The substantial individual heterogeneity that characterizes mental illness is often ignored by classical case-control designs that rely on group mean comparisons. Here, we present a comprehensive, multiscale characterization of individual heterogeneity of brain changes in 1294 cases diagnosed with one of six conditions and 1465 matched healthy controls. Normative models identified that person-specific deviations from population expectations for regional grey matter volume were highly heterogeneous, affecting the same area in <7% of people with the same diagnosis. However, these deviations were embedded within common functional circuits and networks in up to 56% of cases. The salience/ventral attention system was implicated transdiagnostically, with other systems selectively involved in depression, bipolar disorder, schizophrenia, and ADHD. Our findings indicate that while phenotypic differences between cases assigned the same diagnosis may arise from heterogeneity in the location of regional deviations, phenotypic similarities are attributable to dysfunction of common functional circuits and networks.

The substantial individual heterogeneity that characterizes people with mental illness is often ignored by classical case–control research, which relies on group mean comparisons. Here we present a comprehensive, multiscale characterization of the heterogeneity of gray matter volume (GMV) differences in 1,294 cases diagnosed with one of six conditions (attention-deficit/hyperactivity disorder, autism spectrum disorder, bipolar disorder, depression, obsessive–compulsive disorder and schizophrenia) and 1,465 matched controls. Normative models indicated that person-specific deviations from population expectations for regional GMV were highly heterogeneous, affecting the same area in <7% of people with the same diagnosis. However, these deviations were embedded within common functional circuits and networks in up to 56% of cases. The salience–ventral attention system was implicated transdiagnostically, with other systems selectively involved in depression, bipolar disorder, schizophrenia and attention-deficit/hyperactivity disorder. Phenotypic differences between cases assigned the same diagnosis may thus arise from the heterogeneous localization of specific regional deviations, whereas phenotypic similarities may be attributable to the dysfunction of common functional circuits and networks.

Background: Different regions of the brain's grey matter are connected by a complex structural network of white matter fibres which are responsible for the propagation of action potentials and the transport of trophic and other molecules. In neurodegenerative disease, these connections constrain the way in which grey matter volume loss progresses. Here, we investigated whether connectome architecture also shapes the spatial pattern of longitudinal grey matter volume changes attributable to illness and antipsychotic medication in first episode psychosis (FEP). Methods: We conducted a triple-blind randomised placebo-control MRI study where 62 young adults with first episode psychosis received either an atypical antipsychotic or placebo over 6-months. A healthy control group was also recruited. Anatomical MRI scans were acquired at baseline, 3-months and 12-months. Deformation-based morphometry was used to estimate illness-related and antipsychotic-related grey matter volume changes over time. Representative functional and structural brain connectivity patterns were derived from an independent healthy control group using resting-state functional MRI and diffusion-weighted imaging. We used neighbourhood deformation models to predict the extent of brain change in a given area by the changes observed in areas to which it is either structurally connected or functionally coupled. Results: At baseline, we found that empirical illness-related regional volume differences were strongly correlated with predicted differences using a model constrained by structural connectivity weights (ρ = .541; p < .001). At 3-months and 12-months, we also found a strong correlation between longitudinal regional illness-related (ρ > .516; p < .001) and antipsychotic-related volume change (ρ > .591; p < .001) with volumetric changes in structurally connected areas. These correlations were significantly greater than those observed across various null models accounting for lower-order spatial and network properties of the data. Associations between empirical and predicted volume change estimates were much lower for models that only considered binary structural connectivity (all ρ < .376), or which were constrained by inter-regional functional coupling (all ρ < .436). Finally, we found that potential epicentres of volume change emerged posteriorly early in the illness and shifted to the prefrontal cortex by later illness stages. Conclusion: Psychosis- and antipsychotic-related grey matter volume changes are strongly shaped by anatomical brain connectivity. This result is consistent with findings in other neurological disorders and implies that such connections may constrain pathological processes causing brain dysfunction in FEP.

Voxel-based morphometry (VBM) and surface-based morphometry (SBM) are two widely used neuroimaging techniques for investigating brain anatomy. These techniques rely on statistical inferences at individual points (voxels or vertices), clusters of points, or a priori regions-of-interest. They are powerful tools for describing brain anatomy, but offer little insights into the generative processes that shape a particular set of findings. Moreover, they are restricted to a single spatial resolution scale, precluding the opportunity to distinguish anatomical variations that are expressed across multiple scales. Drawing on concepts from classical physics, here we develop an approach, called mode-based morphometry (MBM), that can describe any empirical map of anatomical variations in terms of the fundamental, resonant modes--eigenmodes--of brain anatomy, each tied to a specific spatial scale. Hence, MBM naturally yields a multiscale characterization of the empirical map, affording new opportunities for investigating the spatial frequency content of neuroanatomical variability. Using simulated and empirical data, we show that the validity and reliability of MBM are either comparable or superior to classical vertex-based SBM for capturing differences in cortical thickness maps between two experimental groups. Our approach thus offers a robust, accurate, and informative method for characterizing empirical maps of neuroanatomical variability that can be directly linked to a generative physical process.

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Background: Psychosis has often been linked to abnormal cortical asymmetry, but prior results have been inconsistent. Here, we applied a novel spectral shape analysis to characterize cortical shape asymmetries in patients with early psychosis across different spatial scales. Methods: We used the Human Connectome Project for Early Psychosis dataset (aged 16 to 35), including 56 healthy controls (male = 37, female = 19) and 112 patients with early psychosis (male = 68, female = 44). We quantified shape variations of each hemisphere over different spatial frequencies and applied a generalized linear model to compare differences between healthy control participants and patients with early psychosis. We further used a canonical correlation analysis (CCA) to examine associations between shape asymmetries and clinical symptoms. Results: Cortical shape asymmetries, spanning wavelengths between about 22 mm and 75 mm, were significantly different between healthy control participants and patients with early psychosis (Cohen's d = 0.28 to 0.51), with patients showing greater asymmetry in cortical shape than controls. A single canonical mode linked the asymmetry measures to symptoms (CCA r = 0.45), such that higher cortical asymmetry was correlated with more severe excitement symptoms and less severe emotional distress. In contrast, significant group differences in morphological asymmetries of cortical thickness, surface area, and gyrification at either global or regional levels were not identified. Conclusions: Cortical shape asymmetries are more sensitive than other morphological asymmetries in capturing abnormalities in patients with early psychosis. These abnormalities are expressed at coarse spatial scales and are correlated with specific symptom domains.