Population modeling with machine learning can enhance measures of mental health.

Journal: GigaScience
Published Date: Oct 13, 2021

Abstract

BACKGROUND: Biological aging is revealed by physical measures, e.g., DNA probes or brain scans. In contrast, individual differences in mental function are explained by psychological constructs, e.g., intelligence or neuroticism. These constructs are typically assessed by tailored neuropsychological tests that build on expert judgement and require careful interpretation. Could machine learning on large samples from the general population be used to build proxy measures of these constructs that do not require human intervention?

Authors

  • Kamalaker Dadi
    Inria, CEA, Neurospin, Parietal team, Université Paris Saclay, 91120 Palaiseau, France.
  • Gael Varoquaux
    Parietal, INRIA, NeuroSpin, bat 145 CEA Saclay, 91191, Gif sur Yvette, France.
  • Josselin Houenou
    APHP, Mondor University Hospitals, DMU IMPACT Psychiatry and Addictology, UPEC, Créteil, France.
  • Danilo Bzdok
    Department of Psychiatry at the RWTH Aachen University in Germany and a Visiting Professor at INRIA/Neurospin Saclay in France.
  • Bertrand Thirion
    Parietal, Inria, Université Paris-Saclay, Gif-sur-Yvette, France.
  • Denis Engemann
    Inria, CEA, Neurospin, Parietal team, Université Paris Saclay, 91120 Palaiseau, France.

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