Altered Effective Connectivity of the Primary Motor Cortex in Stroke: A Resting-State fMRI Study with Granger Causality Analysis

Abstract

The primary motor cortex (M1) is often abnormally recruited in stroke patients with motor disabilities. However, little is known about the alterations in the causal connectivity of M1 following stroke. The purpose of the present study was to investigate whether the effective connectivity of the ipsilesional M1 is disturbed in stroke patients who show different outcomes in hand motor function. 23 patients with left-hemisphere subcortical stroke were selected and divided into two subgroups: partially paralyzed hands (PPH) and completely paralyzed hands (CPH). Further, 24 matched healthy controls (HCs) were recruited. A voxel-wise Granger causality analysis (GCA) on the resting-state fMRI data between the ipsilesional M1 and the whole brain was performed to explore differences between the three groups. Our results showed that the influence from the frontoparietal cortices to ipsilesional M1 was diminished in both stroke subgroups and the influence from ipsilesional M1 to the sensorimotor cortices decreased greater in the CPH group than in the PPH group. Moreover, compared with the PPH group, the decreased influence from ipsilesional M1 to the contralesional cerebellum and from the contralesional superior parietal lobe to ipsilesional M1 were observed in the CPH group, and their GCA values were positively correlated with the FMA scores; Conversely, the increased influence from ipsilesional M1 to the ipsilesional middle frontal gyrus and middle temporal gyrus were observed, whose GCA values were negatively correlated with the FMA scores. This study suggests that the abnormalities of casual flow in the ipsilesional M1 are related to the severity of stroke-hand dysfunction, providing valuable information to understand the deficits in resting-state effective connectivity of motor execution and the frontoparietal motor control network during brain plasticity following stroke.

Altered intra‐ and inter‐network functional coupling of resting‐state networks associated with motor dysfunction in stroke

“…In previous motor‐task fMRI and FC studies, the abnormal activity of visual occipital regions has been generally regarded as a noneffective compensatory reorganization in motor deficits after stroke or has been ignored (Park et al, ; Ward et al, ). Our previous study using effective connectivity analysis reported a higher influence from the ipsilesional M1 to the ipsilesional occipital lobe in the stroke group with more severe paralysis and regarded it as compensation for the impaired connectivity to the ipsilesional somatosensory cortex (Zhao et al, ). We also found reduced FC of the ipsilesional inferior parietal lobule within the DAN, further suggesting that the disruption of the attention network contributed to the impairment of the motor function.…”

“…Therefore, a growing number of studies are employing this technique to map the spatio‐temporal covariance structure of networks of spontaneous brain activity to investigate the reorganization of motor function following stroke (Golestani, Tymchuk, Demchuk, Goodyear, & Group, ; Wang et al, ; Yin et al, ). Many resting‐state studies have accordingly shown the altered functional connectivity of both sensorimotor and higher order cognitive control regions (Liu, Tian, Qin, Li, & Yu, ; Park et al, ; Zhao et al, ). In addition, many previous studies that described tasks and seed‐based FC engaged more than basic sensorimotor processes, such as the abnormal activation or FC in temporal and occipital regions (Park et al, ; Ward et al, ; Zhao et al, ).…”

“…Many resting‐state studies have accordingly shown the altered functional connectivity of both sensorimotor and higher order cognitive control regions (Liu, Tian, Qin, Li, & Yu, ; Park et al, ; Zhao et al, ). In addition, many previous studies that described tasks and seed‐based FC engaged more than basic sensorimotor processes, such as the abnormal activation or FC in temporal and occipital regions (Park et al, ; Ward et al, ; Zhao et al, ). However, the link between sensorimotor regions and these nonsensorimotor regions in motor deficits after stroke remains unclear.…”

The impact of hemodynamic variability and signal mixing on the identifiability of effective connectivity structures in BOLD fMRI

“…This voxel‐wise application of GC can be performed in multiple ways: the GC score can be averaged with LASSO regularization (Tang, Bressler, Sylvester, Shulman, & Corbetta, 2012), through a multivoxel pattern‐based causality mapping as proposed by Kim, Kim, Ahmad, & Park (2013), or through hierarchical clustering as proposed by Deshpande, LaConte, James, Peltier, & Hu (2009). This strategic twist is already being used in some studies (Katwal, Gore, Gatenby, & Rogers, 2013; Zhao et al., 2016), however, it is not the most common approach in the field yet [voxel‐wise modeling is, however, increasingly popular for finding activation patterns in cognition from fMRI data (Huth, de Heer, Griffiths, Theunissen, & Gallant, 2016)]. Applying GC voxel‐wise has two major advantages.…”

“…This part of the analysis refers to the studies in which the lagged methods are applied to the unconvolved BOLD, which is often a practice in GC research (Chen et al., 2017; Regner et al., 2016; Zhao et al., 2016). In such case, the utility of lagged methods in fMRI research depends on the variability in the hemodynamic lags.…”