Sex-Based Classification of Grey Matter Volume Using SVM: Implications for Early-Phase Psychotic and Mood Disorders

Introduction Understanding sex differences in brain structure is crucial for advancing personalized medicine. However, the alterations of sex-based neuroanatomical differences during the early phases of mood and psychotic disorders remain largely underexplored. Objectives This study aimed to evaluate neuroanatomical sex-based differences in a healthy cohort and assess potential alterations in clinical populations from the early psychiatric spectrum using a machine learning approach. Methods For this, we developed a Support Vector Machine (SVM) model trained to classify sex based on grey matter volume from a cohort of healthy controls (HC, n = 521), aged 15 to 40 years. After optimization and cross-validation, the model was applied to three distinct clinical populations from the same age range: individuals at clinical high risk for psychosis (CHR, n =334), those with recent onset psychosis (ROP, n = 351), and those with recent onset depression (ROD, n = 309). Results The model achieved a robust balanced accuracy (BAC) of 85.1% (p < .01) in the HC cohort. When applied to the CHR group, the model maintained a BAC of 85.5%, whereas its performance decreased in the ROP (75.8% BAC) and ROD (79.3% BAC) groups. In contrast to previous literature, female participants in the ROP group exhibited a masculinization of brain patterns, while male participants showed no such reversal. In the ROD group, both sexes revealed a slight tendency toward patterns typical of the opposite sex. Conclusions These preliminary findings suggest that sex-based neuroanatomical structures remain preserved at high-risk stages but undergo alterations with disorder progression. Future research will investigate the observed performance decline in relation to other phenotypic factors. These findings offer novel insights into sex-specific neurobiological mechanisms underlying psychotic and mood disorders and their early markers. Disclosure of Interest None Declared

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Journal
European Psychiatry
Published
2025-04-01
DOI
https://doi.org/10.1192/j.eurpsy.2025.1672
Primary Topic
Mental Health Research Topics
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article
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article

Sex-Based Classification of Grey Matter Volume Using SVM: Implications for Early-Phase Psychotic and Mood Disorders

María Fernanda Urquijo, Dominic Dwyer, Anita Riecher‐Rössler, P. Falkai et al.
European Psychiatry
Mental Health Research Topics
article

Sex-Based Classification of Grey Matter Volume Using SVM: Implications for Early-Phase Psychotic and Mood Disorders

María Fernanda Urquijo, Dominic Dwyer, Anita Riecher‐Rössler, P. Falkai, Nikolaos Koutsouleris
article en

Abstract

Introduction Understanding sex differences in brain structure is crucial for advancing personalized medicine. However, the alterations of sex-based neuroanatomical differences during the early phases of mood and psychotic disorders remain largely underexplored. Objectives This study aimed to evaluate neuroanatomical sex-based differences in a healthy cohort and assess potential alterations in clinical populations from the early psychiatric spectrum using a machine learning approach. Methods For this, we developed a Support Vector Machine (SVM) model trained to classify sex based on grey matter volume from a cohort of healthy controls (HC, n = 521), aged 15 to 40 years. After optimization and cross-validation, the model was applied to three distinct clinical populations from the same age range: individuals at clinical high risk for psychosis (CHR, n =334), those with recent onset psychosis (ROP, n = 351), and those with recent onset depression (ROD, n = 309). Results The model achieved a robust balanced accuracy (BAC) of 85.1% (p < .01) in the HC cohort. When applied to the CHR group, the model maintained a BAC of 85.5%, whereas its performance decreased in the ROP (75.8% BAC) and ROD (79.3% BAC) groups. In contrast to previous literature, female participants in the ROP group exhibited a masculinization of brain patterns, while male participants showed no such reversal. In the ROD group, both sexes revealed a slight tendency toward patterns typical of the opposite sex. Conclusions These preliminary findings suggest that sex-based neuroanatomical structures remain preserved at high-risk stages but undergo alterations with disorder progression. Future research will investigate the observed performance decline in relation to other phenotypic factors. These findings offer novel insights into sex-specific neurobiological mechanisms underlying psychotic and mood disorders and their early markers. Disclosure of Interest None Declared

European PsychiatryVol. 68(S1)
University of Udine (IT), The King's College (US), University of Turku (FI), King's College - North Carolina (US), The University of Melbourne (AU), University of Basel (CH), University of Münster (DE), Max Planck Institute of Psychiatry (DE), University Hospital Cologne (DE), Heinrich Heine University Düsseldorf (DE), University of Bari Aldo Moro (IT)
Openalex Percentile: Top 75%
Mental Health Research Topics
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