title: Identifying neuroanatomical signatures of anorexia nervosa: a multivariate machine learning approach.
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.
  
  
  METHODS
  Structural neuroimaging scans were acquired from 15 female patients with AN (age = 20, s.d. = 4 years) and 15 demographically matched female controls (age = 22, s.d. = 3 years). Neuroanatomical volumes were extracted using the FreeSurfer software and input into the Least Absolute Shrinkage and Selection Operator (LASSO) multivariate ML algorithm. LASSO was 'trained' to identify 'novel' individual subjects as either AN patients or healthy controls. Furthermore, the model estimated the probability that an individual subject belonged to the AN group based on an individual scan.
  
  
  RESULTS
  The model correctly predicted 25 out of 30 subjects, translating into 83.3% accuracy (sensitivity 86.7%, specificity 80.0%) (p < 0.001; χ 2 test). Six neuroanatomical regions (cerebellum white matter, choroid plexus, putamen, accumbens, the diencephalon and the third ventricle) were found to be relevant in distinguishing individual AN patients from healthy controls. The predicted probabilities showed a linear relationship with drive for thinness clinical scores (r = 0.52, p < 0.005) and with body mass index (BMI) (r = -0.45, p = 0.01).
  
  
  CONCLUSIONS
  The model achieved a good predictive accuracy and drive for thinness showed a strong neuroanatomical signature. These results indicate that neuroimaging scans coupled with ML techniques have the potential to provide information at an individual subject level that might be relevant to clinical outcomes.
authors.affiliation: UT Center of Excellence on Mood Disorders,Department of Psychiatry and Behavioral Sciences,UT Houston Medical School,Houston,TX,USA.
authors.forename: L
authors.initials: L
authors.lastname: Lavagnino
authors.affiliation: Department of Neuroscience,AOU San Giovanni Battista,Turin,Italy.
authors.forename: F
authors.initials: F
authors.lastname: Amianto
authors.affiliation: UT Center of Excellence on Mood Disorders,Department of Psychiatry and Behavioral Sciences,UT Houston Medical School,Houston,TX,USA.
authors.forename: B
authors.initials: B
authors.lastname: Mwangi
authors.affiliation: Department of Neuroscience,AOU San Giovanni Battista,Turin,Italy.
authors.forename: F
authors.initials: F
authors.lastname: D'Agata
authors.affiliation: Department of Neuroscience,AOU San Giovanni Battista,Turin,Italy.
authors.forename: A
authors.initials: A
authors.lastname: Spalatro
authors.affiliation: UT Center of Excellence on Mood Disorders,Department of Psychiatry and Behavioral Sciences,UT Houston Medical School,Houston,TX,USA.
authors.forename: G B
authors.initials: GB
authors.lastname: Zunta-Soares
authors.affiliation: Department of Neuroscience,AOU San Giovanni Battista,Turin,Italy.
authors.forename: G
authors.initials: G
authors.lastname: Abbate Daga
authors.affiliation: Department of Neuroscience,AOU San Giovanni Battista,Turin,Italy.
authors.forename: P
authors.initials: P
authors.lastname: Mortara
authors.affiliation: Department of Neuroscience,AOU San Giovanni Battista,Turin,Italy.
authors.forename: S
authors.initials: S
authors.lastname: Fassino
authors.affiliation: UT Center of Excellence on Mood Disorders,Department of Psychiatry and Behavioral Sciences,UT Houston Medical School,Houston,TX,USA.
authors.forename: J C
authors.initials: JC
authors.lastname: Soares
mesh_terms: D000293:Adolescent; D000328:Adult; D000465:Algorithms; D000856:Anorexia Nervosa / Q000175:diagnosis*; D001921:Brain / Q000473:pathology*; D016022:Case-Control Studies; D039721:Diagnostic and Statistical Manual of Mental Disorders; D005260:Female; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods; D000069550:Machine Learning / Q000706:statistics & numerical data*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D059906:Neuroimaging / Q000379:methods*; D011336:Probability; D012680:Sensitivity and Specificity; D055815:Young Adult
keywords: Anorexia; drive for thinness; machine learning; magnetic resonance imaging