Assessment of damage to brain fiber tracts is a promising technique to predict post-stroke deficits. Its transition into clinical practice has been limited by the need to acquire diffusion tensor imaging (DTI) data. Here, we present a technique to obtain this information from routinely collected T1 and/or FLAIR images. The Network Modification (NeMo) tool provides Change in Connectivity (ChaCo) scores as estimates of structural connectivity disruption by superimposing individual infarct masks onto a control DTI tractogram reference set. We demonstrate that NeMo’s ChaCo scores, when entered into stepwise multiple regressions, can predict three initial post-stroke linguistic abilities (picture naming, language comprehension, semantic verbal fluency) of 46 post-stroke aphasia patients (43%female; age:M=63.87,SD=9.21) from ChaCo scores of pre-defined language-related regions, lesion size (M=80.06,SD=59.27cm3) and demographics. For comparison, three basic models based on demographics and lesion size only were constructed. Whereas increasing lesion size (-0.60≤≤-0.31) was identified as a negative predictor in all three basic models (0.059≤R2adjusted≤0.267), its predictive power was outweighed by the ChaCo scores in the extended ChaCo models, not only yielding lower Akaike-Information-Criteria, but also better goodness-of-fit values (0.215≤R2adjusted≤0.395). Especially ChaCo scores of the pars opercularis (0.437≤≤1.532), pars triangularis (-1.914≤≤-0.571) (Broca’s area), and the superior temporal pole (-0.678≤≤-0.645) were consistently predictive for initial post-stroke linguistic abilities. Our data demonstrate that information on connectivity disruption is clinically useful and can be obtained without the need for costly DTI data. Additionally, ChaCo scores may be helpful in predicting initial post-stroke language abilities, and perspectively for prognostication, clinical subject stratification, and rehabilitation development.
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