Journal of Neuroscience Methods最新文献

Background: Stroke caused by vascular rupture or blockage has high incidence and leads to significant disability. Motor imagery (MI) electroencephalogram (EEG) is a promising approach to understanding and addressing stroke-related motor impairments. However, the practical application of EEG-based rehabilitation is hindered by an insufficient understanding of the task-specific features and complex temporal patterns inherent in the EEG signals of stroke patients.

New method: In this study, we collected EEG signals from 24 stroke patients performing four unilateral upper limb MI tasks. Among them, 12 subjects performed forward arm raising and lowering, while the remaining 12 performed lateral arm raising and lowering. Moreover, we propose a Temporal Periodicity Convolutional Network (TPCNet) for EEG-based MI classification. TPCNet consists of a convolutional block for extracting shallow spatiotemporal features, a sliding window structure that ensures consistent action initiation across samples, and a temporal periodicity block for capturing variations in periodic patterns associated with MI tasks.

Results: TPCNet achieved a classification accuracy of 86.53% on the stroke patient MI dataset and 82.21% on the BCI Competition IV 2a dataset (left hand, right hand, feet, and tongue). Gradient-weighted Class Activation Mapping (Grad-CAM) analysis suggests that stroke patients may exhibit longer task-specific MI periodicity than healthy subjects.

Comparison with existing methods: The proposed method achieves superior performance on stroke patient MI tasks and competitive results on public MI datasets involving healthy subjects.

Conclusions: The proposed TPCNet model effectively captures the spatiotemporal features and periodic patterns of EEG signals, leading to enhanced classification accuracy.

Background: Small extracellular vesicles (sEVs) mediate intercellular communication in the central nervous system, regulating processes ranging from homeostatic maintenance to injury repair. Although astrocytes are a major source of sEVs in the brain, in vivo investigation of their endogenous and cell-specific signaling remains technically challenging.

New method: To address this limitation, we developed a novel inducible transgenic reporter mouse line, GFAP-CD63-GFP, that enables specific labeling of astrocyte-derived sEVs. The mouse line was generated by crossing GFAP-CreERT2 mice with CD63-emGFPloxP/stop/loxP mice. This system enables Tamoxifen-inducible, astrocyte-specific expression of GFP-tagged CD63, a tetraspanin enriched in sEVs. The model allows visualization and quantification of astrocyte-derived CD63-positive sEVs in vivo.

Results: Following Tamoxifen induction, GFP expression was robustly detected in the brain and spinal cord. Immunogold transmission electron microscopy further identified GFP-positive sEVs within neurons, providing ultrastructural evidence of astrocyte-to-neuron vesicle transfer. As a proof-of-concept, ischemic stroke significantly increased astrocyte-derived sEVs in the ipsilesional cerebral hemisphere and the stroke-impaired side of the spinal cord, accompanied by enhanced neuronal endocytosis.

Comparison with existing methods: Current approaches rely primarily on in vitro EV isolation or nonspecific membrane dyes. The GFAP-CD63-GFP model enables cell type-specific, temporally controlled, and in situ tracking of astrocyte-derived sEVs.

Conclusions: These findings provide the first in vivo demonstration of increased astrocyte-to-neuron sEV communication during post-stroke recovery. The GFAP-CD63-GFP reporter mouse thus provides a powerful platform for investigating astrocyte-derived sEV signaling under both physiological and pathophysiological conditions of the CNS.

Background: Motor imagery (MI)-based electroencephalogram (EEG) brain-computer interfaces (BCIs) facilitate communication for motor-impaired patients by leveraging artificial intelligence to accurately interpret brain signals. However, EEG signal classification remains challenging due to low signal-to-noise ratio (SNR) and individual variability in brain activity.

New method: We propose a novel parallel multi-depth spatial-temporal neural network aimed at enhancing the integration of spatial and temporal features from multichannel EEG signals by leveraging brain functional topography. To improve cortical representations associated with motor imagery, the model incorporates two parallel branches. One branch focuses on inter-channel differences corresponding to contralateral electrode pairs, emphasizing hemispheric disparities, while the other targets the frontal and parietal brain regions. These region-specific enhanced signal representations are then fed into the multi-depth spatial-temporal network for feature extraction and subsequent motor imagery classification. The architecture of the feature extraction network integrates four specialized blocks, ensuring the comprehensive capture of discriminative features that are particularly sensitive to task-relevant frequencies for each MI class. A multi-loss design further optimizes feature integration across networks.

Results: Cross-validation results on the BCI Competition IV 2a dataset and High Gamma dataset achieve accuracies of 82.14% and 95.61%, respectively, with kappa values of 0.76 and 0.93, surpassing state-of-the-art methods.

Conclusion: These experimental results highlight the significance of parallel spatial-temporal networks based on brain partitioning for MI classification in rehabilitation engineering and real-world BCI applications.

Background: Spikes, ripples, and ripples on spikes (RonS) during non-rapid eye movement (NREM) sleep are all important biomarkers associated with epileptic seizures, and accurate detection of these epileptiform waves is vital for epilepsy analysis.

New method: An improved variational mode decomposition (VMD) decomposes frequency bands to isolate target epileptiform waves. Multidimensional handcrafted features are extracted from low and high frequency bands to characterize these waves, with recursive feature elimination (RFE) selecting key ones. Meanwhile, a dual-stream 1-dimension convolutional neural network (1D CNN) with an adaptive scale factor extracts deep features from VMD-decomposed bands, which are then fused with the handcrafted features.

Results: Experimental results show that the proposed algorithm achieves an average precision of 91%, a recall of 90.36%, and an F1-score of 90.62% on the scalp electroencephalogram (EEG) data of 16 children with benign childhood epilepsy with centrotemporal spikes (BECTS) from the Children's Hospital of Zhejiang University School of Medicine (CHZU).

Comparison with existing methods: Previous studies have often focused on only one type of epileptiform discharge. This narrow focus limits the translation of these biomarkers into clinical practice and their comprehensive application. In the present study, three types of epileptiform discharges are focused on simultaneously.

Conclusion: Our method achieves the optimal overall detection performance in the detection of multiple epileptiform waves. It can be concluded that the proposed technique is capable of serving as an effective tool for evaluating multiple epileptiform waves.