The impact of insults to the developing brain upon cognition and behaviour has far-reaching consequences for the child, their family, education and health care systems, and government expenditure. Many variables (illness, environmental) contribute to different outcomes following similar insults, and they exert their influence via the child’s developing brain. Predicting which child will recover from early brain insult and identifying those at risk of poor outcome represents a major challenge, with significant health economic implications. An unexplored question is whether direct measurement of the structure and function of the developing brain can improve our ability to predict outcomes in the long-term. Thus, PROBIt aimed to assess the utility of brain imaging biomarkers to predict individual neuropsychological and neurobehavioural outcomes following paediatric brain injury, and to identify those factors that combine optimally to classify outcomes. The proposal adopts an unorthodox approach of combining heterogeneous injury groups to explore the structural and functional consequences of perturbing developing brain networks. PROBIt sought to integrate data from clinically relevant paediatric cognitive and behavioural assessment, neuroimaging and computational modelling in large cohorts of children with brain insults. Multivariate pattern analysis was to be used to train a statistical classifier to reliably predict individual child outcomes across three core domains: achievement, behaviour and cognitive ability.
PROBIt's aims were addressed through a series of planned objectives including collection of brain imaging data, longutudinal follow up assessment of child neurodevelopmental status, and development of novel analytic methods. Due to early termination of the award the overarching goal could not be delivered. However, considerable planned work was completed and key findings include:
* We developed a new metric, Developmental Divergence, for structural brain scans that enables deviation from typical brain network development to be measured and then used to predict child neurobehavioural outcomes
* We identified that Deep Learning tools developed successfully in adult neuroimaging studies were not applicable to children, and identified ponential solutions to this
* We established a novel measure of network dysconnectivity that could be used in clinically acquired brain MRI scans to predict child neurobehavioural outcomes
* We showed that clinically acquired imaging data could be used to generate structural connectivity network maps in the absence of advanced imaging (i.e. research scans) that are not available in standard paediatric imaging centres thereby expanding the applicability of our methods such that they could be readily translated to the field.
* We identified that in the absence of group differences in white matter brain changes, novel imaging methods were more sensitive to microstructural change following early life traumatic brain injury, providing our broader cohort with a sensitive tool to measure brain development.
* We reported an advance in the field on structural covariance network analysis in which we showed a novel method that yields individual SCN maps, whereas work to date has only been possible at the group level.
