Neuroanatomical heterogeneity and homogeneity in individuals at clinical high risk for psychosis

Abstract

Individuals at Clinical High Risk for Psychosis (CHR-P) demonstrate heterogeneity in clinical profiles and outcome features. However, the extent of neuroanatomical heterogeneity in the CHR-P state is largely undetermined. We aimed to quantify the neuroanatomical heterogeneity in structural magnetic resonance imaging measures of cortical surface area (SA), cortical thickness (CT), subcortical volume (SV), and intracranial volume (ICV) in CHR-P individuals compared with healthy controls (HC), and in relation to subsequent transition to a first episode of psychosis. The ENIGMA CHR-P consortium applied a harmonised analysis to neuroimaging data across 29 international sites, including 1579 CHR-P individuals and 1243 HC, offering the largest pooled CHR-P neuroimaging dataset to date. Regional heterogeneity was indexed with the Variability Ratio (VR) and Coefficient of Variation (CV) ratio applied at the group level. Personalised estimates of heterogeneity of SA, CT and SV brain profiles were indexed with the novel Person-Based Similarity Index (PBSI), with two complementary applications. First, to assess the extent of within-diagnosis similarity or divergence of neuroanatomical profiles between individuals. Second, using a normative modelling approach, to assess the ‘normativeness’ of neuroanatomical profiles in individuals at CHR-P. CHR-P individuals demonstrated no greater regional heterogeneity after applying FDR corrections. However, PBSI scores indicated significantly greater neuroanatomical divergence in global SA, CT and SV profiles in CHR-P individuals compared with HC. Normative PBSI analysis identified 11 CHR-P individuals (0.70%) with marked deviation (>1.5 SD) in SA, 118 (7.47%) in CT and 161 (10.20%) in SV. Psychosis transition was not significantly associated with any measure of heterogeneity. Overall, our examination of neuroanatomical heterogeneity within the CHR-P state indicated greater divergence in neuroanatomical profiles at an individual level, irrespective of psychosis conversion. Further large-scale investigations are required of those who demonstrate marked deviation.

Normative Modeling of Brain Morphometry in Clinical High Risk for Psychosis

“…Also using the ENIGMA CHR-P Working Group data set, Baldwin and colleagues 27 found that individual-level heterogeneity was similar in individuals with CHR-P and healthy individuals and was not associated with increased clinical severity. In the current study, regional deviation from normative patterns in the individuals with CHR-P did not reveal associations with the severity of positive symptoms, both in linear models and multivariate estimation models.…”

Parsing neurobiological heterogeneity of the clinical high-risk state for psychosis: A pseudo-continuous arterial spin labelling study

“…Two advantages of this approach over other methods are that it improves the removal of scanner effects in datasets with small sample sizes, and does not make any assumptions about the neuroimaging technique being used ( 53 , 56 ). This approach has been previously validated in the ENIGMA SCZ ( 57 ) and CHR-P ( 58 ) datasets. It has been shown that ComBat is superior to other harmonization techniques (e.g., global scaling, RAVEL) as it improves the replicability of voxels associated with biological covariates, improves statistical power, is robust to small sample sizes and recovers true effect sizes in imaging data ( 53 ).…”

Systematic review and meta‐analysis of the visual mismatch negativity in schizophrenia

“…Schizophrenia spectrum disorders (SSD) are a complex group of mental disorders defined by the manifestation of positive and negative symptoms, such as hallucinations or diminished emotional expression (DSM-5; American Psychiatric Association, 2013). In light of the challenging task of determining treatment effectiveness and predicting long-term outcomes, recent research has prioritised investigating the heterogeneity and complexity of symptoms in SSD (Baldwin et al, 2022;Chand et al, 2020;Green et al, 2020). However, the pathophysiological mechanisms of SSD remain largely unknown, and diagnosis is symptom constellation only.…”