title: Individualized identification of euthymic bipolar disorder using the Cambridge Neuropsychological Test Automated Battery (CANTAB) and machine learning.
abstract: BACKGROUND
  Previous studies have reported that patients with bipolar disorder (BD) present with cognitive impairments during mood episodes as well as euthymic phase. However, it is still unknown whether reported neurocognitive abnormalities can objectively identify individual BD patients from healthy controls (HC).
  
  
  METHODS
  A total of 21 euthymic BD patients and 21 demographically matched HC were included in the current study. Participants performed the computerized Cambridge Neurocognitive Test Automated Battery (CANTAB) to assess cognitive performance. The least absolute shrinkage selection operator (LASSO) machine learning algorithm was implemented to identify neurocognitive signatures to distinguish individual BD patients from HC.
  
  
  RESULTS
  The LASSO machine learning algorithm identified individual BD patients from HC with an accuracy of 71%, area under receiver operating characteristic curve of 0.7143 and significant at p=0.0053. The LASSO algorithm assigned individual subjects with a probability score (0-healthy, 1-patient). Patients with rapid cycling (RC) were assigned increased probability scores as compared to patients without RC. A multivariate pattern of neurocognitive abnormalities comprising of affective Go/No-go and the Cambridge gambling task was relevant in distinguishing individual patients from HC.
  
  
  CONCLUSIONS
  Our study sample was small as we only considered euthymic BD patients and demographically matched HC.
  
  
  CONCLUSIONS
  Neurocognitive abnormalities can distinguish individual euthymic BD patients from HC with relatively high accuracy. In addition, patients with RC had more cognitive impairments compared to patients without RC. The predictive neurocognitive signature identified in the current study can potentially be used to provide individualized clinical inferences on BD patients.
authors.affiliation: UT Center of Excellence on Mood Disorder, Department of Psychiatry and Behavioral Sciences, The University of Texas Science Center at Houston, Houston, TX, USA.
authors.forename: Mon-Ju
authors.initials: MJ
authors.lastname: Wu
authors.affiliation: UT Center of Excellence on Mood Disorder, Department of Psychiatry and Behavioral Sciences, The University of Texas Science Center at Houston, Houston, TX, USA; Bipolar Disorder Program and Laboratory of Molecular Psychiatry, Federal University of Rio Grande do Sul, Porto Alegre, RS, Brazil.
authors.forename: Ives Cavalcante
authors.initials: IC
authors.lastname: Passos
authors.affiliation: UT Center of Excellence on Mood Disorder, Department of Psychiatry and Behavioral Sciences, The University of Texas Science Center at Houston, Houston, TX, USA.
authors.forename: Isabelle E
authors.initials: IE
authors.lastname: Bauer
authors.affiliation: UT Center of Excellence on Mood Disorder, Department of Psychiatry and Behavioral Sciences, The University of Texas Science Center at Houston, Houston, TX, USA.
authors.forename: Luca
authors.initials: L
authors.lastname: Lavagnino
authors.affiliation: UT Center of Excellence on Mood Disorder, Department of Psychiatry and Behavioral Sciences, The University of Texas Science Center at Houston, Houston, TX, USA.
authors.forename: Bo
authors.initials: B
authors.lastname: Cao
authors.affiliation: UT Center of Excellence on Mood Disorder, Department of Psychiatry and Behavioral Sciences, The University of Texas Science Center at Houston, Houston, TX, USA.
authors.forename: Giovana B
authors.initials: GB
authors.lastname: Zunta-Soares
authors.affiliation: UT Center of Excellence on Mood Disorder, Department of Psychiatry and Behavioral Sciences, The University of Texas Science Center at Houston, Houston, TX, USA; Bipolar Disorder Program and Laboratory of Molecular Psychiatry, Federal University of Rio Grande do Sul, Porto Alegre, RS, Brazil.
authors.forename: Flávio
authors.initials: F
authors.lastname: Kapczinski
authors.affiliation: UT Center of Excellence on Mood Disorder, Department of Psychiatry and Behavioral Sciences, The University of Texas Science Center at Houston, Houston, TX, USA. Electronic address: benson.irungu@uth.tmc.edu.
authors.forename: Benson
authors.initials: B
authors.lastname: Mwangi
authors.affiliation: UT Center of Excellence on Mood Disorder, Department of Psychiatry and Behavioral Sciences, The University of Texas Science Center at Houston, Houston, TX, USA.
authors.forename: Jair C
authors.initials: JC
authors.lastname: Soares
mesh_terms: D000328:Adult; D000465:Algorithms; D001714:Bipolar Disorder / Q000175:diagnosis* / Q000523:psychology; D016022:Case-Control Studies; D003072:Cognition Disorders / Q000175:diagnosis* / Q000523:psychology; D003527:Cyclothymic Disorder / Q000523:psychology*; D005260:Female; D006801:Humans; D000069550:Machine Learning; D008297:Male; D008875:Middle Aged; D009483:Neuropsychological Tests / Q000706:statistics & numerical data*; D011237:Predictive Value of Tests
keywords: Bipolar disorder; CANTAB; Machine learning; Neurocognition; Rapid cycling