Diffusion tensor imaging as a prognostic biomarker for motor recovery and rehabilitation after stroke

Abstract

Purpose: Despite improved acute treatment and new tools to facilitate recovery, most patients have motor deficits after stroke, often causing disability. However, motor impairment varies considerably among patients, and recovery in the acute/subacute phase is difficult to predict using clinical measures alone, particularly in severely impaired patients. Accurate early prediction of recovery would help rationalize rehabilitation goals and improve the design of trials testing strategies to facilitate recovery. Methods: We review the role of diffusion tensor imaging (DTI) in predicting motor recovery after stroke, in monitoring treatment response, and in evaluating white matter remodeling. We critically appraise DTI studies and discuss their limitations, and we explore directions for future study. Results: Growing evidence suggests that combining clinical scores with information about corticospinal tract (CST) integrity can improve predictions about motor outcome. The extent of CST damage on DTI and/or the overlap between the CST and a lesion are key prognostic factor that determines motor performance and outcome. Three main strategies to quantify stroke-related CST damage have been proposed: (i) measuring FA distal to the stroke area, (ii) measuring the number of fibers that go through the stroke with tractography, and (iii) measuring the overlap between the stroke and a CST map derived from healthy age- and gender-matched controls. Conclusion: Recovery of motor function probably involves remodeling of the CST proper and/or a greater reliance on alternative motor tracts through spontaneous and treatment-induced plasticity. DTI-metrics represent promising clinical biomarkers to predict motor recovery and to monitor and predict the response to neurorehabilitative interventions.