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Talpalaru A, Bhagwat N, Devenyi GA, Lepage M, Chakravarty MM. Identifying schizophrenia subgroups using clustering and supervised learning. Schizophr Res 2019; 214:51-59. [PMID: 31455518 DOI: 10.1016/j.schres.2019.05.044] [Citation(s) in RCA: 27] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/02/2018] [Revised: 05/28/2019] [Accepted: 05/30/2019] [Indexed: 01/18/2023]
Abstract
Schizophrenia has a 1% incidence rate world-wide and those diagnosed present with positive (e.g. hallucinations, delusions), negative (e.g. apathy, asociality), and cognitive symptoms. However, both symptom burden and associated brain alterations are highly heterogeneous and intimately linked to prognosis. In this study, we present a method to predict individual symptom profiles by first deriving clinical subgroups and then using machine learning methods to perform subject-level classification based on magnetic resonance imaging (MRI) derived neuroanatomical measures. Symptomatic and MRI data of 167 subjects were used. Subgroups were defined using hierarchical clustering of clinical data resulting in 3 stable clusters: 1) high symptom burden, 2) predominantly positive symptom burden, and 3) mild symptom burden. Cortical thickness estimates were obtained in 78 regions of interest and were input, along with demographic data, into three machine learning models (logistic regression, support vector machine, and random forest) to predict subgroups. Random forest performance metrics for predicting the group membership of the high and mild symptom burden groups exceeded those of the baseline comparison of the entire schizophrenia population versus normal controls (AUC: 0.81 and 0.78 vs. 0.75). Additionally, an analysis of the most important features in the random forest classification demonstrated consistencies with previous findings of regional impairments and symptoms of schizophrenia.
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Affiliation(s)
- Alexandra Talpalaru
- Biological & Biomedical Engineering, McGill University, 845 Sherbrooke Street West, Montreal, Quebec H3A 0G4, Canada; Douglas Mental Health University Institute, 6875 Boulevard LaSalle, Verdun, QC H4H 1R3, Canada.
| | - Nikhil Bhagwat
- Douglas Mental Health University Institute, 6875 Boulevard LaSalle, Verdun, QC H4H 1R3, Canada; Institute of Biomaterials and Biomedical Engineering, University of Toronto, 27 King's College Cir, Toronto, ON M5S 3H7, Canada
| | - Gabriel A Devenyi
- Douglas Mental Health University Institute, 6875 Boulevard LaSalle, Verdun, QC H4H 1R3, Canada; Department of Psychiatry, McGill University, 845 Sherbrooke Street West, Montreal, Quebec H3A 0G4, Canada
| | - Martin Lepage
- Douglas Mental Health University Institute, 6875 Boulevard LaSalle, Verdun, QC H4H 1R3, Canada; Department of Psychiatry, McGill University, 845 Sherbrooke Street West, Montreal, Quebec H3A 0G4, Canada
| | - M Mallar Chakravarty
- Biological & Biomedical Engineering, McGill University, 845 Sherbrooke Street West, Montreal, Quebec H3A 0G4, Canada; Douglas Mental Health University Institute, 6875 Boulevard LaSalle, Verdun, QC H4H 1R3, Canada; Department of Psychiatry, McGill University, 845 Sherbrooke Street West, Montreal, Quebec H3A 0G4, Canada.
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Abstract
We selectively review recent research findings in the field of psychoimmunology which test the hypotheses that immunological dysfunction may be aetiologically related to mental illnesses such as schizophrenia, and that certain morbid affective states such as depression and other forms of psychosocial distress may be the cause of immunosuppression and through this mechanism affect the outcome of illnesses such as cancer. Our examination of research implicating immunological or infective mechanisms in the aetiology of schizophrenia indicates that most studies have been unable to control for major methodological difficulties but the compatibility of these theories with the dopamine hypothesis suggests that further research attention is warranted. More clearly, there is growing evidence demonstrating a link between depression, other states of psychological distress and immunosuppression, but the clinical significance of these findings remains uncertain. The complex relationship between stress and the outcome of illnesses such as cancer is discussed and the possible implications of these findings for clinical psychiatry are suggested.
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Affiliation(s)
- M O'Donnell
- N.S.W. Institute of Psychiatry, Prince of Wales Hospital, Randwick
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