Shared and separate patterns in brain morphometry across transdiagnostic dimensions

McCutcheon, Robert; Pillinger, Toby; Guo, Xin; Rogdaki, Maria; Welby, George; Vano, Luke; Cummings, Connor; Ann-Heron, Toni; Brugger, Stefan; Davies, David; Ghanem, Mawada; Efthimiou, Orestis; Cipriani, Andrea; Howes, Oliver

doi:10.1038/s44220-022-00010-y

Cited by 9 publications

(6 citation statements)

References 37 publications

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“…Even in disorders with significant phenotypic heterogeneity (e.g., SZ) we were able to identify consistent alterations within the rectus, which has previously been associated with a genetic risk for SZ, BP, and psychosis (Luna et al, 2022), and the cerebellum, with varying regions associated with cognitive losses (e.g., long-term and working memory) and psychosis (Clark et al, 2020; Moberget et al, 2018; Moberget & Ivry, 2019). Our findings within the hippocampus are also aligned with previous literature (Brosch et al, 2022; Lorenzetti et al, 2009; McCutcheon et al, 2023; Torres et al, 2016) that found reductions within MDD, BP, and SZ. Other cross-disorder studies found similar shared and disease-specific alterations in the hippocampus, amygdala, thalamus, accumbens of individuals with SZ, BP, and MDD (Okada et al, 2023).…”

Section: Discussionsupporting

confidence: 92%

Cortical Similarities in Psychiatric and Mood Disorders Identified in Federated VBM Analysis via COINSTAC

Rootes-Murdy,

Panta,

Kelly

et al. 2023

Preprint

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Psychiatric disorders such as schizophrenia, major depressive disorder, and bipolar disorder have areas of significant overlap across multiple domains including genetics, neurochemistry, symptom profiles, and regional gray matter alterations. Various structural neuroimaging studies have identified a combination of shared and disorder-specific patterns of gray matter (GM) deficits across these different disorders, though few direct comparisons have been conducted. Given the overlap in symptom presentations and GM alterations, these disorders may have a common etiology or neuroanatomical basis that may relate to a certain vulnerability for mental illness. Pooling large data or heterogeneous data can ensure representation of several participant factors, providing more accurate results. However, ensuring large enough datasets at any one site can be cumbersome, costly, and may take many years of data collection. Large scale collaborative research is already facilitated by current data repositories and neuroinformatics consortia such as the Enhacing NeuroImaging Genetics through Meta-Analysis (ENIGMA) consortium, institutionally supported databases, and data archives. However, these data sharing methodologies can still suffer from significant barriers. Federated approaches can augment these approaches and mitigate some of these barriers by enabling access or more sophisticated, shareable and scaled up analyses of large-scale data which may not be shareable and can easily be scaled up with the number of sites. In the current study, we examined GM alterations using Collaborative Informatics and Neuroimaging Suite Toolkit for Anonymous Computation (COINSTAC). Briefly, COINSTAC (https://coinstac.trendscenter.org) is an open-source decentralized analysis application that provides a venue for analyses of neuroimaging datasets without sharing individual level data, while maintaining granular control of privacy. Through federated analysis, we examined T1-weighted images (N = 3,287) from eight psychiatric diagnostic groups across seven sites. We identified significant overlap in the GM patterns of individuals with schizophrenia, major depressive disorder, and autism spectrum disorder. These results show cortical and subcortical regions, specifically the bilateral insula, that may indicate a possible shared vulnerability to psychiatric disorders.

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Section: Discussionsupporting

confidence: 92%

Cortical Similarities in Psychiatric and Mood Disorders Identified in Federated VBM Analysis via COINSTAC

Rootes-Murdy,

Panta,

Kelly

et al. 2023

Preprint

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“…Various forms of brain morphometry have made it possible to describe brain shape mathematically, yielding variables for statistical evaluation, which have made important contributions towards a better understanding of healthy brain development and aging as well as to disease manifestation and mechanisms (see e.g. Mills et al, 2021; Statsenko et al, 2022; McCutcheon et al, 2023 for recent examples). Large group studies have demonstrated that metrics derived from routine structural MRI scans are sensitive to pathological brain changes (see e.g.…”

Section: Introductionmentioning

confidence: 99%

Cortical thickness and grey-matter volume anomaly detection in individual MRI scans: Comparison of two methods

Romascano,

Rebsamen,

Radojewski

et al. 2024

Preprint

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Over the past decades, morphometric analysis of brain MRI has contributed substantially to the understanding of healthy brain structure, development and aging as well as to improved characterisation of disease related pathologies. Certified commercial tools based on normative modeling of these metrics are meanwhile available for diagnostic purposes, but they are cost intensive and their clinical evaluation is still in its infancy. Here we have compared the performance of "ScanOMetrics", an open-source research-level tool for detection of statistical anomalies in individual MRI scans, depending on whether it is operated on the output of FreeSurfer or of the deep learning based brain morphometry tool DL+DiReCT. When applied to the public OASIS3 dataset, containing patients with Alzheimer's disease (AD) and healthy controls (HC), cortical thickness anomalies in patient scans were mainly detected in regions that are known as predilection areas of cortical atrophy in AD, regardless of the software used for extraction of the metrics. By contrast, anomaly detections in HCs were up to twenty-fold reduced and spatially unspecific using both DL+DiReCT and FreeSurfer. Progression of the atrophy pattern with clinical dementia rating (CDR) was clearly observable with both methods. DL+DiReCT provided results in less than 25 minutes, more than 15 times faster than FreeSurfer. This difference in computation time might be relevant when considering application of this or similar methodology as diagnostic decision support for neuroradiologists.

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“…Various forms of brain morphometry have made it possible to describe brain shape mathematically, yielding variables for statistical evaluation, which have made important contributions towards a better understanding of healthy brain development and aging as well as to disease manifestation and mechanisms (see e.g. Mills et al, 2021 , Statsenko et al, 2022 , McCutcheon et al, 2023 ; Joy et al, 2023 for recent examples). Large group studies have demonstrated that metrics derived from routine structural MRI scans are sensitive to pathological brain changes (see e.g.…”

Section: Introductionmentioning

confidence: 99%

Cortical thickness and grey-matter volume anomaly detection in individual MRI scans: Comparison of two methods

Romascano,

Rebsamen,

Radojewski

et al. 2024

NeuroImage: Clinical

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Shared and separate patterns in brain morphometry across transdiagnostic dimensions

Cited by 9 publications

References 37 publications

Cortical Similarities in Psychiatric and Mood Disorders Identified in Federated VBM Analysis via COINSTAC

Cortical Similarities in Psychiatric and Mood Disorders Identified in Federated VBM Analysis via COINSTAC

Cortical thickness and grey-matter volume anomaly detection in individual MRI scans: Comparison of two methods

Cortical thickness and grey-matter volume anomaly detection in individual MRI scans: Comparison of two methods

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