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Flint AJ, Banerjee S. Pharmacological treatment of psychotic depression. Lancet Psychiatry 2024; 11:162-164. [PMID: 38360020 DOI: 10.1016/s2215-0366(24)00030-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/18/2024] [Accepted: 01/22/2024] [Indexed: 02/17/2024]
Affiliation(s)
- Alastair J Flint
- Department of Psychiatry, University of Toronto, Toronto, ON, Canada; Centre for Mental Health, University Health Network, Toronto, ON, Canada.
| | - Samprit Banerjee
- Department of Population Health Sciences, Weill Cornell Medicine, New York M5G 2C4, NY, USA
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Neufeld NH, Oliver LD, Mulsant BH, Alexopoulos GS, Hoptman MJ, Tani H, Marino P, Meyers BS, Rothschild AJ, Whyte EM, Bingham KS, Flint AJ, Voineskos AN. Effects of antipsychotic medication on functional connectivity in major depressive disorder with psychotic features. Mol Psychiatry 2023; 28:3305-3313. [PMID: 37258617 DOI: 10.1038/s41380-023-02118-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/09/2023] [Revised: 05/02/2023] [Accepted: 05/11/2023] [Indexed: 06/02/2023]
Abstract
The effect of antipsychotic medication on resting state functional connectivity in major depressive disorder (MDD) is currently unknown. To address this gap, we examined patients with MDD with psychotic features (MDDPsy) participating in the Study of the Pharmacotherapy of Psychotic Depression II. All participants were treated with sertraline plus olanzapine and were subsequently randomized to continue sertraline plus olanzapine or be switched to sertraline plus placebo. Participants completed an MRI at randomization and at study endpoint (study completion at Week 36, relapse, or early termination). The primary outcome was change in functional connectivity measured within and between specified networks and the rest of the brain. The secondary outcome was change in network topology measured by graph metrics. Eighty-eight participants completed a baseline scan; 73 completed a follow-up scan, of which 58 were usable for analyses. There was a significant treatment X time interaction for functional connectivity between the secondary visual network and rest of the brain (t = -3.684; p = 0.0004; pFDR = 0.0111). There was no significant treatment X time interaction for graph metrics. Overall, functional connectivity between the secondary visual network and the rest of the brain did not change in participants who stayed on olanzapine but decreased in those switched to placebo. There were no differences in changes in network topology measures when patients stayed on olanzapine or switched to placebo. This suggests that olanzapine may stabilize functional connectivity, particularly between the secondary visual network and the rest of the brain.
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Affiliation(s)
- Nicholas H Neufeld
- Department of Psychiatry, Temerty Faculty of Medicine, University of Toronto, Toronto, ON, Canada
- Centre for Addiction and Mental Health, Toronto, ON, Canada
| | | | - Benoit H Mulsant
- Department of Psychiatry, Temerty Faculty of Medicine, University of Toronto, Toronto, ON, Canada
- Centre for Addiction and Mental Health, Toronto, ON, Canada
| | - George S Alexopoulos
- Department of Psychiatry, Weill Cornell Medicine, Weill Cornell Medical College, Westchester Behavioral Health Center, White Plains, NY, USA
| | - Matthew J Hoptman
- Division of Clinical Research, Nathan S. Kline Institute for Psychiatric Research, Orangeburg, NY, USA
- Department of Psychiatry, New York University Grossman School of Medicine, New York, NY, USA
| | - Hideaki Tani
- Centre for Addiction and Mental Health, Toronto, ON, Canada
- Department of Neuropsychiatry, Keio University School of Medicine, Tokyo, Japan
| | - Patricia Marino
- Department of Psychiatry, Weill Cornell Medicine, Weill Cornell Medical College, Westchester Behavioral Health Center, White Plains, NY, USA
| | - Barnett S Meyers
- Department of Psychiatry, Weill Cornell Medicine, Weill Cornell Medical College, Westchester Behavioral Health Center, White Plains, NY, USA
| | - Anthony J Rothschild
- Department of Psychiatry, University of Massachusetts Chan Medical School and UMass Memorial Health Care, Worcester, MA, USA
| | - Ellen M Whyte
- Department of Psychiatry, University of Pittsburgh School of Medicine and UPMC Western Psychiatric Hospital, Pittsburgh, PA, USA
| | - Kathleen S Bingham
- Department of Psychiatry, Temerty Faculty of Medicine, University of Toronto, Toronto, ON, Canada
- Centre for Mental Health, University Health Network, Toronto, ON, Canada
| | - Alastair J Flint
- Department of Psychiatry, Temerty Faculty of Medicine, University of Toronto, Toronto, ON, Canada
- Centre for Mental Health, University Health Network, Toronto, ON, Canada
| | - Aristotle N Voineskos
- Department of Psychiatry, Temerty Faculty of Medicine, University of Toronto, Toronto, ON, Canada.
- Centre for Addiction and Mental Health, Toronto, ON, Canada.
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Yasin S, Othmani A, Raza I, Hussain SA. Machine learning based approaches for clinical and non-clinical depression recognition and depression relapse prediction using audiovisual and EEG modalities: A comprehensive review. Comput Biol Med 2023; 159:106741. [PMID: 37105109 DOI: 10.1016/j.compbiomed.2023.106741] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2022] [Revised: 02/25/2023] [Accepted: 03/02/2023] [Indexed: 03/07/2023]
Abstract
Mental disorders are rapidly increasing each year and have become a major challenge affecting the social and financial well-being of individuals. There is a need for phenotypic characterization of psychiatric disorders with biomarkers to provide a rich signature for Major Depressive Disorder, improving the understanding of the pathophysiological mechanisms underlying these mental disorders. This comprehensive review focuses on depression and relapse detection modalities such as self-questionnaires, audiovisuals, and EEG, highlighting noteworthy publications in the last ten years. The article concentrates on the literature that adopts machine learning by audiovisual and EEG signals. It also outlines preprocessing, feature extraction, and public datasets for depression detection. The review concludes with recommendations that will help improve the reliability of developed models and the determinism of computational intelligence-based systems in psychiatry. To the best of our knowledge, this survey is the first comprehensive review on depression and relapse prediction by self-questionnaires, audiovisual, and EEG-based approaches. The findings of this review will serve as a useful and structured starting point for researchers studying clinical and non-clinical depression recognition and relapse through machine learning-based approaches.
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Affiliation(s)
- Sana Yasin
- Department of Computer Science, COMSATS University Islamabad, Lahore Campus Lahore, Pakistan; Department of Computer Science, University of Okara, Okara, Pakistan.
| | - Alice Othmani
- Université Paris-Est Créteil (UPEC), LISSI, Vitry sur Seine, 94400, France.
| | - Imran Raza
- Department of Computer Science, COMSATS University Islamabad, Lahore Campus Lahore, Pakistan.
| | - Syed Asad Hussain
- Department of Computer Science, COMSATS University Islamabad, Lahore Campus Lahore, Pakistan.
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