Abnormal intrinsic brain functional network dynamics in stroke and correlation with neuropsychiatric symptoms...

There has been a lack of clarity about the mechanisms of widespread network dysfunctions after stroke. This study aimed to reveal dynamic functional network alternations following stroke based on lesion and brain perfusion. We prospectively enrolled 125 acute ischaemic stroke patients (25 were transient ischemic attack (TIA) patients) and 49 healthy controls with assessed the severity of their depression, anxiety, fatigue, and apathy. We performed dynamic functional network connectivity (DFNC) analysis using the sliding window method. The common static FC biomarkers of stroke were used to define functional states and calculated stroke-specific changes in dynamic indicators. Next, ridge regression (RR) analyses were performed on the dynamic indicators using voxel-wise lesion maps, cerebral blood flow (CBF) difference maps (removal of voxels overlapping lesions) and a combination of both. Mediation analyses were used to characterize the effect of dynamic networks changes on the relationship between lesion, CBF, and neuropsychological scores. Our results showed that DFNC identified three functional states with three dynamic metrics extracted for subsequent analyses. RR analyses show that both CBF and lesions partially explain post-stroke dysfunction (CBF: dynamic indicator1: R² = 0.110, p = 0.163; dynamic indicator2: R² = 0.277, p = 0.006; dynamic indicator3: R² = 0.125, p = 0.121; lesion: dynamic indicator1: R² = 0.132, p = 0.109; dynamic indicator2: R² = 0.238, p = 0.015; dynamic indicator3: R² = 0.131, p = 0.110). In addition, combining the two can improve the efficacy of explanations. Finally, exploratory mediation analyses identified that dynamic functional network changes can mediate between CBF, lesion and neuropsychiatric disorders. Our results suggest that CBF and lesion can be combined to improve the interpretation of dynamic network dysfunction after stroke.