ArticleBiological psychiatry. Cognitive neuroscience and neuroimaging2026
Multiscale Heterogeneity of White Matter Morphometry in Psychiatric Disorders.
Article in Biological psychiatry. Cognitive neuroscience and neuroimaging, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
5 citing papers in PubMed.
- The cross-site reproducibility of MRI morphometric phenotypes in psychiatric disorders.Nature neuroscience · 2026Article
- Mapping ADHD Heterogeneity and Biotypes by Topological Deviations in Morphometric Similarity Networks.JAMA psychiatry · 2026Article
- GWM-HFN, a Gray-White Matter heterogeneous fusion network for functional connectomes.Communications biology · 2025Article
- Mapping ADHD Heterogeneity and Biotypes through Topological Deviations in Morphometric Similarity Networks.medRxiv : the preprint server for health sciences · 2025Article
- Embracing variability in the search for biological mechanisms of psychiatric illness.Trends in cognitive sciences · 2025Review
Corrections and comments
- Update of
Authors and funding
27 authors.
Funding
Abstract
backgroundInterindividual 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.
methodsWe 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.
resultsThe 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.
conclusionsThe 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.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.