Focusing on Comorbidity—A Novel Meta-Analytic Approach and Protocol to Disentangle the Specific Neuroanatomy of Co-occurring Mental Disorders

Abstract

BackgroundIn mental health, comorbidities are the norm rather than the exception. However, current meta-analytic methods for summarizing the neural correlates of mental disorders do not consider comorbidities, reducing them to a source of noise and bias rather than benefitting from their valuable information.ObjectivesWe describe and validate a novel neuroimaging meta-analytic approach that focuses on comorbidities. In addition, we present the protocol for a meta-analysis of all major mental disorders and their comorbidities.MethodsThe novel approach consists of a modification of Seed-based d Mapping—with Permutation of Subject Images (SDM-PSI) in which the linear models have no intercept. As in previous SDM meta-analyses, the dependent variable is the brain anatomical difference between patients and controls in a voxel. However, there is no primary disorder, and the independent variables are the percentages of patients with each disorder and each pair of potentially comorbid disorders. We use simulations to validate and provide an example of this novel approach, which correctly disentangled the abnormalities associated with each disorder and comorbidity. We then describe a protocol for conducting the new meta-analysis of all major mental disorders and their comorbidities. Specifically, we will include all voxel-based morphometry (VBM) studies of mental disorders for which a meta-analysis has already been published, including at least 10 studies. We will use the novel approach to analyze all included studies in two separate single linear models, one for children/adolescents and one for adults.DiscussionThe novel approach is a valid method to focus on comorbidities. The meta-analysis will yield a comprehensive atlas of the neuroanatomy of all major mental disorders and their comorbidities, which we hope might help develop potential diagnostic and therapeutic tools.

Modelling the overlap and divergence of autistic and schizotypal traits on hippocampal subfield volumes and regional cerebral blood flow

“…However, such categorical comparisons typically consider clinical “supra-threshold” expressions of psychiatric phenotypes, vastly neglecting variance within both cases and controls, and only consider disease-related phenotypes as a whole (rather than particular facets of the phenotype). Even with the development of recent novel meta-analytic approaches to co-morbidity mapping [ 97 ], they fail to consider how comorbid conditions interact to influence outcome or the ubiquitous overlap with other disease spectra, including the subclinical expression or psychopathology other than the ones under study.…”

A meta-analysis of brain morphometric aberrations in adolescents who experienced childhood trauma

“…The results of our meta-analysis also show a significant overlap with the results of a VBM-focused meta-analysis by Serra-Blasco et al (2021) , showing correlates of depression, anxiety, and post-traumatic stress disorder (PTSD) in regions such as left inferior frontal gyrus (L IFG), left superior temporal gyrus (L STG), superior frontal gyrus (SFG), fusiform gyrus [we found inferior longitudinal fasciculus (ILF) which connects to fusiform], and cerebellum, which all coincide with our findings. Although the meta-analysis by Serra-Blasco et al (2021) was performed in a mixed population, the same group has published a protocol for a planned VBM-based meta-analysis of all major mental disorders and their comorbidities, in which they will have separate single linear models, one for children/adolescents and one for adults ( Fortea et al, 2022 ). Our results combined with the knowledge that will be gained in this future meta-analysis could help shed light on the differences between manifestations of childhood trauma vs. specific psychiatric disorders in the adolescent brain.…”

Cortical interneurons in schizophrenia – cause or effect?

“…The most consistent finding in postmortem and in vivo studies is a relatively generalized reduction in brain volume, predominantly attributed to a reduction in gray matter of the cerebral cortex (10,11). Nevertheless, the overall thinning of the cerebral cortex does not adequately explain the typical clinical manifestations of schizophrenia, and evidence points to more subtle subcellular abnormalities being the main driving force behind the cognitive disturbances (9,(12)(13)(14)(15)(16). Furthermore, even though the extent to which the white matter is affected varies between studies, white matter abnormalities in schizophrenia are still frequently reported.…”