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Fennema D, Barker GJ, O’Daly O, Duan S, Carr E, Goldsmith K, Young AH, Moll J, Zahn R. The Role of Subgenual Resting-State Connectivity Networks in Predicting Prognosis in Major Depressive Disorder. BIOLOGICAL PSYCHIATRY GLOBAL OPEN SCIENCE 2024; 4:100308. [PMID: 38645404 PMCID: PMC11033067 DOI: 10.1016/j.bpsgos.2024.100308] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2023] [Revised: 12/18/2023] [Accepted: 03/05/2024] [Indexed: 04/23/2024] Open
Abstract
Background A seminal study found higher subgenual frontal cortex resting-state connectivity with 2 left ventral frontal regions and the dorsal midbrain to predict better response to psychotherapy versus medication in individuals with treatment-naïve major depressive disorder (MDD). Here, we examined whether these subgenual networks also play a role in the pathophysiology of clinical outcomes in MDD with early treatment resistance in primary care. Methods Forty-five people with current MDD who had not responded to ≥2 serotonergic antidepressants (n = 43, meeting predefined functional magnetic resonance imaging minimum quality thresholds) were enrolled and followed over 4 months of standard care. Functional magnetic resonance imaging resting-state connectivity between the preregistered subgenual frontal cortex seed and 3 previously identified left ventromedial, ventrolateral prefrontal/insula, and dorsal midbrain regions was extracted. The clinical outcome was the percentage change on the self-reported 16-item Quick Inventory of Depressive Symptomatology. Results We observed a reversal of our preregistered hypothesis in that higher resting-state connectivity between the subgenual cortex and the a priori ventrolateral prefrontal/insula region predicted favorable rather than unfavorable clinical outcomes (rs39 = -0.43, p = .006). This generalized to the sample including participants with suboptimal functional magnetic resonance imaging quality (rs43 = -0.35, p = .02). In contrast, no effects (rs39 = 0.12, rs39 = -0.01) were found for connectivity with the other 2 preregistered regions or in a whole-brain analysis (voxel-based familywise error-corrected p < .05). Conclusions Subgenual connectivity with the ventrolateral prefrontal cortex/insula is relevant for subsequent clinical outcomes in current MDD with early treatment resistance. Its positive association with favorable outcomes could be explained primarily by psychosocial rather than the expected pharmacological changes during the follow-up period.
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Affiliation(s)
- Diede Fennema
- Centre of Affective Disorders, Institute of Psychiatry, Psychology and Neuroscience, Centre for Affective Disorders, King’s College London, London, United Kingdom
| | - Gareth J. Barker
- Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, United Kingdom
| | - Owen O’Daly
- Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, United Kingdom
| | - Suqian Duan
- Centre of Affective Disorders, Institute of Psychiatry, Psychology and Neuroscience, Centre for Affective Disorders, King’s College London, London, United Kingdom
| | - Ewan Carr
- Department of Biostatics and Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, United Kingdom
| | - Kimberley Goldsmith
- Department of Biostatics and Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, United Kingdom
| | - Allan H. Young
- Centre of Affective Disorders, Institute of Psychiatry, Psychology and Neuroscience, Centre for Affective Disorders, King’s College London, London, United Kingdom
- National Service for Affective Disorders, South London and Maudsley National Health Service Foundation Trust, London, United Kingdom
| | - Jorge Moll
- Cognitive and Behavioural Neuroscience Unit, D’Or Institute for Research and Education, Rio de Janeiro, Brazil
| | - Roland Zahn
- Centre of Affective Disorders, Institute of Psychiatry, Psychology and Neuroscience, Centre for Affective Disorders, King’s College London, London, United Kingdom
- Cognitive and Behavioural Neuroscience Unit, D’Or Institute for Research and Education, Rio de Janeiro, Brazil
- National Service for Affective Disorders, South London and Maudsley National Health Service Foundation Trust, London, United Kingdom
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Fennema D, Barker GJ, O'Daly O, Duan S, Carr E, Goldsmith K, Young AH, Moll J, Zahn R. Self-blame-selective hyper-connectivity between anterior temporal and subgenual cortices predicts prognosis in major depressive disorder. Neuroimage Clin 2023; 39:103453. [PMID: 37352570 PMCID: PMC10336192 DOI: 10.1016/j.nicl.2023.103453] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2023] [Revised: 06/12/2023] [Accepted: 06/13/2023] [Indexed: 06/25/2023]
Abstract
BACKGROUND Self-blame-related fMRI measures were shown to predict subsequent recurrence in remitted major depressive disorder (MDD). Their role in current MDD, however, is unknown. We hypothesised that these neural signatures reflect a highly recurrent but remitting course of MDD and therefore predict favourable outcomes over a four-month follow-up period in current MDD. METHODS Forty-five participants with current MDD and non-responders to at least two serotonergic antidepressants, were encouraged to optimise their medication and followed up after receiving four months of primary care treatment-as-usual. Prior to their medication review, participants completed an fMRI paradigm in which they viewed self- and other-blame emotion-evoking statements. Thirty-nine participants met pre-defined fMRI data minimum quality thresholds. Psychophysiological interaction analysis was used to determine baseline connectivity of the right superior anterior temporal lobe (RSATL), with an a priori BA25 region-of-interest for self-blaming vs other-blaming emotions, using Quick Inventory of Depressive Symptomatology (16-item) percentage change as a covariate. RESULTS We corroborated our pre-registered hypothesis that a favourable clinical outcome was associated with higher self-blame-selective RSATL-BA25 connectivity (Family-Wise Error-corrected p <.05 over the a priori BA25 region-of-interest; rs(34) = -0.47, p =.005). This generalised to the sample including participants with suboptimal fMRI quality (rs(39) = -0.32, p =.05). CONCLUSIONS This study shows that neural signatures of overgeneralised self-blame are relevant for prognostic stratification of current treatment-resistant MDD. Future studies need to confirm whether this neural signature indeed represents a trait-like feature of a fully remitting subtype of MDD, or whether it is also modulated by depressive state and related to treatment effects.
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Affiliation(s)
- Diede Fennema
- Centre of Affective Disorders, Institute of Psychiatry, Psychology & Neuroscience, Centre for Affective Disorders, King's College London, London, UK
| | - Gareth J Barker
- Department of Neuroimaging, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
| | - Owen O'Daly
- Department of Neuroimaging, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
| | - Suqian Duan
- Centre of Affective Disorders, Institute of Psychiatry, Psychology & Neuroscience, Centre for Affective Disorders, King's College London, London, UK
| | - Ewan Carr
- Department of Biostatics and Health Informatics, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
| | - Kimberley Goldsmith
- Department of Biostatics and Health Informatics, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
| | - Allan H Young
- Centre of Affective Disorders, Institute of Psychiatry, Psychology & Neuroscience, Centre for Affective Disorders, King's College London, London, UK; National Service for Affective Disorders, South London and Maudsley NHS Foundation Trust, London, UK
| | - Jorge Moll
- Cognitive and Behavioural Neuroscience Unit, D'Or Institute for Research and Education (IDOR), Rio de Janeiro, Brazil
| | - Roland Zahn
- Centre of Affective Disorders, Institute of Psychiatry, Psychology & Neuroscience, Centre for Affective Disorders, King's College London, London, UK; Cognitive and Behavioural Neuroscience Unit, D'Or Institute for Research and Education (IDOR), Rio de Janeiro, Brazil; National Service for Affective Disorders, South London and Maudsley NHS Foundation Trust, London, UK.
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Tsai YC, Li CT, Juan CH. A review of critical brain oscillations in depression and the efficacy of transcranial magnetic stimulation treatment. Front Psychiatry 2023; 14:1073984. [PMID: 37260762 PMCID: PMC10228658 DOI: 10.3389/fpsyt.2023.1073984] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/19/2022] [Accepted: 04/11/2023] [Indexed: 06/02/2023] Open
Abstract
Repetitive transcranial magnetic stimulation (rTMS) and intermittent theta burst stimulation (iTBS) have been proven effective non-invasive treatments for patients with drug-resistant major depressive disorder (MDD). However, some depressed patients do not respond to these treatments. Therefore, the investigation of reliable and valid brain oscillations as potential indices for facilitating the precision of diagnosis and treatment protocols has become a critical issue. The current review focuses on brain oscillations that, mostly based on EEG power analysis and connectivity, distinguish between MDD and controls, responders and non-responders, and potential depression severity indices, prognostic indicators, and potential biomarkers for rTMS or iTBS treatment. The possible roles of each biomarker and the potential reasons for heterogeneous results are discussed, and the directions of future studies are proposed.
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Affiliation(s)
- Yi-Chun Tsai
- Institute of Cognitive Neuroscience, College of Health Sciences and Technology, National Central University, Taoyuan City, Taiwan
| | - Cheng-Ta Li
- Institute of Cognitive Neuroscience, College of Health Sciences and Technology, National Central University, Taoyuan City, Taiwan
- Department of Psychiatry, Taipei Veterans General Hospital, Taipei, Taiwan
- Institute of Brain Science, National Yang-Ming Chiao-Tung University, Taipei, Taiwan
- Division of Psychiatry, Faculty of Medicine, National Yang-Ming Chiao-Tung University, Taipei, Taiwan
| | - Chi-Hung Juan
- Institute of Cognitive Neuroscience, College of Health Sciences and Technology, National Central University, Taoyuan City, Taiwan
- Cognitive Intelligence and Precision Healthcare Center, National Central University, Taoyuan City, Taiwan
- Department of Psychology, Kaohsiung Medical University, Kaohsiung, Taiwan
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Weigand A, Gärtner M, Scheidegger M, Wyss PO, Henning A, Seifritz E, Stippl A, Herrera-Melendez A, Bajbouj M, Aust S, Grimm S. Predicting Antidepressant Effects of Ketamine: the Role of the Pregenual Anterior Cingulate Cortex as a Multimodal Neuroimaging Biomarker. Int J Neuropsychopharmacol 2022; 25:1003-1013. [PMID: 35948274 PMCID: PMC9743970 DOI: 10.1093/ijnp/pyac049] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/02/2022] [Revised: 07/15/2022] [Accepted: 08/10/2022] [Indexed: 01/07/2023] Open
Abstract
BACKGROUND Growing evidence underscores the utility of ketamine as an effective and rapid-acting treatment option for major depressive disorder (MDD). However, clinical outcomes vary between patients. Predicting successful response may enable personalized treatment decisions and increase clinical efficacy. METHODS We here explored the potential of pregenual anterior cingulate cortex (pgACC) activity to predict antidepressant effects of ketamine in relation to ketamine-induced changes in glutamatergic metabolism. Prior to a single i.v. infusion of ketamine, 24 patients with MDD underwent functional magnetic resonance imaging during an emotional picture-viewing task and magnetic resonance spectroscopy. Changes in depressive symptoms were evaluated using the Beck Depression Inventory measured 24 hours pre- and post-intervention. A subsample of 17 patients underwent a follow-up magnetic resonance spectroscopy scan. RESULTS Antidepressant efficacy of ketamine was predicted by pgACC activity during emotional stimulation. In addition, pgACC activity was associated with glutamate increase 24 hours after the ketamine infusion, which was in turn related to better clinical outcome. CONCLUSIONS Our results add to the growing literature implicating a key role of the pgACC in mediating antidepressant effects and highlighting its potential as a multimodal neuroimaging biomarker of early treatment response to ketamine.
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Affiliation(s)
| | | | - Milan Scheidegger
- Department of Psychiatry, Psychotherapy and Psychosomatics, University Hospital of Psychiatry, University of Zurich, Switzerland
| | - Patrik O Wyss
- Department of Radiology, Swiss Paraplegic Centre, Nottwil, Switzerland
| | - Anke Henning
- Advanced Imaging Research Center, UT Southwestern Medical Center, Dallas, Texas, USA
| | - Erich Seifritz
- Department of Psychiatry, Psychotherapy and Psychosomatics, University Hospital of Psychiatry, University of Zurich, Switzerland
| | - Anna Stippl
- Department of Psychiatry and Psychotherapy, Charité – Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin and Berlin Institute of Health, Berlin, Germany
| | - Ana Herrera-Melendez
- Department of Psychiatry and Psychotherapy, Charité – Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin and Berlin Institute of Health, Berlin, Germany
| | - Malek Bajbouj
- Department of Psychiatry and Psychotherapy, Charité – Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin and Berlin Institute of Health, Berlin, Germany
| | - Sabine Aust
- Department of Psychiatry and Psychotherapy, Charité – Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin and Berlin Institute of Health, Berlin, Germany
| | - Simone Grimm
- Correspondence: Simone Grimm, PhD, MSB Medical School Berlin, Rüdesheimer Straße 50, 14197 Berlin, Germany ()
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Gärtner M, Weigand A, Scheidegger M, Lehmann M, Wyss PO, Wunder A, Henning A, Grimm S. Acute effects of ketamine on the pregenual anterior cingulate: linking spontaneous activation, functional connectivity, and glutamate metabolism. Eur Arch Psychiatry Clin Neurosci 2022; 272:703-714. [PMID: 35020021 PMCID: PMC9095553 DOI: 10.1007/s00406-021-01377-2] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/13/2021] [Accepted: 12/16/2021] [Indexed: 11/29/2022]
Abstract
Ketamine exerts its rapid antidepressant effects via modulation of the glutamatergic system. While numerous imaging studies have investigated the effects of ketamine on a functional macroscopic brain level, it remains unclear how altered glutamate metabolism and changes in brain function are linked. To shed light on this topic we here conducted a multimodal imaging study in healthy volunteers (N = 23) using resting state fMRI and proton (1H) magnetic resonance spectroscopy (MRS) to investigate linkage between metabolic and functional brain changes induced by ketamine. Subjects were investigated before and during an intravenous ketamine infusion. The MRS voxel was placed in the pregenual anterior cingulate cortex (pgACC), as this region has been repeatedly shown to be involved in ketamine's effects. Our results showed functional connectivity changes from the pgACC to the right frontal pole and anterior mid cingulate cortex (aMCC). Absolute glutamate and glutamine concentrations in the pgACC did not differ significantly from baseline. However, we found that stronger pgACC activation during ketamine was linked to lower glutamine concentration in this region. Furthermore, reduced functional connectivity between pgACC and aMCC was related to increased pgACC activation and reduced glutamine. Our results thereby demonstrate how multimodal investigations in a single brain region could help to advance our understanding of the association between metabolic and functional changes.
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Affiliation(s)
- Matti Gärtner
- MSB Medical School Berlin, Rüdesheimer Straße 50, 14197, Berlin, Germany. .,Department of Psychiatry and Psychotherapy, Charité, Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität Zu Berlin, Hindenburgdamm 30, 12203, Berlin, Germany.
| | - Anne Weigand
- grid.466457.20000 0004 1794 7698MSB Medical School Berlin, Rüdesheimer Straße 50, 14197 Berlin, Germany
| | - Milan Scheidegger
- grid.7400.30000 0004 1937 0650Department of Psychiatry, Psychotherapy and Psychosomatics, University Hospital of Psychiatry, University of Zurich, Zurich, Switzerland
| | - Mick Lehmann
- grid.7400.30000 0004 1937 0650Department of Psychiatry, Psychotherapy and Psychosomatics, University Hospital of Psychiatry, University of Zurich, Zurich, Switzerland
| | - Patrik O. Wyss
- grid.419769.40000 0004 0627 6016Department of Radiology, Swiss Paraplegic Centre, Nottwil, Switzerland
| | - Andreas Wunder
- grid.420061.10000 0001 2171 7500Translational Medicine and Clinical Pharmacology, Boehringer Ingelheim Pharma GmbH & Co. KG, 88397 Biberach an der Riss, Germany
| | - Anke Henning
- grid.267313.20000 0000 9482 7121Advanced Imaging Research Center, UT Southwestern Medical Center, Dallas, TX USA
| | - Simone Grimm
- grid.466457.20000 0004 1794 7698MSB Medical School Berlin, Rüdesheimer Straße 50, 14197 Berlin, Germany ,grid.6363.00000 0001 2218 4662Department of Psychiatry and Psychotherapy, Charité, Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität Zu Berlin, Hindenburgdamm 30, 12203 Berlin, Germany ,grid.7400.30000 0004 1937 0650Department of Psychiatry, Psychotherapy and Psychosomatics, University Hospital of Psychiatry, University of Zurich, Zurich, Switzerland
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Lin E, Lin CH, Lane HY. Machine Learning and Deep Learning for the Pharmacogenomics of Antidepressant Treatments. CLINICAL PSYCHOPHARMACOLOGY AND NEUROSCIENCE : THE OFFICIAL SCIENTIFIC JOURNAL OF THE KOREAN COLLEGE OF NEUROPSYCHOPHARMACOLOGY 2021; 19:577-588. [PMID: 34690113 PMCID: PMC8553527 DOI: 10.9758/cpn.2021.19.4.577] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/09/2021] [Accepted: 04/10/2021] [Indexed: 12/31/2022]
Abstract
A growing body of evidence now proposes that machine learning and deep learning techniques can serve as a vital foundation for the pharmacogenomics of antidepressant treatments in patients with major depressive disorder (MDD). In this review, we focus on the latest developments for pharmacogenomics research using machine learning and deep learning approaches together with neuroimaging and multi-omics data. First, we review relevant pharmacogenomics studies that leverage numerous machine learning and deep learning techniques to determine treatment prediction and potential biomarkers for antidepressant treatments in MDD. In addition, we depict some neuroimaging pharmacogenomics studies that utilize various machine learning approaches to predict antidepressant treatment outcomes in MDD based on the integration of research on pharmacogenomics and neuroimaging. Moreover, we summarize the limitations in regard to the past pharmacogenomics studies of antidepressant treatments in MDD. Finally, we outline a discussion of challenges and directions for future research. In light of latest advancements in neuroimaging and multi-omics, various genomic variants and biomarkers associated with antidepressant treatments in MDD are being identified in pharmacogenomics research by employing machine learning and deep learning algorithms.
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Affiliation(s)
- Eugene Lin
- Department of Biostatistics, University of Washington, Seattle, WA, USA
- Department of Electrical & Computer Engineering, University of Washington, Seattle, WA, USA
- Graduate Institute of Biomedical Sciences, China Medical University, Taichung, Taiwan
| | - Chieh-Hsin Lin
- Graduate Institute of Biomedical Sciences, China Medical University, Taichung, Taiwan
- Department of Psychiatry, Kaohsiung Chang Gung Memorial Hospital, Chang Gung University College of Medicine, Kaohsiung, Taiwan
- School of Medicine, Chang Gung University, Taoyuan, Taiwan
| | - Hsien-Yuan Lane
- Graduate Institute of Biomedical Sciences, China Medical University, Taichung, Taiwan
- Department of Psychiatry, China Medical University Hospital, Taichung, Taiwan
- Department of Brain Disease Research Center, China Medical University Hospital, Taichung, Taiwan
- Department of Psychology, College of Medical and Health Sciences, Asia University, Taichung, Taiwan
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