Literature DB >> 25547711

A cognitive-emotional biomarker for predicting remission with antidepressant medications: a report from the iSPOT-D trial.

Amit Etkin1, Brian Patenaude1, Yun Ju C Song2, Timothy Usherwood3, William Rekshan4, Alan F Schatzberg5, A John Rush6, Leanne M Williams7.   

Abstract

Depression involves impairments in a range of cognitive and emotional capacities. It is unknown whether these functions can inform medication choice when considered as a composite predictive biomarker. We tested whether behavioral tests, grounded in the neurobiology of cognitive and emotional functions, predict outcome with common antidepressants. Medication-free outpatients with nonpsychotic major depressive disorder (N=1008; 665 completers) were assessed before treatment using 13 computerized tests of psychomotor, executive, memory-attention, processing speed, inhibitory, and emotional functions. Matched healthy controls (N=336) provided a normative reference sample for test performance. Depressed participants were then randomized to escitalopram, sertraline, or venlafaxine-extended release, and were assessed using the 16-item Quick Inventory of Depressive Symptomatology (QIDS-SR16) and the 17-item Hamilton Rating Scale for Depression. Given the heterogeneity of depression, analyses were furthermore stratified by pretreatment performance. We then used pattern classification with cross-validation to determine individual patient-level composite predictive biomarkers of antidepressant outcome based on test performance. A subgroup of depressed participants (approximately one-quarter of patients) were found to be impaired across most cognitive tests relative to the healthy norm, from which they could be discriminated with 91% accuracy. These patients with generally impaired cognitive task performance had poorer treatment outcomes. For this impaired subgroup, task performance furthermore predicted remission on the QIDS-SR16 at 72% accuracy specifically following treatment with escitalopram but not the other medications. Therefore, tests of cognitive and emotional functions can form a clinically meaningful composite biomarker that may help drive general treatment outcome prediction for optimal treatment selection in depression, particularly for escitalopram.

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Year:  2014        PMID: 25547711      PMCID: PMC4397406          DOI: 10.1038/npp.2014.333

Source DB:  PubMed          Journal:  Neuropsychopharmacology        ISSN: 0893-133X            Impact factor:   7.853


  22 in total

1.  Evaluation of outcomes with citalopram for depression using measurement-based care in STAR*D: implications for clinical practice.

Authors:  Madhukar H Trivedi; A John Rush; Stephen R Wisniewski; Andrew A Nierenberg; Diane Warden; Louise Ritz; Grayson Norquist; Robert H Howland; Barry Lebowitz; Patrick J McGrath; Kathy Shores-Wilson; Melanie M Biggs; G K Balasubramani; Maurizio Fava
Journal:  Am J Psychiatry       Date:  2006-01       Impact factor: 18.112

2.  Explicit identification and implicit recognition of facial emotions: II. Core domains and relationships with general cognition.

Authors:  Danielle Mathersul; Donna M Palmer; Ruben C Gur; Raquel E Gur; Nick Cooper; Evian Gordon; Leanne M Williams
Journal:  J Clin Exp Neuropsychol       Date:  2008-08-19       Impact factor: 2.475

3.  The international Study to Predict Optimized Treatment in Depression (iSPOT-D): outcomes from the acute phase of antidepressant treatment.

Authors:  Radu Saveanu; Amit Etkin; Anne-Marie Duchemin; Andrea Goldstein-Piekarski; Anett Gyurak; Charles Debattista; Alan F Schatzberg; Satish Sood; Claire V A Day; Donna M Palmer; William R Rekshan; Evian Gordon; A John Rush; Leanne M Williams
Journal:  J Psychiatr Res       Date:  2014-12-31       Impact factor: 4.791

4.  Cognitive profiles in persons with chronic schizophrenia.

Authors:  Sharron E Dawes; Dilip V Jeste; Barton W Palmer
Journal:  J Clin Exp Neuropsychol       Date:  2011-06-28       Impact factor: 2.475

Review 5.  Cognition and depression: current status and future directions.

Authors:  Ian H Gotlib; Jutta Joormann
Journal:  Annu Rev Clin Psychol       Date:  2010       Impact factor: 18.561

6.  A retrospective study of premorbid ability and aging differences in cognitive clusters of schizophrenia.

Authors:  William P Horan; Gerald Goldstein
Journal:  Psychiatry Res       Date:  2003-06-15       Impact factor: 3.222

Review 7.  The Mini-International Neuropsychiatric Interview (M.I.N.I.): the development and validation of a structured diagnostic psychiatric interview for DSM-IV and ICD-10.

Authors:  D V Sheehan; Y Lecrubier; K H Sheehan; P Amorim; J Janavs; E Weiller; T Hergueta; R Baker; G C Dunbar
Journal:  J Clin Psychiatry       Date:  1998       Impact factor: 4.384

8.  Neuropsychological characteristics as predictors of SSRI treatment response in depressed subjects.

Authors:  Marianne Gorlyn; John G Keilp; Michael F Grunebaum; Bonnie P Taylor; Maria A Oquendo; Gerard E Bruder; Jonathan W Stewart; Gil Zalsman; J John Mann
Journal:  J Neural Transm (Vienna)       Date:  2008-07-16       Impact factor: 3.575

9.  Bupropion-SR, sertraline, or venlafaxine-XR after failure of SSRIs for depression.

Authors:  A John Rush; Madhukar H Trivedi; Stephen R Wisniewski; Jonathan W Stewart; Andrew A Nierenberg; Michael E Thase; Louise Ritz; Melanie M Biggs; Diane Warden; James F Luther; Kathy Shores-Wilson; George Niederehe; Maurizio Fava
Journal:  N Engl J Med       Date:  2006-03-23       Impact factor: 91.245

10.  Data mining methods in the prediction of Dementia: A real-data comparison of the accuracy, sensitivity and specificity of linear discriminant analysis, logistic regression, neural networks, support vector machines, classification trees and random forests.

Authors:  João Maroco; Dina Silva; Ana Rodrigues; Manuela Guerreiro; Isabel Santana; Alexandre de Mendonça
Journal:  BMC Res Notes       Date:  2011-08-17
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  30 in total

1.  Predicting Response to the Antidepressant Bupropion using Pretreatment fMRI.

Authors:  Kevin P Nguyen; Cherise Chin Fatt; Alex Treacher; Cooper Mellema; Madhukar H Trivedi; Albert Montillo
Journal:  Predict Intell Med (2019)       Date:  2019-10-10

2.  Using fMRI connectivity to define a treatment-resistant form of post-traumatic stress disorder.

Authors:  Amit Etkin; Adi Maron-Katz; Wei Wu; Gregory A Fonzo; Julia Huemer; Petra E Vértes; Brian Patenaude; Jonas Richiardi; Madeleine S Goodkind; Corey J Keller; Jaime Ramos-Cejudo; Yevgeniya V Zaiko; Kathy K Peng; Emmanuel Shpigel; Parker Longwell; Russ T Toll; Allison Thompson; Sanno Zack; Bryan Gonzalez; Raleigh Edelstein; Jingyun Chen; Irene Akingbade; Elizabeth Weiss; Roland Hart; Silas Mann; Kathleen Durkin; Steven H Baete; Fernando E Boada; Afia Genfi; Jillian Autea; Jennifer Newman; Desmond J Oathes; Steven E Lindley; Duna Abu-Amara; Bruce A Arnow; Nicolas Crossley; Joachim Hallmayer; Silvia Fossati; Barbara O Rothbaum; Charles R Marmar; Edward T Bullmore; Ruth O'Hara
Journal:  Sci Transl Med       Date:  2019-04-03       Impact factor: 17.956

3.  Cognitive impairment, behavioral impairment, depression, and wish to die in an ALS cohort.

Authors:  Judith Rabkin; Raymond Goetz; Jennifer Mary Murphy; Pam Factor-Litvak; Hiroshi Mitsumoto
Journal:  Neurology       Date:  2016-08-05       Impact factor: 9.910

4.  Targeting treatments for depression: what can our patients tell us?

Authors:  A John Rush
Journal:  Epidemiol Psychiatr Sci       Date:  2016-04-05       Impact factor: 6.892

Review 5.  Computational psychiatry as a bridge from neuroscience to clinical applications.

Authors:  Quentin J M Huys; Tiago V Maia; Michael J Frank
Journal:  Nat Neurosci       Date:  2016-03       Impact factor: 24.884

6.  Pretreatment Reward Sensitivity and Frontostriatal Resting-State Functional Connectivity Are Associated With Response to Bupropion After Sertraline Nonresponse.

Authors:  Yuen-Siang Ang; Roselinde Kaiser; Thilo Deckersbach; Jorge Almeida; Mary L Phillips; Henry W Chase; Christian A Webb; Ramin Parsey; Maurizio Fava; Patrick McGrath; Myrna Weissman; Phil Adams; Patricia Deldin; Maria A Oquendo; Melvin G McInnis; Thomas Carmody; Gerard Bruder; Crystal M Cooper; Cherise R Chin Fatt; Madhukar H Trivedi; Diego A Pizzagalli
Journal:  Biol Psychiatry       Date:  2020-04-23       Impact factor: 13.382

Review 7.  Opioid modulation of cognitive impairment in depression.

Authors:  Moriah L Jacobson; Hildegard A Wulf; Caroline A Browne; Irwin Lucki
Journal:  Prog Brain Res       Date:  2018-09-18       Impact factor: 2.453

Review 8.  Internet-Based Cognitive-Behavioral Therapy for Depression: Current Progress and Future Directions.

Authors:  Christian A Webb; Isabelle M Rosso; Scott L Rauch
Journal:  Harv Rev Psychiatry       Date:  2017 May/Jun       Impact factor: 3.732

9.  Cognitive Control of Emotion in Older Adults: A Review.

Authors:  Crystal Lantrip; Jason H Huang
Journal:  Clin Psychiatry (Wilmington)       Date:  2017-05-17

10.  Effect of antidepressant treatment on cognitive impairments associated with depression: a randomised longitudinal study.

Authors:  Carrie Shilyansky; Leanne M Williams; Anett Gyurak; Anthony Harris; Timothy Usherwood; Amit Etkin
Journal:  Lancet Psychiatry       Date:  2016-03-16       Impact factor: 27.083

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