Alterations of static and dynamic functional network connectivity in acute ischemic brainstem stroke

This study investigates post-stroke cognitive impairment (PSCI) by utilizing spectral dynamic causal modeling (spDCM) to examine changes in effective connectivity (EC) within the default mode network, executive control network, dorsal attention network, and salience network. Forty-one PSCI patients and 41 demographically matched healthy controls underwent 3D-T1WI and resting-state functional magnetic resonance imaging on 3.0T MRI. The study compared the EC among eight representative regions of interest using spDCM and analyzed the correlation between the altered EC and cognitive tests. Subgroup analysis was conducted based on lesion location. The study found a significant increase in EC in the PSCI group, specifically from the posterior cingulate cortex (PCC) to the left and right dorsolateral prefrontal cortex (L_DLPFC and R_DLPFC, respectively), and from the right insula to R_DLPFC (p = 0.01, dNBS-corrected). The study found that these changes were significantly negatively correlated with cognitive scores. Subgroup analysis also showed increased executive control in PSCI patients with left anterior circulation lesions. Validation through low-order functional connectivity analysis revealed abnormalities dominated by large-scale intra- and inter-network functional connectivity increases in patients with PSCI. The study suggests that there is an increase in effective connectivity between networks, particularly within the triple network model. The findings implicate the PCC and R_DLPFC in the pathophysiology of PSCI, which sheds light on its cognitive implications. This study emphasizes the importance of understanding network changes in PSCI from various perspectives and enhances our understanding of the neural mechanisms underlying PSCI, establishing a foundation for future research.

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Functional connectivity and graph theory of impaired central visual pathways in acute ischemic stroke based on fMRI

Chu,

Xue,

et al. 2024

Preprint

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In the study of this paper, we first performed the analysis of whole brain static functional connectivity, we divided the whole brain into 90 regions of interest (ROIs) by applying AAL mapping, we compared the whole brain static functional connectivity analysis of the 14 patients and 26 healthy volunteers (HC) who completed the 3-months experiment (3months), the 14 patients and 26 healthy volunteers who completed the 7-days experiment (7days), the 12 patients and the 12 patients who completed the 7-days experiment (7days), the 12 patients and the 12 patients who completed the 3-months experiment (7days), and the 12 patients and 26 healthy volunteers ( HC), 14 patients who completed the 7-day experiment (7days), and 14 patients who completed the 3-month experiment (3months) were analysed for whole-brain static functional connectivity in all three groups, and 90 ROIs were mapped to the Yeo7 functional network for analysis. sFC analyses revealed significant alterations in the patients' VAN, and DMN networks. Secondly, we performed dynamic functional connectivity analysis based on AAL mapping with the sliding window method separately, and identified two dynamic functional connectivity pattern characteristics, i.e., state 1 with a connectivity pattern dominated by high-frequency weak connectivity, and state 2 with a connectivity pattern dominated by low-frequency strong connectivity.Stroke patients spent significantly more time in state 1, and the number of state switches of the stroke patients in 7days significantly higher and were more likely to switch to the low-frequency strong connectivity mode state 2. Significant changes in connectivity were observed for DMN, VIS, FPN, and LIM. Finally, we built five machine learning models based on SFC features that differ between groups, namely linear support vector machine (SVM), radial basis function support vector machine (SVM-RBF), k nearest neighbours (KNN), random forest (RF), and decision tree (TREE). Based on the maximum AUC we identified the optimal feature subset and found that the SFC within the VIS, DMN, and LIM networks contributed significantly to the classification of AIS patients and HCs alike.The variation of FC within the VIS, DMN, and LIM networks may provide new insights into the neural mechanisms of AIS patients.

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Alterations of static and dynamic functional network connectivity in acute ischemic brainstem stroke

Cited by 3 publications

References 47 publications

Withdrawn: The altered dynamic community structure for adaptive adjustment in stroke patients with multidomain cognitive impairments: A multilayer network analysis

Withdrawn: The altered dynamic community structure for adaptive adjustment in stroke patients with multidomain cognitive impairments: A multilayer network analysis

Network Dynamics in Post-Stroke Cognitive Impairment: Insights from Effective Connectivity Analysis

Functional connectivity and graph theory of impaired central visual pathways in acute ischemic stroke based on fMRI

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