Brain network analysis reveals convergent and divergent aberrations between mild stroke patients with cortical and subcortical infarcts during cognitive task performing

Mengru Xu; Linze Qian; Sujie Wang; Huaying Cai; Yi Sun; Nitish Thakor; Xuchen Qi; Yu Sun

doi:10.3389/fnagi.2023.1193292

Brain network analysis reveals convergent and divergent aberrations between mild stroke patients with cortical and subcortical infarcts during cognitive task performing

Front Aging Neurosci. 2023 Jul 6:15:1193292. doi: 10.3389/fnagi.2023.1193292. eCollection 2023.

Authors

Mengru Xu¹, Linze Qian¹, Sujie Wang¹, Huaying Cai², Yi Sun², Nitish Thakor³, Xuchen Qi^{4

5}, Yu Sun^{1

2

6}

Affiliations

¹ Key Laboratory for Biomedical Engineering of Ministry of Education of China, Department of Biomedical Engineering, Zhejiang University, Hangzhou, China.
² Department of Neurology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China.
³ Department of Biomedical Engineering, National University of Singapore, Singapore, Singapore.
⁴ Department of Neurosurgery, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China.
⁵ Department of Neurosurgery, Shaoxing People's Hospital, Shaoxing, China.
⁶ State Key Laboratory of Brain-Machine Intelligence, Zhejiang University, Hangzhou, China.

Abstract

Although consistent evidence has revealed that cognitive impairment is a common sequela in patients with mild stroke, few studies have focused on it, nor the impact of lesion location on cognitive function. Evidence on the neural mechanisms underlying the effects of mild stroke and lesion location on cognitive function is limited. This prompted us to conduct a comprehensive and quantitative study of functional brain network properties in mild stroke patients with different lesion locations. Specifically, an empirical approach was introduced in the present work to explore the impact of mild stroke-induced cognitive alterations on functional brain network reorganization during cognitive tasks (i.e., visual and auditory oddball). Electroencephalogram functional connectivity was estimated from three groups (i.e., 40 patients with cortical infarctions, 48 patients with subcortical infarctions, and 50 healthy controls). Using graph theoretical analysis, we quantitatively investigated the topological reorganization of functional brain networks at both global and nodal levels. Results showed that both patient groups had significantly worse behavioral performance on both tasks, with significantly longer reaction times and reduced response accuracy. Furthermore, decreased global and local efficiency were found in both patient groups, indicating a mild stroke-related disruption in information processing efficiency that is independent of lesion location. Regarding the nodal level, both divergent and convergent node strength distribution patterns were revealed between both patient groups, implying that mild stroke with different lesion locations would lead to complex regional alterations during visual and auditory information processing, while certain robust cognitive processes were independent of lesion location. These findings provide some of the first quantitative insights into the complex neural mechanisms of mild stroke-induced cognitive impairment and extend our understanding of underlying alterations in cognition-related brain networks induced by different lesion locations, which may help to promote post-stroke management and rehabilitation.

Keywords: EEG; cognitive impairment; functional connectivity; graph theory; mild stroke.

Grants and funding

This study was supported by the Scientific and Technological Innovation 2030 Program of China, major projects (2021ZD0200408), the National Natural Science Foundation of China (82172056), the Zhejiang Provincial Natural Science Foundation of China (LR23F010003 and LTGY23H180015), and the Key Research and Development Program of Zhejiang Province (2022C03064), in part by the Science and Technology Special Project of the Institute of Wenzhou, Zhejiang University (XMGL-KJZX-202203).