Keywords: Traumatic Brain Injury, Traumatic brain injury, network-based statistic, Connectome-based Predictive Modeling, post-concussion syndrome, post-traumatic stress disorder
Motivation: Mild traumatic brain injury (mTBI) presents with a wide array of clinical features due to the great heterogeneity of underlying pathological features.
Goal(s): To identify aberrant structural connectivity in mTBI service members (SMs) and to evaluate the usefulness of predictive models of brain-behavior relationships from structural connectivity data.
Approach: Employ Network-Based Statistic (NBS) and Connectome-based Predictive Modeling (CPM) to evaluate the structural connectome of SMs who had a remote mTBI, as mapped by advanced diffusion MRI.
Results: NBS identified sub-networks involving the default mode networks with decreased connectivity density; CPM revealed an association between the predicted post-concussive symptom scores and the self-report scores.
Impact: Structure connectome-based analysis using advanced diffusion MRI techniques has the potential for the objective evaluation of white matter properties, which can become biomarkers in monitoring clinical symptoms in SMs after a remote brain injury
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