title: Multidimensional prediction of treatment response to antidepressants with cognitive control and functional MRI.
abstract: Predicting treatment response for major depressive disorder can provide a tremendous benefit for our overstretched health care system by reducing number of treatments and time to remission, thereby decreasing morbidity. The present study used neural and performance predictors during a cognitive control task to predict treatment response (% change in Hamilton Depression Rating Scale pre- to post-treatment). Forty-nine individuals diagnosed with major depressive disorder were enrolled with intent to treat in the open-label study; 36 completed treatment, had useable data, and were included in most data analyses. Participants included in the data analysis sample received treatment with escitalopram (n = 22) or duloxetine (n = 14) for 10 weeks. Functional MRI and performance during a Parametric Go/No-go test were used to predict per cent reduction in Hamilton Depression Rating Scale scores after treatment. Haemodynamic response function-based contrasts and task-related independent components analysis (subset of sample: n = 29) were predictors. Independent components analysis component beta weights and haemodynamic response function modelling activation during Commission errors in the rostral and dorsal anterior cingulate, mid-cingulate, dorsomedial prefrontal cortex, and lateral orbital frontal cortex predicted treatment response. In addition, more commission errors on the task predicted better treatment response. Together in a regression model, independent component analysis, haemodynamic response function-modelled, and performance measures predicted treatment response with 90% accuracy (compared to 74% accuracy with clinical features alone), with 84% accuracy in 5-fold, leave-one-out cross-validation. Convergence between performance markers and functional magnetic resonance imaging, including novel independent component analysis techniques, achieved high accuracy in prediction of treatment response for major depressive disorder. The strong link to a task paradigm provided by use of independent component analysis is a potential breakthrough that can inform ways in which prediction models can be integrated for use in clinical and experimental medicine studies.
authors.affiliation: The University of Illinois at Chicago, Department of Psychiatry and the Cognitive Neuroscience Center, Chicago, IL 60612, USA.
authors.forename: Natania A
authors.initials: NA
authors.lastname: Crane
authors.affiliation: The University of Illinois at Chicago, Department of Psychiatry and the Cognitive Neuroscience Center, Chicago, IL 60612, USA.
authors.forename: Lisanne M
authors.initials: LM
authors.lastname: Jenkins
authors.affiliation: The University of Illinois at Chicago, Department of Psychiatry and the Cognitive Neuroscience Center, Chicago, IL 60612, USA.
authors.forename: Runa
authors.initials: R
authors.lastname: Bhaumik
authors.affiliation: The University of Illinois at Chicago, Department of Psychiatry and the Cognitive Neuroscience Center, Chicago, IL 60612, USA.
authors.forename: Catherine
authors.initials: C
authors.lastname: Dion
authors.affiliation: The University of Illinois at Chicago, Department of Psychiatry and the Cognitive Neuroscience Center, Chicago, IL 60612, USA.
authors.forename: Jennifer R
authors.initials: JR
authors.lastname: Gowins
authors.affiliation: The University of Michigan Medical School, Department of Psychiatry, Molecular and Behavioral Neuroscience Institute, Ann Arbor, MI 48104, USA.
authors.forename: Brian J
authors.initials: BJ
authors.lastname: Mickey
authors.affiliation: The University of Michigan Medical School, Department of Psychiatry, Molecular and Behavioral Neuroscience Institute, Ann Arbor, MI 48104, USA.
authors.forename: Jon-Kar
authors.initials: JK
authors.lastname: Zubieta
authors.affiliation: The University of Illinois at Chicago, Department of Psychiatry and the Cognitive Neuroscience Center, Chicago, IL 60612, USA slangenecker@psych.uic.edu.|The University of Michigan Medical School, Department of Psychiatry, Molecular and Behavioral Neuroscience Institute, Ann Arbor, MI 48104, USA.
authors.forename: Scott A
authors.initials: SA
authors.lastname: Langenecker
mesh_terms: D000328:Adult; D000928:Antidepressive Agents / Q000627:therapeutic use*; D015283:Citalopram / Q000627:therapeutic use; D003072:Cognition Disorders / Q000175:diagnosis / Q000000981:diagnostic imaging / Q000188:drug therapy / Q000209:etiology; D003865:Major Depressive Disorder / Q000150:complications / Q000000981:diagnostic imaging / Q000188:drug therapy; D000068736:Duloxetine Hydrochloride / Q000627:therapeutic use; D005260:Female; D006801:Humans; D007091:Image Processing, Computer-Assisted; D008279:Magnetic Resonance Imaging; D008297:Male; D008875:Middle Aged; D009483:Neuropsychological Tests; D010100:Oxygen / Q000097:blood; D011237:Predictive Value of Tests; D016896:Treatment Outcome
chemical_list: D000928:Antidepressive Agents; D015283:Citalopram; D000068736:Duloxetine Hydrochloride; D010100:Oxygen
keywords: duloxetine; escitalopram; functional MRI; independent components analysis; major depressive disorder