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Arnold PIM, Janzing JGE, Hommersom A. Machine learning for antidepressant treatment selection in depression. Drug Discov Today 2024; 29:104068. [PMID: 38925472 DOI: 10.1016/j.drudis.2024.104068] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2023] [Revised: 06/07/2024] [Accepted: 06/19/2024] [Indexed: 06/28/2024]
Abstract
Finding the right antidepressant for the individual patient with major depressive disorder can be a difficult endeavor and is mostly based on trial-and-error. Machine learning (ML) is a promising tool to personalize antidepressant prescription. In this review, we summarize the current evidence of ML in the selection of antidepressants and conclude that its value for clinical practice is still limited. Apart from the current focus on effectiveness, several other factors should be taken into account to make ML-based prediction models useful for clinical application.
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Affiliation(s)
- Prehm I M Arnold
- Department of Psychiatry, Radboudumc, Nijmegen, the Netherlands.
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2
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Prompiengchai S, Dunlop K. Breakthroughs and challenges for generating brain network-based biomarkers of treatment response in depression. Neuropsychopharmacology 2024:10.1038/s41386-024-01907-1. [PMID: 38951585 DOI: 10.1038/s41386-024-01907-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/29/2024] [Revised: 05/17/2024] [Accepted: 06/13/2024] [Indexed: 07/03/2024]
Abstract
Treatment outcomes widely vary for individuals diagnosed with major depressive disorder, implicating a need for deeper understanding of the biological mechanisms conferring a greater likelihood of response to a particular treatment. Our improved understanding of intrinsic brain networks underlying depression psychopathology via magnetic resonance imaging and other neuroimaging modalities has helped reveal novel and potentially clinically meaningful biological markers of response. And while we have made considerable progress in identifying such biomarkers over the last decade, particularly with larger, multisite trials, there are significant methodological and practical obstacles that need to be overcome to translate these markers into the clinic. The aim of this review is to review current literature on brain network structural and functional biomarkers of treatment response or selection in depression, with a specific focus on recent large, multisite trials reporting predictive accuracy of candidate biomarkers. Regarding pharmaco- and psychotherapy, we discuss candidate biomarkers, reporting that while we have identified candidate biomarkers of response to a single intervention, we need more trials that distinguish biomarkers between first-line treatments. Further, we discuss the ways prognostic neuroimaging may help to improve treatment outcomes to neuromodulation-based therapies, such as transcranial magnetic stimulation and deep brain stimulation. Lastly, we highlight obstacles and technical developments that may help to address the knowledge gaps in this area of research. Ultimately, integrating neuroimaging-derived biomarkers into clinical practice holds promise for enhancing treatment outcomes and advancing precision psychiatry strategies for depression management. By elucidating the neural predictors of treatment response and selection, we can move towards more individualized and effective depression interventions, ultimately improving patient outcomes and quality of life.
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Affiliation(s)
| | - Katharine Dunlop
- Centre for Depression and Suicide Studies, Unity Health Toronto, Toronto, ON, Canada.
- Keenan Research Centre for Biomedical Science, Unity Health Toronto, Toronto, ON, Canada.
- Department of Psychiatry and Institute of Medical Science, University of Toronto, Toronto, ON, Canada.
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3
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Tozzi L, Zhang X, Pines A, Olmsted AM, Zhai ES, Anene ET, Chesnut M, Holt-Gosselin B, Chang S, Stetz PC, Ramirez CA, Hack LM, Korgaonkar MS, Wintermark M, Gotlib IH, Ma J, Williams LM. Personalized brain circuit scores identify clinically distinct biotypes in depression and anxiety. Nat Med 2024; 30:2076-2087. [PMID: 38886626 PMCID: PMC11271415 DOI: 10.1038/s41591-024-03057-9] [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] [Accepted: 05/09/2024] [Indexed: 06/20/2024]
Abstract
There is an urgent need to derive quantitative measures based on coherent neurobiological dysfunctions or 'biotypes' to enable stratification of patients with depression and anxiety. We used task-free and task-evoked data from a standardized functional magnetic resonance imaging protocol conducted across multiple studies in patients with depression and anxiety when treatment free (n = 801) and after randomization to pharmacotherapy or behavioral therapy (n = 250). From these patients, we derived personalized and interpretable scores of brain circuit dysfunction grounded in a theoretical taxonomy. Participants were subdivided into six biotypes defined by distinct profiles of intrinsic task-free functional connectivity within the default mode, salience and frontoparietal attention circuits, and of activation and connectivity within frontal and subcortical regions elicited by emotional and cognitive tasks. The six biotypes showed consistency with our theoretical taxonomy and were distinguished by symptoms, behavioral performance on general and emotional cognitive computerized tests, and response to pharmacotherapy as well as behavioral therapy. Our results provide a new, theory-driven, clinically validated and interpretable quantitative method to parse the biological heterogeneity of depression and anxiety. Thus, they represent a promising approach to advance precision clinical care in psychiatry.
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Affiliation(s)
- Leonardo Tozzi
- Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, CA, USA
| | - Xue Zhang
- Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, CA, USA
| | - Adam Pines
- Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, CA, USA
| | - Alisa M Olmsted
- Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, CA, USA
- Sierra-Pacific Mental Illness Research, Education and Clinical Center, Veterans Affairs Palo Alto Health Care System, Palo Alto, CA, USA
| | - Emily S Zhai
- Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, CA, USA
| | - Esther T Anene
- Department of Counseling and Clinical Psychology, Teacher's College, Columbia University, New York, NY, USA
| | - Megan Chesnut
- Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, CA, USA
| | - Bailey Holt-Gosselin
- Interdepartmental Neuroscience Graduate Program, Yale University School of Medicine, New Haven, CT, USA
| | - Sarah Chang
- Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, CA, USA
| | - Patrick C Stetz
- Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, CA, USA
- Sierra-Pacific Mental Illness Research, Education and Clinical Center, Veterans Affairs Palo Alto Health Care System, Palo Alto, CA, USA
| | - Carolina A Ramirez
- Center for Intelligent Imaging, University of California San Francisco, San Francisco, CA, USA
| | - Laura M Hack
- Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, CA, USA
- Sierra-Pacific Mental Illness Research, Education and Clinical Center, Veterans Affairs Palo Alto Health Care System, Palo Alto, CA, USA
| | - Mayuresh S Korgaonkar
- Brain Dynamics Centre, Westmead Institute for Medical Research, University of Sydney, Westmead, New South Wales, Australia
- Department of Radiology, Westmead Hospital, Western Sydney Local Health District, Westmead, New South Wales, Australia
| | - Max Wintermark
- Department of Neuroradiology, the University of Texas MD Anderson Center, Houston, TX, USA
| | - Ian H Gotlib
- Department of Psychology, Stanford University, Stanford, CA, USA
| | - Jun Ma
- Department of Medicine, College of Medicine, University of Illinois Chicago, Chicago, IL, USA
| | - Leanne M Williams
- Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, CA, USA.
- Sierra-Pacific Mental Illness Research, Education and Clinical Center, Veterans Affairs Palo Alto Health Care System, Palo Alto, CA, USA.
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Lu B, Chen X, Xavier Castellanos F, Thompson PM, Zuo XN, Zang YF, Yan CG. The power of many brains: Catalyzing neuropsychiatric discovery through open neuroimaging data and large-scale collaboration. Sci Bull (Beijing) 2024; 69:1536-1555. [PMID: 38519398 DOI: 10.1016/j.scib.2024.03.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2023] [Revised: 12/12/2023] [Accepted: 02/27/2024] [Indexed: 03/24/2024]
Abstract
Recent advances in open neuroimaging data are enhancing our comprehension of neuropsychiatric disorders. By pooling images from various cohorts, statistical power has increased, enabling the detection of subtle abnormalities and robust associations, and fostering new research methods. Global collaborations in imaging have furthered our knowledge of the neurobiological foundations of brain disorders and aided in imaging-based prediction for more targeted treatment. Large-scale magnetic resonance imaging initiatives are driving innovation in analytics and supporting generalizable psychiatric studies. We also emphasize the significant role of big data in understanding neural mechanisms and in the early identification and precise treatment of neuropsychiatric disorders. However, challenges such as data harmonization across different sites, privacy protection, and effective data sharing must be addressed. With proper governance and open science practices, we conclude with a projection of how large-scale imaging resources and collaborations could revolutionize diagnosis, treatment selection, and outcome prediction, contributing to optimal brain health.
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Affiliation(s)
- Bin Lu
- CAS Key Laboratory of Behavioral Science, Institute of Psychology, Beijing 100101, China; Department of Psychology, University of Chinese Academy of Sciences, Beijing 100101, China
| | - Xiao Chen
- CAS Key Laboratory of Behavioral Science, Institute of Psychology, Beijing 100101, China; Department of Psychology, University of Chinese Academy of Sciences, Beijing 100101, China
| | - Francisco Xavier Castellanos
- Department of Child and Adolescent Psychiatry, NYU Grossman School of Medicine, New York 10016, USA; Nathan Kline Institute for Psychiatric Research, Orangeburg 10962, USA
| | - Paul M Thompson
- Imaging Genetics Center, Mark & Mary Stevens Institute for Neuroimaging & Informatics, Keck School of Medicine, University of Southern California, Los Angeles 90033, USA
| | - Xi-Nian Zuo
- Developmental Population Neuroscience Research Center, IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China; National Basic Science Data Center, Beijing 100190, China
| | - Yu-Feng Zang
- Centre for Cognition and Brain Disorders, The Affiliated Hospital of Hangzhou Normal University, Hangzhou 310004, China; Institute of Psychological Science, Hangzhou Normal University, Hangzhou 310030, China; Zhejiang Key Laboratory for Research in Assessment of Cognitive Impairment, Hangzhou 311121, China
| | - Chao-Gan Yan
- CAS Key Laboratory of Behavioral Science, Institute of Psychology, Beijing 100101, China; Department of Psychology, University of Chinese Academy of Sciences, Beijing 100101, China; International Big-Data Center for Depression Research, Institute of Psychology, Chinese Academy of Sciences, Beijing 100101, China; Magnetic Resonance Imaging Research Center, Institute of Psychology, Chinese Academy of Sciences, Beijing 100101, China.
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5
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Song EJ, Tozzi L, Williams LM. Brain Circuit-Derived Biotypes for Treatment Selection in Mood Disorders: A Critical Review and Illustration of a Functional Neuroimaging Tool for Clinical Translation. Biol Psychiatry 2024:S0006-3223(24)01175-2. [PMID: 38552866 DOI: 10.1016/j.biopsych.2024.03.016] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/18/2023] [Revised: 03/16/2024] [Accepted: 03/20/2024] [Indexed: 05/12/2024]
Abstract
Although the lifetime burden due to major depressive disorder is increasing, we lack tools for selecting the most effective treatments for each patient. One-third to one-half of patients with major depressive disorder do not respond to treatment, and we lack strategies for selecting among available treatments or expediting access to new treatment options. This critical review concentrates on functional neuroimaging as a modality of measurement for precision psychiatry. We begin by summarizing the current landscape of how functional neuroimaging-derived circuit predictors can forecast treatment outcomes in depression. Then, we outline the opportunities and challenges in integrating circuit predictors into clinical practice. We highlight one standardized and reproducible approach for quantifying brain circuit function at an individual level, which could serve as a model for clinical translation. We conclude by evaluating the prospects and practicality of employing neuroimaging tools, such as the one that we propose, in routine clinical practice.
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Affiliation(s)
- Evelyn Jiayi Song
- Stanford Center for Precision Mental Health and Wellness, Psychiatry and Behavioral Sciences, Stanford, California; Stanford School of Engineering, Stanford, California
| | - Leonardo Tozzi
- Stanford Center for Precision Mental Health and Wellness, Psychiatry and Behavioral Sciences, Stanford, California
| | - Leanne M Williams
- Stanford Center for Precision Mental Health and Wellness, Psychiatry and Behavioral Sciences, Stanford, California; Mental Illness Research, Education and Clinical Center of Excellence (MIRECC), VA Palo Alto Health Care System, Palo Alto, California.
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Klooster D, Voetterl H, Baeken C, Arns M. Evaluating Robustness of Brain Stimulation Biomarkers for Depression: A Systematic Review of Magnetic Resonance Imaging and Electroencephalography Studies. Biol Psychiatry 2024; 95:553-563. [PMID: 37734515 DOI: 10.1016/j.biopsych.2023.09.009] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/12/2023] [Revised: 08/30/2023] [Accepted: 09/06/2023] [Indexed: 09/23/2023]
Abstract
Noninvasive brain stimulation (NIBS) treatments have gained considerable attention as potential therapeutic intervention for psychiatric disorders. The identification of reliable biomarkers for predicting clinical response to NIBS has been a major focus of research in recent years. Neuroimaging techniques, such as electroencephalography (EEG) and functional magnetic resonance imaging (MRI), have been used to identify potential biomarkers that could predict response to NIBS. However, identifying clinically actionable brain biomarkers requires robustness. In this systematic review, we aimed to summarize the current state of brain biomarker research for NIBS in depression, focusing only on well-powered studies (N ≥ 88) and/or studies that aimed at independently replicating previous findings, either successfully or unsuccessfully. A total of 220 studies were initially identified, of which 18 MRI studies and 18 EEG studies met the inclusion criteria. All focused on repetitive transcranial magnetic stimulation treatment in depression. After reviewing the included studies, we found the following MRI and EEG biomarkers to be most robust: 1) functional MRI-based functional connectivity between the dorsolateral prefrontal cortex and subgenual anterior cingulate cortex, 2) functional MRI-based network connectivity, 3) task-induced EEG frontal-midline theta, and 4) EEG individual alpha frequency. Future prospective studies should further investigate the clinical actionability of these specific EEG and MRI biomarkers to bring biomarkers closer to clinical reality.
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Affiliation(s)
- Debby Klooster
- Ghent Experimental Psychiatry Laboratory, Department of Head and Skin, Faculty of Medicine and Health Sciences, Ghent University, Ghent, Belgium; 4BRAIN Team, Department of Head and Skin, Faculty of Medicine and Health Sciences, Ghent University, Ghent, Belgium; Center for Care and Cure, Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, the Netherlands
| | - Helena Voetterl
- Research Institute Brainclinics, Brainclinics Foundation, Nijmegen, the Netherlands; Department of Cognitive Neuroscience, Faculty of Psychology and Neuroscience, Maastricht University, Maastricht, the Netherlands
| | - Chris Baeken
- Ghent Experimental Psychiatry Laboratory, Department of Head and Skin, Faculty of Medicine and Health Sciences, Ghent University, Ghent, Belgium; Center for Care and Cure, Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, the Netherlands; Vrije Universiteit Brussel, Universitair Ziekenhuis Brussel, Department of Psychiatry, Brussels, Belgium
| | - Martijn Arns
- Research Institute Brainclinics, Brainclinics Foundation, Nijmegen, the Netherlands; Department of Cognitive Neuroscience, Faculty of Psychology and Neuroscience, Maastricht University, Maastricht, the Netherlands.
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Vreijling SR, Chin Fatt CR, Williams LM, Schatzberg AF, Usherwood T, Nemeroff CB, Rush AJ, Uher R, Aitchison KJ, Köhler-Forsberg O, Rietschel M, Trivedi MH, Jha MK, Penninx BWJH, Beekman ATF, Jansen R, Lamers F. Features of immunometabolic depression as predictors of antidepressant treatment outcomes: pooled analysis of four clinical trials. Br J Psychiatry 2024; 224:89-97. [PMID: 38130122 PMCID: PMC10884825 DOI: 10.1192/bjp.2023.148] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/26/2023] [Revised: 10/03/2023] [Accepted: 10/19/2023] [Indexed: 12/23/2023]
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, I2 = 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, I2= 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.
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Affiliation(s)
- Sarah R. Vreijling
- Department of Psychiatry, Amsterdam University Medical Center, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands; and Mental Health Program, Amsterdam Public Health, Amsterdam, The Netherlands
| | - Cherise R. Chin Fatt
- Department of Psychiatry, University of Texas Southwestern Medical Center, Dallas, Texas, USA
| | - Leanne M. Williams
- Department of Psychiatry and Behavioral Sciences, Stanford School of Medicine, Stanford University, Stanford, California, USA
| | - Alan F. Schatzberg
- Department of Psychiatry and Behavioral Sciences, Stanford School of Medicine, Stanford University, Stanford, California, USA
| | - Tim Usherwood
- Department of General Practice, Westmead Clinical School, University of Sydney, Sydney, Australia; Westmead Applied Research Centre, Faculty of Medicine and Health, University of Sydney, Sydney, Australia; and George Institute for Global Health, Sydney, Australia
| | - Charles B. Nemeroff
- Department of Psychiatry and Behavioral Sciences, Dell Medical School, University of Texas, Austin, Texas, USA
| | - A. John Rush
- Department of Psychiatry and Behavioral Health, Duke School of Medicine, Durham, North Carolina, USA; and Duke-National University of Singapore, Singapore, Singapore
| | - Rudolf Uher
- Department of Psychiatry, Dalhousie University, Halifax, Nova Scotia, Canada
| | - Katherine J. Aitchison
- Departments of Psychiatry & Medical Genetics, College of Health Sciences, University of Alberta, Edmonton, Alberta, Canada; Neuroscience and Mental Health Institute, University of Alberta, Edmonton, Alberta, Canada; and Women and Children's Research Institute, University of Alberta, Edmonton, Alberta, Canada
| | - Ole Köhler-Forsberg
- Psychosis Research Unit, Aarhus University Hospital Psychiatry, Aarhus, Denmark; and Department of Clinical Medicine, Aarhus University, Aarhus, Denmark
| | - Marcella Rietschel
- Department of Genetic Epidemiology in Psychiatry, Central Institute of Mental Health, Faculty of Medicine Mannheim, University of Heidelberg, Mannheim, Germany
| | - Madhukar H. Trivedi
- Department of Psychiatry, University of Texas Southwestern Medical Center, Dallas, Texas, USA
| | - Manish K. Jha
- Department of Psychiatry, University of Texas Southwestern Medical Center, Dallas, Texas, USA
| | - Brenda W. J. H. Penninx
- Department of Psychiatry, Amsterdam University Medical Center, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands; Mental Health Program, Amsterdam Public Health, Amsterdam, The Netherlands; and Mood, Anxiety, Psychosis, Sleep & Stress Program, Amsterdam Neuroscience, Amsterdam, The Netherlands
| | - Aartjan T. F. Beekman
- Department of Psychiatry, Amsterdam University Medical Center, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands; Mental Health Program, Amsterdam Public Health, Amsterdam, The Netherlands; and Mood, Anxiety, Psychosis, Sleep & Stress Program, Amsterdam Neuroscience, Amsterdam, The Netherlands
| | - Rick Jansen
- Department of Psychiatry, Amsterdam University Medical Center, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands; and Mood, Anxiety, Psychosis, Sleep & Stress Program, Amsterdam Neuroscience, Amsterdam, The Netherlands
| | - Femke Lamers
- Department of Psychiatry, Amsterdam University Medical Center, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands; and Mental Health Program, Amsterdam Public Health, Amsterdam, The Netherlands
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Meijs H, Voetterl H, Sack AT, van Dijk H, De Wilde B, Van Hecke J, Niemegeers P, Gordon E, Luykx JJ, Arns M. A posterior-alpha ageing network is differentially associated with antidepressant effects of venlafaxine and rTMS. Eur Neuropsychopharmacol 2024; 79:7-16. [PMID: 38000196 DOI: 10.1016/j.euroneuro.2023.11.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/13/2023] [Revised: 11/06/2023] [Accepted: 11/11/2023] [Indexed: 11/26/2023]
Abstract
Major depressive disorder (MDD) is a highly prevalent psychiatric disorder, but chances for remission largely decrease with each failed treatment attempt. It is therefore desirable to assign a given patient to the most promising individual treatment option as early as possible. We used a polygenic score (PGS) informed electroencephalography (EEG) data-driven approach to identify potential predictors for MDD treatment outcome. Post-hoc we conducted exploratory analyses in order to understand the results in depth. First, an EEG independent component analysis produced 54 functional brain networks in a large heterogeneous cohort of psychiatric patients (n = 4,045; 5-84 yrs.). Next, the network that was associated to PGS for antidepressant-response (PRS-AR) in an independent sample (n = 722) was selected: an age-related posterior alpha network that explained >60 % of EEG variance, and was highly stable over recording time. Translational analyses were performed in two other independent datasets to examine if the network was predictive of psychopharmacotherapy (n = 535) and/or repetitive transcranial magnetic stimulation (rTMS) and concomitant psychotherapy (PT; n = 186) outcome. The network predicted remission to venlafaxine (p = 0.015), resulting in a normalized positive predicted value (nPPV) of 138 %, and rTMS + PT - but in opposite direction for women (p = 0.002) relative to men (p = 0.018) - yielding a nPPV of 131 %. Blinded out-of-sample validations for venlafaxine (n = 29) and rTMS + PT (n = 36) confirmed the findings for venlafaxine, while results for rTMS + PT could not be replicated. These data suggest the existence of a relatively stable EEG posterior alpha aging network related to PGS-AR that has potential as MDD treatment predictor.
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Affiliation(s)
- Hannah Meijs
- Research Institute Brainclinics, Brainclinics Foundation, Nijmegen, The Netherlands; Department of Cognitive Neuroscience, Faculty of Psychology and Neuroscience, Maastricht University, Maastricht, The Netherlands
| | - Helena Voetterl
- Research Institute Brainclinics, Brainclinics Foundation, Nijmegen, The Netherlands; Department of Cognitive Neuroscience, Faculty of Psychology and Neuroscience, Maastricht University, Maastricht, The Netherlands
| | - Alexander T Sack
- Department of Cognitive Neuroscience, Faculty of Psychology and Neuroscience, Maastricht University, Maastricht, The Netherlands
| | - Hanneke van Dijk
- Research Institute Brainclinics, Brainclinics Foundation, Nijmegen, The Netherlands
| | - Bieke De Wilde
- Department of Psychiatry, Ziekenhuis Netwerk Antwerpen (ZNA), Antwerp, Belgium
| | - Jan Van Hecke
- Department of Psychiatry, Ziekenhuis Netwerk Antwerpen (ZNA), Antwerp, Belgium
| | - Peter Niemegeers
- Department of Psychiatry, Ziekenhuis Netwerk Antwerpen (ZNA), Antwerp, Belgium
| | - Evian Gordon
- Brain Resource Ltd, San Francisco, CA, United States of America
| | - Jurjen J Luykx
- Research Institute Brainclinics, Brainclinics Foundation, Nijmegen, The Netherlands; Department of Cognitive Neuroscience, Faculty of Psychology and Neuroscience, Maastricht University, Maastricht, The Netherlands
| | - Martijn Arns
- Research Institute Brainclinics, Brainclinics Foundation, Nijmegen, The Netherlands; Department of Cognitive Neuroscience, Faculty of Psychology and Neuroscience, Maastricht University, Maastricht, The Netherlands.
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Ip CT, de Bardeci M, Kronenberg G, Pinborg LH, Seifritz E, Brunovsky M, Olbrich S. EEG-vigilance regulation is associated with and predicts ketamine response in major depressive disorder. Transl Psychiatry 2024; 14:64. [PMID: 38272875 PMCID: PMC10810879 DOI: 10.1038/s41398-024-02761-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/04/2023] [Revised: 01/08/2024] [Accepted: 01/10/2024] [Indexed: 01/27/2024] Open
Abstract
Ketamine offers promising new therapeutic options for difficult-to-treat depression. The efficacy of treatment response, including ketamine, has been intricately linked to EEG measures of vigilance. This research investigated the interplay between intravenous ketamine and alterations in brain arousal, quantified through EEG vigilance assessments in two distinct cohorts of depressed patients (original dataset: n = 24; testing dataset: n = 24). Clinical response was defined as a decrease from baseline of >33% on the Montgomery-Åsberg Depression Rating Scale (MADRS) 24 h after infusion. EEG recordings were obtained pre-, start-, end- and 24 h post- infusion, and the resting EEG was automatically scored using the Vigilance Algorithm Leipzig (VIGALL). Relative to placebo (sodium chloride 0.9%), ketamine increased the amount of low-vigilance stage B1 at end-infusion. This increase in B1 was positively related to serum concentrations of ketamine, but not to norketamine, and was independent of clinical response. In contrast, treatment responders showed a distinct EEG pattern characterized by a decrease in high-vigilance stage A1 and an increase in low-vigilance B2/3, regardless of whether placebo or ketamine had been given. Furthermore, pretreatment EEG differed between responders and non-responders with responders showing a higher percentage of stage A1 (53% vs. 21%). The logistic regression fitted on the percent of A1 stages was able to predict treatment outcomes in the testing dataset with an area under the ROC curve of 0.7. Ketamine affects EEG vigilance in a distinct pattern observed only in responders. Consequently, the percentage of pretreatment stage A1 shows significant potential as a predictive biomarker of treatment response.Clinical Trials Registration: https://www.clinicaltrialsregister.eu/ctr-search/trial/2013-000952-17/CZ Registration number: EudraCT Number: 2013-000952-17.
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Affiliation(s)
- Cheng-Teng Ip
- Center for Cognitive and Brain Sciences, University of Macau, Taipa, Macau SAR, China
- Neurobiology Research Unit, University Hospital Rigshospitalet, Copenhagen, Denmark
| | - Mateo de Bardeci
- Hospital for Psychiatry, Psychotherapy and Psychosomatic; University Zurich, Zurich, Switzerland
| | - Golo Kronenberg
- Hospital for Psychiatry, Psychotherapy and Psychosomatic; University Zurich, Zurich, Switzerland
| | - Lars Hageman Pinborg
- Neurobiology Research Unit, University Hospital Rigshospitalet, Copenhagen, Denmark
- Epilepsy Clinic, University Hospital Rigshospitalet, Copenhagen, Denmark
| | - Erich Seifritz
- Hospital for Psychiatry, Psychotherapy and Psychosomatic; University Zurich, Zurich, Switzerland
| | - Martin Brunovsky
- National Institute of Mental Health, Klecany, Czech Republic
- Charles University, Third Faculty of Medicine, Prague, Czech Republic
| | - Sebastian Olbrich
- Hospital for Psychiatry, Psychotherapy and Psychosomatic; University Zurich, Zurich, Switzerland.
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10
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Fagiolini A, Cardoner N, Pirildar S, Ittsakul P, Ng B, Duailibi K, El Hindy N. Moving from serotonin to serotonin-norepinephrine enhancement with increasing venlafaxine dose: clinical implications and strategies for a successful outcome in major depressive disorder. Expert Opin Pharmacother 2023; 24:1715-1723. [PMID: 37501324 DOI: 10.1080/14656566.2023.2242264] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2023] [Accepted: 07/25/2023] [Indexed: 07/29/2023]
Abstract
INTRODUCTION Mental health disorders, especially depressive and anxiety disorders, are associated with substantial health-related burden. While the second-generation antidepressants are widely accepted as first-line pharmacological treatment for major depressive disorder (MDD), patient response to such treatment is variable, with more than half failing to achieve complete remission, and residual symptoms are frequently present. AREAS COVERED Here, the pharmacodynamics of venlafaxine XR are reviewed in relation to its role as both a selective serotonin reuptake inhibitor (SSRI) and a serotonin-norepinephrine-reuptake inhibitor (SNRI), and we look at how these pharmacodynamic properties can be harnessed to guide clinical practice, asking the question 'is it possible to develop a symptom-cluster-based approach to the treatment of MDD with comorbid anxiety utilizing venlafaxine XR?.' Additionally, three illustrative clinical cases provide practical examples of the utility of venlafaxine-XR in real-world clinical practice. The place of venlafaxine XR in managing fatigue/low energy, a frequent residual symptom in MDD, is explored using pooled data from clinical trials of venlafaxine XR. EXPERT OPINION Venlafaxine XR should be considered as a first-line treatment for MDD with or without comorbid anxiety, and there are clear pharmacodynamic signals supporting a symptom cluster-based treatment paradigm for venlafaxine XR.
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Affiliation(s)
- Andrea Fagiolini
- Department of Molecular Medicine, University of Siena School of Medicine, Siena, Italy
| | - Narcis Cardoner
- Department of Psychiatry and Legal Medicine, Hospital de la Santa Creu I Sant Pau, Biomedical Research Institute Sant Pau (IIB-Sant Pau), Universitat Autònoma de Barcelona (UAB), CIBERSAM, Carlos III Health Institute, Madrid, Spain
| | - Sebnem Pirildar
- Department of Mental Health and Diseases, Ege University Medical School, Izmir, Turkey
| | - Pichai Ittsakul
- Department of Psychiatry, Faculty of Medicine, Ramathibodi Hospital, Mahidol University, Bangkok, Thailand
| | - Bernardo Ng
- Mexican Consortium of Neuropsychopharmacology, Mexico, Sun Valley Research Center, Imperial, California, USA
| | - Kalil Duailibi
- Department of Psychiatry, Santo Amaro University, São Paulo, Brazil
| | - Nasser El Hindy
- American Center Neurology and Psychiatry, Dubai, United Arab Emirates
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11
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Sajjadian M, Uher R, Ho K, Hassel S, Milev R, Frey BN, Farzan F, Blier P, Foster JA, Parikh SV, Müller DJ, Rotzinger S, Soares CN, Turecki G, Taylor VH, Lam RW, Strother SC, Kennedy SH. Prediction of depression treatment outcome from multimodal data: a CAN-BIND-1 report. Psychol Med 2023; 53:5374-5384. [PMID: 36004538 PMCID: PMC10482706 DOI: 10.1017/s0033291722002124] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/20/2021] [Revised: 05/04/2022] [Accepted: 06/20/2022] [Indexed: 11/06/2022]
Abstract
BACKGROUND Prediction of treatment outcomes is a key step in improving the treatment of major depressive disorder (MDD). The Canadian Biomarker Integration Network in Depression (CAN-BIND) aims to predict antidepressant treatment outcomes through analyses of clinical assessment, neuroimaging, and blood biomarkers. METHODS In the CAN-BIND-1 dataset of 192 adults with MDD and outcomes of treatment with escitalopram, we applied machine learning models in a nested cross-validation framework. Across 210 analyses, we examined combinations of predictive variables from three modalities, measured at baseline and after 2 weeks of treatment, and five machine learning methods with and without feature selection. To optimize the predictors-to-observations ratio, we followed a tiered approach with 134 and 1152 variables in tier 1 and tier 2 respectively. RESULTS A combination of baseline tier 1 clinical, neuroimaging, and molecular variables predicted response with a mean balanced accuracy of 0.57 (best model mean 0.62) compared to 0.54 (best model mean 0.61) in single modality models. Adding week 2 predictors improved the prediction of response to a mean balanced accuracy of 0.59 (best model mean 0.66). Adding tier 2 features did not improve prediction. CONCLUSIONS A combination of clinical, neuroimaging, and molecular data improves the prediction of treatment outcomes over single modality measurement. The addition of measurements from the early stages of treatment adds precision. Present results are limited by lack of external validation. To achieve clinically meaningful prediction, the multimodal measurement should be scaled up to larger samples and the robustness of prediction tested in an external validation dataset.
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Affiliation(s)
- Mehri Sajjadian
- Department of Psychiatry, Dalhousie University, Halifax, NS, Canada
| | - Rudolf Uher
- Department of Psychiatry, Dalhousie University, Halifax, NS, Canada
| | - Keith Ho
- University Health Network, 399 Bathurst Street, Toronto, ON, M5T 2S8, Canada
- Unity Health Toronto, St. Michael's Hospital, 193 Yonge Street, 6th floor, Toronto, ON, M5B 1M4, Canada
| | - Stefanie Hassel
- Department of Psychiatry and Mathison Centre for Mental Health Research and Education, Cumming School of Medicine, University of Calgary, 3330 Hospital Dr NW, Calgary, AB, T2N 4N1, Canada
- Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada
| | - Roumen Milev
- Departments of Psychiatry and Psychology, Queen's University, Providence Care Hospital, Kingston, ON, Canada
| | - Benicio N. Frey
- Department of Psychiatry and Behavioural Neurosciences, McMaster University, Hamilton, ON, Canada
- Mood Disorders Program and Women's Health Concerns Clinic, St. Joseph's Healthcare Hamilton, Hamilton, ON, Canada
| | - Faranak Farzan
- eBrain Lab, School of Mechatronic Systems Engineering, Simon Fraser University, Surrey, BC, Canada
| | - Pierre Blier
- The Royal's Institute of Mental Health Research, 1145 Carling Avenue, Ottawa, ON, K1Z 7K4, Canada
- Department of Cellular and Molecular Medicine, University of Ottawa, 451 Smyth Road, Ottawa, ON, K1H 8M5, Canada
- Department of Psychiatry, University of Ottawa, 1145 Carling Avenue, Ottawa, ON, K1Z 7K4, Canada
| | - Jane A. Foster
- Department of Psychiatry & Behavioural Neurosciences, St Joseph's Healthcare, Hamilton, ON, Canada
| | - Sagar V. Parikh
- Department of Psychiatry, University of Michigan, Ann Arbor, MI, USA
| | - Daniel J. Müller
- Campbell Family Mental Health Research Institute, Center for Addiction and Mental Health, Toronto, ON, Canada
- Department of Psychiatry, University of Toronto, Toronto, ON, Canada
| | - Susan Rotzinger
- Department of Psychiatry, University of Toronto, Toronto, ON, Canada
- Department of Psychiatry, St Michael's Hospital, University of Toronto, Toronto, ON, Canada
| | - Claudio N. Soares
- Department of Psychiatry, Queen's University School of Medicine, Kingston, ON, Canada
| | - Gustavo Turecki
- Department of Psychiatry, Douglas Institute, McGill University, Montreal, QC, Canada
| | - Valerie H. Taylor
- Department of Psychiatry, Foothills Medical Centre, University of Calgary, Calgary, AB, Canada
| | - Raymond W. Lam
- Department of Psychiatry, University of British Columbia, Vancouver, BC, Canada
| | - Stephen C. Strother
- Rotman Research Center, Baycrest, Toronto, Canada
- Department of Medical Biophysics, University of Toronto, Toronto, Canada
| | - Sidney H. Kennedy
- Department of Psychiatry, University of Toronto, Toronto, ON, Canada
- Department of Psychiatry, St Michael's Hospital, University of Toronto, Toronto, ON, Canada
- Department of Psychiatry, University Health Network, Toronto, Ontario, Canada
- Krembil Research Centre, University Health Network, University of Toronto, Toronto, Canada
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12
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Wang Y, Zhou J, Ye J, Sun Z, He Y, Zhao Y, Ren S, Zhang G, Liu M, Zheng P, Wang G, Yang J. Multi-omics reveal microbial determinants impacting the treatment outcome of antidepressants in major depressive disorder. MICROBIOME 2023; 11:195. [PMID: 37641148 PMCID: PMC10464022 DOI: 10.1186/s40168-023-01635-6] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/10/2023] [Accepted: 07/30/2023] [Indexed: 08/31/2023]
Abstract
BACKGROUND There is a growing body of evidence suggesting that disturbance of the gut-brain axis may be one of the potential causes of major depressive disorder (MDD). However, the effects of antidepressants on the gut microbiota, and the role of gut microbiota in influencing antidepressant efficacy are still not fully understood. RESULTS To address this knowledge gap, a multi-omics study was undertaken involving 110 MDD patients treated with escitalopram (ESC) for a period of 12 weeks. This study was conducted within a cohort and compared to a reference group of 166 healthy individuals. It was found that ESC ameliorated abnormal blood metabolism by upregulating MDD-depleted amino acids and downregulating MDD-enriched fatty acids. On the other hand, the use of ESC showed a relatively weak inhibitory effect on the gut microbiota, leading to a reduction in microbial richness and functions. Machine learning-based multi-omics integrative analysis revealed that gut microbiota contributed to the changes in plasma metabolites and was associated with several amino acids such as tryptophan and its gut microbiota-derived metabolite, indole-3-propionic acid (I3PA). Notably, a significant correlation was observed between the baseline microbial richness and clinical remission at week 12. Compared to non-remitters, individuals who achieved remission had a higher baseline microbial richness, a lower dysbiosis score, and a more complex and well-organized community structure and bacterial networks within their microbiota. These findings indicate a more resilient microbiota community in remitters. Furthermore, we also demonstrated that it was not the composition of the gut microbiota itself, but rather the presence of sporulation genes at baseline that could predict the likelihood of clinical remission following ESC treatment. The predictive model based on these genes revealed an area under the curve (AUC) performance metric of 0.71. CONCLUSION This study provides valuable insights into the role of the gut microbiota in the mechanism of ESC treatment efficacy for patients with MDD. The findings represent a significant advancement in understanding the intricate relationship among antidepressants, gut microbiota, and the blood metabolome. Additionally, this study offers a microbiota-centered perspective that can potentially improve antidepressant efficacy in clinical practice. By shedding light on the interplay between these factors, this research contributes to our broader understanding of the complex mechanisms underlying the treatment of MDD and opens new avenues for optimizing therapeutic approaches. Video Abstract.
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Affiliation(s)
- Yaping Wang
- Beijing Key Laboratory of Mental Disorders, National Clinical Research Center for Mental Disorders & National Center for Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, 100088, China
- Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing, 100069, China
| | - Jingjing Zhou
- Beijing Key Laboratory of Mental Disorders, National Clinical Research Center for Mental Disorders & National Center for Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, 100088, China
- Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing, 100069, China
| | - Junbin Ye
- Beijing WeGenome Paradigm Co., Ltd, Beijing, China
| | - Zuoli Sun
- Beijing Key Laboratory of Mental Disorders, National Clinical Research Center for Mental Disorders & National Center for Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, 100088, China
- Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing, 100069, China
| | - Yi He
- Beijing Key Laboratory of Mental Disorders, National Clinical Research Center for Mental Disorders & National Center for Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, 100088, China
- Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing, 100069, China
| | - Yingxin Zhao
- Beijing Key Laboratory of Mental Disorders, National Clinical Research Center for Mental Disorders & National Center for Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, 100088, China
- Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing, 100069, China
| | - Siyu Ren
- Beijing Key Laboratory of Mental Disorders, National Clinical Research Center for Mental Disorders & National Center for Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, 100088, China
- Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing, 100069, China
| | - Guofu Zhang
- Beijing Key Laboratory of Mental Disorders, National Clinical Research Center for Mental Disorders & National Center for Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, 100088, China
- Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing, 100069, China
| | - Min Liu
- Beijing Key Laboratory of Mental Disorders, National Clinical Research Center for Mental Disorders & National Center for Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, 100088, China
- Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing, 100069, China
| | - Peng Zheng
- Department of Neurology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, 400016, China
- NHC Key Laboratory of Diagnosis and Treatment On Brain Functional Diseases, The First Affiliated Hospital of Chongqing Medical University, Chongqing, 400016, China
| | - Gang Wang
- Beijing Key Laboratory of Mental Disorders, National Clinical Research Center for Mental Disorders & National Center for Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, 100088, China.
- Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing, 100069, China.
| | - Jian Yang
- Beijing Key Laboratory of Mental Disorders, National Clinical Research Center for Mental Disorders & National Center for Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, 100088, China.
- Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing, 100069, China.
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13
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Hack LM, Tozzi L, Zenteno S, Olmsted AM, Hilton R, Jubeir J, Korgaonkar MS, Schatzberg AF, Yesavage JA, O’Hara R, Williams LM. A Cognitive Biotype of Depression and Symptoms, Behavior Measures, Neural Circuits, and Differential Treatment Outcomes: A Prespecified Secondary Analysis of a Randomized Clinical Trial. JAMA Netw Open 2023; 6:e2318411. [PMID: 37318808 PMCID: PMC10273022 DOI: 10.1001/jamanetworkopen.2023.18411] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/08/2023] [Accepted: 04/27/2023] [Indexed: 06/16/2023] Open
Abstract
Importance Cognitive deficits in depression have been associated with poor functional capacity, frontal neural circuit dysfunction, and worse response to conventional antidepressants. However, it is not known whether these impairments combine together to identify a specific cognitive subgroup (or "biotype") of individuals with major depressive disorder (MDD), and the extent to which these impairments mediate antidepressant outcomes. Objective To undertake a systematic test of the validity of a proposed cognitive biotype of MDD across neural circuit, symptom, social occupational function, and treatment outcome modalities. Design, Setting, and Participants This secondary analysis of a randomized clinical trial implemented data-driven clustering in findings from the International Study to Predict Optimized Treatment in Depression, a pragmatic biomarker trial in which patients with MDD were randomized in a 1:1:1 ratio to antidepressant treatment with escitalopram, sertraline, or venlafaxine extended-release and assessed at baseline and 8 weeks on multimodal outcomes between December 1, 2008, and September 30, 2013. Eligible patients were medication-free outpatients with nonpsychotic MDD in at least the moderate range, and were recruited from 17 clinical and academic practices; a subset of these patients underwent functional magnetic resonance imaging. This prespecified secondary analysis was performed between June 10, 2022, and April 21, 2023. Main Outcomes and Measures Pretreatment and posttreatment behavioral measures of cognitive performance across 9 domains, depression symptoms assessed using 2 standard depression scales, and psychosocial function assessed using the Social and Occupational Functioning Assessment Scale and World Health Organization Quality of Life scale were analyzed. Neural circuit function engaged during a cognitive control task was measured using functional magnetic resonance imaging. Results A total of 1008 patients (571 [56.6%] female; mean [SD] age, 37.8 [12.6] years) participated in the overall trial and 96 patients participated in the imaging substudy (45 [46.7%] female; mean [SD] age, 34.5 [13.5] years). Cluster analysis identified what may be referred to as a cognitive biotype of 27% of depressed patients with prominent behavioral impairment in executive function and response inhibition domains of cognitive control. This biotype was characterized by a specific profile of pretreatment depressive symptoms, worse psychosocial functioning (d = -0.25; 95% CI, -0.39 to -0.11; P < .001), and reduced activation of the cognitive control circuit (right dorsolateral prefrontal cortex: d = -0.78; 95% CI, -1.28 to -0.27; P = .003). Remission was comparatively lower in the cognitive biotype positive subgroup (73 of 188 [38.8%] vs 250 of 524 [47.7%]; P = .04) and cognitive impairments persisted regardless of symptom change (executive function: ηp2 = 0.241; P < .001; response inhibition: ηp2 = 0.750; P < .001). The extent of symptom and functional change was specifically mediated by change in cognition but not the reverse. Conclusions and Relevance Our findings suggest the presence of a cognitive biotype of depression with distinct neural correlates, and a functional clinical profile that responds poorly to standard antidepressants and instead may benefit from therapies specifically targeting cognitive dysfunction. Trial Registration ClinicalTrials.gov Identifier: NCT00693849.
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Affiliation(s)
- Laura M. Hack
- Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, California
- Sierra-Pacific Mental Illness Research, Education, and Clinical Center (MIRECC) Veterans Affairs Palo Alto Health Care System, Palo Alto, California
| | - Leonardo Tozzi
- Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, California
| | - Samantha Zenteno
- Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, California
| | - Alisa M. Olmsted
- Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, California
- Sierra-Pacific Mental Illness Research, Education, and Clinical Center (MIRECC) Veterans Affairs Palo Alto Health Care System, Palo Alto, California
| | - Rachel Hilton
- Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, California
| | - Jenna Jubeir
- Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, California
| | - Mayuresh S. Korgaonkar
- Brain Dynamic Centre, Westmead Institute for Medical Research, The University of Sydney, Westmead, New South Wales, Australia
| | - Alan F. Schatzberg
- Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, California
| | - Jerome A. Yesavage
- Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, California
- Sierra-Pacific Mental Illness Research, Education, and Clinical Center (MIRECC) Veterans Affairs Palo Alto Health Care System, Palo Alto, California
| | - Ruth O’Hara
- Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, California
- Sierra-Pacific Mental Illness Research, Education, and Clinical Center (MIRECC) Veterans Affairs Palo Alto Health Care System, Palo Alto, California
| | - Leanne M. Williams
- Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, California
- Sierra-Pacific Mental Illness Research, Education, and Clinical Center (MIRECC) Veterans Affairs Palo Alto Health Care System, Palo Alto, California
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14
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Hietamies TM, McInnes LA, Klise AJ, Worley MJ, Qian JJ, Williams LM, Heifets BD, Levine SP. The effects of ketamine on symptoms of depression and anxiety in real-world care settings: A retrospective controlled analysis. J Affect Disord 2023:S0165-0327(23)00611-0. [PMID: 37201900 DOI: 10.1016/j.jad.2023.04.141] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/04/2022] [Revised: 03/30/2023] [Accepted: 04/29/2023] [Indexed: 05/20/2023]
Abstract
INTRODUCTION Ketamine intravenous therapy (KIT) appears effective for treating depression in controlled trials testing a short series of infusions. A rapidly proliferating number of clinics offer KIT for depression and anxiety, using protocols without a strong evidence basis. Controlled comparison of mood and anxiety from real-world KIT clinics, and the stability of outcomes, is lacking. METHODS We performed a retrospective controlled analysis on patients treated with KIT in ten community clinics across the US, between 08/2017-03/2020. Depression and anxiety symptoms were evaluated using the Quick Inventory of Depressive Symptomatology-Self Report 16-item (QIDS) and the Generalized Anxiety Disorder 7-item (GAD-7) scales, respectively. Comparison data sets from patients who did not undergo KIT were obtained from previously published real-world studies. RESULTS Of 2758 patients treated, 714 and 836 met criteria for analysis of KIT induction and maintenance outcomes, respectively. Patients exhibited significant and concordant reduction in both anxiety and depression symptoms after induction (Cohen's d = 1.14 and d = 0.9, respectively). Compared to two external datasets of KIT-naive depressed patients or patients starting standard antidepressant therapy, KIT patients experienced a significantly greater reduction in depression symptoms at eight weeks (Cohen's d = -1.03 and d = -0.62 respectively). Furthermore, we identified a subpopulation of late-responders. During maintenance, up to a year post-induction, increases in symptoms were minimal. LIMITATIONS Due to the retrospective nature of the analyses, interpreting this dataset is limited by incomplete patient information and sample attrition. CONCLUSIONS KIT treatment elicited robust symptomatic relief that remained stable up to one year of follow-up.
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Affiliation(s)
- Tuuli M Hietamies
- Stanford University School of Medicine, Stanford, CA, USA; Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Stanford, CA, USA.
| | | | - Andrew J Klise
- Stanford University School of Medicine, Stanford, CA, USA.
| | | | - Jimmy J Qian
- Stanford University School of Medicine, Stanford, CA, USA; Osmind, San Francisco, CA, USA.
| | - Leanne M Williams
- Stanford University School of Medicine, Stanford, CA, USA; Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, CA, USA.
| | - Boris D Heifets
- Stanford University School of Medicine, Stanford, CA, USA; Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Stanford, CA, USA; Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, CA, USA.
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15
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Zhang S, Zhou J, Cui J, Zhang Z, Liu R, Feng Y, Feng L, Wang Y, Chen X, Wu H, Jin Y, Zhou Y, Wang G. Effects of 12-week escitalopram treatment on resting-state functional connectivity of large-scale brain networks in major depressive disorder. Hum Brain Mapp 2023; 44:2572-2584. [PMID: 36773284 PMCID: PMC10028676 DOI: 10.1002/hbm.26231] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2022] [Revised: 01/06/2023] [Accepted: 01/30/2023] [Indexed: 02/12/2023] Open
Abstract
In this study, the effects of antidepressants on large-scale brain networks and the neural basis of individual differences in response were explored. A total of 41 patients with major depressive disorder (MDD) and 42 matched healthy controls (HCs) were scanned by resting-state functional magnetic resonance imaging separately at baseline and after a 12-week follow-up. The patients with MDD received escitalopram for 12 weeks. After treatment, patients were classified into those with MDD in remission [MDDr, endpoint 17-item Hamilton Depression Rating Scale (HAMD) total score ≤7] and those in nonremission (MDDnr). The human Brainnetome Atlas was used to define large-scale networks and compute within- and between-network resting-state functional connectivity (rsFC). Results showed the decreased subcortical network (SCN)-ventral attention network (VAN) connectivity at baseline increased in patients with MDD after 12-week treatment, and it was comparable with that of HCs. This change was only observed in patients with MDDr. However, the decreased within-network rsFC in SCN and default mode network (DMN) persisted in all patients with MDD, including those with MDDr and MDDnr, after treatment. The strength of SCN-VAN connectivity at baseline was significantly negatively correlated with the reduction rate of HAMD score in all patients with MDD. Thus, SCN-VAN connectivity may be an antidepressant target associated with depressive state changes and a predictor of treatment response to serotonin reuptake inhibitors. The within-network rsFC in SCN and DMN may reflect a trait-like abnormality in MDD. These findings provide further insights into the mechanism of antidepressants and their individual differences in response. The trial name is "Appropriate technology study of MDD diagnosis and treatment based on objective indicators and measurement" (URL: http://www.chictr.org.cn/showproj.aspx?proj=21377; registration number: ChiCTR-OOC-17012566).
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Affiliation(s)
- Shudong Zhang
- The National Clinical Research Center for Mental Disorders & Beijing Key Laboratory of Mental Disorders, Beijing Anding Hospital & the Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing, China
| | - Jingjing Zhou
- The National Clinical Research Center for Mental Disorders & Beijing Key Laboratory of Mental Disorders, Beijing Anding Hospital & the Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing, China
| | - Jian Cui
- The National Clinical Research Center for Mental Disorders & Beijing Key Laboratory of Mental Disorders, Beijing Anding Hospital & the Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing, China
- Department of Psychiatry, Shandong Daizhuang Hospital, Jining, China
| | - Zhifang Zhang
- The National Clinical Research Center for Mental Disorders & Beijing Key Laboratory of Mental Disorders, Beijing Anding Hospital & the Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing, China
| | - Rui Liu
- The National Clinical Research Center for Mental Disorders & Beijing Key Laboratory of Mental Disorders, Beijing Anding Hospital & the Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing, China
| | - Yuan Feng
- The National Clinical Research Center for Mental Disorders & Beijing Key Laboratory of Mental Disorders, Beijing Anding Hospital & the Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing, China
| | - Lei Feng
- The National Clinical Research Center for Mental Disorders & Beijing Key Laboratory of Mental Disorders, Beijing Anding Hospital & the Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing, China
| | - Yun Wang
- The National Clinical Research Center for Mental Disorders & Beijing Key Laboratory of Mental Disorders, Beijing Anding Hospital & the Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing, China
| | - Xiongying Chen
- The National Clinical Research Center for Mental Disorders & Beijing Key Laboratory of Mental Disorders, Beijing Anding Hospital & the Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing, China
| | - Hang Wu
- The National Clinical Research Center for Mental Disorders & Beijing Key Laboratory of Mental Disorders, Beijing Anding Hospital & the Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing, China
| | - Yuening Jin
- CAS Key Laboratory of Behavioral Science, Institute of Psychology, Beijing, China
- Department of Psychology, University of Chinese Academy of Sciences, Beijing, China
| | - Yuan Zhou
- The National Clinical Research Center for Mental Disorders & Beijing Key Laboratory of Mental Disorders, Beijing Anding Hospital & the Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing, China
- CAS Key Laboratory of Behavioral Science, Institute of Psychology, Beijing, China
- Department of Psychology, University of Chinese Academy of Sciences, Beijing, China
| | - Gang Wang
- The National Clinical Research Center for Mental Disorders & Beijing Key Laboratory of Mental Disorders, Beijing Anding Hospital & the Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing, China
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16
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Ip CT, Ganz M, Ozenne B, Olbrich S, Beliveau V, Dam VH, Köhler-Forsberg K, Jørgensen MB, Frøkjær VG, Knudsen GM. Association between the loudness dependence of auditory evoked potential, serotonergic neurotransmission and treatment outcome in patients with depression. Eur Neuropsychopharmacol 2023; 70:32-44. [PMID: 36863106 DOI: 10.1016/j.euroneuro.2023.02.008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/08/2022] [Revised: 02/06/2023] [Accepted: 02/08/2023] [Indexed: 03/04/2023]
Abstract
Previous studies have suggested that the loudness dependence of auditory evoked potential (LDAEP) is associated with the effectiveness of antidepressant treatment in patients with major depressive disorders (MDD). Furthermore, both LDAEP and the cerebral serotonin 4 receptor (5-HT4R) density is inversely related to brain serotonin levels. We included 84 patients with MDD and 22 healthy controls to examined the association between LDAEP and treatment response and its association with cerebral 5-HT4R density. Participants underwent both EEG and 5-HT4R neuroimaging with [11C]SB207145 PET. Thirty-nine patients with MDD were re-examined after 8 weeks of treatment with selective serotonin reuptake inhibitors/serotonin noradrenaline reuptake inhibitor (SSRI/SNRI). We found that the cortical source of LDAEP was higher in untreated patients with MDD compared to healthy controls (p=0.03). Prior to SSRI/SNRI treatment, subsequent treatment responders had a negative association between LDAEP and depressive symptoms and a positive association between scalp LDAEP and symptom improvement at week 8. This was not found in source LDAEP. In healthy controls, we found a positive correlation between both scalp and source LDAEP and cerebral 5-HT4R binding but that was not observed in patients with MDD. We did not see any changes in scalp and source LDAEP in response to SSRI/SNRI treatment. These results support a theoretical framework where both LDAEP and cerebral 5-HT4R are indices of cerebral 5-HT levels in healthy individuals while this association seems to be disrupted in MDD. The combination of the two biomarkers may be useful for stratifying patients with MDD. Clinical Trials Registration:https://clinicaltrials.gov/ct2/show/NCT02869035?draw=1Registration number: NCT0286903.
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Affiliation(s)
- Cheng-Teng Ip
- Neurobiology Research Unit and NeuroPharm, University Hospital Rigshospitalet, Copenhagen, Denmark; Center for Cognitive and Brain Sciences, University of Macau, Taipa, Macau SAR, China
| | - Melanie Ganz
- Neurobiology Research Unit and NeuroPharm, University Hospital Rigshospitalet, Copenhagen, Denmark; Department of Computer Science, University of Copenhagen, Copenhagen, Denmark
| | - Brice Ozenne
- Neurobiology Research Unit and NeuroPharm, University Hospital Rigshospitalet, Copenhagen, Denmark; Department of Public Health, Section of Biostatistics, University of Copenhagen, Denmark
| | - Sebastian Olbrich
- Department for Psychiatry, Psychotherapy and Psychosomatic, University Zurich, Switzerland
| | - Vincent Beliveau
- Neurobiology Research Unit and NeuroPharm, University Hospital Rigshospitalet, Copenhagen, Denmark; Department of Neurology, Medical University of Innsbruck, Innsbruck, Austria
| | - Vibeke H Dam
- Neurobiology Research Unit and NeuroPharm, University Hospital Rigshospitalet, Copenhagen, Denmark
| | - Kristin Köhler-Forsberg
- Neurobiology Research Unit and NeuroPharm, University Hospital Rigshospitalet, Copenhagen, Denmark; Department of Psychiatry, Psychiatric Centre Copenhagen, Copenhagen, Denmark
| | - Martin B Jørgensen
- Department of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark; Department of Psychiatry, Psychiatric Centre Copenhagen, Copenhagen, Denmark
| | - Vibe G Frøkjær
- Neurobiology Research Unit and NeuroPharm, University Hospital Rigshospitalet, Copenhagen, Denmark; Department of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark; Department of Psychiatry, Psychiatric Centre Copenhagen, Copenhagen, Denmark
| | - Gitte M Knudsen
- Neurobiology Research Unit and NeuroPharm, University Hospital Rigshospitalet, Copenhagen, Denmark; Department of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark.
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17
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Castro Martínez JC, Santamaría-García H. Understanding mental health through computers: An introduction to computational psychiatry. Front Psychiatry 2023; 14:1092471. [PMID: 36824671 PMCID: PMC9941647 DOI: 10.3389/fpsyt.2023.1092471] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/08/2022] [Accepted: 01/16/2023] [Indexed: 02/10/2023] Open
Abstract
Computational psychiatry recently established itself as a new tool in the study of mental disorders and problems. Integration of different levels of analysis is creating computational phenotypes with clinical and research values, and constructing a way to arrive at precision psychiatry are part of this new branch. It conceptualizes the brain as a computational organ that receives from the environment parameters to respond to challenges through calculations and algorithms in continuous feedback and feedforward loops with a permanent degree of uncertainty. Through this conception, one can seize an understanding of the cerebral and mental processes in the form of theories or hypotheses based on data. Using these approximations, a better understanding of the disorder and its different determinant factors facilitates the diagnostics and treatment by having an individual, ecologic, and holistic approach. It is a tool that can be used to homologate and integrate multiple sources of information given by several theoretical models. In conclusion, it helps psychiatry achieve precision and reproducibility, which can help the mental health field achieve significant advancement. This article is a narrative review of the basis of the functioning of computational psychiatry with a critical analysis of its concepts.
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Affiliation(s)
- Juan Camilo Castro Martínez
- Departamento de Psiquiatría y Salud Mental, Facultad de Medicina, Pontificia Universidad Javeriana, Bogotá, Colombia
| | - Hernando Santamaría-García
- Ph.D. Programa de Neurociencias, Departamento de Psiquiatría y Salud Mental, Pontificia Universidad Javeriana, Bogotá, Colombia
- Centro de Memoria y Cognición Intellectus, Hospital Universitario San Ignacio, Bogotá, Colombia
- Global Brain Health Institute, University of California, San Francisco – Trinity College Dublin, San Francisco, CA, United States
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18
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Wang Y, Li X, Yan H, Zhang Q, Ou Y, Wu W, Shangguan W, Chen W, Yu Y, Liang J, Wu W, Liao H, Liu Z, Mai X, Xie G, Guo W. Multiple examinations indicated associations between abnormal regional homogeneity and cognitive dysfunction in major depressive disorder. Front Psychol 2023; 13:1090181. [PMID: 36778176 PMCID: PMC9909210 DOI: 10.3389/fpsyg.2022.1090181] [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: 11/05/2022] [Accepted: 12/28/2022] [Indexed: 01/27/2023] Open
Abstract
Background This study aimed to investigate the relationships between regional neural activity and multiple related indicators in patients with major depressive disorder (MDD). Methods Forty-two patients and 42 healthy controls (HCs) were enrolled. Pearson/Spearman correlation analyses were applied to examine the associations between abnormal regional homogeneity (ReHo) and different indicators in the patients. Results Compared with HCs, patients with MDD had increased ReHo in the left inferior temporal gyrus (ITG) and decreased ReHo values in the left putamen, anterior cingulate cortex (ACC), and precentral gyrus. The ReHo of the left putamen was positively correlated with the PR interval, Repeatable Battery for the Assessment of Neuropsychological Status 4A, and Discriminant analysis (D), and negatively correlated with Ae (block) and Ae (total) in the patients. The ReHo value of the left ACC was positively correlated with the severity of depression, Stroop Color Word Test of C - 2B + 100 in reaction time, and negatively correlated with Ce (Missay) and Perseverative Responses in the patients. The ReHo of the left ITG was positively correlated with the Neuroticism scores and negatively correlated with the Lie scores in the patients. Conclusion These results suggested that the decreased ReHo of the salience network might be the underpinning of cognitive impairments in patients with MDD.
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Affiliation(s)
- Yun Wang
- Department of Psychiatry, The Third People's Hospital of Foshan, Foshan, Guangdong, China
| | - Xiaoling Li
- Department of Psychiatry, The Third People's Hospital of Foshan, Foshan, Guangdong, China
| | - Haohao Yan
- Department of Psychiatry, National Clinical Research Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha, Hunan, China
| | - Qinqin Zhang
- Department of Psychiatry, The Third People's Hospital of Foshan, Foshan, Guangdong, China
| | - Yangpan Ou
- Department of Psychiatry, National Clinical Research Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha, Hunan, China
| | - Weibin Wu
- Department of Psychiatry, The Third People's Hospital of Foshan, Foshan, Guangdong, China
| | - Webo Shangguan
- Department of Psychiatry, The Third People's Hospital of Foshan, Foshan, Guangdong, China
| | - Wensheng Chen
- Department of Psychiatry, The Third People's Hospital of Foshan, Foshan, Guangdong, China
| | - Yang Yu
- Department of Psychiatry, The Third People's Hospital of Foshan, Foshan, Guangdong, China
| | - Jiaquan Liang
- Department of Psychiatry, The Third People's Hospital of Foshan, Foshan, Guangdong, China
| | - Wanting Wu
- Department of Psychiatry, The Third People's Hospital of Foshan, Foshan, Guangdong, China
| | - Hairong Liao
- Department of Psychiatry, The Third People's Hospital of Foshan, Foshan, Guangdong, China
| | - Zishan Liu
- Department of Psychiatry, The Third People's Hospital of Foshan, Foshan, Guangdong, China
| | - Xiancong Mai
- Department of Psychiatry, The Third People's Hospital of Foshan, Foshan, Guangdong, China
| | - Guojun Xie
- Department of Psychiatry, The Third People's Hospital of Foshan, Foshan, Guangdong, China,*Correspondence: Guojun Xie, ✉
| | - Wenbin Guo
- Department of Psychiatry, National Clinical Research Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha, Hunan, China,Wenbin Guo, ✉
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19
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Lee CT, Palacios J, Richards D, Hanlon AK, Lynch K, Harty S, Claus N, Swords L, O’Keane V, Stephan KE, Gillan CM. The Precision in Psychiatry (PIP) study: Testing an internet-based methodology for accelerating research in treatment prediction and personalisation. BMC Psychiatry 2023; 23:25. [PMID: 36627607 PMCID: PMC9832676 DOI: 10.1186/s12888-022-04462-5] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/28/2022] [Accepted: 12/09/2022] [Indexed: 01/12/2023] Open
Abstract
BACKGROUND Evidence-based treatments for depression exist but not all patients benefit from them. Efforts to develop predictive models that can assist clinicians in allocating treatments are ongoing, but there are major issues with acquiring the volume and breadth of data needed to train these models. We examined the feasibility, tolerability, patient characteristics, and data quality of a novel protocol for internet-based treatment research in psychiatry that may help advance this field. METHODS A fully internet-based protocol was used to gather repeated observational data from patient cohorts receiving internet-based cognitive behavioural therapy (iCBT) (N = 600) or antidepressant medication treatment (N = 110). At baseline, participants provided > 600 data points of self-report data, spanning socio-demographics, lifestyle, physical health, clinical and other psychological variables and completed 4 cognitive tests. They were followed weekly and completed another detailed clinical and cognitive assessment at week 4. In this paper, we describe our study design, the demographic and clinical characteristics of participants, their treatment adherence, study retention and compliance, the quality of the data gathered, and qualitative feedback from patients on study design and implementation. RESULTS Participant retention was 92% at week 3 and 84% for the final assessment. The relatively short study duration of 4 weeks was sufficient to reveal early treatment effects; there were significant reductions in 11 transdiagnostic psychiatric symptoms assessed, with the largest improvement seen for depression. Most participants (66%) reported being distracted at some point during the study, 11% failed 1 or more attention checks and 3% consumed an intoxicating substance. Data quality was nonetheless high, with near perfect 4-week test retest reliability for self-reported height (ICC = 0.97). CONCLUSIONS An internet-based methodology can be used efficiently to gather large amounts of detailed patient data during iCBT and antidepressant treatment. Recruitment was rapid, retention was relatively high and data quality was good. This paper provides a template methodology for future internet-based treatment studies, showing that such an approach facilitates data collection at a scale required for machine learning and other data-intensive methods that hope to deliver algorithmic tools that can aid clinical decision-making in psychiatry.
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Affiliation(s)
- Chi Tak Lee
- grid.8217.c0000 0004 1936 9705School of Psychology, Trinity College Dublin, Dublin, Ireland ,grid.8217.c0000 0004 1936 9705Trinity College Institute of Neuroscience, Trinity College Dublin, Dublin, Ireland
| | - Jorge Palacios
- grid.8217.c0000 0004 1936 9705School of Psychology, Trinity College Dublin, Dublin, Ireland ,grid.487403.c0000 0004 7474 9161SilverCloud Science, SilverCloud Health, Dublin, Ireland
| | - Derek Richards
- grid.8217.c0000 0004 1936 9705School of Psychology, Trinity College Dublin, Dublin, Ireland ,grid.487403.c0000 0004 7474 9161SilverCloud Science, SilverCloud Health, Dublin, Ireland
| | - Anna K. Hanlon
- grid.8217.c0000 0004 1936 9705School of Psychology, Trinity College Dublin, Dublin, Ireland ,grid.8217.c0000 0004 1936 9705Trinity College Institute of Neuroscience, Trinity College Dublin, Dublin, Ireland
| | - Kevin Lynch
- grid.8217.c0000 0004 1936 9705School of Psychology, Trinity College Dublin, Dublin, Ireland ,grid.8217.c0000 0004 1936 9705Trinity College Institute of Neuroscience, Trinity College Dublin, Dublin, Ireland
| | - Siobhan Harty
- grid.8217.c0000 0004 1936 9705School of Psychology, Trinity College Dublin, Dublin, Ireland ,grid.8217.c0000 0004 1936 9705Trinity College Institute of Neuroscience, Trinity College Dublin, Dublin, Ireland ,grid.487403.c0000 0004 7474 9161SilverCloud Science, SilverCloud Health, Dublin, Ireland
| | - Nathalie Claus
- grid.5252.00000 0004 1936 973XDepartment of Psychology, Division of Clinical Psychology and Psychological Treatment, Ludwig-Maximilians-University Munich, Munich, Germany
| | - Lorraine Swords
- grid.8217.c0000 0004 1936 9705School of Psychology, Trinity College Dublin, Dublin, Ireland
| | - Veronica O’Keane
- grid.8217.c0000 0004 1936 9705Trinity College Institute of Neuroscience, Trinity College Dublin, Dublin, Ireland ,grid.8217.c0000 0004 1936 9705School of Medicine, Trinity College Dublin, Dublin, Ireland ,grid.413305.00000 0004 0617 5936Tallaght Hospital, Trinity Centre for Health Sciences, Tallaght University Hospital, Tallaght, Dublin, Ireland
| | - Klaas E Stephan
- grid.5801.c0000 0001 2156 2780Institute for Biomedical Engineering, Translational Neuromodeling Unit, University of Zürich & Eidgenössische Technische Hochschule, Zurich, Switzerland ,grid.418034.a0000 0004 4911 0702Max Planck Institute for Metabolism Research, Cologne, Germany
| | - Claire M Gillan
- School of Psychology, Trinity College Dublin, Dublin, Ireland. .,Trinity College Institute of Neuroscience, Trinity College Dublin, Dublin, Ireland. .,Global Brain Health Institute, Trinity College Dublin, Dublin, Ireland.
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20
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Key AP, Thornton-Wells TA, Smith DG. Electrophysiological biomarkers and age characterize phenotypic heterogeneity among individuals with major depressive disorder. Front Hum Neurosci 2023; 16:1055685. [PMID: 36699961 PMCID: PMC9870293 DOI: 10.3389/fnhum.2022.1055685] [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: 09/28/2022] [Accepted: 12/02/2022] [Indexed: 01/11/2023] Open
Abstract
Introduction: Despite the high need for effective treatments for major depressive disorder (MDD), the development of novel medicines is hampered by clinical, genetic and biological heterogeneity, unclear links between symptoms and neural dysfunction, and tenuous biomarkers for clinical trial contexts of use. Methods: In this study, we examined the International Study to Predict Optimized Treatment in Depression (iSPOT-D) clinical trial database for new relationships between auditory event-related potential (ERP) responses, demographic features, and clinical symptoms and behavior, to inform strategies for biomarker-driven patient stratification that could be used to optimize future clinical trial design and drug development strategy in MDD. Results: We replicate findings from previous analyses of the classic auditory oddball task in the iSPOT-D sample showing smaller than typical N1 and P300 response amplitudes and longer P300 latencies for target and standard stimuli in patients with MDD, suggesting altered bottom-up sensory and top-down attentional processes. We further demonstrate that age is an important contributor to clinical group differences, affecting both topographic distribution of the clinically informative ERP responses and the types of the stimuli sensitive to group differences. In addition, the observed brain-behavior associations indicate that levels of anxiety and stress are major contributing factors to atypical sensory and attentional processing among patients with MDD, particularly in the older subgroups. Discussion: Our novel findings support the possibility of accelerated cognitive aging in patients with MDD and identify the frontal P300 latency as an additional candidate biomarker of MDD. These results from a large, well-phenotyped sample support the view that heterogeneity of the clinical population with MDD can be systematically characterized based on age and neural biomarkers of sensory and attentional processing, informing patient stratification strategies in the design of clinical trials.
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Affiliation(s)
- Alexandra P. Key
- Department of Hearing and Speech Sciences, Vanderbilt University Medical Center, Nashville, TN, United States,*Correspondence: Alexandra P. Key
| | - Tricia A. Thornton-Wells
- Translational Medicine, Pharmaceutical and Early-Stage Clinical Development, Alkermes, Inc., Waltham, MA, United States
| | - Daniel G. Smith
- Translational Medicine, Pharmaceutical and Early-Stage Clinical Development, Alkermes, Inc., Waltham, MA, United States
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21
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Pinotsis DA, Fitzgerald S, See C, Sementsova A, Widge AS. Toward biophysical markers of depression vulnerability. Front Psychiatry 2022; 13:938694. [PMID: 36329919 PMCID: PMC9622949 DOI: 10.3389/fpsyt.2022.938694] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/07/2022] [Accepted: 09/22/2022] [Indexed: 11/13/2022] Open
Abstract
A major difficulty with treating psychiatric disorders is their heterogeneity: different neural causes can lead to the same phenotype. To address this, we propose describing the underlying pathophysiology in terms of interpretable, biophysical parameters of a neural model derived from the electroencephalogram. We analyzed data from a small patient cohort of patients with depression and controls. Using DCM, we constructed biophysical models that describe neural dynamics in a cortical network activated during a task that is used to assess depression state. We show that biophysical model parameters are biomarkers, that is, variables that allow subtyping of depression at a biological level. They yield a low dimensional, interpretable feature space that allowed description of differences between individual patients with depressive symptoms. They could capture internal heterogeneity/variance of depression state and achieve significantly better classification than commonly used EEG features. Our work is a proof of concept that a combination of biophysical models and machine learning may outperform earlier approaches based on classical statistics and raw brain data.
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Affiliation(s)
- D. A. Pinotsis
- Centre for Mathematical Neuroscience and Psychology, Department of Psychology, City, University of London, London, United Kingdom
- The Picower Institute for Learning and Memory, Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, United States
| | - S. Fitzgerald
- Centre for Mathematical Neuroscience and Psychology, Department of Psychology, City, University of London, London, United Kingdom
| | - C. See
- Department of Computer Science, City, University of London, London, United Kingdom
| | - A. Sementsova
- Department of Computer Science, City, University of London, London, United Kingdom
| | - A. S. Widge
- Department of Psychiatry and Behavioral Sciences, University of Minnesota, Minneapolis, MN, United States
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22
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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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23
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Prediction of remission among patients with a major depressive disorder based on the resting-state functional connectivity of emotion regulation networks. Transl Psychiatry 2022; 12:391. [PMID: 36115833 PMCID: PMC9482642 DOI: 10.1038/s41398-022-02152-0] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/08/2022] [Revised: 08/29/2022] [Accepted: 09/02/2022] [Indexed: 11/08/2022] Open
Abstract
The prediction of antidepressant response is critical for psychiatrists to select the initial antidepressant drug for patients with major depressive disorders (MDD). The implicated brain networks supporting emotion regulation (ER) are critical in the pathophysiology of MDD and the prediction of antidepressant response. Therefore, the primary aim of the current study was to identify the neuroimaging biomarkers for the prediction of remission in patients with MDD based on the resting-state functional connectivity (rsFC) of the ER networks. A total of 81 unmedicated adult MDD patients were investigated and they underwent resting-state functional magnetic resonance imagining (fMRI) scans. The patients were treated with escitalopram for 12 weeks. The 17-item Hamilton depression rating scale was used for assessing remission. The 36 seed regions from predefined ER networks were selected and the rsFC matrix was caculated for each participant. The support vector machine algorithm was employed to construct prediction model, which separated the patients with remission from those with non-remission. And leave-one-out cross-validation and the area under the curve (AUC) of the receiver operating characteristic were used for evaluating the performance of the model. The accuracy of the prediction model was 82.08% (sensitivity = 71.43%, specificity = 89.74%, AUC = 0.86). The rsFC between the left medial superior frontal gyrus and the right inferior frontal gyrus as well as the precuneus were the features with the highest discrimination ability in predicting remission from escitalopram among the MDD patients. Results from our study demonstrated that rsFC of the ER brain networks are potential predictors for the response of antidepressant drugs. The trial name: appropriate technology study of MDD diagnosis and treatment based on objective indicators and measurement. URL: http://www.chictr.org.cn/showproj.aspx?proj=21377 . Registration number: ChiCTR-OOC-17012566.
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24
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The emergence of psychoanalytical electrochemistry: the translation of MDD biomarker discovery to diagnosis with electrochemical sensing. Transl Psychiatry 2022; 12:372. [PMID: 36075922 PMCID: PMC9452859 DOI: 10.1038/s41398-022-02138-y] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/05/2022] [Revised: 08/16/2022] [Accepted: 08/23/2022] [Indexed: 01/30/2023] Open
Abstract
The disease burden and healthcare costs of psychiatric diseases along with the pursuit to understand their underlying biochemical mechanisms have led to psychiatric biomarker investigations. Current advances in evaluating candidate biomarkers for psychiatric diseases, such as major depressive disorder (MDD), focus on determining a specific biomarker signature or profile. The origins of candidate biomarkers are heterogenous, ranging from genomics, proteomics, and metabolomics, while incorporating associations with clinical characterization. Prior to clinical use, candidate biomarkers must be validated by large multi-site clinical studies, which can be used to determine the ideal MDD biomarker signature. Therefore, identifying valid biomarkers has been challenging, suggesting the need for alternative approaches. Following validation studies, new technology must be employed to transition from biomarker discovery to diagnostic biomolecular profiling. Current technologies used in discovery and validation, such as mass spectroscopy, are currently limited to clinical research due to the cost or complexity of equipment, sample preparation, or measurement analysis. Thus, other technologies such as electrochemical detection must be considered for point-of-care (POC) testing with the needed characteristics for physicians' offices. This review evaluates the advantages of using electrochemical sensing as a primary diagnostic platform due to its rapidity, accuracy, low cost, biomolecular detection diversity, multiplexed capacity, and instrument flexibility. We evaluate the capabilities of electrochemical methods in evaluating current candidate MDD biomarkers, individually and through multiplexed sensing, for promising applications in detecting MDD biosignatures in the POC setting.
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25
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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: 18] [Impact Index Per Article: 9.0] [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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26
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Férat V, Arns M, Deiber MP, Hasler R, Perroud N, Michel CM, Ros T. Electroencephalographic Microstates as Novel Functional Biomarkers for Adult Attention-Deficit/Hyperactivity Disorder. BIOLOGICAL PSYCHIATRY. COGNITIVE NEUROSCIENCE AND NEUROIMAGING 2022; 7:814-823. [PMID: 34823049 DOI: 10.1016/j.bpsc.2021.11.006] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/25/2021] [Revised: 11/05/2021] [Accepted: 11/06/2021] [Indexed: 05/25/2023]
Abstract
BACKGROUND Research on the electroencephalographic (EEG) signatures of attention-deficit/hyperactivity disorder (ADHD) has historically concentrated on its frequency spectrum or event-related evoked potentials. In this work, we investigate EEG microstates (MSs), an alternative framework defined by the clustering of recurring topographical patterns, as a novel approach for examining large-scale cortical dynamics in ADHD. METHODS Using k-means clustering, we studied the spatiotemporal dynamics of ADHD during the rest condition by comparing the MS segmentations between adult patients with ADHD and neurotypical control subjects across two independent datasets: the first dataset consisted of 66 patients with ADHD and 66 control subjects, and the second dataset comprised 22 patients with ADHD and 22 control subjects and was used for out-of-sample validation. RESULTS Spatially, patients with ADHD and control subjects displayed equivalent MS topographies (canonical maps), indicating the preservation of prototypical EEG generators in patients with ADHD. However, this concordance was accompanied by significant differences in temporal dynamics. At the group level, and across both datasets, ADHD diagnosis was associated with longer mean durations of a frontocentral topography (MS D), indicating that its electrocortical generator(s) could be acting as pronounced attractors of global cortical dynamics. In addition, its spatiotemporal metrics were correlated with sleep disturbance, the latter being known to have a strong relationship with ADHD. Finally, in the first (larger) dataset, we also found evidence of decreased time coverage and mean duration of a left-right diagonal topography (MS A), which inversely correlated with ADHD scores. CONCLUSIONS Overall, our study underlines the value of EEG MSs as promising functional biomarkers for ADHD, offering an additional lens through which to examine its neurophysiological mechanisms.
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Affiliation(s)
- Victor Férat
- Functional Brain Mapping Laboratory, Department of Basic Neurosciences, Campus Biotech, University of Geneva, Geneva, Switzerland.
| | - Martijn Arns
- Research Institute Brainclinics, Brainclinics Foundation, Nijmegen, The Netherlands; Department of Psychiatry, Amsterdam UMC, University of Amsterdam, Location AMC, Amsterdam Neuroscience, Amsterdam, The Netherlands; Department of Cognitive Neuroscience, Faculty of Psychology and Neuroscience, Maastricht University, Maastricht, The Netherlands
| | - Marie-Pierre Deiber
- Department of Psychiatry, Faculty of Medicine, University of Geneva, Geneva, Switzerland; Division of Psychiatric Specialties, Department of Psychiatry, University Hospitals of Geneva, Geneva, Switzerland
| | - Roland Hasler
- Division of Psychiatric Specialties, Department of Psychiatry, University Hospitals of Geneva, Geneva, Switzerland; Department of Psychiatry, Dalhousie University, Nova Scotia, Halifax, Nova Scotia, Canada
| | - Nader Perroud
- Department of Psychiatry, Faculty of Medicine, University of Geneva, Geneva, Switzerland; Division of Psychiatric Specialties, Department of Psychiatry, University Hospitals of Geneva, Geneva, Switzerland
| | - Christoph M Michel
- Functional Brain Mapping Laboratory, Department of Basic Neurosciences, Campus Biotech, University of Geneva, Geneva, Switzerland; Center for Biomedical Imaging, Lausanne, Geneva, Switzerland
| | - Tomas Ros
- Functional Brain Mapping Laboratory, Department of Basic Neurosciences, Campus Biotech, University of Geneva, Geneva, Switzerland; Division of Psychiatric Specialties, Department of Psychiatry, University Hospitals of Geneva, Geneva, Switzerland; Center for Biomedical Imaging, Lausanne, Geneva, Switzerland
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Mapping Neural Circuit Biotypes to Symptoms and Behavioral Dimensions of Depression and Anxiety. Biol Psychiatry 2022; 91:561-571. [PMID: 34482948 PMCID: PMC9511971 DOI: 10.1016/j.biopsych.2021.06.024] [Citation(s) in RCA: 44] [Impact Index Per Article: 22.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/30/2020] [Revised: 06/25/2021] [Accepted: 06/28/2021] [Indexed: 01/07/2023]
Abstract
BACKGROUND Despite tremendous advances in characterizing human neural circuits that govern emotional and cognitive functions impaired in depression and anxiety, we lack a circuit-based taxonomy for depression and anxiety that captures transdiagnostic heterogeneity and informs clinical decision making. METHODS We developed and tested a novel system for quantifying 6 brain circuits reproducibly and at the individual patient level. We implemented standardized circuit definitions relative to a healthy reference sample and algorithms to generate circuit clinical scores for the overall circuit and its constituent regions. RESULTS In new data from primary and generalizability samples of depression and anxiety (N = 250), we demonstrated that overall disconnections within task-free salience and default mode circuits map onto symptoms of anxious avoidance, loss of pleasure, threat dysregulation, and negative emotional biases-core characteristics that transcend diagnoses-and poorer daily function. Regional dysfunctions within task-evoked cognitive control and affective circuits may implicate symptoms of cognitive and valence-congruent emotional functions. Circuit dysfunction scores also distinguished response to antidepressant and behavioral intervention treatments in an independent sample (n = 205). CONCLUSIONS Our findings articulate circuit dimensions that relate to transdiagnostic symptoms across mood and anxiety disorders. Our novel system offers a foundation for deploying standardized circuit assessments across research groups, trials, and clinics to advance more precise classifications and treatment targets for psychiatry.
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Chen X, Lu B, Li HX, Li XY, Wang YW, Castellanos FX, Cao LP, Chen NX, Chen W, Cheng YQ, Cui SX, Deng ZY, Fang YR, Gong QY, Guo WB, Hu ZJY, Kuang L, Li BJ, Li L, Li T, Lian T, Liao YF, Liu YS, Liu ZN, Lu JP, Luo QH, Meng HQ, Peng DH, Qiu J, Shen YD, Si TM, Tang YQ, Wang CY, Wang F, Wang HN, Wang K, Wang X, Wang Y, Wang ZH, Wu XP, Xie CM, Xie GR, Xie P, Xu XF, Yang H, Yang J, Yao SQ, Yu YQ, Yuan YG, Zhang KR, Zhang W, Zhang ZJ, Zhu JJ, Zuo XN, Zhao JP, Zang YF, Yan CG, Chen X, Cao LP, Chen W, Cheng YQ, Fang YR, Gong QY, Guo WB, Kuang L, Li BJ, Li T, Liu YS, Liu ZN, Lu JP, Luo QH, Meng HQ, Peng DH, Qiu J, Shen YD, Si TM, Tang YQ, Wang CY, Wang F, Wang HN, Wang K, Wang X, Wang Y, Wu XP, Xie CM, Xie GR, Xie P, Xu XF, Yang H, Yang J, Yao SQ, Yu YQ, Yuan YG, Zhang KR, Zhang W, Zhang ZJ, Zhu JJ, Zuo XN, Zhao JP, Zang YF, Yan CG. The DIRECT consortium and the REST-meta-MDD project: towards neuroimaging biomarkers of major depressive disorder. PSYCHORADIOLOGY 2022; 2:32-42. [PMID: 38665141 PMCID: PMC10917197 DOI: 10.1093/psyrad/kkac005] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/18/2022] [Revised: 05/06/2022] [Accepted: 05/06/2022] [Indexed: 02/05/2023]
Abstract
Despite a growing neuroimaging literature on the pathophysiology of major depressive disorder (MDD), reproducible findings are lacking, probably reflecting mostly small sample sizes and heterogeneity in analytic approaches. To address these issues, the Depression Imaging REsearch ConsorTium (DIRECT) was launched. The REST-meta-MDD project, pooling 2428 functional brain images processed with a standardized pipeline across all participating sites, has been the first effort from DIRECT. In this review, we present an overview of the motivations, rationale, and principal findings of the studies so far from the REST-meta-MDD project. Findings from the first round of analyses of the pooled repository have included alterations in functional connectivity within the default mode network, in whole-brain topological properties, in dynamic features, and in functional lateralization. These well-powered exploratory observations have also provided the basis for future longitudinal hypothesis-driven research. Following these fruitful explorations, DIRECT has proceeded to its second stage of data sharing that seeks to examine ethnicity in brain alterations in MDD by extending the exclusive Chinese original sample to other ethnic groups through international collaborations. A state-of-the-art, surface-based preprocessing pipeline has also been introduced to improve sensitivity. Functional images from patients with bipolar disorder and schizophrenia will be included to identify shared and unique abnormalities across diagnosis boundaries. In addition, large-scale longitudinal studies targeting brain network alterations following antidepressant treatment, aggregation of diffusion tensor images, and the development of functional magnetic resonance imaging-guided neuromodulation approaches are underway. Through these endeavours, we hope to accelerate the translation of functional neuroimaging findings to clinical use, such as evaluating longitudinal effects of antidepressant medications and developing individualized neuromodulation targets, while building an open repository for the scientific community.
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Affiliation(s)
- Xiao Chen
- CAS Key Laboratory of Behavioral Science, Institute of Psychology, Chinese Academy of Sciences , Beijing 100101, China
- International Big-Data Center for Depression Research, Chinese Academy of Sciences , Beijing 100101, China
- Magnetic Resonance Imaging Research Center, Institute of Psychology, Chinese Academy of Sciences , Beijing 100101, China
- Department of Psychology, University of Chinese Academy of Sciences , Beijing 100049, China
| | - Bin Lu
- International Big-Data Center for Depression Research, Chinese Academy of Sciences , Beijing 100101, China
- Magnetic Resonance Imaging Research Center, Institute of Psychology, Chinese Academy of Sciences , Beijing 100101, China
| | - Hui-Xian Li
- International Big-Data Center for Depression Research, Chinese Academy of Sciences , Beijing 100101, China
- Magnetic Resonance Imaging Research Center, Institute of Psychology, Chinese Academy of Sciences , Beijing 100101, China
| | - Xue-Ying Li
- CAS Key Laboratory of Behavioral Science, Institute of Psychology, Chinese Academy of Sciences , Beijing 100101, China
- International Big-Data Center for Depression Research, Chinese Academy of Sciences , Beijing 100101, China
- Magnetic Resonance Imaging Research Center, Institute of Psychology, Chinese Academy of Sciences , Beijing 100101, China
- Department of Psychology, University of Chinese Academy of Sciences , Beijing 100049, China
- Sino-Danish College, University of Chinese Academy of Sciences , Beijing 101408, China
- Sino-Danish Center for Education and Research, Graduate University of Chinese Academy of Sciences , Beijing 101408, China
| | - Yu-Wei Wang
- International Big-Data Center for Depression Research, Chinese Academy of Sciences , Beijing 100101, China
- Magnetic Resonance Imaging Research Center, Institute of Psychology, Chinese Academy of Sciences , Beijing 100101, China
| | - Francisco Xavier Castellanos
- Department of Child and Adolescent Psychiatry, NYU Grossman School of Medicine , New York, NY 10016, USA
- Nathan Kline Institute for Psychiatric Research , Orangeburg, New York, NY 10962, USA
| | - Li-Ping Cao
- Affiliated Brain Hospital of Guangzhou Medical University , Guangzhou 510370, China
| | | | - Wei Chen
- Department of Psychiatry, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine , Hangzhou 310020, Zhejiang, China
| | - Yu-Qi Cheng
- Department of Psychiatry, First Affiliated Hospital of Kunming Medical University , Kunming, Yunnan 650032, China
| | - Shi-Xian Cui
- International Big-Data Center for Depression Research, Chinese Academy of Sciences , Beijing 100101, China
- Sino-Danish College, University of Chinese Academy of Sciences , Beijing 101408, China
- Sino-Danish Center for Education and Research, Graduate University of Chinese Academy of Sciences , Beijing 101408, China
| | - Zhao-Yu Deng
- International Big-Data Center for Depression Research, Chinese Academy of Sciences , Beijing 100101, China
- Magnetic Resonance Imaging Research Center, Institute of Psychology, Chinese Academy of Sciences , Beijing 100101, China
| | - Yi-Ru Fang
- Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine , Shanghai 200030, China
| | - Qi-Yong Gong
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University , Chengdu, Sichuan 610044, China
- Research Unit of Psychoradiology, Chinese Academy of Medical Sciences , Chengdu, Sichuan 610052, China
| | - Wen-Bin Guo
- Department of Psychiatry, and National Clinical Research Center for Mental Disorders, The Second Xiangya Hospital of Central South University , Changsha 410011, Hunan, China
| | - Zheng-Jia-Yi Hu
- International Big-Data Center for Depression Research, Chinese Academy of Sciences , Beijing 100101, China
- Magnetic Resonance Imaging Research Center, Institute of Psychology, Chinese Academy of Sciences , Beijing 100101, China
| | - Li Kuang
- Department of Psychiatry, The First Affiliated Hospital of Chongqing Medical University , Chongqing 400042, China
| | - Bao-Juan Li
- Xijing Hospital of Air Force Military Medical University , Xi'an, Shaanxi 710032, China
| | - Le Li
- Center for Cognitive Science of Language, Beijing Language and Culture University , Beijing 100083, China
| | - Tao Li
- Affiliated Mental Health Center & Hangzhou Seventh People's Hospital, Zhejiang University School of Medicine , Hangzhou, Zhejiang 310063, China
- Mental Health Center and Psychiatric Laboratory, West China Hospital of Sichuan University , Chengdu, Sichuan 610044, China
| | - Tao Lian
- CAS Key Laboratory of Behavioral Science, Institute of Psychology, Chinese Academy of Sciences , Beijing 100101, China
- International Big-Data Center for Depression Research, Chinese Academy of Sciences , Beijing 100101, China
- Magnetic Resonance Imaging Research Center, Institute of Psychology, Chinese Academy of Sciences , Beijing 100101, China
| | - Yi-Fan Liao
- CAS Key Laboratory of Behavioral Science, Institute of Psychology, Chinese Academy of Sciences , Beijing 100101, China
- International Big-Data Center for Depression Research, Chinese Academy of Sciences , Beijing 100101, China
- Magnetic Resonance Imaging Research Center, Institute of Psychology, Chinese Academy of Sciences , Beijing 100101, China
| | - Yan-Song Liu
- Department of Clinical Psychology, Suzhou Psychiatric Hospital, The Affiliated Guangji Hospital of Soochow University , Suzhou, Jiangsu 215003, China
| | - Zhe-Ning Liu
- Department of Psychiatry, and National Clinical Research Center for Mental Disorders, The Second Xiangya Hospital of Central South University , Changsha 410011, Hunan, China
| | - Jian-Ping Lu
- Shenzhen Kangning Hospital , Shenzhen, Guangzhou 518020, China
| | - Qing-Hua Luo
- Department of Psychiatry, The First Affiliated Hospital of Chongqing Medical University , Chongqing 400042, China
| | - Hua-Qing Meng
- Department of Psychiatry, The First Affiliated Hospital of Chongqing Medical University , Chongqing 400042, China
| | - Dai-Hui Peng
- Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine , Shanghai 200030, China
| | - Jiang Qiu
- Faculty of Psychology, Southwest University , Chongqing 400715, China
| | - Yue-Di Shen
- Department of Diagnostics, Affiliated Hospital, Hangzhou Normal University Medical School , Hangzhou, Zhejiang 311121, China
| | - Tian-Mei Si
- National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital) & Key Laboratory of Mental Health, Ministry of Health (Peking University) , Beijing 100191, China
| | - Yan-Qing Tang
- Department of Psychiatry, First Affiliated Hospital, China Medical University , Shenyang, Liaoning 110122, China
| | - Chuan-Yue Wang
- Beijing Anding Hospital, Capital Medical University , Beijing 100120, China
| | - Fei Wang
- Department of Psychiatry, First Affiliated Hospital, China Medical University , Shenyang, Liaoning 110122, China
- Early Intervention Unit, Department of Psychiatry , Affiliated Nanjing Brain Hospital, Nanjing Medical University, Nanjing 210024, China
| | - Hua-Ning Wang
- Xijing Hospital of Air Force Military Medical University , Xi'an, Shaanxi 710032, China
| | - Kai Wang
- Department of Neurology, The First Affiliated Hospital of Anhui Medical University , Hefei, Anhui 230022, China
| | - Xiang Wang
- Department of Psychiatry, and National Clinical Research Center for Mental Disorders, The Second Xiangya Hospital of Central South University , Changsha 410011, Hunan, China
| | - Ying Wang
- The First Affiliated Hospital of Jinan University , Guangzhou, Guangdong 250024, China
| | - Zi-Han Wang
- International Big-Data Center for Depression Research, Chinese Academy of Sciences , Beijing 100101, China
- Magnetic Resonance Imaging Research Center, Institute of Psychology, Chinese Academy of Sciences , Beijing 100101, China
| | - Xiao-Ping Wu
- Xi'an Central Hospital , Xi'an, Shaanxi 710004, China
| | - Chun-Ming Xie
- Department of Neurology, Affiliated ZhongDa Hospital of Southeast University , Nanjing, Jiangsu 210009, China
| | - Guang-Rong Xie
- Department of Psychiatry, and National Clinical Research Center for Mental Disorders, The Second Xiangya Hospital of Central South University , Changsha 410011, Hunan, China
| | - Peng Xie
- Institute of Neuroscience, Chongqing Medical University , Chongqing 400016, China
- Chongqing Key Laboratory of Neurobiology , Chongqing 400000, China
- Department of Neurology, The First Affiliated Hospital of Chongqing Medical University , Chongqing 400042, China
| | - Xiu-Feng Xu
- Department of Psychiatry, First Affiliated Hospital of Kunming Medical University , Kunming, Yunnan 650032, China
| | - Hong Yang
- Department of Radiology, The First Affiliated Hospital, College of Medicine, Zhejiang University , Hangzhou, Zhejiang 310058, China
| | - Jian Yang
- Chongqing Key Laboratory of Neurobiology , Chongqing 400000, China
| | - Shu-Qiao Yao
- Department of Psychiatry, and National Clinical Research Center for Mental Disorders, The Second Xiangya Hospital of Central South University , Changsha 410011, Hunan, China
| | - Yong-Qiang Yu
- The First Affiliated Hospital of Anhui Medical University , Hefei, Anhui 230032, China
| | - Yong-Gui Yuan
- Department of Psychosomatics and Psychiatry, Zhongda Hospital, School of Medicine, Southeast University , Nanjing, Jiangsu 210009, China
| | - Ke-Rang Zhang
- First Hospital of Shanxi Medical University , Taiyuan, Shanxi 030001, China
| | - Wei Zhang
- West China Hospital of Sichuan University , Chengdu, Sichuan 610044, China
| | - Zhi-Jun Zhang
- Department of Neurology, Affiliated ZhongDa Hospital of Southeast University , Nanjing, Jiangsu 210009, China
| | - Jun-Juan Zhu
- Department of Psychiatry, Shanghai Jiao Tong University School of Medicine , Shanghai 200025, China
| | - Xi-Nian Zuo
- Developmental Population Neuroscience Research Center, IDG/McGovern Institute for Brain Research, Beijing Normal University , Beijing 100091, China
- National Basic Science Data Center , Beijing 100038, China
| | - Jing-Ping Zhao
- Department of Psychiatry, and National Clinical Research Center for Mental Disorders, The Second Xiangya Hospital of Central South University , Changsha 410011, Hunan, China
| | - Yu-Feng Zang
- Center for Cognition and Brain Disorders, The Affiliated Hospital of Hangzhou Normal University , Hangzhou, Zhejiang 310018, China
- Zhejiang Key Laboratory for Research in Assessment of Cognitive Impairments , Hangzhou, Zhejiang 310000, China
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | - Chao-Gan Yan
- CAS Key Laboratory of Behavioral Science, Institute of Psychology, Chinese Academy of Sciences , Beijing 100101, China
- International Big-Data Center for Depression Research, Chinese Academy of Sciences , Beijing 100101, China
- Magnetic Resonance Imaging Research Center, Institute of Psychology, Chinese Academy of Sciences , Beijing 100101, China
- Department of Psychology, University of Chinese Academy of Sciences , Beijing 100049, China
- Sino-Danish College, University of Chinese Academy of Sciences , Beijing 101408, China
- Sino-Danish Center for Education and Research, Graduate University of Chinese Academy of Sciences , Beijing 101408, China
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29
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Automated diagnosis of depression from EEG signals using traditional and deep learning approaches: A comparative analysis. Biocybern Biomed Eng 2022. [DOI: 10.1016/j.bbe.2021.12.005] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
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30
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Wendt K, Denison T, Foster G, Krinke L, Thomson A, Wilson S, Widge AS. Physiologically informed neuromodulation. J Neurol Sci 2021; 434:120121. [PMID: 34998239 PMCID: PMC8976285 DOI: 10.1016/j.jns.2021.120121] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2021] [Revised: 12/19/2021] [Accepted: 12/21/2021] [Indexed: 01/09/2023]
Abstract
The rapid evolution of neuromodulation techniques includes an increasing amount of research into stimulation paradigms that are guided by patients' neurophysiology, to increase efficacy and responder rates. Treatment personalisation and target engagement have shown to be effective in fields such as Parkinson's disease, and closed-loop paradigms have been successfully implemented in cardiac defibrillators. Promising avenues are being explored for physiologically informed neuromodulation in psychiatry. Matching the stimulation frequency to individual brain rhythms has shown some promise in transcranial magnetic stimulation (TMS). Matching the phase of those rhythms may further enhance neuroplasticity, for instance when combining TMS with electroencephalographic (EEG) recordings. Resting-state EEG and event-related potentials may be useful to demonstrate connectivity between stimulation sites and connected areas. These techniques are available today to the psychiatrist to diagnose underlying sleep disorders, epilepsy, or lesions as contributing factors to the cause of depression. These technologies may also be useful in assessing the patient's brain network status prior to deciding on treatment options. Ongoing research using invasive recordings may allow for future identification of mood biomarkers and network structure. A core limitation is that biomarker research may currently be limited by the internal heterogeneity of psychiatric disorders according to the current DSM-based classifications. New approaches are being developed and may soon be validated. Finally, care must be taken when incorporating closed-loop capabilities into neuromodulation systems, by ensuring the safe operation of the system and understanding the physiological dynamics. Neurophysiological tools are rapidly evolving and will likely define the next generation of neuromodulation therapies.
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Affiliation(s)
- Karen Wendt
- Department of Engineering Science and MRC Brain Network Dynamics Unit, University of Oxford, Oxford, UK.
| | - Timothy Denison
- Department of Engineering Science and MRC Brain Network Dynamics Unit, University of Oxford, Oxford, UK
| | - Gaynor Foster
- Welcony Inc., Plymouth, MN, United States of America
| | - Lothar Krinke
- Welcony Inc., Plymouth, MN, United States of America; Department of Neuroscience, School of Medicine, West Virginia University, Morgantown, WV, United States of America
| | - Alix Thomson
- Welcony Inc., Plymouth, MN, United States of America
| | - Saydra Wilson
- Department of Psychiatry and Behavioral Sciences, University of Minnesota-Twin Cities, Minneapolis, MN, United States of America
| | - Alik S Widge
- Department of Psychiatry and Behavioral Sciences, University of Minnesota-Twin Cities, Minneapolis, MN, United States of America; Medical Discovery Team on Additions, University of Minnesota, Minneapolis, MN, United States of America
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31
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Purgato M, Singh R, Acarturk C, Cuijpers P. Moving beyond a 'one-size-fits-all' rationale in global mental health: prospects of a precision psychology paradigm. Epidemiol Psychiatr Sci 2021; 30:e63. [PMID: 34632978 PMCID: PMC8518023 DOI: 10.1017/s2045796021000500] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/19/2021] [Revised: 09/07/2021] [Accepted: 09/08/2021] [Indexed: 11/13/2022] Open
Abstract
Research on the effectiveness of mental health and psychosocial support interventions for common mental disorders in global mental health provides controversial results. These results are based on mean values for different groups, often without due consideration of individual-level characteristics and contextual factors. Against this background, and based on the recent development of a precision theoretical framework in clinical psychology, which is calling for a renewed perspective on the development and implementation of trial designs, we propose to develop a precision psychology paradigm in global mental health, with emphasis not only on individual clinical and socio-demographic data, but also on the social determinants of mental health. A precision psychology paradigm would require a coordinated action of academics, stakeholders and humanitarian workers in planning a global mental health research agenda, including the design of trials aimed at reliably approximate prediction of intervention response at individual level.
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Affiliation(s)
- Marianna Purgato
- Department of Neurosciences, Biomedicine and Movement Sciences, WHO Collaborating Centre for Research and Training in Mental Health and Service Evaluation, University of Verona, Verona,Italy
- Cochrane Global Mental Health, University of Verona, Verona,Italy
| | - Rakesh Singh
- Department of Public Health, Independent Mental Health Researcher, Visiting Faculty, KIST Medical College, Kathmandu, Nepal
| | - Ceren Acarturk
- Department of Psychology, College of Social Sciences and Humanities, Koc University, Istanbul,Turkey
| | - Pim Cuijpers
- Department of Clinical, Neuro, and Developmental Psychology, Amsterdam Public Health Institute, Vrije Universiteit, Amsterdam, Netherlands
- WHO Collaborating Centre for Research and Dissemination of Psychological Interventions, Vrije Universiteit, Amsterdam, Netherlands
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32
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Antidepressant side effects and their impact on treatment outcome in people with major depressive disorder: an iSPOT-D report. Transl Psychiatry 2021; 11:417. [PMID: 34349116 PMCID: PMC8338944 DOI: 10.1038/s41398-021-01533-1] [Citation(s) in RCA: 35] [Impact Index Per Article: 11.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/04/2021] [Revised: 07/14/2021] [Accepted: 07/23/2021] [Indexed: 12/16/2022] Open
Abstract
Side effects to antidepressant medications are common and can impact the prognosis of successful treatment outcome in people with major depressive disorder (MDD). However, few studies have investigated the severity of side effects over the course of treatment and their association with treatment outcome. Here we assessed the severity of side effects and the impact of treatment type and anxiety symptoms over the course of treatment, as well as whether side effects were associated with treatment outcome. Participants were N = 1008 adults with a current diagnosis of single-episode or recurrent, nonpsychotic MDD. Participants were randomised to receive escitalopram, sertraline, or venlafaxine-extended release with equal probability and reassessed at 8 weeks regarding Hamilton Rating Scale Depression (HRSD17) and Quick Inventory of Depressive Symptomatology (QIDS-SR16) remission and response. Severity of side effects were assessed using the Frequency, Intensity, and Burden of Side Effects Rating (FIBSER) scale and assessed at day 4 and weeks 2, 4, 6, and 8. Frequency, intensity, and burden of side effects were greatest at week 2, then only frequency and intensity of side effects gradually decreased up to week 6. Treatment type and anxiety symptoms did not impact the severity of side effects. A greater burden-but not frequency or intensity-of side effects was associated with poorer treatment outcome and as early as 4 days post-treatment. Together, this work provides an informative mapping of the progression of side effects throughout the treatment course and their association with treatment outcome. Importantly, the burden of side effects that are present as early as 4 days post-treatment predicts poorer treatment outcome and should be monitored closely. iSPOT-D: Registry name: ClinicalTrials.gov. Registration number: NCT00693849.
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33
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Braund TA, Breukelaar IA, Griffiths K, Tillman G, Palmer DM, Bryant R, Phillips ML, Harris AWF, Korgaonkar MS. Intrinsic Functional Connectomes Characterize Neuroticism in Major Depressive Disorder and Predict Antidepressant Treatment Outcomes. BIOLOGICAL PSYCHIATRY: COGNITIVE NEUROSCIENCE AND NEUROIMAGING 2021; 7:276-284. [PMID: 34363999 DOI: 10.1016/j.bpsc.2021.07.010] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/17/2021] [Revised: 06/18/2021] [Accepted: 07/20/2021] [Indexed: 12/28/2022]
Abstract
BACKGROUND Antidepressant efficacy in people with major depressive disorder (MDD) remains modest, yet identifying treatment predictive neurobiological markers may improve outcomes. While disruptions in functional connectivity within and between large-scale brain networks predict poorer treatment outcome, it is unclear whether higher trait neuroticism - which has been associated with generally poorer outcome - contributes to these disruptions and to antidepressant-specific treatment outcomes. Here, we used whole-brain functional connectivity analysis to identify a neural connectomic signature of neuroticism and tested whether this signature predicted antidepressant treatment outcome. METHOD Participants were 229 adults with MDD and 68 healthy controls who underwent functional MRI and were assessed on clinical features at baseline. MDD participants were then randomized to one of three commonly prescribed antidepressants and after 8 weeks completed a second functional MRI and were reassessed for depressive symptom remission/response. Baseline intrinsic functional connectivity between each pair of 436 brain regions were analysed using network-based statistics to identify connectomic features associated with neuroticism. Features were then assessed on their ability to predict treatment outcome and whether they changed after 8 weeks of treatment. RESULTS Higher baseline neuroticism was associated with greater connectivity within and between the salience, executive control, and somatomotor brain networks. Greater connectivity across these networks predicted poorer treatment outcome that was not mediated by baseline neuroticism, and connectivity strength decreased after antidepressant treatment. CONCLUSIONS Our findings demonstrate that neuroticism is associated with organization of intrinsic neural networks that predict treatment outcome, elucidating its biological underpinnings and opportunity for better treatment personalization.
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Affiliation(s)
- Taylor A Braund
- Brain Dynamics Centre, The Westmead Institute for Medical Research, Sydney, NSW, Australia; Sydney Medical School, Faculty of Medicine and Health, University of Sydney, Sydney, NSW, Australia; Total Brain, Sydney, NSW, Australia.
| | - Isabella A Breukelaar
- Brain Dynamics Centre, The Westmead Institute for Medical Research, Sydney, NSW, Australia; School of Psychology, University of New South Wales, Sydney, Australia
| | - Kristi Griffiths
- Brain Dynamics Centre, The Westmead Institute for Medical Research, Sydney, NSW, Australia
| | - Gabriel Tillman
- School of Science, Psychology and Sport, Federation University, Australia
| | - Donna M Palmer
- Brain Dynamics Centre, The Westmead Institute for Medical Research, Sydney, NSW, Australia; Total Brain, Sydney, NSW, Australia
| | - Richard Bryant
- School of Psychology, University of New South Wales, Sydney, Australia
| | - Mary L Phillips
- Department of Psychiatry, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania
| | - Anthony W F Harris
- Brain Dynamics Centre, The Westmead Institute for Medical Research, Sydney, NSW, Australia; Sydney Medical School, Faculty of Medicine and Health, University of Sydney, Sydney, NSW, Australia
| | - Mayuresh S Korgaonkar
- Brain Dynamics Centre, The Westmead Institute for Medical Research, Sydney, NSW, Australia; Sydney Medical School, Faculty of Medicine and Health, University of Sydney, Sydney, NSW, Australia; School of Health Sciences, Faculty of Medicine and Health, The University of Sydney, Sydney, NSW, Australia
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34
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Inhibition-related modulation of salience and frontoparietal networks predicts cognitive control ability and inattention symptoms in children with ADHD. Mol Psychiatry 2021; 26:4016-4025. [PMID: 31664176 PMCID: PMC7188596 DOI: 10.1038/s41380-019-0564-4] [Citation(s) in RCA: 37] [Impact Index Per Article: 12.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/01/2019] [Revised: 09/24/2019] [Accepted: 10/12/2019] [Indexed: 01/09/2023]
Abstract
Attention-deficit hyperactivity disorder (ADHD) is associated with pervasive impairments in attention and cognitive control. Although brain circuits underlying these impairments have been extensively investigated with resting-state fMRI, little is known about task-evoked functional brain circuits and their relation to cognitive control deficits and inattention symptoms in children with ADHD. Children with ADHD and age, gender and head motion matched typically developing (TD) children completed a Go/NoGo fMRI task. We used multivariate and dimensional analyses to investigate impairments in two core cognitive control systems: (i) cingulo-opercular "salience" network (SN) anchored in the right anterior insula, dorsal anterior cingulate cortex (rdACC), and ventrolateral prefrontal cortex (rVLPFC) and (ii) dorsal frontoparietal "central executive" (FPN) network anchored in right dorsolateral prefrontal cortex (rDLPFC) and posterior parietal cortex (rPPC). We found that multivariate patterns of task-evoked effective connectivity between brain regions in SN and FPN distinguished the ADHD and TD groups, with rDLPFC-rPPC connectivity emerging as the most distinguishing link. Task-evoked rdACC-rVLPFC connectivity was positively correlated with NoGo accuracy, and negatively correlated with severity of inattention symptoms. Brain-behavior relationships were robust against potential age, gender, and head motion confounds. Our findings highlight aberrancies in task-evoked modulation of SN and FPN connectivity in children with ADHD. Crucially, cingulo-frontal connectivity was a common locus of deficits in cognitive control and clinical measures of inattention symptoms. Our study provides insights into a parsimonious systems neuroscience model of cognitive control deficits in ADHD, and suggests specific circuit biomarkers for predicting treatment outcomes in childhood ADHD.
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Ip CT, Olbrich S, Ganz M, Ozenne B, Köhler-Forsberg K, Dam VH, Beniczky S, Jørgensen MB, Frokjaer VG, Søgaard B, Christensen SR, Knudsen GM. Pretreatment qEEG biomarkers for predicting pharmacological treatment outcome in major depressive disorder: Independent validation from the NeuroPharm study. Eur Neuropsychopharmacol 2021; 49:101-112. [PMID: 33910154 DOI: 10.1016/j.euroneuro.2021.03.024] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/29/2020] [Revised: 02/24/2021] [Accepted: 03/30/2021] [Indexed: 02/04/2023]
Abstract
Several electroencephalogram (EEG) biomarkers for prediction of drug response in major depressive disorder (MDD) have been proposed, but validations in larger independent datasets are missing. In the current study, we investigated the prognostic value of previously suggested EEG biomarkers. We gathered data that matched prior studies in terms of EEG methodology, clinical criteria for MDD, and statistical approach as closely as possible. The NeuroPharm study is a non-randomized and open label prospective clinical trial. One hundred antidepressant free patients with MDD were enrolled in the study and 79 (57 female) were included in the per-protocol analysis. The biomarkers candidates for cross-validation were derived from prior studies such as iSPOT-D and EMBARC and include frontal and occipital alpha power and asymmetry and delta and theta activity at anterior cingulate cortex (ACC). The alpha asymmetry, reported in two out of six prior studies, could be partially validated. We found that in female patients, larger right than left frontal alpha power prior to drug treatment was associated with better clinical outcome 8 weeks later. Moreover, female non-responder had higher central left alpha power relative to the right. In contrast to prior reports, we found that lower theta activity at ACC was present in remitters and was associated with greater improvement at week 8. We provide evidence that in women with MDD, alpha asymmetry seems to be the most promising EEG biomarker for prediction of treatment response. Registration number: NCT02869035.
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Affiliation(s)
- Cheng-Teng Ip
- Department of Clinical Pharmacology, H. Lundbeck A/S, Valby, Denmark; Neurobiology Research Unit, University Hospital Rigshospitalet, Copenhagen, Denmark; Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Sebastian Olbrich
- Department of Psychiatry, Psychotherapy and Psychosomatic, University Zürich, Switzerland
| | - Melanie Ganz
- Neurobiology Research Unit, University Hospital Rigshospitalet, Copenhagen, Denmark; Department of Computer Science, University of Copenhagen, Copenhagen, Denmark
| | - Brice Ozenne
- Neurobiology Research Unit, University Hospital Rigshospitalet, Copenhagen, Denmark; Section of Biostatistics, Department of Public Health, University of Copenhagen, Denmark
| | - Kristin Köhler-Forsberg
- Neurobiology Research Unit, University Hospital Rigshospitalet, Copenhagen, Denmark; Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Vibeke H Dam
- Neurobiology Research Unit, University Hospital Rigshospitalet, Copenhagen, Denmark; Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Sándor Beniczky
- Department of Clinical Neurophysiology, Danish Epilepsy Center, Dianalund, Denmark; Department of Clinical Medicine, University of Aarhus, Aarhus, Denmark; Department of Clinical Neurophysiology, Aarhus University Hospital, Aarhus, Denmark
| | - Martin B Jørgensen
- Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark; Department of Psychiatry, Psychiatric Centre Copenhagen, Copenhagen, Denmark
| | - Vibe G Frokjaer
- Neurobiology Research Unit, University Hospital Rigshospitalet, Copenhagen, Denmark
| | - Birgitte Søgaard
- Department of Clinical Pharmacology, H. Lundbeck A/S, Valby, Denmark
| | | | - Gitte M Knudsen
- Neurobiology Research Unit, University Hospital Rigshospitalet, Copenhagen, Denmark; Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark.
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Magnetic resonance imaging for individual prediction of treatment response in major depressive disorder: a systematic review and meta-analysis. Transl Psychiatry 2021; 11:168. [PMID: 33723229 PMCID: PMC7960732 DOI: 10.1038/s41398-021-01286-x] [Citation(s) in RCA: 28] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/29/2020] [Revised: 02/05/2021] [Accepted: 02/18/2021] [Indexed: 02/06/2023] Open
Abstract
No tools are currently available to predict whether a patient suffering from major depressive disorder (MDD) will respond to a certain treatment. Machine learning analysis of magnetic resonance imaging (MRI) data has shown potential in predicting response for individual patients, which may enable personalized treatment decisions and increase treatment efficacy. Here, we evaluated the accuracy of MRI-guided response prediction in MDD. We conducted a systematic review and meta-analysis of all studies using MRI to predict single-subject response to antidepressant treatment in patients with MDD. Classification performance was calculated using a bivariate model and expressed as area under the curve, sensitivity, and specificity. In addition, we analyzed differences in classification performance between different interventions and MRI modalities. Meta-analysis of 22 samples including 957 patients showed an overall area under the bivariate summary receiver operating curve of 0.84 (95% CI 0.81-0.87), sensitivity of 77% (95% CI 71-82), and specificity of 79% (95% CI 73-84). Although classification performance was higher for electroconvulsive therapy outcome prediction (n = 285, 80% sensitivity, 83% specificity) than medication outcome prediction (n = 283, 75% sensitivity, 72% specificity), there was no significant difference in classification performance between treatments or MRI modalities. Prediction of treatment response using machine learning analysis of MRI data is promising but should not yet be implemented into clinical practice. Future studies with more generalizable samples and external validation are needed to establish the potential of MRI to realize individualized patient care in MDD.
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Meyer T, Brunovsky M, Horacek J, Novak T, Andrashko V, Seifritz E, Olbrich S. Predictive value of heart rate in treatment of major depression with ketamine in two controlled trials. Clin Neurophysiol 2021; 132:1339-1346. [PMID: 33888426 DOI: 10.1016/j.clinph.2021.01.030] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2020] [Revised: 12/30/2020] [Accepted: 01/09/2021] [Indexed: 11/24/2022]
Abstract
OBJECTIVE Ketamine has been shown to be effective in treatment of episodes of major depressive disorder (MDD). This controlled study aimed to analyse the predictive and discriminative power of heart rate (HR) and heart rate variability (HRV) for ketamine treatment in MDD. METHODS In 51 patients, HR and HRV were assessed at baseline before and during ketamine infusion and 24 hours post ketamine infusion. Montgomery-Åsberg Depression Rating Scale (MADRS) was used to assess changes of depressive symptoms. A 30% or 50% reduction of symptoms after 24 hours or within 7 days was defined as response. A linear mixed model was used for analysis. RESULTS Ketamine infusion increased HR and HRV power during and after infusion. Responders to ketamine showed a higher HR during the whole course of investigation, including at baseline with medium effect sizes (Cohen's d = 0.47-0.67). Furthermore, HR and HRV power discriminated between responders and non-responders, while normalized low and high frequencies did not. CONCLUSION The findings show a predictive value of HR and HRV power for ketamine treatment. This further underlines the importance of the autonomous nervous system (ANS) and its possible malfunctions in MDD. SIGNIFICANCE The predictive power of HR and HRV markers should be studied in prospective studies. Neurophysiological markers could improve treatment for MDD via optimizing the choice of treatments.
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Affiliation(s)
- Torsten Meyer
- Department for Psychiatry, Psychotherapy and Psychosomatics, University Hospital of Psychiatry Zurich, Switzerland
| | - Martin Brunovsky
- National Institute of Mental Health, Klecany, Czech Republic; Charles University, Third Faculty of Medicine, Prague, Czech Republic
| | - Jiri Horacek
- National Institute of Mental Health, Klecany, Czech Republic; Charles University, Third Faculty of Medicine, Prague, Czech Republic
| | - Tomas Novak
- National Institute of Mental Health, Klecany, Czech Republic; Charles University, Third Faculty of Medicine, Prague, Czech Republic
| | - Veronika Andrashko
- National Institute of Mental Health, Klecany, Czech Republic; Charles University, Third Faculty of Medicine, Prague, Czech Republic
| | - Erich Seifritz
- Department for Psychiatry, Psychotherapy and Psychosomatics, University Hospital of Psychiatry Zurich, Switzerland
| | - Sebastian Olbrich
- Department for Psychiatry, Psychotherapy and Psychosomatics, University Hospital of Psychiatry Zurich, Switzerland.
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Intrinsic reward circuit connectivity profiles underlying symptom and quality of life outcomes following antidepressant medication: a report from the iSPOT-D trial. Neuropsychopharmacology 2021; 46:809-819. [PMID: 33230268 PMCID: PMC8027440 DOI: 10.1038/s41386-020-00905-3] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/11/2020] [Revised: 10/06/2020] [Accepted: 10/19/2020] [Indexed: 11/09/2022]
Abstract
There is a critical need to better understand the neural basis of antidepressant medication (ADM) response with respect to both symptom alleviation and quality of life (QoL) in major depressive disorder (MDD). Reward neurocircuitry has been implicated in QoL, the neural basis of MDD, and the mechanisms of ADM response. Yet, we do not know whether change in reward neurocircuitry as a function of ADM is associated with change in symptoms and QoL. To address this gap in knowledge, we analyzed data from 128 patients with MDD who participated in the iSPOT-D trial and were assessed with functional neuroimaging pre- and post-ADM treatment (randomized to sertraline, venlafaxine-XR, or escitalopram). 58 matched healthy controls were scanned at the same time points. We quantified functional connectivity (FC) of reward neurocircuitry using nucleus accumbens (NAc) seed regions of interest, and then characterized how changes in FC relate to symptom response (primary outcome) and QoL response (secondary outcome). Symptom responders showed an increase in NAc-dorsal anterior cingulate cortex (ACC) FC relative to non-responders (p < 0.001) which was associated with improvement in physical QoL (p < 0.0003), and a decrease in NAc-inferior parietal lobule FC relative to controls (p < 0.001). QoL response was characterized by increases in FC between NAc-ventral ACC for environmental, NAc-thalamus for physical, and NAc-paracingulate gyrus for social domains (p < 0.001). Symptom responders to sertraline were distinguished by a decrease in NAc-insula FC (p < 0.001) and to venlafaxine-XR by an increase in NAc-inferior temporal gyrus FC (p < 0.005). Findings suggest that change in reward neurocircuitry may underlie differential ADM response profiles with respect to symptoms and QoL in depression.
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van der Vinne N, Vollebregt MA, Rush AJ, Eebes M, van Putten MJAM, Arns M. EEG biomarker informed prescription of antidepressants in MDD: a feasibility trial. Eur Neuropsychopharmacol 2021; 44:14-22. [PMID: 33509659 DOI: 10.1016/j.euroneuro.2020.12.005] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/23/2020] [Revised: 12/02/2020] [Accepted: 12/15/2020] [Indexed: 12/28/2022]
Abstract
Using pre-treatment biomarkers to guide patients to the preferred antidepressant medication treatment could be a promising approach to enhance its current modest response and remission rates. This open-label prospective study assessed the feasibility of using such pre-treatment biomarkers, by using previously identified EEG features (paroxysmal activity; alpha peak frequency; frontal alpha asymmetry) to inform the clinician in selecting among three different antidepressants (ADs; escitalopram, sertraline, venlafaxine) as compared to Treatment As Usual (TAU). EEG data were obtained from 195 outpatients with major depressive disorder prior to eight weeks of AD treatment. Primary outcome measure was the percentage change between before and after treatment on the Beck Depression Inventory-II (BDI-II). We compared TAU and EEG-informed prescription through AN(C)OVAs. Recruitment started with patients receiving TAU to establish baseline effectiveness, after which we recruited patients receiving EEG-informed prescription. 108 patients received EEG-informed prescription and 87 patients received TAU. Clinicians and patients were satisfied with the protocol. Overall, 70 (65%) of the EEG-informed clinicians followed recommendations (compared to 52 (60%) following prescriptions in the TAU group), establishing feasibility. We here confirm that treatment allocation informed by EEG variables previously reported in correlational studies, was feasible.
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Affiliation(s)
- Nikita van der Vinne
- Synaeda Psycho Medisch Centrum, Leeuwarden, the Netherlands; Research Institute Brainclinics, Brainclinics Foundation, Nijmegen, the Netherlands; Department of Clinical Neurophysiology, Technical Medical Centre, University of Twente, Enschede, the Netherlands.
| | - Madelon A Vollebregt
- Research Institute Brainclinics, Brainclinics Foundation, Nijmegen, the Netherlands
| | - A John Rush
- National University of Singapore, Singapore; Duke University School of Medicine, Durham, NC, USA; Texas Tech Health Sciences Center, Odessa, TX, USA
| | - Michiel Eebes
- Synaeda Psycho Medisch Centrum, Leeuwarden, the Netherlands
| | - Michel J A M van Putten
- Department of Clinical Neurophysiology, Technical Medical Centre, University of Twente, Enschede, the Netherlands; Department of Clinical Neurophysiology and Neurology, Medisch Spectrum Twente, Enschede, the Netherlands
| | - Martijn Arns
- Research Institute Brainclinics, Brainclinics Foundation, Nijmegen, the Netherlands; Department of Cognitive Neuroscience, Faculty of Psychology and Neuroscience, Maastricht University, Maastricht, the Netherlands; Department of Psychiatry, Amsterdam UMC, University of Amsterdam, Location AMC, Amsterdam, the Netherlands
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40
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Krepel N, Benschop L, Baeken C, Sack AT, Arns M. An EEG signature of suicidal behavior in female patients with major depressive disorder? A non-replication. Biol Psychol 2021; 161:108058. [PMID: 33647333 DOI: 10.1016/j.biopsycho.2021.108058] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2020] [Revised: 02/23/2021] [Accepted: 02/24/2021] [Indexed: 11/16/2022]
Abstract
INTRODUCTION A recent study showed hypoactivity in the beta/gamma band in female suicide ideators and suicide attempters diagnosed with depression, relative to a low-risk group. The current study aimed to conceptually replicate these results. METHODS In the iSPOT-D sub-sample (n = 402), suicide ideators and low-risk individuals were identified. Confining analyses to females only, differences between low-risk individuals and suicide ideators were tested for using the electroencephalogram (EEG) frequency bands SMR (Sensori-Motor-Rhythm; 12-15 Hz), beta (14.5-30 Hz), beta I (14.5-20 Hz), beta II (20-25 Hz), beta III (25-30 Hz), gamma I (31-49 Hz) using LORETA-software. RESULTS None of the tested frequency bands showed to be significantly different between suicide ideators and low-risk individuals. CONCLUSIONS The current study could not conceptually replicate the earlier published results. Several reasons could explain this non-replication, among which possible electromyographic (EMG) contamination in the beta/gamma band in the original study. TRIAL REGISTRATION ClinicalTrials.gov identifier: NCT00693849. URL: http://clinicaltrials.gov/ct2/show/NCT00693849.
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Affiliation(s)
- Noralie Krepel
- Dept. of Cognitive Neuroscience, Faculty of Psychology and Neuroscience, Maastricht University, Maastricht, the Netherlands; Research Institute Brainclinics, Brainclinics Foundation, Nijmegen, the Netherlands
| | - Lars Benschop
- Ghent University, Department of Psychiatry and Medical Psychology, Ghent Experimental Psychiatry (GHEP) Lab, Ghent, Belgium
| | - Chris Baeken
- Ghent University, Department of Psychiatry and Medical Psychology, Ghent Experimental Psychiatry (GHEP) Lab, Ghent, Belgium; Department of Psychiatry, Vrije Universiteit Brussel (VUB), Universitair Ziekenhuis Brussel (UZBrussel), Laarbeeklaan 101, 1090, Brussels, Belgium; Eindhoven University of Technology, Department of Electrical Engineering, Eindhoven, the Netherlands
| | - Alexander T Sack
- Dept. of Cognitive Neuroscience, Faculty of Psychology and Neuroscience, Maastricht University, Maastricht, the Netherlands
| | - Martijn Arns
- Dept. of Cognitive Neuroscience, Faculty of Psychology and Neuroscience, Maastricht University, Maastricht, the Netherlands; Research Institute Brainclinics, Brainclinics Foundation, Nijmegen, the Netherlands; Amsterdam UMC, University of Amsterdam, Department of Psychiatry (Location AMC), Amsterdam Neuroscience, Amsterdam, the Netherlands.
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Olbrich S, Brunovsky M. The way ahead for predictive EEG biomarkers in treatment of depression. Clin Neurophysiol 2021; 132:616-617. [PMID: 33386211 DOI: 10.1016/j.clinph.2020.12.001] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2020] [Accepted: 12/03/2020] [Indexed: 11/16/2022]
Affiliation(s)
- Sebastian Olbrich
- Department for Psychiatry, Psychotherapy and Psychosomatics, University Hospital of Psychiatry Zurich, Switzerland.
| | - Martin Brunovsky
- National Institute of Mental Health, Klecany, Czech Republic; Third Faculty of Medicine, Charles University, Prague, Czech Republic
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Chen S, Zhang Y, Yuan Y. The Combination of Serum BDNF, Cortisol and IFN-Gamma Can Assist the Diagnosis of Major Depressive Disorder. Neuropsychiatr Dis Treat 2021; 17:2819-2829. [PMID: 34471356 PMCID: PMC8405229 DOI: 10.2147/ndt.s322078] [Citation(s) in RCA: 12] [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: 05/27/2021] [Accepted: 08/13/2021] [Indexed: 11/23/2022] Open
Abstract
PURPOSE Misdiagnosis and ineffective treatment are common in major depressive disorder (MDD) in current clinical practice, while the combination of various serum proteins may assist the correct diagnosis. The study aimed to explore whether the combination of serum inflammatory, stress, and neurotrophic factors could be helpful for the diagnosis of MDD and to investigate the predictors associated with early symptom improvements. PATIENTS AND METHODS Baseline serum levels of C-reactive protein (CRP), interleukin (IL)-6, IL-10, IL-1beta, tumor necrosis factor (TNF)-alpha, interferon (INF)-gamma, cortisol, and brain-derived neurotrophic factor (BDNF) were detected in 30 MDD patients and 30 age- and gender-matched healthy controls. 17-item Hamilton Depression Rating Scale (HAMD-17) and Hamilton Anxiety Rating Scale (HAMA) were applied to assess symptoms both at baseline and two weeks after antidepressant treatment. Stepwise multiple linear regression was employed to identify the early efficacy predictors, and a logistic regression model was built with the above serum proteins. The area under the receiver operating characteristic (AUC) curve was calculated to evaluate the model's diagnostic power. RESULTS Multiple linear regression revealed that baseline scores of retardation (β = -0.432, P = 0.012) and psychological anxiety (β = -0.423, P = 0.014) factors were negatively associated with the reduction rate of HAMD-17. A simple and efficient diagnostic model using serum BDNF, cortisol, and IFN-gamma levels was established by the forward stepwise logistic regression, and the model achieved an AUC of 0.884, with 86.7% sensitivity and 83.3% specificity. CONCLUSION The results showed that combining serum BDNF, cortisol and IFN-gamma could aid the diagnosis of MDD, while baseline retardation and psychological anxiety may predict the poor early symptom improvement.
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Affiliation(s)
- Suzhen Chen
- Department of Psychosomatics and Psychiatry, ZhongDa Hospital, School of Medicine, Southeast University, Nanjing, 210009, People's Republic of China.,Institute of Psychosomatics, School of Medicine, Southeast University, Nanjing, 210009, People's Republic of China
| | - Yuqun Zhang
- School of Nursing, Nanjing University of Chinese Medicine, Nanjing, 210023, People's Republic of China
| | - Yonggui Yuan
- Department of Psychosomatics and Psychiatry, ZhongDa Hospital, School of Medicine, Southeast University, Nanjing, 210009, People's Republic of China.,Institute of Psychosomatics, School of Medicine, Southeast University, Nanjing, 210009, People's Republic of China
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Ang YS, Kaiser R, Deckersbach T, Almeida J, Phillips ML, Chase HW, Webb CA, Parsey R, Fava M, McGrath P, Weissman M, Adams P, Deldin P, Oquendo MA, McInnis MG, Carmody T, Bruder G, Cooper CM, Fatt CRC, Trivedi MH, Pizzagalli DA. Pretreatment Reward Sensitivity and Frontostriatal Resting-State Functional Connectivity Are Associated With Response to Bupropion After Sertraline Nonresponse. Biol Psychiatry 2020; 88:657-667. [PMID: 32507389 PMCID: PMC7529779 DOI: 10.1016/j.biopsych.2020.04.009] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/02/2019] [Revised: 03/24/2020] [Accepted: 04/13/2020] [Indexed: 12/21/2022]
Abstract
BACKGROUND Standard guidelines recommend selective serotonin reuptake inhibitors as first-line antidepressants for adults with major depressive disorder, but success is limited and patients who fail to benefit are often switched to non-selective serotonin reuptake inhibitor agents. This study investigated whether brain- and behavior-based markers of reward processing might be associated with response to bupropion after sertraline nonresponse. METHODS In a two-stage, double-blinded clinical trial, 296 participants were randomized to receive 8 weeks of sertraline or placebo in stage 1. Individuals who responded continued on another 8-week course of the same intervention in stage 2, while sertraline and placebo nonresponders crossed over to bupropion and sertraline, respectively. Data from 241 participants were analyzed. The stage 2 sample comprised 87 patients with major depressive disorder who switched medication and 38 healthy control subjects. A total of 116 participants with major depressive disorder treated with sertraline in stage 1 served as an independent replication sample. The probabilistic reward task and resting-state functional magnetic resonance imaging were administered at baseline. RESULTS Greater pretreatment reward sensitivity and higher resting-state functional connectivity between bilateral nucleus accumbens and rostral anterior cingulate cortex were associated with positive response to bupropion but not sertraline. Null findings for sertraline were replicated in the stage 1 sample. CONCLUSIONS Pretreatment reward sensitivity and frontostriatal connectivity may identify patients likely to benefit from bupropion following selective serotonin reuptake inhibitor failures. Results call for a prospective replication based on these biomarkers to advance clinical care.
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Affiliation(s)
- Yuen-Siang Ang
- Department of Psychiatry, Harvard Medical School, Boston, 25 Shattuck Street, Boston, MA 02115,Center for Depression, Anxiety and Stress Research, McLean Hospital, 115 Mill Street, Belmont, MA 02478
| | - Roselinde Kaiser
- Department of Psychology and Neuroscience, University of Colorado Boulder, Boulder, CO 80302
| | - Thilo Deckersbach
- Department of Psychiatry, Massachusetts General Hospital, 55 Fruit Street, Boston, MA 02114
| | - Jorge Almeida
- Department of Psychiatry, University of Texas at Austin, Dell Medical School, 1601 Trinity St., Austin, TX 78712
| | - Mary L. Phillips
- Department of Psychiatry, University of Pittsburgh, 3811 O’Hara St, Pittsburgh, PA 15213
| | - Henry W. Chase
- Department of Psychiatry, University of Pittsburgh, 3811 O’Hara St, Pittsburgh, PA 15213
| | - Christian A. Webb
- Department of Psychiatry, Harvard Medical School, Boston, 25 Shattuck Street, Boston, MA 02115,Center for Depression, Anxiety and Stress Research, McLean Hospital, 115 Mill Street, Belmont, MA 02478
| | - Ramin Parsey
- Department of Psychiatry, Stony Brook University, Stony Brook, 100 Nicolls Road, Stony Brook, NY 11794
| | - Maurizio Fava
- Department of Psychiatry, Harvard Medical School, Boston, 25 Shattuck Street, Boston, MA 02115,Department of Psychiatry, Massachusetts General Hospital, 55 Fruit Street, Boston, MA 02114
| | - Patrick McGrath
- New York State Psychiatric Institute & Department of Psychiatry, Columbia University Vagelos College of Physicians and Surgeons, 1051 Riverside Drive, New York, NY 10032
| | - Myrna Weissman
- New York State Psychiatric Institute & Department of Psychiatry, Columbia University Vagelos College of Physicians and Surgeons, 1051 Riverside Drive, New York, NY 10032
| | - Phil Adams
- New York State Psychiatric Institute & Department of Psychiatry, Columbia University Vagelos College of Physicians and Surgeons, 1051 Riverside Drive, New York, NY 10032
| | - Patricia Deldin
- Department of Psychiatry, University of Michigan, 500 S State Street, Ann Arbor, MI 48109
| | - Maria A. Oquendo
- Department of Psychiatry, University of Pennsylvania, Perelman School of Medicine, 3400 Civic Center Blvd, Philadelphia, PA 19104
| | - Melvin G. McInnis
- Department of Psychiatry, University of Michigan, 500 S State Street, Ann Arbor, MI 48109
| | - Thomas Carmody
- Department of Psychiatry, University of Texas, Southwestern Medical Center, 5323 Harry Hines Blvd, Dallas, TX 75390
| | - Gerard Bruder
- New York State Psychiatric Institute & Department of Psychiatry, Columbia University Vagelos College of Physicians and Surgeons, 1051 Riverside Drive, New York, NY 10032
| | - Crystal M. Cooper
- Department of Psychiatry, University of Texas, Southwestern Medical Center, 5323 Harry Hines Blvd, Dallas, TX 75390
| | - Cherise R. Chin Fatt
- Department of Psychiatry, University of Texas, Southwestern Medical Center, 5323 Harry Hines Blvd, Dallas, TX 75390
| | - Madhukar H. Trivedi
- Department of Psychiatry, University of Texas, Southwestern Medical Center, 5323 Harry Hines Blvd, Dallas, TX 75390
| | - Diego A. Pizzagalli
- Department of Psychiatry, Harvard Medical School, Boston, 25 Shattuck Street, Boston, MA 02115,Center for Depression, Anxiety and Stress Research, McLean Hospital, 115 Mill Street, Belmont, MA 02478
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Keller AS, Ball TM, Williams LM. Deep phenotyping of attention impairments and the 'Inattention Biotype' in Major Depressive Disorder. Psychol Med 2020; 50:2203-2212. [PMID: 31477195 PMCID: PMC8022888 DOI: 10.1017/s0033291719002290] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Abstract
BACKGROUND Attention impairment is an under-investigated feature and diagnostic criterion of Major Depressive Disorder (MDD) that is associated with poorer outcomes. Despite increasing knowledge regarding mechanisms of attention in healthy adults, we lack a detailed characterization of attention impairments and their neural signatures in MDD. METHODS Here, we focus on selective attention and advance a deep multi-modal characterization of these impairments in MDD, using data acquired from n = 1008 patients and n = 336 age- and sex-matched healthy controls. Selective attention impairments were operationalized and anchored in a behavioral performance measure, assessed within a battery of cognitive tests. We sought to establish the accompanying neural signature using independent measures of functional magnetic resonance imaging (15% of the sample) and electroencephalographic recordings of oscillatory neural activity. RESULTS Greater impairment on the behavioral measure of selective attention was associated with intrinsic hypo-connectivity of the fronto-parietal attention network. Not only was this relationship specific to the fronto-parietal network unlike other large-scale networks; this hypo-connectivity was also specific to selective attention performance unlike other measures of cognition. Selective attention impairment was also associated with lower posterior alpha (8-13 Hz) power at rest and was related to more severe negative bias (frequent misidentifications of neutral faces as sad and lingering attention on sad faces), relevant to clinical features of negative attributions and brooding. Selective attention impairments were independent of overall depression severity and of worrying or sleep problems. CONCLUSIONS These results provide a foundation for the clinical translational development of objective markers and targeted therapeutics for attention impairment in MDD.
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Affiliation(s)
- Arielle S Keller
- Graduate Program in Neurosciences, Stanford University, Stanford, CA, USA
- Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA, USA
| | - Tali M Ball
- Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA, USA
| | - Leanne M Williams
- Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA, USA
- MIRECC, VA Palo Alto Health Care System, Palo Alto, CA, USA
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Tan D, Foster S, Korgaonkar MS, Oxenham V, Whittle T, Klineberg I. The role of progressive oral implant rehabilitation in mastication, cognition and oral health‐related quality of life outcomes—A pilot to define the protocol. J Oral Rehabil 2020; 47:1368-1381. [DOI: 10.1111/joor.13085] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 08/10/2020] [Indexed: 12/23/2022]
Affiliation(s)
- Daniel Tan
- Department of Oral Rehabilitation School of Dentistry Faculty of Medicine and Health University of Sydney Sydney Australia
| | - Sheryl Foster
- School of Health Sciences Faculty of Medicine and Health University of Sydney Sydney Australia
- Radiology Department Westmead Hospital Sydney Australia
| | - Mayuresh S. Korgaonkar
- School of Health Sciences Faculty of Medicine and Health University of Sydney Sydney Australia
- Brain Dynamics Centre Westmead Institute for Medical Research, University of Sydney Sydney Australia
| | - Vincent Oxenham
- Department of Psychology Faculty of Medicine, Health and Human Sciences Macquarie University Sydney Australia
| | - Terry Whittle
- Department of Oral Rehabilitation School of Dentistry Faculty of Medicine and Health University of Sydney Sydney Australia
| | - Iven Klineberg
- Department of Oral Rehabilitation School of Dentistry Faculty of Medicine and Health University of Sydney Sydney Australia
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Pinotsis DA. Statistical decision theory and multiscale analyses of human brain data. J Neurosci Methods 2020; 346:108912. [PMID: 32835705 DOI: 10.1016/j.jneumeth.2020.108912] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2020] [Revised: 08/11/2020] [Accepted: 08/12/2020] [Indexed: 02/02/2023]
Abstract
BACKGROUND In the era of Big Data, large scale electrophysiological data from animal and human studies are abundant. These data contain information at multiple spatiotemporal scales. However, current approaches for the analysis of electrophysiological data often focus on a single spatiotemporal scale only. NEW METHOD We discuss a multiscale approach for the analysis of electrophysiological data. This is based on combining neural models that describe brain data at different scales. It allows us to make laminar-specific inferences about neurobiological properties of cortical sources using non invasive human electrophysiology data. RESULTS We provide a mathematical proof of this approach using statistical decision theory. We also consider its extensions to brain imaging studies including data from the same subjects performing different tasks. As an illustration, we show that changes in gamma oscillations between different people might originate from differences in recurrent connection strengths of inhibitory interneurons in layers 5/6. COMPARISON WITH EXISTING METHODS This is a new approach that follows up on our recent work. It is different from other approaches where the scale of spatiotemporal dynamics is fixed. CONCLUSIONS We discuss a multiscale approach for the analysis of human MEG data. This uses a neural mass model that includes constraints informed by a compartmental model. This has two advantages. First, it allows us to find differences in cortical laminar dynamics and understand neurobiological properties like neuromodulation, excitation to inhibition balance etc. using non invasive data. Second, it allows us to validate macroscale models by exploiting animal data.
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Affiliation(s)
- D A Pinotsis
- Centre for Mathematical Neuroscience and Psychology and Department of Psychology, City -University of London, London EC1V 0HB, United Kingdom; The Picower Institute for Learning & Memory and Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.
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Ju Y, Horien C, Chen W, Guo W, Lu X, Sun J, Dong Q, Liu B, Liu J, Yan D, Wang M, Zhang L, Guo H, Zhao F, Zhang Y, Shen X, Constable RT, Li L. Connectome-based models can predict early symptom improvement in major depressive disorder. J Affect Disord 2020; 273:442-452. [PMID: 32560939 DOI: 10.1016/j.jad.2020.04.028] [Citation(s) in RCA: 24] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/20/2020] [Revised: 03/24/2020] [Accepted: 04/21/2020] [Indexed: 12/12/2022]
Abstract
BACKGROUND Major depressive disorder (MDD) is a debilitating mental illness with more than 50% of patients not achieving an adequate response using first-line treatments. Reliable models that predict antidepressant treatment outcome are needed to guide clinical decision making. We aimed to build predictive models of treatment improvement for MDD patients using machine learning approaches based on fMRI resting-state functional connectivity patterns. METHODS Resting-state fMRI data were acquired from 192 untreated MDD patients at recruitment, and their severity of depression was assessed by Hamilton Rating Scale for Depression (HAMD) at baseline. Patients were given medication after the initial MR scan and their symptoms were monitored through HAMD for a period of six months. Connectome-based predictive modeling (CPM) algorithms were implemented to predict the improvement in HAMD score at one month from resting-state connectivity at baseline. Additionally, by selectively combining the features from all leave-one-out iterations in the model building stage, we created a consensus model that could be generalized to predict improvement in HAMD score in samples of non-overlapping subjects at different time points. RESULTS Using baseline functional connectivity, CPM successfully predicted symptom improvement of depression at one month. In addition, a consensus 'MDD improvement model' could predict symptom improvement for novel individuals at the two-week, one-month, two-month and three-month time points after antidepressant treatment. CONCLUSIONS Individual pre-treatment functional brain networks contain meaningful information that can be gleaned to build predictors of treatment outcome. The identified MDD improvement networks could be an appropriate biomarker for predicting individual therapeutic response of antidepressant treatment. Replication and validation using other large datasets will be a key next step before these models can be used in clinical practice.
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Affiliation(s)
- Yumeng Ju
- Department of Psychiatry, The Second Xiangya Hospital, Central South University, Changsha, Hunan 410011, China; Mental Health Institute of Central South University, China National Clinical Research Center on Mental Disorders (Xiangya), China National Technology Institute on Mental Disorders, Hunan Technology Institute of Psychiatry, Hunan Key Laboratory of Psychiatry and Mental Health, Changsha, Hunan 410011, China; Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA
| | - Corey Horien
- Interdepartmental Neuroscience Program, Yale University School of Medicine, New Haven, CT, USA
| | - Wentao Chen
- Department of Psychiatry, The Second Xiangya Hospital, Central South University, Changsha, Hunan 410011, China; Mental Health Institute of Central South University, China National Clinical Research Center on Mental Disorders (Xiangya), China National Technology Institute on Mental Disorders, Hunan Technology Institute of Psychiatry, Hunan Key Laboratory of Psychiatry and Mental Health, Changsha, Hunan 410011, China
| | - Weilong Guo
- Department of Psychiatry, The Second Xiangya Hospital, Central South University, Changsha, Hunan 410011, China; Mental Health Institute of Central South University, China National Clinical Research Center on Mental Disorders (Xiangya), China National Technology Institute on Mental Disorders, Hunan Technology Institute of Psychiatry, Hunan Key Laboratory of Psychiatry and Mental Health, Changsha, Hunan 410011, China
| | - Xiaowen Lu
- Department of Psychiatry, The Second Xiangya Hospital, Central South University, Changsha, Hunan 410011, China; Mental Health Institute of Central South University, China National Clinical Research Center on Mental Disorders (Xiangya), China National Technology Institute on Mental Disorders, Hunan Technology Institute of Psychiatry, Hunan Key Laboratory of Psychiatry and Mental Health, Changsha, Hunan 410011, China
| | - Jinrong Sun
- Department of Psychiatry, The Second Xiangya Hospital, Central South University, Changsha, Hunan 410011, China; Mental Health Institute of Central South University, China National Clinical Research Center on Mental Disorders (Xiangya), China National Technology Institute on Mental Disorders, Hunan Technology Institute of Psychiatry, Hunan Key Laboratory of Psychiatry and Mental Health, Changsha, Hunan 410011, China
| | - Qiangli Dong
- Department of Psychiatry, The Second Xiangya Hospital, Central South University, Changsha, Hunan 410011, China; Mental Health Institute of Central South University, China National Clinical Research Center on Mental Disorders (Xiangya), China National Technology Institute on Mental Disorders, Hunan Technology Institute of Psychiatry, Hunan Key Laboratory of Psychiatry and Mental Health, Changsha, Hunan 410011, China
| | - Bangshan Liu
- Department of Psychiatry, The Second Xiangya Hospital, Central South University, Changsha, Hunan 410011, China; Mental Health Institute of Central South University, China National Clinical Research Center on Mental Disorders (Xiangya), China National Technology Institute on Mental Disorders, Hunan Technology Institute of Psychiatry, Hunan Key Laboratory of Psychiatry and Mental Health, Changsha, Hunan 410011, China
| | - Jin Liu
- Department of Psychiatry, The Second Xiangya Hospital, Central South University, Changsha, Hunan 410011, China; Mental Health Institute of Central South University, China National Clinical Research Center on Mental Disorders (Xiangya), China National Technology Institute on Mental Disorders, Hunan Technology Institute of Psychiatry, Hunan Key Laboratory of Psychiatry and Mental Health, Changsha, Hunan 410011, China
| | - Danfeng Yan
- Department of Psychiatry, The Second Xiangya Hospital, Central South University, Changsha, Hunan 410011, China; Mental Health Institute of Central South University, China National Clinical Research Center on Mental Disorders (Xiangya), China National Technology Institute on Mental Disorders, Hunan Technology Institute of Psychiatry, Hunan Key Laboratory of Psychiatry and Mental Health, Changsha, Hunan 410011, China
| | - Mi Wang
- Department of Psychiatry, The Second Xiangya Hospital, Central South University, Changsha, Hunan 410011, China; Mental Health Institute of Central South University, China National Clinical Research Center on Mental Disorders (Xiangya), China National Technology Institute on Mental Disorders, Hunan Technology Institute of Psychiatry, Hunan Key Laboratory of Psychiatry and Mental Health, Changsha, Hunan 410011, China
| | - Liang Zhang
- Department of Psychiatry, The Second Xiangya Hospital, Central South University, Changsha, Hunan 410011, China; Mental Health Institute of Central South University, China National Clinical Research Center on Mental Disorders (Xiangya), China National Technology Institute on Mental Disorders, Hunan Technology Institute of Psychiatry, Hunan Key Laboratory of Psychiatry and Mental Health, Changsha, Hunan 410011, China
| | - Hua Guo
- Zhumadian Psychiatric Hospital, Zhumadian, Henan 463000, China
| | - Futao Zhao
- Zhumadian Psychiatric Hospital, Zhumadian, Henan 463000, China
| | - Yan Zhang
- Department of Psychiatry, The Second Xiangya Hospital, Central South University, Changsha, Hunan 410011, China; Mental Health Institute of Central South University, China National Clinical Research Center on Mental Disorders (Xiangya), China National Technology Institute on Mental Disorders, Hunan Technology Institute of Psychiatry, Hunan Key Laboratory of Psychiatry and Mental Health, Changsha, Hunan 410011, China.
| | - Xilin Shen
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, USA
| | - R Todd Constable
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, USA; Department of Neurosurgery, Yale School of Medicine, USA; Interdepartmental Neuroscience Program, Yale School of Medicine, USA
| | - Lingjiang Li
- Department of Psychiatry, The Second Xiangya Hospital, Central South University, Changsha, Hunan 410011, China; Mental Health Institute of Central South University, China National Clinical Research Center on Mental Disorders (Xiangya), China National Technology Institute on Mental Disorders, Hunan Technology Institute of Psychiatry, Hunan Key Laboratory of Psychiatry and Mental Health, Changsha, Hunan 410011, China.
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Rajpurkar P, Yang J, Dass N, Vale V, Keller AS, Irvin J, Taylor Z, Basu S, Ng A, Williams LM. Evaluation of a Machine Learning Model Based on Pretreatment Symptoms and Electroencephalographic Features to Predict Outcomes of Antidepressant Treatment in Adults With Depression: A Prespecified Secondary Analysis of a Randomized Clinical Trial. JAMA Netw Open 2020; 3:e206653. [PMID: 32568399 PMCID: PMC7309440 DOI: 10.1001/jamanetworkopen.2020.6653] [Citation(s) in RCA: 38] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/24/2022] Open
Abstract
IMPORTANCE Despite the high prevalence and potential outcomes of major depressive disorder, whether and how patients will respond to antidepressant medications is not easily predicted. OBJECTIVE To identify the extent to which a machine learning approach, using gradient-boosted decision trees, can predict acute improvement for individual depressive symptoms with antidepressants based on pretreatment symptom scores and electroencephalographic (EEG) measures. DESIGN, SETTING, AND PARTICIPANTS This prognostic study analyzed data collected as part of the International Study to Predict Optimized Treatment in Depression, a randomized, prospective open-label trial to identify clinically useful predictors and moderators of response to commonly used first-line antidepressant medications. Data collection was conducted at 20 sites spanning 5 countries and including 518 adult outpatients (18-65 years of age) from primary care or specialty care practices who received a diagnosis of current major depressive disorder between December 1, 2008, and September 30, 2013. Patients were antidepressant medication naive or willing to undergo a 1-week washout period of any nonprotocol antidepressant medication. Statistical analysis was conducted from January 5 to June 30, 2019. EXPOSURES Participants with major depressive disorder were randomized in a 1:1:1 ratio to undergo 8 weeks of treatment with escitalopram oxalate (n = 162), sertraline hydrochloride (n = 176), or extended-release venlafaxine hydrochloride (n = 180). MAIN OUTCOMES AND MEASURES The primary objective was to predict improvement in individual symptoms, defined as the difference in score for each of the symptoms on the 21-item Hamilton Rating Scale for Depression from baseline to week 8, evaluated using the C index. RESULTS The resulting data set contained 518 patients (274 women; mean [SD] age, 39.0 [12.6] years; mean [SD] 21-item Hamilton Rating Scale for Depression score improvement, 13.0 [7.0]). With the use of 5-fold cross-validation for evaluation, the machine learning model achieved C index scores of 0.8 or higher on 12 of 21 clinician-rated symptoms, with the highest C index score of 0.963 (95% CI, 0.939-1.000) for loss of insight. The importance of any single EEG feature was higher than 5% for prediction of 7 symptoms, with the most important EEG features being the absolute delta band power at the occipital electrode sites (O1, 18.8%; Oz, 6.7%) for loss of insight. Over and above the use of baseline symptom scores alone, the use of both EEG and baseline symptom features was associated with a significant increase in the C index for improvement in 4 symptoms: loss of insight (C index increase, 0.012 [95% CI, 0.001-0.020]), energy loss (C index increase, 0.035 [95% CI, 0.011-0.059]), appetite changes (C index increase, 0.017 [95% CI, 0.003-0.030]), and psychomotor retardation (C index increase, 0.020 [95% CI, 0.008-0.032]). CONCLUSIONS AND RELEVANCE This study suggests that machine learning may be used to identify independent associations of symptoms and EEG features to predict antidepressant-associated improvements in specific symptoms of depression. The approach should next be prospectively validated in clinical trials and settings. TRIAL REGISTRATION ClinicalTrials.gov Identifier: NCT00693849.
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Affiliation(s)
- Pranav Rajpurkar
- Department of Computer Science, Stanford University, Stanford, California
| | - Jingbo Yang
- Department of Computer Science, Stanford University, Stanford, California
| | - Nathan Dass
- Department of Computer Science, Stanford University, Stanford, California
| | - Vinjai Vale
- Department of Computer Science, Stanford University, Stanford, California
| | - Arielle S. Keller
- Stanford Center for Precision Mental Health and Wellness, Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, California
| | - Jeremy Irvin
- Department of Computer Science, Stanford University, Stanford, California
| | - Zachary Taylor
- Stanford Center for Precision Mental Health and Wellness, Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, California
| | - Sanjay Basu
- Center for Primary Care, Harvard Medical School, Boston, Massachusetts
- Research and Analytics, Collective Health, San Francisco, California
- Division of Primary Care and Public Health, Imperial College London School of Public Health, London, United Kingdom
| | - Andrew Ng
- Department of Computer Science, Stanford University, Stanford, California
| | - Leanne M. Williams
- Stanford Center for Precision Mental Health and Wellness, Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, California
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Braund TA, Palmer DM, Williams LM, Harris AWF. Dimensions of anxiety in Major depressive disorder and their use in predicting antidepressant treatment outcome: an iSPOT-D report. Psychol Med 2020; 50:1032-1042. [PMID: 31023398 DOI: 10.1017/s0033291719000941] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/07/2023]
Abstract
BACKGROUND Major depressive disorder (MDD) commonly co-occurs with clinically significant levels of anxiety. However, anxiety symptoms are varied and have been inconsistently associated with clinical, functional, and antidepressant treatment outcomes. We aimed to identify and characterise dimensions of anxiety in people with MDD and their use in predicting antidepressant treatment outcome. METHOD 1008 adults with a current diagnosis of single-episode or recurrent, nonpsychotic, MDD were assessed at baseline on clinical features and cognitive/physiological functioning. Participants were then randomised to one of three commonly prescribed antidepressants and reassessed at 8 weeks regarding symptom change, as well as remission and response, on the 17-item Hamilton Rating Scale Depression (HRSD17) and the 16-item Quick Inventory of Depressive Symptomatology (QIDS-SR16). Exploratory factor analysis was used on items from scales assessing anxiety symptoms, and resulting factors were assessed against clinical features and cognitive/physiological functioning. Factors were also assessed on their ability to predict treatment outcome. RESULTS Three factors emerged relating to stress, cognitive anxiety, and somatic anxiety. All factors showed high internal consistency, minimal cross-loadings, and unique clinical and functional profiles. Furthermore, only higher somatic anxiety was associated with poorer QIDS-SR16 remission, even after adjusting for covariates and multiple comparisons. CONCLUSIONS Anxiety symptoms in people with MDD can be separated onto distinct factors that differentially respond to treatment outcome. Furthermore, these factors do not align with subscales of established measures of anxiety. Future research should consider cognitive and somatic symptoms of anxiety separately when assessing anxiety in MDD and their use in predicting treatment outcome.
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Affiliation(s)
- Taylor A Braund
- Brain Dynamics Centre, The Westmead Institute for Medical Research, Sydney, NSW, Australia
- Discipline of Psychiatry, Sydney Medical School, University of Sydney, Sydney, NSW, Australia
- The Brain Resource Company, Sydney, NSW, Australia
| | - Donna M Palmer
- Brain Dynamics Centre, The Westmead Institute for Medical Research, Sydney, NSW, Australia
- The Brain Resource Company, Sydney, NSW, Australia
| | - Leanne M Williams
- Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA, USA
- Sierra-Pacific Mental Illness Research, Education, and Clinical Center (MIRECC) Veterans Affairs Palo Alto Health Care System, Palo Alto, CA, USA
| | - Anthony W F Harris
- Brain Dynamics Centre, The Westmead Institute for Medical Research, Sydney, NSW, Australia
- Discipline of Psychiatry, Sydney Medical School, University of Sydney, Sydney, NSW, Australia
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50
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Grzenda A, Widge AS. Electroencephalographic Biomarkers for Predicting Antidepressant Response: New Methods, Old Questions. JAMA Psychiatry 2020; 77:347-348. [PMID: 31895448 DOI: 10.1001/jamapsychiatry.2019.3749] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/12/2023]
Affiliation(s)
- Adrienne Grzenda
- Department of Psychiatry, University of California, Los Angeles.,Semel Institute for Neuroscience and Human Behavior, Los Angeles, California
| | - Alik S Widge
- Department of Psychiatry and Behavioral Sciences, University of Minnesota, Minneapolis
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