The neurophysiological mechanisms by which exercise improves cognitive function have not been fully elucidated. A comprehensive and systematic review of current domestic and international neurophysiological evidence on exercise improving cognitive function was conducted from multiple perspectives. At the molecular level, exercise promotes nerve cell regeneration and synaptogenesis and maintains cellular development and homeostasis through the modulation of a variety of neurotrophic factors, receptor activity, neuropeptides, and monoamine neurotransmitters, and by decreasing the levels of inflammatory factors and other modulators of neuroplasticity. At the cellular level, exercise enhances neural activation and control and improves brain structure through nerve regeneration, synaptogenesis, improved glial cell function and angiogenesis. At the structural level of the brain, exercise promotes cognitive function by affecting white and gray matter volumes, neural activation and brain region connectivity, as well as increasing cerebral blood flow. This review elucidates how exercise improves the internal environment at the molecular level, promotes cell regeneration and functional differentiation, and enhances the brain structure and neural efficiency. It provides a comprehensive, multi-dimensional explanation of the neurophysiological mechanisms through which exercise promotes cognitive function.
Animals ; Humans ; Brain/physiology* ; Cognition/physiology* ; Exercise/physiology* ; Nerve Regeneration/physiology* ; Neuronal Plasticity/physiology*

As a common neurological disease in China, stroke has an extremely high rate of death and disability, of which 80% is ischemic stroke (IS), causing a serious burden to individuals and society. Neuronal death is an important factor in the pathogenesis of stroke. Studies have shown that mitochondrial dynamics, as a key mechanism regulating intracellular energy metabolism and cell death, plays an important role in the pathogenesis of IS. In recent years, targeting mitochondrial dynamics has become an emerging therapeutic tool to improve neurological impairment after stroke. This paper reviews the research advance in recent years in IS mitochondrial dynamics, summarizing and discussing the overview of mitochondrial dynamics, the role of mitochondrial dynamics in IS, and the studies on mitochondrial dynamics-based treatment of IS. This paper helps to explore the mechanism of the role of mitochondrial dynamics in IS and effective interventions, and provides a theoretical strategy for targeting mitochondrial dynamics to treat IS in the clinic.
Humans ; Mitochondrial Dynamics/physiology* ; Ischemic Stroke/metabolism* ; Mitochondria/physiology* ; Animals ; Brain Ischemia/physiopathology* ; Energy Metabolism

Astrocytes are a crucial type of glial cells in the central nervous system, not only maintaining brain homeostasis, but also actively participating in the transmission of information within the brain. Astrocytes have a complex structure that includes the soma, various levels of processes, and end-feet. With the advancement of genetically encoded calcium indicators and imaging technologies, researchers have discovered numerous localized and small calcium activities in the fine processes and end-feet. These calcium activities were termed as microdomain calcium activities, which significantly differ from the calcium activities in the soma and can influence the activity of local neurons, synapses, and blood vessels. This article elaborates the detection and analysis, characteristics, sources, and functions of microdomain calcium activities, and discusses the impact of aging and neurodegenerative diseases on these activities, aiming to enhance the understanding of the role of astrocytes in the brain and to provide new insights for the treatment of brain disorders.
Astrocytes/cytology* ; Humans ; Animals ; Calcium/metabolism* ; Calcium Signaling/physiology* ; Brain/physiology* ; Aging/physiology* ; Membrane Microdomains/physiology* ; Neurodegenerative Diseases/physiopathology*

Circadian rhythms are core regulatory mechanisms that evolved to align biological functions with the Earth's rotation. These rhythms are conserved across organisms from unicellular life to multicellular species and play essential roles in metabolism, immune responses, and sleep-wake cycle. Circadian disruptions are strongly associated with various diseases. Over the past decades, genetic studies in Drosophila and mice have identified key conserved clock genes and uncovered transcription-translation feedback loops governing circadian regulation. Additionally, rhythmic neurons in the brain integrate complex neural circuits to precisely regulate physiological and behavioral rhythms. This review highlights recent advances in understanding the neuronal circuit mechanisms of rhythmic neurons in the Drosophila brain and discusses future directions for translating circadian rhythm research into chronomedicine and precision therapies.
Animals ; Circadian Rhythm/genetics* ; Neurons/physiology* ; Drosophila/physiology* ; Brain/physiology* ; Nerve Net/physiology*

This paper proposes a motor imagery recognition algorithm based on feature fusion and transfer adaptive boosting (TrAdaboost) to address the issue of low accuracy in motor imagery (MI) recognition across subjects, thereby increasing the reliability of MI-based brain-computer interfaces (BCI) for cross-individual use. Using the autoregressive model, power spectral density and discrete wavelet transform, time-frequency domain features of MI can be obtained, while the filter bank common spatial pattern is used to extract spatial domain features, and multi-scale dispersion entropy is employed to extract nonlinear features. The IV-2a dataset from the 4 ^th International BCI Competition was used for the binary classification task, with the pattern recognition model constructed by combining the improved TrAdaboost integrated learning algorithm with support vector machine (SVM), k nearest neighbor (KNN), and mind evolutionary algorithm-based back propagation (MEA-BP) neural network. The results show that the SVM-based TrAdaboost integrated learning algorithm has the best performance when 30% of the target domain instance data is migrated, with an average classification accuracy of 86.17%, a Kappa value of 0.723 3, and an AUC value of 0.849 8. These results suggest that the algorithm can be used to recognize MI signals across individuals, providing a new way to improve the generalization capability of BCI recognition models.
Brain-Computer Interfaces ; Humans ; Support Vector Machine ; Algorithms ; Neural Networks, Computer ; Imagination/physiology* ; Pattern Recognition, Automated/methods* ; Electroencephalography ; Wavelet Analysis

Motor imagery (MI) is a mental process that can be recognized by electroencephalography (EEG) without actual movement. It has significant research value and application potential in the field of brain-computer interface (BCI) technology. To address the challenges posed by the non-stationary nature and low signal-to-noise ratio of MI-EEG signals, this study proposed a Riemannian spatial filtering and domain adaptation (RSFDA) method for improving the accuracy and efficiency of cross-session MI-BCI classification tasks. The approach addressed the issue of inconsistent data distribution between source and target domains through a multi-module collaborative framework, which enhanced the generalization capability of cross-session MI-EEG classification models. Comparative experiments were conducted on three public datasets to evaluate RSFDA against eight existing methods in terms of classification accuracy and computational efficiency. The experimental results demonstrated that RSFDA achieved an average classification accuracy of 79.37%, outperforming the state-of-the-art deep learning method Tensor-CSPNet (76.46%) by 2.91% ( P < 0.01). Furthermore, the proposed method showed significantly lower computational costs, requiring only approximately 3 minutes of average training time compared to Tensor-CSPNet's 25 minutes, representing a reduction of 22 minutes. These findings indicate that the RSFDA method demonstrates superior performance in cross-session MI-EEG classification tasks by effectively balancing accuracy and efficiency. However, its applicability in complex transfer learning scenarios remains to be further investigated.
Electroencephalography/methods* ; Brain-Computer Interfaces ; Humans ; Imagination/physiology* ; Signal Processing, Computer-Assisted ; Movement/physiology* ; Signal-To-Noise Ratio ; Deep Learning ; Algorithms

Transcranial temporal interference stimulation (tTIS) is a novel non-invasive transcranial electrical stimulation technique that achieves deep brain stimulation through multiple electrodes applying electric fields of different frequencies. Current studies on the mechanism of tTIS effects are primarily based on rodents, but experimental outcomes are often significantly influenced by electrode configurations. To enhance the performance of tTIS within the limited cranial space of rodents, we proposed various electrode configurations for tTIS and conducted finite element simulations using a realistic mouse model. Results demonstrated that ventral-dorsal, four-channel bipolar, and two-channel configurations performed best in terms of focality, diffusion of activated brain regions, and scalp impact, respectively. Compared to traditional transcranial direct current stimulation (tDCS), these configurations improved by 94.83%, 50.59%, and 3 514.58% in the respective evaluation metrics. This study provides a reference for selecting electrode configurations in future tTIS research on rodents.
Animals ; Transcranial Direct Current Stimulation/instrumentation* ; Electrodes ; Mice ; Computer Simulation ; Finite Element Analysis ; Brain/physiology*

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*

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

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