Abstract
Objective Perinatal stroke causes most hemiparetic cerebral palsy and lifelong disability. Human developmental plasticity models are defining sensorimotor neurophysiology, identifying central targets for neuromodulation. Network connectivity is poorly understood but can be defined with resting-state fMRI (rsfMRI). We hypothesized that sensorimotor connectivity is measureable with rsfMRI in perinatal stroke and associated with functional outcome. Methods Children were recruited through the Alberta Perinatal Stroke Project (APSP), a population-based cohort. Subjects aged 6–18 years with MRI-defined periventricular venous infarction (PVI) were compared to controls. Resting-state BOLD signal was acquired on 3T MRI with children at rest for 5 minutes. Data was analyzed using the FEAT tool of FSL. Functional connectivity was computed between 4 anatomical nodes: S1 and M1 of the left/stroke and right/non-stroke hemispheres and a control node (right temporal pole). Primary outcome was connection strength (coefficient of determination, r^2). Subjects were ranked for motor outcome (Assisting Hand Assessment, AHA), proprioceptive dysfunction (robotic position matching variability, VAR) and corticospinal arrangement defined by single pulse TMS. Results Twenty-five subjects included 10 PVI (mean age 13.50±3.85) and 15 controls (age 12.67±3.89). The network was measureable in all subjects. Strong, symmetrical INTRAhemispheric M1-S1 correlations observed bilaterally in controls (left=0.782±0.10, right=0.785±0.11) were comparable in PVI (stroke=0.758±0.11, non-stroke=0.776±0.09). INTERhemispheric correlations were weaker and comparable between cases and controls: M1-M1=0.371, 0.366, S1-S1=0.368, 0.398. Correlations to the right temporal pole were all Conclusion Sensorimotor network connectivity in perinatal stroke can be assessed with rsfMRI. INTRAhemispheric M1-S1 connectivity is robust across normal and stroke children but may be disordered in some patterns of reorganization. Future connectivity analyses need to assess larger samples, wider networks, and interventional effects.
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