Background and Objectives: Traumatic brain injury (TBI) is heterogeneous, complicating efforts to develop standardized diagnostic and therapeutic approaches. Conventional group-based analyses often obscure individual differences by averaging across diverse injury patterns. In contrast, by comparing deviation from the expected distribution observed in healthy controls, normative modelling can capture patient-specific deviations from expected neuroanatomical norms. This research aims to use normative modelling to capture individual variability in brain morphometry on individuals who have experienced a TBI. Methods: In this study, we applied traditional case-control and a normative modelling approach to cortical and subcortical magnetic resonance imaging (MRI). Data was analysed from the ENIGMA Consortium Adult Moderate-to-Severe TBI Working Group. Primary outcome measures were cortical thickness and subcortical volumes, derived from the Destrieux and Freesurfer subcortical atlases. Casecontrol tests were conducted using linear models controlling for age, sex, site, and intracranial volume. Normative modelling was carried out by accessing the PCNportal, estimating individual deviations using a model of Bayesian linear regression with likelihood warping. Results: A total of 631 (407 TBI, 224 controls) MR images were processed. Conventional case-control analyses identified significant group differences in 153 regions of a possible 178 cortical or subcortical regions. Normative modelling revealed far greater heterogeneity. No more than 23% of TBI participants shared an extreme deviation (z-score > 2 or z < -2) in the same region, but every region experienced at least one extreme positive or negative deviation across individuals. Stratifying by Glasgow Coma Scale (GCS) severity sustained this pattern: even within GCS 13-15, GCS 9-12, and GCS 3-8 subgroups, regional convergence did not exceed 34%. The median number of deviations increased with injury severity (GCS 13-15 = 9, GCS 9-12 = 19, GCS 3- 8 = 22), demonstrating that group averages mask highly individualised morphological abnormalities as injury severity increases. Discussion: Normative modelling detects participant-specific cortical and subcortical abnormalities that conventional group comparisons overlook, better representing the true diversity of TBI-related morphological changes. Generating individualised 'morphological fingerprints' may ultimately advance prognostic accuracy and lay the foundation for personalised interventions in research and clinical practice.
Virginia Newcombe (Wed,) studied this question.