Early deviation from normal structural connectivity: A novel intrinsic severity score for mild TBI. (10th March 2020) - Taylor, Peter Neal; Moreira da Silva, Nádia; Blamire, Andrew; Wang, Yujiang; Forsyth, Rob
Abstract : Objective: Studies of outcome after traumatic brain injury (TBI) are hampered by the lack of robust injury severity measures that can accommodate spatial-anatomical and mechanistic heterogeneity. In this study we introduce a Mahalanobis distance measure ( M ) as an intrinsic injury severity measure that combines in a single score the many ways a given injured brain's connectivity can vary from that of healthy controls. Our objective is to test the hypotheses that M is superior to univariate measures in (1) discriminating patients and controls and (2) correlating with cognitive assessment. Methods: Sixty-five participants (34 with mild TBI, 31 controls) underwent diffusion tensor MRI and extensive neuropsychological testing. Structural connectivity was inferred for all participants for 22 major white matter connections. Twenty-two univariate measures (1 per connection) and 1 multivariate measure ( M ), capturing and summarizing all connectivity change in a single score, were computed. Results: Our multivariate measure ( M ) was able to better discriminate between patients and controls (area under the curve 0.81) than any individual univariate measure. M significantly correlated with cognitive outcome (Spearman ρ = 0.31; p < 0.05). No univariate measure showed significant correlation after correction for multiple comparisons. Conclusions: Heterogeneity in the severity and distribution of injuries after TBI has traditionally complicated the understanding of outcomes after TBI. Our approach provides a single, continuous variable that can fully capture individual heterogeneity. M 's ability to distinguish even mildly injured patients from controls and its correlation with cognitive assessment suggest utility as an imaging-based marker of intrinsic injury severity.