Combining Diffusion Tensor Imaging and Gray Matter Volumetry to Investigate Motor Functioning in Chronic Stroke: e0125038

• Published in: PloS one 2015-05, Vol.10 (5)
• Main Authors: Yang, Ming, Yang, Ya-ru, Li, Hui-jun, Lu, Xue-song, Shi, Yong-mei, Liu, Bin, Chen, Hua-jun, Teng, Gao-jun, Chen, Rong, Herskovits, Edward H
• Format: Article
• Language: English
• ISSN: 1932-6203
• DOI: 10.1371/journal.pone.0125038

Motor impairment after stroke is related to the integrity of the corticospinal tract (CST). However, considerable variability in motor impairment remains unexplained. To increase the accuracy in evaluating long-term motor function after ischemic stroke, we tested the hypothesis that combining diffusion tensor imaging (DTI) and gray matter (GM) volumetry can better characterize long-term motor deficit than either method alone in patients with chronic stroke. We recruited 31 patients whose Medical Research Council strength grade was less than or equal to 3/5 in the extensor muscles of the affected upper extremity in the acute phase. We used the Upper Extremity Fugl-Meyer (UE-FM) assessment to evaluate motor impairment, and as the primary outcome variable. We computed the fractional anisotropy ratio of the entire CST (CSTratio) and the volume of interest ratio (VOIratio), between ipsilesional and contralesional hemispheres, to explain long-term motor impairment. The results showed that CSTratio, VOIratio of motor-related brain regions, and VOIratio in the temporal lobe were correlated with UE-FM. A multiple regression model including CSTratio and VOIratio of the caudate nucleus explained 40.7% of the variability in UE-FM. The adjusted R2 of the regression model with CSTratio as an independent variable was 29.4%, and that of using VOIratio of the caudate nucleus as an independent variable was 23.1%. These results suggest that combining DTI and GM volumetry may achieve better explanation of long-term motor deficit in stroke patients, than using either measure individually. This finding may provide guidance in determining optimal neurorehabilitative interventions.