Multiscale heterogeneity of white matter morphometry in psychiatric disorders

Area of Science:

• Neuroimaging
• Brain Morphology
• Mental Health Research

Background:

• Classical studies overlook individual differences in mental illness neurobiology.
• Normative modeling shows group means don't represent individuals for grey matter.
• Variability in white matter morphometry across individuals with mental illness is not well understood.

Purpose of the Study:

• To map inter-individual variability in white matter volume deviations using normative modeling.
• To characterize the heterogeneity of these deviations across multiple spatial scales.
• To investigate if white matter deviations aggregate in specific brain pathways or networks in various mental disorders.

Main Methods:

• Applied Warped Bayesian Linear Regression normative models to T1-weighted MRI data.
• Analyzed white matter volume deviations in 1,294 individuals with six mental disorders and 1,465 controls.
• Developed a framework to assess deviation heterogeneity from voxels to large-scale brain networks.

Main Results:

• White matter volume deviations were highly heterogeneous across participants, with few consistently affected voxels.
• Negative deviations (reduced volume) aggregated in common tracts/regions for autism and schizophrenia (up to 35%).
• Overall prevalence of white matter deviations was lower than previously seen in grey matter.

Conclusions:

• White matter volume deviations in mental illness are highly person-specific and spatially heterogeneous.
• Aggregation of deviations into common pathways was observed in autism and schizophrenia, but not other disorders.
• Findings highlight the limitations of group-mean approaches and the need for individual-level analysis in mental illness research.