Background and Purpose Stroke impairs cognition and movement. Although clinical severity and infarct volume can predict functional outcomes, variability in patient responses requires advanced structural and functional connectivity methods. Disconnection markers were tested to predict functional outcomes after acute ischemic stroke using diffusion tensor imaging.Methods A probabilistic approach was used to quantify brain damage from white matter (WM) disconnections affecting cortical areas, using lesion masking on a tractography atlas and parcellation of gray matter into functional network nodes. Forty-three patients with acute ischemic stroke were grouped according to functional improvement (change in modified Rankin Scale score from 3–5 at discharge to 0–2 at 3-month follow-up). Significantly different structural disconnection measures between the groups were combined into a principal component and included in a logistic regression model to evaluate prediction accuracy. Fractional anisotropy (FA), radial diffusivity (RD), axial diffusivity, and mean diffusivity of the disconnected WM tracts were analyzed.Results Baseline structural disconnections in the mid-posterior and central corpus callosum predicted poor functional outcomes at 3 months, and increased somatomotor network (SMN) disconnection severity correlated with poor recovery. Age, National Institutes of Health Stroke Scale score, and structural disconnections significantly predicted functional outcomes in logistic regression models. The first principal component analysis of the dysconnectivity measures explained 88% of the total variance and improved prediction accuracy from 53.8% to 76.9%. Differences in FA and RD in the region of interest of the corpus callosum between outcome groups were statistically significant.Conclusions Predictive outcome markers from probabilistic structural disconnection mapping in acute stroke emphasize preserving interhemispheric corpus callosum and SMN connections.
Calle et al. (Thu,) studied this question.