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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 language | English |
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Pages (from-to) | 3922-3937 |
Number of pages | 16 |
Journal | Human Brain Mapping |
Volume | 41 |
Issue number | 14 |
Early online date | 19 Jun 2020 |
DOIs | |
Publication status | Published - 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.Projects
- 1 Finished
Research output
- 23 Citations
- 1 Article
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Spectral Clustering Based on Structural Magnetic Resonance Imaging and its Relationship with Major Depressive Disorder and Cognitive Ability
Yeung, H. W. (Lead / Corresponding author), Shen, X., Stolicyn, A., Harris, M. A., Romaniuk, L., Buchanan, C. R., Waiter, G. D., Sandu, A.-L., McNeil, C. J., Murray, A. D., Steele, D., Campbell, A., Porteous, D. J., Lawrie, S. M., McIntosh, A. M., Cox, S. R., Smith, K. M. & Whalley, H. C. (Lead / Corresponding author), Sept 2021, In: European Journal of Neuroscience. 54, 6, p. 6281-6303 23 p.Research output: Contribution to journal › Article › peer-review
Open AccessFile5 Citations (Scopus)157 Downloads (Pure)
Profiles
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Steele, Douglas
- Neuroscience - Clinical Professor (Teaching and Research) of Neuroimaging
Person: Academic