Accurate identification of Alzheimer's disease (AD) with electroencephalograph (EEG) is crucial in clinical diagnosis of neurological disorders. However, the effectiveness and accuracy of manually labeling EEG signals is barely satisfactory, due to lacking effective biomarkers. In this paper, we propose a novel machine learning method network-based Takagi-Sugeno-Kang (N-TSK) for AD identification, which employs the complex network theory and TSK fuzzy system. With the construction of functional network of AD subjects, the topological features of weighted and unweighted networks are extracted. Taken the network parameters as independent inputs, a fuzzy-system-based TSK model is established and further trained to identify AD EEG signals. Experimental results demonstrate the effectiveness of the proposed scheme in AD identification and ability of N-TSK fuzzy classifiers. The highest accuracy can achieve 97.3% for patients with closed eyes and 94.78% with open eyes. In addition, the performance of weighted N-TSK largely exceeds unweighted N-TSK. By further optimizing the network features utilized in the N-TSK fuzzy classifiers, it is found that local efficiency and clustering coefficient are most effective factors in AD identification. This work provides a potential tool for identifying neurological disorders from the perspective of functional networks with EEG signal, especially contributing to the diagnosis and identification of AD.

The investigation of the human intelligence, cognitive systems and functional complexity of human brain is significantly facilitated by high-performance computational platforms. In this paper, we present a real-time digital neuromorphic system for the simulation of large-scale conductance-based spiking neural networks (LaCSNN), which has the advantages of both high biological realism and large network scale. Using this system, a detailed large-scale cortico-basal ganglia-thalamocortical loop is simulated using a scalable 3-D network-on-chip (NoC) topology with six Altera Stratix III field-programmable gate arrays simulate 1 million neurons. Novel router architecture is presented to deal with the communication of multiple data flows in the multinuclei neural network, which has not been solved in previous NoC studies. At the single neuron level, cost-efficient conductance-based neuron models are proposed, resulting in the average utilization of 95% less memory resources and 100% less DSP resources for multiplier-less realization, which is the foundation of the large-scale realization. An analysis of the modified models is conducted, including investigation of bifurcation behaviors and ionic dynamics, demonstrating the required range of dynamics with a more reduced resource cost. The proposed LaCSNN system is shown to outperform the alternative state-of-the-art approaches previously used to implement the large-scale spiking neural network, and enables a broad range of potential applications due to its real-time computational power.

The clinical diagnosis of Parkinson's disease (PD) is very difficult, especially in the early stage of the disease, because there is no physiological indicator that can be referenced. Drug-free patients with early PD are characterized by clinical symptoms such as impaired motor function and cognitive decline, which was caused by the dysfunction of brain's dynamic activities. The indicators of brain dysfunction in patients with PD at an early unmedicated condition may provide a valuable basis for the diagnosis of early PD and later treatment. In order to find the spatiotemporal characteristic markers of brain dysfunction in PD, the resting-state EEG microstate analysis is used to explore the transient state of the whole brain of 23 drug-free patients with PD on the sub-second timescale compared to 23 healthy controls. EEG microstates reflect a transiently stable brain topological structure with spatiotemporal characteristics, and the spatial characteristic microstate classes and temporal parameters provide insight into the brain's functional activities in PD patients. The further exploration was to explore the relation between temporal microstate parameters and significant clinical symptoms to determine whether these parameters could be used as a basis for clinically assisted diagnosis. Therefore, we used a general linear model (GLM) to explore the relevance of microstate parameters to clinical scales and multiple patient attributes, and the Wilcoxon rank sum test was used to quantify the linear relation between influencing factors and microstate parameters. Results of microstate analysis revealed that there was an unique spatial microstate different from healthy controls in PD, and several other typical microstates had significant differences compared with the normal control group, and these differences were reflected in the microstate parameters, such as longer durations and more occurrences of one class of microstates in PD compared with healthy controls. Furthermore, correlation analysis showed that there was a significant correlation between multiple microstate classes' parameters and significant clinical symptoms, including impaired motor function and cognitive decline. These results indicate that we have found multiple quantifiable feature tags that reflect brain dysfunction in the early stage of PD. Importantly, such temporal dynamics in microstates are correlated with clinical scales which represent the motor function and recognize level. The obtained results may deepen our understanding of the brain dysfunction caused by PD, and obtain some quantifiable signatures to provide an auxiliary reference for the early diagnosis of PD.

Characterizing the neural representations underlying somatic perception is crucial for neural decoding of external stimulations. Acupuncture is an important therapeutic method of traditional Chinese medicine and can effectively modulate brain activity for the treatment of neural diseases. In this work, we investigate the neural representations based on the power spectral density (PSD) estimated from electroencephalogram (EEG) across the whole brain with deep learning. Frequency and spatial characteristics of PSD can reliably represent the dynamical brain responses to acupuncture with different manipulations, manifesting enhanced alpha power in parietal lobe. By removing aperiodic components, periodic spatial spectrum shows a higher representation ability of different brain states during acupuncture stimulations, and twiring-rotating (TR) manipulation have a more pronounced modulatory effect than lifting-thrusting (LT) manipulation. Moreover, we further infer the low-dimensional feature-disentangled representations with generative adversarial network (GAN), i.e., w-latents of StyleGAN, which can capture the latent features of periodic spatial spectrum and strike a balance between separability and generalizability. The effectiveness of feature-disentangled representations is evaluated by decoding the acupuncture states, which can achieve a highest accuracy of 95.71% with Transformer classifier. Compared with high-dimensional spatial spectrum, low-dimensional latent features can best characterize different brain states, indicating a precise representation of somatic perception of acupuncture stimulations. Taken together, our results highlight the significant role of spatial spectral representation underlying somatic perception and serve as an important benchmark for the evaluation of acupuncture effect on human brain.

One of the challenging problems in real-time control of movement disorders is the effective handling of time-variant brain activities that involve stochastic functional networks with nonlinear dynamics. For such challenges in neuromodulation tasks, fuzzy logic control (FLC) has shown significant potential. The objective of this work is to present a FLC-based strategy to treat pathological symptoms of movement-disorder with higher performance. In particular, the strategy is two-fold: 1) develop a design methodology for the FLC system that can robustly control pathological conditions and significantly improve control performance, and 2) develop a hardware-efficient implementation for real-time neuromodulation applications. To enhance control performance, a slowly varying hidden variable in the neural network than can be estimated using an unscented Kalman filter (UKF) is identified as a feedback variable. In comparison with state-of-the-art schemes, the proposed design can adaptively optimize the control signals without requiring particular information of the controlled plant, and can avoid repeated determinations of controller parameters. A field-programmable gate array (FPGA) is used for reconfigurable realization of the entire control strategy based on a modification of the original neural network. The presented design, with enhanced control performance and higher hardware efficiency, has significant potential for clinical treatment of movement disorders, and opens a new perspective for the applications in the fields of neural control engineering and brain-machine interfaces.