Abstract
Mild TBI produces spatially heterogeneous damage to white matter and functional connectivity. In group averaged data, this heterogeneity is reflected as noise rather than a statistically coherent and diagnostically specific signal. Some approaches that classify mTBI patients into subtypes that use symptom or neuropsychological measures are unable to predict functional outcomes successfully. Accuracy in predicting outcomes for mTBI may increase when neurobiological assessments from MRI scans are used in predictive modeling. We aimed to identify neurobiologically-derived, outcome-relevant subtypes of mTBI using resting-state fMRI.
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