Multiscale Heterogeneity of White Matter Morphometry in Psychiatric Disorders

Area of Science:

• Neuroimaging
• Psychiatry
• Brain Morphometry

Background:

• Classical group-mean studies overlook inter-individual variability in mental illness.
• Normative modeling shows group means don't represent most individuals for grey matter.
• Variability in white matter morphometry, crucial for brain function, is not well understood.

Purpose of the Study:

• To investigate inter-individual variability in white matter volume deviations across six mental disorders.
• To characterize the heterogeneity of these deviations at multiple spatial scales.

Main Methods:

• Applied Warped Bayesian Linear Regression normative models to T1-weighted MRI data.
• Analyzed 1,294 cases with attention-deficit/hyperactivity, autism, bipolar, major depressive, obsessive-compulsive, and schizophrenia, and 1,465 controls.
• Developed a framework to analyze deviation heterogeneity from voxels to large-scale brain networks.

Main Results:

• White matter volume deviations were highly heterogeneous across participants, with minimal overlap at the voxel level (<8%).
• Negative deviations (lower volume) aggregated in common tracts, regions, and networks in up to 69% of individuals with autism and schizophrenia.
• Prevalence of white matter deviations was lower than previously seen in grey matter.

Conclusions:

• White matter volume deviations are highly individualized and heterogeneous at voxel resolution.
• Aggregation of reduced white matter volume within specific pathways and networks was evident in schizophrenia and autism.
• Findings highlight the need for person-specific approaches in understanding brain morphometry in mental disorders.