Characterizing Heterogeneity in Brain Morphology in Traumatic Brain Injury Using Normative Modeling.

Area of Science:

• Neuroimaging
• Brain Morphometry
• Computational Neuroscience

Background:

• Traumatic brain injury (TBI) is a heterogeneous condition, making standardized diagnosis and treatment challenging.
• Conventional group analyses obscure individual differences in TBI patients by averaging across diverse injury patterns.
• Normative modeling offers a patient-specific approach by comparing deviations from healthy neuroanatomical norms.

Purpose of the Study:

• To employ normative modeling to characterize individual variability in brain morphometry among TBI patients.
• To compare the findings of normative modeling with traditional case-control analyses in TBI.

Main Methods:

• Applied both traditional case-control and normative modeling approaches to cortical and subcortical MRI data.
• Utilized data from the Enhancing NeuroImaging Genetics through Meta-Analysis Consortium Adult Moderate-to-Severe TBI working group.
• Extracted cortical thickness and subcortical volumes using Destrieux and Freesurfer atlases; normative modeling used Bayesian linear regression.

Main Results:

• Conventional analyses found group differences in 153 of 178 regions, while normative modeling revealed significant patient-specific heterogeneity.
• No more than 23% of TBI patients showed extreme deviations in the same region, highlighting individualized abnormalities.
• The number of deviations increased with injury severity (Glasgow Coma Scale), indicating that group averages mask individual morphologic changes.

Conclusions:

• Normative modeling effectively identifies participant-specific abnormalities missed by conventional group comparisons in TBI.
• This method provides a more accurate representation of the diverse morphologic changes associated with TBI.
• Individualized 'morphologic fingerprints' generated by normative modeling may enhance prognostic accuracy and personalized TBI interventions.