Brain Network Connectivity During Resting-State and a Visuospatial Task as a Biomarker for Spatial Neglect in Stroke Patients.

IF 3.7
Neurorehabilitation and neural repair Pub Date : 2026-06-01 Epub Date: 2026-03-03 DOI:10.1177/15459683261419423
Golnaz Haddadshargh, Richard Gall, Emily S Grattan, Sarah Ostadabbas, George F Wittenberg, Murat Akcakaya
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Abstract

BackgroundSpatial neglect (SN) is a common visual attention deficit affecting stroke patients due to large-scale disruptions within brain networks. Most studies have focused only on resting-state, but effective rehabilitation requires a clearer understanding of how brain networks change during visuospatial tasks.ObjectiveThis study aims to identify network disruptions associated with neglect by comparing resting-state and task-based electroencephalography (EEG) connectivity patterns in stroke patients with and without neglect.MethodsWe recorded EEG data from 28 stroke patients using the augmented reality (AR)-based EEG-guided neglect detection system (AREEN) during resting-state and a visuospatial task. Connectivity was measured using coherence in delta, theta, alpha, and beta bands for both conditions, with gamma-band coherence assessed only during the task. Graph-based metrics were applied to model network-level disruptions. Classification models evaluated the significance of connectivity features to find patterns predictive of neglect.ResultsThe neglect group showed reduced connectivity in frontal and right parieto-occipital (ParOcc) regions, primarily in beta and theta bands, during both conditions, with additional gamma-band connectivity differences in the task condition, compared to the non-neglect group. Conversely, connectivity was greater in central and midline regions, which may indicate a maladaptive shift in network organization. Classification models accurately classified patients into neglect and non-neglect groups (resting-state: 87.0% ± 0.7%; task: 80.9% ± 16.0%). Feature importance analysis identified eigenvector and closeness centrality within frontal, right ParOcc, and central regions as key predictors.ConclusionsNetwork disruptions can effectively identify SN and provide potential targets for connectivity-based rehabilitation. Future studies should investigate whether these interventions improve attention and recovery in stroke patients.This study was registered at ClinicalTrials.gov under ID NCT04187131.

脑卒中患者静息状态下的脑网络连通性和视觉空间任务作为空间忽视的生物标志物。
空间忽视(SN)是一种常见的影响脑卒中患者的视觉注意缺陷,由于大脑网络的大规模破坏。大多数研究只关注静息状态,但有效的康复需要更清楚地了解大脑网络在视觉空间任务中的变化。目的本研究旨在通过比较有和无忽视的脑卒中患者静息状态和基于任务的脑电图(EEG)连接模式来识别与忽视相关的网络中断。方法采用基于增强现实(AR)的脑电图引导忽视检测系统(AREEN)记录28例脑卒中患者静息状态和视觉空间任务时的脑电图数据。在两种情况下,连通性都是通过delta、theta、alpha和beta波段的相干性来测量的,而gamma波段的相干性仅在任务期间进行评估。基于图形的指标被应用于网络级中断模型。分类模型评估连接特征的重要性,以找到预测忽视的模式。结果与非忽视组相比,忽视组在两种情况下均表现出额叶和右侧顶枕区(ParOcc)的连通性降低,主要是在β和θ波段,并且在任务条件下存在额外的γ波段连通性差异。相反,中部和中线地区的连通性更强,这可能表明网络组织的不适应转变。分类模型准确地将患者分为忽视组和非忽视组(静息状态:87.0%±0.7%;任务:80.9%±16.0%)。特征重要性分析确定了特征向量和接近中心性在额、右ParOcc和中心区域作为关键预测因子。结论网络中断可以有效识别SN,为基于连通性的康复提供潜在靶点。未来的研究应该调查这些干预措施是否能改善中风患者的注意力和恢复。本研究在ClinicalTrials.gov注册,编号NCT04187131。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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