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Mol Psychiatry. 2018 Aug 31. doi: 10.1038/s41380-018-0228-9. [Epub ahead of print]

Using structural MRI to identify bipolar disorders - 13 site machine learning study in 3020 individuals from the ENIGMA Bipolar Disorders Working Group.

Author information

1
Department of Psychiatry, Dalhousie University, Halifax, Nova Scotia, Canada.
2
Faculty of Computer Science, Dalhousie University, Halifax, Nova Scotia, Canada.
3
Department of Psychiatry, Brain Center Rudolf Magnus, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands.
4
Interdepartmental Neuroscience Program, University of California, Los Angeles, CA, USA.
5
Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California, Marina del Rey, CA, USA.
6
NORMENT KG Jebsen Centre, University of Oslo, Oslo, Norway.
7
Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway.
8
Department of Psychiatric Research, Diakonhjemmet Hospital, Oslo, Norway.
9
Department of Clinical Neuroscience, Centre for Psychiatric Research, Karolinska Institutet, Stockholm, Sweden.
10
Centre for Neuroimaging and Cognitive Genomics (NICOG), Clinical Neuroimaging Laboratory, NCBES Galway Neuroscience Centre, College of Medicine Nursing and Health Sciences, National University of Ireland Galway, Galway, Ireland.
11
FIDMAG Germanes Hospitalàries Research Foundation, Barcelona, Spain.
12
Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM), Madrid, Spain.
13
Institute of Clinical Radiology, Medical Faculty - University of Muenster - and University Hospital Muenster, Muenster, Germany.
14
Department of Psychiatry, Melbourne Medical School, The University of Melbourne, Parkville, VIC, Australia.
15
Hospital Clinic, University of Barcelona, IDIBAPS, CIBERSAM, Barcelona, Catalonia, Spain.
16
Laboratory of Psychiatric Neuroimaging (LIM-21), Department and Institute of Psychiatry, Faculty of Medicine, University of São Paulo, São Paulo, Brazil.
17
Center for Interdisciplinary Research on Applied Neurosciences (NAPNA), University of São Paulo, São Paulo, Brazil.
18
MRC Centre for Neuropsychiatric Genetics and Genomics, Cardiff University, Cardiff, UK.
19
Department of Psychiatry, University of Münster, Münster, Germany.
20
Research Group in Psychiatry, Department of Psychiatry, Faculty of Medicine, Universidad de Antioquia, Medellín, Antioquia, Colombia.
21
Department of Psychiatry and Psychotherapy, Philipps-University Marburg, Marburg, Germany.
22
NeuroSpin, CEA, Paris-Saclay, Gif sur Yvette, France.
23
Department of Neurology, Oslo Universisty Hospital, Oslo, Norway.
24
Department of Psychiatry, University of California, San Diego, La Jolla, CA, USA.
25
Desert-Pacific Mental Illness Research, Education, and Clinical Center, VA San Diego Healthcare System, La Jolla, CA, USA.
26
Departament de Biologia Evolutiva, Ecologia i Ciències Ambientals, Facultat de Biologia, Universitat de Barcelona, Barcelona, Spain.
27
Cardiff University Brain Research Imaging Centre, Cardiff University, Cardiff, UK.
28
Neuroscience Research Australia, Sydney, NSW, Australia.
29
School of Medical Sciences, University of New South Wales, Sydney, NSW, Australia.
30
Department of Psychiatry, Yale University, New Haven, CT, USA.
31
Olin Neuropsychiatric Research Center, Institute of Living, Hartford Hospital, Hartford, CT, USA.
32
Institut Pasteur, Unité Perception et Mémoire, Paris, France.
33
INSERM U955 Team 15 'Translational Psychiatry', University Paris East, APHP, CHU Mondor, Fondation FondaMental, Créteil, France.
34
Neuroscience Institute, University of Cape Town, Cape Town, South Africa.
35
Translational Neuroscience Group, Department of Psychiatry and Mental Health, Cape Town, South Africa.
36
School of Psychiatry, University of New South Wales, Sydney, NSW, Australia.
37
Department of Psychiatry and Behavioural Sciences, University of New Mexico, Albuquerque, NM, USA.
38
Mood Disorders Program, Hospital Universitario San Vicente Fundación, Medellín, Antioquia, Colombia.
39
Department of Psychiatry, University of Texas Health Science Center at Houston, Houston, TX, USA.
40
Psychosomatic Unit, Division of Mental Health and Dependence, Oslo University Hospital and University of Oslo, Oslo, Norway.
41
University of Oslo, Institute of Clinical Medicine, Oslo, Norway.
42
Black Dog Institute, Prince of Wales Hospital, Sydney, NSW, Australia.
43
Research Group, Instituto de Alta Tecnología Médica (IATM), Medellín, Antioquia, Colombia.
44
Department of Psychiatry, University of Pennsylvania, Philadelphia, PA, USA.
45
Department of Psychiatry, SA MRC Unit on Risk & Resilience in Mental Disorders, University of Cape Town, Cape Town, South Africa.
46
Western Cape Department of Health, Valkenberg Hospital, Cape Town, Western Cape, South Africa.
47
Department of Psychiatry, University of Vermont, Burlington, VT, USA.
48
Department of Child and Adolescent Psychiatry/Psychology, Erasmus Medical Centre, Rotterdam, The Netherlands.
49
Department of Psychology, University of Oslo, Oslo, Norway.
50
Instituto de Ensino e Pesquisa, Hospital Sírio-Libanês, Sao Paulo, Brazil.
51
Department of Psychiatry, Dalhousie University, Halifax, Nova Scotia, Canada. tomas.hajek@dal.ca.

Abstract

Bipolar disorders (BDs) are among the leading causes of morbidity and disability. Objective biological markers, such as those based on brain imaging, could aid in clinical management of BD. Machine learning (ML) brings neuroimaging analyses to individual subject level and may potentially allow for their diagnostic use. However, fair and optimal application of ML requires large, multi-site datasets. We applied ML (support vector machines) to MRI data (regional cortical thickness, surface area, subcortical volumes) from 853 BD and 2167 control participants from 13 cohorts in the ENIGMA consortium. We attempted to differentiate BD from control participants, investigated different data handling strategies and studied the neuroimaging/clinical features most important for classification. Individual site accuracies ranged from 45.23% to 81.07%. Aggregate subject-level analyses yielded the highest accuracy (65.23%, 95% CI = 63.47-67.00, ROC-AUC = 71.49%, 95% CI = 69.39-73.59), followed by leave-one-site-out cross-validation (accuracy = 58.67%, 95% CI = 56.70-60.63). Meta-analysis of individual site accuracies did not provide above chance results. There was substantial agreement between the regions that contributed to identification of BD participants in the best performing site and in the aggregate dataset (Cohen's Kappa = 0.83, 95% CI = 0.829-0.831). Treatment with anticonvulsants and age were associated with greater odds of correct classification. Although short of the 80% clinically relevant accuracy threshold, the results are promising and provide a fair and realistic estimate of classification performance, which can be achieved in a large, ecologically valid, multi-site sample of BD participants based on regional neurostructural measures. Furthermore, the significant classification in different samples was based on plausible and similar neuroanatomical features. Future multi-site studies should move towards sharing of raw/voxelwise neuroimaging data.

PMID:
30171211
DOI:
10.1038/s41380-018-0228-9

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