title: Predicting obsessive-compulsive disorder severity combining neuroimaging and machine learning methods.
abstract: BACKGROUND
  Recently, machine learning methods have been used to discriminate, on an individual basis, patients from healthy controls through brain structural magnetic resonance imaging (MRI). However, the application of these methods to predict the severity of psychiatric symptoms is less common.
  
  
  METHODS
  Herein, support vector regression (SVR) was employed to evaluate whether gray matter volumes encompassing cortical-subcortical loops contain discriminative information to predict obsessive-compulsive disorder (OCD) symptom severity in 37 treatment-naïve adult OCD patients.
  
  
  RESULTS
  The Pearson correlation coefficient between predicted and observed symptom severity scores was 0.49 (p=0.002) for total Dimensional Yale-Brown Obsessive-Compulsive Scale (DY-BOCS) and 0.44 (p=0.006) for total Yale-Brown Obsessive-Compulsive Scale (Y-BOCS). The regions that contained the most discriminative information were the left medial orbitofrontal cortex and the left putamen for both scales.
  
  
  CONCLUSIONS
  Our sample is relatively small and our results must be replicated with independent and larger samples.
  
  
  CONCLUSIONS
  These results indicate that machine learning methods such as SVR analysis may identify neurobiological markers to predict OCD symptom severity based on individual structural MRI datasets.
authors.affiliation: Department & Institute of Psychiatry, University of São Paulo Medical School, Rua Ovídio Pires de Campos 785, 3° Andar Ala Norte, Sala 9 (PROTOC), CEP 05403-010, São Paulo, Brazil. mqhoexter@gmail.com
authors.forename: Marcelo Q
authors.initials: MQ
authors.lastname: Hoexter
authors.forename: Euripedes C
authors.initials: EC
authors.lastname: Miguel
authors.forename: Juliana B
authors.initials: JB
authors.lastname: Diniz
authors.forename: Roseli G
authors.initials: RG
authors.lastname: Shavitt
authors.forename: Geraldo F
authors.initials: GF
authors.lastname: Busatto
authors.forename: João R
authors.initials: JR
authors.lastname: Sato
mesh_terms: D000293:Adolescent; D000328:Adult; D001185:Artificial Intelligence; D002540:Cerebral Cortex / Q000473:pathology*; D005260:Female; D006801:Humans; D008279:Magnetic Resonance Imaging; D008297:Male; D008875:Middle Aged; D059906:Neuroimaging; D009771:Obsessive-Compulsive Disorder / Q000175:diagnosis* / Q000473:pathology / Q000523:psychology; D011379:Prognosis; D011699:Putamen / Q000473:pathology; D012720:Severity of Illness Index; D055815:Young Adult
keywords: Machine learning; Magnetic resonance imaging; Neuroimaging; Obsessive–compulsive disorder; Support vector regression; Symptom severity