Analysis of Time-Dependent Brain Network on Active and MI Tasks for Chronic Stroke Patients

Estimation of Stroke's Motor Function Ability Using Multimodal Biomarkers and the Role of Contralesional Motor Area

PURPOSE

In the chronic phase, many stroke survivors did not regain their pre-stroke upper limb movement capabilities. This emphasizes the crucial role of assessing motor function in patients with stroke, as it provides valuable insights into setting effective rehabilitation goals. Accordingly, this study aimed to investigate the electroencephalography (EEG)-based functional brain network properties in stroke patients during motor tasks and assess their utility in predicting the upper limb Fugl-Meyer Assessment (UL-FMA) scores.

METHODS

We performed a comparative analysis of brain properties, including EEG power and network characteristics, in stroke patients and a healthy control (HC) group. Also, we selected prognostic factors of brain properties during voluntary movement for patients' motor function ability using stepwise regression analysis.

FINDINGS

Stroke patients manifested reduced global efficiency relative to the HC group, signifying impaired information processing attributed to brain injury. Local analyses highlighted pronounced disparities in the contralesional motor area (MA) between stroke patients and the HC group, revealing patterns indicative of compensatory mechanisms. Leveraging a multimodal approach incorporating EEG power and network features within the contralesional MA yielded a robust model for motor function estimation, outperforming unimodal models (adjusted R2 0.99, RMSE 0.13). The findings of this study outperformed other models for estimating the motor abilities of chronic stroke patients. Another chronic stroke dataset was used to externally validate this study, and it had an adjusted R2 of 0.95. This suggests that the results of this study can be generalized.

CONCLUSION

Our findings provide insight into the brain properties of stroke-related motor impairment. These results underscore the pivotal role of the contralesional MA in assessing UL-FMA scores and represent how a multimodal approach to this area can suggest the possibility of using it as a meaningful biomarker for motor function. They also have potential implications for the development of individualized rehabilitation strategies, particularly during the chronic phase of recovery.

Enhancing motor imagery in the third-person perspective by manipulating sense of body ownership with virtual reality

Virtual reality (VR)-guided motor imagery (MI) is a widely used approach for motor rehabilitation, especially for patients with severe motor impairments. Most approaches provide visual guidance from the first-person perspective (1PP). MI training with visual guidance from the third-person perspective (3PP) remains largely unexplored. We argue that 3PP MI training has its own advantages and can supplement 1PP MI. For some movements beyond the view of 1PP, such as shoulder shrugging and other axial movements, MI are suitable performed under 3PP. However, the efficiency of existing paradigms for 3PP MI is unsatisfactory. We speculate that the absence of sense of body ownership (SOO) from 3PP could be one possible factor and hypothesize that 3PP MI could be enhanced by eliciting SOO over a 3PP avatar. Based on our hypothesis, a novel paradigm was proposed to enhance 3PP MI by inducing full-body illusion (FBI) from 3PP, which is similar to the so-called out-of-body experience (OBE), using synchronous visuo-tactile stimulus with VR. The event-related Electroencephalograph (EEG) desynchronization (ERD) at motor-related regions from 31 healthy participants were calculated and compared with a control paradigm without "OBE" FBI induction. This study attempts to enhance 3PP MI with FBI induction. It offers an opportunity to perform MI guided by action observation from 3PP with elicited SOO to the observed avatar. We believe that 3PP MI could provide more possibilities for effective rehabilitation training, when SOO could be elicited to a virtual avatar and the present work demonstrates its viability and effectiveness.

Brain network characteristics between subacute and chronic stroke survivors in active, imagery, passive movement task: a pilot study

Background

The activation patterns and functional network characteristics between stroke survivors and healthy individuals based on resting-or task-state neuroimaging and neurophysiological techniques have been extensively explored. However, the discrepancy between stroke patients at different recovery stages remains unclear.

Objective

To investigate the changes in brain connectivity and network topology between subacute and chronic patients, and hope to provide a basis for rehabilitation strategies at different stages after stroke.

Methods

Fifteen stroke survivors were assigned to the subacute group (SG, N = 9) and chronic group (CG, N = 6). They were asked to perform hand grasping under active, passive, and MI conditions when recording EEG. The Fugl-Meyer Assessment Upper Extremity subscale (FMA_UE), modified Ashworth Scale (MAS), Manual Muscle Test (MMT), grip and pinch strength, modified Barthel Index (MBI), and Berg Balance Scale (BBS) were measured.

Results

Functional connectivity analyses showed significant interactions on frontal, parietal and occipital lobes connections in each frequency band, particularly in the delta band. The coupling strength of premotor cortex, M1, S1 and several connections linked to frontal, parietal, and occipital lobes in subacute subjects were lower than in chronic subjects in low alpha, high alpha, low beta, and high beta bands. Nodal clustering coefficient (CC) analyses revealed that the CC in chronic subjects was higher than in subacute subjects in the ipsilesional S1 and occipital area, contralesional dorsolateral prefrontal cortex and parietal area. Characteristic path length (CPL) analyses showed that CPL in subacute subjects was lower than in chronic subjects in low beta, high beta, and gamma bands. There were no significant differences between subacute and chronic subjects for small-world property.

Conclusion

Subacute stroke survivors were characterized by higher transfer efficiency of the entire brain network and weak local nodal effects. Transfer efficiency was reduced, the local nodal role was strengthened, and more neural resources needed to be mobilized to perform motor tasks for chronic survivors. Overall, these results may help to understand the remodeling pattern of the brain network for different post-stroke stages on task conditions and the mechanism of spontaneous recovery.

Alcoholic EEG signal classification with Correlation Dimension based distance metrics approach and Modified Adaboost classification

The basic function of the brain is severely affected by alcoholism. For the easy depiction and assessment of the mental condition of a human brain, Electroencephalography (EEG) signals are highly useful as it can record and measure the electrical activities of the brain much to the satisfaction of doctors and researchers. Utilizing the standard conventional techniques is quite hectic to derive the useful information as these signals are highly non-linear and non-stationary in nature. While recording the EEG signals, the activities of the neurons are recorded from various scalp regions which has varied characteristics and has a very low magnitude. Therefore, human interpretation of such signals is very difficult and consumes a lot of time. Hence, with the advent of Computer Aided Diagnosis (CAD) Techniques, identifying the normal versus alcoholic EEG signals has been of great utility in the medical field. In this work, we perform the initial clustering of the alcoholic EEG signals by means of using Correlation Dimension (CD) for easy feature extraction and then the suitable features are selected in it by means of employing various distance metrics like correlation distance, city block distance, cosine distance and chebyshev distance. Proceeding in such a methodology aids and assures that a good discrimination could be achieved between normal and alcoholic EEG signals using non-linear features. Finally, classification is then carried out with the suitable classifiers chosen such as Adaboost.RT classifier, the proposed Modified Adaboost.RT classifier by means of introducing Ridge and Lasso based soft thresholding technique, Random Forest with bootstrap resampling technique, Artificial Neural Networks (ANN) such as Radial Basis Functions (RBF) and Multi-Layer Perceptron (MLP), Support Vector Machine (SVM) with Linear, Polynomial and RBF Kernel, Naïve Bayesian Classifier (NBC), K-means classifier, and K Nearest Neighbor (KNN) Classifier and the results are analyzed. Results report a comparatively high classification accuracy of about 98.99% when correlation distance metrics are utilized with CD and the proposed Modified Adaboost.RT classifier using Ridge based soft thresholding technique.