Conditional Granger Causality Analysis of Effective Connectivity during Motor Imagery and Motor Execution in Stroke Patients

Abstract

Aims. Motor imagery has emerged as a promising technique for the improvement of motor function following stroke, but the mechanism of functional network reorganization in patients during this process remains unclear. The aim of this study is to evaluate the cortical motor network patterns of effective connectivity in stroke patients. Methods. Ten stroke patients with right hand hemiplegia and ten normal control subjects were recruited. We applied conditional Granger causality analysis (CGCA) to explore and compare the functional connectivity between motor execution and motor imagery. Results. Compared with the normal controls, the patient group showed lower effective connectivity to the primary motor cortex (M1), the premotor cortex (PMC), and the supplementary motor area (SMA) in the damaged hemisphere but stronger effective connectivity to the ipsilesional PMC and M1 in the intact hemisphere during motor execution. There were tighter connections in the cortical motor network in the patients than in the controls during motor imagery, and the patients showed more effective connectivity in the intact hemisphere. Conclusions. The increase in effective connectivity suggests that motor imagery enhances core corticocortical interactions, promotes internal interaction in damaged hemispheres in stroke patients, and may facilitate recovery of motor function.

BCI training effects on chronic stroke correlate with functional reorganization in motor-related regions: A concurrent EEG and fMRI study

“…Mean- 8 while, a strong but not significant correlation between the influence from SMA to 9 iM1 and recovery was also observed. 10 Considering the findings from previous studies that stroke patients obtained 11 weaker effective connection from cPMA to iM1 during motor imagery and exe- 12 cution compared with healthy subjects [86], our result validated this point from 13 another perspective by showing that the enhanced effective interaction from cPMA 14 to iM1 implied a better rehabilitation recovery. This maybe partially due to the 15 motor imagery procedure of the training therapy since the premotor cortex has 16 been identified as the key node of motor imagery [87].…”

Longitudinal Electroencephalography Analysis in Subacute Stroke Patients During Intervention of Brain–Computer Interface With Exoskeleton Feedback

“…A patient (OME1), who showed activations in the contralesional hemisphere in the first week, had focal ERD in the ipsilesional hemisphere after several BCI sessions. It was reasonable because the sensorimotor areas should be mostly involved in motor-related tasks Wang et al, 2016). Patients, who presented with extensive activations at the very start, became focused on the sensorimotor cortex FIGURE 7 | Topographies of 1.4-1.6 s after the task onset of the seven subjects during the 12-session BCI training in the BCI group.…”

Dynamic Information Flow Based on EEG and Diffusion MRI in Stroke: A Proof-of-Principle Study

“…Nevertheless, the underdetermined nature of the inverse problem of EEG calls for structural, physiological and functional information to be combined to better estimate the location of active sources at the cortex and the causal interactions between sources, i.e., effective connectivity, related to a specific form of stimulus. Various computational approaches such as dynamic causal modeling (DCM) and conditional Granger causality analysis have been proposed and used to estimate the effective connectivity (Bajaj et al, 2015 ; Schulz et al, 2016 ; Wang et al, 2016 ). However, most of the current methods either require prior assumptions on the model structure (e.g., DCM) or exclusively rely on the signal correlations without considering anatomical constraints in the model (e.g., Granger causality analysis).…”