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Williams LM, Whitfield Gabrieli S. Neuroimaging for precision medicine in psychiatry. Neuropsychopharmacology 2024:10.1038/s41386-024-01917-z. [PMID: 39039140 DOI: 10.1038/s41386-024-01917-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/15/2024] [Revised: 06/24/2024] [Accepted: 06/27/2024] [Indexed: 07/24/2024]
Abstract
Although the lifetime burden due to mental disorders is increasing, we lack tools for more precise diagnosing and treating prevalent and disabling disorders such as major depressive disorder. 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, focusing on major depressive and anxiety disorders. We begin by outlining evidence for the use of functional neuroimaging to stratify the heterogeneity of these disorders, based on underlying circuit dysfunction. We then review the current landscape of how functional neuroimaging-derived circuit predictors can predict treatment outcomes and clinical trajectories in depression and anxiety. Future directions for advancing clinically appliable neuroimaging measures are considered. We conclude by considering the opportunities and challenges of translating neuroimaging measures into practice. As an illustration, we highlight one approach for quantifying brain circuit function at an individual level, which could serve as a model for clinical translation.
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Affiliation(s)
- Leanne M Williams
- Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, CA, 94305, USA.
- Sierra-Pacific Mental Illness Research, Education, and Clinical Center (MIRECC) Veterans Affairs Palo Alto Health Care System, Palo Alto, CA, 94304, USA.
| | - Susan Whitfield Gabrieli
- Department of Psychology, Northeastern University, 805 Columbus Ave, Boston, MA, 02120, USA
- Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA, 02114, USA
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2
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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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3
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Buch AM, Liston C, Grosenick L. Simple and Scalable Algorithms for Cluster-Aware Precision Medicine. PROCEEDINGS OF MACHINE LEARNING RESEARCH 2024; 238:136-144. [PMID: 39015742 PMCID: PMC11251711] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Subscribe] [Scholar Register] [Indexed: 07/18/2024]
Abstract
AI-enabled precision medicine promises a transformational improvement in healthcare outcomes. However, training on biomedical data presents significant challenges as they are often high dimensional, clustered, and of limited sample size. To overcome these challenges, we propose a simple and scalable approach for cluster-aware embedding that combines latent factor methods with a convex clustering penalty in a modular way. Our novel approach overcomes the complexity and limitations of current joint embedding and clustering methods and enables hierarchically clustered principal component analysis (PCA), locally linear embedding (LLE), and canonical correlation analysis (CCA). Through numerical experiments and real-world examples, we demonstrate that our approach outperforms fourteen clustering methods on highly underdetermined problems (e.g., with limited sample size) as well as on large sample datasets. Importantly, our approach does not require the user to choose the desired number of clusters, yields improved model selection if they do, and yields interpretable hierarchically clustered embedding dendrograms. Thus, our approach improves significantly on existing methods for identifying patient subgroups in multiomics and neuroimaging data and enables scalable and interpretable biomarkers for precision medicine.
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Affiliation(s)
- Amanda M Buch
- Dept. of Psychiatry & BMRI, Weill Cornell Medicine, Cornell University
| | - Conor Liston
- Dept. of Psychiatry & BMRI, Weill Cornell Medicine, Cornell University
| | - Logan Grosenick
- Dept. of Psychiatry & BMRI, Weill Cornell Medicine, Cornell University
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4
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Willinger D, Häberling I, Ilioska I, Berger G, Walitza S, Brem S. Weakened effective connectivity between salience network and default mode network during resting state in adolescent depression. Front Psychiatry 2024; 15:1386984. [PMID: 38638415 PMCID: PMC11024787 DOI: 10.3389/fpsyt.2024.1386984] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/16/2024] [Accepted: 03/18/2024] [Indexed: 04/20/2024] Open
Abstract
Adolescent major depressive disorder (MDD) is associated with altered resting-state connectivity between the default mode network (DMN) and the salience network (SN), which are involved in self-referential processing and detecting and filtering salient stimuli, respectively. Using spectral dynamical causal modelling, we investigated the effective connectivity and input sensitivity between key nodes of these networks in 30 adolescents with MDD and 32 healthy controls while undergoing resting-state fMRI. We found that the DMN received weaker inhibition from the SN and that the medial prefrontal cortex and the anterior cingulate cortex showed reduced self-inhibition in MDD, making them more prone to external influences. Moreover, we found that selective serotonin reuptake inhibitor (SSRI) intake was associated with decreased and increased self-inhibition of the SN and DMN, respectively, in patients. Our findings suggest that adolescent MDD is characterized by a hierarchical imbalance between the DMN and the SN, which could affect the integration of emotional and self-related information. We propose that SSRIs may help restore network function by modulating excitatory/inhibitory balance in the DMN and the SN. Our study highlights the potential of prefrontal-amygdala interactions as a biomarker and a therapeutic target for adolescent depression.
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Affiliation(s)
- David Willinger
- Department of Child and Adolescent Psychiatry and Psychotherapy, University Hospital of Psychiatry Zurich, University of Zurich, Zurich, Switzerland
- Neuroscience Center Zurich, University of Zurich and ETH Zurich, Zurich, Switzerland
- Department of Psychology and Psychodynamics, Karl Landsteiner University of Health Sciences, Krems an der Donau, Austria
| | - Isabelle Häberling
- Department of Child and Adolescent Psychiatry and Psychotherapy, University Hospital of Psychiatry Zurich, University of Zurich, Zurich, Switzerland
| | - Iva Ilioska
- Department of Cognitive Neuroscience, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Centre, Nijmegen, Netherlands
| | - Gregor Berger
- Department of Child and Adolescent Psychiatry and Psychotherapy, University Hospital of Psychiatry Zurich, University of Zurich, Zurich, Switzerland
| | - Susanne Walitza
- Department of Child and Adolescent Psychiatry and Psychotherapy, University Hospital of Psychiatry Zurich, University of Zurich, Zurich, Switzerland
- Neuroscience Center Zurich, University of Zurich and ETH Zurich, Zurich, Switzerland
| | - Silvia Brem
- Department of Child and Adolescent Psychiatry and Psychotherapy, University Hospital of Psychiatry Zurich, University of Zurich, Zurich, Switzerland
- Neuroscience Center Zurich, University of Zurich and ETH Zurich, Zurich, Switzerland
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5
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Fu CHY, Antoniades M, Erus G, Garcia JA, Fan Y, Arnone D, Arnott SR, Chen T, Choi KS, Fatt CC, Frey BN, Frokjaer VG, Ganz M, Godlewska BR, Hassel S, Ho K, McIntosh AM, Qin K, Rotzinger S, Sacchet MD, Savitz J, Shou H, Singh A, Stolicyn A, Strigo I, Strother SC, Tosun D, Victor TA, Wei D, Wise T, Zahn R, Anderson IM, Craighead WE, Deakin JFW, Dunlop BW, Elliott R, Gong Q, Gotlib IH, Harmer CJ, Kennedy SH, Knudsen GM, Mayberg HS, Paulus MP, Qiu J, Trivedi MH, Whalley HC, Yan CG, Young AH, Davatzikos C. Neuroanatomical dimensions in medication-free individuals with major depressive disorder and treatment response to SSRI antidepressant medications or placebo. NATURE. MENTAL HEALTH 2024; 2:164-176. [PMID: 38948238 PMCID: PMC11211072 DOI: 10.1038/s44220-023-00187-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/18/2022] [Accepted: 11/17/2023] [Indexed: 07/02/2024]
Abstract
Major depressive disorder (MDD) is a heterogeneous clinical syndrome with widespread subtle neuroanatomical correlates. Our objective was to identify the neuroanatomical dimensions that characterize MDD and predict treatment response to selective serotonin reuptake inhibitor (SSRI) antidepressants or placebo. In the COORDINATE-MDD consortium, raw MRI data were shared from international samples (N = 1,384) of medication-free individuals with first-episode and recurrent MDD (N = 685) in a current depressive episode of at least moderate severity, but not treatment-resistant depression, as well as healthy controls (N = 699). Prospective longitudinal data on treatment response were available for a subset of MDD individuals (N = 359). Treatments were either SSRI antidepressant medication (escitalopram, citalopram, sertraline) or placebo. Multi-center MRI data were harmonized, and HYDRA, a semi-supervised machine-learning clustering algorithm, was utilized to identify patterns in regional brain volumes that are associated with disease. MDD was optimally characterized by two neuroanatomical dimensions that exhibited distinct treatment responses to placebo and SSRI antidepressant medications. Dimension 1 was characterized by preserved gray and white matter (N = 290 MDD), whereas Dimension 2 was characterized by widespread subtle reductions in gray and white matter (N = 395 MDD) relative to healthy controls. Although there were no significant differences in age of onset, years of illness, number of episodes, or duration of current episode between dimensions, there was a significant interaction effect between dimensions and treatment response. Dimension 1 showed a significant improvement in depressive symptoms following treatment with SSRI medication (51.1%) but limited changes following placebo (28.6%). By contrast, Dimension 2 showed comparable improvements to either SSRI (46.9%) or placebo (42.2%) (β = -18.3, 95% CI (-34.3 to -2.3), P = 0.03). Findings from this case-control study indicate that neuroimaging-based markers can help identify the disease-based dimensions that constitute MDD and predict treatment response.
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Affiliation(s)
- Cynthia H. Y. Fu
- School of Psychology, University of East London, London, UK
- Centre for Affective Disorders, Department of Psychological Medicine, Institute of Psychiatry, Psychology, and Neuroscience, King’s College London, London, UK
| | - Mathilde Antoniades
- Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA USA
| | - Guray Erus
- Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA USA
| | - Jose A. Garcia
- Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA USA
| | - Yong Fan
- Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA USA
| | - Danilo Arnone
- Centre for Affective Disorders, Department of Psychological Medicine, Institute of Psychiatry, Psychology, and Neuroscience, King’s College London, London, UK
| | | | - Taolin Chen
- Huaxi MR Research Center, Department of Radiology, West China Hospital, Sichuan University, Chengdu, China
- Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, China
| | - Ki Sueng Choi
- Nash Family Center for Advanced Circuit Therapeutics, Icahn School of Medicine at Mount Sinai, New York, NY USA
| | - Cherise Chin Fatt
- Department of Psychiatry, Center for Depression Research and Clinical Care, University of Texas Southwestern Medical Center, Dallas, TX USA
| | - Benicio N. Frey
- Department of Psychiatry and Behavioural Neurosciences, McMaster University, Hamilton, Ontario Canada
- Mood Disorders Treatment and Research Centre and Women’s Health Concerns Clinic, St Joseph’s Healthcare Hamilton, Hamilton, Ontario Canada
| | - Vibe G. Frokjaer
- Neurobiology Research Unit, University Hospital Rigshospitalet, Copenhagen, Denmark
- Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
- Department of Psychiatry, Psychiatric Centre Copenhagen, Copenhagen, Denmark
| | - Melanie Ganz
- Neurobiology Research Unit, University Hospital Rigshospitalet, Copenhagen, Denmark
- Department of Computer Science, University of Copenhagen, Copenhagen, Denmark
| | - Beata R. Godlewska
- Department of Psychiatry, University of Oxford, Oxford, UK
- Oxford Health NHS Foundation Trust, Warneford Hospital, Oxford, UK
| | - Stefanie Hassel
- Mathison Centre for Mental Health Research and Education, University of Calgary, Calgary, Alberta Canada
- Department of Psychiatry, Cumming School of Medicine, University of Calgary, Calgary, Alberta Canada
| | - Keith Ho
- Department of Psychiatry, University Health Network, Toronto, Ontario Canada
| | - Andrew M. McIntosh
- Division of Psychiatry, Royal Edinburgh Hospital, University of Edinburgh, Edinburgh, UK
| | - Kun Qin
- Huaxi MR Research Center, Department of Radiology, West China Hospital, Sichuan University, Chengdu, China
- Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, China
- Department of Radiology, Taihe Hospital, Hubei University of Medicine, Shiyan, China
| | - Susan Rotzinger
- Department of Psychiatry, University Health Network, Toronto, Ontario Canada
- Centre for Depression and Suicide Studies, Unity Health Toronto, Toronto, Ontario Canada
| | - Matthew D. Sacchet
- Meditation Research Program, Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA USA
| | | | - Haochang Shou
- Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA USA
- Penn Statistics in Imaging and Visualization Endeavor (PennSIVE) Center, Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania, Philadelphia, PA USA
| | - Ashish Singh
- Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA USA
| | - Aleks Stolicyn
- Division of Psychiatry, Royal Edinburgh Hospital, University of Edinburgh, Edinburgh, UK
| | - Irina Strigo
- Department of Psychiatry, University of California San Francisco, San Francisco, USA
| | - Stephen C. Strother
- Rotman Research Institute, Baycrest Centre, Toronto, Ontario Canada
- Department of Medical Biophysics, University of Toronto, Toronto, Ontario Canada
| | - Duygu Tosun
- Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, CA USA
| | | | - Dongtao Wei
- School of Psychology, Southwest University, Chongqing, China
| | - Toby Wise
- Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, United Kingdom
| | - Roland Zahn
- Centre for Affective Disorders, Department of Psychological Medicine, Institute of Psychiatry, Psychology, and Neuroscience, King’s College London, London, UK
| | - Ian M. Anderson
- Division of Neuroscience and Experimental Psychology, University of Manchester, Manchester, UK
| | - W. Edward Craighead
- Department of Psychiatry and Behavioral Sciences, Emory University School of Medicine, Atlanta, GA USA
- Department of Psychology, Emory University, Atlanta, GA USA
| | - J. F. William Deakin
- Division of Neuroscience and Experimental Psychology, University of Manchester, Manchester, UK
| | - Boadie W. Dunlop
- Department of Psychiatry and Behavioral Sciences, Emory University School of Medicine, Atlanta, GA USA
| | - Rebecca Elliott
- Division of Neuroscience and Experimental Psychology, University of Manchester, Manchester, UK
| | - Qiyong Gong
- Huaxi MR Research Center, Department of Radiology, West China Hospital, Sichuan University, Chengdu, China
- Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, China
| | - Ian H. Gotlib
- Department of Psychology, Stanford University, Stanford, CA USA
| | | | - Sidney H. Kennedy
- Department of Psychiatry, University Health Network, Toronto, Ontario Canada
- Centre for Depression and Suicide Studies, Unity Health Toronto, Toronto, Ontario Canada
| | - Gitte M. Knudsen
- Neurobiology Research Unit, University Hospital Rigshospitalet, Copenhagen, Denmark
- Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Helen S. Mayberg
- Nash Family Center for Advanced Circuit Therapeutics, Icahn School of Medicine at Mount Sinai, New York, NY USA
| | | | - Jiang Qiu
- School of Psychology, Southwest University, Chongqing, China
| | - Madhukar H. Trivedi
- Department of Psychiatry, Center for Depression Research and Clinical Care, University of Texas Southwestern Medical Center, Dallas, TX USA
| | - Heather C. Whalley
- Division of Psychiatry, Royal Edinburgh Hospital, University of Edinburgh, Edinburgh, UK
| | - Chao-Gan Yan
- Key Laboratory of Behavioral Science, Institute of Psychology, Chinese Academy of Sciences, Beijing, China
| | - Allan H. Young
- Centre for Affective Disorders, Department of Psychological Medicine, Institute of Psychiatry, Psychology, and Neuroscience, King’s College London, London, UK
- South London and Maudsley NHS Foundation Trust, Bethlem Royal Hospital, London, UK
| | - Christos Davatzikos
- Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA USA
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6
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Gallo S, El-Gazzar A, Zhutovsky P, Thomas RM, Javaheripour N, Li M, Bartova L, Bathula D, Dannlowski U, Davey C, Frodl T, Gotlib I, Grimm S, Grotegerd D, Hahn T, Hamilton PJ, Harrison BJ, Jansen A, Kircher T, Meyer B, Nenadić I, Olbrich S, Paul E, Pezawas L, Sacchet MD, Sämann P, Wagner G, Walter H, Walter M, van Wingen G. Functional connectivity signatures of major depressive disorder: machine learning analysis of two multicenter neuroimaging studies. Mol Psychiatry 2023; 28:3013-3022. [PMID: 36792654 PMCID: PMC10615764 DOI: 10.1038/s41380-023-01977-5] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/22/2021] [Revised: 01/12/2023] [Accepted: 01/19/2023] [Indexed: 02/17/2023]
Abstract
The promise of machine learning has fueled the hope for developing diagnostic tools for psychiatry. Initial studies showed high accuracy for the identification of major depressive disorder (MDD) with resting-state connectivity, but progress has been hampered by the absence of large datasets. Here we used regular machine learning and advanced deep learning algorithms to differentiate patients with MDD from healthy controls and identify neurophysiological signatures of depression in two of the largest resting-state datasets for MDD. We obtained resting-state functional magnetic resonance imaging data from the REST-meta-MDD (N = 2338) and PsyMRI (N = 1039) consortia. Classification of functional connectivity matrices was done using support vector machines (SVM) and graph convolutional neural networks (GCN), and performance was evaluated using 5-fold cross-validation. Features were visualized using GCN-Explainer, an ablation study and univariate t-testing. The results showed a mean classification accuracy of 61% for MDD versus controls. Mean accuracy for classifying (non-)medicated subgroups was 62%. Sex classification accuracy was substantially better across datasets (73-81%). Visualization of the results showed that classifications were driven by stronger thalamic connections in both datasets, while nearly all other connections were weaker with small univariate effect sizes. These results suggest that whole brain resting-state connectivity is a reliable though poor biomarker for MDD, presumably due to disease heterogeneity as further supported by the higher accuracy for sex classification using the same methods. Deep learning revealed thalamic hyperconnectivity as a prominent neurophysiological signature of depression in both multicenter studies, which may guide the development of biomarkers in future studies.
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Affiliation(s)
- Selene Gallo
- Amsterdam UMC location University of Amsterdam, Department of Psychiatry, Meibergdreef 9, Amsterdam, The Netherlands
- Amsterdam Neuroscience, Amsterdam, The Netherlands
| | - Ahmed El-Gazzar
- Amsterdam UMC location University of Amsterdam, Department of Psychiatry, Meibergdreef 9, Amsterdam, The Netherlands.
- Amsterdam Neuroscience, Amsterdam, The Netherlands.
| | - Paul Zhutovsky
- Amsterdam UMC location University of Amsterdam, Department of Psychiatry, Meibergdreef 9, Amsterdam, The Netherlands
- Amsterdam Neuroscience, Amsterdam, The Netherlands
| | - Rajat M Thomas
- Amsterdam UMC location University of Amsterdam, Department of Psychiatry, Meibergdreef 9, Amsterdam, The Netherlands
- Amsterdam Neuroscience, Amsterdam, The Netherlands
| | - Nooshin Javaheripour
- Department Of Psychiatry and Psychotherapy, Jena University Hospital, Jena, Germany
| | - Meng Li
- Department Of Psychiatry and Psychotherapy, Jena University Hospital, Jena, Germany
| | - Lucie Bartova
- Department of Psychiatry and Psychotherapy, Medical University of Vienna, Vienna, Austria
| | | | - Udo Dannlowski
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
| | - Christopher Davey
- Department of Psychiatry, The University of Melbourne, Melbourne, VIC, Australia
| | - Thomas Frodl
- Department of Psychiatry and Psychotherapy, Otto von Guericke University Magdeburg, Magdeburg, Germany
- German center for mental health, CIRC, Magdeburg, Germany
| | - Ian Gotlib
- Department of Psychology, Stanford University, Stanford, CA, 94305, USA
| | - Simone Grimm
- Department of Psychiatry, Charité Universitätsmedizin Berlin, Berlin, Germany
| | - Dominik Grotegerd
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
| | - Tim Hahn
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
| | - Paul J Hamilton
- Center for Social and Affective Neuroscience, Department of Biomedical and Clinical Sciences, Linköping University, Linköping, Sweden
| | - Ben J Harrison
- Department of Psychiatry, The University of Melbourne, Melbourne, VIC, Australia
| | - Andreas Jansen
- Department Of Psychiatry, University of Marburg, Marburg, Germany
| | - Tilo Kircher
- Department Of Psychiatry, University of Marburg, Marburg, Germany
| | - Bernhard Meyer
- Department of Psychiatry and Psychotherapy, Medical University of Vienna, Vienna, Austria
| | - Igor Nenadić
- Department Of Psychiatry, University of Marburg, Marburg, Germany
| | - Sebastian Olbrich
- Department of Psychiatry, Psychotherapy and Psychosomatics, University Hospital of Zurich, Zurich, Switzerland
| | - Elisabeth Paul
- Center for Social and Affective Neuroscience, Department of Biomedical and Clinical Sciences, Linköping University, Linköping, Sweden
| | - Lukas Pezawas
- Department of Psychiatry and Psychotherapy, Medical University of Vienna, Vienna, Austria
| | - Matthew D Sacchet
- Center for Depression, Anxiety, and Stress Research, McLean Hospital, Harvard Medical School, Belmont, MA, USA
| | | | - Gerd Wagner
- Department Of Psychiatry and Psychotherapy, Jena University Hospital, Jena, Germany
| | - Henrik Walter
- Charité-Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Department of Psychiatry and Psychotherapy, Charitéplatz 1, D-10117, Berlin, Germany
| | - Martin Walter
- Department of Psychiatry and Psychotherapy, Otto von Guericke University Magdeburg, Magdeburg, Germany
- German center for mental health, CIRC, Magdeburg, Germany
| | - Guido van Wingen
- Amsterdam UMC location University of Amsterdam, Department of Psychiatry, Meibergdreef 9, Amsterdam, The Netherlands
- Amsterdam Neuroscience, Amsterdam, The Netherlands
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7
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Talishinsky A, Downar J, Vértes PE, Seidlitz J, Dunlop K, Lynch CJ, Whalley H, McIntosh A, Vila-Rodriguez F, Daskalakis ZJ, Blumberger DM, Liston C. Regional gene expression signatures are associated with sex-specific functional connectivity changes in depression. Nat Commun 2022; 13:5692. [PMID: 36171190 PMCID: PMC9519925 DOI: 10.1038/s41467-022-32617-1] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2020] [Accepted: 08/09/2022] [Indexed: 12/02/2022] Open
Abstract
The neural substrates of depression may differ in men and women, but the underlying mechanisms are incompletely understood. Here, we show that depression is associated with sex-specific patterns of abnormal functional connectivity in the default mode network and in five regions of interest with sexually dimorphic transcriptional effects. Regional differences in gene expression in two independent datasets explained the neuroanatomical distribution of abnormal connectivity. These gene sets varied by sex and were strongly enriched for genes implicated in depression, synapse function, immune signaling, and neurodevelopment. In an independent sample, we confirmed the prediction that individual differences in default mode network connectivity are explained by inferred brain expression levels for six depression-related genes, including PCDH8, a brain-specific protocadherin integral membrane protein implicated in activity-related synaptic reorganization. Together, our results delineate both shared and sex-specific changes in the organization of depression-related functional networks, with implications for biomarker development and fMRI-guided therapeutic neuromodulation.
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Affiliation(s)
- Aleksandr Talishinsky
- Department of Psychiatry, Weill Cornell Medicine, New York, NY, USA
- Feil Family Brain and Mind Research Institute, Weill Cornell Medicine, New York, NY, USA
| | - Jonathan Downar
- Krembil Research Institute and Centre for Mental Health, University Health Network, Toronto, ON, USA.
- Department of Psychiatry, University of Toronto, Toronto, ON, USA.
| | - Petra E Vértes
- Department of Psychiatry, University of Cambridge, Cambridge, UK
| | - Jakob Seidlitz
- Department of Child and Adolescent Psychiatry and Behavioral Science, Children's Hospital of Philadelphia, Philadelphia, PA, USA
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA, USA
| | - Katharine Dunlop
- Department of Psychiatry, Weill Cornell Medicine, New York, NY, USA
- Feil Family Brain and Mind Research Institute, Weill Cornell Medicine, New York, NY, USA
| | - Charles J Lynch
- Department of Psychiatry, Weill Cornell Medicine, New York, NY, USA
- Feil Family Brain and Mind Research Institute, Weill Cornell Medicine, New York, NY, USA
| | - Heather Whalley
- Center for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK
| | - Andrew McIntosh
- Center for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK
| | - Fidel Vila-Rodriguez
- Non-Invasive Neurostimulation Therapies Lab and Department of Psychiatry, University of British Columbia, Vancouver, BC, USA
| | | | - Daniel M Blumberger
- Department of Psychiatry, University of Toronto, Toronto, ON, USA
- Temerty Centre for Therapeutic Brain Intervention, Centre for Addiction and Mental Health, Toronto, ON, USA
| | - Conor Liston
- Department of Psychiatry, Weill Cornell Medicine, New York, NY, USA.
- Feil Family Brain and Mind Research Institute, Weill Cornell Medicine, New York, NY, USA.
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8
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Linking interindividual variability in brain structure to behaviour. Nat Rev Neurosci 2022; 23:307-318. [PMID: 35365814 DOI: 10.1038/s41583-022-00584-7] [Citation(s) in RCA: 66] [Impact Index Per Article: 33.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 03/14/2022] [Indexed: 12/15/2022]
Abstract
What are the brain structural correlates of interindividual differences in behaviour? More than a decade ago, advances in structural MRI opened promising new avenues to address this question. The initial wave of research then progressively led to substantial conceptual and methodological shifts, and a replication crisis unveiled the limitations of traditional approaches, which involved searching for associations between local measurements of neuroanatomy and behavioural variables in small samples of healthy individuals. Given these methodological issues and growing scepticism regarding the idea of one-to-one mapping of psychological constructs to brain regions, new perspectives emerged. These not only embrace the multivariate nature of brain structure-behaviour relationships and promote generalizability but also embrace the representation of the relationships between brain structure and behavioural data by latent dimensions of interindividual variability. Here, we examine the past and present of the study of brain structure-behaviour associations in healthy populations and address current challenges and open questions for future investigations.
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9
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Zebley B, Wolk D, McAllister M, Lynch CJ, Mikofsky R, Liston C. Individual Differences in the Affective Response to Pandemic-related Stressors in COVID-19 Healthcare Workers. BIOLOGICAL PSYCHIATRY GLOBAL OPEN SCIENCE 2021; 1:336-344. [PMID: 34704087 PMCID: PMC8529885 DOI: 10.1016/j.bpsgos.2021.08.008] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2021] [Revised: 08/12/2021] [Accepted: 08/27/2021] [Indexed: 11/16/2022] Open
Abstract
Background We investigated the evolving prevalence of mood and anxiety symptoms among healthcare workers from May, 2020 to January, 2021; risk factors for adverse outcomes; and characteristic modes of affective responses to pandemic-related stressors. Methods 2,307 healthcare workers (78.9% female, modal age 25-34) participated in an online survey assessing depression (Patient Health Questionnaire [PHQ-9]) and anxiety symptoms (Generalized Anxiety Disorder scale [GAD-7]), demographic variables, and self-reported impact of pandemic-related stressors. 334 subjects were reassessed ∼6 months later. Results The prevalence of clinically significant depression and anxiety was 45.3% and 43.3%, respectively, and a majority (59.9%-62.9%) of those individuals had persistent significant symptoms at 6-month follow-up. Younger age, female gender, and specific occupations (support staff > nurses > physicians) were associated with increased depressive and anxiety symptoms. The most important risk factors were social isolation and fear of contracting COVID-19. The prevalence of clinically significant mood and anxiety symptoms increased by 39.8% from May, 2020 to January, 2021. PHQ-9 and GAD-7 scores were highly correlated and associated with nearly identical risk factors, suggesting that they are not capturing independent constructs in this sample. Principal components analysis identified seven orthogonal symptom domains with unique risk factors. Conclusions Clinically significant mood and anxiety symptoms are highly prevalent and persistent among healthcare workers, and are associated with numerous risk factors, the strongest of which are related to pandemic stressors and potentially modifiable. Interventions aimed at reducing social isolation and mitigating the impact of fear of infection warrant further study.
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Affiliation(s)
- Benjamin Zebley
- Department of Psychiatry and Brain and Mind Research Institute, Weill Cornell Medicine
| | - Danielle Wolk
- Department of Psychiatry and Brain and Mind Research Institute, Weill Cornell Medicine
| | - Mary McAllister
- Department of Psychiatry and Brain and Mind Research Institute, Weill Cornell Medicine
| | - Charles J Lynch
- Department of Psychiatry and Brain and Mind Research Institute, Weill Cornell Medicine
| | - Rachel Mikofsky
- Department of Psychiatry and Brain and Mind Research Institute, Weill Cornell Medicine
| | - Conor Liston
- Department of Psychiatry and Brain and Mind Research Institute, Weill Cornell Medicine
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10
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Abstract
Improvements in understanding the neurobiological basis of mental illness have unfortunately not translated into major advances in treatment. At this point, it is clear that psychiatric disorders are exceedingly complex and that, in order to account for and leverage this complexity, we need to collect longitudinal data sets from much larger and more diverse samples than is practical using traditional methods. We discuss how smartphone-based research methods have the potential to dramatically advance our understanding of the neuroscience of mental health. This, we expect, will take the form of complementing lab-based hard neuroscience research with dense sampling of cognitive tests, clinical questionnaires, passive data from smartphone sensors, and experience-sampling data as people go about their daily lives. Theory- and data-driven approaches can help make sense of these rich data sets, and the combination of computational tools and the big data that smartphones make possible has great potential value for researchers wishing to understand how aspects of brain function give rise to, or emerge from, states of mental health and illness.
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Affiliation(s)
- Claire M Gillan
- School of Psychology, Trinity College Institute of Neuroscience, and Global Brain Health Institute, Trinity College Dublin, Dublin 2, Ireland;
| | - Robb B Rutledge
- Department of Psychology, Yale University, New Haven, Connecticut 06520, USA;
- Max Planck UCL Centre for Computational Psychiatry and Ageing Research, University College London, London WC1B 5EH, United Kingdom
- Wellcome Centre for Human Neuroimaging, University College London, London WC1N 3AR, United Kingdom
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11
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Deng ZD, Luber B, Balderston NL, Velez Afanador M, Noh MM, Thomas J, Altekruse WC, Exley SL, Awasthi S, Lisanby SH. Device-Based Modulation of Neurocircuits as a Therapeutic for Psychiatric Disorders. Annu Rev Pharmacol Toxicol 2020; 60:591-614. [PMID: 31914895 DOI: 10.1146/annurev-pharmtox-010919-023253] [Citation(s) in RCA: 24] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
Device-based neuromodulation of brain circuits is emerging as a promising new approach in the study and treatment of psychiatric disorders. This work presents recent advances in the development of tools for identifying neurocircuits as therapeutic targets and in tools for modulating neurocircuits. We review clinical evidence for the therapeutic efficacy of circuit modulation with a range of brain stimulation approaches, including subthreshold, subconvulsive, convulsive, and neurosurgical techniques. We further discuss strategies for enhancing the precision and efficacy of neuromodulatory techniques. Finally, we survey cutting-edge research in therapeutic circuit modulation using novel paradigms and next-generation devices.
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Affiliation(s)
- Zhi-De Deng
- Noninvasive Neuromodulation Unit, Experimental Therapeutics and Pathophysiology Branch, National Institute of Mental Health, National Institutes of Health, Bethesda, Maryland 20892, USA; .,Department of Psychiatry and Behavioral Sciences, Duke University School of Medicine, Durham, North Carolina 27710, USA
| | - Bruce Luber
- Noninvasive Neuromodulation Unit, Experimental Therapeutics and Pathophysiology Branch, National Institute of Mental Health, National Institutes of Health, Bethesda, Maryland 20892, USA;
| | - Nicholas L Balderston
- Section on Neurobiology of Fear and Anxiety, National Institute of Mental Health, National Institutes of Health, Bethesda, Maryland 20892, USA
| | - Melbaliz Velez Afanador
- Noninvasive Neuromodulation Unit, Experimental Therapeutics and Pathophysiology Branch, National Institute of Mental Health, National Institutes of Health, Bethesda, Maryland 20892, USA;
| | - Michelle M Noh
- Noninvasive Neuromodulation Unit, Experimental Therapeutics and Pathophysiology Branch, National Institute of Mental Health, National Institutes of Health, Bethesda, Maryland 20892, USA;
| | - Jeena Thomas
- Noninvasive Neuromodulation Unit, Experimental Therapeutics and Pathophysiology Branch, National Institute of Mental Health, National Institutes of Health, Bethesda, Maryland 20892, USA;
| | - William C Altekruse
- Noninvasive Neuromodulation Unit, Experimental Therapeutics and Pathophysiology Branch, National Institute of Mental Health, National Institutes of Health, Bethesda, Maryland 20892, USA;
| | - Shannon L Exley
- Noninvasive Neuromodulation Unit, Experimental Therapeutics and Pathophysiology Branch, National Institute of Mental Health, National Institutes of Health, Bethesda, Maryland 20892, USA;
| | - Shriya Awasthi
- Noninvasive Neuromodulation Unit, Experimental Therapeutics and Pathophysiology Branch, National Institute of Mental Health, National Institutes of Health, Bethesda, Maryland 20892, USA;
| | - Sarah H Lisanby
- Noninvasive Neuromodulation Unit, Experimental Therapeutics and Pathophysiology Branch, National Institute of Mental Health, National Institutes of Health, Bethesda, Maryland 20892, USA; .,Department of Psychiatry and Behavioral Sciences, Duke University School of Medicine, Durham, North Carolina 27710, USA
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12
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Zhuang X, Yang Z, Cordes D. A technical review of canonical correlation analysis for neuroscience applications. Hum Brain Mapp 2020; 41:3807-3833. [PMID: 32592530 PMCID: PMC7416047 DOI: 10.1002/hbm.25090] [Citation(s) in RCA: 63] [Impact Index Per Article: 15.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2020] [Accepted: 05/23/2020] [Indexed: 12/11/2022] Open
Abstract
Collecting comprehensive data sets of the same subject has become a standard in neuroscience research and uncovering multivariate relationships among collected data sets have gained significant attentions in recent years. Canonical correlation analysis (CCA) is one of the powerful multivariate tools to jointly investigate relationships among multiple data sets, which can uncover disease or environmental effects in various modalities simultaneously and characterize changes during development, aging, and disease progressions comprehensively. In the past 10 years, despite an increasing number of studies have utilized CCA in multivariate analysis, simple conventional CCA dominates these applications. Multiple CCA-variant techniques have been proposed to improve the model performance; however, the complicated multivariate formulations and not well-known capabilities have delayed their wide applications. Therefore, in this study, a comprehensive review of CCA and its variant techniques is provided. Detailed technical formulation with analytical and numerical solutions, current applications in neuroscience research, and advantages and limitations of each CCA-related technique are discussed. Finally, a general guideline in how to select the most appropriate CCA-related technique based on the properties of available data sets and particularly targeted neuroscience questions is provided.
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Affiliation(s)
- Xiaowei Zhuang
- Cleveland Clinic Lou Ruvo Center for Brain HealthLas VegasNevadaUSA
| | - Zhengshi Yang
- Cleveland Clinic Lou Ruvo Center for Brain HealthLas VegasNevadaUSA
| | - Dietmar Cordes
- Cleveland Clinic Lou Ruvo Center for Brain HealthLas VegasNevadaUSA
- University of ColoradoBoulderColoradoUSA
- Department of Brain HealthUniversity of NevadaLas VegasNevadaUSA
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13
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Lynch CJ, Gunning FM, Liston C. Causes and Consequences of Diagnostic Heterogeneity in Depression: Paths to Discovering Novel Biological Depression Subtypes. Biol Psychiatry 2020; 88:83-94. [PMID: 32171465 DOI: 10.1016/j.biopsych.2020.01.012] [Citation(s) in RCA: 72] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/26/2019] [Revised: 12/13/2019] [Accepted: 01/18/2020] [Indexed: 12/17/2022]
Abstract
Depression is a highly heterogeneous syndrome that bears only modest correlations with its biological substrates, motivating a renewed interest in rethinking our approach to diagnosing depression for research purposes and new efforts to discover subtypes of depression anchored in biology. Here, we review the major causes of diagnostic heterogeneity in depression, with consideration of both clinical symptoms and behaviors (symptomatology and trajectory of depressive episodes) and biology (genetics and sexually dimorphic factors). Next, we discuss the promise of using data-driven strategies to discover novel subtypes of depression based on functional neuroimaging measures, including dimensional, categorical, and hybrid approaches to parsing diagnostic heterogeneity and understanding its biological basis. The merits of using resting-state functional magnetic resonance imaging functional connectivity techniques for subtyping are considered along with a set of technical challenges and potential solutions. We conclude by identifying promising future directions for defining neurobiologically informed depression subtypes and leveraging them in the future for predicting treatment outcomes and informing clinical decision making.
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Affiliation(s)
- Charles J Lynch
- Brain and Mind Research Institute and Department of Psychiatry, Weill Cornell Medicine, New York, New York
| | - Faith M Gunning
- Brain and Mind Research Institute and Department of Psychiatry, Weill Cornell Medicine, New York, New York
| | - Conor Liston
- Brain and Mind Research Institute and Department of Psychiatry, Weill Cornell Medicine, New York, New York.
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14
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Abstract
The search for more effective treatments for depression is a long-standing primary objective in both psychiatry and translational neuroscience. From initial models centered on neurochemical deficits, such as the monoamine hypothesis, research toward this goal has shifted toward a focus on network and circuit models to explain how key nodes in the limbic system and beyond interact to produce persistent shifts in affective states. To build these models, researchers have turned to two complementary approaches: neuroimaging studies in human patients (and their healthy counterparts) and neurophysiology studies in animal models, facilitated in large part by optogenetic and chemogenetic techniques. As the authors discuss, functional neuroimaging studies in humans have included largely task-oriented experiments, which have identified brain regions differentially activated during processing of affective stimuli, and resting-state functional MRI experiments, which have identified brain-wide networks altered in depressive states. Future work in this area will build on a multisite approach, assembling large data sets across diverse populations, and will also leverage the statistical power afforded by longitudinal imaging studies in patient samples. Translational studies in rodents have used optogenetic and chemogenetic tools to identify not just nodes but also connections within the networks of the limbic system that are both critical and permissive for the expression of motivated behavior and affective phenotypes. Future studies in this area will exploit mesoscale imaging and multisite electrophysiology recordings to construct network models with cell-type specificity and high statistical power, identifying candidate circuit and molecular pathways for therapeutic intervention.
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Affiliation(s)
- Timothy Spellman
- Department of Psychiatry and Brain and Mind Research Institute, Weill Cornell Medicine, New York
| | - Conor Liston
- Department of Psychiatry and Brain and Mind Research Institute, Weill Cornell Medicine, New York
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15
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Mihalik A, Adams RA, Huys Q. Canonical Correlation Analysis for Identifying Biotypes of Depression. BIOLOGICAL PSYCHIATRY: COGNITIVE NEUROSCIENCE AND NEUROIMAGING 2020; 5:478-480. [PMID: 32224000 DOI: 10.1016/j.bpsc.2020.02.002] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/03/2020] [Accepted: 02/04/2020] [Indexed: 12/28/2022]
Affiliation(s)
- Agoston Mihalik
- Centre for Medical Image Computing, University College London, London, United Kingdom; Max Planck-University College London Centre for Computational Psychiatry and Ageing Research, University College London, London, United Kingdom.
| | - Rick A Adams
- Centre for Medical Image Computing, University College London, London, United Kingdom; Max Planck-University College London Centre for Computational Psychiatry and Ageing Research, University College London, London, United Kingdom
| | - Quentin Huys
- Max Planck-University College London Centre for Computational Psychiatry and Ageing Research, University College London, London, United Kingdom; Division of Psychiatry, University College London, London, United Kingdom
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16
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Grosenick L, Liston C. Reply to: A Closer Look at Depression Biotypes: Correspondence Relating to Grosenick et al. (2019). BIOLOGICAL PSYCHIATRY: COGNITIVE NEUROSCIENCE AND NEUROIMAGING 2020; 5:556. [PMID: 31926903 DOI: 10.1016/j.bpsc.2019.11.002] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/07/2019] [Accepted: 11/07/2019] [Indexed: 01/08/2023]
Affiliation(s)
- Logan Grosenick
- Feil Family Brain and Mind Research Institute, Weill Cornell Medicine, New York, New York; Department of Psychiatry, Weill Cornell Medicine, New York, New York; Simons Foundation, Columbia University, New York, New York; Department of Statistics, Columbia University, New York, New York
| | - Conor Liston
- Feil Family Brain and Mind Research Institute, Weill Cornell Medicine, New York, New York; Department of Psychiatry, Weill Cornell Medicine, New York, New York.
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17
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Zhang Z, Deng T, Wu M, Zhu A, Zhu G. Botanicals as modulators of depression and mechanisms involved. Chin Med 2019; 14:24. [PMID: 31338119 PMCID: PMC6628492 DOI: 10.1186/s13020-019-0246-9] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2019] [Accepted: 07/10/2019] [Indexed: 12/11/2022] Open
Abstract
Depression is the most disastrous mood disorder affecting the health of individuals. Conventional treatments with chemical compounds for depression have limitations, while herbal medicine has unique therapeutic effects. This paper introduces the pharmacological basis and biological mechanisms underlying the botanical antidepressants over the past 5 years. Based upon the specific therapeutic targets or mechanisms, we analyzed the pathological roles of monoamine neurotransmitters, the hypothalamic-pituitary-adrenal axis, inflammation, oxidative stress, synaptic plasticity performed in antidepressant of the botanicals. In addition, gut flora and neurogenesis were also preferentially discussed as treatment approaches. Based on the complex pathogenesis of depression, we suggested that mixed use of botanicals, namely prescription would be more suitable for treatment of depression. In addition, neural circuit affected by botanicals or active components should also attract attention as the botanicals have potential to be developed into fast-acting antidepressants. Finally, gut flora might be a new systemic target for the treatment of depression by botanicals. This review would strength botanical medicine as the antidepressant and also provides an overview of the potential mechanisms involved.
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Affiliation(s)
- Zhengrong Zhang
- Key Laboratory of Xin’an Medicine, Ministry of Education, Anhui University of Chinese Medicine, Meishan Road 103, Hefei, 230038 China
| | - Taomei Deng
- School of Pharmacy, Anhui University of Chinese Medicine, Hefei, 230038 China
| | - Manli Wu
- School of Pharmacy, Anhui University of Chinese Medicine, Hefei, 230038 China
| | - Aisong Zhu
- College of Basic Medicine, Zhejiang Chinese Medical University, Hangzhou, 310053 China
| | - Guoqi Zhu
- Key Laboratory of Xin’an Medicine, Ministry of Education, Anhui University of Chinese Medicine, Meishan Road 103, Hefei, 230038 China
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18
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Abstract
PURPOSE OF REVIEW Poor treatment response is a hallmark of major depressive disorder. To tackle this problem, recent neuroimaging studies have sought to characterize antidepressant response in terms of pretreatment differences in intrinsic functional brain networks. Our aim is to review recent studies that predict antidepressant response using intrinsic network connectivity. We discuss current methodological limitations and directions for future antidepressant biomarker studies. RECENT FINDINGS Functional connectivity stemming from the subgenual and rostral anterior cingulate has shown particular consistency in predicting antidepressant response. Differences in this connectivity may prove fruitful in differentiating treatment responders to many antidepressant interventions. Future biomarker studies should integrate biological MDD subtypes to address the disorder's inherent clinical heterogeneity. These clinical and scientific advancements have the potential to address this population marked by limited treatment response. Methodological considerations, including patient selection, response criteria, and model overfitting, will require future investigation to ensure that biomarkers generalize for prospective prediction of treatment response.
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Affiliation(s)
- Katharine Dunlop
- Brain and Mind Research Institute, Weill Cornell Medicine, 413 East 69th Street, Box 240, New York, NY, 10021, USA.
| | - Aleksandr Talishinsky
- 000000041936877Xgrid.5386.8Brain and Mind Research Institute, Weill Cornell Medicine, 413 East 69th Street, Box 240, New York, NY 10021 USA
| | - Conor Liston
- 000000041936877Xgrid.5386.8Brain and Mind Research Institute, Weill Cornell Medicine, 413 East 69th Street, Box 240, New York, NY 10021 USA ,000000041936877Xgrid.5386.8Department of Psychiatry, Weill Cornell Medicine, New York, NY USA
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