Multimodal brain-age prediction and cardiovascular risk: The Whitehall II MRI sub-study
Ann-Marie G. de Lange,
Melis Anatürk,
Sana Suri,
Tobias Kaufmann,
James H. Cole,
Ludovica Griffanti,
Enikő Zsoldos,
Daria E.A. Jensen,
Nicola Filippini,
Archana Singh-Manoux,
Mika Kivimäki,
Lars T. Westlye,
Klaus P. Ebmeier
Affiliations
Ann-Marie G. de Lange
Department of Psychiatry, University of Oxford, Oxford, UK; Department of Psychology, University of Oslo, Oslo, Norway; NORMENT, Institute of Clinical Medicine, University of Oslo, & Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway; Corresponding author.
Melis Anatürk
Department of Psychiatry, University of Oxford, Oxford, UK; Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford, UK
Sana Suri
Department of Psychiatry, University of Oxford, Oxford, UK; Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford, UK
Tobias Kaufmann
NORMENT, Institute of Clinical Medicine, University of Oslo, & Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
James H. Cole
Centre for Medical Image Computing, Department of Computer Science, University College London, London, UK; Dementia Research Centre, Institute of Neurology, University College London, London, UK
Ludovica Griffanti
Department of Psychiatry, University of Oxford, Oxford, UK; Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford, UK
Enikő Zsoldos
Department of Psychiatry, University of Oxford, Oxford, UK; Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford, UK
Daria E.A. Jensen
Department of Psychiatry, University of Oxford, Oxford, UK; Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford, UK
Nicola Filippini
Department of Psychiatry, University of Oxford, Oxford, UK; Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford, UK
Archana Singh-Manoux
Epidemiology of Ageing and Neurodegenerative Diseases, Universit de Paris, INSERM U1153, Paris France; Department of Epidemiology and Public Health, University College London, London, UK
Mika Kivimäki
Department of Epidemiology and Public Health, University College London, London, UK
Lars T. Westlye
Department of Psychology, University of Oslo, Oslo, Norway; NORMENT, Institute of Clinical Medicine, University of Oslo, & Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
Klaus P. Ebmeier
Department of Psychiatry, University of Oxford, Oxford, UK
Brain age is becoming a widely applied imaging-based biomarker of neural aging and potential proxy for brain integrity and health. We estimated multimodal and modality-specific brain age in the Whitehall II (WHII) MRI cohort using machine learning and imaging-derived measures of gray matter (GM) morphology, white matter microstructure (WM), and resting state functional connectivity (FC). The results showed that the prediction accuracy improved when multiple imaging modalities were included in the model (R2 = 0.30, 95% CI [0.24, 0.36]). The modality-specific GM and WM models showed similar performance (R2 = 0.22 [0.16, 0.27] and R2 = 0.24 [0.18, 0.30], respectively), while the FC model showed the lowest prediction accuracy (R2 = 0.002 [-0.005, 0.008]), indicating that the FC features were less related to chronological age compared to structural measures. Follow-up analyses showed that FC predictions were similarly low in a matched sub-sample from UK Biobank, and although FC predictions were consistently lower than GM predictions, the accuracy improved with increasing sample size and age range. Cardiovascular risk factors, including high blood pressure, alcohol intake, and stroke risk score, were each associated with brain aging in the WHII cohort. Blood pressure showed a stronger association with white matter compared to gray matter, while no differences in the associations of alcohol intake and stroke risk with these modalities were observed. In conclusion, machine-learning based brain age prediction can reduce the dimensionality of neuroimaging data to provide meaningful biomarkers of individual brain aging. However, model performance depends on study-specific characteristics including sample size and age range, which may cause discrepancies in findings across studies.