Identifying neuroanatomical signatures of anorexia nervosa: a multivariate machine learning approach.

Journal: Psychological medicine
PMID: 25990697

Abstract

BACKGROUND: There are currently no neuroanatomical biomarkers of anorexia nervosa (AN) available to make clinical inferences at an individual subject level. We present results of a multivariate machine learning (ML) approach utilizing structural neuroanatomical scan data to differentiate AN patients from matched healthy controls at an individual subject level.

Authors

  • L Lavagnino
    UT Center of Excellence on Mood Disorders,Department of Psychiatry and Behavioral Sciences,UT Houston Medical School,Houston,TX,USA.
  • F Amianto
    Department of Neuroscience,AOU San Giovanni Battista,Turin,Italy.
  • B Mwangi
    UT Center of Excellence on Mood Disorders,Department of Psychiatry and Behavioral Sciences,UT Houston Medical School,Houston,TX,USA.
  • F D'Agata
    Department of Neuroscience,AOU San Giovanni Battista,Turin,Italy.
  • A Spalatro
    Department of Neuroscience,AOU San Giovanni Battista,Turin,Italy.
  • G B Zunta-Soares
    UT Center of Excellence on Mood Disorders,Department of Psychiatry and Behavioral Sciences,UT Houston Medical School,Houston,TX,USA.
  • G Abbate Daga
    Department of Neuroscience,AOU San Giovanni Battista,Turin,Italy.
  • P Mortara
    Department of Neuroscience,AOU San Giovanni Battista,Turin,Italy.
  • S Fassino
    Department of Neuroscience,AOU San Giovanni Battista,Turin,Italy.
  • J C Soares
    UT Center of Excellence on Mood Disorders,Department of Psychiatry and Behavioral Sciences,UT Houston Medical School,Houston,TX,USA.

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