Effective connectivity differences in motor network during passive movement of paretic and non-paretic ankles in subacute stroke patients

PeerJ

article

Marianna Nagy¹  Csaba Aranyi²  Gábor Opposits²  Tamás Papp¹  Levente Lánczi¹  Ervin Berényi¹  Csilla Vér⁴  László Csiba⁴  Péter Katona³  Tamás Spisák⁵  Miklós Emri² 

[1] Faculty of Medicine, Department of Medical Imaging, Division of Radiology and Imaging Science, University of Debrecen;Faculty of Medicine, Department of Medical Imaging, Division of Nuclear Medicine and Translational Imaging, University of Debrecen;Department of Diagnostic Radiology, Kenézy University Hospital;Clinical Center, Department of Neurology, University of Debrecen;Department of Neurology, University Hospital Essen

关键词: Subacute stroke;    fMRI;    Effective connectivity;    Motor network;    DCM;    BMC;    Hemodynamic response;   

DOI  :  10.7717/peerj.8942

学科分类：社会科学、人文和艺术（综合）

来源: Inra

【 摘 要 】

Background A better understanding of the neural changes associated with paresis in stroke patients could have important implications for therapeutic approaches. Dynamic Causal Modeling (DCM) for functional magnetic resonance imaging (fMRI) is commonly used for analyzing effective connectivity patterns of brain networks due to its significant property of modeling neural states behind fMRI signals. We applied this technique to analyze the differences between motor networks (MNW) activated by continuous passive movement (CPM) of paretic and non-paretic ankles in subacute stroke patients. This study aimed to identify CPM induced connectivity characteristics of the primary sensory area (S1) and the differences in extrinsic directed connections of the MNW and to explain the hemodynamic differences of brain regions of MNW. Methods For the network analysis, we used ten stroke patients’ task fMRI data collected under CPMs of both ankles. Regions for the MNW, the primary motor cortex (M1), the premotor cortex (PM), the supplementary motor area (SMA) and the S1 were defined in a data-driven way, by independent component analysis. For the network analysis of both CPMs, we compared twelve models organized into two model-families, depending on the S1 connections and input stimulus modeling. Using DCM, we evaluated the extrinsic connectivity strengths and hemodynamic parameters of both stimulations of all patients. Results After a statistical comparison of the extrinsic connections and their modulations of the “best model”, we concluded that three contralateral self-inhibitions (cM1, cS1 and cSMA), one contralateral inter-regional connection (cSMA→cM1), and one interhemispheric connection (cM1→iM1) were significantly different. Our research shows that hemodynamic parameters can be estimated with the Balloon model using DCM but the parameters do not change with stroke. Conclusions Our results confirm that the DCM-based connectivity analyses combined with Bayesian model selection may be a useful technique for quantifying the alteration or differences in the characteristics of the motor network in subacute stage stroke patients and in determining the degree of MNW changes.

【 授权许可】

CC BY