An artificial neural network model for clinical score prediction in Alzheimer disease using structural neuroimaging measures.

Journal: Journal of psychiatry & neuroscience : JPN
Published Date: Jul 1, 2019

Abstract

BACKGROUND: The development of diagnostic and prognostic tools for Alzheimer disease is complicated by substantial clinical heterogeneity in prodromal stages. Many neuroimaging studies have focused on case–control classification and predicting conversion from mild cognitive impairment to Alzheimer disease, but predicting scores from clinical assessments (such as the Alzheimer’s Disease Assessment Scale or the Mini Mental State Examination) using MRI data has received less attention. Predicting clinical scores can be crucial in providing a nuanced prognosis and inferring symptomatic severity.

Authors

  • Nikhil Bhagwat
    Computational Brain Anatomy Laboratory, Douglas Mental Health Institute, McGill University, Montreal, Quebec, Canada; Institute of Biomaterials and Biomedical Engineering, University of Toronto, Toronto, Ontario, Canada; Kimel Family Translational Imaging-Genetics Research Lab, Research Imaging Centre, Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, Ontario, Canada.
  • Jon Pipitone
    From the Institute of Biomaterials and Biomedical Engineering, University of Toronto, Toronto, Ont. (Bhagwat, Chakravarty); the Cerebral Imaging Centre, Douglas Mental Health University Institute, Verdun, Que. (Bhagwat, Chakravarty); the Kimel Family Translational Imaging-Genetics Research Lab, Research Imaging Centre, Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, Ont. (Bhagwat, Pipitone, Voineskos); the Department of Psychiatry, University of Toronto, Toronto, Ont. (Voineskos); and the Department of Psychiatry, McGill University, Montreal, Que. (Chakravarty), Canada.
  • Aristotle N. Voineskos
    From the Institute of Biomaterials and Biomedical Engineering, University of Toronto, Toronto, Ont. (Bhagwat, Chakravarty); the Cerebral Imaging Centre, Douglas Mental Health University Institute, Verdun, Que. (Bhagwat, Chakravarty); the Kimel Family Translational Imaging-Genetics Research Lab, Research Imaging Centre, Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, Ont. (Bhagwat, Pipitone, Voineskos); the Department of Psychiatry, University of Toronto, Toronto, Ont. (Voineskos); and the Department of Psychiatry, McGill University, Montreal, Que. (Chakravarty), Canada.
  • M. Mallar Chakravarty
    From the Institute of Biomaterials and Biomedical Engineering, University of Toronto, Toronto, Ont. (Bhagwat, Chakravarty); the Cerebral Imaging Centre, Douglas Mental Health University Institute, Verdun, Que. (Bhagwat, Chakravarty); the Kimel Family Translational Imaging-Genetics Research Lab, Research Imaging Centre, Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, Ont. (Bhagwat, Pipitone, Voineskos); the Department of Psychiatry, University of Toronto, Toronto, Ont. (Voineskos); and the Department of Psychiatry, McGill University, Montreal, Que. (Chakravarty), Canada.

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