awards Research/ ; },
mesh = {Humans ; *Magnetic Resonance Imaging/methods ; Biomarkers ; *Brain/diagnostic imaging/metabolism/physiopathology ; *Dementia/diagnostic imaging/diagnosis/metabolism ; Male ; Female ; Amyloid beta-Peptides/metabolism ; *Electric Conductivity ; tau Proteins/metabolism ; Aged ; Cognitive Dysfunction ; Positron-Emission Tomography ; },
abstract = {INTRODUCTION: Dementia, particularly Alzheimer's disease, involves cognitive decline linked to amyloid beta (Aβ) and tau protein aggregation. Magnetic resonance imaging (MRI)-based brain tissue conductivity, which increases in dementia, may serve as a non-invasive biomarker for protein aggregation. We investigate the relationship between MRI-based brain electrical conductivity, protein aggregation, cognition, and gene expression.

METHODS: Brain conductivity maps were reconstructed and correlated with PET protein signals, cognitive performance, and plasma protein levels. The diagnostic potential of conductivity for dementia was assessed, and transcriptomic analysis using the Allen Human Brain Atlas elucidated the underlying biological processes.

RESULTS: Increased brain conductivity was associated with Aβ and tau aggregation in specific brain regions, cognitive decline, and plasma protein levels. Conductivity also improved dementia discrimination performance, and higher gene expression related to ion transport, cellular development, and signaling pathways was observed.

DISCUSSION: Brain electrical conductivity shows promise as a biomarker for dementia, correlating with protein aggregation and relevant cellular processes.

HIGHLIGHTS: Brain tissue conductivity correlates with Aβ and tau aggregation in dementia. Brain tissue conductivity correlates with cognitive scores and GMV. CSF conductivity correlates with plasma protein levels. Combining conductivity with GMV improves dementia diagnosis accuracy. Gene expression in ion processes, cell development, and signaling links to conductivity.},
}

Humans
*Magnetic Resonance Imaging/methods
Biomarkers
*Brain/diagnostic imaging/metabolism/physiopathology
*Dementia/diagnostic imaging/diagnosis/metabolism
Male
Female
Amyloid beta-Peptides/metabolism
*Electric Conductivity
tau Proteins/metabolism
Aged
Cognitive Dysfunction
Positron-Emission Tomography

@article {pmid40550006,
year = {2025},
author = {Liu, Y and Fan, P and Pan, Y and Ping, J},
title = {Flexible Microinterventional Sensors for Advanced Biosignal Monitoring.},
journal = {Chemical reviews},
volume = {},
number = {},
pages = {},
doi = {10.1021/acs.chemrev.5c00115},
pmid = {40550006},
issn = {1520-6890},
abstract = {Flexible microinterventional sensors represent a transformative technology that enables the minimal intervention required to access and monitor complex biosignals (e.g., bioelectrical, biophysical, and biochemical signals) originating from deep tissues, thereby providing accurate data for diagnostics, robotics, prosthetics, brain-computer interfaces, and therapeutic systems. However, fully unlocking their potential hinges on establishing a nondisruptive, intimate, and nonrestrictive interface with the tissue surface, facilitating efficient integration between the microinterventional sensor and the target tissue. In this comprehensive review, we highlight the critical tissue characteristics in both physiologically and pathologically relevant contexts that are pivotal for the design of microinterventional sensors. We also summarize recent advancements in flexible substrate materials and conductive materials, which are tailored to facilitate effective information interaction between bioelectronic components and biological tissues. Furthermore, we classify various electrode architectures─spanning 1D, 2D, and 3D─designed to accommodate the mechanics of soft tissues and enable nonrestrictive interfaces in diverse sensing scenarios. Additionally, we outline critical challenges for next-generation microinterventional sensors and propose integrating advanced materials, innovative fabrication, and embedded intelligence to drive breakthroughs in biosignal sensing. Ultimately, we aim to both provide foundational understanding and highlight emerging strategies in biosignal capture, leveraging recent advancements in these critical components.},
}

@article {pmid40549688,
year = {2025},
author = {Meijs, S and Andreis, FR and Kjærgaard, B and Janjua, TAM and Jensen, W},
title = {Chronic Cranial Window Technique for Repeated Cortical Recordings During Anesthesia in Pigs.},
journal = {Journal of visualized experiments : JoVE},
volume = {},
number = {220},
pages = {},
doi = {10.3791/67931},
pmid = {40549688},
issn = {1940-087X},
mesh = {Animals ; Swine ; *Electrocorticography/methods/instrumentation ; *Anesthesia/methods ; *Somatosensory Cortex/physiology ; Dura Mater/surgery ; Electrodes, Implanted ; },
abstract = {Cortical recordings are essential for extracting neuronal signals to inform various applications, including brain-computer interfaces and disease diagnostics. Each application places specific requirements on the recording technique, and invasive solutions are often selected for long-term recordings. However, invasive recording methods are challenged by device failure and adverse tissue responses, which compromise long-term signal quality. To improve the reliability and quality of chronic cortical recordings while minimizing risks related to device failure and tissue reactions, we developed a cranial window technique. In this protocol, we report methods to implant and access a cranial window in juvenile landrace pigs, which facilitates temporary electrocorticography (ECoG) array placement on the dura mater. We further describe how cortical signals can be recorded using the cranial window technique. Cranial window access can be repeated several times, but a minimum of 2 weeks between implant and access surgeries is advised to facilitate recovery and tissue healing. The cranial window approach successfully minimized common electrode failure modes and tissue responses, resulting in stable and reliable cortical recordings over time. We recorded event-related potentials (ERPs) from the primary somatosensory cortex as an example. The method provided highly reliable recordings, which also allowed the assessment of the effect of an intervention (high-frequency stimulation) on the ERPs. The absence of significant device failures and the reduced number of electrodes used (two electrodes, 43 recording sessions, 16 animals) suggest an improved research economy. While minor surgical access is required for electrode placement, the method offers advantages such as reduced infection risk and improved animal welfare. This study presents a scalable, reliable, and reproducible method for chronic cortical recordings, with potential applications in various fields of neuroscience, including pain research and neurological disease diagnosis. Future adaptations may extend its use to other species and recording modalities, such as intracortical recordings and imaging techniques.},
}

Animals
Swine
*Electrocorticography/methods/instrumentation
*Anesthesia/methods
*Somatosensory Cortex/physiology
Dura Mater/surgery
Electrodes, Implanted

@article {pmid40549518,
year = {2025},
author = {Yu, F and Rao, Z and Chen, N and Liu, L and Jiang, M},
title = {ArmBCIsys: Robot Arm BCI System With Time-Frequency Network for Multiobject Grasping.},
journal = {IEEE transactions on neural networks and learning systems},
volume = {PP},
number = {},
pages = {},
doi = {10.1109/TNNLS.2025.3579332},
pmid = {40549518},
issn = {2162-2388},
abstract = {Brain-computer interface (BCI) offers a direct communication and control channel between the human brain and external devices, presenting new pathways for individuals with physical disabilities to operate robotic arms for complex tasks. However, achieving multiobject grasping tasks under low signal-to-noise ratio (SNR) consumer-grade EEG signals is a significant challenge due to the lack of robust decoding algorithms and precise visual tracking methods. This article proposes, ArmBCIsys, an integrated robotic arm system that combines a novel dual-branch frequency-enhanced network (DBFENet) to robustly decode EEG signals under noisy conditions with the high-precision vision-guided grasping module. The proposed DBFENet designs the scaling temporal convolution block (STCB) to extract multiscale spatiotemporal features from the time domain, while the designed DropScale projected Transformer (DSPT) utilizes discrete cosine transform (DCT) to capture key frequency-domain features, significantly improving decoding robustness. We fine-tune the masked-attention mask Transformer (Mask2Former) model on the Jacquard dataset and incorporate the multiframe centroid-intersection over union (IoU) tracking algorithm to build visual grasp segmenter (VisGraspSeg), enabling reliable segmentation and dynamic tracking for diverse daily objects. Experimental validations on both self-built code-modulated visual evoked potential (c-VEP) dataset (1344 samples) and two public c-VEP datasets demonstrate that DBFENet achieves the state-of-the-art recognition performance, and the system integrates the DBFENet and proposed vision-guided module and ensures stable multiobject selecting and automatic object grasping in dynamic environments, extending promising applications in healthcare robotics, assistive technology, and industrial automation. The self-built dataset has been made publicly accessible at https://github.com/wtu1020/ ArmBCIsys-Self-built-cVEP-Dataset.},
}

@article {pmid40548156,
year = {2025},
author = {Tzimourta, KD},
title = {Human-Centered Design and Development in Digital Health: Approaches, Challenges, and Emerging Trends.},
journal = {Cureus},
volume = {17},
number = {6},
pages = {e85897},
pmid = {40548156},
issn = {2168-8184},
abstract = {Human-centered design (HCD) has emerged as a critical approach for developing digital health technologies that are usable, acceptable, and effective within complex healthcare environments. Rooted in systems theory, ergonomics, and information systems research, HCD prioritizes the needs, capabilities, and limitations of diverse user groups - including patients, clinicians, and caregivers - throughout the design and implementation process. This review synthesizes current applications of HCD in four key domains: brain-computer interfaces (BCIs), augmented and virtual reality (AR/VR), artificial intelligence (AI)-based clinical decision support systems AI-CDSS, and mobile health (mHealth) technologies. It explores design frameworks, usability strategies, and models of human-technology collaboration that contribute to successful adoption and sustained use. Ethical and legal considerations - such as data privacy, informed consent, and algorithmic fairness - are also addressed, particularly in contexts involving biometric and neurophysiological data. While HCD practices have shown substantial potential to improve digital health outcomes, their implementation remains uneven across technologies and settings. Emerging trends, including adaptive personalization, explainable AI, and participatory co-design, are identified as promising directions for the development of more inclusive, trustworthy, and sustainable digital health innovations.},
}

@article {pmid40546334,
year = {2025},
author = {Cruz, MV and Jamal, S and Sethuraman, SC},
title = {A Comprehensive Survey of Brain-Computer Interface Technology in Health care: Research Perspectives.},
journal = {Journal of medical signals and sensors},
volume = {15},
number = {},
pages = {16},
pmid = {40546334},
issn = {2228-7477},
abstract = {The brain-computer interface (BCI) technology has emerged as a groundbreaking innovation with profound implications across diverse domains, particularly in health care. By establishing a direct communication pathway between the human brain and external devices, BCI systems offer unprecedented opportunities for diagnosis, treatment, and rehabilitation, thereby reshaping the landscape of medical practice. However, despite its immense potential, the widespread adoption of BCI technology in clinical settings faces several challenges. These include the need for robust signal acquisition and processing techniques and optimizing user training and adaptation. Overcoming these challenges is crucial to unleashing the complete potential of BCI technology in health care and realizing its promise of personalized, patient-centric care. This review work underscores the transformative potential of BCI technology in revolutionizing medical practice. This paper offers a comprehensive analysis of medical-oriented BCI applications by exploring the various uses of BCI technology and its potential to transform patient care.},
}

@article {pmid40545006,
year = {2025},
author = {Feng, J and Jia, W and Li, Z},
title = {Electroencephalography: A Valuable Tool for Assessing Motor Impairment and Recovery Post-Stroke.},
journal = {Journal of neuroscience methods},
volume = {},
number = {},
pages = {110518},
doi = {10.1016/j.jneumeth.2025.110518},
pmid = {40545006},
issn = {1872-678X},
abstract = {Stroke is a leading cause of adult disability, and restoring motor function post-stroke is critical to improving the well-being and quality of life of affected individuals. Accurate and timely assessment of motor function is essential for developing effective rehabilitation strategies and predicting recovery outcomes. Electroencephalography (EEG) offers a non-invasive, real-time monitoring of brain activity, offering personalized insights into motor impairment and recovery. Its simplicity and bedside applicability make EEG a valuable tool and a potential biomarker for brain function. In recent years, the integration of EEG with the brain-computer interface technology and neuromodulation techniques has revolutionized personalized rehabilitation therapy, offering new hope for patients with motor dysfunction following stroke. This review synthesizes evidence on EEG-derived biomarkers and their integration with brain-computer interface and neuromodulation techniques, proposing a framework for personalized rehabilitation strategies in stroke recovery.},
}

@article {pmid40544658,
year = {2025},
author = {Mathon, B and Navarro, V and Pons, T and Lecas, S and Roussel, D and Charpier, S and Carpentier, A},
title = {Ultrasound-induced blood-brain barrier opening and selenium-nanoparticle injection lower seizure activity: A mouse model of temporal lobe epilepsy.},
journal = {Ultrasonics},
volume = {155},
number = {},
pages = {107734},
doi = {10.1016/j.ultras.2025.107734},
pmid = {40544658},
issn = {1874-9968},
abstract = {BACKGROUND: Given the limitations of current treatment options for drug-resistant mesial temporal lobe epilepsy (MTLE), the development of novel, nonablative and minimally invasive surgical techniques is essential.

OBJECTIVE AND METHODS: In this study, low-intensity pulsed ultrasound (LIPU)- and microbubble-induced (henceforth LIPU) blood-brain barrier (BBB) opening combined with selenium-nanoparticle (SeNP) intravenous injection in a mouse model of mesial temporal lobe optimized the latter's bioavailability in the brain epileptic tissue of the kainic acid (KA) mouse model of MTLE. We aimed to assess the safety and antiepileptic potential of LIPU-enhanced SeNP delivery against KA-induced seizures using long-term intracranial electroencephalogram video recordings and evaluating neuroinflammation, astrogliosis, neuronal apoptosis and neurogenesis in the hippocampal tissues of mice.

RESULTS: First, we established that SeNP intravenous injection combined with LIPU-induced BBB disruption was the most effective method to achieve high and sustained selenium levels in the brain. The safety of this treatment was demonstrated after three treatment sessions, 1-week apart, with no adverse effects observed. Our results further showed a significantly lower frequency of epileptic seizures (-90 %, P = 0.001) in KA mice treated with LIPU + SeNPs compared to sham-treated controls. Short- and long-term histological changes were seen after that combined regimen, including less aberrant neurogenesis in the hippocampus hilum, less neuronal death throughout the hippocampus and less hippocampal microglial activation, which might collectively contribute to the observed antiseizure effect.

CONCLUSION: SeNP injection combined with LIPU-induced BBB disruption demonstrated potential as a promising approach to reduce seizure activity in MTLE; however, statistical comparison did not conclusively establish superiority over SeNPs alone. Further investigations are necessary to consider translational studies in humans.},
}

@article {pmid40542951,
year = {2025},
author = {Chen, J and Sun, G and Zhang, Y and Chen, W and Zheng, X and Zhang, S and Hao, Y},
title = {Interactively Integrating Reach and Grasp Information in Macaque Premotor Cortex.},
journal = {Neuroscience bulletin},
volume = {},
number = {},
pages = {},
pmid = {40542951},
issn = {1995-8218},
abstract = {Reach-to-grasp movements require integrating information on both object location and grip type, but how these elements are planned and to what extent they interact remains unclear. We designed a new experimental paradigm in which monkeys sequentially received reach and grasp cues with delays, requiring them to retain and integrate both cues to grasp the goal object with appropriate hand gestures. Neural activity in the dorsal premotor cortex (PMd) revealed that reach and grasp were similarly represented yet not independent. Upon receiving the second cue, the PMd continued encoding the first, but over half of the neurons displayed incongruent modulations: enhanced, attenuated, or even reversed. Population-level analysis showed significant changes in encoding structure, forming distinct neural patterns. Leveraging canonical correlation analysis, we identified a shared subspace preserving the initial cue's encoding, contributed by both congruent and incongruent neurons. Together, these findings reveal a novel perspective on the interactive planning of reach and grasp within the PMd, providing insights into potential applications for brain-machine interfaces.},
}

@article {pmid40541755,
year = {2025},
author = {Yang, A and Tian, J and Wang, W and Zhou, L and Zhou, K},
title = {Shared and distinct neural signatures of feature and spatial attention.},
journal = {NeuroImage},
volume = {},
number = {},
pages = {121296},
doi = {10.1016/j.neuroimage.2025.121296},
pmid = {40541755},
issn = {1095-9572},
abstract = {The debate on whether feature attention (FA) and spatial attention (SA) share a common neural mechanism remains unresolved. Previous neuroimaging studies have identified fronto-parietal-temporal attention-related regions that exhibited consistent activation during various visual attention tasks. However, these studies have been limited by small sample sizes and methodological constraints inherent in univariate analysis. Here, we utilized a between-subject whole-brain machine learning approach with a large sample size (N=235) to investigate the neural signatures of FA (FAS) and SA (SAS). Both FAS and SAS showed cross-task predictive capabilities, though inter-task prediction was weaker than intra-task prediction, suggesting both shared and distinct mechanisms. Specifically, the frontoparietal network exhibited the highest predictive performance for FA, while the visual network excelled in predicting SA, highlighting their respective prominence in the two attention processes. Moreover, both signatures demonstrated distributed representations across large-scale brain networks, as each cluster within the signatures was sufficient for predicting FA and SA, but none of them were deemed necessary for either FA or SA. Our study challenges traditional network-centric models of attention, emphasizing distributed brain functioning in attention, and provides comprehensive evidence for shared and distinct neural mechanisms underlying FA and SA.},
}

@article {pmid40502202,
year = {2025},
author = {Srinivasan, A and Wairagkar, M and Iacobacci, C and Hou, X and Card, NS and Jacques, BG and Pritchard, AL and Bechefsky, PH and Hochberg, LR and AuYong, N and Pandarinath, C and Brandman, DM and Stavisky, SD},
title = {Encoding of speech modes and loudness in ventral precentral gyrus.},
journal = {bioRxiv : the preprint server for biology},
volume = {},
number = {},
pages = {},
pmid = {40502202},
issn = {2692-8205},
abstract = {The ability to vary the mode and loudness of speech is an important part of the expressive range of human vocal communication. However, the encoding of these behaviors in the ventral precentral gyrus (vPCG) has not been studied at the resolution of neuronal firing rates. We investigated this in two participants who had intracortical microelectrode arrays implanted in their vPCG as part of a speech neuroprosthesis clinical trial. Neuronal firing rates modulated strongly in vPCG as a function of attempted mimed, whispered, normal or loud speech. At the neural ensemble level, mode/loudness and phonemic content were encoded in distinct neural subspaces. Attempted mode/loudness could be decoded from vPCG with an accuracy of 94% and 89% for two participants respectively, and corresponding neural preparatory activity could be detected hundreds of milliseconds before speech onset. We then developed a closed-loop loudness decoder that achieved 94% online accuracy in modulating a brain-to-text speech neuroprosthesis output based on attempted loudness. These findings demonstrate the feasibility of decoding mode and loudness from vPCG, paving the way for speech neuroprostheses capable of synthesizing more expressive speech.},
}

@article {pmid40541523,
year = {2025},
author = {Cao, S and Yin, Y and Li, W and Liu, Z and Chen, Z},
title = {Time-varying formation control for heterogeneous multi-agent systems in the presence of actuator faults and deception attacks.},
journal = {ISA transactions},
volume = {},
number = {},
pages = {},
doi = {10.1016/j.isatra.2025.06.004},
pmid = {40541523},
issn = {1879-2022},
abstract = {This paper explores the control of time-varying formations in a class of heterogeneous multi-agent systems. The key innovation lies in the simultaneous consideration of hybrid actuator faults and deception attacks. To achieve the control objective, a novel distributed double-layer control scheme, comprising a network layer and a physical layer, is proposed. In the network layer, a distributed observer with secure output feedback control is developed to mitigate severe deception attacks, ensuring that the mean square observer error remains within an acceptable range. In the physical layer, fault compensators are designed to address both additive and multiplicative faults. As a result, the followers achieve time-varying formation control, and closed-loop stability analysis is conducted using the Lyapunov method. Finally, to verify the validity of the theoretical findings, numerical simulations are subsequently conducted.},
}

@article {pmid40538971,
year = {2025},
author = {Miao, Y and Li, K and Zhao, W and Zhang, Y},
title = {EA-EEG: a novel model for efficient motor imagery EEG classification with whitening and multi-scale feature integration.},
journal = {Cognitive neurodynamics},
volume = {19},
number = {1},
pages = {94},
pmid = {40538971},
issn = {1871-4080},
abstract = {Electroencephalography (EEG) is a non-invasive technique widely used in neuroscience and brain-computer interfaces (BCI) due to its high temporal resolution. In motor imagery EEG (MI-EEG) tasks, EEG signals reflect movement-related brain activity, making them ideal for BCI control. However, the non-stationary nature of MI-EEG signals poses significant challenges for classification, as frequency characteristics vary across tasks and individuals. Traditional preprocessing methods, such as bandpass filtering and standardization, may struggle to adapt to these variations, potentially limiting classification performance. To address this issue, this study introduces EA-EEG, an improved MI-EEG classification model that incorporates whitening as a preprocessing step to reduce channel correlation and enhance the model feature extraction ability. EA-EEG further leverages a multi-scale pooling strategy, combining convolutional networks and root mean square pooling to extract key spatial and temporal features, and applies prototype-based classification to improve MI-EEG classification performance. Experiments on the BCI4-2A and BCI4-2B datasets demonstrate that EA-EEG achieves state-of-the-art performance, with 85.33% accuracy (Kappa = 0.804) on BCI4-2A and 88.05% accuracy (Kappa = 0.761) on BCI4-2B, surpassing existing approaches. These results confirm EA-EEG's effectiveness in handling non-stationary MI-EEG signals, demonstrating its potential for robust BCI applications, including rehabilitation, prosthetic control, and cognitive monitoring.},
}

@article {pmid40538970,
year = {2025},
author = {Lin, C and Lu, H and Pan, C and Ma, S and Zhang, Z and Tian, R},
title = {MBRSTCformer: a knowledge embedded local-global spatiotemporal transformer for emotion recognition.},
journal = {Cognitive neurodynamics},
volume = {19},
number = {1},
pages = {95},
pmid = {40538970},
issn = {1871-4080},
abstract = {Emotion recognition is an essential prerequisite for realizing generalized BCI, which possesses an extensive range of applications in real life. EEG-based emotion recognition has become mainstream due to its real-time mapping of brain emotional activities, so a robust EEG-based emotion recognition model is of great interest. However, most existing deep learning emotion recognition methods treat the EEG signal as a whole feature extraction, which will destroy its local stimulation differences and fail to extract local features of the brain region well. Inspired by the cognitive mechanisms of the brain, we propose the multi-brain regions spatiotemporal collaboration transformer (MBRSTCfromer) framework for EEG-based emotion recognition. First, inspired by the prior knowledge, we propose the Multi-Brain Regions Collaboration Network. The EEG data are processed separately after being divided by brain regions, and stimulation scores are presented to quantify the stimulation produced by different brain regions and feedback on the stimulation degree to the MBRSTCfromer. Second, we propose a Cascade Pyramid Spatial Fusion Temporal Convolution Network for multi-brain regions EEG features fusion. Finally, we conduct comprehensive experiments on two mainstream emotion recognition datasets to validate the effectiveness of our proposed MBRSTCfromer framework. We achieved 98.63 % , 98.15 % , and 98.58 % accuracy on the three dimensions (arousal, valence, and dominance) on the DEAP dataset; and 97.66 % , 97.07 % , and 97.97 % on the DREAMER dataset.},
}

@article {pmid40536865,
year = {2025},
author = {Li, Y and Su, D and Yang, X and Wang, X and Zhao, H and Zhang, J},
title = {From Frequency to Temporal: Three Simple Steps Achieve Lightweight High-Performance Motor Imagery Decoding.},
journal = {IEEE transactions on bio-medical engineering},
volume = {PP},
number = {},
pages = {},
doi = {10.1109/TBME.2025.3579528},
pmid = {40536865},
issn = {1558-2531},
abstract = {OBJECTIVE: To address the challenges of high data noise and substantial model computational complexity in Electroencephalography (EEG)-based motor imagery decoding, this study aims to develop a decoding method with both high accuracy and low computational cost.

METHODS: First, frequency domain analysis was performed to reveal the frequency modeling patterns of deep learning models. Utilizing prior knowledge from brain science regarding the key frequency bands for motor imagery, we adjusted the convolution kernels and pooling sizes of EEGNet to focus on effective frequency bands. Subsequently, a residual network was introduced to preserve high-frequency detailed features. Finally, temporal convolution modules were used to deeply capture temporal dependencies, significantly enhancing feature discriminability.

RESULTS: Experiments were conducted on the BCI Competition IV 2a and 2b datasets. Our method achieved average classification accuracies of 86.23% and 86.75% respectively, surpassing advanced models like EEG-Conformer and EEG-TransNet. Meanwhile, the Multiply-accumulate operations (MACs) were 27.16M, a reduction of over 50% compared to the comparison models, and the Forward/Backward Pass Size was 14.33MB.

CONCLUSION: By integrating prior knowledge from brain science with deep learning techniques-specifically frequency domain analysis, residual networks, and temporal convolutions-it is possible to effectively improve the accuracy of EEG motor imagery decoding while substantially reducing model computational complexity.

SIGNIFICANCE: This paper employs the simplest and most fundamental techniques in its design, highlighting the critical role of brain science knowledge in model development. The proposed method demonstrates broad application potential.},
}

@article {pmid40536747,
year = {2025},
author = {Fei, SW and Chen, JL and Hu, YB},
title = {A novel time-frequency feature extraction method of EEG signals utilizing fractional synchrosqueezing wavelet transform.},
journal = {Physical and engineering sciences in medicine},
volume = {},
number = {},
pages = {},
pmid = {40536747},
issn = {2662-4737},
abstract = {In order to improve the accuracy of Electroencephalogram (EEG) classification, Fractional Synchrosqueezing Wavelet Transform (FSSWT) is proposed to effectively overcome the contradiction between energy concentration and frequency separation in traditional time-frequency analysis methods. Firstly, the principle of FSSWT is introduced, and the time-frequency transformation equation for FSSWT applied to multi-frequency signals is established. The examples of synthetic signal and EEG signal show that the proposed method can suppress the mode aliasing of MI-EEG significantly while maintaining high resolution characteristics, and the energy concentration and related intermediate indexes perform well. The experimental results show that the proposed FSSWT-EEGDNN-ResNet model achieves an average classification accuracy of 95.17% under the condition of the MI-EEG signals processed by FSSWT of eight subjects, demonstrating the effectiveness of FSSWT in EEG signal feature extraction and classification.},
}

@article {pmid40536356,
year = {2025},
author = {Rizzo, M and Dawson, JD},
title = {AI in Neurology: Everything, Everywhere, All at Once Part 1: Principles and Practice.},
journal = {Annals of neurology},
volume = {},
number = {},
pages = {},
doi = {10.1002/ana.27225},
pmid = {40536356},
issn = {1531-8249},
support = {//University of Nebraska Foundation/ ; U54GM115458//National Institute of General Medical Sciences/ ; R01AG017177//National Institute on Aging/ ; },
abstract = {Artificial intelligence (AI) is rapidly transforming healthcare, yet it often remains opaque to clinicians, scientists, and patients alike. This review, part 1 of a 3-part series, provides neurologists and neuroscientists with a foundational understanding of AI's key concepts, terminology, and applications. We begin by tracing AI's origins in mathematics, human logic, and brain-inspired neural networks to establish a context for its development. The review highlights AI's growing role in neurological diagnostics and treatment, emphasizing machine learning applications, such as computer vision, brain-machine interfaces, and precision care. By mapping the evolution of AI tools and linking them to neuroscience and human reasoning, we illustrate how AI is reshaping neurological practice and research. We end the review with an overview of model selection in AI and a case scenario illustrating how AI may drive precision neurological care. Part 1 sets the stage for part 2, which will focus on practical applications of AI in real-world scenarios where humans and AI collaborate as joint cognitive systems. Part 3 will examine AI's integration with extensive healthcare and neurology networks, innovative clinical trials, and massive datasets, expanding our vision of AI's global impact on neurology, healthcare systems, and society. ANN NEUROL 2025.},
}

@article {pmid40535306,
year = {2025},
author = {Xavier Fidêncio, A and Grün, F and Klaes, C and Iossifidis, I},
title = {Hybrid brain-computer interface using error-related potential and reinforcement learning.},
journal = {Frontiers in human neuroscience},
volume = {19},
number = {},
pages = {1569411},
pmid = {40535306},
issn = {1662-5161},
abstract = {Brain-computer interfaces (BCIs) offer alternative communication methods for individuals with motor disabilities, aiming to improve their quality of life through external device control. However, non-invasive BCIs using electroencephalography (EEG) often suffer from performance limitations due to non-stationarities arising from changes in mental state or device characteristics. Addressing these challenges motivates the development of adaptive systems capable of real-time adjustment. This study investigates a novel approach for creating an adaptive, error-related potential (ErrP)-based BCI using reinforcement learning (RL) to dynamically adapt to EEG signal variations. The framework was validated through experiments on a publicly available motor imagery dataset and a novel fast-paced protocol designed to enhance user engagement. Results showed that RL agents effectively learned control policies from user interactions, maintaining robust performance across datasets. However, findings from the game-based protocol revealed that fast-paced motor imagery tasks were ineffective for most participants, highlighting critical challenges in real-time BCI task design. Overall, the results demonstrate the potential of RL for enhancing BCI adaptability while identifying practical constraints in task complexity and user responsiveness.},
}

@article {pmid40535187,
year = {2025},
author = {Bao, Y and Zhou, H and Geng, F and Hu, Y},
title = {The relation between game disorder and interruption during game is mediated by game craving.},
journal = {Frontiers in psychology},
volume = {16},
number = {},
pages = {1579016},
pmid = {40535187},
issn = {1664-1078},
abstract = {The burgeoning user base and potential negative effects of excessive involvement in gaming, particularly Internet Gaming Disorder (IGD), demand significant attention. While existing research has explored the susceptibility of individuals with IGD to game-related stimuli, the question of why it is challenging for these individuals to disengage from gaming remains under-explored. Drawing parallels with the concept of interruption, we hypothesize that negative emotions triggered during gaming interruptions would drive individuals' craving for the game and compelling them to continue playing, reinforcing the IGD cycle. In this study, 42 male 'League of Legends' players, aged 19 to 29, experienced controlled interruptions every 3 min during gaming and non-gaming control tasks. Our findings demonstrate that interruptions during gaming elicited significantly higher levels of anger and anxiety compared to the control tasks. Further, we found a positive correlation between the severity of IGD symptoms and the intensity of anger and anxiety induced by gaming interruptions. Additionally, our analysis suggests that craving partially mediates the relationship between anger arousal during gaming interruptions and IGD severity. These findings provide new insights into how emotional responses to gaming interruptions contribute to IGD, offering a novel perspective for future research and potential treatment approaches.},
}

@article {pmid40534746,
year = {2025},
author = {Zheng, K and Guo, L and Liang, W and Liu, P},
title = {Comparison of the effects of transcranial direct current stimulation combined with different rehabilitation interventions on motor function in people suffering from stroke-related symptoms: a systematic review and network meta-analysis.},
journal = {Frontiers in neurology},
volume = {16},
number = {},
pages = {1586685},
pmid = {40534746},
issn = {1664-2295},
abstract = {BACKGROUND: This study employs network meta-analysis to assess the efficacy of transcranial direct current stimulation (tDCS) combined with different rehabilitation approaches in enhancing motor function in people suffering from stroke-related symptoms (PSSS). The objective is to determine the most effective tDCS-based rehabilitation approach and offer valuable evidence to guide clinical decision-making.

METHODS: This study included randomized controlled trials (RCTs) published before September 23, 2024. We conducted a systematic search across eight databases: PubMed, Embase, Cochrane Library, Web of Science, China National Knowledge Infrastructure (CNKI), China Biology Medicine (SinoMed), Wanfang, and VIP. Network meta-analysis (NMA) was conducted utilizing R Studio and Stata 15.0 for data analysis.

RESULTS: A total of 74 RCTs were included in this study, encompassing 4,335 PSSS and 11 intervention strategies. The NMA revealed that brain-computer interface therapy (BCIT) in combination with tDCS [surface under the cumulative ranking curve (SUCRA) = 88.34%] was the most effective tDCS-based intervention for improving the Fugl-Meyer Assessment for Upper Extremity score in PSSS. Mirror therapy (MT) in combination with tDCS (SUCRA = 85.96%) was identified as the optimal intervention for enhancing the Action Research Arm Test score in PSSS. MT + tDCS (SUCRA = 84.29%) was the best approach for improving the Fugl-Meyer Assessment for Lower Extremity score. Additionally, acupuncture and moxibustion (AM) in combination with tDCS (SUCRA = 77.16%) was the most effective intervention for increasing the Berg Balance Scale score in PSSS. The two-dimensional clustering analysis showed that MT + tDCS (SUCRA = 75.83%/85.96%) was the optimal tDCS-based rehabilitation strategy for treating upper limb motor dysfunction in PSSS, while AM+tDCS (SUCRA = 76.94%/77.16%) was the best tDCS-based rehabilitation strategy for improving lower limb motor dysfunction in PSSS.

CONCLUSION: BCIT+tDCS was identified as the optimal tDCS-based rehabilitation strategy for improving upper limb motor ability in PSSS, MT + tDCS was the most effective intervention for enhancing arm mobility, MT + tDCS was the best protocol for improving lower limb motor ability, while AM+tDCS was the best strategy for improving balance ability. Furthermore, MT + tDCS was the optimal tDCS-based rehabilitation approach for treating upper limb motor dysfunction, whereas AM+tDCS was the most effective strategy for addressing lower limb motor dysfunction in PSSS. Future studies may focus on investigating the therapeutic effects of MT combined with tDCS on Berg Balance Scale score in PSSS, as well as the effects of AM combined with tDCS on Action Research Arm Test score, in order to further explore the therapeutic potential of these two intervention strategies.

https://www.crd.york.ac.uk/PROSPERO/view/CRD42024621998, Identifier PROSPERO CRD42024621998.},
}

@article {pmid40534671,
year = {2025},
author = {Zhai, H and Wang, H and Li, H and Wang, X},
title = {The Intersection of Psychedelics and Sleep: Exploring the Impacts on Sleep Architecture, Dream States, and Therapeutic Implications.},
journal = {ACS pharmacology & translational science},
volume = {8},
number = {6},
pages = {1832-1836},
pmid = {40534671},
issn = {2575-9108},
abstract = {The interplay between psychedelics, such as psilocybin, lysergic acid diethylamide (LSD) and dimethyltryptamine (DMT), and sleep is an emerging area, but their impact on sleep remains relatively underexplored. This viewpoint provides a perspective on how psychedelics may alter sleep phases, dreaming, and their potential therapeutic applications for sleep disorders.},
}

@article {pmid40533772,
year = {2025},
author = {Lv, S and Ran, X and Xia, M and Zhang, Y and Pang, T and Zhou, X and Zhao, Z and Yu, Y and Gao, Z},
title = {Classification of left and right-hand motor imagery in acute stroke patients using EEG microstate.},
journal = {Journal of neuroengineering and rehabilitation},
volume = {22},
number = {1},
pages = {137},
pmid = {40533772},
issn = {1743-0003},
support = {221100310500//the Major Science and Technology Projects of Henan Province/ ; 82302298//the National Natural Science Foundation of China/ ; 82201709//the National Natural Science Foundation of China/ ; 24IRTSTHN042//Innovative Research Team (in Science and Technology) in University of Henan Province/ ; XTkf01//the Open Project Program of Henan Collaborative Innovation Center of Prevention and Treatment of Mental Disorder/ ; 242102521012//International Science and Technology Cooperation Project of Henan Province/ ; 242102310055//the Science and Technology Research Project of Henan Province/ ; },
mesh = {Humans ; Male ; Female ; *Electroencephalography/methods ; Middle Aged ; *Brain-Computer Interfaces ; *Imagination/physiology ; *Stroke/physiopathology ; Aged ; *Hand/physiopathology ; *Stroke Rehabilitation/methods ; *Functional Laterality/physiology ; Support Vector Machine ; Adult ; },
abstract = {BACKGROUND: Stroke is one of the leading causes of adult disability, often resulting in motor dysfunction and brain network reorganization. Brain-computer interface (BCI) systems offer a novel approach to post-stroke motor rehabilitation, with motor imagery (MI) serving as a key paradigm that requires decoding left and right-hand MI differences to optimize system performance. However, the neural dynamics underlying these differences, especially from the perspective of Electroencephalography(EEG) microstate, remain poorly understood in acute stroke patients.

METHODS: This study enrolled 14 acute stroke patients and recorded their EEG data during left and right-hand MI tasks. Four EEG microstate (A, B, C, and D) were analyzed to extract temporal feature parameters, including Duration, Occurrence Coverage, and transition probabilities(TP). Significant features were used to construct classification models using Linear Discriminant Analysis(LDA), Support Vector Machines(SVM), and K-Nearest Neighbors(KNN) algorithms.

RESULTS: Microstate analysis revealed significant differences in temporal features of microstate A and C during left and right-hand MI tasks. During left-hand MI, microstate A exhibited longer Duration(Pfdr=0.032), higher Occurrence(Pfdr=0.018), and greater Coverage(Pfdr=0.004) compared to the right-hand, whereas microstate C showed the opposite pattern(Pfdr=0.044, Pfdr=0.004, Pfdr=0.004). Additionally, the TP from microstate B→A, D→A and D→C also demonstrated significant differences(Pfdr=0.04, Pfdr<0.001, Pfdr=0.006). Among classification models, the KNN algorithm achieved the highest accuracy of 75.00%, outperforming LDA and SVM. Fisher analysis indicated that the Occurrence of microstate C was the most discriminative feature for distinguishing between left and right-hand MI tasks in acute stroke patients.

CONCLUSION: Differences in EEG microstate features during left and right-hand MI tasks in acute stroke patients may reflect lateralized mechanisms of brain network reorganization. Microstate features hold significant potential for both post-stroke brain function assessment and the optimization of BCI systems. These features could enhance adaptive BCI strategies in acute stroke rehabilitation.},
}

Humans
Male
Female
*Electroencephalography/methods
Middle Aged
*Brain-Computer Interfaces
*Imagination/physiology
*Stroke/physiopathology
Aged
*Hand/physiopathology
*Stroke Rehabilitation/methods
*Functional Laterality/physiology
Support Vector Machine
Adult

@article {pmid40532880,
year = {2025},
author = {Luo, X and Dong, J and Li, T},
title = {Comparative cytokine signatures and cognitive deficits in early-onset schizophrenia and adolescent major depression: Toward refined diagnostic classification frameworks.},
journal = {Journal of affective disorders},
volume = {},
number = {},
pages = {119667},
doi = {10.1016/j.jad.2025.119667},
pmid = {40532880},
issn = {1573-2517},
abstract = {BACKGROUND: This study analyzed plasma cytokine patterns in individuals with schizophrenia (SCZ), major depressive disorder (MDD), and healthy controls, explored the link between cytokine levels and cognitive function, and created machine learning models to evaluate the diagnostic potential of cytokine and cognitive assessments.

METHODS: This study involved 64 early-onset SCZ patients, 53 adolescents with MDD, and 33 healthy controls. The plasma concentrations of 44 cytokines were measured using the LUMINEX multiplex assay. Cognitive function was tested with the Cambridge Neuropsychological Test Automated Battery. Random Forest and Extreme Gradient Boosting models were used for classification, with their effectiveness evaluated via ROC curve analysis.

RESULTS: SCZ patients exhibited significantly elevated levels of CCL11, IL-2 and IL-13, while MDD patients displayed increased CXCL2 and G-CSF levels but decreased CCL20 and CCL11 levels. SCZ patients showed significant cognitive impairments compared to healthy controls. Elevated CCL11 were associated with poorer memory accuracy, and higher G-CSF were linked to worse executive function. The XGBoost model was more sensitive in classifying MDD than the Random Forest model, but both struggled to differentiate SCZ patients from healthy controls due to low specificity.

CONCLUSION: Early-onset SCZ and adolescent MDD patients showed unique peripheral cytokine profiles, with SCZ patients experiencing significant cognitive deficits. The cytokine CCL11 was found to have a significant association with cognitive dysfunction. Cytokine levels and cognitive assessments may serve as potential biomarkers for the diagnosis of MDD.},
}

@article {pmid40530005,
year = {2025},
author = {Shao, W and Meng, W and Zuo, J and Li, X and Ming, D},
title = {Opportunities and Challenges of Brain-on-a-Chip Interfaces.},
journal = {Cyborg and bionic systems (Washington, D.C.)},
volume = {6},
number = {},
pages = {0287},
pmid = {40530005},
issn = {2692-7632},
abstract = {The convergence of life sciences and information technology is driving a new wave of scientific and technological innovation, with brain-on-a-chip interfaces (BoCIs) emerging as a prominent area of focus in the brain-computer interface field. BoCIs aim to create an interactive bridge between lab-grown brains and the external environment, utilizing advanced encoding and decoding technologies alongside electrodes. Unlike classical brain-computer interfaces that rely on human or animal brains, BoCIs employ lab-grown brains, offering greater experimental controllability and scalability. Central to this innovation is the advancement of stem cell and microelectrode array technologies, which facilitate the development of neuro-electrode hybrid structures to ensure effective signal transmission in lab-grown brains. Furthermore, the evolution of BoCI systems depends on a range of stimulation strategies and novel decoding algorithms, including artificial-intelligence-driven methods, which has expanded BoCI applications to pattern recognition and robotic control. Biological neural networks inherently grant BoCI systems neuro-inspired computational properties-such as ultralow energy consumption and dynamic plasticity-that surpass those of conventional artificial intelligence. Functionally, BoCIs offer a novel framework for hybrid intelligence, merging the cognitive capabilities of biological systems (e.g., learning and memory) with the computational efficiency of machines. However, critical challenges span 4 domains: optimizing neural maturation and functional regionalization, engineering high-fidelity bioelectronic interfaces for robust signal transduction, enhancing adaptive neuroplasticity mechanisms in lab-grown brains, and achieving biophysically coherent integration with artificial intelligence architectures. Addressing these limitations could offer insights into emergent intelligence while enabling next-generation biocomputing solutions.},
}

@article {pmid40529543,
year = {2025},
author = {Faber, J and Tsytsarev, V and Pais-Vieira, M and Aksenova, T},
title = {Editorial: Sensorimotor decoding: characterization and modeling for rehabilitation and assistive technologies, volume II.},
journal = {Frontiers in human neuroscience},
volume = {19},
number = {},
pages = {1619232},
pmid = {40529543},
issn = {1662-5161},
}

@article {pmid40527877,
year = {2025},
author = {Moreira, JPC and Carvalho, VR and Mendes, EMAM and Fallah, A and Sejnowski, TJ and Lainscsek, C and Comstock, L},
title = {An open-access EEG dataset for speech decoding: Exploring the role of articulation and coarticulation.},
journal = {Scientific data},
volume = {12},
number = {1},
pages = {1017},
pmid = {40527877},
issn = {2052-4463},
mesh = {Humans ; *Electroencephalography ; *Speech ; *Brain-Computer Interfaces ; Phonetics ; Transcranial Magnetic Stimulation ; Adult ; Male ; Female ; },
abstract = {Electroencephalography (EEG) holds promise for brain-computer interface (BCI) devices as a non-invasive measure of neural activity. With increased attention to EEG-based BCI systems, publicly available datasets incorporating the complex stimuli found in naturalistic speech are necessary to establish a common standard of performance within the BCI community. Effective solutions must overcome noise in the EEG signal and remain reliable across sessions and stimuli that reflect types of real-world linguistic complexity without overfitting to a dataset or task. We present two validated datasets (N=8 and N=16) for classification at the phoneme and word level and by the articulatory properties of phonemes. EEG signals were recorded from 64 channels while subjects listened to and repeated six consonants and five vowels. Individual phonemes were combined in different phonetic environments to produce coarticulated variation in 40 consonant-vowel pairs, 20 real words, and 20 pseudowords. Phoneme pairs and words were presented during a control condition and during transcranial magnetic stimulation (TMS) to assess whether stimulation would augment the EEG signal associated with specific articulatory processes.},
}

Humans
*Electroencephalography
*Speech
*Brain-Computer Interfaces
Phonetics
Transcranial Magnetic Stimulation
Adult
Male
Female

@article {pmid40527666,
year = {2025},
author = {Toner, AA and Eberlin, L and Pichaimuthu, R and Tompkins, T and Szekeres, M},
title = {The use of robotics and artificial intelligence in upper extremity rehabilitation following traumatic injury: A scoping review.},
journal = {Journal of hand therapy : official journal of the American Society of Hand Therapists},
volume = {},
number = {},
pages = {},
doi = {10.1016/j.jht.2025.04.009},
pmid = {40527666},
issn = {1545-004X},
abstract = {BACKGROUND: With the recent advances in technology and its increased use in society, healthcare practices work to identify areas where technology can be implemented to enhance patient care. Rehabilitation has begun to incorporate the use of robotics and artificial intelligence to facilitate positive outcomes and assist in achieving patient goals following injury. While traumatic upper extremity injuries can result in increased levels of pain and disability for an individual, it is not clear how robotics and artificial intelligence have been used in hand rehabilitation to address these issues.

PURPOSE: The objective of this study is to understand the extent of the use of robotics and artificial intelligence for traumatic upper extremity injuries.

STUDY DESIGN: Scoping review.

METHODS: The search strategy was conducted in Embase, CINAHL, MEDLINE, and PsycINFO and identified 7105 studies published between 2014 and 2024. Following title and abstract screening and removal of duplicates, 122 full-text articles were screened. A total of 13 papers were included that used artificial intelligence, robotics, or other technology in rehabilitation programs for individuals with traumatic upper extremity injuries.

RESULTS: Of the 13 included studies: 11 used robotics such as the KINARM Exoskeleton, the Hybrid Assistive Limb, and the WRISTBOT, and two used artificial intelligence including chatbots and brain-computer interface. Multiple outcomes were reported with the most common including range of motion, strength, pain, function, and joint sense.

CONCLUSIONS: Currently, there is a wide variety of different forms of robotics with very little reported use of artificial intelligence for traumatic upper extremity injuries. There exists opportunities for future research to further investigate how these technologies can influence clinical outcomes for patients with traumatic upper extremity injuries.},
}

@article {pmid40527337,
year = {2025},
author = {Temmar, H and Willsey, MS and Costello, JT and Mender, MJ and Cubillos, LH and DeMatteo, JC and Lam, JLW and Wallace, DM and Kelberman, MM and Patil, PG and Chestek, CA},
title = {Investigating the benefits of artificial neural networks over linear approaches to BMI decoding.},
journal = {Journal of neural engineering},
volume = {},
number = {},
pages = {},
doi = {10.1088/1741-2552/ade568},
pmid = {40527337},
issn = {1741-2552},
abstract = {OBJECTIVE: Brain-machine interfaces (BMI) aim to restore function to persons living with spinal cord injuries by 'decoding' neural signals into behavior. Recently, nonlinear BMI decoders have outperformed previous state-of-the-art linear decoders, but few studies have investigated what specific improvements these nonlinear approaches provide. In this study, we compare how temporally convolved feedforward neural networks (tcFNNs) and linear approaches predict individuated finger movements in open and closed-loop settings. Approach. One adult male rhesus macque was implanted with Utah arrays in the motor cortex and performed a 2D dexterous finger movement task for a juice reward. Multiple linear and nonlinear 'decoders' were used to map from recorded spiking band power into movement kinematics. Performance of these decoders was compared and analyzed to determine how nonlinear decoders perform in both open and closed-loop scenarios. Main Results. We show that nonlinear decoders generate more naturalistic movements, producing distributions of velocities 85.3% closer to true hand control than linear decoders. Addressing concerns that neural networks may come to inconsistent solutions, we find that regularization techniques improve the consistency of tcFNN convergence by 194.6%, along with improving average performance, and training speed. Finally, we show that tcFNN can leverage training data from multiple task variations to improve generalization.

SIGNIFICANCE: The results of this study show that nonlinear methods produce more naturalistic movements and show potential for generalizing over less constrained tasks. .},
}

@article {pmid40527331,
year = {2025},
author = {Xin, H and Li, H and Qi, H},
title = {A novel paradigm for two-degree-of-freedom BCI control based on ERP in-duced by overt and covert visual attention.},
journal = {Journal of neural engineering},
volume = {},
number = {},
pages = {},
doi = {10.1088/1741-2552/ade56a},
pmid = {40527331},
issn = {1741-2552},
abstract = {This study developed a novel brain-computer interface (BCI) paradigm based on event-related potentials (ERPs) to achieve simultaneous two-degree-of-freedom (2-DOF) control through overt and covert visual selective attention. Methods: In this paradigm, three stimuli were arranged equidistantly around the cursor. Participants selected two stimuli as attention targets based on the relative position of the cursor and the intended movement destination, focusing overtly on one while covertly attending to the other. EEG data collected during offline experiments were used to train classifiers for overt and covert targets, and the outputs of these classifiers were employed in online experiments to construct movement vectors for controlling the cursor in a 2D space. Results: EEG analysis demonstrated that overt and covert targets elicited distinct ERP signals, with classification accuracies of 96.2% and 92.4%, respectively. The accuracy of simultaneously identifying both targets reached 91.0%. In online experiments, the success rate of moving the cursor to the target region was 96%, and 91% of cursor movements were in the desired direction. These results confirm the feasibility of achieving 2D control through ERP-based selective attention and validate the effectiveness of the proposed paradigm. Conclusion: This study introduces a novel EEG-based approach for multi-degree-of-freedom control, expanding the capabilities of traditional ERP-based BCIs, which have primarily been limited to single-degree-of-freedom applications. .},
}

@article {pmid40526548,
year = {2025},
author = {Li, C and Cao, Z and Pan, Y and Zhu, P and Li, P and Li, F and Chen, H and Lu, BL and Wan, F and Yao, D and Xu, P},
title = {EEG-Based Emotion Monitoring and Regulation System by Learning the Discriminative Brain Network Manifold.},
journal = {IEEE transactions on neural networks and learning systems},
volume = {PP},
number = {},
pages = {},
doi = {10.1109/TNNLS.2025.3576182},
pmid = {40526548},
issn = {2162-2388},
abstract = {Emotion recognition based on electroencephalogram (EEG) is fundamentally associated with human-like intelligence system. However, due to the noise-sensitive characteristics of EEGs and the individual variability of emotions, it is very challenging to extract inherent emotion dependent patterns from emotional EEG signals. In this work, we propose a L1-norm space defined discriminative brain network manifold learning model (L1-SGL), in which the EEG noise outliers can be effectively separated and the pseudolabeled samples caused by subjective feelings can be automatically corrected. Off-line experimental results consistently indicate that the L1-SGL can effectively suppress the influence of noise and achieve an incomparable superiority performance over other existing methods in EEG emotion recognition. Besides, benefiting from the time efficiency of the L1-SGL, an online emotion monitoring and regulation system is further implemented in this work. On-line emotion decoding experimental results (86.30%) of 25 participants prove that the L1-SGL can effectively satisfy the real-time requirements of on-line emotional monitoring applications, and the significant negative emotion regulation experimental results ($p \lt 0.001$) further confirm the feasibility and effectiveness of L1-SGL model in real-time emotion regulation and interactive applications. Overall, the L1-SGL provides a promising solution for the real-time online affective brain-computer interfaces (aBCIs) and the intelligent clinical closed-loop treatments.},
}

@article {pmid40526539,
year = {2025},
author = {Jia, T and Long, H and Ji, L and Guan, X},
title = {EEG-based Spatial-Channel Interaction Attention Neural Networks for Detecting Empathy in Motor Collaboration.},
journal = {IEEE journal of biomedical and health informatics},
volume = {PP},
number = {},
pages = {},
doi = {10.1109/JBHI.2025.3580617},
pmid = {40526539},
issn = {2168-2208},
abstract = {Embodied intelligence and humanoid robots aim to mimic interpersonal interactions to achieve affective human-robot interaction (HRI). A major challenge in advancing HRI lies in effectively emulating interpersonal affective interactions and evaluating the resulting artificial empathy. To address these challenges, we propose SpatialChannel Interaction Attention Neural Networks (SCIANN)-a novel EEG-based architecture that combines topological brain activation and connectivity patterns to decode empathy in motor collaboration. A private EEG dataset from a collaborative brain-computer interface motor control experiment and a public EEG dataset from a dyadic perceptual crossing experiment were used for evaluating SCIANN's performance with comparisons with five baseline models. Results showed that SCIANN outperformed the state-of-the-art baseline models. In the private dataset, SCIANN reached an accuracy of 100% both in inter-subject and cross-subject tests for detecting whether empathy is induced or not. For classifying 4-class empathy, it achieved an accuracy of 98.3% in the inter-subject test, and 48.1% in the cross-subject test. In the public dataset, SCIANN reached a classification accuracy of 92.2% in inter-subject and 91.7% in cross-subject tests for detecting whether empathy is induced or not. Feature visualization results revealed that contributing EEG channel importance features and channel interaction features aligned with established neurophysiological findings. These results collectively demonstrate SCIANN's potential as a robust, generalizable framework for artificial empathy assessment in HRI applications.},
}

@article {pmid40526534,
year = {2025},
author = {Luo, T and Zhang, J and Qiu, Y and Zhang, L and Hu, Y and Yu, Z and Liang, Z},
title = {M3D: Manifold-based Domain Adaptation with Dynamic Distribution for Non-Deep Transfer Learning in Cross-subject and Cross-session EEG-based Emotion Recognition.},
journal = {IEEE journal of biomedical and health informatics},
volume = {PP},
number = {},
pages = {},
doi = {10.1109/JBHI.2025.3580612},
pmid = {40526534},
issn = {2168-2208},
abstract = {Emotion decoding using Electroencephalography (EEG)-based affective brain-computer interfaces (aBCIs) is crucial for affective computing but is hindered by EEG's non-stationarity, individual variability, and the high cost of large-scale labeled data. Deep learning-based approaches, while effective, require substantial computational resources and large datasets, limiting their practicality. To address these challenges, we propose Manifold-based Domain Adaptation with Dynamic Distribution (M3D), a lightweight non-deep transfer learning framework. M3D includes four main modules: manifold feature transformation, dynamic distribution alignment, classifier learning, and ensemble learning. The data undergoes a transformation onto an optimal Grassmann manifold space, enabling dynamic alignment of the source and target domains. This process prioritizes both marginal and conditional distributions according to their significance, ensuring enhanced adaptation efficiency across various types of data. In the classifier learning, the principle of structural risk minimization is integrated to develop robust classification models. This is complemented by dynamic distribution alignment, which refines the classifier iteratively. Additionally, the ensemble learning module aggregates the classifiers obtained at different stages of the optimization process, which leverages the diversity of the classifiers to enhance the overall prediction accuracy. The proposed M3D framework is evaluated on three benchmark EEG emotion recognition datasets using two validation protocols (cross-subject single-session and cross-subject cross-session), as well as on a clinical EEG dataset of Major Depressive Disorder (MDD). Experimental results demonstrate that M3D outperforms traditional non-deep learning methods, achieving an average improvement of 6.67%, while achieving deep learning-comparable performance with significantly lower data and computational requirements. These findings highlight the potential of M3D to enhance the practicality and applicability of aBCIs in real-world scenarios.},
}

@article {pmid40524963,
year = {2025},
author = {Zhao, W and Lu, H and Zhang, B and Zheng, X and Wang, W and Zhou, H},
title = {TCANet: a temporal convolutional attention network for motor imagery EEG decoding.},
journal = {Cognitive neurodynamics},
volume = {19},
number = {1},
pages = {91},
pmid = {40524963},
issn = {1871-4080},
abstract = {Decoding motor imagery electroencephalogram (MI-EEG) signals is fundamental to the development of brain-computer interface (BCI) systems. However, robust decoding remains a challenge due to the inherent complexity and variability of MI-EEG signals. This study proposes the Temporal Convolutional Attention Network (TCANet), a novel end-to-end model that hierarchically captures spatiotemporal dependencies by progressively integrating local, fused, and global features. Specifically, TCANet employs a multi-scale convolutional module to extract local spatiotemporal representations across multiple temporal resolutions. A temporal convolutional module then fuses and compresses these multi-scale features while modeling both short- and long-term dependencies. Subsequently, a stacked multi-head self-attention mechanism refines the global representations, followed by a fully connected layer that performs MI-EEG classification. The proposed model was systematically evaluated on the BCI IV-2a and IV-2b datasets under both subject-dependent and subject-independent settings. In subject-dependent classification, TCANet achieved accuracies of 83.06% and 88.52% on BCI IV-2a and IV-2b respectively, with corresponding Kappa values of 0.7742 and 0.7703, outperforming multiple representative baselines. In the more challenging subject-independent setting, TCANet achieved competitive performance on IV-2a and demonstrated potential for improvement on IV-2b. The code is available at https://github.com/snailpt/TCANet.},
}

@article {pmid40523119,
year = {2025},
author = {Kamitani, Y and Tanaka, M and Shirakawa, K},
title = {Visual Image Reconstruction from Brain Activity via Latent Representation.},
journal = {Annual review of vision science},
volume = {},
number = {},
pages = {},
doi = {10.1146/annurev-vision-110423-023616},
pmid = {40523119},
issn = {2374-4650},
abstract = {Visual image reconstruction, the decoding of perceptual content from brain activity into images, has advanced significantly with the integration of deep neural networks (DNNs) and generative models. This review traces the field's evolution from early classification approaches to sophisticated reconstructions that capture detailed, subjective visual experiences, emphasizing the roles of hierarchical latent representations, compositional strategies, and modular architectures. Despite notable progress, challenges remain, such as achieving true zero-shot generalization for unseen images and accurately modeling the complex, subjective aspects of perception. We discuss the need for diverse datasets, refined evaluation metrics aligned with human perceptual judgments, and compositional representations that strengthen model robustness and generalizability. Ethical issues, including privacy, consent, and potential misuse, are underscored as critical considerations for responsible development. Visual image reconstruction offers promising insights into neural coding and enables new psychological measurements of visual experiences, with applications spanning clinical diagnostics and brain-machine interfaces.},
}

@article {pmid40522886,
year = {2025},
author = {Nam, J and Shin, H and You, C and Baeg, E and Kim, JG and Yang, S and Han, MR},
title = {Cortical Stimulation-Based Transcriptome Shifts on Parkinson's Disease Animal Model.},
journal = {ASN neuro},
volume = {17},
number = {1},
pages = {2513881},
doi = {10.1080/17590914.2025.2513881},
pmid = {40522886},
issn = {1759-0914},
mesh = {Animals ; *Transcriptome/physiology ; Male ; Disease Models, Animal ; *Motor Cortex/metabolism ; *Parkinson Disease/genetics/therapy/metabolism ; Mice ; Mice, Inbred C57BL ; },
abstract = {Parkinson's disease is the second most prevalent neurodegenerative disorder and is characterized by the degeneration of dopaminergic neurons. Significant improvements in gait balance, particularly in step length and velocity, were observed with less invasive wireless cortical stimulation. Transcriptome sequencing was performed to demonstrate the cellular mechanism, specifically targeting the primary motor cortex, where stimulation was applied. Our findings indicated that 38 differentially expressed genes (DEGs), initially downregulated following Parkinson's disease induction, were subsequently restored to normal levels after cortical stimulation. These 38 DEGs are potential targets for the treatment of motor disorders in Parkinson's disease. These genes are implicated in crucial processes, such as astrocyte-mediated blood vessel development and microglia-mediated phagocytosis of damaged motor neurons, suggesting their significant roles in improving behavioral disorders. Moreover, these biomarkers not only facilitate the rapid and accurate diagnosis of Parkinson's disease but also assist in precision medicine approaches.},
}

Animals
*Transcriptome/physiology
Male
Disease Models, Animal
*Motor Cortex/metabolism
*Parkinson Disease/genetics/therapy/metabolism
Mice
Mice, Inbred C57BL

@article {pmid40522806,
year = {2025},
author = {Wang, P and Qi, Y and Pan, G},
title = {Partial Domain Adaptation for Stable Neural Decoding in Disentangled Latent Subspaces.},
journal = {IEEE transactions on bio-medical engineering},
volume = {PP},
number = {},
pages = {},
doi = {10.1109/TBME.2025.3577222},
pmid = {40522806},
issn = {1558-2531},
abstract = {OBJECTIVE: Brain-Computer Interfaces (BCI) have demonstrated significant potential in neural rehabilitation. However, the variability of non-stationary neural signals often leads to instabilities of behavioral decoding, posing critical obstacles to chronic applications. Domain adaptation technique offers a promising solution by obtaining the invariant neural representation against non-stationary signals through distribution alignment. Here, we demonstrate domain adaptation that directly applied to neural data may lead to unstable performance, mostly due to the common presence of task-irrelevant components within neural signals. To address this, we aim to identify task-relevant components to achieve more stable neural alignment.

METHODS: In this work, we propose a novel partial domain adaptation (PDA) framework that performs neural alignment within the task-relevant latent subspace. With pre-aligned short-time windows as input, the proposed latent space is constructed based on a causal dynamical system, enabling more flexible neural decoding. Within this latent space, task-relevant dynamical features are disentangled from task-irrelevant components through VAE-based representation learning and adversarial alignment. The aligned task-relevant features are then employed for neural decoding across domains.

RESULTS: Using Lyapunov theory, we analytically validated the improved stability of late our neural representations through alignment. Experiments with various neural datasets verified that PDA significantly enhanced the cross-session decoding performance.

CONCLUSION: PDA successfully achieved stable neural representations across different experimental days, enabling reliable long-term decoding.

SIGNIFICANCE: Our approach provides a novel aspect for addressing the challenge of chronic reliability in real-world BCI deployments.},
}

@article {pmid40522801,
year = {2025},
author = {Yao, Y and Swaef, W and Geirnaert, S and Bertrand, A},
title = {EEG-Based Decoding of Selective Visual Attention in Superimposed Videos.},
journal = {IEEE journal of biomedical and health informatics},
volume = {PP},
number = {},
pages = {},
doi = {10.1109/JBHI.2025.3580261},
pmid = {40522801},
issn = {2168-2208},
abstract = {Selective attention enables humans to efficiently process visual stimuli by enhancing important elements and filtering out irrelevant information. Locating visual attention is fundamental in neuroscience with potential applications in brain-computer interfaces. Conventional paradigms often use synthetic stimuli or static images, but visual stimuli in real life contain smooth and highly irregular dynamics. We show that these irregular dynamics can be decoded from electroencephalography (EEG) signals for selective visual attention decoding. To this end, we propose a free-viewing paradigm in which participants attend to one of two superimposed videos, each showing a center-aligned person performing a stage act. Superimposing ensures that the relative differences in the neural responses are not driven by differences in object locations. A stimulus-informed decoder is trained to extract EEG components correlated with the motion patterns of the attended object, and can detect the attended object in unseen data with significantly above-chance accuracy. This shows that the EEG responses to naturalistic motion are modulated by selective attention. Eye movements are also found to be correlated to the motion patterns in the attended video, despite the spatial overlap with the distractor. We further show that these eye movements do not dominantly drive the EEG-based decoding and that complementary information exists in EEG and gaze data. Moreover, our results indicate that EEG may also capture neural responses to unattended objects. To our knowledge, this study is the first to explore EEG-based selective visual attention decoding on natural videos, opening new possibilities for experiment design.},
}

@article {pmid40522765,
year = {2026},
author = {Kinfe, T and Brenner, S and Etminan, N},
title = {Brain-computer interfaces re-shape functional neurosurgery.},
journal = {Neural regeneration research},
volume = {21},
number = {3},
pages = {1122-1123},
doi = {10.4103/NRR.NRR-D-24-01336},
pmid = {40522765},
issn = {1673-5374},
}

@article {pmid40522539,
year = {2025},
author = {Ullah, A and Bookwalter, J and Sant, H and Azapagic, A and Shea, J and Berlet, R and Jha, N and Bailes, J and Gale, BK},
title = {An Osmosis-driven 3D-printed brain implant for drug delivery.},
journal = {Biomedical microdevices},
volume = {27},
number = {3},
pages = {29},
pmid = {40522539},
issn = {1572-8781},
mesh = {*Printing, Three-Dimensional ; *Osmosis ; *Drug Delivery Systems/instrumentation ; *Brain/surgery/metabolism ; *Brain Neoplasms/drug therapy ; Humans ; *Prostheses and Implants ; Glioblastoma/drug therapy ; },
abstract = {Glioblastoma is a highly malignant brain tumor with limited survival rates due to challenges in complete surgical excision, high recurrence (> 90%), and the inefficacy of systemic drug delivery. Significant efforts have been made to develop drug-loaded brain implants, catheters, and wafers aimed at enhancing survival rates by suppressing tumor recurrence. However, these devices often fail due to clogging, reflux, and the inability to be fully implanted intracranially. Furthermore, a lack of tissue penetration, diffusion distance, and duration of therapy have limited effectiveness of these implants. To address existing challenges, this study reports an osmosis-driven, 3D-printed brain implant with the potential for precise device customization to meet therapeutic needs, while negating systemic toxicity. It is capable of being loaded with two distinct therapeutic agents and implanted directly into the tumor resection cavity during surgery. The device features dual reservoirs, osmotic membranes, and precision-engineered needles for anchoring the device in the resection cavity and perfusing. Further, the device was characterized in vitro using 0.2% agarose gel as a brain tissue analog, with food dye as a drug analog and sodium chloride serving as an osmogen. A design of experiment approach was implemented to investigate various parameters, including membrane pore size, osmogen concentration, needle length, and their effects on release rates. The results demonstrated that the optimized implant achieves flow rates of 2.5 ± 0.1 µl/Hr and diffusion distance of up to 15.5 ± 0.4 mm, using 25 nm pore osmotic membranes with 25.3% osmogen concentration, aligning with model predictions.},
}

*Printing, Three-Dimensional
*Osmosis
*Drug Delivery Systems/instrumentation
*Brain/surgery/metabolism
*Brain Neoplasms/drug therapy
Humans
*Prostheses and Implants
Glioblastoma/drug therapy

@article {pmid40520823,
year = {2025},
author = {Ivan Brown, A and MacDuffie, KE and Goering, S and Klein, E},
title = {The "wheels that keep me goin'": invisible forms of support for brain pioneers.},
journal = {Neuroethics},
volume = {18},
number = {1},
pages = {},
pmid = {40520823},
issn = {1874-5490},
abstract = {Research participants in long-term, first-in-human trials of implantable neural devices (i.e., brain pioneers) are critical to the success of the emerging field of neurotechnology. How these participants fare in studies can make or break a research program. Yet, their ability to enroll, participate, and seamlessly exit studies relies on both the support of family/caregivers and care from researchers that is often hidden from view. The present study offers an initial exploration of the different kinds of support that play a role in neural device trials from the perspectives of brain pioneers and their support partners (spouses, paid caregivers, parents, etc.). Using a mixed methods approach (semi-structured, open-ended interviews and a survey) with interpretive grounded theory, we present narratives from a study of six pioneers -- four in brain-computer interface (BCI) trials, and two in deep brain stimulation (DBS) trials -- and five support partners, about their experiences of being supported and supporting participants in implantable neural device studies. Our findings indicate the substantial amount of work involved on the part of pioneers - and some support partners - to make these studies successful. A central finding of the study is that non-logistical forms of support - social, emotional, and epistemic support - play a role, alongside more widely acknowledged forms of support, such as transportation and physical and clinical care. We argue that developing a better understanding of the kinds of support that enable neurotechnology studies to go well can help bridge the gap between abstract ethical principles of caring for subjects and on-the-ground practice.},
}

@article {pmid40520820,
year = {2025},
author = {Aubinet, C and Gillet, A and Regnier, A},
title = {Disorders of Consciousness, Language and Communication Following Severe Brain Injury.},
journal = {Psychologica Belgica},
volume = {65},
number = {1},
pages = {169-188},
pmid = {40520820},
issn = {2054-670X},
abstract = {Patients with severe brain injuries and disorders of consciousness (DoC) represent a complex clinical population in terms of diagnosis, prognosis, and management, including critical ethical considerations. Behavioral assessment scales remain the primary tools for evaluating the level of consciousness of these patients following a coma; however, they heavily depend on language and communication abilities. This reliance can lead to underestimating residual consciousness in cases where language impairments go undetected. Accordingly, the latest international guidelines on DoC diagnosis have highlighted aphasia as a significant confounding factor that must be addressed. On the other hand, accurately assessing residual language abilities is essential for better characterizing the patient's cognitive profile. This, in turn, enables neuropsychologists and speech-language therapists to tailor and plan effective rehabilitation programs. This review examines the current literature on language function and communication skills in patients with DoC, detailing the latest tools for assessing and managing language and consciousness in individuals with severe brain injuries. We explore the critical role of language function in evaluating residual consciousness, particularly in DoC behavioral diagnoses and in identifying covert consciousness through neuroimaging passive or active paradigms. Furthermore, we discuss how therapies aimed at recovering consciousness-such as pharmacological treatments, electromagnetic therapies, sensory or cognitive stimulation, and communication aids like brain-computer interfaces-may also impact or rely on language function and communication abilities. Further research is needed to refine methodologies and better understand the interplay between language processing, communication and levels of consciousness.},
}

@article {pmid40519866,
year = {2025},
author = {Wang, K and Ren, S and Jia, Y and Yan, X and Wang, L and Fan, Y},
title = {Neuromorphic chips for biomedical engineering.},
journal = {Mechanobiology in medicine},
volume = {3},
number = {3},
pages = {100133},
pmid = {40519866},
issn = {2949-9070},
abstract = {The modern medical field faces two critical challenges: the dramatic increase in data complexity and the explosive growth in data size. Especially in current research, medical diagnostic, and data processing devices relying on traditional computer architecture are increasingly showing limitations when faced with dynamic temporal and spatial processing requirements, as well as high-dimensional data processing tasks. Neuromorphic devices provide a new way for biomedical data processing due to their low energy consumption and high dynamic information processing capabilities. This paper aims to reveal the advantages of neuromorphic devices in biomedical applications. First, this review emphasizes the urgent need of biomedical engineering for diversify clinical diagnostic techniques. Secondly, the feasibility of the application in biomedical engineering is demonstrated by reviewing the historical development of neuromorphic devices from basic modeling to multimodal signal processing. In addition, this paper demonstrates the great potential of neuromorphic chips for application in the fields of biosensing technology, medical image processing and generation, rehabilitation medical engineering, and brain-computer interfaces. Finally, this review provides the pathways for constructing standardized experimental protocols using biocompatible technologies, personalized treatment strategies, and systematic clinical validation. In summary, neuromorphic devices will drive technological innovation in the biomedical field and make significant contributions to life health.},
}

@article {pmid40519178,
year = {2025},
author = {Kumar, R and Waisberg, E and Ong, J and Lee, AG},
title = {Response to letter to the editor on "the potential power of neuralink - how brain-machine interfaces can revolutionize medicine".},
journal = {Expert review of medical devices},
volume = {},
number = {},
pages = {},
doi = {10.1080/17434440.2025.2521399},
pmid = {40519178},
issn = {1745-2422},
}

@article {pmid40519177,
year = {2025},
author = {Cordero, DA},
title = {Letter to the editor on "the potential power of neuralink - how brain-machine interfaces can revolutionize medicine".},
journal = {Expert review of medical devices},
volume = {},
number = {},
pages = {},
doi = {10.1080/17434440.2025.2521393},
pmid = {40519177},
issn = {1745-2422},
}

@article {pmid40514107,
year = {2025},
author = {Zuo, M and Chen, X and Sui, L},
title = {A novel STA-EEGNet combined with channel selection for classification of EEG evoked in 2D and 3D virtual reality.},
journal = {Medical engineering & physics},
volume = {141},
number = {},
pages = {104363},
doi = {10.1016/j.medengphy.2025.104363},
pmid = {40514107},
issn = {1873-4030},
mesh = {*Virtual Reality ; *Electroencephalography ; Humans ; Male ; *Signal Processing, Computer-Assisted ; Adult ; Brain-Computer Interfaces ; Female ; Young Adult ; Attention ; *Neural Networks, Computer ; },
abstract = {Virtual reality (VR), particularly through 3D presentations, significantly boosts user engagement and task efficiency in fields such as gaming, education, and healthcare, offering more immersive and interactive experiences than traditional 2D formats. This study investigates EEG classification in response to 2D and 3D VR stimuli to deepen our understanding of the neural mechanisms driving VR interactions, with implications for brain-computer interfaces (BCIs). We introduce STA-EEGNet, an innovative model that enhances EEGNet by incorporating spatial-temporal attention (STA), improving EEG signal classification from VR environments. A one-way analysis of variance (ANOVA) was utilized to optimize channel selection, enhancing model accuracy. Comparative experiments showed that STA-EEGNet surpassed traditional EEGNet, achieving a peak accuracy of 99.78 % with channel selection. These findings highlight the benefits of spatial-temporal attention and optimal channel selection in classifying VR-evoked EEG data. This study underscores the importance of integrating spatial-temporal attention with compact convolutional neural networks like EEGNet, not only improving EEG signal classification but also advancing neural decoding and optimizing BCI applications.},
}

*Virtual Reality
*Electroencephalography
Humans
Male
*Signal Processing, Computer-Assisted
Adult
Brain-Computer Interfaces
Female
Young Adult
Attention
*Neural Networks, Computer

@article {pmid40513959,
year = {2025},
author = {Yang, Q and Guo, W and Wang, L and Zhang, Y and Tian, Y and Ming, D and Xiao, X and Yang, J},
title = {Effects of Fstl1 on neuroinflammation and microglia activation in lipopolysaccharide-induced acute depression-like mice.},
journal = {Behavioural brain research},
volume = {},
number = {},
pages = {115696},
doi = {10.1016/j.bbr.2025.115696},
pmid = {40513959},
issn = {1872-7549},
abstract = {Depression is the most prevalent psychiatric illness, and its pathogenesis is associated with neuroinflammation. Follistatinlike protein 1 (FSTL1), a novel inflammatory protein, participates in the pathogenesis of diseases related to neuroinflammation. Therefore, we aimed to investigate the effect of FSTL1 in the pathogenesis of depression mediated using neuroinflammation-mediated models. Our results showed that lipopolysaccharide (LPS) administration could induce despair-like behavior and increase proinflammatory cytokine levels in both male and female mice. Then, a significant positive correlation between hippocampal Fstl1 mRNA expression, microglial activation and desperate-like behaviors was observed in male mice. Moreover, knockdown FSTL1 significantly reduced microglial activation and the expression of proinflammatory cytokines, while overexpression of Fstl1 in hippocampus could exacerbate the activation of microglial under the LPS-induced condition in male mice. Mechanically, knockdown Fstl1 inhibited LPS-induced activation of BV2 microglia and reduced the production of proinflammatory cytokines, thereby protecting the survival of HT22 neurons. In conclusion, our results implied that Fstl1 may modulate despair-like behaviors through regulation of microglial activation and neuronal viability, which would lay the experimental and theoretical foundation for the neuroinflammatory mechanisms underlying depression.},
}

@article {pmid40513226,
year = {2025},
author = {Vasilyev, AN and Svirin, EP and Dubynin, IA and Butorina, AV and Nuzhdin, YO and Ossadtchi, AE and Stroganova, TA and Shishkin, SL},
title = {Intentionally versus spontaneously prolonged Gaze: A MEG study of active gaze-based interaction.},
journal = {Cortex; a journal devoted to the study of the nervous system and behavior},
volume = {189},
number = {},
pages = {76-96},
doi = {10.1016/j.cortex.2025.05.010},
pmid = {40513226},
issn = {1973-8102},
abstract = {Eye fixations are increasingly employed to control computers through gaze-sensitive interfaces, yet the brain mechanisms supporting this non-visual use of gaze remain poorly understood. In this study, we employed 306-channel magnetoencephalography (MEG) to find out what is specific to brain activity when gaze is used voluntarily for control. MEG was recorded while participants played a video game controlled by their eye movements. Each move required object selection by fixating it for at least 500 msec. Gaze dwells were classified as intentional if followed by a confirmation gaze on a designated location and as spontaneous otherwise. We identified both induced oscillatory and sustained phase-locked MEG activity differentiating intentional and spontaneous gaze dwells. Induced power analysis revealed prominent alpha-beta band synchronization (8-30 Hz) localized in the frontal cortex, with location broadly consistent with the frontal eye fields. This synchronization began 500-750 msec before intentional fixation onset and peaked shortly after it, suggesting proactive inhibition of saccadic activity. Sustained evoked responses further distinguished the two conditions, showing gradually rising cortical activation with a maximum at 200 msec post-onset in the inferior temporal cortex during intentional fixations, likely indicative of focused attentional engagement on spatial targets. These findings illuminate the neural dynamics underlying intentional gaze control, shedding light on the roles of proactive inhibitory mechanisms and attentional processes in voluntary behavior. By leveraging a naturalistic gaze-based interaction paradigm, this study offers a novel framework for investigating voluntary control under free behavior conditions and holds potential applications for enhancing hybrid eye-brain-computer interfaces.},
}

@article {pmid40512634,
year = {2025},
author = {Yan, T and Ming, Z and Huang, Y and Liu, Z and Chen, Q and Zhang, D and Liu, M and Suo, D and Zhang, J and Liu, S},
title = {Enhanced Brain-Controlled Mobile Robot based on SE-VEP Paradigm with Single Stimulus.},
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.2025.3579373},
pmid = {40512634},
issn = {1558-0210},
abstract = {Brain-computer interface (BCI) systems based on steady-state visually evoked potentials (SSVEPs) have been widely adopted because of their efficiency and accuracy. However, the traditional SSVEP method has limitations, including visual fatigue and interference between different stimuli. To address these issues, a new BCI paradigm, namely, a spatial encoding-visually evoked potential (SE-VEP) model, is proposed in this work. This paradigm involves deploying four target points to implement gaze restrictions around a stimulus block and optimizing the locations of these target points through offline data acquisition. This design facilitates electroencephalogram (EEG) encoding for four instructions while using only one stimulus block. Data with varying eccentricities are classified using the Riemann kernel-based support vector machine (R-SVM) approach, which achieves a classification accuracy of up to 86.11%. As the eccentricity increases, the classification accuracy initially increases but subsequently decreases. By evaluating the information transfer rate (ITR), the optimal time window length for online BCIs is determined to be 1.2 s. Additionally, an online brain-controlled robotic virtual system is developed to validate the feasibility of the proposed paradigm for online brain-computer interface applications. The results confirm the effectiveness of the proposed paradigm in implementing an online BCI control system. An evaluation conducted with scales and the information transfer rate for a single stimulus (ITRSS) indicates that compared with the traditional BCI system, the proposed paradigm yields greater reductions in user fatigue (2.8 ± 0.5 vs. 4.1 ± 0.6) and stimulus block utilization (24.6 ± 2.3 vs. 8.2 ± 1.1 bits/min).},
}

@article {pmid40511861,
year = {2025},
author = {Ramirez, P},
title = {Alternative ways to access AAC technologies.},
journal = {Augmentative and alternative communication (Baltimore, Md. : 1985)},
volume = {},
number = {},
pages = {1-3},
doi = {10.1080/07434618.2025.2513902},
pmid = {40511861},
issn = {1477-3848},
abstract = {More than 21 years ago, I had a car accident that led to a brain stem stroke, leaving me paralyzed and unable to speak. I was desperate to communicate. One day, my sister wrote down the alphabet and pointed to each letter accordingly. I nodded, yes or no, and she wrote my message down. Later, I used a laser light with a letter board and then a laptop with a head pointer. More recently, I started using a gyroscopic air mouse. During outings, I use the laser and the letter board. They are easy to carry and use. Plus, I can communicate in English and Spanish which is very important because my family does not speak English. I am currently enrolled in a clinical trial at the University of California, San Francisco to investigate brain computer interface to control a robotic arm and communicate. They placed an implant in the surface of my brain; the implant connects to a computer system that collects brain signals and translates neural activity from my sensorimotor cortex into intended speech and motor actions. This type of research is needed to enhance communication and improve lives.},
}

@article {pmid40510263,
year = {2025},
author = {Amande, TJ and Kaszyk, V and Brown, F},
title = {Identification of OqxB Efflux Pump and Tigecycline Resistance Gene Cluster tmexC3D2-toprJ3 in Multidrug-Resistant Pseudomonas Stutzeri Isolate G3.},
journal = {Infection and drug resistance},
volume = {18},
number = {},
pages = {2889-2899},
pmid = {40510263},
issn = {1178-6973},
abstract = {PURPOSE: To identify antibiotic resistance genes (ARGs) and understand the molecular basis of multidrug resistance in P. stutzeri isolate G3.

METHODS: Whole-genome sequencing of isolate G3 was conducted at 30X coverage using Illumina NovaSeq 6000. Reads were trimmed using Trimmomatic and assessed using a combination of scripts that incorporated Samtools, BedTools, and bwa-mem. De novo assembly was performed using SPAdes, and assembly metrics were evaluated using QUAST. The assembled genome was uploaded to a Type Strain Genome Server (TYGS) for taxonomic identification. Genome annotation was performed using the KBase and Proksee software using PROKKA. ARGs were identified using the Comprehensive Antibiotic Resistance Database (CARD).

RESULTS: P. stutzeri isolate G3 demonstrated resistance to most antibiotics tested, including meropenem (10 µg), ciprofloxacin (5 µg), gentamicin (10 µg), and tetracycline (30 µg). The ARGs identified were PmpM, AdeF, rsmA, vgb(A), BcI, cipA, OCH-2, and tet(45). A tigecycline-resistant gene cluster, tmexC3D2-toprJ3, was found in NODE_84, while the oqxB gene, encoding a resistance-nodulation-division (RND) efflux pump, was in NODE_309. Phylogenetic analysis showed OqxB clustered with Pseudomonas species, distinct from Klebsiella and Enterobacter. Comparative analysis of oqxB revealed P. stutzeri isolate G3 shared 78-100% identity with Pseudomonas aeruginosa strain 1334/14 in key components of the multidrug efflux system, including the transcriptional regulator MexT, periplasmic adaptor subunit MexE, and permease subunit MexF.

CONCLUSION: Our findings offer new insights into the reservoir of ARGs in the draft genome of Pseudomonas stutzeri isolate G3, including the tmexC3D2-toprJ3 and oqxB genes, highlighting its genomic plasticity and public health significance. This adaptability enables P. stutzeri to thrive in clinical environments, despite its natural habitat association. This study advances our understanding of the molecular mechanisms driving resistance in P. stutzeri and offers valuable insights to inform strategies for combating the spread of antimicrobial resistance in clinical and environmental settings.},
}

@article {pmid40510210,
year = {2025},
author = {Gazerani, P},
title = {A Hybrid Digital-4E Strategy for comorbid migraine and depression: a medical hypothesis on an AI-driven, neuroadaptive, and exposome-aware approach.},
journal = {Frontiers in neurology},
volume = {16},
number = {},
pages = {1587296},
pmid = {40510210},
issn = {1664-2295},
abstract = {OBJECTIVE: The co-occurrence of migraines and depression presents a critical clinical challenge, affecting up to 50% of individuals with either condition. This comorbidity leads to greater disability, higher healthcare costs, and poorer treatment outcomes than either disorder alone. Despite a bidirectional pathophysiological relationship, current models remain static and fragmented, treating each condition separately. This paper proposes a Hybrid Digital-4E Strategy, deployed on an AI-driven neuroadaptive digital health platform, integrating closed-loop therapy, digital phenotyping, and exposome tracking to enable real-time, personalized care.

METHODS: Grounded in the 4E cognition framework (Embodied, Embedded, Enactive, and Extended cognition), this strategy reconceptualizes migraine-depression as an interactive system rather than two separate conditions. The platform integrates real-time biomarker tracking, neuromorphic AI, and precision environmental analytics to dynamically optimize treatment. Adaptive chronotherapy, brain-computer interfaces (BCIs), and virtual reality (VR)-based neuroplasticity training further enhance intervention precision.

CONCLUSION: A closed-loop, AI-driven neuroadaptive system could improve outcomes by enabling early detection, real-time intervention, and precision care tailored to individual neurophysiological and environmental profiles. Addressing AI bias, data privacy, and clinical validation is crucial for implementation. If validated, this Hybrid Digital-4E Strategy could redefine migraine-depression management, paving the way for precision neuropsychiatry.},
}

@article {pmid40506548,
year = {2025},
author = {Wairagkar, M and Card, NS and Singer-Clark, T and Hou, X and Iacobacci, C and Miller, LM and Hochberg, LR and Brandman, DM and Stavisky, SD},
title = {An instantaneous voice-synthesis neuroprosthesis.},
journal = {Nature},
volume = {},
number = {},
pages = {},
pmid = {40506548},
issn = {1476-4687},
abstract = {Brain-computer interfaces (BCIs) have the potential to restore communication for people who have lost the ability to speak owing to a neurological disease or injury. BCIs have been used to translate the neural correlates of attempted speech into text[1-3]. However, text communication fails to capture the nuances of human speech, such as prosody and immediately hearing one's own voice. Here we demonstrate a brain-to-voice neuroprosthesis that instantaneously synthesizes voice with closed-loop audio feedback by decoding neural activity from 256 microelectrodes implanted into the ventral precentral gyrus of a man with amyotrophic lateral sclerosis and severe dysarthria. We overcame the challenge of lacking ground-truth speech for training the neural decoder and were able to accurately synthesize his voice. Along with phonemic content, we were also able to decode paralinguistic features from intracortical activity, enabling the participant to modulate his BCI-synthesized voice in real time to change intonation and sing short melodies. These results demonstrate the feasibility of enabling people with paralysis to speak intelligibly and expressively through a BCI.},
}

@article {pmid40506484,
year = {2025},
author = {Liu, J and Liu, H and Zhu, J and Han, X and Bai, Y and Ni, G and Ming, D},
title = {A Dataset of Pinna-Related Transfer Functions Using High-Resolution Pinna Models.},
journal = {Scientific data},
volume = {12},
number = {1},
pages = {992},
pmid = {40506484},
issn = {2052-4463},
mesh = {Humans ; *Sound Localization ; *Ear Auricle/physiology ; Acoustics ; },
abstract = {The pinna-related transfer function (PRTF) is critical for localizing and perceiving sound in three-dimensional space. PRTF largely depends on individual spectral cues and the unique physiology of the pinna, necessitating high-resolution data for accurate acoustic modeling. The accuracy of personalized acoustic models could be significantly improved using high-precision physiological data and incorporating advanced simulation methods such as the boundary element method (BEM). We describe a comprehensive dataset of 150 bilateral PRTFs from 75 participants to support developing, improving, and validating personalized PRTF modeling methods. The dataset includes simulated results from binaural laser-scanned models that are accurately validated through empirical measurements. This comprehensive dataset will contribute to acoustic and spatial audio research and support the ongoing advancements in personalized PRTF modeling techniques.},
}

Humans
*Sound Localization
*Ear Auricle/physiology
Acoustics