Mild Traumatic Brain Injury Outcome Prediction Based on Both Graph and K-nn Methods
Cognitive impairment has mainly two, non mutually exclusive, etiologies: structural or connectivity lesions. Analogously, we present here a methodology aimed at investigating magnetic resonance imaging (MRI) scans of subject after a traumatic brain injury (TBI) to detect the presence of these heterogeneous lesions and access the information content within. In particular, we use (i) complex network topological features to capture the effect of disease on connectivity and (ii) morphological brain measurements to describe anomalous patterns from a structural perspective. This integrated base of knowledge is then used to emphasize differences arising within a cohort including normal controls and patients labeled as category-I and category-II according to their outcome after TBI. Results suggest that topological measurements provide a suitable measurement to detect category-I subjects, while structural features are effective to distinguish controls from category-II subjects.
KeywordsTBI MRI Complex networks Graph theory K-nn
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