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Your search keyword '"Jha, Manish K."' showing total 318 results
Start Over Author "Jha, Manish K." Publication Type Academic Journals
318 results on '"Jha, Manish K."'

9. An electroencephalographic signature predicts antidepressant response in major depression

Author

Wu, Wei, Zhang, Yu, Jiang, Jing, Lucas, Molly V, Fonzo, Gregory A, Rolle, Camarin E, Cooper, Crystal, Chin-Fatt, Cherise, Krepel, Noralie, Cornelssen, Carena A, Wright, Rachael, Toll, Russell T, Trivedi, Hersh M, Monuszko, Karen, Caudle, Trevor L, Sarhadi, Kamron, Jha, Manish K, Trombello, Joseph M, Deckersbach, Thilo, Adams, Phil, McGrath, Patrick J, Weissman, Myrna M, Fava, Maurizio, Pizzagalli, Diego A, Arns, Martijn, Trivedi, Madhukar H, and Etkin, Amit

Subjects
Serious Mental Illness, Neurosciences, Mental Health, Major Depressive Disorder, Depression, Clinical Research, Brain Disorders, 6.1 Pharmaceuticals, Evaluation of treatments and therapeutic interventions, Mental health, Good Health and Well Being, Antidepressive Agents, Depressive Disorder, Major, Double-Blind Method, Electroencephalography, Humans, Machine Learning, Membrane Potentials, Models, Neurological, Predictive Value of Tests, Prefrontal Cortex, Reproducibility of Results, Sertraline, Transcranial Magnetic Stimulation, Treatment Outcome
Abstract

Antidepressants are widely prescribed, but their efficacy relative to placebo is modest, in part because the clinical diagnosis of major depression encompasses biologically heterogeneous conditions. Here, we sought to identify a neurobiological signature of response to antidepressant treatment as compared to placebo. We designed a latent-space machine-learning algorithm tailored for resting-state electroencephalography (EEG) and applied it to data from the largest imaging-coupled, placebo-controlled antidepressant study (n = 309). Symptom improvement was robustly predicted in a manner both specific for the antidepressant sertraline (versus placebo) and generalizable across different study sites and EEG equipment. This sertraline-predictive EEG signature generalized to two depression samples, wherein it reflected general antidepressant medication responsivity and related differentially to a repetitive transcranial magnetic stimulation treatment outcome. Furthermore, we found that the sertraline resting-state EEG signature indexed prefrontal neural responsivity, as measured by concurrent transcranial magnetic stimulation and EEG. Our findings advance the neurobiological understanding of antidepressant treatment through an EEG-tailored computational model and provide a clinical avenue for personalized treatment of depression.

Published
2020

10. Brain regulation of emotional conflict predicts antidepressant treatment response for depression

Author

Fonzo, Gregory A, Etkin, Amit, Zhang, Yu, Wu, Wei, Cooper, Crystal, Chin-Fatt, Cherise, Jha, Manish K, Trombello, Joseph, Deckersbach, Thilo, Adams, Phil, McInnis, Melvin, McGrath, Patrick J, Weissman, Myrna M, Fava, Maurizio, and Trivedi, Madhukar H

Subjects
Biological Psychology, Biomedical and Clinical Sciences, Clinical Sciences, Psychology, Clinical Trials and Supportive Activities, Neurosciences, Brain Disorders, Clinical Research, Mind and Body, Behavioral and Social Science, Mental Health, Depression, 6.1 Pharmaceuticals, Evaluation of treatments and therapeutic interventions, Mental health, Good Health and Well Being, Antidepressive Agents, Antidepressive Agents, Second-Generation, Brain, Clinical Trials as Topic, Depressive Disorder, Major, Humans, Magnetic Resonance Imaging, Neuroimaging, Treatment Outcome, Biomedical and clinical sciences, Health sciences
Abstract

The efficacy of antidepressant treatment for depression is controversial due to the only modest superiority demonstrated over placebo. However, neurobiological heterogeneity within depression may limit overall antidepressant efficacy. We sought to identify a neurobiological phenotype responsive to antidepressant treatment by testing pretreatment brain activation during response to, and regulation of, emotional conflict as a moderator of the clinical benefit of the antidepressant sertraline versus placebo. Using neuroimaging data from a large randomized controlled trial, we found widespread moderation of clinical benefits by brain activity during regulation of emotional conflict, in which greater downregulation of conflict-responsive regions predicted better sertraline outcomes. Treatment-predictive machine learning using brain metrics outperformed a model trained on clinical and demographic variables. Our findings demonstrate that antidepressant response is predicted by brain activity underlying a key self-regulatory emotional capacity. Leveraging brain-based measures in psychiatry will forge a path toward better treatment personalization, refined mechanistic insights and improved outcomes.

Published
2019

14. Neural substrates of emotional conflict with anxiety in major depressive disorder: Findings from the Establishing Moderators and biosignatures of Antidepressant Response in Clinical Care (EMBARC) randomized controlled trial

Author

Trombello, Joseph M., Cooper, Crystal M., Fatt, Cherise Chin, Grannemann, Bruce D., Carmody, Thomas J., Jha, Manish K., Mayes, Taryn L., Greer, Tracy L., Yezhuvath, Uma, Aslan, Sina, Pizzagalli, Diego A., Weissman, Myrna M., Webb, Christian A., Dillon, Daniel G., McGrath, Patrick J., Fava, Maurizio, Parsey, Ramin V., McInnis, Melvin G., Etkin, Amit, and Trivedi, Madhukar H.

Published
2022
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28. Comprehensive phenotyping of depression disease trajectory and risk: Rationale and design of Texas Resilience Against Depression study (T-RAD)

Author

Trivedi, Madhukar H., Chin Fatt, Cherise R., Jha, Manish K., Cooper, Crystal M., Trombello, Joseph M., Mason, Brittany L., Hughes, Jennifer, Gadad, Bharathi S., Czysz, Andrew H., Toll, Russell T., Fuller, Anne K., Sethuram, Sangita, Mayes, Taryn L., Minhajuddin, Abu, Carmody, Thomas, and Greer, Tracy L.

Published
2020
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31. Double-blind, placebo-controlled, dose-ranging trial of intravenous ketamine as adjunctive therapy in treatment-resistant depression (TRD)

Author

Fava, Maurizio, Freeman, Marlene P., Flynn, Martina, Judge, Heidi, Hoeppner, Bettina B., Cusin, Cristina, Ionescu, Dawn F., Mathew, Sanjay J., Chang, Lee C., Iosifescu, Dan V., Murrough, James, Debattista, Charles, Schatzberg, Alan F., Trivedi, Madhukar H., Jha, Manish K, Sanacora, Gerard, Wilkinson, Samuel T., and Papakostas, George I.

Published
2020
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36. Features of immunometabolic depression as predictors of antidepressant treatment outcomes: pooled analysis of four clinical trials.

Author

Vreijling, Sarah R., Chin Fatt, Cherise R., Williams, Leanne M., Schatzberg, Alan F., Usherwood, Tim, Nemeroff, Charles B., Rush, A. John, Uher, Rudolf, Aitchison, Katherine J., Köhler-Forsberg, Ole, Rietschel, Marcella, Trivedi, Madhukar H., Jha, Manish K., Penninx, Brenda W. J. H., Beekman, Aartjan T. F., Jansen, Rick, and Lamers, Femke

Subjects
SEROTONIN uptake inhibitors, TREATMENT effectiveness, ANTIDEPRESSANTS, CLINICAL trials, MENTAL depression
Abstract

Background: Profiling patients on a proposed 'immunometabolic depression' (IMD) dimension, described as a cluster of atypical depressive symptoms related to energy regulation and immunometabolic dysregulations, may optimise personalised treatment. Aims: To test the hypothesis that baseline IMD features predict poorer treatment outcomes with antidepressants. Method: Data on 2551 individuals with depression across the iSPOT-D (n = 967), CO-MED (n = 665), GENDEP (n = 773) and EMBARC (n = 146) clinical trials were used. Predictors included baseline severity of atypical energy-related symptoms (AES), body mass index (BMI) and C-reactive protein levels (CRP, three trials only) separately and aggregated into an IMD index. Mixed models on the primary outcome (change in depressive symptom severity) and logistic regressions on secondary outcomes (response and remission) were conducted for the individual trial data-sets and pooled using random-effects meta-analyses. Results: Although AES severity and BMI did not predict changes in depressive symptom severity, higher baseline CRP predicted smaller reductions in depressive symptoms (n = 376, βpooled = 0.06, P = 0.049, 95% CI 0.0001–0.12, I 2 = 3.61%); this was also found for an IMD index combining these features (n = 372, βpooled = 0.12, s.e. = 0.12, P = 0.031, 95% CI 0.01–0.22, I 2 = 23.91%), with a higher – but still small – effect size compared with CRP. Confining analyses to selective serotonin reuptake inhibitor users indicated larger effects of CRP (βpooled = 0.16) and the IMD index (βpooled = 0.20). Baseline IMD features, both separately and combined, did not predict response or remission. Conclusions: Depressive symptoms of people with more IMD features improved less when treated with antidepressants. However, clinical relevance is limited owing to small effect sizes in inconsistent associations. Whether these patients would benefit more from treatments targeting immunometabolic pathways remains to be investigated. [ABSTRACT FROM AUTHOR]

Published
2024
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41. Characterizing anxiety subtypes and the relationship to behavioral phenotyping in major depression: Results from the EMBARC study

Author

Trombello, Joseph M., Pizzagalli, Diego A., Weissman, Myrna M., Grannemann, Bruce D., Cooper, Crystal M., Greer, Tracy L., Malchow, Ashley L., Jha, Manish K., Carmody, Thomas J., Kurian, Benji T., Webb, Christian A., Dillon, Daniel G., McGrath, Patrick J., Bruder, Gerard, Fava, Maurizio, Parsey, Ramin V., McInnis, Melvin G., Adams, Phil, and Trivedi, Madhukar H.

Published
2018
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43. A Structured Approach to Detecting and Treating Depression in Primary Care: VitalSign6 Project

Author

Jha, Manish K., Grannemann, Bruce D., Trombello, Joseph M., Will Clark, E., Eidelman, Sara Levinson, Lawson, Tiffany, Greer, Tracy L., John Rush, A., and Trivedi, Madhukar H.

Subjects
Major depressive disorder -- Diagnosis -- Care and treatment -- Patient outcomes, Primary health care -- Quality management -- Patient outcomes, Health, Science and technology
Abstract

PURPOSE This report describes outcomes of an ongoing quality-improvement project (VitalSign6) in a large US metropolitan area to improve recognition, treatment, and outcomes of depressed patients in 16 primary care clinics (6 charity clinics, 6 federally qualified health care centers, 2 private clinics serving lowincome populations, and 2 private clinics serving patients with either Medicare or private insurance). METHODS Inclusion in this retrospective analysis was restricted to the first 25,000 patients (aged >12 years) screened with the 2-item Patient Health Questionnaire (PHQ-2) in the aforementioned quality-improvement project. Further evaluations with self-reports and clinician assessments were recorded for those with positive screen (PHQ-2 [greater than or equal to]2). Data collected from August 2014 though November 2016 were available at 3 levels: (1) initial PHQ-2 (n = 25,000), (2) positive screen (n = 4,325), and (3) clinician-diagnosed depressive disorder with 18 or more weeks of enrollment (n = 2,160). RESULTS Overall, 17.3% (4,325/25,000) of patients screened positive for depression. Of positive screens, 56.1% (2,426/4,325) had clinician-diagnosed depressive disorder. Of those enrolled for 18 or more weeks, 64.8% were started on measurement-based pharmacotherapy and 8.9% referred externally. Of the 1,400 patients started on pharmacotherapy, 45.5%, 30.2%, 12.6%, and 11.6% had 0, 1, 2, and 3 or more follow-up visits, respectively. Remission rates were 20.3% (86/423), 31.6% (56/177), and 41.7% (68/163) for those with 1, 2, and 3 or more follow-up visits, respectively. Baseline characteristics associated with higher attrition were: non-white, positive drug-abuse screen, lower depression/anxiety symptom severity, and younger age. CONCLUSION Although remission rates are high in those with 3 or more followup visits after routine screening and treatment of depression, attrition from care is a significant issue adversely affecting outcomes., INTRODUCTION Major Depressive Disorder (MDD) affects 5% to 10% of adults in the United States every year. (1-3) One-half of the patients with MDD seen in medical settings are not [...]

Published
2019
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