Prediction of Epilepsy Based on Tensor Decomposition and Functional Brain Network

Altered Functional Brain Network Structure between Patients with High and Low Generalized Anxiety Disorder

To investigate the differences in functional brain network structures between patients with a high level of generalized anxiety disorder (HGAD) and those with a low level of generalized anxiety disorder (LGAD), a resting-state electroencephalogram (EEG) was recorded in 30 LGAD patients and 21 HGAD patients. Functional connectivity between all pairs of brain regions was determined by the Phase Lag Index (PLI) to construct a functional brain network. Then, the characteristic path length, clustering coefficient, and small world were calculated to estimate functional brain network structures. The results showed that the PLI values of HGAD were significantly increased in alpha2, and significantly decreased in the theta and alpha1 rhythms, and the small-world attributes for both HGAD patients and LGAD patients were less than one for all the rhythms. Moreover, the small-world values of HGAD were significantly lower than those of LGAD in the theta and alpha2 rhythms, which indicated that the brain functional network structure would deteriorate with the increase in generalized anxiety disorder (GAD) severity. Our findings may play a role in the development and understanding of LGAD and HGAD to determine whether interventions that target these brain changes may be effective in treating GAD.

A sequential learning model with GNN for EEG-EMG-based stroke rehabilitation BCI

Introduction

Brain-computer interfaces (BCIs) have the potential in providing neurofeedback for stroke patients to improve motor rehabilitation. However, current BCIs often only detect general motor intentions and lack the precise information needed for complex movement execution, mainly due to insufficient movement execution features in EEG signals.

Methods

This paper presents a sequential learning model incorporating a Graph Isomorphic Network (GIN) that processes a sequence of graph-structured data derived from EEG and EMG signals. Movement data are divided into sub-actions and predicted separately by the model, generating a sequential motor encoding that reflects the sequential features of the movements. Through time-based ensemble learning, the proposed method achieves more accurate prediction results and execution quality scores for each movement.

Results

A classification accuracy of 88.89% is achieved on an EEG-EMG synchronized dataset for push and pull movements, significantly outperforming the benchmark method's performance of 73.23%.

Discussion

This approach can be used to develop a hybrid EEG-EMG brain-computer interface to provide patients with more accurate neural feedback to aid their recovery.

Computerized application for epilepsy in China: Does the era of artificial intelligence comes?

Epilepsy, one of the most common neurological diseases in China, is notorious for its spontaneous, unprovoked and recurrent seizures. The etiology of epilepsy varies among individual patients, including congenital gene mutation, traumatic injury, infections, etc. This heterogeneity partly hampered the accurate diagnosis and choice of appropriate treatments. Encouragingly, great achievements have been achieved in computational science, making it become a key player in medical fields gradually and bringing new hope for rapid and accurate diagnosis as well as targeted therapies in epilepsy. Here, we historically review the advances of computerized applications in epilepsy-especially those tremendous findings achieved in China-for different purposes including seizure prediction, localization of epileptogenic zone, post-surgical prognosis, etc. Special attentions are paid to the great progress based on artificial intelligence (AI), which is more "sensitive", "smart" and "in-depth" than human capacities. At last, we give a comprehensive discussion about the disadvantages and limitations of current computerized applications for epilepsy and propose some future directions as further stepping stones to embrace "the era of AI" in epilepsy.

Osteogenic Effect of Pregabalin in Human Primary Mesenchymal Stem Cells, Osteoblasts, and Osteosarcoma Cells

Seventy million patients worldwide are suffering from epilepsy. The long-term use of antiepileptic drugs causes the alteration of the bone tissue and its metabolism, thus increasing the risk of fractures. Clinical and pre-clinical studies have highlighted conflicting data on the influence of the relatively new antiepileptic drug pregabalin (Lyrica^®). The objective of the present study was therefore to investigate its cytotoxicity in primary human osteoblasts (hOB). HOB and human mesenchymal stem cells (hMSC) were isolated from patients. The human osteosarcoma cells MG63 were included as established cell line. Cells were incubated with pregabalin at concentrations ranging from 0 to 40 μg/mL. Time-dependent cell proliferation was measured by automatic cell counting, and metabolism was determined by XTT assay and osseous differentiation by alkaline phosphatase (ALP) activity. Histological examinations of calcium deposit were performed with ALP, Alizarin Red, and von Kossa staining. A concentration-dependent increase in the proliferation of hOB and hMSC was observed after treatment with pregabalin. All cells showed a significant increase in cell metabolism. The osteogenic differentiation, confirmed by the increase of calcium deposit, was promoted by the administration of pregabalin. This effect was already significant at the therapeutic plasma concentration of pregabalin (10 μg/mL). In contrast to the other antiepileptic drugs, pregabalin showed no osteocatabolic effects. Conflicting in-vivo data must therefore be attributed to systemic effects of pregabalin.