Multiscale Heterogeneity of White Matter Morphometry in Psychiatric Disorders

Interindividual variability in the neurobiological and clinical characteristics of mental illnesses are often overlooked by classical group-mean case-control studies. Studies using normative modeling to infer person-specific deviations of gray matter volume have indicated that group means are not representative of most individuals. The extent to which this variability is present in white matter morphometry, which is integral to brain function, remains unclear.

We applied warped Bayesian linear regression normative models to T1-weighted magnetic resonance imaging data and mapped interindividual variability in person-specific white matter volume (WMV) deviations in 1294 cases (58% male) diagnosed with one of 6 disorders (attention-deficit/hyperactivity disorder, autism, bipolar disorder, major depressive disorder, obsessive-compulsive disorder, and schizophrenia) and 1465 matched control participants (54% male) recruited across 25 scan sites. We developed a framework to characterize deviation heterogeneity on multiple spatial scales from individual voxels through interregional connections, specific brain regions, and spatially extended brain networks.

The specific locations of WMV deviations were highly heterogeneous across participants, affecting the same voxel in fewer than 8% of individuals with the same diagnosis. For autism and schizophrenia, negative deviations (i.e., areas where volume is lower than normative expectations) aggregated into common tracts, regions, and large-scale networks in up to 69% of individuals.

The prevalence of WMV deviations was lower than previously observed in gray matter, and the specific location of these deviations was highly heterogeneous when considering voxelwise spatial resolution. Evidence of aggregation within common pathways and networks was apparent in schizophrenia and autism but not in other disorders.