Altered resting-state effective connectivity of fronto-parietal motor control systems on the primary motor network following stroke

Cory S Inman,Justin K Rajendra,Giuseppe Pagnoni,Andrew J Butler,G Andrew James,Stephan Hamann

doi:10.1016/j.neuroimage.2011.07.083

Abstract

Previous brain imaging work suggests that stroke alters the effective connectivity (the influence neural regions exert upon each other) of motor execution networks. The present study examines the intrinsic effective connectivity of top-down motor control in stroke survivors (n=13) relative to healthy participants (n=12). Stroke survivors exhibited significant deficits in motor function, as assessed by the Fugl-Meyer Motor Assessment. We used structural equation modeling (SEM) of resting-state fMRI data to investigate the relationship between motor deficits and the intrinsic effective connectivity between brain regions involved in motor control and motor execution. An exploratory adaptation of SEM determined the optimal model of motor execution effective connectivity in healthy participants, and confirmatory SEM assessed stroke survivors' fit to that model. We observed alterations in spontaneous resting-state effective connectivity from fronto-parietal guidance systems to the motor network in stroke survivors. More specifically, diminished connectivity was found in connections from the superior parietal cortex to primary motor cortex and supplementary motor cortex. Furthermore, the paths demonstrated large individual variance in stroke survivors but less variance in healthy participants. These findings suggest that characterizing the deficits in resting-state connectivity of top-down processes in stroke survivors may help optimize cognitive and physical rehabilitation therapies by individually targeting specific neural pathway.

Highlights

Stroke is the leading cause of severe, long-term disability in the United States (Rosamond, Flegal, Friday, et al, 2007)
The path from parietal area (PAR) to supplementary motor area (SMA) has an almost identical path weight identified in the Solodkin motor execution model and our resting-state motor network (Motor Exec. path coef. = 0.3–0.6; rsmotor network path coef. =0.51; Figure 2B: PAR to SMA)
The present study found a disruption in the influence of a region implicated in top-down attentional control (Corbetta and Shulman, 2002) on primary motor regions in stroke survivors with heterogeneous stroke locations, during the resting-state

Summary

Introduction

Stroke is the leading cause of severe, long-term disability in the United States (Rosamond, Flegal, Friday, et al, 2007). The emphasis thereby changes from the influence of individual brain regions active in each condition to the influence neural regions have upon each other, an aspect of brain function known as effective connectivity (McIntosh and Gonzalez-Lima, 1994; Buchel and Friston, 1997; Buchel, Coull, and Friston, 1999; Friston, Harrison, and Penny, 2003). In their pioneering study, Solodkin et al (Solodkin, Hlustik, et al, 2004) used structural equation modeling (SEM) to assess motor network activation during motor execution, visual imagery, and kinesthetic imagery in healthy volunteers. With regard to the present study, data from Solodkin et al provide an established template of the motor imagery/execution network upon which to establish comparisons between the same motor execution networks in healthy individuals and stroke survivors with known motor deficits

Methods

Results

Discussion

Conclusion