Toward a Neuroimaging Treatment Selection Biomarker for Major Depressive Disorder
Callie L. McGrath,Mary E. Kelley,Paul E. Holtzheimer,Boadie W. Dunlop,W. Edward Craighead,Alexandre Rosa Franco,R. Cameron Craddock,R. Cameron Craddock,Helen S. Mayberg +8 more
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TLDR
If verified with prospective testing, the insula metabolism-based treatment-specific biomarker defined in this study provides the first objective marker, to the authors' knowledge, to guide initial treatment selection for depression.Abstract:
Importance Currently, fewer than 40% of patients treated for major depressive disorder achieve remission with initial treatment. Identification of a biological marker that might improve these odds could have significant health and economic impact. Objective To identify a candidate neuroimaging “treatment-specific biomarker” that predicts differential outcome to either medication or psychotherapy. Design Brain glucose metabolism was measured with positron emission tomography prior to treatment randomization to either escitalopram oxalate or cognitive behavior therapy for 12 weeks. Patients who did not remit on completion of their phase 1 treatment were offered enrollment in phase 2 comprising an additional 12 weeks of treatment with combination escitalopram and cognitive behavior therapy. Setting Mood and anxiety disorders research program at an academic medical center. Participants Men and women aged 18 to 60 years with currently untreated major depressive disorder. Intervention Randomized assignment to 12 weeks of treatment with either escitalopram oxalate (10-20 mg/d) or 16 sessions of manual-based cognitive behavior therapy. Main Outcome and Measure Remission, defined as a 17-item Hamilton Depression Rating Scale score of 7 or less at both weeks 10 and 12, as assessed by raters blinded to treatment. Results Positive and negative predictors of remission were identified with a 2-way analysis of variance treatment (escitalopram or cognitive behavior therapy) × outcome (remission or nonresponse) interaction. Of 65 protocol completers, 38 patients with clear outcomes and usable positron emission tomography scans were included in the primary analysis: 12 remitters to cognitive behavior therapy, 11 remitters to escitalopram, 9 nonresponders to cognitive behavior therapy, and 6 nonresponders to escitalopram. Six limbic and cortical regions were identified, with the right anterior insula showing the most robust discriminant properties across groups (effect size = 1.43). Insula hypometabolism (relative to whole-brain mean) was associated with remission to cognitive behavior therapy and poor response to escitalopram, while insula hypermetabolism was associated with remission to escitalopram and poor response to cognitive behavior therapy. Conclusions and Relevance If verified with prospective testing, the insula metabolism-based treatment-specific biomarker defined in this study provides the first objective marker, to our knowledge, to guide initial treatment selection for depression. Trial Registration Registered at clinicaltrials.gov (NCT00367341)read more
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Major depressive disorder
Christian Otte,Stefan M. Gold,Stefan M. Gold,Brenda W.J.H. Penninx,Carmine M. Pariante,Amit Etkin,Maurizio Fava,David C. Mohr,Alan F. Schatzberg +8 more
TL;DR: An overview of the current evidence of major depressive disorder, including its epidemiology, aetiology, pathophysiology, diagnosis and treatment, is provided.
Journal ArticleDOI
Resting-state connectivity biomarkers define neurophysiological subtypes of depression
Andrew T. Drysdale,Logan Grosenick,Logan Grosenick,Jonathan Downar,Katharine Dunlop,Farrokh Mansouri,Yue Meng,Robert N. Fetcho,Benjamin D. Zebley,Desmond J. Oathes,Amit Etkin,Alan F. Schatzberg,Keith Sudheimer,Jennifer Keller,Helen S. Mayberg,Faith M. Gunning,George S. Alexopoulos,Michael D. Fox,Alvaro Pascual-Leone,Henning U. Voss,B. J. Casey,Marc J. Dubin,Conor Liston +22 more
TL;DR: It is shown here that patients with depression can be subdivided into four neurophysiological subtypes defined by distinct patterns of dysfunctional connectivity in limbic and frontostriatal networks, which may be useful for identifying the individuals who are most likely to benefit from targeted neurostimulation therapies.
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The NIMH Research Domain Criteria (RDoC) Project: Precision Medicine for Psychiatry
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Interoceptive predictions in the brain
TL;DR: The Embodied Predictive Interoception Coding model is introduced, which integrates an anatomical model of corticocortical connections with Bayesian active inference principles, to propose that agranular visceromotor cortices contribute to interoception by issuing interoceptive predictions.
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Computational psychiatry as a bridge from neuroscience to clinical applications
TL;DR: This work reviews recent advances in data driven and theory driven Computational psychiatry, with an emphasis on clinical applications, and highlights the utility of combining them.
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