title: Brain effective connectivity during motor-imagery and execution following stroke and rehabilitation.
abstract: Brain areas within the motor system interact directly or indirectly during motor-imagery and motor-execution tasks. These interactions and their functionality can change following stroke and recovery. How brain network interactions reorganize and recover their functionality during recovery and treatment following stroke are not well understood. To contribute to answering these questions, we recorded blood oxygenation-level dependent (BOLD) functional magnetic resonance imaging (fMRI) signals from 10 stroke survivors and evaluated dynamical causal modeling (DCM)-based effective connectivity among three motor areas: primary motor cortex (M1), pre-motor cortex (PMC) and supplementary motor area (SMA), during motor-imagery and motor-execution tasks. We compared the connectivity between affected and unaffected hemispheres before and after mental practice and combined mental practice and physical therapy as treatments. The treatment (intervention) period varied in length between 14 to 51 days but all patients received the same dose of 60 h of treatment. Using Bayesian model selection (BMS) approach in the DCM approach, we found that, after intervention, the same network dominated during motor-imagery and motor-execution tasks but modulatory parameters suggested a suppressive influence of SM A on M1 during the motor-imagery task whereas the influence of SM A on M1 was unrestricted during the motor-execution task. We found that the intervention caused a reorganization of the network during both tasks for unaffected as well as for the affected hemisphere. Using Bayesian model averaging (BMA) approach, we found that the intervention improved the regional connectivity among the motor areas during both the tasks. The connectivity between PMC and M1 was stronger in motor-imagery tasks whereas the connectivity from PMC to M1, SM A to M1 dominated in motor-execution tasks. There was significant behavioral improvement (p = 0.001) in sensation and motor movements because of the intervention as reflected by behavioral Fugl-Meyer (FMA) measures, which were significantly correlated (p = 0.05) with a subset of connectivity. These findings suggest that PMC and M1 play a crucial role during motor-imagery as well as during motor-execution task. In addition, M1 causes more exchange of causal information among motor areas during a motor-execution task than during a motor-imagery task due to its interaction with SM A. This study expands our understanding of motor network involved during two different tasks, which are commonly used during rehabilitation following stroke. A clear understanding of the effective connectivity networks leads to a better treatment in helping stroke survivors regain motor ability.
authors.affiliation: Department of Physics and Astronomy, Georgia State University, Atlanta, GA, USA.
authors.forename: Sahil
authors.initials: S
authors.lastname: Bajaj
authors.affiliation: Byrdine F. Lewis School of Nursing & Health Professions, Georgia State University, Atlanta, GA, USA ; Department of Veteran's Affairs, Atlanta Rehabilitation Research and Development Center of Excellence, Decatur, GA, USA ; Neuroscience Institute, Georgia State University, Atlanta, GA, USA.
authors.forename: Andrew J
authors.initials: AJ
authors.lastname: Butler
authors.affiliation: Byrdine F. Lewis School of Nursing & Health Professions, Georgia State University, Atlanta, GA, USA.
authors.forename: Daniel
authors.initials: D
authors.lastname: Drake
authors.affiliation: Department of Physics and Astronomy, Georgia State University, Atlanta, GA, USA ; Neuroscience Institute, Georgia State University, Atlanta, GA, USA ; Center for Nano-Optics, Center for Behavioral Neuroscience, Center for Diagnostics and Therapeutics, Georgia State University, Atlanta, GA, USA.
authors.forename: Mukesh
authors.initials: M
authors.lastname: Dhamala
mesh_terms: D000368:Aged; D063132:Connectome / Q000379:methods*; D005260:Female; D006801:Humans; D007092:Imagination / Q000502:physiology*; D008279:Magnetic Resonance Imaging; D008297:Male; D008875:Middle Aged; D009043:Motor Activity / Q000502:physiology*; D009044:Motor Cortex / Q000503:physiopathology*; D009415:Nerve Net / Q000503:physiopathology*; D020521:Stroke / Q000503:physiopathology*; D000071939:Stroke Rehabilitation
keywords: BMA, Bayesian model averaging; BMS, Bayesian model selection; Bayesian model averaging; Bayesian model selection; DCM, dynamical causal modeling; Dynamical causal modeling; Effective connectivity; Functional magnetic resonance imaging; IA, imagine affected; IU, imagine unaffected; ME, motor-execution.; MI, motor imagery; PA, pinch affected; PU, pinch unaffected