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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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Chang KY, Tik M, Mizutani-Tiebel Y, Schuler AL, Taylor P, Campana M, Vogelmann U, Huber B, Dechantsreiter E, Thielscher A, Bulubas L, Padberg F, Keeser D. Neural response during prefrontal theta burst stimulation: Interleaved TMS-fMRI of full iTBS protocols. Neuroimage 2024; 291:120596. [PMID: 38554783 DOI: 10.1016/j.neuroimage.2024.120596] [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: 11/13/2023] [Revised: 03/25/2024] [Accepted: 03/28/2024] [Indexed: 04/02/2024] Open
Abstract
BACKGROUND Left prefrontal intermittent theta-burst stimulation (iTBS) has emerged as a safe and effective transcranial magnetic stimulation (TMS) treatment protocol in depression. Though network effects after iTBS have been widely studied, the deeper mechanistic understanding of target engagement is still at its beginning. Here, we investigate the feasibility of a novel integrated TMS-fMRI setup and accelerated echo planar imaging protocol to directly observe the immediate effects of full iTBS treatment sessions. OBJECTIVE/HYPOTHESIS In our effort to explore interleaved iTBS-fMRI feasibility, we hypothesize that TMS will induce acute BOLD signal changes in both the stimulated area and interconnected neural regions. METHODS Concurrent TMS-fMRI with full sessions of neuronavigated iTBS (i.e. 600 pulses) of the left dorsolateral prefrontal cortex (DLPFC) was investigated in 18 healthy participants. In addition, we conducted four TMS-fMRI sessions in a single patient on long-term maintenance iTBS for bipolar depression to test the transfer to clinical cases. RESULTS Concurrent TMS-fMRI was feasible for iTBS sequences with 600 pulses. During interleaved iTBS-fMRI, an increase of the BOLD signal was observed in a network including bilateral DLPFC regions. In the clinical case, a reduced BOLD response was found in the left DLPFC and the subgenual anterior cingulate cortex, with high variability across individual sessions. CONCLUSIONS Full iTBS sessions as applied for the treatment of depressive disorders can be established in the interleaved iTBS-fMRI paradigm. In the future, this experimental approach could be valuable in clinical samples, for demonstrating target engagement by iTBS protocols and investigating their mechanisms of therapeutic action.
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Affiliation(s)
- Kai-Yen Chang
- Department of Psychiatry and Psychotherapy, University Hospital, LMU Munich, Munich, Germany; Neuroimaging Core Unit Munich - NICUM, University Hospital, LMU Munich, Munich, Germany
| | - Martin Tik
- High Field MR Center, Center for Medical Physics and Biomedical Engineering, Medical University of Vienna, Vienna, Austria; Brain Stimulation Lab, Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, USA.
| | - Yuki Mizutani-Tiebel
- Department of Psychiatry and Psychotherapy, University Hospital, LMU Munich, Munich, Germany; Neuroimaging Core Unit Munich - NICUM, University Hospital, LMU Munich, Munich, Germany
| | - Anna-Lisa Schuler
- Lise Meitner Research Group Cognition and Plasticity, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
| | - Paul Taylor
- Department of Psychology, LMU Munich, Munich, Germany
| | - Mattia Campana
- Department of Psychiatry and Psychotherapy, University Hospital, LMU Munich, Munich, Germany; Neuroimaging Core Unit Munich - NICUM, University Hospital, LMU Munich, Munich, Germany
| | - Ulrike Vogelmann
- Department of Psychiatry and Psychotherapy, University Hospital, LMU Munich, Munich, Germany
| | - Barbara Huber
- Department of Psychiatry and Psychotherapy, University Hospital, LMU Munich, Munich, Germany
| | - Esther Dechantsreiter
- Department of Psychiatry and Psychotherapy, University Hospital, LMU Munich, Munich, Germany
| | - Axel Thielscher
- Department of Health Technology, Technical University of Denmark, Kgs. Lyngby, Denmark; Danish Research Centre for Magnetic Resonance, Centre for Functional and Diagnostic Imaging and Research, Copenhagen University Hospital Amager and Hvidovre, Denmark
| | - Lucia Bulubas
- Department of Psychiatry and Psychotherapy, University Hospital, LMU Munich, Munich, Germany; Neuroimaging Core Unit Munich - NICUM, University Hospital, LMU Munich, Munich, Germany
| | - Frank Padberg
- Department of Psychiatry and Psychotherapy, University Hospital, LMU Munich, Munich, Germany; Neuroimaging Core Unit Munich - NICUM, University Hospital, LMU Munich, Munich, Germany
| | - Daniel Keeser
- Department of Psychiatry and Psychotherapy, University Hospital, LMU Munich, Munich, Germany; Neuroimaging Core Unit Munich - NICUM, University Hospital, LMU Munich, Munich, Germany.
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3
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Fennema D, Barker GJ, O’Daly O, Duan S, Carr E, Goldsmith K, Young AH, Moll J, Zahn R. The Role of Subgenual Resting-State Connectivity Networks in Predicting Prognosis in Major Depressive Disorder. BIOLOGICAL PSYCHIATRY GLOBAL OPEN SCIENCE 2024; 4:100308. [PMID: 38645404 PMCID: PMC11033067 DOI: 10.1016/j.bpsgos.2024.100308] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2023] [Revised: 12/18/2023] [Accepted: 03/05/2024] [Indexed: 04/23/2024] Open
Abstract
Background A seminal study found higher subgenual frontal cortex resting-state connectivity with 2 left ventral frontal regions and the dorsal midbrain to predict better response to psychotherapy versus medication in individuals with treatment-naïve major depressive disorder (MDD). Here, we examined whether these subgenual networks also play a role in the pathophysiology of clinical outcomes in MDD with early treatment resistance in primary care. Methods Forty-five people with current MDD who had not responded to ≥2 serotonergic antidepressants (n = 43, meeting predefined functional magnetic resonance imaging minimum quality thresholds) were enrolled and followed over 4 months of standard care. Functional magnetic resonance imaging resting-state connectivity between the preregistered subgenual frontal cortex seed and 3 previously identified left ventromedial, ventrolateral prefrontal/insula, and dorsal midbrain regions was extracted. The clinical outcome was the percentage change on the self-reported 16-item Quick Inventory of Depressive Symptomatology. Results We observed a reversal of our preregistered hypothesis in that higher resting-state connectivity between the subgenual cortex and the a priori ventrolateral prefrontal/insula region predicted favorable rather than unfavorable clinical outcomes (rs39 = -0.43, p = .006). This generalized to the sample including participants with suboptimal functional magnetic resonance imaging quality (rs43 = -0.35, p = .02). In contrast, no effects (rs39 = 0.12, rs39 = -0.01) were found for connectivity with the other 2 preregistered regions or in a whole-brain analysis (voxel-based familywise error-corrected p < .05). Conclusions Subgenual connectivity with the ventrolateral prefrontal cortex/insula is relevant for subsequent clinical outcomes in current MDD with early treatment resistance. Its positive association with favorable outcomes could be explained primarily by psychosocial rather than the expected pharmacological changes during the follow-up period.
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Affiliation(s)
- Diede Fennema
- Centre of Affective Disorders, Institute of Psychiatry, Psychology and Neuroscience, Centre for Affective Disorders, King’s College London, London, United Kingdom
| | - Gareth J. Barker
- Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, United Kingdom
| | - Owen O’Daly
- Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, United Kingdom
| | - Suqian Duan
- Centre of Affective Disorders, Institute of Psychiatry, Psychology and Neuroscience, Centre for Affective Disorders, King’s College London, London, United Kingdom
| | - Ewan Carr
- Department of Biostatics and Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, United Kingdom
| | - Kimberley Goldsmith
- Department of Biostatics and Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, United Kingdom
| | - Allan H. Young
- Centre of Affective Disorders, Institute of Psychiatry, Psychology and Neuroscience, Centre for Affective Disorders, King’s College London, London, United Kingdom
- National Service for Affective Disorders, South London and Maudsley National Health Service Foundation Trust, London, United Kingdom
| | - Jorge Moll
- Cognitive and Behavioural Neuroscience Unit, D’Or Institute for Research and Education, Rio de Janeiro, Brazil
| | - Roland Zahn
- Centre of Affective Disorders, Institute of Psychiatry, Psychology and Neuroscience, Centre for Affective Disorders, King’s College London, London, United Kingdom
- Cognitive and Behavioural Neuroscience Unit, D’Or Institute for Research and Education, Rio de Janeiro, Brazil
- National Service for Affective Disorders, South London and Maudsley National Health Service Foundation Trust, London, United Kingdom
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Poggi G, Klaus F, Pryce CR. Pathophysiology in cortico-amygdala circuits and excessive aversion processing: the role of oligodendrocytes and myelination. Brain Commun 2024; 6:fcae140. [PMID: 38712320 PMCID: PMC11073757 DOI: 10.1093/braincomms/fcae140] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2023] [Revised: 12/27/2023] [Accepted: 04/16/2024] [Indexed: 05/08/2024] Open
Abstract
Stress-related psychiatric illnesses, such as major depressive disorder, anxiety and post-traumatic stress disorder, present with alterations in emotional processing, including excessive processing of negative/aversive stimuli and events. The bidirectional human/primate brain circuit comprising anterior cingulate cortex and amygdala is of fundamental importance in processing emotional stimuli, and in rodents the medial prefrontal cortex-amygdala circuit is to some extent analogous in structure and function. Here, we assess the comparative evidence for: (i) Anterior cingulate/medial prefrontal cortex<->amygdala bidirectional neural circuits as major contributors to aversive stimulus processing; (ii) Structural and functional changes in anterior cingulate cortex<->amygdala circuit associated with excessive aversion processing in stress-related neuropsychiatric disorders, and in medial prefrontal cortex<->amygdala circuit in rodent models of chronic stress-induced increased aversion reactivity; and (iii) Altered status of oligodendrocytes and their oligodendrocyte lineage cells and myelination in anterior cingulate/medial prefrontal cortex<->amygdala circuits in stress-related neuropsychiatric disorders and stress models. The comparative evidence from humans and rodents is that their respective anterior cingulate/medial prefrontal cortex<->amygdala circuits are integral to adaptive aversion processing. However, at the sub-regional level, the anterior cingulate/medial prefrontal cortex structure-function analogy is incomplete, and differences as well as similarities need to be taken into account. Structure-function imaging studies demonstrate that these neural circuits are altered in both human stress-related neuropsychiatric disorders and rodent models of stress-induced increased aversion processing. In both cases, the changes include altered white matter integrity, albeit the current evidence indicates that this is decreased in humans and increased in rodent models. At the cellular-molecular level, in both humans and rodents, the current evidence is that stress disorders do present with changes in oligodendrocyte lineage, oligodendrocytes and/or myelin in these neural circuits, but these changes are often discordant between and even within species. Nonetheless, by integrating the current comparative evidence, this review provides a timely insight into this field and should function to inform future studies-human, monkey and rodent-to ascertain whether or not the oligodendrocyte lineage and myelination are causally involved in the pathophysiology of stress-related neuropsychiatric disorders.
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Affiliation(s)
- Giulia Poggi
- Preclinical Laboratory for Translational Research into Affective Disorders, Department of Psychiatry, Psychotherapy and Psychosomatics, Psychiatric Hospital, University of Zurich, CH-8008 Zurich, Switzerland
| | - Federica Klaus
- Department of Psychiatry, University of California San Diego, San Diego, CA 92093, USA
- Desert-Pacific Mental Illness Research Education and Clinical Center, VA San Diego Healthcare System, San Diego, CA 92093, USA
| | - Christopher R Pryce
- Preclinical Laboratory for Translational Research into Affective Disorders, Department of Psychiatry, Psychotherapy and Psychosomatics, Psychiatric Hospital, University of Zurich, CH-8008 Zurich, Switzerland
- Neuroscience Center Zurich, University of Zurich and ETH Zurich, 8057 Zurich, Switzerland
- URPP Adaptive Brain Circuits in Development and Learning (AdaBD), University of Zurich, 8057 Zurich, Switzerland
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Bhattacharyya S, MahmoudianDehkordi S, Sniatynski MJ, Belenky M, Marur VR, Rush AJ, Craighead WE, Mayberg HS, Dunlop BW, Kristal BS, Kaddurah-Daouk R. Metabolomics Signatures of serotonin reuptake inhibitor (Escitalopram), serotonin norepinephrine reuptake inhibitor (Duloxetine) and Cognitive Behavior Therapy on Key Neurotransmitter Pathways in Major Depressive Disorder. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.04.02.24304677. [PMID: 38633777 PMCID: PMC11023644 DOI: 10.1101/2024.04.02.24304677] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/19/2024]
Abstract
Metabolomics provides powerful tools that can inform about heterogeneity in disease and response to treatments. In this study, we employed an electrochemistry-based targeted metabolomics platform to assess the metabolic effects of three randomly-assigned treatments: escitalopram, duloxetine, and Cognitive Behavior Therapy (CBT) in 163 treatment-naïve outpatients with major depressive disorder. Serum samples from baseline and 12 weeks post-treatment were analyzed using targeted liquid chromatography-electrochemistry for metabolites related to tryptophan, tyrosine metabolism and related pathways. Changes in metabolite concentrations related to each treatment arm were identified and compared to define metabolic signatures of exposure. In addition, association between metabolites and depressive symptom severity (assessed with the 17-item Hamilton Rating Scale for Depression [HRSD17]) and anxiety symptom severity (assessed with the 14-item Hamilton Rating Scale for Anxiety [HRSA14]) were evaluated, both at baseline and after 12 weeks of treatment. Significant reductions in serum serotonin level and increases in tryptophan-derived indoles that are gut bacterially derived were observed with escitalopram and duloxetine arms but not in CBT arm. These include indole-3-propionic acid (I3PA), indole-3-lactic acid (I3LA) and Indoxyl sulfate (IS), a uremic toxin. Purine-related metabolites were decreased across all arms. Different metabolites correlated with improved symptoms in the different treatment arms revealing potentially different mechanisms between response to antidepressant medications and to CBT.
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Affiliation(s)
- Sudeepa Bhattacharyya
- Department of Biological Sciences, Arkansas Biosciences Institute, Arkansas State University, Jonesboro, AR, United States
| | | | - Matthew J Sniatynski
- Division of Sleep and Circadian Disorders, Department of Medicine, Brigham and Women's Hospital, 221 Longwood Ave, LM322B, Boston, MA 02115, USA and Division of Sleep Medicine, Department of Medicine, Harvard Medical School, Boston, MA 02115, USA
| | - Marina Belenky
- Division of Sleep and Circadian Disorders, Department of Medicine, Brigham and Women's Hospital, 221 Longwood Ave, LM322B, Boston, MA 02115, USA and Division of Sleep Medicine, Department of Medicine, Harvard Medical School, Boston, MA 02115, USA
| | - Vasant R Marur
- Division of Sleep and Circadian Disorders, Department of Medicine, Brigham and Women's Hospital, 221 Longwood Ave, LM322B, Boston, MA 02115, USA and Division of Sleep Medicine, Department of Medicine, Harvard Medical School, Boston, MA 02115, USA
| | - A John Rush
- Department of Psychiatry and Behavioral Sciences, Duke University, Durham, NC, United States
- Duke-National University of Singapore, Singapore, Singapore
| | - W Edward Craighead
- Department of Psychiatry and Behavioral Sciences, Emory University School of Medicine, Atlanta, GA, United States
| | - Helen S Mayberg
- Department of Neurology and Neurosurgery, Icahn School of Medicine at Mount Sinai, New York, United States
| | - Boadie W Dunlop
- Department of Psychiatry and Behavioral Sciences, Emory University School of Medicine, Atlanta, GA, United States
| | - Bruce S Kristal
- Division of Sleep and Circadian Disorders, Department of Medicine, Brigham and Women's Hospital, 221 Longwood Ave, LM322B, Boston, MA 02115, USA and Division of Sleep Medicine, Department of Medicine, Harvard Medical School, Boston, MA 02115, USA
| | - Rima Kaddurah-Daouk
- Department of Psychiatry and Behavioral Sciences, Duke University, Durham, NC, United States
- Department of Medicine, Duke University, Durham, NC, United States
- Duke Institute of Brain Sciences, Duke University, Durham, NC, United States
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Zhou Z, Gao Y, Bao W, Liang K, Cao L, Tang M, Li H, Hu X, Zhang L, Sun H, Roberts N, Gong Q, Huang X. Distinctive intrinsic functional connectivity alterations of anterior cingulate cortex subdivisions in major depressive disorder: A systematic review and meta-analysis. Neurosci Biobehav Rev 2024; 159:105583. [PMID: 38365137 DOI: 10.1016/j.neubiorev.2024.105583] [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: 09/30/2023] [Revised: 01/22/2024] [Accepted: 02/11/2024] [Indexed: 02/18/2024]
Abstract
Evidence of whether the intrinsic functional connectivity of anterior cingulate cortex (ACC) and its subregions is altered in major depressive disorder (MDD) remains inconclusive. A systematic review and meta-analysis were therefore performed on the whole-brain resting-state functional connectivity (rsFC) studies using the ACC and its subregions as seed regions in MDD, in order to draw more reliable conclusions. Forty-four ACC-based rsFC studies were included, comprising 25 subgenual ACC-based studies, 11 pregenual ACC-based studies, and 17 dorsal ACC-based studies. Specific alterations of rsFC were identified for each ACC subregion in patients with MDD, with altered rsFC of subgenual ACC in emotion-related brain regions, of pregenual ACC in sensorimotor-related regions, and of dorsal ACC in cognition-related regions. Furthermore, meta-regression analysis revealed a significant negative correlation between the pgACC-caudate hypoconnectivity and percentage of female patients in the study cohort. This meta-analysis provides robust evidence of altered intrinsic functional connectivity of the ACC subregions in MDD, which may hold relevance to understanding the origin of, and treating, the emotional, sensorimotor and cognitive dysfunctions that are often observed in these patients.
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Affiliation(s)
- Zilin Zhou
- Department of Radiology and Huaxi MR Research Center (HMRRC), Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital, Sichuan University, Chengdu, China
| | - Yingxue Gao
- Department of Radiology and Huaxi MR Research Center (HMRRC), Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital, Sichuan University, Chengdu, China
| | - Weijie Bao
- Department of Radiology and Huaxi MR Research Center (HMRRC), Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital, Sichuan University, Chengdu, China
| | - Kaili Liang
- Department of Radiology and Huaxi MR Research Center (HMRRC), Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital, Sichuan University, Chengdu, China
| | - Lingxiao Cao
- Department of Radiology and Huaxi MR Research Center (HMRRC), Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital, Sichuan University, Chengdu, China
| | - Mengyue Tang
- Department of Radiology and Huaxi MR Research Center (HMRRC), Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital, Sichuan University, Chengdu, China
| | - Hailong Li
- Department of Radiology and Huaxi MR Research Center (HMRRC), Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital, Sichuan University, Chengdu, China
| | - Xinyue Hu
- Department of Radiology and Huaxi MR Research Center (HMRRC), Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital, Sichuan University, Chengdu, China
| | - Lianqing Zhang
- Department of Radiology and Huaxi MR Research Center (HMRRC), Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital, Sichuan University, Chengdu, China
| | - Huaiqiang Sun
- Department of Radiology and Huaxi MR Research Center (HMRRC), Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital, Sichuan University, Chengdu, China; Research Unit of Psychoradiology, Chinese Academy of Medical Science, Chengdu, China
| | - Neil Roberts
- Department of Radiology and Huaxi MR Research Center (HMRRC), Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital, Sichuan University, Chengdu, China; Centre for Reproductive Health (CRH), School of Clinical Sciences, University of Edinburgh, Edinburgh, UK
| | - Qiyong Gong
- Department of Radiology and Huaxi MR Research Center (HMRRC), Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital, Sichuan University, Chengdu, China; Research Unit of Psychoradiology, Chinese Academy of Medical Science, Chengdu, China; The Xiaman Key Lab of psychoradiology and neuromodulation, West China Xiamen Hospital of Sichuan University, Xiamen, Fujian, China
| | - Xiaoqi Huang
- Department of Radiology and Huaxi MR Research Center (HMRRC), Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital, Sichuan University, Chengdu, China; Research Unit of Psychoradiology, Chinese Academy of Medical Science, Chengdu, China; The Xiaman Key Lab of psychoradiology and neuromodulation, West China Xiamen Hospital of Sichuan University, Xiamen, Fujian, China.
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7
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Sanader Vukadinovic B, Karch S, Paolini M, Reidler P, Rauchmann B, Koller G, Pogarell O, Keeser D. Neurofeedback for alcohol addiction: Changes in resting state network activity ✰. Psychiatry Res Neuroimaging 2024; 339:111786. [PMID: 38281353 DOI: 10.1016/j.pscychresns.2024.111786] [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: 08/08/2023] [Revised: 12/09/2023] [Accepted: 01/08/2024] [Indexed: 01/30/2024]
Abstract
Alcohol dependence continues to be a major global burden despite significant research progress and treatment development. The aim of this study was to investigate whether neurofeedback training can alter resting state fMRI activity in brain regions that play a crucial role in addiction disorders in patients with alcohol dependence. For this purpose, a total of 52 patients were recruited for the present study, randomized, and divided into an active and a sham group. Patients in the active group received three sessions of neurofeedback training. We compared the resting state data in the active group as part of the NF training on six measurement days. When comparing the results of the active group from neurofeedback day 3 with baseline 1, a significant reduction in activated voxels in the ventral attention network area was seen. This suggests that reduced activity over the course of therapy in subjects may lead to greater independence from external stimuli. Overall, a global decrease in activated voxels within all three analysed networks compared to baseline was observed in the study. The use of resting-state data as potential biomarkers, as activity changes within these networks, may be to help restore cognitive processes and alcohol abuse-related craving and emotions.
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Affiliation(s)
- B Sanader Vukadinovic
- Department of Psychiatry and Psychotherapy, University Hospital LMU, Munich, Germany; University College London Hospitals NHS Foundation Trust (UCLH), London, United Kingdom.
| | - S Karch
- Department of Psychiatry and Psychotherapy, University Hospital LMU, Munich, Germany
| | - M Paolini
- Department of Radiology, University Hospital LMU, Munich, Germany
| | - P Reidler
- Department of Radiology, University Hospital LMU, Munich, Germany
| | - B Rauchmann
- Department of Radiology, University Hospital LMU, Munich, Germany
| | - G Koller
- Department of Psychiatry and Psychotherapy, University Hospital LMU, Munich, Germany
| | - O Pogarell
- Department of Psychiatry and Psychotherapy, University Hospital LMU, Munich, Germany
| | - D Keeser
- Department of Psychiatry and Psychotherapy, University Hospital LMU, Munich, Germany; NeuroImaging Core Unit Munich (NICUM), University Hospital LMU, Munich, Germany
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8
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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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9
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Valentim WL, Tylee DS, Polimanti R. A perspective on translating genomic discoveries into targets for brain-machine interface and deep brain stimulation devices. WIREs Mech Dis 2024; 16:e1635. [PMID: 38059513 DOI: 10.1002/wsbm.1635] [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: 03/20/2023] [Revised: 10/22/2023] [Accepted: 11/17/2023] [Indexed: 12/08/2023]
Abstract
Mental illnesses have a huge impact on individuals, families, and society, so there is a growing need for more efficient treatments. In this context, brain-computer interface (BCI) technology has the potential to revolutionize the options for neuropsychiatric therapies. However, the development of BCI-based therapies faces enormous challenges, such as power dissipation constraints, lack of credible feedback mechanisms, uncertainty of which brain areas and frequencies to target, and even which patients to treat. Some of these setbacks are due to the large gap in our understanding of brain function. In recent years, large-scale genomic analyses uncovered an unprecedented amount of information regarding the biology of the altered brain function observed across the psychopathology spectrum. We believe findings from genetic studies can be useful to refine BCI technology to develop novel treatment options for mental illnesses. Here, we assess the latest advancements in both fields, the possibilities that can be generated from their intersection, and the challenges that these research areas will need to address to ensure that translational efforts can lead to effective and reliable interventions. Specifically, starting from highlighting the overlap between mechanisms uncovered by large-scale genetic studies and the current targets of deep brain stimulation treatments, we describe the steps that could help to translate genomic discoveries into BCI targets. Because these two research areas have not been previously presented together, the present article can provide a novel perspective for scientists with different research backgrounds. This article is categorized under: Neurological Diseases > Genetics/Genomics/Epigenetics Neurological Diseases > Biomedical Engineering.
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Affiliation(s)
- Wander L Valentim
- Faculty of Medicine, Federal University of Minas Gerais, Belo Horizonte, Brazil
- Department of Psychiatry, Yale University School of Medicine, New Haven, Connecticut, USA
| | - Daniel S Tylee
- Department of Psychiatry, Yale University School of Medicine, New Haven, Connecticut, USA
- VA CT Healthcare Center, West Haven, Connecticut, USA
| | - Renato Polimanti
- Department of Psychiatry, Yale University School of Medicine, New Haven, Connecticut, USA
- VA CT Healthcare Center, West Haven, Connecticut, USA
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10
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Williams LM, Carpenter WT, Carretta C, Papanastasiou E, Vaidyanathan U. Precision psychiatry and Research Domain Criteria: Implications for clinical trials and future practice. CNS Spectr 2024; 29:26-39. [PMID: 37675453 DOI: 10.1017/s1092852923002420] [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] [Indexed: 09/08/2023]
Abstract
Psychiatric disorders are associated with significant social and economic burdens, many of which are related to issues with current diagnosis and treatments. The coronavirus (COVID-19) pandemic is estimated to have increased the prevalence and burden of major depressive and anxiety disorders, indicating an urgent need to strengthen mental health systems globally. To date, current approaches adopted in drug discovery and development for psychiatric disorders have been relatively unsuccessful. Precision psychiatry aims to tailor healthcare more closely to the needs of individual patients and, when informed by neuroscience, can offer the opportunity to improve the accuracy of disease classification, treatment decisions, and prevention efforts. In this review, we highlight the growing global interest in precision psychiatry and the potential for the National Institute of Health-devised Research Domain Criteria (RDoC) to facilitate the implementation of transdiagnostic and improved treatment approaches. The need for current psychiatric nosology to evolve with recent scientific advancements and increase awareness in emerging investigators/clinicians of the value of this approach is essential. Finally, we examine current challenges and future opportunities of adopting the RDoC-associated translational and transdiagnostic approaches in clinical studies, acknowledging that the strength of RDoC is that they form a dynamic framework of guiding principles that is intended to evolve continuously with scientific developments into the future. A collaborative approach that recruits expertise from multiple disciplines, while also considering the patient perspective, is needed to pave the way for precision psychiatry that can improve the prognosis and quality of life of psychiatric patients.
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Affiliation(s)
- 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
| | - William T Carpenter
- Department of Psychiatry, University of Maryland School of Medicine, Baltimore, MD, USA
| | | | - Evangelos Papanastasiou
- Boehringer Ingelheim Pharma GmbH & Co, Ingelheim am Rhein, Rhineland-Palatinate, Germany
- HMNC Holding GmbH, Wilhelm-Wagenfeld-Strasse 20, 80807Munich, Bavaria, Germany
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11
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Kaneko N, Wada M, Nakajima S, Takano M, Taniguchi K, Honda S, Mimura M, Noda Y. Neuroplasticity of the left dorsolateral prefrontal cortex in patients with treatment-resistant depression as indexed with paired associative stimulation: a TMS-EEG study. Cereb Cortex 2024; 34:bhad515. [PMID: 38204301 PMCID: PMC10839839 DOI: 10.1093/cercor/bhad515] [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: 10/10/2023] [Revised: 12/12/2023] [Accepted: 12/13/2023] [Indexed: 01/12/2024] Open
Abstract
Major depressive disorder affects over 300 million people globally, with approximately 30% experiencing treatment-resistant depression (TRD). Given that impaired neuroplasticity underlies depression, the present study focused on neuroplasticity in the dorsolateral prefrontal cortex (DLPFC). Here, we aimed to investigate the differences in neuroplasticity between 60 individuals with TRD and 30 age- and sex-matched healthy controls (HCs). To induce neuroplasticity, participants underwent a paired associative stimulation (PAS) paradigm involving peripheral median nerve stimulation and transcranial magnetic stimulation (TMS) targeting the left DLPFC. Neuroplasticity was assessed by using measurements combining TMS with EEG before and after PAS. Both groups exhibited significant increases in the early component of TMS-evoked potentials (TEP) after PAS (P < 0.05, paired t-tests with the bootstrapping method). However, the HC group demonstrated a greater increase in TEPs than the TRD group (P = 0.045, paired t-tests). Additionally, event-related spectral perturbation analysis highlighted that the gamma power significantly increased after PAS in the HC group, whereas it was decreased in the TRD group (P < 0.05, paired t-tests with the bootstrapping method). This gamma power modulation revealed a significant group difference (P = 0.006, paired t-tests), indicating an inverse relationship for gamma power modulation. Our findings underscore the impaired neuroplasticity of the DLPFC in individuals with TRD.
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Affiliation(s)
- Naotsugu Kaneko
- Department of Life Sciences, Graduate School of Arts and Sciences, The University of Tokyo, 3-8-1 Komaba, Meguro, Tokyo 153-8902, Japan
| | - Masataka Wada
- Department of Neuropsychiatry, Keio University School of Medicine, 35 Shinanomachi, Shinjuku, Tokyo 160-8582, Japan
| | - Shinichiro Nakajima
- Department of Neuropsychiatry, Keio University School of Medicine, 35 Shinanomachi, Shinjuku, Tokyo 160-8582, Japan
| | - Mayuko Takano
- Department of Neuropsychiatry, Keio University School of Medicine, 35 Shinanomachi, Shinjuku, Tokyo 160-8582, Japan
- Teijin Pharma Limited, 4-3-2 Asahigaoka, Hino, Tokyo 191-8512, Japan
| | - Keita Taniguchi
- Department of Neuropsychiatry, Keio University School of Medicine, 35 Shinanomachi, Shinjuku, Tokyo 160-8582, Japan
| | - Shiori Honda
- Department of Neuropsychiatry, Keio University School of Medicine, 35 Shinanomachi, Shinjuku, Tokyo 160-8582, Japan
| | - Masaru Mimura
- Department of Neuropsychiatry, Keio University School of Medicine, 35 Shinanomachi, Shinjuku, Tokyo 160-8582, Japan
| | - Yoshihiro Noda
- Department of Neuropsychiatry, Keio University School of Medicine, 35 Shinanomachi, Shinjuku, Tokyo 160-8582, Japan
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12
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Dunlop K, Grosenick L, Downar J, Vila-Rodriguez F, Gunning FM, Daskalakis ZJ, Blumberger DM, Liston C. Dimensional and Categorical Solutions to Parsing Depression Heterogeneity in a Large Single-Site Sample. Biol Psychiatry 2024:S0006-3223(24)00055-6. [PMID: 38280408 DOI: 10.1016/j.biopsych.2024.01.012] [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: 07/07/2023] [Revised: 12/21/2023] [Accepted: 01/13/2024] [Indexed: 01/29/2024]
Abstract
BACKGROUND Recent studies have reported significant advances in modeling the biological basis of heterogeneity in major depressive disorder, but investigators have also identified important technical challenges, including scanner-related artifacts, a propensity for multivariate models to overfit, and a need for larger samples with more extensive clinical phenotyping. The goals of the current study were to evaluate dimensional and categorical solutions to parsing heterogeneity in depression that are stable and generalizable in a large, single-site sample. METHODS We used regularized canonical correlation analysis to identify data-driven brain-behavior dimensions that explain individual differences in depression symptom domains in a large, single-site dataset comprising clinical assessments and resting-state functional magnetic resonance imaging data for 328 patients with major depressive disorder and 461 healthy control participants. We examined the stability of clinical loadings and model performance in held-out data. Finally, hierarchical clustering on these dimensions was used to identify categorical depression subtypes. RESULTS The optimal regularized canonical correlation analysis model yielded 3 robust and generalizable brain-behavior dimensions that explained individual differences in depressed mood and anxiety, anhedonia, and insomnia. Hierarchical clustering identified 4 depression subtypes, each with distinct clinical symptom profiles, abnormal resting-state functional connectivity patterns, and antidepressant responsiveness to repetitive transcranial magnetic stimulation. CONCLUSIONS Our results define dimensional and categorical solutions to parsing neurobiological heterogeneity in major depressive disorder that are stable, generalizable, and capable of predicting treatment outcomes, each with distinct advantages in different contexts. They also provide additional evidence that regularized canonical correlation analysis and hierarchical clustering are effective tools for investigating associations between functional connectivity and clinical symptoms.
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Affiliation(s)
- Katharine Dunlop
- Centre for Depression and Suicide Studies, St Michael's Hospital, Toronto, Ontario, Canada; Keenan Research Centre for Biomedical Science, St. Michael's Hospital, Toronto, Ontario, Canada; Department of Psychiatry and Institute of Medical Sciences, University of Toronto, Toronto, Ontario, Canada
| | - Logan Grosenick
- Department of Psychiatry, Weill Cornell Medicine, New York, New York
| | - Jonathan Downar
- Department of Psychiatry and Institute of Medical Sciences, University of Toronto, Toronto, Ontario, Canada
| | - Fidel Vila-Rodriguez
- Non-Invasive Neurostimulation Therapies Laboratory, Department of Psychiatry, University of British Columbia, Vancouver, British Columbia, Canada
| | - Faith M Gunning
- Institute of Geriatric Psychiatry, Weill Cornell Medicine, White Plains, New York
| | - Zafiris J Daskalakis
- Department of Psychiatry, University of California San Diego, San Diego, California
| | - Daniel M Blumberger
- Department of Psychiatry and Institute of Medical Sciences, University of Toronto, Toronto, Ontario, Canada; Department of Psychiatry, Weill Cornell Medicine, New York, New York; Temerty Centre for Therapeutic Brain Intervention and Campbell Family Research Institute, Centre for Addiction and Mental Health, Toronto, Ontario, Canada
| | - Conor Liston
- Department of Psychiatry, Weill Cornell Medicine, New York, New York; Feil Family Brain and Mind Research Institute, Weill Cornell Medicine, New York, New York.
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13
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Xu C, Wang S, Su BB, Ozuna K, Mao C, Dai Z, Wang K. Associations of adolescent substance use and depressive symptoms with adult major depressive disorder in the United States: NSDUH 2016-2019. J Affect Disord 2024; 344:397-406. [PMID: 37844780 DOI: 10.1016/j.jad.2023.10.066] [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: 03/11/2023] [Revised: 09/16/2023] [Accepted: 10/09/2023] [Indexed: 10/18/2023]
Abstract
BACKGROUND Few studies have focused on the associations of adolescent substance use and depressive symptoms with adult major depressive disorder (MDD). METHODS Data from 168,859 adults, among which, 15,959 had experienced MDD in the past year, as indicated by a major depressive episode (MDE) marked by MDD symptoms, were from the 2016-2019 National Surveys on Drug Use and Health. Weighted multivariable logistic regression (MLR) analyses were used to determine the associations. RESULTS The overall MDD prevalence was 7.2 %, whereas the prevalence for adults without early onset depressive symptoms prior to age 18 was 4.6 %. Variable cluster analysis revealed that adolescent use of alcohol, cigarettes, marijuana, cocaine, hallucinogen use, and inhalants prior to age 18 were in one cluster. MLR analyses showed that the presence of depressive symptoms prior to age 18 was the major risk factor for MDD, while adolescent use of alcohol, marijuana, and inhalants prior to age 18 were associated with increased odds of MDD (p < 0.05) both in the whole data and the subset of adults without depressive symptoms prior to age 18. Adolescent use of cocaine prior to age 18 were associated with MDD only in the whole data, whereas adolescent smokeless tobacco use was associated with MDD only in those without depressive symptoms prior to age 18. CONCLUSIONS These findings highlight the comorbid early substance use and depressive symptoms during adolescence with adult MDD. Intervention strategies should simultaneously address early-onset substance use and depressive symptoms prior to age 18.
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Affiliation(s)
- Chun Xu
- Department of Health and Biomedical Sciences, College of Health Affairs, University of Texas Rio Grande Valley, Brownsville, TX, USA.
| | - Silas Wang
- Department of Statistics & Data Science, Dietrich College of Humanities and Social Sciences, Carnegie Mellon University, Pittsburgh, PA, USA
| | - Brenda Bin Su
- Department of Pediatrics - Allergy and Immunology, Baylor College of Medicine, Houston, TX, USA
| | - Kaysie Ozuna
- Department of Health and Biomedical Sciences, College of Health Affairs, University of Texas Rio Grande Valley, Brownsville, TX, USA
| | - ChunXiang Mao
- Department of Health and Biomedical Sciences, College of Health Affairs, University of Texas Rio Grande Valley, Brownsville, TX, USA
| | - Zheng Dai
- Health Affairs Institute, Health Sciences Center, West Virginia University, Morgantown, WV, USA
| | - Kesheng Wang
- Department of Family and Community Health, School of Nursing, Health Sciences Center, West Virginia University, Morgantown, WV, USA.
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14
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Hagan KE. Unlocking the Potential of Effective Connectivity as a Neuroimaging Biomarker: A Path Toward Personalized Depression Treatment in Youth. Biol Psychiatry 2023; 94:910-912. [PMID: 37968029 DOI: 10.1016/j.biopsych.2023.09.010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/14/2023] [Accepted: 09/14/2023] [Indexed: 11/17/2023]
Affiliation(s)
- Kelsey E Hagan
- Department of Psychiatry, Virginia Commonwealth University, Richmond, Virginia; Institute for Women's Health, Virginia Commonwealth University, Richmond, Virginia.
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15
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Young ME, Spencer-Salmon C, Mosher C, Tamang S, Rajan K, Rudebeck PH. Temporally specific patterns of neural activity in interconnected corticolimbic structures during reward anticipation. Neuron 2023; 111:3668-3682.e5. [PMID: 37586366 PMCID: PMC10840822 DOI: 10.1016/j.neuron.2023.07.012] [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: 02/04/2021] [Revised: 04/25/2023] [Accepted: 07/20/2023] [Indexed: 08/18/2023]
Abstract
Functional neuroimaging studies indicate that interconnected parts of the subcallosal anterior cingulate cortex (ACC), striatum, and amygdala play a fundamental role in affect in health and disease. Yet, although the patterns of neural activity engaged in the striatum and amygdala during affective processing are well established, especially during reward anticipation, less is known about subcallosal ACC. Here, we recorded neural activity in non-human primate subcallosal ACC and compared this with interconnected parts of the basolateral amygdala and rostromedial striatum while macaque monkeys performed reward-based tasks. Applying multiple analysis approaches, we found that neurons in subcallosal ACC and rostromedial striatum preferentially signal anticipated reward using short bursts of activity that form temporally specific patterns. By contrast, the basolateral amygdala uses a mixture of both temporally specific and more sustained patterns of activity to signal anticipated reward. Thus, dynamic patterns of neural activity across populations of neurons are engaged in affect, especially in subcallosal ACC.
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Affiliation(s)
- Megan E Young
- Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, New York, NY 10029, USA
| | - Camille Spencer-Salmon
- Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, New York, NY 10029, USA
| | - Clayton Mosher
- Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, New York, NY 10029, USA
| | - Sarita Tamang
- Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, New York, NY 10029, USA
| | - Kanaka Rajan
- Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, New York, NY 10029, USA
| | - Peter H Rudebeck
- Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, New York, NY 10029, USA.
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16
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Paolini M, Harrington Y, Colombo F, Bettonagli V, Poletti S, Carminati M, Colombo C, Benedetti F, Zanardi R. Hippocampal and parahippocampal volume and function predict antidepressant response in patients with major depression: A multimodal neuroimaging study. J Psychopharmacol 2023; 37:1070-1081. [PMID: 37589290 PMCID: PMC10647896 DOI: 10.1177/02698811231190859] [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] [Indexed: 08/18/2023]
Abstract
BACKGROUND For many patients with major depressive disorder (MDD) adequate treatment remains elusive. Neuroimaging techniques received attention for their potential use in guiding and predicting response, but were rarely investigated in real-world psychiatric settings. AIMS To identify structural and functional Magnetic Resonance Imaging (MRI) biomarkers associated with antidepressant response in a real-world clinical sample. METHODS We studied 100 MDD inpatients admitted to our psychiatric ward, treated with various antidepressants upon clinical need. Hamilton Depression Rating Scale percentage decrease from admission to discharge was used as a measure of response. All patients underwent 3.0 T MRI scanning. Grey matter (GM) volumes were investigated both in a voxel-based morphometry (VBM), and in a regions of interest (ROI) analysis. In a subsample of patients, functional resting-state connectivity patterns were also explored. RESULTS In the VBM analysis, worse response was associated to lower GM volumes in two clusters, encompassing the left hippocampus and parahippocampal gyrus, and the right superior and middle temporal gyrus. Investigating ROIs, lower bilateral hippocampi and amygdalae volumes predicted worse treatment outcomes. Functional connectivity in the right temporal and parahippocampal gyrus was also associated to response. CONCLUSION Our results expand existing literature on the relationship between the structure and function of several brain regions and treatment response in MDD. While we are still far from routine use of MRI biomarkers in clinical practice, we confirm a possible role of these techniques in guiding treatment choices and predicting their efficacy.
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Affiliation(s)
- Marco Paolini
- Vita-Salute San Raffaele University, Milano, Italy
- Psychiatry and Clinical Psychobiology, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Yasmin Harrington
- Vita-Salute San Raffaele University, Milano, Italy
- Psychiatry and Clinical Psychobiology, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Federica Colombo
- Vita-Salute San Raffaele University, Milano, Italy
- Psychiatry and Clinical Psychobiology, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | | | - Sara Poletti
- Vita-Salute San Raffaele University, Milano, Italy
- Psychiatry and Clinical Psychobiology, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Matteo Carminati
- Vita-Salute San Raffaele University, Milano, Italy
- Mood Disorders Unit, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Cristina Colombo
- Vita-Salute San Raffaele University, Milano, Italy
- Mood Disorders Unit, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Francesco Benedetti
- Vita-Salute San Raffaele University, Milano, Italy
- Psychiatry and Clinical Psychobiology, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Raffaella Zanardi
- Mood Disorders Unit, IRCCS San Raffaele Scientific Institute, Milan, Italy
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17
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Wei Y, Zhang R, Wang Y, Womer FY, Dong S, Zheng J, Zhang X, Wang F. Towards a neuroimaging biomarker for predicting cognitive behavioural therapy outcomes in treatment-naive depression: Preliminary findings. Psychiatry Res 2023; 329:115542. [PMID: 37890407 DOI: 10.1016/j.psychres.2023.115542] [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: 06/02/2023] [Revised: 10/08/2023] [Accepted: 10/09/2023] [Indexed: 10/29/2023]
Abstract
Clear prognostic indicators of cognitive behavioural therapy (CBT) are lacking for depression. This study aims to identify a biomarker that predicts CBT outcomes in depression. We developed a machine learning algorithm to predict post-CBT Hamilton Depression Rating Scale (HAMD) using pre-CBT regional homogeneity (ReHo). We examined transcriptomic signatures of regions with CBT-related ReHo changes. Twenty-five patients completed CBT and had increased ReHo in the dorsolateral prefrontal cortex (DLPFC) following CBT. Pre-CBT ReHo in left DLPFC was shown to be a predictor of post-HAMD scores. We identified left DLPFC ReHo as a neuroimaging biomarker for therapeutic effects of CBT in depression.
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Affiliation(s)
- Yange Wei
- Early Intervention Unit, Department of Psychiatry, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China; Department of Psychiatry, The Second Affiliated Hospital of Xinxiang Medical University, Henan Mental Hospital, Xinxiang, China; Department of Psychiatry, The First Affiliated Hospital of China Medical University, Shenyang, China
| | - Ran Zhang
- Early Intervention Unit, Department of Psychiatry, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China; Department of Psychiatry, The First Affiliated Hospital of China Medical University, Shenyang, China
| | - Yang Wang
- Early Intervention Unit, Department of Psychiatry, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China; Department of Psychiatry, The First Affiliated Hospital of China Medical University, Shenyang, China
| | - Fay Y Womer
- Department of Psychiatry and Behavioural Sciences, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Shuai Dong
- School of Biomedical Engineering and Informatics, Nanjing Medical University, Nanjing, China
| | - Junjie Zheng
- Early Intervention Unit, Department of Psychiatry, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
| | - Xizhe Zhang
- School of Biomedical Engineering and Informatics, Nanjing Medical University, Nanjing, China
| | - Fei Wang
- Early Intervention Unit, Department of Psychiatry, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China; Department of Psychiatry, The First Affiliated Hospital of China Medical University, Shenyang, China.
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18
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Ahmed R, Boyd BD, Elson D, Albert K, Begnoche P, Kang H, Landman BA, Szymkowicz SM, Andrews P, Vega J, Taylor WD. Influences of resting-state intrinsic functional brain connectivity on the antidepressant treatment response in late-life depression. Psychol Med 2023; 53:6261-6270. [PMID: 36482694 PMCID: PMC10250562 DOI: 10.1017/s0033291722003579] [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: 05/04/2022] [Revised: 09/04/2022] [Accepted: 10/24/2022] [Indexed: 12/14/2022]
Abstract
BACKGROUND Late-life depression (LLD) is characterized by differences in resting state functional connectivity within and between intrinsic functional networks. This study examined whether clinical improvement to antidepressant medications is associated with pre-randomization functional connectivity in intrinsic brain networks. METHODS Participants were 95 elders aged 60 years or older with major depressive disorder. After clinical assessments and baseline MRI, participants were randomized to escitalopram or placebo with a two-to-one allocation for 8 weeks. Non-remitting participants subsequently entered an 8-week trial of open-label bupropion. The main clinical outcome was depression severity measured by MADRS. Resting state functional connectivity was measured between a priori key seeds in the default mode (DMN), cognitive control, and limbic networks. RESULTS In primary analyses of blinded data, lower post-treatment MADRS score was associated with higher resting connectivity between: (a) posterior cingulate cortex (PCC) and left medial prefrontal cortex; (b) PCC and subgenual anterior cingulate cortex (ACC); (c) right medial PFC and subgenual ACC; (d) right orbitofrontal cortex and left hippocampus. Lower post-treatment MADRS was further associated with lower connectivity between: (e) the right orbitofrontal cortex and left amygdala; and (f) left dorsolateral PFC and left dorsal ACC. Secondary analyses associated mood improvement on escitalopram with anterior DMN hub connectivity. Exploratory analyses of the bupropion open-label trial associated improvement with subgenual ACC, frontal, and amygdala connectivity. CONCLUSIONS Response to antidepressants in LLD is related to connectivity in the DMN, cognitive control and limbic networks. Future work should focus on clinical markers of network connectivity informing prognosis. REGISTRATION ClinicalTrials.gov NCT02332291.
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Affiliation(s)
- Ryan Ahmed
- Department of Psychiatry and Behavioral Sciences, Vanderbilt University Medical Center, The Vanderbilt Center for Cognitive Medicine, Nashville, TN, USA
| | - Brian D. Boyd
- Department of Psychiatry and Behavioral Sciences, Vanderbilt University Medical Center, The Vanderbilt Center for Cognitive Medicine, Nashville, TN, USA
| | - Damian Elson
- Department of Psychiatry and Behavioral Sciences, Vanderbilt University Medical Center, The Vanderbilt Center for Cognitive Medicine, Nashville, TN, USA
| | - Kimberly Albert
- Department of Psychiatry and Behavioral Sciences, Vanderbilt University Medical Center, The Vanderbilt Center for Cognitive Medicine, Nashville, TN, USA
| | - Patrick Begnoche
- Department of Psychiatry and Behavioral Sciences, Vanderbilt University Medical Center, The Vanderbilt Center for Cognitive Medicine, Nashville, TN, USA
| | - Hakmook Kang
- Department of Biostatistics, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Bennett A. Landman
- Department of Psychiatry and Behavioral Sciences, Vanderbilt University Medical Center, The Vanderbilt Center for Cognitive Medicine, Nashville, TN, USA
- Department of Electrical and Computer Engineering, Vanderbilt University, Nashville, TN, USA
| | - Sarah M. Szymkowicz
- Department of Psychiatry and Behavioral Sciences, Vanderbilt University Medical Center, The Vanderbilt Center for Cognitive Medicine, Nashville, TN, USA
| | - Patricia Andrews
- Department of Psychiatry and Behavioral Sciences, Vanderbilt University Medical Center, The Vanderbilt Center for Cognitive Medicine, Nashville, TN, USA
| | - Jennifer Vega
- Department of Psychiatry and Behavioral Sciences, Vanderbilt University Medical Center, The Vanderbilt Center for Cognitive Medicine, Nashville, TN, USA
| | - Warren D. Taylor
- Department of Psychiatry and Behavioral Sciences, Vanderbilt University Medical Center, The Vanderbilt Center for Cognitive Medicine, Nashville, TN, USA
- Geriatric Research, Education, and Clinical Center, Veterans Affairs Tennessee Valley Health System, Nashville, TN, USA
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19
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Alagapan S, Choi KS, Heisig S, Riva-Posse P, Crowell A, Tiruvadi V, Obatusin M, Veerakumar A, Waters AC, Gross RE, Quinn S, Denison L, O'Shaughnessy M, Connor M, Canal G, Cha J, Hershenberg R, Nauvel T, Isbaine F, Afzal MF, Figee M, Kopell BH, Butera R, Mayberg HS, Rozell CJ. Cingulate dynamics track depression recovery with deep brain stimulation. Nature 2023; 622:130-138. [PMID: 37730990 PMCID: PMC10550829 DOI: 10.1038/s41586-023-06541-3] [Citation(s) in RCA: 23] [Impact Index Per Article: 23.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2021] [Accepted: 08/09/2023] [Indexed: 09/22/2023]
Abstract
Deep brain stimulation (DBS) of the subcallosal cingulate (SCC) can provide long-term symptom relief for treatment-resistant depression (TRD)1. However, achieving stable recovery is unpredictable2, typically requiring trial-and-error stimulation adjustments due to individual recovery trajectories and subjective symptom reporting3. We currently lack objective brain-based biomarkers to guide clinical decisions by distinguishing natural transient mood fluctuations from situations requiring intervention. To address this gap, we used a new device enabling electrophysiology recording to deliver SCC DBS to ten TRD participants (ClinicalTrials.gov identifier NCT01984710). At the study endpoint of 24 weeks, 90% of participants demonstrated robust clinical response, and 70% achieved remission. Using SCC local field potentials available from six participants, we deployed an explainable artificial intelligence approach to identify SCC local field potential changes indicating the patient's current clinical state. This biomarker is distinct from transient stimulation effects, sensitive to therapeutic adjustments and accurate at capturing individual recovery states. Variable recovery trajectories are predicted by the degree of preoperative damage to the structural integrity and functional connectivity within the targeted white matter treatment network, and are matched by objective facial expression changes detected using data-driven video analysis. Our results demonstrate the utility of objective biomarkers in the management of personalized SCC DBS and provide new insight into the relationship between multifaceted (functional, anatomical and behavioural) features of TRD pathology, motivating further research into causes of variability in depression treatment.
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Affiliation(s)
- Sankaraleengam Alagapan
- School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA, USA
| | - Ki Sueng Choi
- Nash Family Center for Advanced Circuit Therapeutics, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Radiology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Neurosurgery, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Stephen Heisig
- Nash Family Center for Advanced Circuit Therapeutics, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Patricio Riva-Posse
- Department of Psychiatry and Behavioral Sciences, Emory University School of Medicine, Atlanta, GA, USA
| | - Andrea Crowell
- Department of Psychiatry and Behavioral Sciences, Emory University School of Medicine, Atlanta, GA, USA
| | - Vineet Tiruvadi
- Wallace H. Coulter Department of Biomedical Engineering at Georgia Institute of Technology and Emory University, Atlanta, GA, USA
- Emory University School of Medicine, Atlanta, GA, USA
| | - Mosadoluwa Obatusin
- Nash Family Center for Advanced Circuit Therapeutics, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Ashan Veerakumar
- Department of Psychiatry, Schulich School of Medicine and Dentistry at Western University, London, Ontario, Canada
| | - Allison C Waters
- Nash Family Center for Advanced Circuit Therapeutics, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Neuroscience, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Robert E Gross
- Wallace H. Coulter Department of Biomedical Engineering at Georgia Institute of Technology and Emory University, Atlanta, GA, USA
- Department of Neurosurgery, Emory University School of Medicine, Atlanta, GA, USA
- Department of Neurology, Emory University School of Medicine, Atlanta, GA, USA
| | - Sinead Quinn
- Department of Psychiatry and Behavioral Sciences, Emory University School of Medicine, Atlanta, GA, USA
| | - Lydia Denison
- Emory University School of Medicine, Atlanta, GA, USA
| | - Matthew O'Shaughnessy
- School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA, USA
| | - Marissa Connor
- School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA, USA
| | - Gregory Canal
- School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA, USA
| | - Jungho Cha
- Nash Family Center for Advanced Circuit Therapeutics, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Rachel Hershenberg
- Department of Psychiatry and Behavioral Sciences, Emory University School of Medicine, Atlanta, GA, USA
| | - Tanya Nauvel
- Nash Family Center for Advanced Circuit Therapeutics, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Faical Isbaine
- Department of Neurosurgery, Emory University School of Medicine, Atlanta, GA, USA
| | - Muhammad Furqan Afzal
- Nash Family Center for Advanced Circuit Therapeutics, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Martijn Figee
- Nash Family Center for Advanced Circuit Therapeutics, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Neuroscience, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Brian H Kopell
- Nash Family Center for Advanced Circuit Therapeutics, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Neurosurgery, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Neuroscience, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Robert Butera
- School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA, USA
- Wallace H. Coulter Department of Biomedical Engineering at Georgia Institute of Technology and Emory University, Atlanta, GA, USA
| | - Helen S Mayberg
- Nash Family Center for Advanced Circuit Therapeutics, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
- Department of Neurosurgery, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
- Department of Neuroscience, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
- Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
| | - Christopher J Rozell
- School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA, USA.
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20
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Elias GJB, Germann J, Boutet A, Beyn ME, Giacobbe P, Song HN, Choi KS, Mayberg HS, Kennedy SH, Lozano AM. Local neuroanatomical and tract-based proxies of optimal subcallosal cingulate deep brain stimulation. Brain Stimul 2023; 16:1259-1272. [PMID: 37611657 DOI: 10.1016/j.brs.2023.08.014] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2023] [Revised: 08/02/2023] [Accepted: 08/19/2023] [Indexed: 08/25/2023] Open
Abstract
BACKGROUND Deep brain stimulation of the subcallosal cingulate area (SCC-DBS) is a promising neuromodulatory therapy for treatment-resistant depression (TRD). Biomarkers of optimal target engagement are needed to guide surgical targeting and stimulation parameter selection and to reduce variance in clinical outcome. OBJECTIVE/HYPOTHESIS We aimed to characterize the relationship between stimulation location, white matter tract engagement, and clinical outcome in a large (n = 60) TRD cohort treated with SCC-DBS. A smaller cohort (n = 22) of SCC-DBS patients with differing primary indications (bipolar disorder/anorexia nervosa) was utilized as an out-of-sample validation cohort. METHODS Volumes of tissue activated (VTAs) were constructed in standard space using high-resolution structural MRI and individual stimulation parameters. VTA-based probabilistic stimulation maps (PSMs) were generated to elucidate voxelwise spatial patterns of efficacious stimulation. A whole-brain tractogram derived from Human Connectome Project diffusion-weighted MRI data was seeded with VTA pairs, and white matter streamlines whose overlap with VTAs related to outcome ('discriminative' streamlines; Puncorrected < 0.05) were identified using t-tests. Linear modelling was used to interrogate the potential clinical relevance of VTA overlap with specific structures. RESULTS PSMs varied by hemisphere: high-value left-sided voxels were located more anterosuperiorly and squarely in the lateral white matter, while the equivalent right-sided voxels fell more posteroinferiorly and involved a greater proportion of grey matter. Positive discriminative streamlines localized to the bilateral (but primarily left) cingulum bundle, forceps minor/rostrum of corpus callosum, and bilateral uncinate fasciculus. Conversely, negative discriminative streamlines mostly belonged to the right cingulum bundle and bilateral uncinate fasciculus. The best performing linear model, which utilized information about VTA volume overlap with each of the positive discriminative streamline bundles as well as the negative discriminative elements of the right cingulum bundle, explained significant variance in clinical improvement in the primary TRD cohort (R = 0.46, P < 0.001) and survived repeated 10-fold cross-validation (R = 0.50, P = 0.040). This model was also able to predict outcome in the out-of-sample validation cohort (R = 0.43, P = 0.047). CONCLUSION(S) These findings reinforce prior indications of the importance of white matter engagement to SCC-DBS treatment success while providing new insights that could inform surgical targeting and stimulation parameter selection decisions.
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Affiliation(s)
- Gavin J B Elias
- Division of Neurosurgery, Department of Surgery, University Health Network and University of Toronto, Toronto, M5T 2S8, Canada; Krembil Research Institute, University of Toronto, Toronto, M5T 0S8, Canada
| | - Jürgen Germann
- Division of Neurosurgery, Department of Surgery, University Health Network and University of Toronto, Toronto, M5T 2S8, Canada; Krembil Research Institute, University of Toronto, Toronto, M5T 0S8, Canada
| | - Alexandre Boutet
- Division of Neurosurgery, Department of Surgery, University Health Network and University of Toronto, Toronto, M5T 2S8, Canada; Krembil Research Institute, University of Toronto, Toronto, M5T 0S8, Canada; Joint Department of Medical Imaging, University of Toronto, Toronto, M5T 1W7, Canada
| | - Michelle E Beyn
- Division of Neurosurgery, Department of Surgery, University Health Network and University of Toronto, Toronto, M5T 2S8, Canada
| | - Peter Giacobbe
- Department of Psychiatry, Sunnybrook Health Sciences Centre and University of Toronto, Toronto, M4N 3M5, Canada
| | - Ha Neul Song
- Nash Family Center for Advanced Circuit Therapeutics, Mount Sinai West, Icahn School of Medicine at Mount Sinai, New York, NY, 10019, USA
| | - Ki Sueng Choi
- Nash Family Center for Advanced Circuit Therapeutics, Mount Sinai West, Icahn School of Medicine at Mount Sinai, New York, NY, 10019, USA; Department of Radiology, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Helen S Mayberg
- Nash Family Center for Advanced Circuit Therapeutics, Mount Sinai West, Icahn School of Medicine at Mount Sinai, New York, NY, 10019, USA; Departments of Neurology and Neurosurgery, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Sidney H Kennedy
- Krembil Research Institute, University of Toronto, Toronto, M5T 0S8, Canada; ASR Suicide and Depression Studies Unit, St. Michael's Hospital, University of Toronto, M5B 1M8, Canada; Department of Psychiatry, University Health Network and University of Toronto, Toronto, M5T 2S8, Canada
| | - Andres M Lozano
- Division of Neurosurgery, Department of Surgery, University Health Network and University of Toronto, Toronto, M5T 2S8, Canada; Krembil Research Institute, University of Toronto, Toronto, M5T 0S8, Canada.
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21
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Xu C, Qi L, Wang X, Schaper FLWVJ, Wu D, Yu T, Yan X, Jin G, Wang Q, Wang X, Huang X, Wang Y, Chen Y, Liu J, Wang Y, Horn A, Fisher RS, Ren L. Functional connectomic profile correlates with effective anterior thalamic stimulation for refractory epilepsy. Brain Stimul 2023; 16:1302-1309. [PMID: 37633491 DOI: 10.1016/j.brs.2023.08.020] [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: 11/30/2022] [Revised: 08/19/2023] [Accepted: 08/21/2023] [Indexed: 08/28/2023] Open
Abstract
BACKGROUND Deep brain stimulation of the anterior nucleus of the thalamus (ANT-DBS) is an effective treatment for refractory epilepsy; however, seizure outcome varies among individuals. Identifying a reliable noninvasive biomarker to predict good responders would be helpful. OBJECTIVES To test whether the functional connectivity between the ANT-DBS sites and the seizure foci correlates with effective seizure control in refractory epilepsy. METHODS We performed a proof-of-concept pilot study of patients with focal refractory epilepsy receiving ANT-DBS. Using normative human connectome data derived from 1000 healthy participants, we investigated whether intrinsic functional connectivity between the seizure foci and the DBS site was associated with seizure outcome. We repeated this analysis controlling for the extent of seizure foci, distance between the seizure foci and DBS site, and using functional connectivity of the ANT instead of the DBS site to test the contribution of variance in DBS sites. RESULTS Eighteen patients with two or more seizure foci were included. Greater functional connectivity between the seizure foci and the DBS site correlated with more favorable outcome. The degree of functional connectivity accounted for significant variance in clinical outcomes (DBS site: |r| = 0.773, p < 0.001 vs ANT-atlas: |r| = 0.715, p = 0.001), which remained significant when controlling for the extent of the seizure foci (|r| = 0.773, p < 0.001) and the distance between the seizure foci and DBS site (|r| = 0.777, p < 0.001). Significant correlations were independent of variance in the DBS sites (|r| = 0.148, p = 0.57). CONCLUSION These findings suggest that functional connectomic profile is a potential reliable non-invasive biomarker to predict ANT-DBS outcomes. Accordingly, the identification of ANT responders could decrease the surgical risk for patients who may not benefit and optimize the cost-effective allocation of health care resources.
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Affiliation(s)
- Cuiping Xu
- National Center for Neurological Disorders, Beijing, China; Department of Functional Neurosurgery, Beijing Institute of Functional Neurosurgery, Xuanwu Hospital, Clinical Center for Epilepsy, Capital Medical University, Beijing, China
| | - Lei Qi
- National Center for Neurological Disorders, Beijing, China; Department of Neurology, Xuanwu Hospital, Clinical Center for Epilepsy, Capital Medical University, Beijing, China
| | - Xueyuan Wang
- National Center for Neurological Disorders, Beijing, China; Department of Functional Neurosurgery, Beijing Institute of Functional Neurosurgery, Xuanwu Hospital, Clinical Center for Epilepsy, Capital Medical University, Beijing, China
| | - Frédéric L W V J Schaper
- Center for Brain Circuit Therapeutics, Departments of Neurology, Psychiatry, and Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, United States
| | - Di Wu
- National Center for Neurological Disorders, Beijing, China; Department of Neurology, Xuanwu Hospital, Clinical Center for Epilepsy, Capital Medical University, Beijing, China
| | - Tao Yu
- National Center for Neurological Disorders, Beijing, China; Department of Functional Neurosurgery, Beijing Institute of Functional Neurosurgery, Xuanwu Hospital, Clinical Center for Epilepsy, Capital Medical University, Beijing, China
| | - Xiaoming Yan
- National Center for Neurological Disorders, Beijing, China; Department of Functional Neurosurgery, Beijing Institute of Functional Neurosurgery, Xuanwu Hospital, Clinical Center for Epilepsy, Capital Medical University, Beijing, China
| | - Guangyuan Jin
- National Center for Neurological Disorders, Beijing, China; Department of Neurology, Xuanwu Hospital, Clinical Center for Epilepsy, Capital Medical University, Beijing, China
| | - Qiao Wang
- National Center for Neurological Disorders, Beijing, China; Department of Neurology, Xuanwu Hospital, Clinical Center for Epilepsy, Capital Medical University, Beijing, China
| | - Xiaopeng Wang
- National Center for Neurological Disorders, Beijing, China; Department of Neurology, Xuanwu Hospital, Clinical Center for Epilepsy, Capital Medical University, Beijing, China
| | - Xinqi Huang
- National Center for Neurological Disorders, Beijing, China; Department of Neurology, Xuanwu Hospital, Clinical Center for Epilepsy, Capital Medical University, Beijing, China
| | - Yuke Wang
- National Center for Neurological Disorders, Beijing, China; Department of Neurology, Xuanwu Hospital, Clinical Center for Epilepsy, Capital Medical University, Beijing, China
| | - Yuanhong Chen
- National Center for Neurological Disorders, Beijing, China; Department of Neurology, Xuanwu Hospital, Clinical Center for Epilepsy, Capital Medical University, Beijing, China
| | - Jinghui Liu
- National Center for Neurological Disorders, Beijing, China; Department of Neurology, Xuanwu Hospital, Clinical Center for Epilepsy, Capital Medical University, Beijing, China
| | - Yuping Wang
- National Center for Neurological Disorders, Beijing, China; Department of Neurology, Xuanwu Hospital, Clinical Center for Epilepsy, Capital Medical University, Beijing, China
| | - Andreas Horn
- Center for Brain Circuit Therapeutics, Departments of Neurology, Psychiatry, and Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, United States; Department of Neurology, Charité-Universitätsmedizin Berlin, Berlin, Germany; MGH Neurosurgery & Center for Neurotechnology and Neurorecovery (CNTR) at MGH Neurology, Massachusetts General Hospital, Harvard Medical School, United States
| | - Robert S Fisher
- Department of Neurology and Neurological Sciences and Neurosurgery by Courtesy, Stanford University School of Medicine, Palo Alto, CA, USA
| | - Liankun Ren
- National Center for Neurological Disorders, Beijing, China; Department of Neurology, Xuanwu Hospital, Clinical Center for Epilepsy, Capital Medical University, Beijing, China; Chinese Institute for Brain Research, Beijing, China.
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22
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Chai Y, Sheline YI, Oathes DJ, Balderston NL, Rao H, Yu M. Functional connectomics in depression: insights into therapies. Trends Cogn Sci 2023; 27:814-832. [PMID: 37286432 PMCID: PMC10476530 DOI: 10.1016/j.tics.2023.05.006] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2022] [Revised: 05/09/2023] [Accepted: 05/09/2023] [Indexed: 06/09/2023]
Abstract
Depression is a common mental disorder characterized by heterogeneous cognitive and behavioral symptoms. The emerging research paradigm of functional connectomics has provided a quantitative theoretical framework and analytic tools for parsing variations in the organization and function of brain networks in depression. In this review, we first discuss recent progress in depression-associated functional connectome variations. We then discuss treatment-specific brain network outcomes in depression and propose a hypothetical model highlighting the advantages and uniqueness of each treatment in relation to the modulation of specific brain network connectivity and symptoms of depression. Finally, we look to the future promise of combining multiple treatment types in clinical practice, using multisite datasets and multimodal neuroimaging approaches, and identifying biological depression subtypes.
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Affiliation(s)
- Ya Chai
- Key Laboratory of Brain-Machine Intelligence for Information Behavior (Ministry of Education and Shanghai), School of Business and Management, Shanghai International Studies University, Shanghai, China; Center for Functional Neuroimaging, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Yvette I Sheline
- Center for Neuromodulation in Depression and Stress (CNDS), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA; Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA; Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA; Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
| | - Desmond J Oathes
- Center for Neuromodulation in Depression and Stress (CNDS), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA; Penn Brain Science, Translation, Innovation and Modulation Center (brainSTIM), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
| | - Nicholas L Balderston
- Center for Neuromodulation in Depression and Stress (CNDS), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Hengyi Rao
- Key Laboratory of Brain-Machine Intelligence for Information Behavior (Ministry of Education and Shanghai), School of Business and Management, Shanghai International Studies University, Shanghai, China; Center for Functional Neuroimaging, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
| | - Meichen Yu
- Indiana Alzheimer's Disease Research Center, Indiana University School of Medicine, Indianapolis, IN, USA; Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN, USA; Indiana University Network Science Institute, Bloomington, IN, USA.
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23
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Strege MV, Siegle GJ, Richey JA, Krawczak RA, Young K. Cingulate prediction of response to antidepressant and cognitive behavioral therapies for depression: Meta-analysis and empirical application. Brain Imaging Behav 2023; 17:450-460. [PMID: 36622532 PMCID: PMC10329727 DOI: 10.1007/s11682-022-00756-0] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2022] [Revised: 10/26/2022] [Accepted: 12/16/2022] [Indexed: 01/10/2023]
Abstract
We sought to identify baseline (pre-treatment) neural markers associated with treatment response in major depressive disorder (MDD), specific to treatment type, Cognitive Behavioral Therapy (CBT) or pharmacotherapy (selective serotonin reuptake inhibitors; SSRI). We conducted a meta-analysis of functional magnetic resonance imaging (fMRI) studies to identify neural prognostic indicators of response to CBT or SSRI. To verify the regions derived from literature, the meta-analytic regions were used to predict clinical change in a verification sample of participants with MDD who received either CBT (n = 60) or an SSRI (n = 19) as part of prior clinical trials. The meta-analysis consisted of 21 fMRI studies that used emotion-related tasks. It yielded prognostic regions of the perigenual (meta pgACC) and subgenual anterior cingulate cortex (meta sgACC), associated with SSRI and CBT response, respectively. When applying the meta-analytic regions to predict treatment response in the verification sample, reactivity of the meta pgACC was prognostic for SSRI response, yet the effect direction was opposite of most prior studies. Meta sgACC reactivity failed to be prognostic for CBT response. Results confirm the prognostic potential of neural reactivity of ACC subregions in MDD but further research is necessary for clinical translation.
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Affiliation(s)
- Marlene V Strege
- Department of Psychiatry, University of Pittsburgh, Pittsburgh, United States.
| | - Greg J Siegle
- Department of Psychiatry, University of Pittsburgh, Pittsburgh, United States
| | - John A Richey
- Department of Psychology, Virginia Polytechnic Institute and State University, Blacksburg, United States
| | | | - Kymberly Young
- Department of Psychiatry, University of Pittsburgh, Pittsburgh, United States
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24
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Han S, Cui Q, Zheng R, Li S, Zhou B, Fang K, Sheng W, Wen B, Liu L, Wei Y, Chen H, Chen Y, Cheng J, Zhang Y. Parsing altered gray matter morphology of depression using a framework integrating the normative model and non-negative matrix factorization. Nat Commun 2023; 14:4053. [PMID: 37422463 PMCID: PMC10329663 DOI: 10.1038/s41467-023-39861-z] [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: 09/12/2022] [Accepted: 06/27/2023] [Indexed: 07/10/2023] Open
Abstract
The high inter-individual heterogeneity in individuals with depression limits neuroimaging studies with case-control approaches to identify promising biomarkers for individualized clinical decision-making. We put forward a framework integrating the normative model and non-negative matrix factorization (NMF) to quantitatively assess altered gray matter morphology in depression from a dimensional perspective. The proposed framework parses altered gray matter morphology into overlapping latent disease factors, and assigns patients distinct factor compositions, thus preserving inter-individual variability. We identified four robust disease factors with distinct clinical symptoms and cognitive processes in depression. In addition, we showed the quantitative relationship between the group-level gray matter morphological differences and disease factors. Furthermore, this framework significantly predicted factor compositions of patients in an independent dataset. The framework provides an approach to resolve neuroanatomical heterogeneity in depression.
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Affiliation(s)
- Shaoqiang Han
- Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Henan Province, China.
- Key Laboratory for Functional Magnetic Resonance Imaging and Molecular Imaging of Henan Province, Henan Province, China.
- Engineering Technology Research Center for Detection and Application of Brain Function of Henan Province, Henan Province, China.
- Engineering Research Center of Medical Imaging Intelligent Diagnosis and Treatment of Henan Province, Henan Province, China.
- Key Laboratory of Magnetic Resonance and Brain Function of Henan Province, Henan Province, China.
- Key Laboratory of Brain Function and Cognitive Magnetic Resonance Imaging of Zhengzhou, Henan Province, China.
- Key Laboratory of Imaging Intelligence Research medicine of Henan Province, Henan Province, China.
- Henan Engineering Research Center of Brain Function Development and Application, Henan Province, China.
| | - Qian Cui
- School of Public Affairs and Administration, University of Electronic Science and Technology of China, Chengdu, China
| | - Ruiping Zheng
- Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Henan Province, China
- Key Laboratory for Functional Magnetic Resonance Imaging and Molecular Imaging of Henan Province, Henan Province, China
- Engineering Technology Research Center for Detection and Application of Brain Function of Henan Province, Henan Province, China
- Engineering Research Center of Medical Imaging Intelligent Diagnosis and Treatment of Henan Province, Henan Province, China
- Key Laboratory of Magnetic Resonance and Brain Function of Henan Province, Henan Province, China
- Key Laboratory of Brain Function and Cognitive Magnetic Resonance Imaging of Zhengzhou, Henan Province, China
- Key Laboratory of Imaging Intelligence Research medicine of Henan Province, Henan Province, China
- Henan Engineering Research Center of Brain Function Development and Application, Henan Province, China
| | - Shuying Li
- Department of Psychiatry, The First Affiliated Hospital of Zhengzhou University, Henan Province, China
| | - Bingqian Zhou
- Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Henan Province, China
- Key Laboratory for Functional Magnetic Resonance Imaging and Molecular Imaging of Henan Province, Henan Province, China
- Engineering Technology Research Center for Detection and Application of Brain Function of Henan Province, Henan Province, China
- Engineering Research Center of Medical Imaging Intelligent Diagnosis and Treatment of Henan Province, Henan Province, China
- Key Laboratory of Magnetic Resonance and Brain Function of Henan Province, Henan Province, China
- Key Laboratory of Brain Function and Cognitive Magnetic Resonance Imaging of Zhengzhou, Henan Province, China
- Key Laboratory of Imaging Intelligence Research medicine of Henan Province, Henan Province, China
- Henan Engineering Research Center of Brain Function Development and Application, Henan Province, China
| | - Keke Fang
- Department of Pharmacy, Affiliated Cancer Hospital of Zhengzhou University, Henan Cancer Hospital, Henan Province, China
| | - Wei Sheng
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Baohong Wen
- Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Henan Province, China
- Key Laboratory for Functional Magnetic Resonance Imaging and Molecular Imaging of Henan Province, Henan Province, China
- Engineering Technology Research Center for Detection and Application of Brain Function of Henan Province, Henan Province, China
- Engineering Research Center of Medical Imaging Intelligent Diagnosis and Treatment of Henan Province, Henan Province, China
- Key Laboratory of Magnetic Resonance and Brain Function of Henan Province, Henan Province, China
- Key Laboratory of Brain Function and Cognitive Magnetic Resonance Imaging of Zhengzhou, Henan Province, China
- Key Laboratory of Imaging Intelligence Research medicine of Henan Province, Henan Province, China
- Henan Engineering Research Center of Brain Function Development and Application, Henan Province, China
| | - Liang Liu
- Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Henan Province, China
- Key Laboratory for Functional Magnetic Resonance Imaging and Molecular Imaging of Henan Province, Henan Province, China
- Engineering Technology Research Center for Detection and Application of Brain Function of Henan Province, Henan Province, China
- Engineering Research Center of Medical Imaging Intelligent Diagnosis and Treatment of Henan Province, Henan Province, China
- Key Laboratory of Magnetic Resonance and Brain Function of Henan Province, Henan Province, China
- Key Laboratory of Brain Function and Cognitive Magnetic Resonance Imaging of Zhengzhou, Henan Province, China
- Key Laboratory of Imaging Intelligence Research medicine of Henan Province, Henan Province, China
- Henan Engineering Research Center of Brain Function Development and Application, Henan Province, China
| | - Yarui Wei
- Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Henan Province, China
- Key Laboratory for Functional Magnetic Resonance Imaging and Molecular Imaging of Henan Province, Henan Province, China
- Engineering Technology Research Center for Detection and Application of Brain Function of Henan Province, Henan Province, China
- Engineering Research Center of Medical Imaging Intelligent Diagnosis and Treatment of Henan Province, Henan Province, China
- Key Laboratory of Magnetic Resonance and Brain Function of Henan Province, Henan Province, China
- Key Laboratory of Brain Function and Cognitive Magnetic Resonance Imaging of Zhengzhou, Henan Province, China
- Key Laboratory of Imaging Intelligence Research medicine of Henan Province, Henan Province, China
- Henan Engineering Research Center of Brain Function Development and Application, Henan Province, China
| | - Huafu Chen
- Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Henan Province, China.
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China.
| | - Yuan Chen
- Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Henan Province, China.
- Key Laboratory for Functional Magnetic Resonance Imaging and Molecular Imaging of Henan Province, Henan Province, China.
- Engineering Technology Research Center for Detection and Application of Brain Function of Henan Province, Henan Province, China.
- Engineering Research Center of Medical Imaging Intelligent Diagnosis and Treatment of Henan Province, Henan Province, China.
- Key Laboratory of Magnetic Resonance and Brain Function of Henan Province, Henan Province, China.
- Key Laboratory of Brain Function and Cognitive Magnetic Resonance Imaging of Zhengzhou, Henan Province, China.
- Key Laboratory of Imaging Intelligence Research medicine of Henan Province, Henan Province, China.
- Henan Engineering Research Center of Brain Function Development and Application, Henan Province, China.
| | - Jingliang Cheng
- Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Henan Province, China.
- Key Laboratory for Functional Magnetic Resonance Imaging and Molecular Imaging of Henan Province, Henan Province, China.
- Engineering Technology Research Center for Detection and Application of Brain Function of Henan Province, Henan Province, China.
- Engineering Research Center of Medical Imaging Intelligent Diagnosis and Treatment of Henan Province, Henan Province, China.
- Key Laboratory of Magnetic Resonance and Brain Function of Henan Province, Henan Province, China.
- Key Laboratory of Brain Function and Cognitive Magnetic Resonance Imaging of Zhengzhou, Henan Province, China.
- Key Laboratory of Imaging Intelligence Research medicine of Henan Province, Henan Province, China.
- Henan Engineering Research Center of Brain Function Development and Application, Henan Province, China.
| | - Yong Zhang
- Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Henan Province, China
- Key Laboratory for Functional Magnetic Resonance Imaging and Molecular Imaging of Henan Province, Henan Province, China
- Engineering Technology Research Center for Detection and Application of Brain Function of Henan Province, Henan Province, China
- Engineering Research Center of Medical Imaging Intelligent Diagnosis and Treatment of Henan Province, Henan Province, China
- Key Laboratory of Magnetic Resonance and Brain Function of Henan Province, Henan Province, China
- Key Laboratory of Brain Function and Cognitive Magnetic Resonance Imaging of Zhengzhou, Henan Province, China
- Key Laboratory of Imaging Intelligence Research medicine of Henan Province, Henan Province, China
- Henan Engineering Research Center of Brain Function Development and Application, Henan Province, China
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Hanuka S, Olson EA, Admon R, Webb CA, Killgore WDS, Rauch SL, Rosso IM, Pizzagalli DA. Reduced anhedonia following internet-based cognitive-behavioral therapy for depression is mediated by enhanced reward circuit activation. Psychol Med 2023; 53:4345-4354. [PMID: 35713110 DOI: 10.1017/s0033291722001106] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
BACKGROUND Major depressive disorder (MDD) is a highly prevalent psychiatric condition, yet many patients do not receive adequate treatment. Novel and highly scalable interventions such as internet-based cognitive-behavioral-therapy (iCBT) may help to address this treatment gap. Anhedonia, a hallmark symptom of MDD that refers to diminished interest and ability to experience pleasure, has been associated with reduced reactivity in a neural reward circuit that includes medial prefrontal and striatal brain regions. Whether iCBT can reduce anhedonia severity in MDD patients, and whether these therapeutic effects are accompanied by enhanced reward circuit reactivity has yet to be examined. METHODS Fifty-two MDD patients were randomly assigned to either 10-week iCBT (n = 26) or monitored attention control (MAC, n = 26) programs. All patients completed pre- and post-treatment assessments of anhedonia (Snaith-Hamilton Pleasure Scale; SHAPS) and reward circuit reactivity [monetary incentive delay (MID) task during functional magnetic resonance imaging (fMRI)]. Healthy control participants (n = 42) also underwent two fMRI scans while completing the MID task 10 weeks apart. RESULTS Both iCBT and MAC groups exhibited a reduction in anhedonia severity post-treatment. Nevertheless, only the iCBT group exhibited enhanced nucleus accumbens (Nacc) and subgenual anterior cingulate cortex (sgACC) activation and functional connectivity from pre- to post-treatment in response to reward feedback. Enhanced Nacc and sgACC activations were associated with reduced anhedonia severity following iCBT treatment, with enhanced Nacc activation also mediating the reduction in anhedonia severity post-treatment. CONCLUSIONS These findings suggest that increased reward circuit reactivity may contribute to a reduction in anhedonia severity following iCBT treatment for depression.
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Affiliation(s)
- Shir Hanuka
- School of Psychological Sciences, University of Haifa, Haifa, Israel
| | - Elizabeth A Olson
- Center for Depression, Anxiety and Stress Research, McLean Hospital, Belmont, MA, USA
- Department of Psychiatry, Harvard Medical School, Boston, MA, USA
| | - Roee Admon
- School of Psychological Sciences, University of Haifa, Haifa, Israel
- The Integrated Brain and Behavior Research Center (IBBRC), University of Haifa, Haifa, Israel
| | - Christian A Webb
- Center for Depression, Anxiety and Stress Research, McLean Hospital, Belmont, MA, USA
- Department of Psychiatry, Harvard Medical School, Boston, MA, USA
| | | | - Scott L Rauch
- Center for Depression, Anxiety and Stress Research, McLean Hospital, Belmont, MA, USA
- Department of Psychiatry, Harvard Medical School, Boston, MA, USA
| | - Isabelle M Rosso
- Center for Depression, Anxiety and Stress Research, McLean Hospital, Belmont, MA, USA
- Department of Psychiatry, Harvard Medical School, Boston, MA, USA
| | - Diego A Pizzagalli
- Center for Depression, Anxiety and Stress Research, McLean Hospital, Belmont, MA, USA
- Department of Psychiatry, Harvard Medical School, Boston, MA, USA
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26
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Katayama N, Nakagawa A, Umeda S, Terasawa Y, Shinagawa K, Kikuchi T, Tabuchi H, Abe T, Mimura M. Functional connectivity changes between frontopolar cortex and nucleus accumbens following cognitive behavioral therapy in major depression: A randomized clinical trial. Psychiatry Res Neuroimaging 2023; 332:111643. [PMID: 37060839 DOI: 10.1016/j.pscychresns.2023.111643] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/17/2022] [Revised: 03/19/2023] [Accepted: 04/05/2023] [Indexed: 04/17/2023]
Abstract
Cognitive behavioral therapy (CBT) is a psychotherapy that challenges distorted cognitions; however, the neural mechanisms that underpin CBT remain unclear. Hence, we aimed to assess the treatment-related resting-state functional connectivity (rsFC) changes in the brain regions associated with future thinking and the associations between rsFC changes and clinical improvements. Thirty-eight adult patients with MDD were randomly assigned with equal likelihood to receive 16-week individual CBT or talking control with a 12-month follow-up period. We evaluated the rsFC changes in the frontal regions, nucleus accumbens, amygdala, and limbic structures key to the depression pathophysiology and future thinking with 2 × 2 mixed ANOVA interaction analysis. Pearson's correlation analysis with Bonferroni's correction was also performed to examine the associations with clinical symptoms, such as depression severity and automatic thoughts in follow-up evaluations. Treatment-specific changes include enhancement in frontopolar connectivity with the nucleus accumbens. An increased rsFC was associated with lower negative automatic thoughts postoperatively, together with lower depressive symptoms and higher positive automatic thoughts at follow-up. Conclusively, rsFC changes in the fronto-limbic neural control circuit after CBT, particularly between the frontal pole and nucleus accumbens, may be clinically meaningful functional changes related to the depression recovery process.
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Affiliation(s)
- Nariko Katayama
- Department of Neuropsychiatry, School of Medicine, Keio University, Tokyo, Japan
| | - Atsuo Nakagawa
- Department of Neuropsychiatry, School of Medicine, Keio University, Tokyo, Japan; Department of Neuropsychiatry, School of Medicine, St. Marianna University, Kawasaki, Japan.
| | - Satoshi Umeda
- Department of Psychology, Faculty of Letters, Keio University, Tokyo, Japan
| | - Yuri Terasawa
- Department of Psychology, Faculty of Letters, Keio University, Tokyo, Japan
| | - Kazushi Shinagawa
- Department of Psychology, Faculty of Letters, Keio University, Tokyo, Japan
| | - Toshiaki Kikuchi
- Department of Neuropsychiatry, School of Medicine, Keio University, Tokyo, Japan
| | - Hajime Tabuchi
- Department of Neuropsychiatry, School of Medicine, Keio University, Tokyo, Japan
| | - Takayuki Abe
- Department of Neuropsychiatry, School of Medicine, St. Marianna University, Kawasaki, Japan; School of Data Science, Yokohama City University, Yokohama, Japan
| | - Masaru Mimura
- Department of Neuropsychiatry, School of Medicine, Keio University, Tokyo, Japan
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27
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Tura A, Goya-Maldonado R. Brain connectivity in major depressive disorder: a precision component of treatment modalities? Transl Psychiatry 2023; 13:196. [PMID: 37296121 DOI: 10.1038/s41398-023-02499-y] [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: 12/17/2021] [Revised: 05/15/2023] [Accepted: 05/30/2023] [Indexed: 06/12/2023] Open
Abstract
Major depressive disorder (MDD) is a very prevalent mental disorder that imposes an enormous burden on individuals, society, and health care systems. Most patients benefit from commonly used treatment methods such as pharmacotherapy, psychotherapy, electroconvulsive therapy (ECT), and repetitive transcranial magnetic stimulation (rTMS). However, the clinical decision on which treatment method to use remains generally informed and the individual clinical response is difficult to predict. Most likely, a combination of neural variability and heterogeneity in MDD still impedes a full understanding of the disorder, as well as influences treatment success in many cases. With the help of neuroimaging methods like functional magnetic resonance imaging (fMRI) and diffusion tensor imaging (DTI), the brain can be understood as a modular set of functional and structural networks. In recent years, many studies have investigated baseline connectivity biomarkers of treatment response and the connectivity changes after successful treatment. Here, we systematically review the literature and summarize findings from longitudinal interventional studies investigating the functional and structural connectivity in MDD. By compiling and discussing these findings, we recommend the scientific and clinical community to deepen the systematization of findings to pave the way for future systems neuroscience roadmaps that include brain connectivity parameters as a possible precision component of the clinical evaluation and therapeutic decision.
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Affiliation(s)
- Asude Tura
- Laboratory of Systems Neuroscience and Imaging in Psychiatry (SNIP-Lab), Department of Psychiatry and Psychotherapy, University Medical Center Göttingen (UMG), Göttingen, Germany
| | - Roberto Goya-Maldonado
- Laboratory of Systems Neuroscience and Imaging in Psychiatry (SNIP-Lab), Department of Psychiatry and Psychotherapy, University Medical Center Göttingen (UMG), Göttingen, Germany.
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28
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Jellinger KA. The heterogeneity of late-life depression and its pathobiology: a brain network dysfunction disorder. J Neural Transm (Vienna) 2023:10.1007/s00702-023-02648-z. [PMID: 37145167 PMCID: PMC10162005 DOI: 10.1007/s00702-023-02648-z] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2023] [Accepted: 04/28/2023] [Indexed: 05/06/2023]
Abstract
Depression is frequent in older individuals and is often associated with cognitive impairment and increasing risk of subsequent dementia. Late-life depression (LLD) has a negative impact on quality of life, yet the underlying pathobiology is still poorly understood. It is characterized by considerable heterogeneity in clinical manifestation, genetics, brain morphology, and function. Although its diagnosis is based on standard criteria, due to overlap with other age-related pathologies, the relationship between depression and dementia and the relevant structural and functional cerebral lesions are still controversial. LLD has been related to a variety of pathogenic mechanisms associated with the underlying age-related neurodegenerative and cerebrovascular processes. In addition to biochemical abnormalities, involving serotonergic and GABAergic systems, widespread disturbances of cortico-limbic, cortico-subcortical, and other essential brain networks, with disruption in the topological organization of mood- and cognition-related or other global connections are involved. Most recent lesion mapping has identified an altered network architecture with "depressive circuits" and "resilience tracts", thus confirming that depression is a brain network dysfunction disorder. Further pathogenic mechanisms including neuroinflammation, neuroimmune dysregulation, oxidative stress, neurotrophic and other pathogenic factors, such as β-amyloid (and tau) deposition are in discussion. Antidepressant therapies induce various changes in brain structure and function. Better insights into the complex pathobiology of LLD and new biomarkers will allow earlier and better diagnosis of this frequent and disabling psychopathological disorder, and further elucidation of its complex pathobiological basis is warranted in order to provide better prevention and treatment of depression in older individuals.
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Affiliation(s)
- Kurt A Jellinger
- Institute of Clinical Neurobiology, Alberichgasse 5/13, 1150, Vienna, Austria.
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29
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Li F, Jörg F, Merkx MJM, Feenstra T. Early symptom change contributes to the outcome prediction of cognitive behavioral therapy for depression patients: A machine learning approach. J Affect Disord 2023; 334:352-357. [PMID: 37149055 DOI: 10.1016/j.jad.2023.04.111] [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: 09/08/2022] [Revised: 04/19/2023] [Accepted: 04/28/2023] [Indexed: 05/08/2023]
Abstract
BACKGROUND Limited evidence exists regarding the association between early symptom change and later outcomes of cognitive behavioral therapy (CBT). This study aimed to apply machine learning algorithms to predict continuous treatment outcomes based on pre-treatment predictors and early symptom changes and to uncover whether additional variance could be explained compared to regression methods. Additionally, the study examined early subscale symptom changes to determine the most significant predictors of treatment outcome. METHODS We investigated CBT outcomes in a large naturalistic dataset (N = 1975 depression patients). The sociodemographic profile, pre-treatment predictors, and early symptom change, including total and subscale scores were used to predict the Symptom Questionnaire (SQ)48 score at the 10th session as a continuous outcome. Different machine learners were compared to linear regression. RESULTS Early symptom change and baseline symptom score were the only significant predictors. Models with early symptom change explained 22.0 % to 23.3 % more variance than those without early symptom change. Specifically, the baseline total symptom score, and the early symptom score changes of the subscales pertaining to depression and anxiety were the top three predictors of treatment outcome. LIMITATION Excluded patients with missing treatment outcomes had slightly higher symptom scores at baseline, indicating possible selection bias. CONCLUSION Early symptom change improved the prediction of treatment outcomes. The prediction performance achieved is far from clinical relevance: the best learner could only explain 51.2 % of the variance in outcomes. Compared to linear regression, more sophisticated preprocessing and learning methods did not substantially improve performance.
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Affiliation(s)
- Fang Li
- University of Groningen, Faculty of Science and Engineering, Groningen Research Institute of Pharmacy, Groningen, the Netherlands.
| | - Frederike Jörg
- University of Groningen, University Medical Center Groningen, University Center Psychiatry, Rob Giel Research Center, Interdisciplinary Centre for Psychopathology and Emotion Regulation, the Netherlands
| | | | - Talitha Feenstra
- University of Groningen, Faculty of Science and Engineering, Groningen Research Institute of Pharmacy, Groningen, the Netherlands; Center for Nutrition, Prevention and Health Services Research, National Institute for Public Health and the Environment (RIVM), Bilthoven, the Netherlands
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30
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Jaeckle T, Williams SCR, Barker GJ, Basilio R, Carr E, Goldsmith K, Colasanti A, Giampietro V, Cleare A, Young AH, Moll J, Zahn R. Self-blame in major depression: a randomised pilot trial comparing fMRI neurofeedback with self-guided psychological strategies. Psychol Med 2023; 53:2831-2841. [PMID: 34852855 PMCID: PMC10235657 DOI: 10.1017/s0033291721004797] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/24/2020] [Revised: 02/19/2021] [Accepted: 11/02/2021] [Indexed: 11/06/2022]
Abstract
BACKGROUND Overgeneralised self-blame and worthlessness are key symptoms of major depressive disorder (MDD) and have previously been associated with self-blame-selective changes in connectivity between right superior anterior temporal lobe (rSATL) and subgenual frontal cortices. Another study showed that remitted MDD patients were able to modulate this neural signature using functional magnetic resonance imaging (fMRI) neurofeedback training, thereby increasing their self-esteem. The feasibility and potential of using this approach in symptomatic MDD were unknown. METHOD This single-blind pre-registered randomised controlled pilot trial probed a novel self-guided psychological intervention with and without additional rSATL-posterior subgenual cortex (BA25) fMRI neurofeedback, targeting self-blaming emotions in people with insufficiently recovered MDD and early treatment-resistance (n = 43, n = 35 completers). Participants completed three weekly self-guided sessions to rebalance self-blaming biases. RESULTS As predicted, neurofeedback led to a training-induced reduction in rSATL-BA25 connectivity for self-blame v. other-blame. Both interventions were safe and resulted in a 46% reduction on the Beck Depression Inventory-II, our primary outcome, with no group differences. Secondary analyses, however, revealed that patients without DSM-5-defined anxious distress showed a superior response to neurofeedback compared with the psychological intervention, and the opposite pattern in anxious MDD. As predicted, symptom remission was associated with increases in self-esteem and this correlated with the frequency with which participants employed the psychological strategies in daily life. CONCLUSIONS These findings suggest that self-blame-rebalance neurofeedback may be superior over a solely psychological intervention in non-anxious MDD, although further confirmatory studies are needed. Simple self-guided strategies tackling self-blame were beneficial, but need to be compared against treatment-as-usual in further trials. https://doi.org/10.1186/ISRCTN10526888.
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Affiliation(s)
- Tanja Jaeckle
- Department of Psychological Medicine, Centre for Affective Disorders, London, UK
| | - Steven C. R. Williams
- Department of Neuroimaging, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
| | - Gareth J. Barker
- Department of Neuroimaging, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
| | - Rodrigo Basilio
- Cognitive and Behavioral Neuroscience Unit and Neuroinformatics Workgroup, D'Or Institute for Research and Education (IDOR), Rio de Janeiro, Brazil
| | - Ewan Carr
- Department of Biostatistics, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
| | - Kimberley Goldsmith
- Department of Biostatistics, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
| | - Alessandro Colasanti
- Department of Psychological Medicine, Centre for Affective Disorders, London, UK
| | - Vincent Giampietro
- Department of Neuroimaging, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
| | - Anthony Cleare
- Department of Psychological Medicine, Centre for Affective Disorders, London, UK
| | - Allan H. Young
- Department of Psychological Medicine, Centre for Affective Disorders, London, UK
- South London and Maudsley NHS Foundation Trust, Bethlem Royal Hospital, Monks Orchard Road, Beckenham, Kent, BR3 3BX, UK
| | - Jorge Moll
- Cognitive and Behavioral Neuroscience Unit and Neuroinformatics Workgroup, D'Or Institute for Research and Education (IDOR), Rio de Janeiro, Brazil
| | - Roland Zahn
- Department of Psychological Medicine, Centre for Affective Disorders, London, UK
- Cognitive and Behavioral Neuroscience Unit and Neuroinformatics Workgroup, D'Or Institute for Research and Education (IDOR), Rio de Janeiro, Brazil
- South London and Maudsley NHS Foundation Trust, Bethlem Royal Hospital, Monks Orchard Road, Beckenham, Kent, BR3 3BX, UK
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31
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Dunlop BW, Cha J, Choi KS, Rajendra JK, Nemeroff CB, Craighead WE, Mayberg HS. Shared and Unique Changes in Brain Connectivity Among Depressed Patients After Remission With Pharmacotherapy Versus Psychotherapy. Am J Psychiatry 2023; 180:218-229. [PMID: 36651624 DOI: 10.1176/appi.ajp.21070727] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/19/2023]
Abstract
OBJECTIVE The authors sought to determine the shared and unique changes in brain resting-state functional connectivity (rsFC) between patients with major depressive disorder who achieved remission with cognitive-behavioral therapy (CBT) or with antidepressant medication. METHODS The Predictors of Remission in Depression to Individual and Combined Treatments (PReDICT) trial randomized adults with treatment-naive major depressive disorder to 12 weeks of treatment with CBT (16 1-hour sessions) or medication (duloxetine 30-60 mg/day or escitalopram 10-20 mg/day). Resting-state functional MRI scans were performed at baseline and at week 12. The primary outcome was change in the whole-brain rsFC of four seeded brain networks among participants who achieved remission. RESULTS Of the 131 completers with usable MRI data (74 female; mean age, 39.8 years), remission was achieved by 19 of 40 CBT-treated and 45 of 91 medication-treated patients. Three patterns of connectivity changes were observed. First, those who remitted with either treatment shared a pattern of reduction in rsFC between the subcallosal cingulate cortex and the motor cortex. Second, reciprocal rsFC changes were observed across multiple networks, primarily increases in CBT remitters and decreases in medication remitters. And third, in CBT remitters only, rsFC increased within the executive control network and between the executive control network and parietal attention regions. CONCLUSIONS Remission from major depression via treatment with CBT or medication is associated with changes in rsFC that are mostly specific to the treatment modality, providing biological support for the clinical practice of switching between or combining these treatment approaches. Medication is associated with broadly inhibitory effects. In CBT remitters, the increase in rsFC strength between networks involved in cognitive control and attention provides biological support for the theorized mechanism of CBT. Reducing affective network connectivity with motor systems is a shared process important for remission with both CBT and medication.
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Affiliation(s)
- Boadie W Dunlop
- Department of Psychiatry and Behavioral Sciences, Emory University School of Medicine, Atlanta (Dunlop, Craighead); Department of Neurology and Neurosurgery, Icahn School of Medicine at Mount Sinai, New York (Cha, Choi, Mayberg); Scientific and Statistical Computational Core, NIMH, Bethesda (Rajendra); Department of Psychiatry and Behavioral Sciences, Institute for Early Life Adversity Research, University of Texas at Austin Dell Medical School, Austin (Nemeroff); Department of Psychology, Emory University, Atlanta (Craighead)
| | - Jungho Cha
- Department of Psychiatry and Behavioral Sciences, Emory University School of Medicine, Atlanta (Dunlop, Craighead); Department of Neurology and Neurosurgery, Icahn School of Medicine at Mount Sinai, New York (Cha, Choi, Mayberg); Scientific and Statistical Computational Core, NIMH, Bethesda (Rajendra); Department of Psychiatry and Behavioral Sciences, Institute for Early Life Adversity Research, University of Texas at Austin Dell Medical School, Austin (Nemeroff); Department of Psychology, Emory University, Atlanta (Craighead)
| | - Ki Sueng Choi
- Department of Psychiatry and Behavioral Sciences, Emory University School of Medicine, Atlanta (Dunlop, Craighead); Department of Neurology and Neurosurgery, Icahn School of Medicine at Mount Sinai, New York (Cha, Choi, Mayberg); Scientific and Statistical Computational Core, NIMH, Bethesda (Rajendra); Department of Psychiatry and Behavioral Sciences, Institute for Early Life Adversity Research, University of Texas at Austin Dell Medical School, Austin (Nemeroff); Department of Psychology, Emory University, Atlanta (Craighead)
| | - Justin K Rajendra
- Department of Psychiatry and Behavioral Sciences, Emory University School of Medicine, Atlanta (Dunlop, Craighead); Department of Neurology and Neurosurgery, Icahn School of Medicine at Mount Sinai, New York (Cha, Choi, Mayberg); Scientific and Statistical Computational Core, NIMH, Bethesda (Rajendra); Department of Psychiatry and Behavioral Sciences, Institute for Early Life Adversity Research, University of Texas at Austin Dell Medical School, Austin (Nemeroff); Department of Psychology, Emory University, Atlanta (Craighead)
| | - Charles B Nemeroff
- Department of Psychiatry and Behavioral Sciences, Emory University School of Medicine, Atlanta (Dunlop, Craighead); Department of Neurology and Neurosurgery, Icahn School of Medicine at Mount Sinai, New York (Cha, Choi, Mayberg); Scientific and Statistical Computational Core, NIMH, Bethesda (Rajendra); Department of Psychiatry and Behavioral Sciences, Institute for Early Life Adversity Research, University of Texas at Austin Dell Medical School, Austin (Nemeroff); Department of Psychology, Emory University, Atlanta (Craighead)
| | - W Edward Craighead
- Department of Psychiatry and Behavioral Sciences, Emory University School of Medicine, Atlanta (Dunlop, Craighead); Department of Neurology and Neurosurgery, Icahn School of Medicine at Mount Sinai, New York (Cha, Choi, Mayberg); Scientific and Statistical Computational Core, NIMH, Bethesda (Rajendra); Department of Psychiatry and Behavioral Sciences, Institute for Early Life Adversity Research, University of Texas at Austin Dell Medical School, Austin (Nemeroff); Department of Psychology, Emory University, Atlanta (Craighead)
| | - Helen S Mayberg
- Department of Psychiatry and Behavioral Sciences, Emory University School of Medicine, Atlanta (Dunlop, Craighead); Department of Neurology and Neurosurgery, Icahn School of Medicine at Mount Sinai, New York (Cha, Choi, Mayberg); Scientific and Statistical Computational Core, NIMH, Bethesda (Rajendra); Department of Psychiatry and Behavioral Sciences, Institute for Early Life Adversity Research, University of Texas at Austin Dell Medical School, Austin (Nemeroff); Department of Psychology, Emory University, Atlanta (Craighead)
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Scangos KW, State MW, Miller AH, Baker JT, Williams LM. New and emerging approaches to treat psychiatric disorders. Nat Med 2023; 29:317-333. [PMID: 36797480 DOI: 10.1038/s41591-022-02197-0] [Citation(s) in RCA: 12] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2022] [Accepted: 12/21/2022] [Indexed: 02/18/2023]
Abstract
Psychiatric disorders are highly prevalent, often devastating diseases that negatively impact the lives of millions of people worldwide. Although their etiological and diagnostic heterogeneity has long challenged drug discovery, an emerging circuit-based understanding of psychiatric illness is offering an important alternative to the current reliance on trial and error, both in the development and in the clinical application of treatments. Here we review new and emerging treatment approaches, with a particular emphasis on the revolutionary potential of brain-circuit-based interventions for precision psychiatry. Limitations of circuit models, challenges of bringing precision therapeutics to market and the crucial advances needed to overcome these obstacles are presented.
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Affiliation(s)
- Katherine W Scangos
- Department of Psychiatry and Behavioral Sciences, UCSF Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, USA.
| | - Matthew W State
- Department of Psychiatry and Behavioral Sciences, UCSF Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, USA
| | - Andrew H Miller
- Department of Psychiatry and Behavioral Sciences, Emory University School of Medicine, Atlanta, GA, USA
| | - Justin T Baker
- McLean Hospital Institute for Technology in Psychiatry, Belmont, MA, USA
| | - Leanne M Williams
- Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA, USA
- Mental Illness Research Education and Clinical Center (MIRECC), VA Palo Alto Health Care System, Palo Alto, CA, USA
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33
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Fu CHY, Erus G, Fan Y, Antoniades M, Arnone D, Arnott SR, Chen T, Choi KS, Fatt CC, Frey BN, Frokjaer VG, Ganz M, Garcia J, Godlewska BR, Hassel S, Ho K, McIntosh AM, Qin K, Rotzinger S, Sacchet MD, Savitz J, Shou H, Singh A, Stolicyn A, Strigo I, Strother SC, Tosun D, Victor TA, Wei D, Wise T, Woodham RD, Zahn R, Anderson IM, Deakin JFW, Dunlop BW, Elliott R, Gong Q, Gotlib IH, Harmer CJ, Kennedy SH, Knudsen GM, Mayberg HS, Paulus MP, Qiu J, Trivedi MH, Whalley HC, Yan CG, Young AH, Davatzikos C. AI-based dimensional neuroimaging system for characterizing heterogeneity in brain structure and function in major depressive disorder: COORDINATE-MDD consortium design and rationale. BMC Psychiatry 2023; 23:59. [PMID: 36690972 PMCID: PMC9869598 DOI: 10.1186/s12888-022-04509-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/29/2022] [Accepted: 12/29/2022] [Indexed: 01/24/2023] Open
Abstract
BACKGROUND Efforts to develop neuroimaging-based biomarkers in major depressive disorder (MDD), at the individual level, have been limited to date. As diagnostic criteria are currently symptom-based, MDD is conceptualized as a disorder rather than a disease with a known etiology; further, neural measures are often confounded by medication status and heterogeneous symptom states. METHODS We describe a consortium to quantify neuroanatomical and neurofunctional heterogeneity via the dimensions of novel multivariate coordinate system (COORDINATE-MDD). Utilizing imaging harmonization and machine learning methods in a large cohort of medication-free, deeply phenotyped MDD participants, patterns of brain alteration are defined in replicable and neurobiologically-based dimensions and offer the potential to predict treatment response at the individual level. International datasets are being shared from multi-ethnic community populations, first episode and recurrent MDD, which are medication-free, in a current depressive episode with prospective longitudinal treatment outcomes and in remission. Neuroimaging data consist of de-identified, individual, structural MRI and resting-state functional MRI with additional positron emission tomography (PET) data at specific sites. State-of-the-art analytic methods include automated image processing for extraction of anatomical and functional imaging variables, statistical harmonization of imaging variables to account for site and scanner variations, and semi-supervised machine learning methods that identify dominant patterns associated with MDD from neural structure and function in healthy participants. RESULTS We are applying an iterative process by defining the neural dimensions that characterise deeply phenotyped samples and then testing the dimensions in novel samples to assess specificity and reliability. Crucially, we aim to use machine learning methods to identify novel predictors of treatment response based on prospective longitudinal treatment outcome data, and we can externally validate the dimensions in fully independent sites. CONCLUSION We describe the consortium, imaging protocols and analytics using preliminary results. Our findings thus far demonstrate how datasets across many sites can be harmonized and constructively pooled to enable execution of this large-scale project.
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Affiliation(s)
- Cynthia H Y Fu
- Department of Psychological Sciences, University of East London, London, UK.
- Centre for Affective Disorders, Department of Psychological Medicine, Institute of Psychiatry, Psychology, and Neuroscience, King's College London, London, UK.
| | - Guray Erus
- Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, USA
| | - Yong Fan
- Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, USA
| | - Mathilde Antoniades
- Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, USA
| | - Danilo Arnone
- Centre for Affective Disorders, Department of Psychological Medicine, Institute of Psychiatry, Psychology, and Neuroscience, King's College London, London, UK
- Department of Psychiatry and Behavioral Science, College of Medicine and Health Sciences, United Arab Emirates University, Al Ain, United Arab Emirates
| | | | - Taolin Chen
- Huaxi MR Research Center, Department of Radiology, West China Hospital, Sichuan University, Chengdu, China
- Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, China
| | - Ki Sueng Choi
- Nash Family Center for Advanced Circuit Therapeutics, Icahn School of Medicine at Mount Sinai, New York, USA
| | - Cherise Chin Fatt
- Department of Psychiatry, Center for Depression Research and Clinical Care, University of Texas Southwestern Medical Center, Dallas, USA
| | - Benicio N Frey
- Department of Psychiatry and Behavioural Neurosciences, McMaster University, Hamilton, Canada
- Mood Disorders Treatment and Research Centre and Women's Health Concerns Clinic, St Joseph's Healthcare Hamilton, Hamilton, Canada
| | - Vibe G Frokjaer
- Neurobiology Research Unit, University Hospital Rigshospitalet, Copenhagen, Denmark
- Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
- Department of Psychiatry, Psychiatric Centre Copenhagen, Copenhagen, Denmark
| | - Melanie Ganz
- Neurobiology Research Unit, University Hospital Rigshospitalet, Copenhagen, Denmark
- Department of Computer Science, University of Copenhagen, Copenhagen, Denmark
| | - Jose Garcia
- Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, USA
| | - Beata R Godlewska
- Department of Psychiatry, University of Oxford, Oxford, UK
- Oxford Health NHS Foundation Trust, Warneford Hospital, Oxford, UK
| | - Stefanie Hassel
- Mathison Centre for Mental Health Research and Education, University of Calgary, Calgary, Canada
- Department of Psychiatry, Cumming School of Medicine, University of Calgary, Calgary, Canada
| | - Keith Ho
- Department of Psychiatry, University Health Network, Toronto, Canada
| | - Andrew M McIntosh
- Division of Psychiatry, Royal Edinburgh Hospital, University of Edinburgh, Edinburgh, UK
| | - Kun Qin
- Huaxi MR Research Center, Department of Radiology, West China Hospital, Sichuan University, Chengdu, China
- Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, China
| | - Susan Rotzinger
- Department of Psychiatry, University Health Network, Toronto, Canada
- Centre for Depression and Suicide Studies, Unity Health Toronto, Toronto, Canada
| | - Matthew D Sacchet
- Meditation Research Program, Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, USA
| | | | - Haochang Shou
- Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, USA
- Penn Statistics in Imaging and Visualization Endeavor (PennSIVE) Center, Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania, Philadelphia, USA
| | - Ashish Singh
- Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, USA
| | - Aleks Stolicyn
- Division of Psychiatry, Royal Edinburgh Hospital, University of Edinburgh, Edinburgh, UK
| | - Irina Strigo
- Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, USA
| | - Stephen C Strother
- Rotman Research Institute, Baycrest Centre, Toronto, Canada
- Department of Medical Biophysics, University of Toronto, Toronto, Canada
| | - Duygu Tosun
- Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, USA
| | | | - Dongtao Wei
- School of Psychology, Southwest University, Chongqing, China
| | - Toby Wise
- Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Rachel D Woodham
- Department of Psychological Sciences, University of East London, London, UK
| | - Roland Zahn
- Centre for Affective Disorders, Department of Psychological Medicine, Institute of Psychiatry, Psychology, and Neuroscience, King's College London, London, UK
| | - Ian M Anderson
- Division of Neuroscience and Experimental Psychology, University of Manchester, Manchester, UK
| | - J F William Deakin
- Division of Neuroscience and Experimental Psychology, University of Manchester, Manchester, UK
| | - Boadie W Dunlop
- Department of Psychiatry and Behavioral Sciences, Emory University School of Medicine, Atlanta, USA
| | - Rebecca Elliott
- Division of Neuroscience and Experimental Psychology, University of Manchester, Manchester, UK
| | - Qiyong Gong
- Huaxi MR Research Center, Department of Radiology, West China Hospital, Sichuan University, Chengdu, China
- Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, China
| | - Ian H Gotlib
- Department of Psychology, Stanford University, Stanford, USA
| | | | - Sidney H Kennedy
- Department of Psychiatry, University Health Network, Toronto, Canada
- Centre for Depression and Suicide Studies, Unity Health Toronto, Toronto, Canada
- Unity Health Toronto, Toronto, Canada
| | - Gitte M Knudsen
- Neurobiology Research Unit, University Hospital Rigshospitalet, Copenhagen, Denmark
- Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Helen S Mayberg
- Nash Family Center for Advanced Circuit Therapeutics, Icahn School of Medicine at Mount Sinai, New York, USA
| | | | - Jiang Qiu
- School of Psychology, Southwest University, Chongqing, China
| | - Madhukar H Trivedi
- Department of Psychiatry, Center for Depression Research and Clinical Care, University of Texas Southwestern Medical Center, Dallas, USA
| | - Heather C Whalley
- Division of Psychiatry, Royal Edinburgh Hospital, University of Edinburgh, Edinburgh, UK
| | - Chao-Gan Yan
- CAS Key Laboratory of Behavioral Science, Institute of Psychology, Beijing, China
| | - Allan H Young
- Centre for Affective Disorders, Department of Psychological Medicine, Institute of Psychiatry, Psychology, and Neuroscience, King's College London, London, UK
- South London and Maudsley NHS Foundation Trust, Bethlem Royal Hospital, London, UK
| | - Christos Davatzikos
- Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, USA
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Ausderau KK, Colman RJ, Kabakov S, Schultz-Darken N, Emborg ME. Evaluating depression- and anxiety-like behaviors in non-human primates. Front Behav Neurosci 2023; 16:1006065. [PMID: 36744101 PMCID: PMC9892652 DOI: 10.3389/fnbeh.2022.1006065] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2022] [Accepted: 12/29/2022] [Indexed: 01/20/2023] Open
Abstract
Depression and anxiety are some of the most prevalent and debilitating mental health conditions in humans. They can present on their own or as co-morbidities with other disorders. Like humans, non-human primates (NHPs) can develop depression- and anxiety-like signs. Here, we first define human depression and anxiety, examine equivalent species-specific behaviors in NHPs, and consider models and current methods to identify and evaluate these behaviors. We also discuss knowledge gaps, as well as the importance of evaluating the co-occurrence of depression- and anxiety-like behaviors in animal models of human disease. Lastly, we consider ethical challenges in depression and anxiety research on NHPs in order to ultimately advance the understanding and the personalized treatment of these disorders.
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Affiliation(s)
- Karla K. Ausderau
- Wisconsin National Primate Research Center, University of Wisconsin—Madison, Madison, WI, United States
- Waisman Center, University of Wisconsin—Madison, Madison, WI, United States
- Department of Kinesiology, University of Wisconsin—Madison, Madison, WI, United States
| | - Ricki J. Colman
- Wisconsin National Primate Research Center, University of Wisconsin—Madison, Madison, WI, United States
- Department of Cell and Regenerative Biology, University of Wisconsin—Madison, Madison, WI, United States
| | - Sabrina Kabakov
- Department of Kinesiology, University of Wisconsin—Madison, Madison, WI, United States
| | - Nancy Schultz-Darken
- Wisconsin National Primate Research Center, University of Wisconsin—Madison, Madison, WI, United States
| | - Marina E. Emborg
- Wisconsin National Primate Research Center, University of Wisconsin—Madison, Madison, WI, United States
- Department of Medical Physics, University of Wisconsin—Madison, Madison, WI, United States
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Alexander L, Wood CM, Roberts AC. The ventromedial prefrontal cortex and emotion regulation: lost in translation? J Physiol 2023; 601:37-50. [PMID: 35635793 PMCID: PMC10084434 DOI: 10.1113/jp282627] [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] [Subscribe] [Scholar Register] [Received: 11/30/2021] [Accepted: 05/13/2022] [Indexed: 01/03/2023] Open
Abstract
Neuroimaging studies implicate the ventromedial prefrontal cortex (vmPFC) in a wide range of emotional and cognitive functions, and changes in activity within vmPFC have been linked to the aetiology and successful treatment of depression. However, this is a large, structurally heterogeneous region and the extent to which this structural heterogeneity reflects functional heterogeneity remains unclear. Causal studies in animals should help address this question but attempts to map findings from vmPFC studies in rodents onto human imaging studies highlight cross-species discrepancies between structural homology and functional analogy. Bridging this gap, recent studies in marmosets - a species of new world monkey in which the overall organization of vmPFC is more like humans than that of rodents - have revealed that over-activation of the caudal subcallosal region of vmPFC, area 25, but not neighbouring area 32, heightens reactivity to negatively valenced stimuli whilst blunting responsivity to positively valenced stimuli. These co-occurring states resemble those seen in depressed patients, which are associated with increased activity in caudal subcallosal regions. In contrast, only reward blunting but not heightening of threat reactivity is seen following over-activation of the structurally homologous region in rodents. To further advance understanding of the role of vmPFC in the aetiology and treatment of depression, future work should focus on the behaviourally specific networks by which vmPFC regions have their effects, together with characterization of cross-species similarities and differences in function.
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Affiliation(s)
- Laith Alexander
- St Thomas’ HospitalLondonUK
- Department of Psychological MedicineSchool of Academic PsychiatryInstitute of Psychiatry, Psychology and NeuroscienceKing's College LondonLondonUK
| | - Christian M. Wood
- Department of PhysiologyDevelopment and NeuroscienceUniversity of CambridgeCambridgeUK
- Behavioural and Clinical Neuroscience InstituteUniversity of CambridgeCambridgeUK
| | - Angela C. Roberts
- Department of PhysiologyDevelopment and NeuroscienceUniversity of CambridgeCambridgeUK
- Behavioural and Clinical Neuroscience InstituteUniversity of CambridgeCambridgeUK
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Carreon RL, Rivas-Grajales AM, Murphy N, Mathew SJ, Jha MK. Biomarkers in Psychiatric Drug Development: From Precision Medicine to Novel Therapeutics. ADVANCES IN NEUROBIOLOGY 2023; 30:287-297. [PMID: 36928855 DOI: 10.1007/978-3-031-21054-9_12] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/18/2023]
Abstract
Burden of psychiatric disorders is compounded by their wide prevalence as well as the limited efficacy of currently available treatments and the current approaches for prescribing these treatments. The selection of treatments continues to be subjective and often results in a trial-and-error approach. Emerging research suggests that biological markers (or biomarkers) can be used to develop precision medicine approaches for psychiatric disorders. Furthermore, the biomarkers also promise to elucidate the underlying pathophysiological mechanisms which in turn can be used to develop novel therapeutic treatments. In this chapter we have focused on mood disorders and reviewed studies on electroencephalography (EEG), magnetic resonance imaging (MRI), and blood-based biomarkers that can guide selection of one treatment versus another (treatment-selection biomarker) as well as biomarkers that can guide the development of novel therapeutics. These studies suggest that the use of objective physiological data is poised to alter the landscape of psychiatric diagnosis and treatment. However, practical and economic barriers remain as major hurdles. The key to finding such translational diagnostic and therapeutic biomarkers is a better understanding of the underlying pathophysiology, and despite the tremendous advances in neuroscience, it is clear there remains much left to be elucidated.
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Affiliation(s)
- Rudy Lozano Carreon
- Menninger Department of Psychiatry and Behavioral Sciences, Baylor College of Medicine, Houston, TX, USA
- Michael E. Debakey VA Medical Center, Houston, TX, USA
| | - Ana Maria Rivas-Grajales
- Department of Psychiatry, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, USA
| | - Nicholas Murphy
- Menninger Department of Psychiatry and Behavioral Sciences, Baylor College of Medicine, Houston, TX, USA
- The Menninger Clinic, Houston, TX, USA
| | - Sanjay J Mathew
- Menninger Department of Psychiatry and Behavioral Sciences, Baylor College of Medicine, Houston, TX, USA
- Michael E. Debakey VA Medical Center, Houston, TX, USA
- The Menninger Clinic, Houston, TX, USA
| | - Manish K Jha
- Center for Depression Research and Clinical Care, O'Donnell Brain Institute, UT Southwestern Medical Center, Dallas, TX, USA.
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Heinzel S, Schwefel M, Sanchez A, Heinen D, Fehm L, Henze R, Terán C, Kallies G, Rapp MA, Fydrich T, Ströhle A, Heissel A. Physical exercise training as preceding treatment to cognitive behavioral therapy in mild to moderate major depressive disorder: A randomized controlled trial. J Affect Disord 2022; 319:90-98. [PMID: 36113693 DOI: 10.1016/j.jad.2022.09.024] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/02/2022] [Revised: 07/28/2022] [Accepted: 09/11/2022] [Indexed: 11/30/2022]
Abstract
BACKGROUND Many patients with major depressive disorder (MDD) remain untreated or do not respond to cognitive behavioral therapy (CBT). Physical exercise shows antidepressive effects and may serve as an effective augmentation treatment. However, research on combining exercise with CBT is sparse in MDD and underlying mechanisms of exercise are not well understood to date. METHODS 120 outpatients with MDD were randomized to either a high intensity exercise group (HEX), a low intensity exercise group (LEX), or a waiting list control group (WL). After 12 weeks of exercise training or waiting period, all patients received a manualized CBT. RESULTS Seventy-five patients with MDD completed both the exercise program/ waiting period and the CBT. While physical fitness improved in HEX after the exercise program, it did not change in LEX and WL. Depressive symptoms improved in all three groups from baseline to post-CBT and the group by time interaction was not significant. Regression analyses revealed that the amount of fitness improvement during exercise predicted the subsequent CBT response. LIMITATIONS The dropout rate was relatively high, preparatory CBT sessions during exercise / waiting period may have influenced depressive symptoms, and no patients with severe MDD were included. CONCLUSIONS High intense physical exercise did not lead to a general enhancement of CBT outcome, but higher increases in physical fitness seem to improve symptom change during CBT. Our results suggest that the implementation of more individually tailored exercise programs could be a promising approach for future research and clinical practice.
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Affiliation(s)
- Stephan Heinzel
- Clinical Psychology and Psychotherapy, Department of Education and Psychology, Freie Universität Berlin, Berlin, Germany.
| | - Melanie Schwefel
- Clinical Psychology and Psychotherapy, Department of Education and Psychology, Freie Universität Berlin, Berlin, Germany
| | - Alba Sanchez
- Social and Preventive Medicine, University of Potsdam, Potsdam, Germany
| | - Darlene Heinen
- Social and Preventive Medicine, University of Potsdam, Potsdam, Germany
| | - Lydia Fehm
- Department of Psychology, Humboldt-Universität zu Berlin, Berlin, Germany
| | - Romy Henze
- Department of Psychology, Humboldt-Universität zu Berlin, Berlin, Germany
| | - Christina Terán
- Social and Preventive Medicine, University of Potsdam, Potsdam, Germany
| | - Gunnar Kallies
- Department of Psychiatry and Psychotherapy, Campus Charité Mitte, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Michael A Rapp
- Social and Preventive Medicine, University of Potsdam, Potsdam, Germany
| | - Thomas Fydrich
- Department of Psychology, Humboldt-Universität zu Berlin, Berlin, Germany
| | - Andreas Ströhle
- Department of Psychiatry and Psychotherapy, Campus Charité Mitte, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Andreas Heissel
- Social and Preventive Medicine, University of Potsdam, Potsdam, Germany
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Dhami P, Quilty LC, Schwartzmann B, Uher R, Allen TA, Kloiber S, Lam RW, MacQueen G, Frey BN, Milev R, Müller DJ, Rotzinger S, Kennedy SH, Farzan F. Alterations in the neural correlates of affective inhibitory control following cognitive behavioral therapy for depression: A Canadian biomarker integration network for depression (CAN-BIND) study. JOURNAL OF AFFECTIVE DISORDERS REPORTS 2022. [DOI: 10.1016/j.jadr.2022.100413] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/15/2022] Open
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Kennedy JC, Dunlop BW, Craighead LW, Nemeroff CB, Mayberg HS, Craighead WE. Assessing in-session rumination and its effects on CBT for depression. Behav Res Ther 2022; 159:104209. [PMID: 36283238 DOI: 10.1016/j.brat.2022.104209] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2022] [Revised: 08/29/2022] [Accepted: 10/05/2022] [Indexed: 12/14/2022]
Abstract
The study evaluated if rumination of patients during therapy (i.e., in-session rumination) relates to whether or not they do less well in CBT treatment. We developed a reliably assessed in-session rumination observational measure and evaluated its relationship to depression over the course of CBT. Rated sessions came from 63 treatment-naïve patients with major depressive disorder who participated in CBT in the PReDICT study (Dunlop et al., 2017). In-session rumination was operationalized as repetitive, negative, and passive talking about depressive topics. Trained undergraduates rated the intensity and duration of in-session rumination occurring during 57 initial therapy sessions (i.e., session one) and 45 sessions in the middle of treatment (i.e., session eight). The observational ratings were sufficiently reliable (all ICCs > 0.69). Mixed model results indicated that greater intensity of in-session rumination during the initial treatment session predicted higher levels of subsequent clinician-rated depressive symptoms (p < .023). Regression results indicated that greater intensity and duration of in-session rumination at session 8 significantly predicted higher clinician-rated symptoms at end of treatment (p's < 0.02). In-session rumination intensity and duration were not, however, related to subsequent self-reported depressive symptoms. The results support efforts to identify which patients might benefit from rumination-specific interventions.
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Affiliation(s)
- J C Kennedy
- Department of Psychiatry and Behavioral Sciences, School of Medicine, Emory University School of Medicine, Atlanta, GA, United States; Department of Psychology, Emory University, Atlanta, GA, United States
| | - B W Dunlop
- Department of Psychiatry and Behavioral Sciences, School of Medicine, Emory University School of Medicine, Atlanta, GA, United States
| | - L W Craighead
- Department of Psychology, Emory University, Atlanta, GA, United States
| | - C B Nemeroff
- Department of Psychiatry and Behavioral Sciences, Dell School of Medicine, University of Texas, Austin, TX, United States
| | - H S Mayberg
- The Nash Family Center for Advanced Circuit Therapeutics, Icahn School of Medicine at Mount Sinai, New York, NY, United States
| | - W E Craighead
- Department of Psychiatry and Behavioral Sciences, School of Medicine, Emory University School of Medicine, Atlanta, GA, United States; Department of Psychology, Emory University, Atlanta, GA, United States.
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Ye Y, Wang C, Lan X, Li W, Fu L, Zhang F, Liu H, Wu K, Zhou Y, Ning Y. Baseline patterns of resting functional connectivity within posterior default-mode intranetwork associated with remission to antidepressants in major depressive disorder. Neuroimage Clin 2022; 36:103230. [PMID: 36274375 PMCID: PMC9668631 DOI: 10.1016/j.nicl.2022.103230] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2022] [Revised: 10/08/2022] [Accepted: 10/10/2022] [Indexed: 11/11/2022]
Abstract
BACKGROUND The default mode network (DMN) is implicated in the pathophysiology of major depressive disorder (MDD), and functional connectivity (FC) involved in DMN is suggested to be associated with antidepressant remission. The goal of this study is to recognize relationships between FC within DMN and early amelioration in MDD patients and to further test the capacity of FC to predict early efficacy. METHODS In total 66 MDD patients and 57 healthy controls were recruited for resting-state functional magnetic resonance imaging scans at baseline. After four weeks of treatment with Escitalopram or Venlafaxine, patients were divided into subgroups with remitters (R, n = 31) and non-remitters (NR, n = 35). Independent component analysis (ICA) was used to compare intranetwork functional connectivity (intra-FC) in DMN between the three groups. RESULTS Relative to NR-MDD group and HCs, the R-MDD group showed significantly higher intra-FC in the right angular gyrus of DMN, and the intra-FC was positively correlated with the reduction ratio of the depressive symptom scores. The ROC curve analysis revealed that intra-FC exhibited a high diagnostic value for remission. CONCLUSION These findings indicated that intra-FC related to the DMN is a prognostic marker that can potentially predict early remission of symptoms after antidepressant treatment.
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Affiliation(s)
- Yanxiang Ye
- The Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou, China; Guangdong Engineering Technology Research Center for Translational Medicine of Metal Disorders, Guangzhou, China
| | - Chengyu Wang
- The Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou, China; Guangdong Engineering Technology Research Center for Translational Medicine of Metal Disorders, Guangzhou, China
| | - Xiaofeng Lan
- The Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou, China; Guangdong Engineering Technology Research Center for Translational Medicine of Metal Disorders, Guangzhou, China
| | - Weicheng Li
- The Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou, China; The First School of Clinical Medicine, Southern Medical University, Guangzhou, China; Guangdong Engineering Technology Research Center for Translational Medicine of Metal Disorders, Guangzhou, China
| | - Ling Fu
- The Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou, China; The First School of Clinical Medicine, Southern Medical University, Guangzhou, China; Guangdong Engineering Technology Research Center for Translational Medicine of Metal Disorders, Guangzhou, China
| | - Fan Zhang
- The Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou, China; The First School of Clinical Medicine, Southern Medical University, Guangzhou, China; Guangdong Engineering Technology Research Center for Translational Medicine of Metal Disorders, Guangzhou, China
| | - Haiyan Liu
- The Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou, China; Guangdong Engineering Technology Research Center for Translational Medicine of Metal Disorders, Guangzhou, China
| | - Kai Wu
- Department of Biomedical Engineering, School of Material Science and Engineering, South China University of Technology, China
| | - Yanling Zhou
- The Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou, China; Guangdong Engineering Technology Research Center for Translational Medicine of Metal Disorders, Guangzhou, China.
| | - Yuping Ning
- The Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou, China; The First School of Clinical Medicine, Southern Medical University, Guangzhou, China; Guangdong Engineering Technology Research Center for Translational Medicine of Metal Disorders, Guangzhou, China.
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Kaiser RH, Chase HW, Phillips ML, Deckersbach T, Parsey RV, Fava M, McGrath PJ, Weissman M, Oquendo MA, McInnis MG, Carmody T, Cooper CM, Trivedi MH, Pizzagalli DA. Dynamic Resting-State Network Biomarkers of Antidepressant Treatment Response. Biol Psychiatry 2022; 92:533-542. [PMID: 35680431 PMCID: PMC10640874 DOI: 10.1016/j.biopsych.2022.03.020] [Citation(s) in RCA: 10] [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: 11/15/2021] [Revised: 03/02/2022] [Accepted: 03/23/2022] [Indexed: 12/26/2022]
Abstract
BACKGROUND Delivery of effective antidepressant treatment has been hampered by a lack of objective tools for predicting or monitoring treatment response. This study aimed to address this gap by testing novel dynamic resting-state functional network markers of antidepressant response. METHODS The Establishing Moderators and Biosignatures of Antidepressant Response in Clinical Care (EMBARC) study randomized adults with major depressive disorder to 8 weeks of either sertraline or placebo, and depression severity was evaluated longitudinally. Participants completed resting-state neuroimaging pretreatment and again after 1 week of treatment (n = 259 eligible for analyses). Coactivation pattern analyses identified recurrent whole-brain states of spatial coactivation, and computed time spent in each state for each participant was the main dynamic measure. Multilevel modeling estimated the associations between pretreatment network dynamics and sertraline response and between early (pretreatment to 1 week) changes in network dynamics and sertraline response. RESULTS Dynamic network markers of early sertraline response included increased time in network states consistent with canonical default and salience networks, together with decreased time in network states characterized by coactivation of cingulate and ventral limbic or temporal regions. The effect of sertraline on depression recovery was mediated by these dynamic network changes. In contrast, early changes in dynamic functioning of corticolimbic and frontoinsular-default networks were related to patterns of symptom recovery common across treatment groups. CONCLUSIONS Dynamic resting-state markers of early antidepressant response or general recovery may assist development of clinical tools for monitoring and predicting effective intervention.
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Affiliation(s)
- Roselinde H Kaiser
- Department of Psychology and Neuroscience, University of Colorado Boulder, Boulder, Colorado; Institute of Cognitive Science, University of Colorado Boulder, Boulder, Colorado; Renée Crown Wellness Institute, University of Colorado Boulder, Boulder, Colorado.
| | - Henry W Chase
- Department of Psychiatry, University of Pittsburgh, Pittsburgh, Pennsylvania
| | - Mary L Phillips
- Department of Psychiatry, University of Pittsburgh, Pittsburgh, Pennsylvania
| | - Thilo Deckersbach
- Department of Psychiatry, Harvard Medical School and Massachusetts General Hospital, Boston, Massachusetts
| | - Ramin V Parsey
- Department of Psychiatry, Stony Brook University, Stony Brook, New York
| | - Maurizio Fava
- Department of Psychiatry, Harvard Medical School and Massachusetts General Hospital, Boston, Massachusetts
| | - Patrick J McGrath
- Department of Psychiatry, New York State Psychiatric Institute and Columbia University Vagelos College of Physicians and Surgeons, New York, New York
| | - Myrna Weissman
- Department of Psychiatry, New York State Psychiatric Institute and Columbia University Vagelos College of Physicians and Surgeons, New York, New York
| | - Maria A Oquendo
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Melvin G McInnis
- Department of Psychiatry, University of Michigan, Ann Arbor, Michigan
| | - Thomas Carmody
- Department of Psychiatry, University of Texas, Southwestern Medical Center, Dallas, Texas
| | - Crystal M Cooper
- Department of Psychiatry, University of Texas, Southwestern Medical Center, Dallas, Texas
| | - Madhukar H Trivedi
- Department of Psychiatry, University of Texas, Southwestern Medical Center, Dallas, Texas
| | - Diego A Pizzagalli
- Department of Psychiatry, Harvard Medical School and McLean Hospital, Boston, Massachusetts
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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: 16] [Impact Index Per Article: 8.0] [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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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: 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: 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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Wade BSC, Loureiro J, Sahib A, Kubicki A, Joshi SH, Hellemann G, Espinoza RT, Woods RP, Congdon E, Narr KL. Anterior default mode network and posterior insular connectivity is predictive of depressive symptom reduction following serial ketamine infusion. Psychol Med 2022; 52:2376-2386. [PMID: 35578581 PMCID: PMC9527672 DOI: 10.1017/s0033291722001313] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/22/2021] [Revised: 12/20/2021] [Accepted: 04/19/2022] [Indexed: 11/07/2022]
Abstract
BACKGROUND Ketamine is a rapidly-acting antidepressant treatment with robust response rates. Previous studies have reported that serial ketamine therapy modulates resting state functional connectivity in several large-scale networks, though it remains unknown whether variations in brain structure, function, and connectivity impact subsequent treatment success. We used a data-driven approach to determine whether pretreatment multimodal neuroimaging measures predict changes along symptom dimensions of depression following serial ketamine infusion. METHODS Patients with depression (n = 60) received structural, resting state functional, and diffusion MRI scans before treatment. Depressive symptoms were assessed using the 17-item Hamilton Depression Rating Scale (HDRS-17), the Inventory of Depressive Symptomatology (IDS-C), and the Rumination Response Scale (RRS) before and 24 h after patients received four (0.5 mg/kg) infusions of racemic ketamine over 2 weeks. Nineteen unaffected controls were assessed at similar timepoints. Random forest regression models predicted symptom changes using pretreatment multimodal neuroimaging and demographic measures. RESULTS Two HDRS-17 subscales, the HDRS-6 and core mood and anhedonia (CMA) symptoms, and the RRS: reflection (RRSR) scale were predicted significantly with 19, 27, and 1% variance explained, respectively. Increased right medial prefrontal cortex/anterior cingulate and posterior insula (PoI) and lower kurtosis of the superior longitudinal fasciculus predicted reduced HDRS-6 and CMA symptoms following treatment. RRSR change was predicted by global connectivity of the left posterior cingulate, left insula, and right superior parietal lobule. CONCLUSIONS Our findings support that connectivity of the anterior default mode network and PoI may serve as potential biomarkers of antidepressant outcomes for core depressive symptoms.
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Affiliation(s)
- Benjamin S. C. Wade
- Ahmanson-Lovelace Brain Mapping Center, Department of Neurology, University of California, Los Angeles, CA, USA
| | - Joana Loureiro
- Ahmanson-Lovelace Brain Mapping Center, Department of Neurology, University of California, Los Angeles, CA, USA
| | - Ashish Sahib
- Ahmanson-Lovelace Brain Mapping Center, Department of Neurology, University of California, Los Angeles, CA, USA
| | - Antoni Kubicki
- Ahmanson-Lovelace Brain Mapping Center, Department of Neurology, University of California, Los Angeles, CA, USA
| | - Shantanu H. Joshi
- Ahmanson-Lovelace Brain Mapping Center, Department of Neurology, University of California, Los Angeles, CA, USA
| | - Gerhard Hellemann
- Department of Psychiatry and Biobehavioral Sciences, Semel Institute, UCLA, Los Angeles, USA
| | - Randall T. Espinoza
- Department of Psychiatry and Biobehavioral Sciences, Semel Institute, UCLA, Los Angeles, USA
| | - Roger P. Woods
- Ahmanson-Lovelace Brain Mapping Center, Department of Neurology, University of California, Los Angeles, CA, USA
- Department of Psychiatry and Biobehavioral Sciences, Semel Institute, UCLA, Los Angeles, USA
| | - Eliza Congdon
- Department of Psychiatry and Biobehavioral Sciences, Semel Institute, UCLA, Los Angeles, USA
| | - Katherine L. Narr
- Ahmanson-Lovelace Brain Mapping Center, Department of Neurology, University of California, Los Angeles, CA, USA
- Department of Psychiatry and Biobehavioral Sciences, Semel Institute, UCLA, Los Angeles, USA
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Holodny AI. Resting-State Functional MRI Changes in Normal Human Aging. Radiology 2022; 304:633-634. [PMID: 35503020 PMCID: PMC9434810 DOI: 10.1148/radiol.220201] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2022] [Revised: 02/10/2022] [Accepted: 02/15/2022] [Indexed: 11/11/2022]
Affiliation(s)
- Andrei I. Holodny
- From the Department of Radiology, Memorial Sloan Kettering Cancer
Center, 1275 York Ave, New York, NY 10065; Department of Radiology, Weill
Medical College of Cornell University, New York, NY; and Department of
Neuroscience, Weill-Cornell Graduate School of the Medical Sciences, New York,
NY
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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: 10] [Impact Index Per Article: 5.0] [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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Barbosa DAN, Kuijper FM, Duda J, Wang AR, Cartmell SCD, Saluja S, Cunningham T, Shivacharan RS, Bhati MT, Safer DL, Lock JD, Malenka RC, de Oliveira-Souza R, Williams NR, Grossman M, Gee JC, McNab JA, Bohon C, Halpern CH. Aberrant impulse control circuitry in obesity. Mol Psychiatry 2022; 27:3374-3384. [PMID: 35697760 PMCID: PMC9192250 DOI: 10.1038/s41380-022-01640-5] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/26/2022] [Revised: 05/11/2022] [Accepted: 05/18/2022] [Indexed: 01/09/2023]
Abstract
The ventromedial prefrontal cortex (vmPFC) to nucleus accumbens (NAc) circuit has been implicated in impulsive reward-seeking. This disinhibition has been implicated in obesity and often manifests as binge eating, which is associated with worse treatment outcomes and comorbidities. It remains unclear whether the vmPFC-NAc circuit is perturbed in impulsive eaters with obesity. Initially, we analyzed publicly available, high-resolution, normative imaging data to localize where vmPFC structural connections converged within the NAc. These structural connections were found to converge ventromedially in the presumed NAc shell subregion. We then analyzed multimodal clinical and imaging data to test the a priori hypothesis that the vmPFC-NAc shell circuit is linked to obesity in a sample of female participants that regularly engaged in impulsive eating (i.e., binge eating). Functionally, vmPFC-NAc shell resting-state connectivity was inversely related to body mass index (BMI) and decreased in the obese state. Structurally, vmPFC-NAc shell structural connectivity and vmPFC thickness were inversely correlated with BMI; obese binge-prone participants exhibited decreased vmPFC-NAc structural connectivity and vmPFC thickness. Finally, to examine a causal link to binge eating, we directly probed this circuit in one binge-prone obese female using NAc deep brain stimulation in a first-in-human trial. Direct stimulation of the NAc shell subregion guided by local behaviorally relevant electrophysiology was associated with a decrease in number of weekly episodes of uncontrolled eating and decreased BMI. This study unraveled vmPFC-NAc shell circuit aberrations in obesity that can be modulated to restore control over eating behavior in obesity.
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Affiliation(s)
- Daniel A N Barbosa
- Department of Neurosurgery, Pennsylvania Hospital, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Department of Neurosurgery, Stanford University School of Medicine, Stanford, CA, USA
| | - Fiene Marie Kuijper
- Department of Neurosurgery, Pennsylvania Hospital, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Jeffrey Duda
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Allan R Wang
- Department of Neurosurgery, Pennsylvania Hospital, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Samuel C D Cartmell
- Department of Neurosurgery, Pennsylvania Hospital, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Sabir Saluja
- Department of Neurosurgery, Pennsylvania Hospital, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Tricia Cunningham
- Department of Neurosurgery, Stanford University School of Medicine, Stanford, CA, USA
| | - Rajat S Shivacharan
- Department of Neurosurgery, Stanford University School of Medicine, Stanford, CA, USA
| | - Mahendra T Bhati
- Department of Neurosurgery, Stanford University School of Medicine, Stanford, CA, USA
- Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, CA, USA
| | - Debra L Safer
- Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, CA, USA
| | - James D Lock
- Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, CA, USA
| | - Robert C Malenka
- Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, CA, USA
- Nancy Pritzker Laboratory, Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, CA, USA
| | - Ricardo de Oliveira-Souza
- Department of Specialized Medicine, Gaffrée e Guinle University Hospital, The Federal University of the State of Rio de Janeiro, Rio de Janeiro, Brazil
| | - Nolan R Williams
- Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, CA, USA
| | - Murray Grossman
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - James C Gee
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Jennifer A McNab
- Department of Radiology, Stanford University School of Medicine, Stanford, CA, USA
| | - Cara Bohon
- Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, CA, USA
| | - Casey H Halpern
- Department of Neurosurgery, Pennsylvania Hospital, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
- Department of Neurosurgery, Stanford University School of Medicine, Stanford, CA, USA.
- Department of Surgery, Corporal Michael J. Crescenz Veterans Affairs Medical Center, PA, Philadelphia, USA.
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MahmoudianDehkordi S, Bhattacharyya S, Brydges CR, Jia W, Fiehn O, Rush AJ, Dunlop BW, Kaddurah-Daouk R. Gut Microbiome-Linked Metabolites in the Pathobiology of Major Depression With or Without Anxiety—A Role for Bile Acids. Front Neurosci 2022; 16:937906. [PMID: 35937867 PMCID: PMC9350527 DOI: 10.3389/fnins.2022.937906] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2022] [Accepted: 06/24/2022] [Indexed: 12/12/2022] Open
Abstract
Background The gut microbiome may play a role in the pathogenesis of neuropsychiatric diseases including major depressive disorder (MDD). Bile acids (BAs) are steroid acids that are synthesized in the liver from cholesterol and further processed by gut-bacterial enzymes, thus requiring both human and gut microbiome enzymatic processes in their metabolism. BAs participate in a range of important host functions such as lipid transport and metabolism, cellular signaling and regulation of energy homeostasis. BAs have recently been implicated in the pathophysiology of Alzheimer's and several other neuropsychiatric diseases, but the biochemical underpinnings of these gut microbiome-linked metabolites in the pathophysiology of depression and anxiety remains largely unknown. Method Using targeted metabolomics, we profiled primary and secondary BAs in the baseline serum samples of 208 untreated outpatients with MDD. We assessed the relationship of BA concentrations and the severity of depressive and anxiety symptoms as defined by the 17-item Hamilton Depression Rating Scale (HRSD17) and the 14-item Hamilton Anxiety Rating Scale (HRSA-Total), respectively. We also evaluated whether the baseline metabolic profile of BA informs about treatment outcomes. Results The concentration of the primary BA chenodeoxycholic acid (CDCA) was significantly lower at baseline in both severely depressed (log2 fold difference (LFD) = −0.48; p = 0.021) and highly anxious (LFD = −0.43; p = 0.021) participants compared to participants with less severe symptoms. The gut bacteria-derived secondary BAs produced from CDCA such as lithocholic acid (LCA) and several of its metabolites, and their ratios to primary BAs, were significantly higher in the more anxious participants (LFD's range = [0.23, 1.36]; p's range = [6.85E-6, 1.86E-2]). The interaction analysis of HRSD17 and HRSA-Total suggested that the BA concentration differences were more strongly correlated to the symptoms of anxiety than depression. Significant differences in baseline CDCA (LFD = −0.87, p = 0.0009), isoLCA (LFD = −1.08, p = 0.016) and several BA ratios (LFD's range [0.46, 1.66], p's range [0.0003, 0.049]) differentiated treatment failures from remitters. Conclusion In patients with MDD, BA profiles representing changes in gut microbiome compositions are associated with higher levels of anxiety and increased probability of first-line treatment failure. If confirmed, these findings suggest the possibility of developing gut microbiome-directed therapies for MDD characterized by gut dysbiosis.
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Affiliation(s)
- Siamak MahmoudianDehkordi
- Department of Psychiatry and Behavioral Sciences, Duke University School of Medicine, Durham, NC, United States
| | - Sudeepa Bhattacharyya
- Department of Biological Sciences, Arkansas Biosciences Institute, Arkansas State University, Jonesboro, AR, United States
| | - Christopher R. Brydges
- West Coast Metabolomics Center, University of California, Davis, Davis, CA, United States
| | - Wei Jia
- HKBU Phenome Research Centre, School of Chinese Medicine, Hong Kong Baptist University, Kowloon, Hong Kong SAR, China
| | - Oliver Fiehn
- West Coast Metabolomics Center, University of California, Davis, Davis, CA, United States
| | - A. John Rush
- Department of Psychiatry and Behavioral Sciences, Duke University School of Medicine, Durham, NC, United States
- Department of Psychiatry, Health Sciences Center, Texas Tech University, Odessa, Ukraine
- Duke-National University of Singapore, Singapore, Singapore
| | - Boadie W. Dunlop
- Department of Psychiatry and Behavioral Sciences, Emory University School of Medicine, Atlanta, GA, United States
- *Correspondence: Boadie W. Dunlop
| | - Rima Kaddurah-Daouk
- Department of Psychiatry and Behavioral Sciences, Duke University School of Medicine, Durham, NC, United States
- Department of Medicine, Duke University, Durham, NC, United States
- Duke Institute of Brain Sciences, Duke University, Durham, NC, United States
- Rima Kaddurah-Daouk
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Feng HX, Wang MX, Zhao HM, Hou XX, Xu B, Gui Q, Wu GH, Dong XF, Xu QR, Shen MQ, Shi QR, Cheng QZ, Xue SR. Effect of cognitive behavioral intervention on anxiety, depression, and quality of life in patients with epilepsy. Am J Transl Res 2022; 14:5077-5087. [PMID: 35958485 PMCID: PMC9360885] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2022] [Accepted: 06/22/2022] [Indexed: 06/15/2023]
Abstract
OBJECTIVE This study aimed to investigate the effect of cognitive behavioral therapy (CBT) on quality of life, anxiety, and depression in patients with epilepsy. METHODS Each study subject was randomly assigned to a CBT (n=46) or control (n=49) group (1:1 ratio), and the first group underwent an 8-week CBT treatment. Anxiety, depression, and quality of life (QOLIE-31) were assessed at both baseline and endpoint using the Self-Rating Anxiety Scale (SAS), Hamilton Depression Scale (HDMA) and quality of life in Epilepsy-31 (QOLIE-31) scales. The statistical analyses included between-and within-group comparisons of the effects of CBT on these measures and associations with demographic and clinical variables. RESULTS No differences were found between variables at baseline (P>0.05). The repeated-measures analyses found that CBT group had greater improvement in depression score compared to the control group (P<0.05). The analysis of anxiety score showed that compared to the control group, CBT intervention had no statistical significance in the total anxiety population. However, the CBT intervention decreased anxiety in women and Combined-drug group (P<0.05). The CBT group had greater improvement in overall score, medication effect, and seizure worry score than the control group (P<0.05). Stratified analysis found total and medication effects score of CBT intervention group for the combined-drug group were higher than those of the single drug group (P<0.05). CONCLUSION Increases in overall scores, seizure worry, cognitive functioning, and medication effect were better in the CBT group. CBT can improve anxiety, depression, and quality of life in patients with epilepsy. Women and combined-drug patients with epilepsy benefit most from CBT.
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Affiliation(s)
- Hong-Xuan Feng
- Department of Neurology, The First Affiliated Hospital of Soochow UniversitySuzhou 215006, Jiangsu, China
- Department of Neurology, Suzhou Hospital Affiliated to Nanjing Medical University (Suzhou Municipal Hospital)Suzhou 215002, Jiangsu, China
| | - Mei-Xia Wang
- Department of Neurology, Suzhou Hospital Affiliated to Nanjing Medical University (Suzhou Municipal Hospital)Suzhou 215002, Jiangsu, China
| | - Hui-Min Zhao
- Department of Neurology, Suzhou Hospital Affiliated to Nanjing Medical University (Suzhou Municipal Hospital)Suzhou 215002, Jiangsu, China
| | - Xiao-Xia Hou
- Department of Neurology, Suzhou Hospital Affiliated to Nanjing Medical University (Suzhou Municipal Hospital)Suzhou 215002, Jiangsu, China
| | - Bo Xu
- Department of Neurology, Suzhou Hospital Affiliated to Nanjing Medical University (Suzhou Municipal Hospital)Suzhou 215002, Jiangsu, China
| | - Qian Gui
- Department of Neurology, Suzhou Hospital Affiliated to Nanjing Medical University (Suzhou Municipal Hospital)Suzhou 215002, Jiangsu, China
| | - Guan-Hui Wu
- Department of Neurology, Suzhou Hospital Affiliated to Nanjing Medical University (Suzhou Municipal Hospital)Suzhou 215002, Jiangsu, China
| | - Xiao-Feng Dong
- Department of Neurology, Suzhou Hospital Affiliated to Nanjing Medical University (Suzhou Municipal Hospital)Suzhou 215002, Jiangsu, China
| | - Qin-Rong Xu
- Department of Neurology, Suzhou Hospital Affiliated to Nanjing Medical University (Suzhou Municipal Hospital)Suzhou 215002, Jiangsu, China
| | - Ming-Qiang Shen
- Department of Neurology, Suzhou Hospital Affiliated to Nanjing Medical University (Suzhou Municipal Hospital)Suzhou 215002, Jiangsu, China
| | - Qian-Ru Shi
- Department of Neurology, Suzhou Hospital Affiliated to Nanjing Medical University (Suzhou Municipal Hospital)Suzhou 215002, Jiangsu, China
| | - Qing-Zhang Cheng
- Department of Neurology, Suzhou Hospital Affiliated to Nanjing Medical University (Suzhou Municipal Hospital)Suzhou 215002, Jiangsu, China
| | - Shou-Ru Xue
- Department of Neurology, The First Affiliated Hospital of Soochow UniversitySuzhou 215006, Jiangsu, China
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50
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Cakmak JD, Liu L, Poirier SE, Schaefer B, Poolacherla R, Burhan AM, Sabesan P, St. Lawrence K, Théberge J, Hicks JW, Finger E, Palaniyappan L, Anazodo UC. The functional and structural associations of aberrant microglial activity in major depressive disorder. J Psychiatry Neurosci 2022; 47:E197-E208. [PMID: 35654450 PMCID: PMC9343118 DOI: 10.1503/jpn.210124] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/23/2021] [Revised: 12/13/2021] [Accepted: 03/13/2022] [Indexed: 11/03/2022] Open
Abstract
BACKGROUND Major depressive disorder (MDD) is a debilitating mental illness that has been linked to increases in markers of inflammation, as well as to changes in brain functional and structural connectivity, particularly between the insula and the subgenual anterior cingulate cortex (sgACC). In this study, we directly related inflammation and dysconnectivity in treatment-resistant MDD by concurrently measuring the following: microglial activity with [18F]N-2-(fluoroethoxyl)benzyl-N-(4phenoxypyridin-3-yl)acetamide ([18F]FEPPA) positron emission tomography (PET); the severity of MDD; and functional or structural connectivity among insula or sgACC nodes. METHODS Twelve patients with treatment-resistant MDD (8 female, 4 male; mean age ± standard deviation 54.9 ± 4.5 years and 23 healthy controls (11 female, 12 male; 60.3 ± 8.5 years) completed a hybrid [18F]FEPPA PET and MRI acquisition. From these, we extracted relative standardized uptake values for [18F]FEPPA activity and Pearson r-to-z scores representing functional connectivity from our regions of interest. We extracted diffusion tensor imaging metrics from the cingulum bundle, a key white matter bundle in MDD. We performed regressions to relate microglial activity with functional connectivity, structural connectivity and scores on the 17-item Hamilton Depression Rating Scale. RESULTS We found significantly increased [18F]FEPPA uptake in the left sgACC in patients with treatment-resistant MDD compared to healthy controls. Patients with MDD also had a reduction in connectivity between the sgACC and the insula. The [18F]FEPPA uptake in the left sgACC was significantly related to functional connectivity with the insula, and to the structural connectivity of the cingulum bundle. [18F]FEPPA uptake also predicted scores on the Hamilton Depression Rating Scale.Limitations: A relatively small sample size, lack of functional task data and concomitant medication use may have affected our findings. CONCLUSION We present preliminary evidence linking a network-level dysfunction relevant to the pathophysiology of depression and related to increased microglial activity in MDD.
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Affiliation(s)
| | | | | | | | | | | | | | | | | | | | | | - Lena Palaniyappan
- From the Department of Neuroscience, Western University, London, Ont. (Cakmak, Schaefer, Sabesan, Palaniyappan); the Robarts Research Institute, Western University, London, Ont. (Cakmak, Palaniyappan); the Lawson Health Research Institute, London, Ont. (Liu, Poirier, Burhan, St. Lawrence, Théberge, Hicks, Finger, Anazodo); the Department of Medical Biophysics, Western University, London, Ont. (Poirier, Sabesan, St. Lawrence, Théberge, Hicks, Anazodo); the London Health Sciences Centre, London, Ont. (Schaefer, Poolacherla, Palaniyappan); the Department of Psychiatry, Western University, London, Ont. (Burhan, Théberge, Palaniyappan); the Department of Psychiatry, University of Toronto, Toronto, Ont. (Burhan); the Ontario Shores Centre for Mental Health Sciences, Whitby, Ont. (Burhan); the Department of Clinical Neurological Sciences, Western University, London, Ont. (Finger); the Department of Anesthesia and Perioperative Medicine, Western University, London, Ont. (Poolacherla)
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