Children who experience a traumatic brain injury (TBI) are at elevated risk for a range of negative cognitive and neuropsychological outcomes. Identifying which children are at greatest risk for negative outcomes can be difficult due to the heterogeneity of TBI. To address this barrier, the current study applied a novel method of characterizing brain connectivity networks, Bayesian multi-subject vector autoregressive modelling (BVAR-connect), which used white matter integrity as priors to evaluate effective connectivity-the time-dependent relationship in functional magnetic resonance imaging (fMRI) activity between two brain regions-within the default mode network (DMN). In a prospective longitudinal study, children ages 8-15 years with mild to severe TBI underwent diffusion tensor imaging and resting state fMRI 7 weeks after injury; post-concussion and anxiety symptoms were assessed 7 months after injury. The goals of this study were to (1) characterize differences in positive effective connectivity of resting-state DMN circuitry between healthy controls and children with TBI, (2) determine if severity of TBI was associated with differences in DMN connectivity and (3) evaluate whether patterns of DMN effective connectivity predicted persistent post-concussion symptoms and anxiety. Healthy controls had unique positive connectivity that mostly emerged from the inferior temporal lobes. In contrast, children with TBI had unique effective connectivity among orbitofrontal and parietal regions. These positive orbitofrontal-parietal DMN effective connectivity patterns also differed by TBI severity and were associated with persisting behavioural outcomes. Effective connectivity may be a sensitive neuroimaging marker of TBI severity as well as a predictor of chronic post-concussion symptoms and anxiety.
Keywords: Bayes theorem; anxiety; brain injuries; child; magnetic resonance imaging; post-concussion syndrome.
© 2021 Federation of European Neuroscience Societies and John Wiley & Sons Ltd.