Machine Learning Technique Reveals Intrinsic EEG Connectivity Characteristics of Patients With Mild Stroke During Cognitive Task Performing

Abstract

Although convergent evidence has shown that patients with mild stroke (MS) are commonly accompanied by post-stroke cognitive and/or memory impairment, only disproportionate attention was paid compared to severe stroke. To promote post-stroke management for early intervention in MS-related cognitive impairment, a feasible and convenient method for MS detection is therefore favorable. A data-driven classification framework combined with quantitative graph theoretical analysis was introduced in this work, aiming to provide a comprehensive appreciation of MS-related brain network alterations. EEG functional connectivity (FC) was constructed from 45 patients with MS and 45 healthy participants during two cognitive tasks (i.e., visual and auditory oddball) and set as input for the classification model and graph theoretical analysis. As expected, patients showed significantly reduced behavioral performance in both tasks. Furthermore, we achieved a satisfactory classification accuracy of 88.9% with a decision fusion strategy from classification models of both tasks. The spatio-spectral characteristics of the discriminative FC revealed complex topological distributions in both tasks. Moreover, significantly decreased global efficiency was found, suggesting a MS-related disruption in parallel information processing. Overall, these results demonstrated the potential of FC as salient biomarkers for detecting MS, and extended our understanding of the underlying MS-related neural mechanisms during cognitive processing.