Automated Classification of Depression from Structural Brain Measures across Two Independent Community-based Cohorts

Aleks Stolicyn (Lead / Corresponding author), Mathew A. Harris, Xuey Shen, Miruna C. Barbu, Mark J. Adams, Emma L. Hawkins, Laura de Nooij, Hon Wah Yeung, Alison D. Murray, Stephen M. Lawrie, J. Douglas Steele, Andrew M. McIntosh, Heather C. Whalley

    Research output: Contribution to journalArticlepeer-review

    19 Citations (Scopus)
    83 Downloads (Pure)

    Abstract

    Major depressive disorder (MDD) has been the subject of many neuroimaging case-control classification studies. Although some studies report accuracies ≥80%, most have investigated relatively small samples of clinically-ascertained, currently symptomatic cases, and did not attempt replication in larger samples. We here first aimed to replicate previously reported classification accuracies in a small, well-phenotyped community-based group of current MDD cases with clinical interview-based diagnoses (from STratifying Resilience and Depression Longitudinally cohort, 'STRADL'). We performed a set of exploratory predictive classification analyses with measures related to brain morphometry and white matter integrity. We applied three classifier types-SVM, penalised logistic regression or decision tree-either with or without optimisation, and with or without feature selection. We then determined whether similar accuracies could be replicated in a larger independent population-based sample with self-reported current depression (UK Biobank cohort). Additional analyses extended to lifetime MDD diagnoses-remitted MDD in STRADL, and lifetime-experienced MDD in UK Biobank. The highest cross-validation accuracy (75%) was achieved in the initial current MDD sample with a decision tree classifier and cortical surface area features. The most frequently selected decision tree split variables included surface areas of bilateral caudal anterior cingulate, left lingual gyrus, left superior frontal, right precentral and paracentral regions. High accuracy was not achieved in the larger samples with self-reported current depression (53.73%), with remitted MDD (57.48%), or with lifetime-experienced MDD (52.68-60.29%). Our results indicate that high predictive classification accuracies may not immediately translate to larger samples with broader criteria for depression, and may not be robust across different classification approaches.

    Original languageEnglish
    Pages (from-to)3922-3937
    Number of pages16
    JournalHuman Brain Mapping
    Volume41
    Issue number14
    Early online date19 Jun 2020
    DOIs
    Publication statusPublished - 1 Oct 2020

    Keywords

    • brain structure
    • classification
    • depression
    • diffusion MRI
    • machine learning
    • major depressive disorder
    • structural MRI

    ASJC Scopus subject areas

    • Anatomy
    • Radiological and Ultrasound Technology
    • Radiology Nuclear Medicine and imaging
    • Neurology
    • Clinical Neurology

    Fingerprint

    Dive into the research topics of 'Automated Classification of Depression from Structural Brain Measures across Two Independent Community-based Cohorts'. Together they form a unique fingerprint.
    • STRADL - Provision of MRI Scans

      Steele, D. (Investigator)

      23/06/1623/07/19

      Project: Research

    Cite this