Patient classification as an outlier detection problem: An application of the One-Class Support Vector Machine
Open accessAbstract
Pattern recognition approaches, such as the Support Vector Machine (SVM), have been successfully used to classify groups of individuals based on their patterns of brain activity or structure. However these approaches focus on finding group differences and are not applicable to situations where one is interested in accessing deviations from a specific class or population. In the present work we propose an application of the one-class SVM (OC-SVM) to investigate if patterns of fMRI response to sad facial expressions in depressed patients would be classified as outliers in relation to patterns of healthy control subjects. We defined features based on whole brain voxels and anatomical regions. In both cases we found a significant correlation between the OC-SVM predictions and the patients' Hamilton Rating Scale for Depression (HRSD), i.e. the more depressed the patients were the more of an outlier they were. In addition the OC-SVM split the patient groups into two subgroups whose membership was associated with future response to treatment. When applied to region-based features the OC-SVM classified 52% of patients as outliers. However among the patients classified as outliers 70% did not respond to treatment and among those classified as non-outliers 89% responded to treatment. In addition 89% of the healthy controls were classified as non-outliers.
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Our approach has advantages over existing approaches to normative modeling based on multivariate regression and the one-class support vector machine.
“Moreover, our approach has advantages over existing approaches to normative modeling based on multivariate regression (18) and the one-class support vector machine (15).”
From Understanding heterogeneity in clinical cohorts using normative models: Beyond case-control studies (2016) · cites this paper as [15]
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- Authors
- Mourão-Miranda, J., Hardoon, D. R., Hahn, T., Marquand, A. F., Williams, S. C. R., Shawe-Taylor, J., & Brammer, M.
- Affiliations
- King's College London (GB)
- Goethe University Frankfurt (DE)
- University College London (GB)
- Venue
- NeuroImage
- Publisher
- Elsevier BV
- Published
- 2011-10
- Volume
- 58
- Issue
- 3
- Pages
- 793-804
- ISSN
- 1053-8119 (ISSN-L)
- PMC
- PMC3191277
- OpenAlex
- W2080951458
- Cited by
- 131
- References
- 40
- Citations by year
- 2026: 4 · 2025: 5 · 2024: 6 · 2023: 13 · 2022: 9 · 2021: 9 · 2020: 12 · 2019: 7 · 2018: 14 · 2017: 4 · 2016: 16 · 2015: 6 · 2014: 7 · 2013: 9 · 2012: 9
- FWCI
- 7.64
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- NeuroImage (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- UCL Discovery (University College London) (submittedVersion)
- Research Portal (King's College London) (submittedVersion)
- Online Publication Service of Würzburg University (Würzburg University) (submittedVersion, cc-by)
- Publication Server of Goethe University Frankfurt am Main (Goethe University Frankfurt) (submittedVersion, cc-by)
- Funders
- Wellcome Trust Career Development Fellowship (WT086565/Z/08/Z)
- EPSRC (WT088641/Z/09/Z)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Anomaly Detection Techniques and Applications
- Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
- Health Sciences › Medicine › Pharmacology › Treatment of Major Depression
- Keywords
- one-class support vector machine, outlier detection, depression, functional MRI, sad facial expressions, treatment response prediction, Hamilton Depression Rating Scale, brain activation patterns, region-based features, voxel features, patient classification
- MeSH terms
- Adult, Depression, Emotions, Facial Expression, Female, Humans, Image Interpretation, Computer-Assisted, Magnetic Resonance Imaging, Male, Middle Aged, Support Vector Machine
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