Deep neural networks learn general and clinically relevant representations of the ageing brain
Esten H. Leonardsen,
Han Peng,
Tobias Kaufmann,
Ingrid Agartz,
Ole A. Andreassen,
Elisabeth Gulowsen Celius,
Thomas Espeseth,
Hanne F. Harbo,
Einar A. Høgestøl,
Ann-Marie de Lange,
Andre F. Marquand,
Didac Vidal-Piñeiro,
James M. Roe,
Geir Selbæk,
Øystein Sørensen,
Stephen M. Smith,
Lars T. Westlye,
Thomas Wolfers,
Yunpeng Wang
Affiliations
Esten H. Leonardsen
Corresponding author. Postboks 1094 Blindern, 0317 OSLO.; Department of Psychology, University of Oslo, Oslo, Norway; Norwegian Centre for Mental Disorders Research (NORMENT), Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
Han Peng
Wellcome Centre for Integrative Neuroimaging (WIN FMRIB), University of Oxford, Oxford, OX3 9DU, United Kingdom
Tobias Kaufmann
Norwegian Centre for Mental Disorders Research (NORMENT), Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway; Department of Psychiatry and Psychotherapy, Tübingen Center for Mental Health, University of Tübingen, Germany
Ingrid Agartz
Norwegian Centre for Mental Disorders Research (NORMENT), Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway; Department of Psychiatric Research, Diakonhjemmet Hospital, Oslo, Norway; Centre for Psychiatry Research, Department of Clinical Neuroscience, Karolinska Institutet & Stockholm Health Care Services, Stockholm County Council, Stockholm, Sweden
Ole A. Andreassen
Norwegian Centre for Mental Disorders Research (NORMENT), Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
Elisabeth Gulowsen Celius
Department of Neurology, Oslo University Hospital, Norway; Institute of Clinical Medicine, University of Oslo, Oslo, Norway
Thomas Espeseth
Department of Psychology, University of Oslo, Oslo, Norway; Department of Psychology, Bjørknes University College, Oslo, Norway
Hanne F. Harbo
Department of Neurology, Oslo University Hospital, Norway; Institute of Clinical Medicine, University of Oslo, Oslo, Norway
Einar A. Høgestøl
Department of Psychology, University of Oslo, Oslo, Norway; Norwegian Centre for Mental Disorders Research (NORMENT), Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway; Department of Neurology, Oslo University Hospital, Norway
Ann-Marie de Lange
Department of Psychology, University of Oslo, Oslo, Norway; LREN, Centre for Research in Neurosciences-Department of Clinical Neurosciences, CHUV and University of Lausanne, Lausanne, Switzerland; Department of Psychiatry, University of Oxford, Oxford, UK
Andre F. Marquand
Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Centre, Nijmegen, Netherlands
Didac Vidal-Piñeiro
Department of Psychology, University of Oslo, Oslo, Norway
James M. Roe
Department of Psychology, University of Oslo, Oslo, Norway
Geir Selbæk
Norwegian National Advisory Unit on Aging and Health, Vestfold Hospital Trust, Tønsberg, Norway; Department of Geriatric Medicine, Oslo University Hospital, Oslo, Norway
Øystein Sørensen
Department of Psychology, University of Oslo, Oslo, Norway
Stephen M. Smith
Wellcome Centre for Integrative Neuroimaging (WIN FMRIB), University of Oxford, Oxford, OX3 9DU, United Kingdom
Lars T. Westlye
Department of Psychology, University of Oslo, Oslo, Norway; Norwegian Centre for Mental Disorders Research (NORMENT), Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway; KG Jebsen Center for Neurodevelopmental Disorders, University of Oslo, Oslo, Norway
Thomas Wolfers
Department of Psychology, University of Oslo, Oslo, Norway; Norwegian Centre for Mental Disorders Research (NORMENT), Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
Yunpeng Wang
Department of Psychology, University of Oslo, Oslo, Norway
The discrepancy between chronological age and the apparent age of the brain based on neuroimaging data — the brain age delta — has emerged as a reliable marker of brain health. With an increasing wealth of data, approaches to tackle heterogeneity in data acquisition are vital. To this end, we compiled raw structural magnetic resonance images into one of the largest and most diverse datasets assembled (n=53542), and trained convolutional neural networks (CNNs) to predict age. We achieved state-of-the-art performance on unseen data from unknown scanners (n=2553), and showed that higher brain age delta is associated with diabetes, alcohol intake and smoking. Using transfer learning, the intermediate representations learned by our model complemented and partly outperformed brain age delta in predicting common brain disorders. Our work shows we can achieve generalizable and biologically plausible brain age predictions using CNNs trained on heterogeneous datasets, and transfer them to clinical use cases.
