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Köhler-Forsberg K, Dam VH, Ozenne B, Sankar A, Beliveau V, Landman EB, Larsen SV, Poulsen AS, Ip CT, Jørgensen A, Meyer M, Stenbæk DS, Eiberg HRL, Madsen J, Svarer C, Jørgensen MB, Frokjaer VG, Knudsen GM. Serotonin 4 Receptor Brain Binding in Major Depressive Disorder and Association With Memory Dysfunction. JAMA Psychiatry 2023; 80:296-304. [PMID: 36753296 PMCID: PMC9909578 DOI: 10.1001/jamapsychiatry.2022.4539] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/01/2022] [Accepted: 11/05/2022] [Indexed: 02/09/2023]
Abstract
Importance The cerebral serotonin 4 (5-HT4) receptor is a promising novel target for treatment of major depressive disorder (MDD), and pharmacological stimulation of the 5-HT4 receptor has been associated with improved learning and memory in healthy individuals. Objective To map the neurobiological signatures of patients with untreated MDD compared with healthy controls and to examine the association between cerebral 5-HT4 receptor binding and cognitive functions in the depressed state. Design, Setting, and Participants This case-control study used baseline data from the NeuroPharm clinical depression trial in Denmark. Adult participants included antidepressant-free outpatients with a current moderate to severe depressive episode and healthy controls. All participants completed positron emission tomography (PET) scanning with [11C]SB207145 for quantification of brain 5-HT4 receptor binding, but only the patients underwent cognitive testing. Data analyses were performed from January 21, 2020, to April 22, 2022. Main Outcomes and Measures The main study outcome was the group difference in cerebral 5-HT4 receptor binding between patients with MDD and healthy controls. In addition, the association between 5-HT4 receptor binding and verbal memory performance in the patient group was tested. Other cognitive domains (working memory, reaction time, emotion recognition bias, and negative social emotions) were assessed as secondary outcomes. Results A total of 90 patients with untreated MDD (mean [SD] age, 27.1 [8.2] years; 64 women [71.1%]) and 91 healthy controls (mean [SD] age, 27.1 [8.0] years; 55 women [60.4%]) were included in the analysis. Patients with current MDD had significantly lower cerebral 5-HT4 receptor binding than healthy controls (-7.0%; 95% CI, -11.2 to -2.7; P = .002). In patients with MDD, there was a correlation between cerebral 5-HT4 receptor binding and verbal memory (r = 0.29; P = .02). Conclusions and Relevance Results of this study show that cerebral 5-HT4 receptor binding was lower in patients with MDD than in healthy controls and that the memory dysfunction in patients with MDD was associated with lower cerebral 5-HT4 receptor binding. The cerebral 5-HT4 receptor is a promising treatment target for memory dysfunction in patients with MDD.
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Affiliation(s)
- Kristin Köhler-Forsberg
- Neurobiology Research Unit, Copenhagen University Hospital Rigshospitalet, Copenhagen, Denmark
- Department of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark
- Psychiatric Center Copenhagen, Rigshospitalet, Copenhagen, Denmark
| | - Vibeke H. Dam
- Neurobiology Research Unit, Copenhagen University Hospital Rigshospitalet, Copenhagen, Denmark
- Department of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark
| | - Brice Ozenne
- Neurobiology Research Unit, Copenhagen University Hospital Rigshospitalet, Copenhagen, Denmark
- Department of Public Health, Section of Biostatistics, University of Copenhagen, Copenhagen, Denmark
| | - Anjali Sankar
- Neurobiology Research Unit, Copenhagen University Hospital Rigshospitalet, Copenhagen, Denmark
| | - Vincent Beliveau
- Neurobiology Research Unit, Copenhagen University Hospital Rigshospitalet, Copenhagen, Denmark
- Department of Neurology, Medical University of Innsbruck, Innsbruck, Austria
| | - Elizabeth B. Landman
- Neurobiology Research Unit, Copenhagen University Hospital Rigshospitalet, Copenhagen, Denmark
| | - Søren V. Larsen
- Neurobiology Research Unit, Copenhagen University Hospital Rigshospitalet, Copenhagen, Denmark
- Department of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark
| | - Asbjørn S. Poulsen
- Neurobiology Research Unit, Copenhagen University Hospital Rigshospitalet, Copenhagen, Denmark
| | - Cheng-Teng Ip
- Neurobiology Research Unit, Copenhagen University Hospital Rigshospitalet, Copenhagen, Denmark
- Department of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark
- Department of Clinical Pharmacology, H. Lundbeck A/S, Valby, Denmark
| | - Anders Jørgensen
- Department of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark
- Psychiatric Center Copenhagen, Rigshospitalet, Copenhagen, Denmark
| | - Michal Meyer
- Center for Referral and Diagnostics, Mental Health Services, Capital Region of Denmark, Copenhagen, Denmark
| | - Dea S. Stenbæk
- Neurobiology Research Unit, Copenhagen University Hospital Rigshospitalet, Copenhagen, Denmark
- Department of Psychology, University of Copenhagen, Copenhagen, Denmark
| | - Hans R. L. Eiberg
- Department of Cellular and Molecular Medicine, University of Copenhagen, Copenhagen, Denmark
| | - Jacob Madsen
- Department of Clinical Physiology and Nuclear Medicine, Copenhagen University Hospital Rigshospitalet, Copenhagen, Denmark
| | - Claus Svarer
- Neurobiology Research Unit, Copenhagen University Hospital Rigshospitalet, Copenhagen, Denmark
| | - Martin B. Jørgensen
- Neurobiology Research Unit, Copenhagen University Hospital Rigshospitalet, Copenhagen, Denmark
- Department of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark
- Psychiatric Center Copenhagen, Rigshospitalet, Copenhagen, Denmark
| | - Vibe G. Frokjaer
- Neurobiology Research Unit, Copenhagen University Hospital Rigshospitalet, Copenhagen, Denmark
- Department of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark
- Psychiatric Center Copenhagen, Rigshospitalet, Copenhagen, Denmark
| | - Gitte M. Knudsen
- Neurobiology Research Unit, Copenhagen University Hospital Rigshospitalet, Copenhagen, Denmark
- Department of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark
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2
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Jensen KHR, Dam VH, Ganz M, Fisher PM, Ip CT, Sankar A, Marstrand-Joergensen MR, Ozenne B, Osler M, Penninx BWJH, Pinborg LH, Frokjaer VG, Knudsen GM, Jørgensen MB. Deep phenotyping towards precision psychiatry of first-episode depression - the Brain Drugs-Depression cohort. BMC Psychiatry 2023; 23:151. [PMID: 36894940 PMCID: PMC9999625 DOI: 10.1186/s12888-023-04618-x] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/12/2022] [Accepted: 02/19/2023] [Indexed: 03/11/2023] Open
Abstract
BACKGROUND Major Depressive Disorder (MDD) is a heterogenous brain disorder, with potentially multiple psychosocial and biological disease mechanisms. This is also a plausible explanation for why patients do not respond equally well to treatment with first- or second-line antidepressants, i.e., one-third to one-half of patients do not remit in response to first- or second-line treatment. To map MDD heterogeneity and markers of treatment response to enable a precision medicine approach, we will acquire several possible predictive markers across several domains, e.g., psychosocial, biochemical, and neuroimaging. METHODS All patients are examined before receiving a standardised treatment package for adults aged 18-65 with first-episode depression in six public outpatient clinics in the Capital Region of Denmark. From this population, we will recruit a cohort of 800 patients for whom we will acquire clinical, cognitive, psychometric, and biological data. A subgroup (subcohort I, n = 600) will additionally provide neuroimaging data, i.e., Magnetic Resonance Imaging, and Electroencephalogram, and a subgroup of patients from subcohort I unmedicated at inclusion (subcohort II, n = 60) will also undergo a brain Positron Emission Tomography with the [11C]-UCB-J tracer binding to the presynaptic glycoprotein-SV2A. Subcohort allocation is based on eligibility and willingness to participate. The treatment package typically lasts six months. Depression severity is assessed with the Quick Inventory of Depressive Symptomatology (QIDS) at baseline, and 6, 12 and 18 months after treatment initiation. The primary outcome is remission (QIDS ≤ 5) and clinical improvement (≥ 50% reduction in QIDS) after 6 months. Secondary endpoints include remission at 12 and 18 months and %-change in QIDS, 10-item Symptom Checklist, 5-item WHO Well-Being Index, and modified Disability Scale from baseline through follow-up. We also assess psychotherapy and medication side-effects. We will use machine learning to determine a combination of characteristics that best predict treatment outcomes and statistical models to investigate the association between individual measures and clinical outcomes. We will assess associations between patient characteristics, treatment choices, and clinical outcomes using path analysis, enabling us to estimate the effect of treatment choices and timing on the clinical outcome. DISCUSSION The BrainDrugs-Depression study is a real-world deep-phenotyping clinical cohort study of first-episode MDD patients. TRIAL REGISTRATION Registered at clinicaltrials.gov November 15th, 2022 (NCT05616559).
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Affiliation(s)
- Kristian Høj Reveles Jensen
- BrainDrugs, Copenhagen University Hospital Rigshospitalet, Copenhagen, Denmark.,Neurobiology Research Unit, Copenhagen University Hospital, Rigshospitalet, Copenhagen, Denmark.,Department of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark.,Psychiatric Centre Copenhagen, Copenhagen, Denmark
| | - Vibeke H Dam
- BrainDrugs, Copenhagen University Hospital Rigshospitalet, Copenhagen, Denmark.,Neurobiology Research Unit, Copenhagen University Hospital, Rigshospitalet, Copenhagen, Denmark
| | - Melanie Ganz
- Neurobiology Research Unit, Copenhagen University Hospital, Rigshospitalet, Copenhagen, Denmark.,Department of Computer Science, University of Copenhagen, Copenhagen, Denmark
| | - Patrick MacDonald Fisher
- BrainDrugs, Copenhagen University Hospital Rigshospitalet, Copenhagen, Denmark.,Neurobiology Research Unit, Copenhagen University Hospital, Rigshospitalet, Copenhagen, Denmark
| | - Cheng-Teng Ip
- Neurobiology Research Unit, Copenhagen University Hospital, Rigshospitalet, Copenhagen, Denmark.,Center for Cognitive and Brain Sciences, University of Macau, Taipa, Macau SAR, China
| | - Anjali Sankar
- BrainDrugs, Copenhagen University Hospital Rigshospitalet, Copenhagen, Denmark.,Neurobiology Research Unit, Copenhagen University Hospital, Rigshospitalet, Copenhagen, Denmark
| | - Maja Rou Marstrand-Joergensen
- BrainDrugs, Copenhagen University Hospital Rigshospitalet, Copenhagen, Denmark.,Neurobiology Research Unit, Copenhagen University Hospital, Rigshospitalet, Copenhagen, Denmark.,Department of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark.,Department of Neurology, Copenhagen University Hospital Rigshospitalet, Copenhagen, Denmark
| | - Brice Ozenne
- Neurobiology Research Unit, Copenhagen University Hospital, Rigshospitalet, Copenhagen, Denmark.,Department of Public Health, Section of Biostatistics, University of Copenhagen, Copenhagen, Denmark
| | - Merete Osler
- Center for Clinical Research and Prevention, Bispebjerg & Frederiksberg Hospitals, Copenhagen, Denmark.,Department of Public Health, Section of Epidemiology, University of Copenhagen, Copenhagen, Denmark
| | - Brenda W J H Penninx
- Department of Psychiatry, Amsterdam UMC, Vrije Universiteit, Amsterdam, the Netherlands
| | - Lars H Pinborg
- BrainDrugs, Copenhagen University Hospital Rigshospitalet, Copenhagen, Denmark.,Neurobiology Research Unit, Copenhagen University Hospital, Rigshospitalet, Copenhagen, Denmark.,Department of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark.,Department of Neurology, Copenhagen University Hospital Rigshospitalet, Copenhagen, Denmark
| | - Vibe Gedsø Frokjaer
- BrainDrugs, Copenhagen University Hospital Rigshospitalet, Copenhagen, Denmark.,Neurobiology Research Unit, Copenhagen University Hospital, Rigshospitalet, Copenhagen, Denmark.,Department of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark.,Psychiatric Centre Copenhagen, Copenhagen, Denmark
| | - Gitte Moos Knudsen
- BrainDrugs, Copenhagen University Hospital Rigshospitalet, Copenhagen, Denmark.,Neurobiology Research Unit, Copenhagen University Hospital, Rigshospitalet, Copenhagen, Denmark.,Department of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark.,Department of Neurology, Copenhagen University Hospital Rigshospitalet, Copenhagen, Denmark
| | - Martin Balslev Jørgensen
- BrainDrugs, Copenhagen University Hospital Rigshospitalet, Copenhagen, Denmark. .,Neurobiology Research Unit, Copenhagen University Hospital, Rigshospitalet, Copenhagen, Denmark. .,Department of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark. .,Psychiatric Centre Copenhagen, Copenhagen, Denmark.
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3
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Fu CHY, Erus G, Fan Y, Antoniades M, Arnone D, Arnott SR, Chen T, Choi KS, Fatt CC, Frey BN, Frokjaer VG, Ganz M, Garcia J, Godlewska BR, Hassel S, Ho K, McIntosh AM, Qin K, Rotzinger S, Sacchet MD, Savitz J, Shou H, Singh A, Stolicyn A, Strigo I, Strother SC, Tosun D, Victor TA, Wei D, Wise T, Woodham RD, Zahn R, Anderson IM, Deakin JFW, Dunlop BW, Elliott R, Gong Q, Gotlib IH, Harmer CJ, Kennedy SH, Knudsen GM, Mayberg HS, Paulus MP, Qiu J, Trivedi MH, Whalley HC, Yan CG, Young AH, Davatzikos C. AI-based dimensional neuroimaging system for characterizing heterogeneity in brain structure and function in major depressive disorder: COORDINATE-MDD consortium design and rationale. BMC Psychiatry 2023; 23:59. [PMID: 36690972 PMCID: PMC9869598 DOI: 10.1186/s12888-022-04509-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/29/2022] [Accepted: 12/29/2022] [Indexed: 01/24/2023] Open
Abstract
BACKGROUND Efforts to develop neuroimaging-based biomarkers in major depressive disorder (MDD), at the individual level, have been limited to date. As diagnostic criteria are currently symptom-based, MDD is conceptualized as a disorder rather than a disease with a known etiology; further, neural measures are often confounded by medication status and heterogeneous symptom states. METHODS We describe a consortium to quantify neuroanatomical and neurofunctional heterogeneity via the dimensions of novel multivariate coordinate system (COORDINATE-MDD). Utilizing imaging harmonization and machine learning methods in a large cohort of medication-free, deeply phenotyped MDD participants, patterns of brain alteration are defined in replicable and neurobiologically-based dimensions and offer the potential to predict treatment response at the individual level. International datasets are being shared from multi-ethnic community populations, first episode and recurrent MDD, which are medication-free, in a current depressive episode with prospective longitudinal treatment outcomes and in remission. Neuroimaging data consist of de-identified, individual, structural MRI and resting-state functional MRI with additional positron emission tomography (PET) data at specific sites. State-of-the-art analytic methods include automated image processing for extraction of anatomical and functional imaging variables, statistical harmonization of imaging variables to account for site and scanner variations, and semi-supervised machine learning methods that identify dominant patterns associated with MDD from neural structure and function in healthy participants. RESULTS We are applying an iterative process by defining the neural dimensions that characterise deeply phenotyped samples and then testing the dimensions in novel samples to assess specificity and reliability. Crucially, we aim to use machine learning methods to identify novel predictors of treatment response based on prospective longitudinal treatment outcome data, and we can externally validate the dimensions in fully independent sites. CONCLUSION We describe the consortium, imaging protocols and analytics using preliminary results. Our findings thus far demonstrate how datasets across many sites can be harmonized and constructively pooled to enable execution of this large-scale project.
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Affiliation(s)
- Cynthia H Y Fu
- Department of Psychological Sciences, University of East London, London, UK.
- Centre for Affective Disorders, Department of Psychological Medicine, Institute of Psychiatry, Psychology, and Neuroscience, King's College London, London, UK.
| | - Guray Erus
- Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, USA
| | - Yong Fan
- Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, USA
| | - Mathilde Antoniades
- Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, USA
| | - Danilo Arnone
- Centre for Affective Disorders, Department of Psychological Medicine, Institute of Psychiatry, Psychology, and Neuroscience, King's College London, London, UK
- Department of Psychiatry and Behavioral Science, College of Medicine and Health Sciences, United Arab Emirates University, Al Ain, United Arab Emirates
| | | | - Taolin Chen
- Huaxi MR Research Center, Department of Radiology, West China Hospital, Sichuan University, Chengdu, China
- Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, China
| | - Ki Sueng Choi
- Nash Family Center for Advanced Circuit Therapeutics, Icahn School of Medicine at Mount Sinai, New York, USA
| | - Cherise Chin Fatt
- Department of Psychiatry, Center for Depression Research and Clinical Care, University of Texas Southwestern Medical Center, Dallas, USA
| | - Benicio N Frey
- Department of Psychiatry and Behavioural Neurosciences, McMaster University, Hamilton, Canada
- Mood Disorders Treatment and Research Centre and Women's Health Concerns Clinic, St Joseph's Healthcare Hamilton, Hamilton, Canada
| | - Vibe G Frokjaer
- Neurobiology Research Unit, University Hospital Rigshospitalet, Copenhagen, Denmark
- Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
- Department of Psychiatry, Psychiatric Centre Copenhagen, Copenhagen, Denmark
| | - Melanie Ganz
- Neurobiology Research Unit, University Hospital Rigshospitalet, Copenhagen, Denmark
- Department of Computer Science, University of Copenhagen, Copenhagen, Denmark
| | - Jose Garcia
- Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, USA
| | - Beata R Godlewska
- Department of Psychiatry, University of Oxford, Oxford, UK
- Oxford Health NHS Foundation Trust, Warneford Hospital, Oxford, UK
| | - Stefanie Hassel
- Mathison Centre for Mental Health Research and Education, University of Calgary, Calgary, Canada
- Department of Psychiatry, Cumming School of Medicine, University of Calgary, Calgary, Canada
| | - Keith Ho
- Department of Psychiatry, University Health Network, Toronto, Canada
| | - Andrew M McIntosh
- Division of Psychiatry, Royal Edinburgh Hospital, University of Edinburgh, Edinburgh, UK
| | - Kun Qin
- Huaxi MR Research Center, Department of Radiology, West China Hospital, Sichuan University, Chengdu, China
- Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, China
| | - Susan Rotzinger
- Department of Psychiatry, University Health Network, Toronto, Canada
- Centre for Depression and Suicide Studies, Unity Health Toronto, Toronto, Canada
| | - Matthew D Sacchet
- Meditation Research Program, Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, USA
| | | | - Haochang Shou
- Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, USA
- Penn Statistics in Imaging and Visualization Endeavor (PennSIVE) Center, Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania, Philadelphia, USA
| | - Ashish Singh
- Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, USA
| | - Aleks Stolicyn
- Division of Psychiatry, Royal Edinburgh Hospital, University of Edinburgh, Edinburgh, UK
| | - Irina Strigo
- Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, USA
| | - Stephen C Strother
- Rotman Research Institute, Baycrest Centre, Toronto, Canada
- Department of Medical Biophysics, University of Toronto, Toronto, Canada
| | - Duygu Tosun
- Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, USA
| | | | - Dongtao Wei
- School of Psychology, Southwest University, Chongqing, China
| | - Toby Wise
- Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Rachel D Woodham
- Department of Psychological Sciences, University of East London, London, UK
| | - Roland Zahn
- Centre for Affective Disorders, Department of Psychological Medicine, Institute of Psychiatry, Psychology, and Neuroscience, King's College London, London, UK
| | - Ian M Anderson
- Division of Neuroscience and Experimental Psychology, University of Manchester, Manchester, UK
| | - J F William Deakin
- Division of Neuroscience and Experimental Psychology, University of Manchester, Manchester, UK
| | - Boadie W Dunlop
- Department of Psychiatry and Behavioral Sciences, Emory University School of Medicine, Atlanta, USA
| | - Rebecca Elliott
- Division of Neuroscience and Experimental Psychology, University of Manchester, Manchester, UK
| | - Qiyong Gong
- Huaxi MR Research Center, Department of Radiology, West China Hospital, Sichuan University, Chengdu, China
- Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, China
| | - Ian H Gotlib
- Department of Psychology, Stanford University, Stanford, USA
| | | | - Sidney H Kennedy
- Department of Psychiatry, University Health Network, Toronto, Canada
- Centre for Depression and Suicide Studies, Unity Health Toronto, Toronto, Canada
- Unity Health Toronto, Toronto, Canada
| | - Gitte M Knudsen
- Neurobiology Research Unit, University Hospital Rigshospitalet, Copenhagen, Denmark
- Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Helen S Mayberg
- Nash Family Center for Advanced Circuit Therapeutics, Icahn School of Medicine at Mount Sinai, New York, USA
| | | | - Jiang Qiu
- School of Psychology, Southwest University, Chongqing, China
| | - Madhukar H Trivedi
- Department of Psychiatry, Center for Depression Research and Clinical Care, University of Texas Southwestern Medical Center, Dallas, USA
| | - Heather C Whalley
- Division of Psychiatry, Royal Edinburgh Hospital, University of Edinburgh, Edinburgh, UK
| | - Chao-Gan Yan
- CAS Key Laboratory of Behavioral Science, Institute of Psychology, Beijing, China
| | - Allan H Young
- Centre for Affective Disorders, Department of Psychological Medicine, Institute of Psychiatry, Psychology, and Neuroscience, King's College London, London, UK
- South London and Maudsley NHS Foundation Trust, Bethlem Royal Hospital, London, UK
| | - Christos Davatzikos
- Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, USA
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4
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Beliveau V, Hedeboe E, Fisher PM, Dam VH, Jørgensen MB, Frokjaer VG, Knudsen GM, Ganz M. Generalizability of treatment outcome prediction in major depressive disorder using structural MRI: A NeuroPharm study. Neuroimage Clin 2022; 36:103224. [PMID: 36252556 PMCID: PMC9668596 DOI: 10.1016/j.nicl.2022.103224] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2022] [Revised: 10/04/2022] [Accepted: 10/06/2022] [Indexed: 11/11/2022]
Abstract
Brain morphology has been suggested to be predictive of drug treatment outcome in major depressive disorders (MDD). The current study aims at evaluating the performance of pretreatment structural brain magnetic resonance imaging (MRI) measures in predicting the outcome of a drug treatment of MDD in a large single-site cohort, and, importantly, to assess the generalizability of these findings in an independent cohort. The random forest, boosted trees, support vector machines and elastic net classifiers were evaluated in predicting treatment response and remission following an eight week drug treatment of MDD using structural brain measures derived with FastSurfer (FreeSurfer). Models were trained and tested within a nested cross-validation framework using the NeuroPharm dataset (n = 79, treatment: escitalopram); their generalizability was assessed using an independent clinical dataset, EMBARC (n = 64, treatment: sertraline). Prediction of antidepressant treatment response in the Neuropharm cohort was statistically significant for the random forest (p = 0.048), whereas none of the models could significantly predict remission. Furthermore, none of the models trained using the entire NeuroPharm dataset could significantly predict treatment outcome in the EMBARC dataset. Although our primary findings in the NeuroPharm cohort support some, but limited value in using pretreatment structural brain MRI to predict drug treatment outcome in MDD, the models did not generalize to an independent cohort suggesting limited clinical applicability. This study emphasizes the importance of assessing model generalizability for establishing clinical utility.
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Affiliation(s)
- Vincent Beliveau
- Neurobiology Research Unit and NeuroPharm, Copenhagen University Hospital Rigshospitalet, Copenhagen, Denmark,Department of Neurology, Medical University of Innsbruck, Innsbruck, Austria,Corresponding author at: Neurobiology Research Unit, Rigshospitalet, Blegdamsvej 9, 2100 Copenhagen, Denmark.
| | - Ella Hedeboe
- Neurobiology Research Unit and NeuroPharm, Copenhagen University Hospital Rigshospitalet, Copenhagen, Denmark
| | - Patrick M. Fisher
- Neurobiology Research Unit and NeuroPharm, Copenhagen University Hospital Rigshospitalet, Copenhagen, Denmark
| | - Vibeke H. Dam
- Neurobiology Research Unit and NeuroPharm, Copenhagen University Hospital Rigshospitalet, Copenhagen, Denmark
| | - Martin B. Jørgensen
- Neurobiology Research Unit and NeuroPharm, Copenhagen University Hospital Rigshospitalet, Copenhagen, Denmark,Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark,Psychiatric Center Copenhagen, Rigshospitalet, Copenhagen, Denmark
| | - Vibe G. Frokjaer
- Neurobiology Research Unit and NeuroPharm, Copenhagen University Hospital Rigshospitalet, Copenhagen, Denmark,Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark,Psychiatric Center Copenhagen, Rigshospitalet, Copenhagen, Denmark
| | - Gitte M. Knudsen
- Neurobiology Research Unit and NeuroPharm, Copenhagen University Hospital Rigshospitalet, Copenhagen, Denmark,Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Melanie Ganz
- Neurobiology Research Unit and NeuroPharm, Copenhagen University Hospital Rigshospitalet, Copenhagen, Denmark,Department of Computer Science, University of Copenhagen, Copenhagen, Denmark
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5
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Karvelis P, Charlton CE, Allohverdi SG, Bedford P, Hauke DJ, Diaconescu AO. Computational approaches to treatment response prediction in major depression using brain activity and behavioral data: A systematic review. Netw Neurosci 2022; 6:1066-1103. [PMID: 38800454 PMCID: PMC11117101 DOI: 10.1162/netn_a_00233] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2021] [Accepted: 01/14/2022] [Indexed: 05/29/2024] Open
Abstract
Major depressive disorder is a heterogeneous diagnostic category with multiple available treatments. With the goal of optimizing treatment selection, researchers are developing computational models that attempt to predict treatment response based on various pretreatment measures. In this paper, we review studies that use brain activity data to predict treatment response. Our aim is to highlight and clarify important methodological differences between various studies that relate to the incorporation of domain knowledge, specifically within two approaches delineated as data-driven and theory-driven. We argue that theory-driven generative modeling, which explicitly models information processing in the brain and thus can capture disease mechanisms, is a promising emerging approach that is only beginning to be utilized in treatment response prediction. The predictors extracted via such models could improve interpretability, which is critical for clinical decision-making. We also identify several methodological limitations across the reviewed studies and provide suggestions for addressing them. Namely, we consider problems with dichotomizing treatment outcomes, the importance of investigating more than one treatment in a given study for differential treatment response predictions, the need for a patient-centered approach for defining treatment outcomes, and finally, the use of internal and external validation methods for improving model generalizability.
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Affiliation(s)
- Povilas Karvelis
- Krembil Centre for Neuroinformatics, Centre for Addiction and Mental Health (CAMH), Toronto, ON, Canada
| | - Colleen E. Charlton
- Krembil Centre for Neuroinformatics, Centre for Addiction and Mental Health (CAMH), Toronto, ON, Canada
| | - Shona G. Allohverdi
- Krembil Centre for Neuroinformatics, Centre for Addiction and Mental Health (CAMH), Toronto, ON, Canada
| | - Peter Bedford
- Krembil Centre for Neuroinformatics, Centre for Addiction and Mental Health (CAMH), Toronto, ON, Canada
| | - Daniel J. Hauke
- Krembil Centre for Neuroinformatics, Centre for Addiction and Mental Health (CAMH), Toronto, ON, Canada
- Department of Psychiatry (UPK), University of Basel, Basel, Switzerland
- Department of Mathematics and Computer Science, University of Basel, Basel, Switzerland
| | - Andreea O. Diaconescu
- Krembil Centre for Neuroinformatics, Centre for Addiction and Mental Health (CAMH), Toronto, ON, Canada
- University of Toronto, Department of Psychiatry, Toronto, ON, Canada
- Institute of Medical Sciences, University of Toronto, Toronto, ON, Canada
- Department of Psychology, University of Toronto, Toronto, ON, Canada
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6
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Gerlach AR, Karim HT, Peciña M, Ajilore O, Taylor WD, Butters MA, Andreescu C. MRI predictors of pharmacotherapy response in major depressive disorder. Neuroimage Clin 2022; 36:103157. [PMID: 36027717 PMCID: PMC9420953 DOI: 10.1016/j.nicl.2022.103157] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2022] [Revised: 07/11/2022] [Accepted: 08/15/2022] [Indexed: 02/08/2023]
Abstract
Major depressive disorder is among the most prevalent psychiatric disorders, exacting a substantial personal, social, and economic toll. Antidepressant treatment typically involves an individualized trial and error approach with an inconsistent success rate. Despite a pressing need, no reliable biomarkers for predicting treatment outcome have yet been discovered. Brain MRI measures hold promise in this regard, though clinical translation remains elusive. In this review, we summarize structural MRI and functional MRI (fMRI) measures that have been investigated as predictors of treatment outcome. We broadly divide these into five categories including three structural measures: volumetric, white matter burden, and white matter integrity; and two functional measures: resting state fMRI and task fMRI. Currently, larger hippocampal volume is the most widely replicated predictor of successful treatment. Lower white matter hyperintensity burden has shown robustness in late life depression. However, both have modest discriminative power. Higher fractional anisotropy of the cingulum bundle and frontal white matter, amygdala hypoactivation and anterior cingulate cortex hyperactivation in response to negative emotional stimuli, and hyperconnectivity within the default mode network (DMN) and between the DMN and executive control network also show promise as predictors of successful treatment. Such network-focused measures may ultimately provide a higher-dimensional measure of treatment response with closer ties to the underlying neurobiology.
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Affiliation(s)
- Andrew R Gerlach
- Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA, USA
| | - Helmet T Karim
- Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA, USA; Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA, USA
| | - Marta Peciña
- Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA, USA
| | - Olusola Ajilore
- Department of Psychiatry, University of Illinois-Chicago, Chicago, IL, USA
| | - Warren D Taylor
- Department of Psychiatry and Behavioral Sciences, Vanderbilt University Medical Center, Nashville, TN, USA; Geriatric Research, Education, and Clinical Center, Veterans Affairs Tennessee Valley Health System, Nashville, TN, USA
| | - Meryl A Butters
- Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA, USA
| | - Carmen Andreescu
- Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA, USA.
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7
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Jorgensen A, Köhler-Forsberg K, Henriksen T, Weimann A, Brandslund I, Ellervik C, Poulsen HE, Knudsen GM, Frokjaer VG, Jorgensen MB. Systemic DNA and RNA damage from oxidation after serotonergic treatment of unipolar depression. Transl Psychiatry 2022; 12:204. [PMID: 35577781 PMCID: PMC9110351 DOI: 10.1038/s41398-022-01969-z] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/18/2021] [Revised: 04/27/2022] [Accepted: 05/04/2022] [Indexed: 11/09/2022] Open
Abstract
Previous studies have indicated that antidepressants that inhibit the serotonin transporter reduces oxidative stress. DNA and RNA damage from oxidation is involved in aging and a range of age-related pathophysiological processes. Here, we studied the urinary excretion of markers of DNA and RNA damage from oxidation, 8-oxodG and 8-oxoGuo, respectively, in the NeuroPharm cohort of 100 drug-free patients with unipolar depression and in 856 non-psychiatric community controls. Patients were subsequently treated for 8 weeks with escitalopram in flexible doses of 5-20 mg; seven of these switched to duloxetine by week 4, as allowed by the protocol. At week 8, 82 patients were followed up clinically and with measurements of 8-oxodG/8-oxoGuo. Contextual data were collected in patients, including markers of cortisol excretion and low-grade inflammation. The intervention was associated with a substantial reduction in both 8-oxodG/8-oxoGuo excretion (25% and 10%, respectively). The change was not significantly correlated to measures of clinical improvement. Both markers were strongly and negatively correlated to cortisol, as measured by the area under the curve for the full-day salivary cortisol excretion. Surprisingly, patients had similar levels of 8-oxodG excretion and lower levels of 8-oxoGuo excretion at baseline compared to the controls. We conclude that intervention with serotonin reuptake inhibitors in unipolar depression is associated with a reduction in systemic DNA and RNA damage from oxidation. To our knowledge, this to date the largest intervention study to characterize this phenomenon, and the first to include a marker of RNA oxidation.
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Affiliation(s)
- Anders Jorgensen
- Psychiatric Center Copenhagen, Mental Health Services, Copenhagen, Denmark. .,Neurobiology Research Unit, Copenhagen University Hospital Rigshospitalet, Copenhagen, Denmark. .,Institute of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark.
| | - Kristin Köhler-Forsberg
- grid.466916.a0000 0004 0631 4836Psychiatric Center Copenhagen, Mental Health Services, Copenhagen, Denmark ,grid.475435.4Neurobiology Research Unit, Copenhagen University Hospital Rigshospitalet, Copenhagen, Denmark
| | - Trine Henriksen
- grid.4973.90000 0004 0646 7373Department of Clinical Pharmacology, University Hospital Copenhagen, Bispebjerg and Frederiksberg, Denmark
| | - Allan Weimann
- grid.4973.90000 0004 0646 7373Department of Clinical Pharmacology, University Hospital Copenhagen, Bispebjerg and Frederiksberg, Denmark
| | - Ivan Brandslund
- grid.459623.f0000 0004 0587 0347Department of Clinical Immunology and Biochemistry, Lillebælt Hospital, Vejle, Denmark ,grid.10825.3e0000 0001 0728 0170Faculty of Health Science, Institute of Regional Health Research, University of Southern Denmark, Odense, Denmark
| | - Christina Ellervik
- grid.5254.60000 0001 0674 042XInstitute of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark ,grid.38142.3c000000041936754XHarvard Medical School, Boston, USA
| | - Henrik E. Poulsen
- grid.5254.60000 0001 0674 042XInstitute of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark ,grid.4973.90000 0004 0646 7373Department of Clinical Pharmacology, University Hospital Copenhagen, Bispebjerg and Frederiksberg, Denmark ,grid.4973.90000 0004 0646 7373Department of Cardiology, Copenhagen University Hospital Hillerød, Copenhagen, Denmark ,grid.4973.90000 0004 0646 7373Department of Endocrinology, Copenhagen University Hospital Hillerød, Copenhagen, Denmark
| | - Gitte Moos Knudsen
- grid.475435.4Neurobiology Research Unit, Copenhagen University Hospital Rigshospitalet, Copenhagen, Denmark ,grid.5254.60000 0001 0674 042XInstitute of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark
| | - Vibe G. Frokjaer
- grid.466916.a0000 0004 0631 4836Psychiatric Center Copenhagen, Mental Health Services, Copenhagen, Denmark ,grid.475435.4Neurobiology Research Unit, Copenhagen University Hospital Rigshospitalet, Copenhagen, Denmark ,grid.5254.60000 0001 0674 042XInstitute of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark
| | - Martin B. Jorgensen
- grid.466916.a0000 0004 0631 4836Psychiatric Center Copenhagen, Mental Health Services, Copenhagen, Denmark ,grid.475435.4Neurobiology Research Unit, Copenhagen University Hospital Rigshospitalet, Copenhagen, Denmark ,grid.5254.60000 0001 0674 042XInstitute of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark
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8
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Fisher PM, Ozenne B, Ganz M, Frokjaer VG, Dam VN, Penninx BW, Sankar A, Miskowiak K, Jensen PS, Knudsen GM, Jorgensen MB. Emotional faces processing in major depressive disorder and prediction of antidepressant treatment response: A NeuroPharm study. J Psychopharmacol 2022; 36:626-636. [PMID: 35549538 DOI: 10.1177/02698811221089035] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
BACKGROUND Major depressive disorder (MDD) is a prevalent neuropsychiatric illness for which it is important to resolve underlying brain mechanisms. Current treatments are often unsuccessful, precipitating a need to identify predictive markers. AIM We evaluated (1) alterations in brain responses to an emotional faces functional magnetic resonance imaging (fMRI) paradigm in individuals with MDD, compared to controls, (2) whether pretreatment brain responses predicted antidepressant treatment response, and (3) pre-post change in brain responses following treatment. METHODS Eighty-nine medication-free, depressed individuals and 115 healthy controls completed the fMRI paradigm. Depressed individuals completed a nonrandomized, open-label, 8-week treatment with escitalopram, including the option to switch to duloxetine after 4 weeks. We examined patient-control group differences in regional fMRI responses at baseline, whether baseline fMRI responses predicted treatment response at 8 weeks, including early life stress moderating effects, and change in fMRI responses in 36 depressed individuals rescanned following 8 weeks of treatment. RESULTS Task reaction time was 5% slower in patients. Multiple brain regions showed significant task-related responses, but we observed no statistically significant patient-control group differences (Cohen's d < 0.35). Patient pretreatment brain responses did not predict antidepressant treatment response (area under the curve of the receiver operator characteristic (AUC-ROC) < 0.6) and brain responses were not statistically significantly changed after treatment (Cohen's d < 0.33). CONCLUSION This represents the largest prediction study to date examining emotional faces fMRI features as predictors of antidepressant treatment response. Brain response to this fMRI emotional faces paradigm did not distinguish depressed individuals from healthy controls, nor was it predictive of antidepressant treatment response.Clinical Trial Registration: Site: https://clinicaltrials.gov, Trial Number: NCT02869035, Trial Title: Treatment Outcome in Major Depressive Disorder.
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Affiliation(s)
- Patrick M Fisher
- Neurobiology Research Unit, Copenhagen University Hospital Rigshospitalet, Copenhagen, Denmark.,NeuroPharm, Copenhagen University Hospital Rigshospitalet, Copenhagen, Denmark.,BrainDrugs, Copenhagen University Hospital Rigshospitalet, Copenhagen, Denmark
| | - Brice Ozenne
- Neurobiology Research Unit, Copenhagen University Hospital Rigshospitalet, Copenhagen, Denmark.,NeuroPharm, Copenhagen University Hospital Rigshospitalet, Copenhagen, Denmark.,BrainDrugs, Copenhagen University Hospital Rigshospitalet, Copenhagen, Denmark.,Department of Public Health, Section of Biostatistics, University of Copenhagen, Copenhagen, Denmark
| | - Melanie Ganz
- Neurobiology Research Unit, Copenhagen University Hospital Rigshospitalet, Copenhagen, Denmark.,BrainDrugs, Copenhagen University Hospital Rigshospitalet, Copenhagen, Denmark.,Department of Computer Science, University of Copenhagen, Copenhagen, Denmark
| | - Vibe G Frokjaer
- Neurobiology Research Unit, Copenhagen University Hospital Rigshospitalet, Copenhagen, Denmark.,NeuroPharm, Copenhagen University Hospital Rigshospitalet, Copenhagen, Denmark.,BrainDrugs, Copenhagen University Hospital Rigshospitalet, Copenhagen, Denmark.,Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark.,Psychiatric Center Copenhagen, Copenhagen University Hospital Rigshospitalet, Copenhagen, Denmark
| | - Vibeke Nh Dam
- Neurobiology Research Unit, Copenhagen University Hospital Rigshospitalet, Copenhagen, Denmark.,NeuroPharm, Copenhagen University Hospital Rigshospitalet, Copenhagen, Denmark.,BrainDrugs, Copenhagen University Hospital Rigshospitalet, Copenhagen, Denmark
| | - Brenda Wjh Penninx
- BrainDrugs, Copenhagen University Hospital Rigshospitalet, Copenhagen, Denmark.,Amsterdam UMC, Vrije Universiteit, Psychiatry, Amsterdam Public Health Research Institute, Amsterdam, The Netherlands
| | - Anajli Sankar
- Neurobiology Research Unit, Copenhagen University Hospital Rigshospitalet, Copenhagen, Denmark.,BrainDrugs, Copenhagen University Hospital Rigshospitalet, Copenhagen, Denmark
| | - Kamilla Miskowiak
- BrainDrugs, Copenhagen University Hospital Rigshospitalet, Copenhagen, Denmark.,Copenhagen Affective Disorder Research Centre (CADIC), Psychiatric Centre Copenhagen, Copenhagen University Hospital, Rigshospitalet, Copenhagen, Denmark.,Department of Psychology, University of Copenhagen, Copenhagen, Denmark
| | - Peter S Jensen
- Neurobiology Research Unit, Copenhagen University Hospital Rigshospitalet, Copenhagen, Denmark.,NeuroPharm, Copenhagen University Hospital Rigshospitalet, Copenhagen, Denmark.,BrainDrugs, Copenhagen University Hospital Rigshospitalet, Copenhagen, Denmark
| | - Gitte M Knudsen
- Neurobiology Research Unit, Copenhagen University Hospital Rigshospitalet, Copenhagen, Denmark.,NeuroPharm, Copenhagen University Hospital Rigshospitalet, Copenhagen, Denmark.,BrainDrugs, Copenhagen University Hospital Rigshospitalet, Copenhagen, Denmark.,Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Martin B Jorgensen
- Neurobiology Research Unit, Copenhagen University Hospital Rigshospitalet, Copenhagen, Denmark.,NeuroPharm, Copenhagen University Hospital Rigshospitalet, Copenhagen, Denmark.,BrainDrugs, Copenhagen University Hospital Rigshospitalet, Copenhagen, Denmark.,Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark.,Psychiatric Center Copenhagen, Copenhagen University Hospital Rigshospitalet, Copenhagen, Denmark
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9
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Demchenko I, Tassone VK, Kennedy SH, Dunlop K, Bhat V. Intrinsic Connectivity Networks of Glutamate-Mediated Antidepressant Response: A Neuroimaging Review. Front Psychiatry 2022; 13:864902. [PMID: 35722550 PMCID: PMC9199367 DOI: 10.3389/fpsyt.2022.864902] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/29/2022] [Accepted: 04/12/2022] [Indexed: 11/23/2022] Open
Abstract
Conventional monoamine-based pharmacotherapy, considered the first-line treatment for major depressive disorder (MDD), has several challenges, including high rates of non-response. To address these challenges, preclinical and clinical studies have sought to characterize antidepressant response through monoamine-independent mechanisms. One striking example is glutamate, the brain's foremost excitatory neurotransmitter: since the 1990s, studies have consistently reported altered levels of glutamate in MDD, as well as antidepressant effects following molecular targeting of glutamatergic receptors. Therapeutically, this has led to advances in the discovery, testing, and clinical application of a wide array of glutamatergic agents, particularly ketamine. Notably, ketamine has been demonstrated to rapidly improve mood symptoms, unlike monoamine-based interventions, and the neurobiological basis behind this rapid antidepressant response is under active investigation. Advances in brain imaging techniques, including functional magnetic resonance imaging, magnetic resonance spectroscopy, and positron emission tomography, enable the identification of the brain network-based characteristics distinguishing rapid glutamatergic modulation from the effect of slow-acting conventional monoamine-based pharmacology. Here, we review brain imaging studies that examine brain connectivity features associated with rapid antidepressant response in MDD patients treated with glutamatergic pharmacotherapies in contrast with patients treated with slow-acting monoamine-based treatments. Trends in recent brain imaging literature suggest that the activity of brain regions is organized into coherent functionally distinct networks, termed intrinsic connectivity networks (ICNs). We provide an overview of major ICNs implicated in depression and explore how treatment response following glutamatergic modulation alters functional connectivity of limbic, cognitive, and executive nodes within ICNs, with well-characterized anti-anhedonic effects and the enhancement of "top-down" executive control. Alterations within and between the core ICNs could potentially exert downstream effects on the nodes within other brain networks of relevance to MDD that are structurally and functionally interconnected through glutamatergic synapses. Understanding similarities and differences in brain ICNs features underlying treatment response will positively impact the trajectory and outcomes for adults suffering from MDD and will facilitate the development of biomarkers to enable glutamate-based precision therapeutics.
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Affiliation(s)
- Ilya Demchenko
- Interventional Psychiatry Program, Mental Health and Addictions Service, St. Michael's Hospital, Unity Health Toronto, Toronto, ON, Canada.,Center for Depression and Suicide Studies, St. Michael's Hospital, Unity Health Toronto, Toronto, ON, Canada
| | - Vanessa K Tassone
- Interventional Psychiatry Program, Mental Health and Addictions Service, St. Michael's Hospital, Unity Health Toronto, Toronto, ON, Canada
| | - Sidney H Kennedy
- Interventional Psychiatry Program, Mental Health and Addictions Service, St. Michael's Hospital, Unity Health Toronto, Toronto, ON, Canada.,Center for Depression and Suicide Studies, St. Michael's Hospital, Unity Health Toronto, Toronto, ON, Canada.,Keenan Research Center for Biomedical Science, St. Michael's Hospital, Unity Health Toronto, Toronto, ON, Canada.,Department of Psychiatry, Faculty of Medicine, University of Toronto, Toronto, ON, Canada
| | - Katharine Dunlop
- Interventional Psychiatry Program, Mental Health and Addictions Service, St. Michael's Hospital, Unity Health Toronto, Toronto, ON, Canada.,Center for Depression and Suicide Studies, St. Michael's Hospital, Unity Health Toronto, Toronto, ON, Canada.,Keenan Research Center for Biomedical Science, St. Michael's Hospital, Unity Health Toronto, Toronto, ON, Canada.,Department of Psychiatry, Faculty of Medicine, University of Toronto, Toronto, ON, Canada
| | - Venkat Bhat
- Interventional Psychiatry Program, Mental Health and Addictions Service, St. Michael's Hospital, Unity Health Toronto, Toronto, ON, Canada.,Center for Depression and Suicide Studies, St. Michael's Hospital, Unity Health Toronto, Toronto, ON, Canada.,Keenan Research Center for Biomedical Science, St. Michael's Hospital, Unity Health Toronto, Toronto, ON, Canada.,Department of Psychiatry, Faculty of Medicine, University of Toronto, Toronto, ON, Canada
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