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Ho CC, Peng SJ, Yu YH, Chu YR, Huang SS, Kuo PH. In perspective of specific symptoms of major depressive disorder: Functional connectivity analysis of electroencephalography and potential biomarkers of treatment response. J Affect Disord 2024:S0165-0327(24)01372-7. [PMID: 39187193 DOI: 10.1016/j.jad.2024.08.139] [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: 10/11/2023] [Revised: 08/01/2024] [Accepted: 08/23/2024] [Indexed: 08/28/2024]
Abstract
BACKGROUND The symptom variability in major depressive disorder (MDD) complicates treatment assessment, necessitating a thorough understanding of MDD symptoms and potential biomarkers. METHODS In this prospective study, we enrolled 54 MDD patients and 39 controls. Over the course of weeks 1, 2, and 4 participants underwent evaluations, with electroencephalograms (EEG) recorded at baseline and week 1. Our investigation considered five previously identified syndromal factors derived from the 17-item Hamilton Depression Rating Scale (17-item HAMD) for assessing depression: core, insomnia, somatic anxiety, psychomotor-insight, and anorexia. We assessed treatment response and EEG characteristics across all syndromal factors and total scores, all of which are based on the 17-item HAMD. To analyze the topology of brain networks, we employed functional connectivity (FC) and a graph theory-based method across various frequency bands. RESULTS The healthy control group had notably higher values in delta band EEG FC compared to the MDD patient group. Similar distinctions were observed between the responder and non-responder patient groups. Further exploration of baseline FC values across distinct syndromal factors revealed significant variations among the core, psychomotor-insight, and anorexia subgroups when using a specific graph theory-based approach, focusing on global efficiency and average clustering coefficient. LIMITATIONS Different antidepressants were included in this study. Therefore, the results should be interpreted with caution. CONCLUSIONS Our findings suggest that delta band EEG FC holds promise as a valuable predictor of antidepressant efficacy. It demonstrates an ability to adapt to individual variations in depressive symptomatology, offering insights into personalized treatment for patients with depression.
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Affiliation(s)
- Chao-Chung Ho
- Department of Medical Education, Taipei Veterans General Hospital, Taipei, Taiwan; Department of Psychiatry, Taichung Veterans General Hospital, Taichung, Taiwan
| | - Syu-Jyun Peng
- Program in Artificial Intelligence in Medicine, College of Medicine, Taipei Medical University, Taipei, Taiwan
| | - Yu-Hsiang Yu
- Division of Neurology, Taipei Veterans General Hospital, Taipei, Taiwan; College of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan
| | - Yeong-Ruey Chu
- Department of Public Health, China Medical University, Taichung, Taiwan
| | - Shiau-Shian Huang
- Department of Medical Education, Taipei Veterans General Hospital, Taipei, Taiwan; College of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan; School of Public Health, National Defense Medical Center, Taipei, Taiwan.
| | - Po-Hsiu Kuo
- Institute of Epidemiology and Preventive Medicine, College of Public Health, National Taiwan University, Taipei, Taiwan; Department of Psychiatry, National Taiwan University Hospital, Taipei, Taiwan
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2
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Arasappan D, Spears A, Shah S, Mayfield RD, Akula N, McMahon FJ, Jabbi M. Brain transcriptomic signatures for mood disorders and suicide phenotypes: an anterior insula and subgenual ACC network postmortem study. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.08.14.606080. [PMID: 39185191 PMCID: PMC11343154 DOI: 10.1101/2024.08.14.606080] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/27/2024]
Abstract
Mood disorders affect over ten percent of humans, but studies dissecting the brain anatomical and molecular neurobiological mechanisms underlying mood (dys)functions have not consistently identified the patterns of pathological changes in relevant brain regions. Recent studies have identified pathological changes in the anterior insula (Ant-Ins) and subgenual anterior cingulate (sgACC) brain network in mood disorders, in line with this network's role in regulating mood/affective feeling states. Here, we applied whole-tissue RNA-sequencing measures of differentially expressed genes (DEGs) in mood disorders versus (vs.) psychiatrically unaffected controls (controls) to identify postmortem molecular pathological markers for mood disorder phenotypes. Using data-driven factor analysis of the postmortem phenotypic variables to determine relevant sources of population variances, we identified DEGs associated with mood disorder-related diagnostic phenotypes by combining gene co-expression, differential gene expression, and pathway-enrichment analyses. We found downregulation/under expression of inflammatory, and protein synthesis-related genes associated with psychiatric morbidity (i.e., all co-occurring mental disorders and suicide outcomes/death by suicide) in Ant-Ins, in contrasts to upregulation of synaptic membrane and ion channel-related genes with increased psychiatric morbidity in sgACC. Our results identified a preponderance of downregulated metabolic, protein synthesis, inflammatory, and synaptic membrane DEGs associated with suicide outcomes in relation to a factor representing longevity in the Ant-Ins and sgACC (AIAC) network. Our study revealed a critical brain network molecular repertoire for mood disorder phenotypes, including suicide outcomes and longevity, and provides a framework for defining dosage-sensitive (i.e., downregulated vs. upregulated) molecular signatures for mood disorder phenotypic complexity and pathological outcomes.
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Affiliation(s)
- Dhivya Arasappan
- Center for Biomedical Research Support, The University of Texas at Austin, Dell Medical School, Austin, Texas, USA
| | - Abigail Spears
- Department of Psychiatry and Behavioral Sciences, The University of Texas at Austin, Dell Medical School, Austin, Texas, USA
| | - Simran Shah
- Department of Psychiatry and Behavioral Sciences, The University of Texas at Austin, Dell Medical School, Austin, Texas, USA
| | - Roy D Mayfield
- Department of Neuroscience and Waggoner Center for Addiction Research, The University of Texas at Austin
| | - Nirmala Akula
- Genetic Basis of Mood & Anxiety Section, Intramural Research Program, NIMH, NIH, Bethesda, MD USA
| | - Francis J McMahon
- Genetic Basis of Mood & Anxiety Section, Intramural Research Program, NIMH, NIH, Bethesda, MD USA
| | - Mbemba Jabbi
- Department of Psychiatry and Behavioral Sciences, The University of Texas at Austin, Dell Medical School, Austin, Texas, USA
- Center for Learning and Memory, The University of Texas at Austin, Dell Medical School, Austin, Texas, USA
- Mulva clinics for the Neurosciences, Dell Medical School, Austin, Texas, USA
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3
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Laubacher C, Imhoff-Smith TP, Klaus DR, Frye CJ, Esnault S, Busse WW, Rosenkranz MA. Salience network resting state functional connectivity during airway inflammation in asthma: A feature of mental health resilience? Brain Behav Immun 2024; 122:9-17. [PMID: 39097203 DOI: 10.1016/j.bbi.2024.07.042] [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: 03/15/2024] [Revised: 07/07/2024] [Accepted: 07/28/2024] [Indexed: 08/05/2024] Open
Abstract
BACKGROUND Inflammation is an established contributor to the pathophysiology of depression and the prevalence of depression in those with chronic inflammatory disease is two- to four-fold higher than the general population. Yet little is known about the neurobiological changes that confer depression or resilience to depression, that occur when episodes of heightened inflammation are frequent or span many years. METHODS We used an innovative combination of longitudinal resting state functional magnetic resonance imaging coupled to segmental bronchial provocation with allergen (SBP-Ag) to assess changes in resting state functional connectivity (rsFC) of the salience network (SN) caused by an acute inflammatory exacerbation in twenty-six adults (15 female) with asthma and varying levels of depressive symptoms. Eosinophils measured in bronchoalveolar lavage fluid and blood provided an index of allergic inflammation and the Beck Depression Inventory provided an index of depressive symptoms. RESULTS We found that in those with the highest symptoms of depression at baseline, SN rsFC declined most from pre- to post-SBP-Ag in the context of a robust eosinophilic response to challenge, but in those with low depressive symptoms SN rsFC was maintained or increased, even in those with the most pronounced SBP-Ag response. CONCLUSIONS Thus, the maintenance of SN rsFC during inflammation may be a biomarker of resilience to depression, perhaps via more effective orchestration of large-scale brain network dynamics by the SN. These findings advance our understanding of the functional role of the SN during inflammation and inform treatment recommendations for those with comorbid inflammatory disease and depression.
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Affiliation(s)
- Claire Laubacher
- Center for Healthy Minds, University of Wisconsin-Madison, 625 W. Washington Ave, Madison, WI 53703, USA
| | - Theodore P Imhoff-Smith
- Department of Radiology, University of Wisconsin School of Medicine and Public Health, Madison, 600 Highland Ave, Madison, WI 53792, USA
| | - Danika R Klaus
- Department of Medicine, University of Wisconsin School of Medicine and Public Health, Madison, 600 Highland Ave, Madison, WI 53792, USA
| | - Corrina J Frye
- Waisman Center, University of Wisconsin-Madison, 1500 Highland Ave, Madison, WI 53705, USA
| | - Stephane Esnault
- Department of Medicine, University of Wisconsin School of Medicine and Public Health, Madison, 600 Highland Ave, Madison, WI 53792, USA; University of Lille, INSERM, CHU Lille, U1286 - INFINITE - Institute for Translational Research in Inflammation, F-59000 Lille, France
| | - William W Busse
- Department of Medicine, University of Wisconsin School of Medicine and Public Health, Madison, 600 Highland Ave, Madison, WI 53792, USA
| | - Melissa A Rosenkranz
- Center for Healthy Minds, University of Wisconsin-Madison, 625 W. Washington Ave, Madison, WI 53703, USA; Department of Psychiatry, University of Wisconsin-Madison, 6001 Research Park Blvd, Madison, WI 53719, USA.
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Briley PM, Webster L, Boutry C, Oh H, Auer DP, Liddle PF, Morriss R. Magnetic resonance imaging connectivity features associated with response to transcranial magnetic stimulation in major depressive disorder. Psychiatry Res Neuroimaging 2024; 342:111846. [PMID: 38908353 DOI: 10.1016/j.pscychresns.2024.111846] [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: 07/24/2023] [Revised: 03/23/2024] [Accepted: 06/11/2024] [Indexed: 06/24/2024]
Abstract
Transcranial magnetic stimulation (TMS) is an FDA-approved neuromodulation treatment for major depressive disorder (MDD), thought to work by altering dysfunctional brain connectivity pathways, or by indirectly modulating the activity of subcortical brain regions. Clinical response to TMS remains highly variable, highlighting the need for baseline predictors of response and for understanding brain changes associated with response. This systematic review examined brain connectivity features, and changes in connectivity features, associated with clinical improvement following TMS in MDD. Forty-one studies met inclusion criteria, including 1097 people with MDD. Most studies delivered one of two types of TMS to left dorsolateral prefrontal cortex and measured connectivity using resting-state functional MRI. The subgenual anterior cingulate cortex was the most well-studied brain region, particularly its connectivity with the TMS target or with the "executive control network" of brain regions. There was marked heterogeneity in findings. There is a need for greater understanding of how cortical TMS modulates connectivity with, and the activity of, subcortical regions, and how these effects change within and across treatment sessions.
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Affiliation(s)
- P M Briley
- Mental Health and Clinical Neurosciences, School of Medicine, University of Nottingham, Nottingham, United Kingdom; Nottingham National Institute for Health and Care Research (NIHR) Biomedical Research Centre, Nottingham, United Kingdom; Institute of Mental Health, Nottinghamshire Healthcare NHS Foundation Trust, Nottingham, United Kingdom.
| | - L Webster
- Nottingham National Institute for Health and Care Research (NIHR) Biomedical Research Centre, Nottingham, United Kingdom; Institute of Mental Health, Nottinghamshire Healthcare NHS Foundation Trust, Nottingham, United Kingdom
| | - C Boutry
- Mental Health and Clinical Neurosciences, School of Medicine, University of Nottingham, Nottingham, United Kingdom; Institute of Mental Health, Nottinghamshire Healthcare NHS Foundation Trust, Nottingham, United Kingdom; NIHR Applied Research Collaboration East Midlands, University of Nottingham, Nottingham, United Kingdom
| | - H Oh
- Mental Health and Clinical Neurosciences, School of Medicine, University of Nottingham, Nottingham, United Kingdom; Nottingham National Institute for Health and Care Research (NIHR) Biomedical Research Centre, Nottingham, United Kingdom; Sir Peter Mansfield Imaging Centre, University of Nottingham, Nottingham, United Kingdom
| | - D P Auer
- Mental Health and Clinical Neurosciences, School of Medicine, University of Nottingham, Nottingham, United Kingdom; Nottingham National Institute for Health and Care Research (NIHR) Biomedical Research Centre, Nottingham, United Kingdom; Sir Peter Mansfield Imaging Centre, University of Nottingham, Nottingham, United Kingdom
| | - P F Liddle
- Mental Health and Clinical Neurosciences, School of Medicine, University of Nottingham, Nottingham, United Kingdom; Institute of Mental Health, Nottinghamshire Healthcare NHS Foundation Trust, Nottingham, United Kingdom
| | - R Morriss
- Mental Health and Clinical Neurosciences, School of Medicine, University of Nottingham, Nottingham, United Kingdom; Nottingham National Institute for Health and Care Research (NIHR) Biomedical Research Centre, Nottingham, United Kingdom; Institute of Mental Health, Nottinghamshire Healthcare NHS Foundation Trust, Nottingham, United Kingdom; NIHR Applied Research Collaboration East Midlands, University of Nottingham, Nottingham, United Kingdom; NIHR Mental Health (MindTech) Health Technology Collaboration, University of Nottingham, Nottingham, United Kingdom
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5
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Shu YP, Zhang Q, Hou YZ, Liang S, Zheng ZL, Li JL, Wu G. Multimodal abnormalities of brain structures in adolescents and young adults with major depressive disorder: An activation likelihood estimation meta-analysis. World J Psychiatry 2024; 14:1106-1117. [PMID: 39050198 PMCID: PMC11262923 DOI: 10.5498/wjp.v14.i7.1106] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/30/2024] [Revised: 05/10/2024] [Accepted: 05/27/2024] [Indexed: 07/12/2024] Open
Abstract
BACKGROUND Major depressive disorder (MDD) in adolescents and young adults contributes significantly to global morbidity, with inconsistent findings on brain structural changes from structural magnetic resonance imaging studies. Activation likelihood estimation (ALE) offers a method to synthesize these diverse findings and identify consistent brain anomalies. AIM To identify consistent brain structural changes in adolescents and young adults with MDD using ALE meta-analysis. METHODS We performed a comprehensive literature search in PubMed, Web of Science, Embase, and Chinese National Knowledge Infrastructure databases for neuroimaging studies on MDD among adolescents and young adults published up to November 19, 2023. Two independent researchers performed the study selection, quality assessment, and data extraction. The ALE technique was employed to synthesize findings on localized brain function anomalies in MDD patients, which was supplemented by sensitivity analyses. RESULTS Twenty-two studies comprising fourteen diffusion tensor imaging (DTI) studies and eight voxel-based morphometry (VBM) studies, and involving 451 MDD patients and 465 healthy controls (HCs) for DTI and 664 MDD patients and 946 HCs for VBM, were included. DTI-based ALE demonstrated significant reductions in fractional anisotropy (FA) values in the right caudate head, right insula, and right lentiform nucleus putamen in adolescents and young adults with MDD compared to HCs, with no regions exhibiting increased FA values. VBM-based ALE did not demonstrate significant alterations in gray matter volume. Sensitivity analyses highlighted consistent findings in the right caudate head (11 of 14 analyses), right insula (10 of 14 analyses), and right lentiform nucleus putamen (11 of 14 analyses). CONCLUSION Structural alterations in the right caudate head, right insula, and right lentiform nucleus putamen in young MDD patients may contribute to its recurrent nature, offering insights for targeted therapies.
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Affiliation(s)
- Yan-Ping Shu
- Department of Psychiatry of Women and Children, The Second People’s Hospital of Guizhou Province, Guiyang 550000, Guizhou Province, China
| | - Qin Zhang
- Department of Radiology, The Second People’s Hospital of Guizhou Province, Guiyang 550000, Guizhou Province, China
- Department of Radiology, Guizhou Provincial People’s Hospital, Guiyang 550000, Guizhou Province, China
| | - Yong-Zhe Hou
- Department of Psychiatry of Women and Children, The Second People’s Hospital of Guizhou Province, Guiyang 550000, Guizhou Province, China
| | - Shuang Liang
- Department of Radiology, The Second People’s Hospital of Guizhou Province, Guiyang 550000, Guizhou Province, China
| | - Zu-Li Zheng
- Department of Psychiatry of Women and Children, The Second People’s Hospital of Guizhou Province, Guiyang 550000, Guizhou Province, China
| | - Jia-Lin Li
- Medical Humanities College, Guizhou Medical University, Guiyang 550000, Guizhou Province, China
| | - Gang Wu
- Department of Psychiatry of Women and Children, The Second People’s Hospital of Guizhou Province, Guiyang 550000, Guizhou Province, China
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6
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Nan J, Grennan G, Ravichandran S, Ramanathan D, Mishra J. Neural activity during inhibitory control predicts suicidal ideation with machine learning. NPP-DIGITAL PSYCHIATRY AND NEUROSCIENCE 2024; 2:10. [PMID: 38988507 PMCID: PMC11230903 DOI: 10.1038/s44277-024-00012-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/05/2024] [Revised: 04/04/2024] [Accepted: 06/04/2024] [Indexed: 07/12/2024]
Abstract
Suicide is a leading cause of death in the US and worldwide. Current strategies for preventing suicide are often focused on the identification and treatment of risk factors, especially suicidal ideation (SI). Hence, developing data-driven biomarkers of SI may be key for suicide prevention and intervention. Prior attempts at biomarker-based prediction models for SI have primarily used expensive neuroimaging technologies, yet clinically scalable and affordable biomarkers remain elusive. Here, we investigated the classification of SI using machine learning (ML) on a dataset of 76 subjects with and without SI(+/-) (n = 38 each), who completed a neuro-cognitive assessment session synchronized with electroencephalography (EEG). SI+/- groups were matched for age, sex, and mental health symptoms of depression and anxiety. EEG was recorded at rest and while subjects engaged in four cognitive tasks of inhibitory control, interference processing, working memory, and emotion bias. We parsed EEG signals in physiologically relevant theta (4-8 Hz), alpha (8-13 Hz), and beta (13-30 Hz) frequencies and performed cortical source imaging on the neural signals. These data served as SI predictors in ML models. The best ML model was obtained for beta band power during the inhibitory control (IC) task, demonstrating high sensitivity (89%), specificity (98%). Shapley explainer plots further showed top neural predictors as feedback-related power in the visual and posterior default mode networks and response-related power in the ventral attention, fronto-parietal, and sensory-motor networks. We further tested the external validity of the model in an independent clinically depressed sample (n = 35, 12 SI+) that engaged in an adaptive test version of the IC task, demonstrating 50% sensitivity and 61% specificity in this sample. Overall, the study suggests a promising, scalable EEG-based biomarker approach to predict SI that may serve as a target for risk identification and intervention.
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Affiliation(s)
- Jason Nan
- Neural Engineering and Translation Labs, University of California, San Diego, La Jolla, CA USA
- Department of Bioengineering, University of California, San Diego, La Jolla, CA USA
| | - Gillian Grennan
- Neural Engineering and Translation Labs, University of California, San Diego, La Jolla, CA USA
| | - Soumya Ravichandran
- Neural Engineering and Translation Labs, University of California, San Diego, La Jolla, CA USA
| | - Dhakshin Ramanathan
- Neural Engineering and Translation Labs, University of California, San Diego, La Jolla, CA USA
- Department of Psychiatry, University of California, San Diego, La Jolla, CA USA
- Department of Mental Health, VA San Diego Medical Center, San Diego, CA USA
- Center of Excellence for Stress and Mental Health, VA San Diego Medical Center, San Diego, CA USA
| | - Jyoti Mishra
- Neural Engineering and Translation Labs, University of California, San Diego, La Jolla, CA USA
- Department of Psychiatry, University of California, San Diego, La Jolla, CA USA
- Center of Excellence for Stress and Mental Health, VA San Diego Medical Center, San Diego, CA USA
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7
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Prompiengchai S, Dunlop K. Breakthroughs and challenges for generating brain network-based biomarkers of treatment response in depression. Neuropsychopharmacology 2024:10.1038/s41386-024-01907-1. [PMID: 38951585 DOI: 10.1038/s41386-024-01907-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/29/2024] [Revised: 05/17/2024] [Accepted: 06/13/2024] [Indexed: 07/03/2024]
Abstract
Treatment outcomes widely vary for individuals diagnosed with major depressive disorder, implicating a need for deeper understanding of the biological mechanisms conferring a greater likelihood of response to a particular treatment. Our improved understanding of intrinsic brain networks underlying depression psychopathology via magnetic resonance imaging and other neuroimaging modalities has helped reveal novel and potentially clinically meaningful biological markers of response. And while we have made considerable progress in identifying such biomarkers over the last decade, particularly with larger, multisite trials, there are significant methodological and practical obstacles that need to be overcome to translate these markers into the clinic. The aim of this review is to review current literature on brain network structural and functional biomarkers of treatment response or selection in depression, with a specific focus on recent large, multisite trials reporting predictive accuracy of candidate biomarkers. Regarding pharmaco- and psychotherapy, we discuss candidate biomarkers, reporting that while we have identified candidate biomarkers of response to a single intervention, we need more trials that distinguish biomarkers between first-line treatments. Further, we discuss the ways prognostic neuroimaging may help to improve treatment outcomes to neuromodulation-based therapies, such as transcranial magnetic stimulation and deep brain stimulation. Lastly, we highlight obstacles and technical developments that may help to address the knowledge gaps in this area of research. Ultimately, integrating neuroimaging-derived biomarkers into clinical practice holds promise for enhancing treatment outcomes and advancing precision psychiatry strategies for depression management. By elucidating the neural predictors of treatment response and selection, we can move towards more individualized and effective depression interventions, ultimately improving patient outcomes and quality of life.
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Affiliation(s)
| | - Katharine Dunlop
- Centre for Depression and Suicide Studies, Unity Health Toronto, Toronto, ON, Canada.
- Keenan Research Centre for Biomedical Science, Unity Health Toronto, Toronto, ON, Canada.
- Department of Psychiatry and Institute of Medical Science, University of Toronto, Toronto, ON, Canada.
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Mathew J, Adhia DB, Hall M, De Ridder D, Mani R. EEG-Based Cortical Alterations in Individuals With Chronic Knee Pain Secondary to Osteoarthritis: A Cross-sectional Investigation. THE JOURNAL OF PAIN 2024; 25:104429. [PMID: 37989404 DOI: 10.1016/j.jpain.2023.11.012] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/07/2023] [Revised: 11/05/2023] [Accepted: 11/10/2023] [Indexed: 11/23/2023]
Abstract
Chronic painful knee osteoarthritis (OA) is a disabling physical health condition. Alterations in brain responses to arthritic changes in the knee may explain persistent pain. This study investigated source localized, resting-state electroencephalography activity and functional connectivity in people with knee OA, compared to healthy controls. Adults aged 44 to 85 years with knee OA (n = 37) and healthy control (n = 39) were recruited. Resting-state electroencephalography was collected for 10 minutes and decomposed into infraslow frequency (ISF) to gamma frequency bands. Standard low-resolution electromagnetic brain tomography statistical nonparametric maps were conducted, current densities of regions of interest were compared between groups and correlation analyses were performed between electroencephalography (EEG) measures and clinical pain and functional outcomes in the knee OA group. Standard low-resolution electromagnetic brain tomography nonparametric maps revealed higher (P = .006) gamma band activity over the right insula (RIns) in the knee OA group. A significant (P < .0001) reduction in ISF band activity at the pregenual anterior cingulate cortex, whereas higher theta, alpha, beta, and gamma band activity at the dorsal anterior cingulate cortex, pregenual anterior cingulate cortex, the somatosensory cortex, and RIns in the knee OA group were identified. ISF activity of the dorsal anterior cingulate cortex was positively correlated with pain measures and psychological distress scores. Theta and alpha activity of RIns were negatively correlated with pain interference. In conclusion, aberrations in infraslow and faster frequency EEG oscillations at sensory discriminative, motivational-affective, and descending inhibitory cortical regions were demonstrated in people with chronic painful knee OA. Moreover, EEG oscillations were correlated with pain and functional outcome measures. PERSPECTIVE: This study confirms alterations in the rsEEG oscillations and its relationship with pain experience in people with knee OA. The study provides potential cortical targets and the EEG frequency bands for neuromodulatory interventions for managing chronic pain experience in knee OA.
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Affiliation(s)
- Jerin Mathew
- Centre for Health, Activity, and Rehabilitation Research, School of Physiotherapy, University of Otago, New Zealand; Department of Anatomy, School of Biomedical Sciences, University of Otago, New Zealand; Pain@Otago Research Theme, University of Otago, New Zealand
| | - Divya B Adhia
- Pain@Otago Research Theme, University of Otago, New Zealand; Division of Neurosurgery, Department of Surgical Sciences, Dunedin School of Medicine, University of Otago, New Zealand
| | - Matthew Hall
- Division of Neurosurgery, Department of Surgical Sciences, Dunedin School of Medicine, University of Otago, New Zealand
| | - Dirk De Ridder
- Pain@Otago Research Theme, University of Otago, New Zealand; Division of Neurosurgery, Department of Surgical Sciences, Dunedin School of Medicine, University of Otago, New Zealand
| | - Ramakrishnan Mani
- Centre for Health, Activity, and Rehabilitation Research, School of Physiotherapy, University of Otago, New Zealand; Pain@Otago Research Theme, University of Otago, New Zealand
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9
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Hong C, Ding C, Chen Y, Cao S, Hou Y, Hu W, Yang D. Mindfulness-based intervention reduce interference of negative stimuli to working memory in individuals with subclinical depression: A randomized controlled fMRI study. Int J Clin Health Psychol 2024; 24:100459. [PMID: 38660392 PMCID: PMC11039314 DOI: 10.1016/j.ijchp.2024.100459] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2023] [Accepted: 04/04/2024] [Indexed: 04/26/2024] Open
Abstract
Background Individuals with subclinical depression are prone to major depression and experience emotional responses and attentional biases to negative stimuli. Method In a randomized controlled study (N = 42) using functional magnetic resonance imaging (fMRI), we examined the neurocognitive mechanisms behind mindfulness-based cognitive therapy (MBCT) combining loving-kindness meditation (LKM) on a group with subclinical depression compared with the relaxation group across emotional face n-back (EFNBACK) tasks and resting state. We also collected behavioral and self-reported data to confirm neurocognitive results. Results During EFNBACK, the MBCT+LKM group showed greater activation in the left lingual gyrus and right inferior lateral occipital cortex. During rest, the MBCT+LKM group demonstrated increased connectivity of the anterior cingulate cortex and right inferior lateral occipital cortex, right anterior insula and left precentral gyrus. From amplitude of low frequency fluctuation (ALFF) data, activity in brain regions associated with cognitive control decreased and activity in brain regions associated with sensorimotor increased. Conclusion These results suggest that MBCT+LKM alleviate depression for subclinical individuals through improving executive function when they face negative stimuli.
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Affiliation(s)
- Chengjin Hong
- Faculty of Psychology, Southwest University, Chongqing 400715, China
- Key Lab of Cognition and Personality (Ministry of Education), Southwest University, Chongqing 400715, China
| | - Cody Ding
- Faculty of Psychology, Southwest University, Chongqing 400715, China
- Key Lab of Cognition and Personality (Ministry of Education), Southwest University, Chongqing 400715, China
- Education Science & Professional Programs, University of Missouri-St. Louis, United States
| | - Yiwen Chen
- Faculty of International Business Management, University of Nottingham, United Kingdom
| | - Shiyue Cao
- Faculty of Psychology, Southwest University, Chongqing 400715, China
- Key Lab of Cognition and Personality (Ministry of Education), Southwest University, Chongqing 400715, China
| | - Yi Hou
- Faculty of Psychology, Southwest University, Chongqing 400715, China
- Key Lab of Cognition and Personality (Ministry of Education), Southwest University, Chongqing 400715, China
| | - Weiyu Hu
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, 100875, China
| | - Dong Yang
- Faculty of Psychology, Southwest University, Chongqing 400715, China
- Key Lab of Cognition and Personality (Ministry of Education), Southwest University, Chongqing 400715, China
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10
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Wang X, Xue L, Shao J, Dai Z, Hua L, Yan R, Yao Z, Lu Q. Distinct MRI-based functional and structural connectivity for antidepressant response prediction in major depressive disorder. Clin Neurophysiol 2024; 160:19-27. [PMID: 38367310 DOI: 10.1016/j.clinph.2024.02.004] [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: 05/31/2023] [Revised: 12/28/2023] [Accepted: 02/06/2024] [Indexed: 02/19/2024]
Abstract
OBJECTIVE Emerging studies have identified treatment-related connectome predictors in major depressive disorder (MDD). However, quantifying treatment-responsive patterns in structural connectivity (SC) and functional connectivity (FC) simultaneously remains underexplored. We aimed to evaluate whether spatial distributions of FC and SC associated treatment responses are shared or unique. METHODS Diffusion tensor imaging and resting-state functional magnetic resonance imaging were collected from 210 patients with MDD at baseline. We separately developed connectome-based prediction models (CPM) to predict reduction of depressive severity after 6-week monotherapy based on structural, functional, and combined connectomes, then validated them on the external dataset. We identified the predictive SC and FC from CPM with high occurrence frequencies during the cross-validation. RESULTS Structural connectomes (r = 0.2857, p < 0.0001), functional connectomes (r = 0.2057, p = 0.0025), and their combined CPM (r = 0.4, p < 0.0001) can significantly predict a reduction of depressive severity. We didn't find shared connectivity between predictive FC and SC. Specifically, the most predictive FC stemmed from the default mode network, while predictive SC was mainly characterized by within-network SC of fronto-limbic networks. CONCLUSIONS These distinct patterns suggest that SC and FC capture unique connectivity concerning the antidepressant response. SIGNIFICANCE Our findings provide comprehensive insights into the neurophysiology of antidepressants response.
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Affiliation(s)
- Xinyi Wang
- School of Biological Sciences & Medical Engineering, Southeast University, Nanjing 210096, China; Child Development and Learning Science, Key Laboratory of Ministry of Education, Nanjing 210096, China
| | - Li Xue
- School of Biological Sciences & Medical Engineering, Southeast University, Nanjing 210096, China; Child Development and Learning Science, Key Laboratory of Ministry of Education, Nanjing 210096, China
| | - Junneng Shao
- School of Biological Sciences & Medical Engineering, Southeast University, Nanjing 210096, China; Child Development and Learning Science, Key Laboratory of Ministry of Education, Nanjing 210096, China
| | - Zhongpeng Dai
- School of Biological Sciences & Medical Engineering, Southeast University, Nanjing 210096, China; Child Development and Learning Science, Key Laboratory of Ministry of Education, Nanjing 210096, China
| | - Lingling Hua
- Department of Psychiatry, the Affiliated Brain Hospital of Nanjing Medical University, Nanjing 210029, China
| | - Rui Yan
- Department of Psychiatry, the Affiliated Brain Hospital of Nanjing Medical University, Nanjing 210029, China
| | - Zhijian Yao
- Department of Psychiatry, the Affiliated Brain Hospital of Nanjing Medical University, Nanjing 210029, China; Nanjing Brain Hospital, Medical School of Nanjing University, Nanjing 210093, China.
| | - Qing Lu
- School of Biological Sciences & Medical Engineering, Southeast University, Nanjing 210096, China; Child Development and Learning Science, Key Laboratory of Ministry of Education, Nanjing 210096, China.
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11
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Wang J, Wu DD, DeLorenzo C, Yang J. Examining factors related to low performance of predicting remission in participants with major depressive disorder using neuroimaging data and other clinical features. PLoS One 2024; 19:e0299625. [PMID: 38547128 PMCID: PMC10977765 DOI: 10.1371/journal.pone.0299625] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2023] [Accepted: 02/13/2024] [Indexed: 04/02/2024] Open
Abstract
Major depressive disorder (MDD), a prevalent mental health issue, affects more than 8% of the US population, and almost 17% in the young group of 18-25 years old. Since Covid-19, its prevalence has become even more significant. However, the remission (being free of depression) rates of first-line antidepressant treatments on MDD are only about 30%. To improve treatment outcomes, researchers have built various predictive models for treatment responses and yet none of them have been adopted in clinical use. One reason is that most predictive models are based on data from subjective questionnaires, which are less reliable. Neuroimaging data are promising objective prognostic factors, but they are expensive to obtain and hence predictive models using neuroimaging data are limited and such studies were usually in small scale (N<100). In this paper, we proposed an advanced machine learning (ML) pipeline for small training dataset with large number of features. We implemented multiple imputation for missing data and repeated K-fold cross validation (CV) to robustly estimate predictive performances. Different feature selection methods and stacking methods using 6 general ML models including random forest, gradient boosting decision tree, XGBoost, penalized logistic regression, support vector machine (SVM), and neural network were examined to evaluate the model performances. All predictive models were compared using model performance metrics such as accuracy, balanced accuracy, area under ROC curve (AUC), sensitivity and specificity. Our proposed ML pipeline was applied to a training dataset and obtained an accuracy and AUC above 0.80. But such high performance failed while applying our ML pipeline using an external validation dataset from the EMBARC study which is a multi-center study. We further examined the possible reasons especially the site heterogeneity issue.
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Affiliation(s)
- Junying Wang
- Department of Applied Mathematics and Statistics, Stony Brook University, New York, New York, United states of America
| | - David D. Wu
- School of Engineering, University of Michigan, Ann Arbor, Michigan, United States of America
| | - Christine DeLorenzo
- Department of Psychiatry and Behavioral Health, Stony Brook University, Stony Brook, New York, United States of America
- Department of Biomedical Engineering, Stony Brook University, Stony Brook, New York, United States of America
| | - Jie Yang
- Department of Family, Population & Preventive Medicine, Stony Brook University, Stony Brook, New York, United States of America
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12
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Ramasubbu R, Brown EC, Mouches P, Moore JA, Clark DL, Molnar CP, Kiss ZHT, Forkert ND. Multimodal imaging measures in the prediction of clinical response to deep brain stimulation for refractory depression: A machine learning approach. World J Biol Psychiatry 2024; 25:175-187. [PMID: 38185882 DOI: 10.1080/15622975.2023.2300795] [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: 09/07/2023] [Accepted: 12/27/2023] [Indexed: 01/09/2024]
Abstract
OBJECTIVES This study compared machine learning models using unimodal imaging measures and combined multi-modal imaging measures for deep brain stimulation (DBS) outcome prediction in treatment resistant depression (TRD). METHODS Regional brain glucose metabolism (CMRGlu), cerebral blood flow (CBF), and grey matter volume (GMV) were measured at baseline using 18F-fluorodeoxy glucose (18F-FDG) positron emission tomography (PET), arterial spin labelling (ASL) magnetic resonance imaging (MRI), and T1-weighted MRI, respectively, in 19 patients with TRD receiving subcallosal cingulate (SCC)-DBS. Responders (n = 9) were defined by a 50% reduction in HAMD-17 at 6 months from the baseline. Using an atlas-based approach, values of each measure were determined for pre-selected brain regions. OneR feature selection algorithm and the naïve Bayes model was used for classification. Leave-out-one cross validation was used for classifier evaluation. RESULTS The performance accuracy of the CMRGlu classification model (84%) was greater than CBF (74%) or GMV (74%) models. The classification model using the three image modalities together led to a similar accuracy (84%0 compared to the CMRGlu classification model. CONCLUSIONS CMRGlu imaging measures may be useful for the development of multivariate prediction models for SCC-DBS studies for TRD. The future of multivariate methods for multimodal imaging may rest on the selection of complementing features and the developing better models.Clinical Trial Registration: ClinicalTrials.gov (#NCT01983904).
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Affiliation(s)
- Rajamannar Ramasubbu
- Department of Psychiatry, Clinical Neurosciences, Mathison Centre for Mental Health Research & Education, Calgary, Alberta, Canada
- Hotchkiss Brain Institute Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada
| | - Elliot C Brown
- School of Health and Care Management, Arden University, Berlin, Germany
| | - Pauline Mouches
- Department of Radiology, Clinical Neurosciences, Hotchkiss Brain Institute, Cumming school of medicine, University of Calgary, Calgary, Alberta, Canada
| | - Jasmine A Moore
- Department of Radiology, Clinical Neurosciences, Hotchkiss Brain Institute, Cumming school of medicine, University of Calgary, Calgary, Alberta, Canada
| | - Darren L Clark
- Department of Psychiatry, Clinical Neurosciences, Mathison Centre for Mental Health Research & Education, Calgary, Alberta, Canada
- Hotchkiss Brain Institute Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada
| | - Christine P Molnar
- Department of Radiology, Cumming school of medicine, University of Calgary, Calgary, Alberta, Canada
| | - Zelma H T Kiss
- Department of Psychiatry, Clinical Neurosciences, Mathison Centre for Mental Health Research & Education, Calgary, Alberta, Canada
- Hotchkiss Brain Institute Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada
| | - Nils D Forkert
- Department of Radiology, Clinical Neurosciences, Hotchkiss Brain Institute, Cumming school of medicine, University of Calgary, Calgary, Alberta, Canada
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13
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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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14
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van Rooij SJH, Arulpragasam AR, McDonald WM, Philip NS. Accelerated TMS - moving quickly into the future of depression treatment. Neuropsychopharmacology 2024; 49:128-137. [PMID: 37217771 PMCID: PMC10700378 DOI: 10.1038/s41386-023-01599-z] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/28/2023] [Revised: 04/20/2023] [Accepted: 04/22/2023] [Indexed: 05/24/2023]
Abstract
Accelerated TMS is an emerging application of Transcranial Magnetic Stimulation (TMS) aimed to reduce treatment length and improve response time. Extant literature generally shows similar efficacy and safety profiles compared to the FDA-cleared protocols for TMS to treat major depressive disorder (MDD), yet accelerated TMS research remains at a very early stage in development. The few applied protocols have not been standardized and vary significantly across a set of core elements. In this review, we consider nine elements that include treatment parameters (i.e., frequency and inter-stimulation interval), cumulative exposure (i.e., number of treatment days, sessions per day, and pulses per session), individualized parameters (i.e., treatment target and dose), and brain state (i.e., context and concurrent treatments). Precisely which of these elements is critical and what parameters are most optimal for the treatment of MDD remains unclear. Other important considerations for accelerated TMS include durability of effect, safety profiles as doses increase over time, the possibility and advantage of individualized functional neuronavigation, use of biological readouts, and accessibility for patients most in need of the treatment. Overall, accelerated TMS appears to hold promise to reduce treatment time and achieve rapid reduction in depressive symptoms, but at this time significant work remains to be done. Rigorous clinical trials combining clinical outcomes and neuroscientific measures such as electroencephalogram, magnetic resonance imaging and e-field modeling are needed to define the future of accelerated TMS for MDD.
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Affiliation(s)
- Sanne J H van Rooij
- Emory University School of Medicine, Department of Psychiatry and Behavioral Sciences, Atlanta, GA, USA
| | - Amanda R Arulpragasam
- Alpert Medical School of Brown University, Department of Psychiatry and Human Behavior, Providence, RI, USA
- VA RR&D Center for Neurorestoration and Neurotechnology, VA Providence Healthcare System, Providence, RI, USA
| | - William M McDonald
- Emory University School of Medicine, Department of Psychiatry and Behavioral Sciences, Atlanta, GA, USA
| | - Noah S Philip
- Alpert Medical School of Brown University, Department of Psychiatry and Human Behavior, Providence, RI, USA.
- VA RR&D Center for Neurorestoration and Neurotechnology, VA Providence Healthcare System, Providence, RI, USA.
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15
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Tröger A, Miguel C, Ciharova M, de Ponti N, Durman G, Cuijpers P, Karyotaki E. Baseline depression severity as moderator on depression outcomes in psychotherapy and pharmacotherapy. J Affect Disord 2024; 344:86-99. [PMID: 37820960 DOI: 10.1016/j.jad.2023.10.047] [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: 06/20/2023] [Revised: 10/02/2023] [Accepted: 10/08/2023] [Indexed: 10/13/2023]
Abstract
BACKGROUND Evidence-based treatments for adult depression include psychotherapy and pharmacotherapy, yet little is known about how baseline depression severity moderates treatment outcome. OBJECTIVES We aimed to compare the effects of psychotherapy and pharmacotherapy for adult depression and to examine the association between baseline depression severity and treatment outcome, converting multiple baseline depression measures into the scores of the Beck Depression Inventory, second edition (BDI-II). METHODS We conducted systematic searches in bibliographical databases up to September 2022 to identify randomized controlled trials (RCTs) in which psychotherapy was compared with pharmacotherapy in the treatment of adult depression. Various meta-regressions using the baseline depression severity as predictor of the relative effects of psychotherapy and pharmacotherapy were performed. RESULTS We identified 65 RCTs including 7250 participants for the meta-analyses and 56 RCTs including 5548 participants for the meta-regression. We found no significant difference between psychotherapy and pharmacotherapy (g = -0.08, 95 % CI: -0.2 to 0.04, p = 0.193) and baseline depression severity was not significantly associated with the relative effects of psychotherapy and pharmacotherapy (B = 0.0032, SE = 0.0096, p = 0.74). Results were similar in several sensitivity analyses. LIMITATIONS Limitations included the low quality of the included studies, and the omission of long-term effects and within-study variability. CONCLUSIONS We found no indication for a moderation effect of baseline depression severity on the relative effects of psychotherapy and pharmacotherapy. Thus, other factors such as availability and patients' preference must be considered when deciding for treatment options.
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Affiliation(s)
- Anna Tröger
- Department of Clinical, Neuro and Developmental Psychology, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands; Department of Psychological Medicine, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK; Department of Psychiatry and Psychotherapy, University Hospital Carl Gustav Carus, Technische Universitat Dresden, Dresden, Germany.
| | - Clara Miguel
- Department of Clinical, Neuro and Developmental Psychology, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
| | - Marketa Ciharova
- Department of Clinical, Neuro and Developmental Psychology, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
| | - Nino de Ponti
- Department of Clinical, Neuro and Developmental Psychology, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
| | - Güldehan Durman
- Department of Clinical, Neuro and Developmental Psychology, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
| | - Pim Cuijpers
- Department of Clinical, Neuro and Developmental Psychology, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
| | - Eirini Karyotaki
- Department of Clinical, Neuro and Developmental Psychology, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
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16
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Lu F, Cui Q, Zou Y, Guo Y, Luo W, Yu Y, Gao J, Cai X, Fu L, Yuan S, Huang J, Zhang Y, Xie J, Sheng W, Tang Q, Gao Q, He Z, Chen H. Effects of rTMS Intervention on Functional Neuroimaging Activities in Adolescents with Major Depressive Disorder Measured Using Resting-State fMRI. Bioengineering (Basel) 2023; 10:1374. [PMID: 38135965 PMCID: PMC10740826 DOI: 10.3390/bioengineering10121374] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2023] [Revised: 11/10/2023] [Accepted: 11/22/2023] [Indexed: 12/24/2023] Open
Abstract
Repetitive transcranial magnetic stimulation (rTMS) to the left dorsolateral prefrontal cortex (L-DLPFC) is commonly used for the clinical treatment of major depressive disorder (MDD). The neuroimaging biomarkers and mechanisms of rTMS are still not completely understood. This study aimed to explore the functional neuroimaging changes induced by rTMS in adolescents with MDD. A total of ten sessions of rTMS were administrated to the L-DLPFC in thirteen adolescents with MDD once a day for two weeks. All of them were scanned using resting-state functional magnetic resonance imaging at baseline and after rTMS treatment. The regional homogeneity (ReHo), amplitude of low-frequency fluctuation (ALFF), and the subgenual anterior cingulate cortex (sgACC)-based functional connectivity (FC) were computed as neuroimaging indicators. The correlation between changes in the sgACC-based FC and the improvement in depressive symptoms was also analyzed. After rTMS treatment, ReHo and ALFF were significantly increased in the L-DLPFC, the left medial prefrontal cortex, bilateral medial orbital frontal cortex, and the left ACC. ReHo and ALFF decreased mainly in the left middle occipital gyrus, the right middle cingulate cortex (MCC), bilateral calcarine, the left cuneus, and the left superior occipital gyrus. Furthermore, the FCs between the left sgACC and the L-DLPFC, the right IFGoper, the left MCC, the left precuneus, bilateral post-central gyrus, the left supplementary motor area, and the left superior marginal gyrus were enhanced after rTMS treatment. Moreover, the changes in the left sgACC-left MCC FC were associated with an improvement in depressive symptoms in early improvers. This study showed that rTMS treatment in adolescents with MDD causes changes in brain activities and sgACC-based FC, which may provide basic neural biomarkers for rTMS clinical trials.
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Affiliation(s)
- Fengmei Lu
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, China; (F.L.); (Y.Z.); (Y.G.); (W.L.); (Y.Y.); (X.C.); (L.F.); (S.Y.); (J.H.); (Y.Z.); (J.X.); (W.S.); (Q.T.)
| | - Qian Cui
- School of Public Affairs and Administration, University of Electronic Science and Technology of China, Chengdu 611731, China
| | - Yang Zou
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, China; (F.L.); (Y.Z.); (Y.G.); (W.L.); (Y.Y.); (X.C.); (L.F.); (S.Y.); (J.H.); (Y.Z.); (J.X.); (W.S.); (Q.T.)
| | - Yuanhong Guo
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, China; (F.L.); (Y.Z.); (Y.G.); (W.L.); (Y.Y.); (X.C.); (L.F.); (S.Y.); (J.H.); (Y.Z.); (J.X.); (W.S.); (Q.T.)
| | - Wei Luo
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, China; (F.L.); (Y.Z.); (Y.G.); (W.L.); (Y.Y.); (X.C.); (L.F.); (S.Y.); (J.H.); (Y.Z.); (J.X.); (W.S.); (Q.T.)
| | - Yue Yu
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, China; (F.L.); (Y.Z.); (Y.G.); (W.L.); (Y.Y.); (X.C.); (L.F.); (S.Y.); (J.H.); (Y.Z.); (J.X.); (W.S.); (Q.T.)
| | - Jingjing Gao
- School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China;
| | - Xiao Cai
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, China; (F.L.); (Y.Z.); (Y.G.); (W.L.); (Y.Y.); (X.C.); (L.F.); (S.Y.); (J.H.); (Y.Z.); (J.X.); (W.S.); (Q.T.)
| | - Linna Fu
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, China; (F.L.); (Y.Z.); (Y.G.); (W.L.); (Y.Y.); (X.C.); (L.F.); (S.Y.); (J.H.); (Y.Z.); (J.X.); (W.S.); (Q.T.)
| | - Shuai Yuan
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, China; (F.L.); (Y.Z.); (Y.G.); (W.L.); (Y.Y.); (X.C.); (L.F.); (S.Y.); (J.H.); (Y.Z.); (J.X.); (W.S.); (Q.T.)
| | - Juan Huang
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, China; (F.L.); (Y.Z.); (Y.G.); (W.L.); (Y.Y.); (X.C.); (L.F.); (S.Y.); (J.H.); (Y.Z.); (J.X.); (W.S.); (Q.T.)
| | - Yajun Zhang
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, China; (F.L.); (Y.Z.); (Y.G.); (W.L.); (Y.Y.); (X.C.); (L.F.); (S.Y.); (J.H.); (Y.Z.); (J.X.); (W.S.); (Q.T.)
| | - Jing Xie
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, China; (F.L.); (Y.Z.); (Y.G.); (W.L.); (Y.Y.); (X.C.); (L.F.); (S.Y.); (J.H.); (Y.Z.); (J.X.); (W.S.); (Q.T.)
| | - 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 611731, China; (F.L.); (Y.Z.); (Y.G.); (W.L.); (Y.Y.); (X.C.); (L.F.); (S.Y.); (J.H.); (Y.Z.); (J.X.); (W.S.); (Q.T.)
| | - Qin Tang
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, China; (F.L.); (Y.Z.); (Y.G.); (W.L.); (Y.Y.); (X.C.); (L.F.); (S.Y.); (J.H.); (Y.Z.); (J.X.); (W.S.); (Q.T.)
| | - Qing Gao
- School of Mathematical Sciences, University of Electronic Science and Technology of China, Chengdu 611731, China
| | - Zongling He
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, China; (F.L.); (Y.Z.); (Y.G.); (W.L.); (Y.Y.); (X.C.); (L.F.); (S.Y.); (J.H.); (Y.Z.); (J.X.); (W.S.); (Q.T.)
| | - Huafu Chen
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, China; (F.L.); (Y.Z.); (Y.G.); (W.L.); (Y.Y.); (X.C.); (L.F.); (S.Y.); (J.H.); (Y.Z.); (J.X.); (W.S.); (Q.T.)
- MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu 611731, China
- High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu 611731, China
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17
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Lou J, Liu K, Wen J, He Y, Sun Y, Tian X, Hu K, Deng Y, Liu B, Wen G. Deciphering the neural mechanisms of miR-134 in major depressive disorder with population-based and person-specific imaging transcriptomic techniques. Psychiatry Res 2023; 329:115551. [PMID: 37871377 DOI: 10.1016/j.psychres.2023.115551] [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: 08/26/2023] [Revised: 10/07/2023] [Accepted: 10/17/2023] [Indexed: 10/25/2023]
Abstract
MiR-134 has emerged as a potential molecular biomarker for the detection and management of major depressive disorder (MDD). Nevertheless, the specific effects of miR-134 as a regulatory element on brain function and its implications for the clinical presentation of MDD are not yet fully understood. In order to investigate the potential neural mechanisms that contribute to the relationship between miR-134 and MDD, we employed a parallel two-stage cross-scale multi-omics approach. This involved utilizing the anterior cingulate cortex (ACC) functional connectivity as a means to connect microscopic molecular structures with macroscopic brain function in two separate cohorts: the MDD-I dataset (56 MDD patients and 51 healthy controls) and the MDD-II dataset (57 MDD patients and 52 healthy controls). We found a stable ACC functional dysconnectivity pattern of MDD and established the hierarchical cross-scale association from molecular organizations of miR-134 target genes to macroscopic brain functional dysconnectivity and associated behavior, as revealed by population-based analysis. Additionally, our person-specific imaging transcriptomic study revealed that individual exosomal miR-134 expression levels impact on individual clinical symptoms of MDD by modulating ACC-related functional dysconnectivity. Together, our findings provide compelling evidence of the correlation between miR-134 and depression across multi scales within the gene-brain-behavior context.
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Affiliation(s)
- Jing Lou
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China
| | - Kai Liu
- School of Medical Imaging, Xuzhou Medical University, Xuzhou 221006,China; Department of Radiology, Affiliated Hospital of Xuzhou Medical University, Xuzhou Medical University, Xuzhou 221004,China
| | - Junyan Wen
- Department of Imaging, Nanfang Hospital, Southern Medical University, Guangzhou 510515, China
| | - Yini He
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China
| | - Yuqing Sun
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China
| | - Xiaohan Tian
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China
| | - Ke Hu
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China; School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049,China
| | - Yanjia Deng
- School of Medical Imaging, Xuzhou Medical University, Xuzhou 221006,China.
| | - Bing Liu
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China; IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China; Chinese Institute for Brain Research, Beijing 102206, China.
| | - Ge Wen
- Department of Imaging, Nanfang Hospital, Southern Medical University, Guangzhou 510515, China.
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18
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Wang Z, He D, Yang L, Wang P, Zou Z, Xiao J, Min W, He Y, Zhu H. Common and distinct patterns of task-related neural activation abnormalities in patients with remitted and current major depressive disorder: A systematic review and coordinate-based meta-analysis. Neurosci Biobehav Rev 2023; 152:105284. [PMID: 37315658 DOI: 10.1016/j.neubiorev.2023.105284] [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: 02/14/2023] [Revised: 05/20/2023] [Accepted: 06/11/2023] [Indexed: 06/16/2023]
Abstract
Whether remitted major depressive disorder (rMDD) and MDD present common or distinct neuropathological mechanisms remains unclear. We performed a meta-analysis of task-related whole-brain functional magnetic resonance imaging (fMRI) using anisotropic effect-size signed differential mapping software to compare brain activation between rMDD/MDD patients and healthy controls (HCs). We included 18 rMDD studies (458 patients and 476 HCs) and 120 MDD studies (3746 patients and 3863 HCs). The results showed that MDD and rMDD patients shared increased neural activation in the right temporal pole and right superior temporal gyrus. Several brain regions, including the right middle temporal gyrus, left inferior parietal, prefrontal cortex, left superior frontal gyrus and striatum, differed significantly between MDD and rMDD. Meta-regression analyses revealed that the percentage of females with MDD was positively associated with brain activity in the right lenticular nucleus/putamen. Our results provide valuable insights into the underlying neuropathology of brain dysfunction in MDD, developing more targeted and efficacious treatment and intervention strategies, and more importantly, providing potential neuroimaging targets for the early screening of MDD.
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Affiliation(s)
- Zuxing Wang
- Sichuan Provincial Center for Mental Health, The Center of Psychosomatic Medicine of Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, China
| | - Danmei He
- Mental Health Center, West China Hospital, Sichuan University, Chengdu 610041, China; Huaxi Brain Research Center, West China Hospital of Sichuan University, Chengdu, Sichuan 610041, China; Med-X Center for Informatics, Sichuan University, Chengdu, Sichuan 610041, China
| | - Lin Yang
- Mental Health Center, West China Hospital, Sichuan University, Chengdu 610041, China; Huaxi Brain Research Center, West China Hospital of Sichuan University, Chengdu, Sichuan 610041, China; Med-X Center for Informatics, Sichuan University, Chengdu, Sichuan 610041, China
| | - Peijia Wang
- Sichuan Provincial Center for Mental Health, The Center of Psychosomatic Medicine of Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, China
| | - Zhili Zou
- Sichuan Provincial Center for Mental Health, The Center of Psychosomatic Medicine of Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, China
| | - Jun Xiao
- Sichuan Provincial Center for Mental Health, The Center of Psychosomatic Medicine of Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, China
| | - Wenjiao Min
- Sichuan Provincial Center for Mental Health, The Center of Psychosomatic Medicine of Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, China
| | - Ying He
- Sichuan Provincial Center for Mental Health, The Center of Psychosomatic Medicine of Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, China
| | - Hongru Zhu
- Mental Health Center, West China Hospital, Sichuan University, Chengdu 610041, China; Huaxi Brain Research Center, West China Hospital of Sichuan University, Chengdu, Sichuan 610041, China; Med-X Center for Informatics, Sichuan University, Chengdu, Sichuan 610041, China.
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19
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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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20
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Sajjadian M, Uher R, Ho K, Hassel S, Milev R, Frey BN, Farzan F, Blier P, Foster JA, Parikh SV, Müller DJ, Rotzinger S, Soares CN, Turecki G, Taylor VH, Lam RW, Strother SC, Kennedy SH. Prediction of depression treatment outcome from multimodal data: a CAN-BIND-1 report. Psychol Med 2023; 53:5374-5384. [PMID: 36004538 PMCID: PMC10482706 DOI: 10.1017/s0033291722002124] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/20/2021] [Revised: 05/04/2022] [Accepted: 06/20/2022] [Indexed: 11/06/2022]
Abstract
BACKGROUND Prediction of treatment outcomes is a key step in improving the treatment of major depressive disorder (MDD). The Canadian Biomarker Integration Network in Depression (CAN-BIND) aims to predict antidepressant treatment outcomes through analyses of clinical assessment, neuroimaging, and blood biomarkers. METHODS In the CAN-BIND-1 dataset of 192 adults with MDD and outcomes of treatment with escitalopram, we applied machine learning models in a nested cross-validation framework. Across 210 analyses, we examined combinations of predictive variables from three modalities, measured at baseline and after 2 weeks of treatment, and five machine learning methods with and without feature selection. To optimize the predictors-to-observations ratio, we followed a tiered approach with 134 and 1152 variables in tier 1 and tier 2 respectively. RESULTS A combination of baseline tier 1 clinical, neuroimaging, and molecular variables predicted response with a mean balanced accuracy of 0.57 (best model mean 0.62) compared to 0.54 (best model mean 0.61) in single modality models. Adding week 2 predictors improved the prediction of response to a mean balanced accuracy of 0.59 (best model mean 0.66). Adding tier 2 features did not improve prediction. CONCLUSIONS A combination of clinical, neuroimaging, and molecular data improves the prediction of treatment outcomes over single modality measurement. The addition of measurements from the early stages of treatment adds precision. Present results are limited by lack of external validation. To achieve clinically meaningful prediction, the multimodal measurement should be scaled up to larger samples and the robustness of prediction tested in an external validation dataset.
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Affiliation(s)
- Mehri Sajjadian
- Department of Psychiatry, Dalhousie University, Halifax, NS, Canada
| | - Rudolf Uher
- Department of Psychiatry, Dalhousie University, Halifax, NS, Canada
| | - Keith Ho
- University Health Network, 399 Bathurst Street, Toronto, ON, M5T 2S8, Canada
- Unity Health Toronto, St. Michael's Hospital, 193 Yonge Street, 6th floor, Toronto, ON, M5B 1M4, Canada
| | - Stefanie Hassel
- Department of Psychiatry and Mathison Centre for Mental Health Research and Education, Cumming School of Medicine, University of Calgary, 3330 Hospital Dr NW, Calgary, AB, T2N 4N1, Canada
- Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada
| | - Roumen Milev
- Departments of Psychiatry and Psychology, Queen's University, Providence Care Hospital, Kingston, ON, Canada
| | - Benicio N. Frey
- Department of Psychiatry and Behavioural Neurosciences, McMaster University, Hamilton, ON, Canada
- Mood Disorders Program and Women's Health Concerns Clinic, St. Joseph's Healthcare Hamilton, Hamilton, ON, Canada
| | - Faranak Farzan
- eBrain Lab, School of Mechatronic Systems Engineering, Simon Fraser University, Surrey, BC, Canada
| | - Pierre Blier
- The Royal's Institute of Mental Health Research, 1145 Carling Avenue, Ottawa, ON, K1Z 7K4, Canada
- Department of Cellular and Molecular Medicine, University of Ottawa, 451 Smyth Road, Ottawa, ON, K1H 8M5, Canada
- Department of Psychiatry, University of Ottawa, 1145 Carling Avenue, Ottawa, ON, K1Z 7K4, Canada
| | - Jane A. Foster
- Department of Psychiatry & Behavioural Neurosciences, St Joseph's Healthcare, Hamilton, ON, Canada
| | - Sagar V. Parikh
- Department of Psychiatry, University of Michigan, Ann Arbor, MI, USA
| | - Daniel J. Müller
- Campbell Family Mental Health Research Institute, Center for Addiction and Mental Health, Toronto, ON, Canada
- Department of Psychiatry, University of Toronto, Toronto, ON, Canada
| | - Susan Rotzinger
- Department of Psychiatry, University of Toronto, Toronto, ON, Canada
- Department of Psychiatry, St Michael's Hospital, University of Toronto, Toronto, ON, Canada
| | - Claudio N. Soares
- Department of Psychiatry, Queen's University School of Medicine, Kingston, ON, Canada
| | - Gustavo Turecki
- Department of Psychiatry, Douglas Institute, McGill University, Montreal, QC, Canada
| | - Valerie H. Taylor
- Department of Psychiatry, Foothills Medical Centre, University of Calgary, Calgary, AB, Canada
| | - Raymond W. Lam
- Department of Psychiatry, University of British Columbia, Vancouver, BC, Canada
| | - Stephen C. Strother
- Rotman Research Center, Baycrest, Toronto, Canada
- Department of Medical Biophysics, University of Toronto, Toronto, Canada
| | - Sidney H. Kennedy
- Department of Psychiatry, University of Toronto, Toronto, ON, Canada
- Department of Psychiatry, St Michael's Hospital, University of Toronto, Toronto, ON, Canada
- Department of Psychiatry, University Health Network, Toronto, Ontario, Canada
- Krembil Research Centre, University Health Network, University of Toronto, Toronto, Canada
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21
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Batail JM, Xiao X, Azeez A, Tischler C, Kratter IH, Bishop JH, Saggar M, Williams NR. Network effects of Stanford Neuromodulation Therapy (SNT) in treatment-resistant major depressive disorder: a randomized, controlled trial. Transl Psychiatry 2023; 13:240. [PMID: 37400432 DOI: 10.1038/s41398-023-02537-9] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/09/2023] [Revised: 06/20/2023] [Accepted: 06/21/2023] [Indexed: 07/05/2023] Open
Abstract
Here, we investigated the brain functional connectivity (FC) changes following a novel accelerated theta burst stimulation protocol known as Stanford Neuromodulation Therapy (SNT) which demonstrated significant antidepressant efficacy in treatment-resistant depression (TRD). In a sample of 24 patients (12 active and 12 sham), active stimulation was associated with significant pre- and post-treatment modulation of three FC pairs, involving the default mode network (DMN), amygdala, salience network (SN) and striatum. The most robust finding was the SNT effect on amygdala-DMN FC (group*time interaction F(1,22) = 14.89, p < 0.001). This FC change correlated with improvement in depressive symptoms (rho (Spearman) = -0.45, df = 22, p = 0.026). The post-treatment FC pattern showed a change in the direction of the healthy control group and was sustained at the one-month follow-up. These results are consistent with amygdala-DMN connectivity dysfunction as an underlying mechanism of TRD and bring us closer to the goal of developing imaging biomarkers for TMS treatment optimization.Trial registration: ClinicalTrials.gov NCT03068715.
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Affiliation(s)
- Jean-Marie Batail
- Stanford Brain Stimulation Lab, Stanford, CA, USA
- Pôle Hospitalo-Universitaire de Psychiatrie Adulte, Centre Hospitalier Guillaume Régnier, Rennes, France
| | | | | | | | - Ian H Kratter
- Stanford Brain Stimulation Lab, Stanford, CA, USA
- Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA, USA
| | | | - Manish Saggar
- Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA, USA
| | - Nolan R Williams
- Stanford Brain Stimulation Lab, Stanford, CA, USA.
- Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA, USA.
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22
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Ten Doesschate F, Bruin W, Zeidman P, Abbott CC, Argyelan M, Dols A, Emsell L, van Eijndhoven PFP, van Exel E, Mulders PCR, Narr K, Tendolkar I, Rhebergen D, Sienaert P, Vandenbulcke M, Verdijk J, van Verseveld M, Bartsch H, Oltedal L, van Waarde JA, van Wingen GA. Effective resting-state connectivity in severe unipolar depression before and after electroconvulsive therapy. Brain Stimul 2023; 16:1128-1134. [PMID: 37517467 DOI: 10.1016/j.brs.2023.07.054] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2023] [Revised: 07/06/2023] [Accepted: 07/27/2023] [Indexed: 08/01/2023] Open
Abstract
BACKGROUND Electroconvulsive therapy (ECT) is one of the most effective treatments for severe depressive disorders. A recent multi-center study found no consistent changes in correlation-based (undirected) resting-state connectivity after ECT. Effective (directed) connectivity may provide more insight into the working mechanism of ECT. OBJECTIVE We investigated whether there are consistent changes in effective resting-state connectivity. METHODS This multi-center study included data from 189 patients suffering from severe unipolar depression and 59 healthy control participants. Longitudinal data were available for 81 patients and 24 healthy controls. We used dynamic causal modeling for resting-state functional magnetic resonance imaging to determine effective connectivity in the default mode, salience and central executive networks before and after a course of ECT. Bayesian general linear models were used to examine differences in baseline and longitudinal effective connectivity effects associated with ECT and its effectiveness. RESULTS Compared to controls, depressed patients showed many differences in effective connectivity at baseline, which varied according to the presence of psychotic features and later treatment outcome. Additionally, effective connectivity changed after ECT, which was related to ECT effectiveness. Notably, treatment effectiveness was associated with decreasing and increasing effective connectivity from the posterior default mode network to the left and right insula, respectively. No effects were found using correlation-based (undirected) connectivity. CONCLUSIONS A beneficial response to ECT may depend on how brain regions influence each other in networks important for emotion and cognition. These findings further elucidate the working mechanisms of ECT and may provide directions for future non-invasive brain stimulation research.
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Affiliation(s)
- Freek Ten Doesschate
- Department of Psychiatry, Rijnstate Hospital, Arnhem, the Netherlands; Amsterdam UMC Location University of Amsterdam, Department of Psychiatry, Amsterdam, the Netherlands; Amsterdam Neuroscience, Amsterdam, the Netherlands.
| | - Willem Bruin
- Amsterdam UMC Location University of Amsterdam, Department of Psychiatry, Amsterdam, the Netherlands; Amsterdam Neuroscience, Amsterdam, the Netherlands
| | - Peter Zeidman
- Wellcome Centre for Human Neuroimaging, 12 Queen Square, London, WC1N 3AR, UK
| | - Christopher C Abbott
- Department of Psychiatry, University of New Mexico School of Medicine, Albuquerque, NM, USA
| | - Miklos Argyelan
- Center for Psychiatric Neuroscience at the Feinstein Institute for Medical Research, New York, NY, USA
| | - Annemieke Dols
- GGZ inGeest Specialized Mental Health Care, Department of Old Age Psychiatry, Oldenaller 1, 1081 HJ, Amsterdam, the Netherlands; Amsterdam UMC, Vrije Universiteit, Psychiatry, Amsterdam Public Health Research Institute, the Netherlands; Amsterdam UMC, Vrije Universiteit, Psychiatry, Amsterdam Neuroscience, the Netherlands
| | - Louise Emsell
- Katholieke Universiteit Leuven, University Psychiatric Center Katholieke Universiteit Leuven, Leuven, Belgium
| | - Philip F P van Eijndhoven
- Donders Institute for Brain, Cognition and Behavior, Department of Psychiatry, Nijmegen, the Netherlands; Department of Psychiatry, Radboud University Medical Centre, Huispost 961, P.O. Box 9101, 6500 HB, Nijmegen, the Netherlands
| | - Eric van Exel
- GGZ inGeest Specialized Mental Health Care, Department of Old Age Psychiatry, Oldenaller 1, 1081 HJ, Amsterdam, the Netherlands; Amsterdam UMC, Vrije Universiteit, Psychiatry, Amsterdam Public Health Research Institute, the Netherlands; Amsterdam UMC, Vrije Universiteit, Psychiatry, Amsterdam Neuroscience, the Netherlands
| | - Peter C R Mulders
- Donders Institute for Brain, Cognition and Behavior, Department of Psychiatry, Nijmegen, the Netherlands; Department of Psychiatry, Radboud University Medical Centre, Huispost 961, P.O. Box 9101, 6500 HB, Nijmegen, the Netherlands
| | - Katherine Narr
- Departments of Neurology, Psychiatry, and Biobehavioral Sciences, University of California, Los Angeles, CA, USA
| | - Indira Tendolkar
- Donders Institute for Brain, Cognition and Behavior, Department of Psychiatry, Nijmegen, the Netherlands; Department of Psychiatry, Radboud University Medical Centre, Huispost 961, P.O. Box 9101, 6500 HB, Nijmegen, the Netherlands
| | - Didi Rhebergen
- Amsterdam UMC, Vrije Universiteit, Psychiatry, Amsterdam Public Health Research Institute, the Netherlands; Amsterdam UMC, Vrije Universiteit, Psychiatry, Amsterdam Neuroscience, the Netherlands
| | - Pascal Sienaert
- Academic Center for ECT and Neuromodulation (AcCENT), University Psychiatric Center KU Leuven (Catholic University of Leuven), Leuven, Belgium
| | - Mathieu Vandenbulcke
- Katholieke Universiteit Leuven, University Psychiatric Center Katholieke Universiteit Leuven, Leuven, Belgium
| | - Joey Verdijk
- Department of Psychiatry, Rijnstate Hospital, Arnhem, the Netherlands
| | | | - Hauke Bartsch
- Mohn Medical Imaging and Visualization Center, Department of Radiology, Haukeland University Hospital, Bergen, Norway
| | - Leif Oltedal
- Mohn Medical Imaging and Visualization Center, Department of Radiology, Haukeland University Hospital, Bergen, Norway; Department of Clinical Medicine, University of Bergen, Bergen, Norway
| | | | - Guido A van Wingen
- Amsterdam UMC Location University of Amsterdam, Department of Psychiatry, Amsterdam, the Netherlands; Amsterdam Neuroscience, Amsterdam, the Netherlands
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23
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Bracké KFM, Steegers CPM, van der Harst T, Dremmen MHG, Vernooij MW, White TJH, Dieleman GC. Can neuroimaging measures differentiate the disease course of anorexia nervosa? A systematic review. J Psychiatr Res 2023; 163:337-349. [PMID: 37263169 DOI: 10.1016/j.jpsychires.2023.05.059] [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: 02/24/2023] [Revised: 04/20/2023] [Accepted: 05/16/2023] [Indexed: 06/03/2023]
Abstract
Anorexia nervosa (AN) entails many uncertainties regarding the clinical outcome, due to large heterogeneity in the disease course. AN is associated with global decrease in brain volumes and altered brain functioning during acute illness. However, it is unclear whether structural and functional brain alterations can predict clinical outcome. We aimed to systematically review the predictive value of volumetric and functional brain outcome measures of structural and functional brain magnetic resonance imaging (MRI) on the disease course of AN. Four databases (Embase, Medline, Psycinfo, and Cochrane Central Register) were systematically searched. A total of 15 studies (structural MRI: n = 6, functional MRI: n = 9) were reviewed. In total 464 unique AN patients, and 328 controls were included. Follow-up time ranged between 1 and 43 months. Structural neuroimaging studies showed that lower brain volumes of the cerebellum, subcortical grey matter, and cortical white matter at admission predicted a worse clinical outcome. A smaller increase of the anterior cingulate cortex volume in the early phase of the disease predicted a worse clinical outcome. Lower overall gyrification, and a higher clustering coefficient predicted a worse clinical outcome. Functional MRI studies showed that frontal, parietal and temporal activity during task-based algorithms predicted follow-up body mass index, although results were bidirectional possibly due to the large heterogeneity in methodological approaches. Neuroimaging measures may predict the clinical outcome of AN. However, there is a lack of replication studies. Future studies are needed to validate the prognostic utility of neuroimaging measures in AN patients, and should harmonize demographic, clinical and neuroimaging features in order to enhance comparability.
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Affiliation(s)
- Katrien F M Bracké
- Department of Child and Adolescent Psychiatry/Psychology, Erasmus MC-Sophia Children's Hospital, Rotterdam, the Netherlands; Department of Radiology and Nuclear Medicine, Erasmus MC, Rotterdam, the Netherlands
| | - Cathelijne P M Steegers
- Department of Child and Adolescent Psychiatry/Psychology, Erasmus MC-Sophia Children's Hospital, Rotterdam, the Netherlands
| | - Tess van der Harst
- Department of Child and Adolescent Psychiatry/Psychology, Erasmus MC-Sophia Children's Hospital, Rotterdam, the Netherlands
| | - Marjolein H G Dremmen
- Department of Radiology and Nuclear Medicine, Erasmus MC, Rotterdam, the Netherlands
| | - Meike W Vernooij
- Department of Radiology and Nuclear Medicine, Erasmus MC, Rotterdam, the Netherlands
| | - Tonya J H White
- Department of Child and Adolescent Psychiatry/Psychology, Erasmus MC-Sophia Children's Hospital, Rotterdam, the Netherlands; Department of Radiology and Nuclear Medicine, Erasmus MC, Rotterdam, the Netherlands; Section of Social and Cognitive Developmental Neuroscience, National Institutes of Health, Bethesda, MD, USA
| | - Gwen C Dieleman
- Department of Child and Adolescent Psychiatry/Psychology, Erasmus MC-Sophia Children's Hospital, Rotterdam, the Netherlands.
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24
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Flint J. The genetic basis of major depressive disorder. Mol Psychiatry 2023; 28:2254-2265. [PMID: 36702864 PMCID: PMC10611584 DOI: 10.1038/s41380-023-01957-9] [Citation(s) in RCA: 28] [Impact Index Per Article: 28.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/18/2022] [Revised: 12/30/2022] [Accepted: 01/11/2023] [Indexed: 01/27/2023]
Abstract
The genetic dissection of major depressive disorder (MDD) ranks as one of the success stories of psychiatric genetics, with genome-wide association studies (GWAS) identifying 178 genetic risk loci and proposing more than 200 candidate genes. However, the GWAS results derive from the analysis of cohorts in which most cases are diagnosed by minimal phenotyping, a method that has low specificity. I review data indicating that there is a large genetic component unique to MDD that remains inaccessible to minimal phenotyping strategies and that the majority of genetic risk loci identified with minimal phenotyping approaches are unlikely to be MDD risk loci. I show that inventive uses of biobank data, novel imputation methods, combined with more interviewer diagnosed cases, can identify loci that contribute to the episodic severe shifts of mood, and neurovegetative and cognitive changes that are central to MDD. Furthermore, new theories about the nature and causes of MDD, drawing upon advances in neuroscience and psychology, can provide handles on how best to interpret and exploit genetic mapping results.
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Affiliation(s)
- Jonathan Flint
- Department of Psychiatry and Biobehavioral Sciences, Billy and Audrey Wilder Endowed Chair in Psychiatry and Neuroscience, Center for Neurobehavioral Genetics, 695 Charles E. Young Drive South, 3357B Gonda, Box 951761, Los Angeles, CA, 90095-1761, USA.
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25
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Jakary A, Lupo JM, Mackin S, Yin A, Murray D, Yang T, Mukherjee P, Larson P, Xu D, Eisendrath S, Luks T, Li Y. Evaluation of major depressive disorder using 7 Tesla phase sensitive neuroimaging before and after mindfulness-based cognitive therapy. J Affect Disord 2023; 335:383-391. [PMID: 37192691 DOI: 10.1016/j.jad.2023.05.038] [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: 10/04/2022] [Revised: 04/21/2023] [Accepted: 05/12/2023] [Indexed: 05/18/2023]
Abstract
OBJECTIVE We applied 7 Tesla phase sensitive imaging to evaluate the impact of brain iron levels on depression severity and cognitive function in individuals with major depressive disorder (MDD) treated with mindfulness-based cognitive therapy (MBCT). METHODS Seventeen unmedicated MDD participants underwent MRI, evaluation of depression severity, and cognitive testing before and after receiving MBCT, compared to fourteen healthy controls (HC). Local field shift (LFS) values, measures of brain iron levels, were derived from phase images in the putamen, caudate, globus pallidus (GP), anterior cingulate cortex (ACC) and thalamus. RESULTS Compared to the HC group, the MDD group had significantly lower baseline LFS (indicative of higher iron) in the left GP and left putamen and had a higher number of subjects with impairment in a test of information processing speed. In the MDD group, lower LFS values in the left and right ACC, right putamen, right GP, and right thalamus were significantly associated with depression severity; and lower LFS in the right GP was correlated with worse performance on measures of attention. All MBCT participants experienced depression relief. MBCT treatment also significantly improved executive function and attention. MBCT participants with lower baseline LFS values in the right caudate experienced significantly greater improvement in depression severity with treatment; and those with lower LFS values in the right ACC, right caudate, and right GB at baseline performed better on measures of verbal learning and memory after MBCT. CONCLUSIONS Our study highlights the potential contribution of subtle differences in brain iron to MDD symptoms and their successful treatment.
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Affiliation(s)
- Angela Jakary
- Department of Radiology and Biomedical Imaging, UCSF, San Francisco, CA, United States of America
| | - Janine M Lupo
- Department of Radiology and Biomedical Imaging, UCSF, San Francisco, CA, United States of America
| | - Scott Mackin
- Department of Psychiatry and Behavioral Sciences, UCSF, San Francisco, CA, United States of America
| | - Audrey Yin
- Department of Radiology and Biomedical Imaging, UCSF, San Francisco, CA, United States of America
| | - Donna Murray
- Department of Psychiatry and Behavioral Sciences, UCSF, San Francisco, CA, United States of America
| | - Tony Yang
- Department of Psychiatry and Behavioral Sciences, UCSF, San Francisco, CA, United States of America
| | - Pratik Mukherjee
- Department of Radiology and Biomedical Imaging, UCSF, San Francisco, CA, United States of America
| | - Peder Larson
- Department of Radiology and Biomedical Imaging, UCSF, San Francisco, CA, United States of America
| | - Duan Xu
- Department of Radiology and Biomedical Imaging, UCSF, San Francisco, CA, United States of America
| | - Stuart Eisendrath
- Department of Psychiatry and Behavioral Sciences, UCSF, San Francisco, CA, United States of America
| | - Tracy Luks
- Department of Radiology and Biomedical Imaging, UCSF, San Francisco, CA, United States of America
| | - Yan Li
- Department of Radiology and Biomedical Imaging, UCSF, San Francisco, CA, United States of America.
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Cammisuli DM, Castelnuovo G. Neuroscience-based psychotherapy: A position paper. Front Psychol 2023; 14:1101044. [PMID: 36860785 PMCID: PMC9968886 DOI: 10.3389/fpsyg.2023.1101044] [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: 11/17/2022] [Accepted: 01/11/2023] [Indexed: 02/15/2023] Open
Abstract
In the recent years, discoveries in neuroscience have greatly impacted upon the need to modify therapeutic practice starting from the evidence showing some cerebral mechanisms capable of coping with mental health crisis and traumatic events of the individual's life history by redesigning the narrative plot and the person's sense of the Self. The emerging dialogue between neuroscience and psychotherapy is increasingly intense and modern psychotherapy cannot ignore the heritage deriving from studies about neuropsychological modification of memory traces, neurobiology of attachment theory, cognitive mechanisms involved in psychopathology, neurophysiology of human empathy, neuroimaging evidence about psychotherapeutic treatment, and somatoform disorders connecting the brain and the body. In the present article, we critically examined sectorial literature and claimed that psychotherapy has to referred to a neuroscience-based approach in order to adopt the most tailored interventions for specific groups of patients or therapy settings. We also provided recommendations for care implementation in clinical practice and illustrated challenges of future research.
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Affiliation(s)
| | - Gianluca Castelnuovo
- Department of Psychology, Catholic University, Milan, Italy,Psychology Research Laboratory, Istituto Auxologico Italiano IRCCS, Milan, Italy,*Correspondence: Gianluca Castelnuovo ✉
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Luo J, Yi P, Liang M, Zhang S, Tao Q, Li N, Zhang H, Wen J, Xue X, Fan C, Li X. Distinct brain activity alterations of treatment for bipolar disorders with psychotherapy and drug therapy: activation likelihood estimation meta-analysis. Psychol Med 2023; 53:625-637. [PMID: 36722029 DOI: 10.1017/s0033291722003889] [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: 02/02/2023]
Abstract
BACKGROUNDS Many studies suggest that both psychotherapy and drug therapy are effective in the treatment of bipolar disorders (BDs). However, the pathophysiology of both types of intervention has not been established definitively. METHODS An activation likelihood estimation meta-analysis was performed to identify the distinct brain activity alterations between psychotherapy and drug therapy for the treatment of BDs. Articles were identified by searching databases including PubMed, Embase, Cochrane Library, and Web of Science databases. Eligible studies on BDs were published up until 10 June 2021. RESULTS 21 studies were included and we conducted a meta-analysis for different therapies and imaging tasks. After receiving psychotherapy, BD patients showed increased activation in the inferior frontal gyrus (IFG) and superior temporal gyrus. While after taking drug therapy, BD patients displayed increased activation in the anterior cingulate cortex, medial frontal gyrus, IFG, and decreased activation in the posterior cingulate cortex. The regions of brain activity changes caused by psychotherapy were mostly focused on the frontal areas, while drug therapy mainly impacted on the limbic areas. Different type of tasks also affected brain regions which were activated. CONCLUSIONS Our comprehensive meta-analysis indicates that these two treatments might have effect on BD in their own therapeutic modes. Psychotherapy might have a top-down effect, while drug therapy might have a bottom-up effect. This study may contribute to differential diagnosis of BDs and would be helpful to finding more accurate neuroimaging biomarkers for BD treatment.
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Affiliation(s)
- Jingyi Luo
- Centre for Translational Medicine, Second Affiliated Hospital, Anhui Medical University, Hefei, Anhui, China
- Department of Medical Psychology, School of Mental Health and Psychological Science, Anhui Medical University, Hefei, China
| | - Pengcheng Yi
- Department of Clinical Psychology, the Third People's Hospital of Xiangshan County, Ningbo, China
| | - Meng Liang
- Department of Medical Psychology, School of Mental Health and Psychological Science, Anhui Medical University, Hefei, China
| | - Shuyu Zhang
- School of Psychology, the Australian National University, Canberra, Australia
| | - Qian Tao
- Department of Psychology, School of Basic Medicine, Jinan University, Guangzhou, China
| | - Ni Li
- Department of Medical Psychology, School of Mental Health and Psychological Science, Anhui Medical University, Hefei, China
| | - Han Zhang
- Department of Medical Psychology, School of Mental Health and Psychological Science, Anhui Medical University, Hefei, China
| | - Jialin Wen
- Department of Medical Psychology, School of Mental Health and Psychological Science, Anhui Medical University, Hefei, China
| | - Xinrong Xue
- Department of Medical Psychology, School of Mental Health and Psychological Science, Anhui Medical University, Hefei, China
| | - Chuan Fan
- Department of Psychiatry, the First Affiliated Hospital of Anhui Medical University, Hefei 230022, China
| | - Xiaoming Li
- Centre for Translational Medicine, Second Affiliated Hospital, Anhui Medical University, Hefei, Anhui, China
- Department of Medical Psychology, School of Mental Health and Psychological Science, Anhui Medical University, Hefei, China
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van der Velden AM, Scholl J, Elmholdt EM, Fjorback LO, Harmer CJ, Lazar SW, O'Toole MS, Smallwood J, Roepstorff A, Kuyken W. Mindfulness Training Changes Brain Dynamics During Depressive Rumination: A Randomized Controlled Trial. Biol Psychiatry 2023; 93:233-242. [PMID: 36328822 DOI: 10.1016/j.biopsych.2022.06.038] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/11/2021] [Revised: 06/23/2022] [Accepted: 06/23/2022] [Indexed: 12/27/2022]
Abstract
BACKGROUND Depression is a leading cause of disability worldwide and its prevalence is on the rise. One of the most debilitating aspects of depression is the dominance and persistence of depressive rumination, a state of mind that is linked to onset and recurrence of depression. Mindfulness meditation trains adaptive attention regulation and present-moment embodied awareness, skills that may be particularly useful during depressive mind states characterized by negative ruminative thoughts. METHODS In a randomized controlled functional magnetic resonance imaging study (N = 80), we looked at the neurocognitive mechanisms behind mindfulness-based cognitive therapy (n = 50) for recurrent depression compared with treatment as usual (n = 30) across experimentally induced states of rest, mindfulness practice and rumination, and the relationship with dispositional psychological processes. RESULTS Mindfulness-based cognitive therapy compared with treatment as usual led to decreased salience network connectivity to the lingual gyrus during a ruminative state, and this change in salience network connectivity mediated improvements in the ability to sustain and control attention to body sensations. CONCLUSIONS These findings showed that a clinically effective mindfulness intervention modulates neurocognitive functioning during depressive rumination and the ability to sustain attention to the body.
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Affiliation(s)
- Anne Maj van der Velden
- Department of Clinical Medicine, Aarhus University, Aarhus, Denmark; Department of Psychiatry, Oxford University and Oxford Health NHS Foundation Trust, Warneford Hospital, Oxford, United Kingdom.
| | - Jacqueline Scholl
- Department of Psychiatry, Oxford University and Oxford Health NHS Foundation Trust, Warneford Hospital, Oxford, United Kingdom; Department of Experimental Psychology, Oxford University, Oxford, United Kingdom; Lyon Neuroscience Research Centre, INSERM U1028, CNRS UMR5292, PSYR2 Team, University of Lyon, Lyon, France; Centre Hospitalier Le Vinatier, Bron, France
| | - Else-Marie Elmholdt
- School of Business and Social Sciences, Aarhus University, Aarhus, Denmark; NIDO, Regional Hospital West Jutland, Herning, Denmark
| | - Lone O Fjorback
- Department of Clinical Medicine, Aarhus University, Aarhus, Denmark
| | - Catherine J Harmer
- Department of Psychiatry, Oxford University and Oxford Health NHS Foundation Trust, Warneford Hospital, Oxford, United Kingdom
| | - Sara W Lazar
- Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts
| | - Mia S O'Toole
- School of Business and Social Sciences, Aarhus University, Aarhus, Denmark
| | | | - Andreas Roepstorff
- Department of Clinical Medicine, Aarhus University, Aarhus, Denmark; School of Culture and Society, Aarhus University, Aarhus, Denmark
| | - Willem Kuyken
- Department of Psychiatry, Oxford University and Oxford Health NHS Foundation Trust, Warneford Hospital, Oxford, United Kingdom
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Gartlehner G, Dobrescu A, Chapman A, Toromanova A, Emprechtinger R, Persad E, Affengruber L, Pieh C, Klerings I, Wagner G. Nonpharmacologic and Pharmacologic Treatments of Adult Patients With Major Depressive Disorder: A Systematic Review and Network Meta-analysis for a Clinical Guideline by the American College of Physicians. Ann Intern Med 2023; 176:196-211. [PMID: 36689750 DOI: 10.7326/m22-1845] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/24/2023] Open
Abstract
BACKGROUND Primary care patients and clinicians may prefer alternative options to second-generation antidepressants for major depressive disorder (MDD). PURPOSE To compare the benefits and harms of nonpharmacologic treatments with second-generation antidepressants as first-step interventions for acute MDD, and to compare second-step treatment strategies for patients who did not achieve remission after an initial attempt with antidepressants. DATA SOURCES English-language studies from several electronic databases from 1 January 1990 to 8 August 2022, trial registries, gray literature databases, and reference lists to identify unpublished research. STUDY SELECTION 2 investigators independently selected randomized trials of at least 6 weeks' duration. DATA EXTRACTION Reviewers abstracted data about study design and conduct, participants, interventions, and outcomes. They dually rated the risk of bias of studies and the certainty of evidence for outcomes of interest. DATA SYNTHESIS 65 randomized trials met the inclusion criteria; eligible data from nonrandomized studies were not found. Meta-analyses and network meta-analyses indicated similar benefits of most nonpharmacologic treatments and antidepressants as first-step treatments. Antidepressants had higher risks for discontinuation because of adverse events than most other treatments. For second-step therapies, different switching and augmentation strategies provided similar symptomatic relief. The certainty of evidence for most comparisons is low; findings should be interpreted cautiously. LIMITATIONS Many studies had methodological limitations or dosing inequalities; publication bias might have affected some comparisons. In some cases, conclusions could not be drawn because of insufficient evidence. CONCLUSION Although benefits seem to be similar among first- and second-step MDD treatments, the certainty of evidence is low for most comparisons. Clinicians and patients should focus on options with the most reliable evidence and take adverse event profiles and patient preferences into consideration. PRIMARY FUNDING SOURCE American College of Physicians. (PROSPERO: CRD42020204703).
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Affiliation(s)
- Gerald Gartlehner
- Cochrane Austria, Department for Evidence-based Medicine and Evaluation, University of Krems, Krems, Austria, and RTI International, Research Triangle Park, North Carolina (G.G.)
| | - Andreea Dobrescu
- Cochrane Austria, Department for Evidence-based Medicine and Evaluation, University of Krems, Krems, Austria (A.D., A.C., A.T., E.P., I.K., G.W.)
| | - Andrea Chapman
- Cochrane Austria, Department for Evidence-based Medicine and Evaluation, University of Krems, Krems, Austria (A.D., A.C., A.T., E.P., I.K., G.W.)
| | - Ana Toromanova
- Cochrane Austria, Department for Evidence-based Medicine and Evaluation, University of Krems, Krems, Austria (A.D., A.C., A.T., E.P., I.K., G.W.)
| | | | - Emma Persad
- Cochrane Austria, Department for Evidence-based Medicine and Evaluation, University of Krems, Krems, Austria (A.D., A.C., A.T., E.P., I.K., G.W.)
| | - Lisa Affengruber
- Cochrane Austria, Department for Evidence-based Medicine and Evaluation, University of Krems, Krems, Austria, and Department of Family Medicine, Care and Public Health Research Institute, Maastricht University, Maastricht, The Netherlands (L.A.)
| | - Christoph Pieh
- Department for Psychotherapy and Biopsychosocial Health, University of Krems, Krems, Austria (C.P.)
| | - Irma Klerings
- Cochrane Austria, Department for Evidence-based Medicine and Evaluation, University of Krems, Krems, Austria (A.D., A.C., A.T., E.P., I.K., G.W.)
| | - Gernot Wagner
- Cochrane Austria, Department for Evidence-based Medicine and Evaluation, University of Krems, Krems, Austria (A.D., A.C., A.T., E.P., I.K., G.W.)
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30
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Lu F, Cui Q, Chen Y, He Z, Sheng W, Tang Q, Yang Y, Luo W, Yu Y, Chen J, Li D, Deng J, Zeng Y, Chen H. Insular-associated causal network of structural covariance evaluating progressive gray matter changes in major depressive disorder. Cereb Cortex 2023; 33:831-843. [PMID: 35357431 DOI: 10.1093/cercor/bhac105] [Citation(s) in RCA: 12] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2021] [Revised: 01/17/2022] [Accepted: 02/15/2022] [Indexed: 02/03/2023] Open
Abstract
BACKGROUND Morphometric studies demonstrated wide-ranging distribution of brain structural abnormalities in major depressive disorder (MDD). OBJECTIVE This study explored the progressive gray matter volume (GMV) changes pattern of structural network in 108 MDD patients throughout the illness duration by using voxel-based morphometric analysis. METHODS The causal structural covariance network method was applied to map the causal effects of GMV alterations between the original source of structural changes and other brain regions as the illness duration prolonged in MDD. This was carried out by utilizing the Granger causality analysis to T1-weighted data ranked based on the disease progression information. RESULTS With greater illness duration, the GMV reduction was originated from the right insula and progressed to the frontal lobe, and then expanded to the occipital lobe, temporal lobe, dorsal striatum (putamen and caudate) and the cerebellum. Importantly, results revealed that the right insula was the prominent node projecting positive causal influences (i.e., GMV decrease) to frontal lobe, temporal lobe, postcentral gyrus, putamen, and precuneus. While opposite causal effects were detected from the right insula to the angular, parahippocampus, supramarginal gyrus and cerebellum. CONCLUSIONS This work may provide further information and vital evidence showing that MDD is associated with progressive brain structural alterations.
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Affiliation(s)
- Fengmei Lu
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, PR China
| | - Qian Cui
- School of Public Affairs and Administration, University of Electronic Science and Technology of China, Chengdu 611731, PR China
| | - Yuyan Chen
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, PR China
| | - Zongling He
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, PR 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 611731, PR China
| | - Qin Tang
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, PR China
| | - Yang Yang
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, PR China
| | - Wei Luo
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, PR China
| | - Yue Yu
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, PR China
| | - Jiajia Chen
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, PR China
| | - Di Li
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, PR China
| | - Jiaxin Deng
- School of Public Affairs and Administration, University of Electronic Science and Technology of China, Chengdu 611731, PR China
| | - Yuhong Zeng
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, PR China
| | - Huafu Chen
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, PR China.,MOE Key Lab for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu 611731, PR China
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31
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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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32
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Gray JP, Manuello J, Alexander-Bloch AF, Leonardo C, Franklin C, Choi KS, Cauda F, Costa T, Blangero J, Glahn DC, Mayberg HS, Fox PT. Co-alteration Network Architecture of Major Depressive Disorder: A Multi-modal Neuroimaging Assessment of Large-scale Disease Effects. Neuroinformatics 2022; 21:443-455. [PMID: 36469193 DOI: 10.1007/s12021-022-09614-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 11/06/2022] [Indexed: 12/12/2022]
Abstract
Major depressive disorder (MDD) exhibits diverse symptomology and neuroimaging studies report widespread disruption of key brain areas. Numerous theories underpinning the network degeneration hypothesis (NDH) posit that neuropsychiatric diseases selectively target brain areas via meaningful network mechanisms rather than as indistinct disease effects. The present study tests the hypothesis that MDD is a network-based disorder, both structurally and functionally. Coordinate-based meta-analysis and Activation Likelihood Estimation (CBMA-ALE) were used to assess the convergence of findings from 92 previously published studies in depression. An extension of CBMA-ALE was then used to generate a node-and-edge network model representing the co-alteration of brain areas impacted by MDD. Standardized measures of graph theoretical network architecture were assessed. Co-alteration patterns among the meta-analytic MDD nodes were then tested in independent, clinical T1-weighted structural magnetic resonance imaging (MRI) and resting-state functional (rs-fMRI) data. Differences in co-alteration profiles between MDD patients and healthy controls, as well as between controls and clinical subgroups of MDD patients, were assessed. A 65-node 144-edge co-alteration network model was derived for MDD. Testing of co-alteration profiles in replication data using the MDD nodes provided distinction between MDD and healthy controls in structural data. However, co-alteration profiles were not distinguished between patients and controls in rs-fMRI data. Improved distinction between patients and healthy controls was observed in clinically homogenous MDD subgroups in T1 data. MDD abnormalities demonstrated both structural and functional network architecture, though only structural networks exhibited between-groups differences. Our findings suggest improved utility of structural co-alteration networks for ongoing biomarker development.
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33
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Ali FZ, Wengler K, He X, Nguyen MH, Parsey RV, DeLorenzo C. Gradient boosting decision-tree-based algorithm with neuroimaging for personalized treatment in depression. NEUROSCIENCE INFORMATICS 2022; 2:100110. [PMID: 36699194 PMCID: PMC9873411 DOI: 10.1016/j.neuri.2022.100110] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/13/2022]
Abstract
Introduction Pretreatment positron emission tomography (PET) with 2-deoxy-2-[18F]fluoro-D-glucose (FDG) and magnetic resonance spectroscopy (MRS) may identify biomarkers for predicting remission (absence of depression). Yet, no such image-based biomarkers have achieved clinical validity. The purpose of this study was to identify biomarkers of remission using machine learning (ML) with pretreatment FDG-PET/MRS neuroimaging, to reduce patient suffering and economic burden from ineffective trials. Methods This study used simultaneous PET/MRS neuroimaging from a double-blind, placebo-controlled, randomized antidepressant trial on 60 participants with major depressive disorder (MDD) before initiating treatment. After eight weeks of treatment, those with ≤ 7 on 17-item Hamilton Depression Rating Scale were designated a priori as remitters (free of depression, 37%). Metabolic rate of glucose uptake (metabolism) from 22 brain regions were acquired from PET. Concentrations (mM) of glutamine and glutamate and gamma-aminobutyric acid (GABA) in anterior cingulate cortex were quantified from MRS. The data were randomly split into 67% train and cross-validation (n = 40), and 33% test (n = 20) sets. The imaging features, along with age, sex, handedness, and treatment assignment (selective serotonin reuptake inhibitor or SSRI vs. placebo) were entered into the eXtreme Gradient Boosting (XGBoost) classifier for training. Results In test data, the model showed 62% sensitivity, 92% specificity, and 77% weighted accuracy. Pretreatment metabolism of left hippocampus from PET was the most predictive of remission. Conclusions The pretreatment neuroimaging takes around 60 minutes but has potential to prevent weeks of failed treatment trials. This study effectively addresses common issues for neuroimaging analysis, such as small sample size, high dimensionality, and class imbalance.
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Affiliation(s)
- Farzana Z. Ali
- Department of Biomedical Engineering, Stony Brook University, Stony Brook, NY, USA
| | - Kenneth Wengler
- Department of Biomedical Engineering, Stony Brook University, Stony Brook, NY, USA
- Department of Psychiatry, Columbia University and New York State Psychiatric Institute, New York, NY, USA
| | - Xiang He
- Department of Radiology, Stony Brook Medicine, Stony Brook, NY, USA
- Department of Radiology, Northshore University Hospital, Manhasset, NY, USA
| | - Minh Hoai Nguyen
- Department of Computer Science, Stony Brook University, Stony Brook, NY, USA
| | - Ramin V. Parsey
- Department of Psychiatry, Renaissance School of Medicine at Stony Brook University, Stony Brook, NY, USA
| | - Christine DeLorenzo
- Department of Biomedical Engineering, Stony Brook University, Stony Brook, NY, USA
- Department of Psychiatry, Columbia University and New York State Psychiatric Institute, New York, NY, USA
- Department of Psychiatry, Renaissance School of Medicine at Stony Brook University, Stony Brook, NY, USA
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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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Huys QJM, Russek EM, Abitante G, Kahnt T, Gollan JK. Components of Behavioral Activation Therapy for Depression Engage Specific Reinforcement Learning Mechanisms in a Pilot Study. COMPUTATIONAL PSYCHIATRY (CAMBRIDGE, MASS.) 2022; 6:238-255. [PMID: 38774780 PMCID: PMC11104310 DOI: 10.5334/cpsy.81] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/10/2021] [Accepted: 09/28/2022] [Indexed: 11/20/2022]
Abstract
Background Behavioral activation is an evidence-based treatment for depression. Theoretical considerations suggest that treatment response depends on reinforcement learning mechanisms. However, which reinforcement learning mechanisms are engaged by and mediate the therapeutic effect of behavioral activation remains only partially understood, and there are no procedures to measure such mechanisms. Objective To perform a pilot study to examine whether reinforcement learning processes measured through tasks or self-report are related to treatment response to behavioral activation. Method The pilot study enrolled 13 outpatients (12 completers) with major depressive disorder, from July of 2018 through February of 2019, into a nine-week trial with BA. Psychiatric evaluations, decision-making tests and self-reported reward experience and anticipations were acquired before, during and after the treatment. Task and self-report data were analysed by using reinforcement-learning models. Inferred parameters were related to measures of depression severity through linear mixed effects models. Results Treatment effects during different phases of the therapy were captured by specific decision-making processes in the task. During the weeks focusing on the active pursuit of reward, treatment effects were more pronounced amongst those individuals who showed an increase in Pavlovian appetitive influence. During the weeks focusing on the avoidance of punishments, treatment responses were more pronounced in those individuals who showed an increase in Pavlovian avoidance. Self-reported anticipation of reinforcement changed according to formal RL rules. Individual differences in the extent to which learning followed RL rules related to changes in anhedonia. Conclusions In this pilot study both task- and self-report-derived measures of reinforcement learning captured individual differences in treatment response to behavioral activation. Appetitive and aversive Pavlovian reflexive processes appeared to be modulated by separate psychotherapeutic interventions, and the modulation strength covaried with response to specific interventions. Self-reported changes in reinforcement expectations are also related to treatment response. Trial Registry Name Set Your Goal: Engaging in GO/No-Go Active Learning, #NCT03538535, http://www.clinicaltrials.gov.
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Affiliation(s)
- Quentin J. M. Huys
- Division of Psychiatry, University College London, London, UK
- Max Planck UCL Centre for Computational Psychiatry and Ageing Research and Wellcome Trust Centre for Human Neuroimaging, Institute of Neurology, University College London, London, UK
- Camden and Islington NHS Foundation Trust, London, UK
| | - Evan M. Russek
- Max Planck UCL Centre for Computational Psychiatry and Ageing Research and Wellcome Trust Centre for Human Neuroimaging, Institute of Neurology, University College London, London, UK
| | - George Abitante
- Department of Psychiatry and Behavioral Sciences, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
| | - Thorsten Kahnt
- National Institute on Drug Abuse Intramural Research Program, Baltimore, MD, USA
- Department of Neurology, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
- Department of Psychiatry and Behavioral Science, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
| | - Jacqueline K. Gollan
- Department of Psychiatry and Behavioral Science, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
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36
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Jackson NA, Jabbi MM. Integrating biobehavioral information to predict mood disorder suicide risk. Brain Behav Immun Health 2022; 24:100495. [PMID: 35990401 PMCID: PMC9388879 DOI: 10.1016/j.bbih.2022.100495] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2022] [Revised: 08/04/2022] [Accepted: 08/05/2022] [Indexed: 11/25/2022] Open
Abstract
The will to live and the ability to maintain one's well-being are crucial for survival. Yet, almost a million people die by suicide globally each year (Aleman and Denys, 2014), making premature deaths due to suicide a significant public health problem (Saxena et al., 2013). The expression of suicidal behaviors is a complex phenotype with documented biological, psychological, clinical, and sociocultural risk factors (Turecki et al., 2019). From a brain disease perspective, suicide is associated with neuroanatomical, neurophysiological, and neurochemical dysregulations of brain networks involved in integrating and contextualizing cognitive and emotional regulatory behaviors. From a symptom perspective, diagnostic measures of dysregulated mood states like major depressive symptoms are associated with over sixty percent of suicide deaths worldwide (Saxena et al., 2013). This paper reviews the neurobiological and clinical phenotypic correlates for mood dysregulations and suicidal phenotypes. We further propose machine learning approaches to integrate neurobiological measures with dysregulated mood symptoms to elucidate the role of inflammatory processes as neurobiological risk factors for suicide.
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Affiliation(s)
- Nicholas A. Jackson
- Department of Psychiatry and Behavioral Sciences, Dell Medical School, The University of Texas at Austin, USA
- Institute for Neuroscience, The University of Texas at Austin, USA
| | - Mbemba M. Jabbi
- Department of Psychiatry and Behavioral Sciences, Dell Medical School, The University of Texas at Austin, USA
- Mulva Clinics for the Neurosciences
- Institute for Neuroscience, The University of Texas at Austin, USA
- Department of Psychology, The University of Texas at Austin, USA
- Center for Learning and Memory, The University of Texas at Austin, USA
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Hayden BY, Park HS, Zimmermann J. Automated pose estimation in primates. Am J Primatol 2022; 84:e23348. [PMID: 34855257 PMCID: PMC9160209 DOI: 10.1002/ajp.23348] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2021] [Revised: 11/05/2021] [Accepted: 11/09/2021] [Indexed: 11/11/2022]
Abstract
Understanding the behavior of primates is important for primatology, for psychology, and for biology more broadly. It is also important for biomedicine, where primates are an important model organism, and whose behavior is often an important variable of interest. Our ability to rigorously quantify behavior has, however, long been limited. On one hand, we can rigorously quantify low-information measures like preference, looking time, and reaction time; on the other, we can use more gestalt measures like behavioral categories tracked via ethogram, but at high cost and with high variability. Recent technological advances have led to a major revolution in behavioral measurement that offers affordable and scalable rigor. Specifically, digital video cameras and automated pose tracking software can provide measures of full-body position (i.e., pose) of primates over time (i.e., behavior) with high spatial and temporal resolution. Pose-tracking technology in turn can be used to infer behavioral states, such as eating, sleeping, and mating. We call this technological approach behavioral imaging. In this review, we situate the behavioral imaging revolution in the history of the study of behavior, argue for investment in and development of analytical and research techniques that can profit from the advent of the era of big behavior, and propose that primate centers and zoos will take on a more central role in relevant fields of research than they have in the past.
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Affiliation(s)
- Benjamin Y. Hayden
- Department of Neuroscience, Center for Magnetic Resonance Research, Department of Biomedical Engineering
| | - Hyun Soo Park
- Department of Computer Science and Engineering, University of Minnesota, Minneapolis MN 55455
| | - Jan Zimmermann
- Department of Neuroscience, Center for Magnetic Resonance Research, Department of Biomedical Engineering
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Kroemer NB, Opel N, Teckentrup V, Li M, Grotegerd D, Meinert S, Lemke H, Kircher T, Nenadić I, Krug A, Jansen A, Sommer J, Steinsträter O, Small DM, Dannlowski U, Walter M. Functional Connectivity of the Nucleus Accumbens and Changes in Appetite in Patients With Depression. JAMA Psychiatry 2022; 79:993-1003. [PMID: 36001327 PMCID: PMC9403857 DOI: 10.1001/jamapsychiatry.2022.2464] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/29/2022] [Accepted: 06/14/2022] [Indexed: 11/14/2022]
Abstract
Importance Major depressive disorder (MDD) is characterized by a substantial burden on health, including changes in appetite and body weight. Heterogeneity of depressive symptoms has hampered the identification of biomarkers that robustly generalize to most patients, thus calling for symptom-based mapping. Objective To define the functional architecture of the reward circuit subserving increases vs decreases in appetite and body weight in patients with MDD by specifying their contributions and influence on disease biomarkers using resting-state functional connectivity (FC). Design, Setting, and Participants In this case-control study, functional magnetic resonance imaging (fMRI) data were taken from the Marburg-Münster FOR 2107 Affective Disorder Cohort Study (MACS), collected between September 2014 and November 2016. Cross-sectional data of patients with MDD (n = 407) and healthy control participants (n = 400) were analyzed from March 2018 to June 2022. Main Outcomes and Measures Changes in appetite during the depressive episode and their association with FC were examined using fMRI. By taking the nucleus accumbens (NAcc) as seed of the reward circuit, associations with opposing changes in appetite were mapped, and a sparse symptom-specific elastic-net model was built with 10-fold cross-validation. Results Among 407 patients with MDD, 249 (61.2%) were women, and the mean (SD) age was 36.79 (13.4) years. Reduced NAcc-based FC to the ventromedial prefrontal cortex (vmPFC) and the hippocampus was associated with reduced appetite (vmPFC: bootstrap r = 0.13; 95% CI, 0.02-0.23; hippocampus: bootstrap r = 0.15; 95% CI, 0.05-0.26). In contrast, reduced NAcc-based FC to the insular ingestive cortex was associated with increased appetite (bootstrap r = -0.14; 95% CI, -0.24 to -0.04). Critically, the cross-validated elastic-net model reflected changes in appetite based on NAcc FC and explained variance increased with increasing symptom severity (all patients: bootstrap r = 0.24; 95% CI, 0.16-0.31; patients with Beck Depression Inventory score of 28 or greater: bootstrap r = 0.42; 95% CI, 0.25-0.58). In contrast, NAcc FC did not classify diagnosis (MDD vs healthy control). Conclusions and Relevance In this study, NAcc-based FC reflected important individual differences in appetite and body weight in patients with depression that can be leveraged for personalized prediction. However, classification of diagnosis using NAcc-based FC did not exceed chance levels. Such symptom-specific associations emphasize the need to map biomarkers onto more confined facets of psychopathology to improve the classification and treatment of MDD.
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Affiliation(s)
- Nils B. Kroemer
- Department of Psychiatry and Psychotherapy, Tübingen Center for Mental Health, University of Tübingen, Tübingen, Germany
- Department of Psychiatry and Psychotherapy, University of Bonn, Bonn, Germany
| | - Nils Opel
- Department of Psychiatry and Psychotherapy, University Hospital Jena, Jena, Germany
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
| | - Vanessa Teckentrup
- Department of Psychiatry and Psychotherapy, Tübingen Center for Mental Health, University of Tübingen, Tübingen, Germany
- Institute of Neuroscience, Trinity College Dublin, Dublin, Ireland
| | - Meng Li
- Department of Psychiatry and Psychotherapy, Tübingen Center for Mental Health, University of Tübingen, Tübingen, Germany
- Department of Psychiatry and Psychotherapy, University Hospital Jena, Jena, Germany
- Department of Psychiatry and Psychotherapy, Otto-von-Guericke University Magdeburg, Magdeburg, Germany
| | - Dominik Grotegerd
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
| | - Susanne Meinert
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
| | - Hannah Lemke
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
| | - Tilo Kircher
- Department of Psychiatry and Psychotherapy, University of Marburg, Marburg, Germany
| | - Igor Nenadić
- Department of Psychiatry and Psychotherapy, University of Marburg, Marburg, Germany
| | - Axel Krug
- Department of Psychiatry and Psychotherapy, University of Bonn, Bonn, Germany
- Department of Psychiatry and Psychotherapy, University of Marburg, Marburg, Germany
| | - Andreas Jansen
- Department of Psychiatry and Psychotherapy, University of Marburg, Marburg, Germany
| | - Jens Sommer
- Department of Psychiatry and Psychotherapy, University of Marburg, Marburg, Germany
| | - Olaf Steinsträter
- Department of Psychiatry and Psychotherapy, University of Marburg, Marburg, Germany
| | - Dana M. Small
- Departments of Psychiatry and Psychology, Yale University, New Haven, Connecticut
- Modern Diet and Physiology Research Center, Yale University, New Haven, Connecticut
| | - Udo Dannlowski
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
| | - Martin Walter
- Department of Psychiatry and Psychotherapy, Tübingen Center for Mental Health, University of Tübingen, Tübingen, Germany
- Department of Psychiatry and Psychotherapy, University Hospital Jena, Jena, Germany
- Department of Psychiatry and Psychotherapy, Otto-von-Guericke University Magdeburg, Magdeburg, Germany
- Leibniz Institute for Neurobiology, Magdeburg, Germany
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Gärtner M, de Rover M, Václavů L, Scheidegger M, van Osch MJP, Grimm S. Increase in thalamic cerebral blood flow is associated with antidepressant effects of ketamine in major depressive disorder. World J Biol Psychiatry 2022; 23:643-652. [PMID: 34985394 DOI: 10.1080/15622975.2021.2020900] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/25/2023]
Abstract
Ketamine is a promising treatment option for patients with Major Depressive Disorder (MDD) and has become an important research tool to investigate antidepressant mechanisms of action. However, imaging studies attempting to characterise ketamine's mechanism of action using blood oxygen level-dependent signal (BOLD) imaging have yielded inconsistent results- at least partly due to intrinsic properties of the BOLD contrast, which measures a complex signal related to neural activity. To circumvent the limitations associated with the BOLD signal, we used arterial spin labelling (ASL) as an unambiguous marker of neuronal activity-related changes in cerebral blood flow (CBF). We measured CBF in 21 MDD patients at baseline and 24 h after receiving a single intravenous infusion of subanesthetic ketamine and examined relationships with clinical outcomes. Our findings demonstrate that increase in thalamus perfusion 24 h after ketamine administration is associated with greater improvement of depressive symptoms. Furthermore, lower thalamus perfusion at baseline is associated both with larger increases in perfusion 24 h after ketamine administration and with stronger reduction of depressive symptoms. These findings indicate that ASL is not only a useful tool to broaden our understanding of ketamine's mechanism of action but might also have the potential to inform treatment decisions based on CBF-defined regional disruptions.
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Affiliation(s)
- Matti Gärtner
- MSB-Medical School Berlin, Berlin, Germany.,Department of Psychiatry and Psychotherapy, Campus Benjamin Franklin, Charité-Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Berlin, Germany
| | - Mischa de Rover
- Laboratory for Neurophysiology, Department of Cell and Chemical Biology, Leiden University Medical Center, Leiden, Netherlands.,Department of Clinical Psychology, Institute of Psychology, Leiden University, Leiden, Netherlands
| | - Lena Václavů
- Department of Radiology, C.J. Gorter Center for High Field MRI, Leiden University Medical Center, Leiden, Netherlands
| | - Milan Scheidegger
- Department of Psychiatry, Psychotherapy and Psychosomatics, Psychiatric Hospital, University of Zurich, Zurich, Switzerland
| | - Matthias J P van Osch
- Department of Radiology, C.J. Gorter Center for High Field MRI, Leiden University Medical Center, Leiden, Netherlands
| | - Simone Grimm
- MSB-Medical School Berlin, Berlin, Germany.,Department of Psychiatry and Psychotherapy, Campus Benjamin Franklin, Charité-Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Berlin, Germany.,Department of Psychiatry, Psychotherapy and Psychosomatics, Psychiatric Hospital, University of Zurich, Zurich, Switzerland
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40
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Cai G, Lu Y, Chen J, Yang D, Yan R, Ren M, He S, Wu S, Zhao Y. Brain-wide mapping of c-Fos expression with fluorescence micro-optical sectioning tomography in a chronic sleep deprivation mouse model. Neurobiol Stress 2022; 20:100478. [PMID: 35991686 PMCID: PMC9389418 DOI: 10.1016/j.ynstr.2022.100478] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2022] [Revised: 07/26/2022] [Accepted: 07/29/2022] [Indexed: 11/23/2022] Open
Abstract
Chronic sleep deprivation (SD) is a common problem for humans and can lead to many deleterious effects, including depression, anxiety, stroke, permanent cognitive deficits, stress, and other physiological diseases. It is vital to acquire information about the relevant neural activities at the whole-brain level to systematically explore the mechanisms of brain dysfunction related to SD. Expression of the immediate-early gene (IEG) Fos in the mouse brain has been widely used as a functional marker of brain activity in the field of neuroscience. However, most previous studies only analyzed the change of c-Fos in several specific brain regions using traditional research methods or in short-term SD model. Here, we applied c-Fos mapping through the fluorescence micro-optical sectioning tomography (fMOST) technique and AAV-PHP.eB to comprehensive analysis the state of cumulative activation across the whole brain in a mouse model of chronic SD. The chronic rapid eyes movement (REM) SD model was induced by moving mice to a separate holding area filled with water. The experimental period lasted for 6 h per day. The results showed that after 14 days of SD, the mice displayed anxiety-like behaviors in open field test and elevated plus maze test, and displayed depression-like behaviors in tail suspension test and the sucrose preference test. The c-Fos + cells were detected in a maximum of 230 brain regions. SD-induced stress model evoked c-Fos expression in several brain regions compared to the control group. In particular, the isocortex-cerebral cortex plate area, including the retrosplenial, anterior cingulate, agranular insular, gustatory, and parasubiculum, appear to be the most sensitive regions after chronic REM SD.
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Affiliation(s)
- Guohong Cai
- Department of Gastroenterology, First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, 710065, China
- Department of Neurobiology, School of Basic Medicine, Fourth Military Medical University, Xi'an, 710032, China
| | - Yifan Lu
- Department of Neurobiology, School of Basic Medicine, Fourth Military Medical University, Xi'an, 710032, China
- College of Life Sciences & Research Center for Resource Peptide Drugs, Shaanxi Engineering & Technological Research Center for Conversation & Utilization of Regional Biological Resources, Yanan University, Yanan, 716000, China
| | - Jing Chen
- Department of Anatomy, Histology and Embryology and K. K. Leung Brain Research Centre, Fourth Military Medical University, Xi'an, 710032, China
| | - Dingding Yang
- Department of Neurobiology, School of Basic Medicine, Fourth Military Medical University, Xi'an, 710032, China
| | - Ruixuan Yan
- Department of Neurobiology, School of Basic Medicine, Fourth Military Medical University, Xi'an, 710032, China
| | - Mudan Ren
- Department of Gastroenterology, First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, 710065, China
| | - Shuixiang He
- Department of Gastroenterology, First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, 710065, China
| | - Shengxi Wu
- Department of Neurobiology, School of Basic Medicine, Fourth Military Medical University, Xi'an, 710032, China
| | - Yan Zhao
- Department of Gastroenterology, First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, 710065, China
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Cervenka S, Frick A, Bodén R, Lubberink M. Application of positron emission tomography in psychiatry-methodological developments and future directions. Transl Psychiatry 2022; 12:248. [PMID: 35701411 PMCID: PMC9198063 DOI: 10.1038/s41398-022-01990-2] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/22/2021] [Revised: 05/20/2022] [Accepted: 05/25/2022] [Indexed: 11/09/2022] Open
Abstract
Mental disorders represent an increasing source of disability and high costs for societies globally. Molecular imaging techniques such as positron emission tomography (PET) represent powerful tools with the potential to advance knowledge regarding disease mechanisms, allowing the development of new treatment approaches. Thus far, most PET research on pathophysiology in psychiatric disorders has focused on the monoaminergic neurotransmission systems, and although a series of discoveries have been made, the results have not led to any material changes in clinical practice. We outline areas of methodological development that can address some of the important obstacles to fruitful progress. First, we point towards new radioligands and targets that can lead to the identification of processes upstream, or parallel to disturbances in monoaminergic systems. Second, we describe the development of new methods of PET data quantification and PET systems that may facilitate research in psychiatric populations. Third, we review the application of multimodal imaging that can link molecular imaging data to other aspects of brain function, thus deepening our understanding of disease processes. Fourth, we highlight the need to develop imaging study protocols to include longitudinal and interventional paradigms, as well as frameworks to assess dimensional symptoms such that the field can move beyond cross-sectional studies within current diagnostic boundaries. Particular effort should be paid to include also the most severely ill patients. Finally, we discuss the importance of harmonizing data collection and promoting data sharing to reach the desired sample sizes needed to fully capture the phenotype of psychiatric conditions.
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Affiliation(s)
- Simon Cervenka
- Department of Medical Sciences, Psychiatry, Uppsala University, Uppsala, Sweden. .,Centre for Psychiatry Research, Department of Clinical Neuroscience, Karolinska Institutet and Stockholm Health Care Services, Region Stockholm, Stockholm, Sweden.
| | - Andreas Frick
- grid.8993.b0000 0004 1936 9457Department of Medical Sciences, Psychiatry, Uppsala University, Uppsala, Sweden
| | - Robert Bodén
- grid.8993.b0000 0004 1936 9457Department of Medical Sciences, Psychiatry, Uppsala University, Uppsala, Sweden
| | - Mark Lubberink
- grid.8993.b0000 0004 1936 9457Department of Surgical Sciences, Uppsala University, Uppsala, Sweden
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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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Farb NAS, Desormeau P, Anderson AK, Segal ZV. Static and treatment-responsive brain biomarkers of depression relapse vulnerability following prophylactic psychotherapy: Evidence from a randomized control trial. Neuroimage Clin 2022; 34:102969. [PMID: 35367955 PMCID: PMC8978278 DOI: 10.1016/j.nicl.2022.102969] [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] [Subscribe] [Scholar Register] [Received: 06/02/2021] [Revised: 01/18/2022] [Accepted: 02/17/2022] [Indexed: 12/18/2022]
Abstract
A prospective study of neural biomarkers of relapse in remitted depressed patients. Assessed neural response to dysphoric mood-induction before and after psychotherapy. Relapse over a 2-year follow-up linked to dysphoria-evoked sensory inhibition. Relapse risk was lower when dorsolateral prefrontal reactivity decreased over time. Depression prophylaxis may involve reducing dysphoria-evoked sensory inhibition.
Background Neural reactivity to dysphoric mood induction indexes the tendency for distress to promote cognitive reactivity and sensory avoidance. Linking these responses to illness prognosis following recovery from Major Depressive Disorder informs our understanding of depression vulnerability and provides engagement targets for prophylactic interventions. Methods A prospective fMRI neuroimaging design investigated the relationship between dysphoric reactivity and relapse following prophylactic intervention. Remitted depressed outpatients (N = 85) were randomized to 8 weeks of Cognitive Therapy with a Well-Being focus or Mindfulness Based Cognitive Therapy. Participants were assessed before and after therapy and followed for 2 years to assess relapse status. Neural reactivity common to both assessment points identified static biomarkers of relapse, whereas reactivity change identified dynamic biomarkers. Results Dysphoric mood induction evoked prefrontal activation and sensory deactivation. Controlling for past episodes, concurrent symptoms and medication status, somatosensory deactivation was associated with depression recurrence in a static pattern that was unaffected by prophylactic treatment, HR 0.04, 95% CI [0.01, 0.14], p < .001. Treatment-related prophylaxis was linked to reduced activation of the left lateral prefrontal cortex (LPFC), HR 3.73, 95% CI [1.33, 10.46], p = .013. Contralaterally, the right LPFC showed dysphoria-evoked inhibitory connectivity with the right somatosensory biomarker Conclusions These findings support a two-factor model of depression relapse vulnerability, in which: enduring patterns of dysphoria-evoked sensory deactivation contribute to episode return, but vulnerability may be mitigated by targeting prefrontal regions responsive to clinical intervention. Emotion regulation during illness remission may be enhanced by reducing prefrontal cognitive processes in favor of sensory representation and integration.
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Affiliation(s)
- Norman A S Farb
- Department of Psychology, University of Toronto Mississauga, 3359 Mississauga Road, Mississauga, Ontario L5L 1C6, Canada; Graduate Department of Psychological Clinical Science, University of Toronto Scarborough, 1265 Military Trail, Toronto, ON M1C 1A4, Canada.
| | - Philip Desormeau
- Graduate Department of Psychological Clinical Science, University of Toronto Scarborough, 1265 Military Trail, Toronto, ON M1C 1A4, Canada
| | - Adam K Anderson
- College of Human Ecology, Cornell University, Ithaca, NY 14853, USA
| | - Zindel V Segal
- Graduate Department of Psychological Clinical Science, University of Toronto Scarborough, 1265 Military Trail, Toronto, ON M1C 1A4, Canada
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Trombello JM, Cooper CM, Fatt CC, Grannemann BD, Carmody TJ, Jha MK, Mayes TL, Greer TL, Yezhuvath U, Aslan S, Pizzagalli DA, Weissman MM, Webb CA, Dillon DG, McGrath PJ, Fava M, Parsey RV, McInnis MG, Etkin A, Trivedi MH. Neural substrates of emotional conflict with anxiety in major depressive disorder: Findings from the Establishing Moderators and biosignatures of Antidepressant Response in Clinical Care (EMBARC) randomized controlled trial. J Psychiatr Res 2022; 149:243-251. [PMID: 35290819 PMCID: PMC9746288 DOI: 10.1016/j.jpsychires.2022.03.015] [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: 11/05/2021] [Revised: 02/16/2022] [Accepted: 03/07/2022] [Indexed: 12/16/2022]
Abstract
BACKGROUND The brain circuitry of depression and anxiety/fear is well-established, involving regions such as the limbic system and prefrontal cortex. We expand prior literature by examining the extent to which four discrete factors of anxiety (immediate state anxiety, physiological/panic, neuroticism/worry, and agitation/restlessness) among depressed outpatients are associated with differential responses during reactivity to and regulation of emotional conflict. METHODS A total of 172 subjects diagnosed with major depressive disorder underwent functional magnetic resonance imaging while performing an Emotional Stroop Task. Two main contrasts were examined using whole brain voxel wise analyses: emotional reactivity and emotion regulation. We also evaluated the association of these contrasts with the four aforementioned anxiety factors. RESULTS During emotional reactivity, participants with higher immediate state anxiety showed potentiated activation in the rolandic operculum and insula, while individuals with higher levels of physiological/panic demonstrated decreased activation in the posterior cingulate. No significant results emerged for any of the four factors on emotion regulation. When re-analyzing these statistically-significant brain regions through analyses of a subsample with (n = 92) and without (n = 80) a current anxiety disorder, no significant associations occurred among those without an anxiety disorder. Among those with an anxiety disorder, results were similar to the full sample, except the posterior cingulate was associated with the neuroticism/worry factor. CONCLUSIONS Divergent patterns of task-related brain activation across four discrete anxiety factors could be used to inform treatment decisions and target specific aspects of anxiety that involve intrinsic processing to attenuate overactive responses to emotional stimuli.
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Affiliation(s)
- Joseph M. Trombello
- Department of Psychiatry, Center for Depression Research and Clinical Care, University of Texas Southwestern Medical Center, Dallas, TX, USA,Janssen Research & Development, LLC, Titusville, NJ, USA
| | - Crystal M. Cooper
- Department of Psychiatry, Center for Depression Research and Clinical Care, University of Texas Southwestern Medical Center, Dallas, TX, USA,Neuroscience Research, Cook Children’s Medical Center, Fort Worth, TX, USA
| | - Cherise Chin Fatt
- Department of Psychiatry, Center for Depression Research and Clinical Care, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Bruce D. Grannemann
- Department of Psychiatry, Center for Depression Research and Clinical Care, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Thomas J. Carmody
- Department of Psychiatry, Center for Depression Research and Clinical Care, University of Texas Southwestern Medical Center, Dallas, TX, USA,Department of Population and Data Sciences, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Manish K. Jha
- Department of Psychiatry, Center for Depression Research and Clinical Care, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Taryn L. Mayes
- Department of Psychiatry, Center for Depression Research and Clinical Care, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Tracy L. Greer
- Department of Psychiatry, Center for Depression Research and Clinical Care, University of Texas Southwestern Medical Center, Dallas, TX, USA,Department of Psychology, The University of Texas at Arlington, Arlington, TX, USA
| | | | - Sina Aslan
- Department of Psychiatry, Center for Depression Research and Clinical Care, University of Texas Southwestern Medical Center, Dallas, TX, USA,Advance MRI LLC, Frisco, TX, USA
| | - Diego A. Pizzagalli
- Harvard Medical School, McLean Hospital, Department of Psychiatry, Boston, MA, USA
| | - Myrna M. Weissman
- Columbia University, Department of Psychiatry, New York, NY, USA,New York State Psychiatric Institute and Department of Psychiatry, College of Physicians and Surgeons of Columbia University, New York, NY, USA
| | - Christian A. Webb
- Harvard Medical School, McLean Hospital, Department of Psychiatry, Boston, MA, USA
| | - Daniel G. Dillon
- Harvard Medical School, McLean Hospital, Department of Psychiatry, Boston, MA, USA
| | - Patrick J. McGrath
- Columbia University, Department of Psychiatry, New York, NY, USA,New York State Psychiatric Institute and Department of Psychiatry, College of Physicians and Surgeons of Columbia University, New York, NY, USA
| | - Maurizio Fava
- Massachusetts General Hospital, Department of Psychiatry, Boston, MA, USA
| | - Ramin V. Parsey
- Stony Brook University, Department of Psychiatry, Stony Brook, NY, USA
| | - Melvin G. McInnis
- University of Michigan, Department of Psychiatry, Ann Arbor, MI, USA
| | - Amit Etkin
- Stanford University School of Medicine, Department of Psychiatry, Palo Alto, CA, USA
| | - Madhukar H. Trivedi
- Department of Psychiatry, Center for Depression Research and Clinical Care, University of Texas Southwestern Medical Center, Dallas, TX, USA,Corresponding author. Center for Depression Research and Clinical Care, Peter O’Donnell Jr. Brain Institute, University of Texas Southwestern Medical Center, USA. (M.H. Trivedi)
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45
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Sheikh-Wu SF, Gerber KS, Pinto MD, Downs CA. Mechanisms and Methods to Understand Depressive Symptoms. Issues Ment Health Nurs 2022; 43:434-446. [PMID: 34752200 DOI: 10.1080/01612840.2021.1998261] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
Abstract
Depressive symptoms, feelings of sadness, anger, and loss that interfere with a person's daily life, are prevalent health concerns across populations that significantly result in adverse health outcomes with direct and indirect economic burdens at a national and global level. This article aims to synthesize known mechanisms of depressive symptoms and the established and emerging methodologies used to understand depressive symptoms; implications and directions for future nursing research are discussed. A comprehensive search was performed by Cumulative Index to Nursing and Allied Health Literature, MEDLINE, and PUBMED databases between 2000-2021 to examine contributing factors of depressive symptoms. Many environmental, psychological, and physiological factors are associated with the development or increased severity of depressive symptoms (anhedonia, fatigue, sleep and appetite disturbances to depressed mood). This paper discusses biological and psychological theories that guide our understanding of depressive symptoms, as well as known biomarkers (gut microbiome, specific genes, multi-cytokine, and hormones) and established and emerging methods. Disruptions within the nervous system, hormonal and neurotransmitters levels, brain structure, gut-brain axis, leaky-gut syndrome, immune and inflammatory process, and genetic variations are significant mediating mechanisms in depressive symptomology. Nursing research and practice are at the forefront of furthering depressive symptoms' mechanisms and methods. Utilizing advanced technology and measurement tools (big data, machine learning/artificial intelligence, and multi-omic approaches) can provide insight into the psychological and biological mechanisms leading to effective intervention development. Thus, understanding depressive symptomology provides a pathway to improve patients' health outcomes, leading to reduced morbidity and mortality and the overall nation-wide economic burden.Supplemental data for this article is available online at https://doi.org/10.1080/01612840.2021.1998261 .
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Affiliation(s)
- Sameena F Sheikh-Wu
- School of Nursing and Health Studies, University of Miami, Coral Gables, Florida, USA
| | - Kathryn S Gerber
- School of Nursing and Health Studies, University of Miami, Coral Gables, Florida, USA
| | - Melissa D Pinto
- Sue and Bill Gross School of Nursing, University of California, Irvine, California, USA
| | - Charles A Downs
- School of Nursing and Health Studies, University of Miami, Coral Gables, Florida, USA
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46
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Teng C, Liu T, Zhang N, Zhong Y, Wang C. Cognitive behavioral therapy may rehabilitate abnormally functional communication pattern among the triple-network in major depressive disorder: A follow-up study. J Affect Disord 2022; 304:28-39. [PMID: 35192866 DOI: 10.1016/j.jad.2022.02.050] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/11/2021] [Revised: 02/12/2022] [Accepted: 02/15/2022] [Indexed: 12/14/2022]
Abstract
BACKGROUND Cognitive behavioral therapy (CBT) is an established treatment for Major Depressive Disorder (MDD). MDD is characterized by imbalanced communication patterns among three networks: the central executive network (CEN), the default mode network (DMN) and the salience network (SN). The effect of CBT in restoring communications among these networks in MDD is unknown. METHODS Thirty-three patients with MDD and 27 healthy controls (HC) participated in the study. Patients were treated with CBT. Resting-state functional magnetic resonance imaging (rs-fMRI) data were obtained in patients at three stages (T0: before treatment; T1: after 6 weeks CBT; T2: after 28 weeks CBT) and in HC (only T0). Both independent component analysis (ICA) and granger causality analysis (GCA) were used to explore dynamic causal communication patterns among the three networks (CEN, DMN, SN) over a course of CBT treatment. RESULTS In the HC group, the SN had an inhibitory causal effect on CEN; the CEN and DMN had an excitatory causal effect on the SN. The SN had an inhibitory causal effect on the CEN and the DMN; only the DMN had an excitatory causal effect on the SN in the MDD patients at the T0 stage. As the CBT treatment went on for MDD patients, the CEN restored excitatory causal effect on the SN, and the SN lost inhibitory effect on the DMN. This result mimicked the one found in the HC group. Four regions, left ventromedial prefrontal cortex (lvmPFC), posterior cingulate gyrus (PCC), right inferior parietal lobule (rIPL) and right insula, were implicated in mediating network communications. LIMITATIONS The findings should be considered preliminary given the small sample sizes, and assessed only one stage in HC subjects. CONCLUSION CBT may enhance the regulatory function of the SN, and rehabilitate the imbalanced brain network communication mode in the MDD. PCC, lvmPFC and rIPL may all be potential targets of CBT.
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Affiliation(s)
- Changjun Teng
- Nanjing Brain Hospital Affiliated to Nanjing Medical University, Nanjing, Jiangsu, China; Functional Brain Imaging Institute of Nanjing Medical University, Nanjing, Jiangsu, China; Cognitive Behavioral Therapy Institute of Nanjing Medical University, Nanjing, Jiangsu, China
| | - Tianchen Liu
- School of Psychology, Nanjing Normal University, Nanjing, Jiangsu, China
| | - Ning Zhang
- Nanjing Brain Hospital Affiliated to Nanjing Medical University, Nanjing, Jiangsu, China; Functional Brain Imaging Institute of Nanjing Medical University, Nanjing, Jiangsu, China; Cognitive Behavioral Therapy Institute of Nanjing Medical University, Nanjing, Jiangsu, China; School of Psychology, Nanjing Normal University, Nanjing, Jiangsu, China
| | - Yuan Zhong
- School of Psychology, Nanjing Normal University, Nanjing, Jiangsu, China.
| | - Chun Wang
- Nanjing Brain Hospital Affiliated to Nanjing Medical University, Nanjing, Jiangsu, China; Functional Brain Imaging Institute of Nanjing Medical University, Nanjing, Jiangsu, China; Cognitive Behavioral Therapy Institute of Nanjing Medical University, Nanjing, Jiangsu, China; School of Psychology, Nanjing Normal University, Nanjing, Jiangsu, China.
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47
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Rauch SA, Kim HM, Venners MR, Porter K, Norman SB, Simon NM, Rothbaum BO, Tuerk PW, Acierno R, Bui E, Powell C, Smith ER, Goetter E, McSweeney L. Change in posttraumatic stress disorder-related thoughts during treatment: Do thoughts drive change when pills are involved? J Trauma Stress 2022; 35:496-507. [PMID: 34973039 PMCID: PMC9446312 DOI: 10.1002/jts.22762] [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: 05/24/2021] [Revised: 08/16/2021] [Accepted: 08/28/2021] [Indexed: 11/09/2022]
Abstract
Posttraumatic negative thoughts about one's self and the world are related to posttraumatic stress disorder (PTSD) symptom severity and change in cognitive behavioral treatment (CBT), but little is known about this association when CBT is delivered with medication. The current study presents a planned comparison of changes in negative posttraumatic thoughts during (a) prolonged exposure (PE) plus pill placebo (PE+PLB), (b) sertraline plus enhanced medication management (SERT+EMM), and (c) PE plus sertraline (PE+SERT) as part of a randomized clinical trial in a sample of 176 veterans. Lagged regression modeling revealed that change in posttraumatic negative thoughts was associated with PTSD symptom change in the conditions in which participants received sertraline, ds = 0.14-0.25, ps = 0.04-.001). However, contrary to previous research, the models that started with symptom change were also statistically significant, d = 0.23, p < .001, for the lagged effect of symptoms on negative thoughts about self in the SERT+EMM condition, indicating a bidirectional association between such thoughts and PTSD symptoms. In the PE+PLB condition, no significant association between posttraumatic thoughts and PTSD symptoms emerged in either direction. These results suggest that the previously demonstrated role of change in posttraumatic thoughts leading to PTSD symptom reduction in PE may be altered when combined with pill administration, either active or placebo.
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Affiliation(s)
- Sheila A.M. Rauch
- Atlanta VA Medical Center, 1670 Clairmont Road, Decatur, GA, 30033,Emory University School of Medicine, 12 Executive Park, 3 Floor, Atlanta, GA, 30029
| | - H. Myra Kim
- VA Ann Arbor Healthcare System, 2215 Fuller Road, Ann Arbor, MI, 48105,University of Michigan, Consulting for Statistics, Computing and Analytics Research, 3550 Rackham, 950 E. Washington Street, Ann Arbor, MI, 48109
| | | | - Katherine Porter
- VA Ann Arbor Healthcare System, 2215 Fuller Road, Ann Arbor, MI, 48105,University of Michigan, Department of Psychiatry, 4250 Plymouth Road, Ann Arbor, MI, 48109
| | - Sonya B. Norman
- National Center for PTSD, 215 N. Main Street, White River Junction, VT 05009,VA Center of Excellence for Stress and Mental Health, 3350 La Jolla Village Drive, MC116B, San Diego, CA 92161,University of California, San Diego, School of Medicine, 9500 Gilman Drive, La Jolla, CA 92037
| | - Naomi M. Simon
- Massachusetts General Hospital, Department of Psychiatry, One Bowdoin Square, 6th Floor, Boston, MA 02114,New York University Medical School, Department of Psychiatry, One Park Avenue 8 Floor, New York NY 10016
| | - Barbara O. Rothbaum
- Emory University School of Medicine, 12 Executive Park, 3 Floor, Atlanta, GA, 30029
| | - Peter W. Tuerk
- University of Virginia, Department of Human Services, 417 Emmet St. South, PO Box 400270, Charlottesville, VA 22903
| | - Ron Acierno
- Ralph H. Johnson VA Medical Center, 109 Bee Street, Charleston, SC, 29401,Medical University of South Carolina, College of Nursing, Jonathan Lucas Street, Charleston, SC 29425
| | - Eric Bui
- Massachusetts General Hospital, Department of Psychiatry, One Bowdoin Square, 6th Floor, Boston, MA 02114,University of Caen Normandy & Caen University Hospital, avenue de la Côte de Nacre, 14000 Caen, France
| | - Corey Powell
- University of Michigan, Consulting for Statistics, Computing and Analytics Research, 3550 Rackham, 950 E. Washington Street, Ann Arbor, MI, 48109
| | - Erin R. Smith
- VA Ann Arbor Healthcare System, 2215 Fuller Road, Ann Arbor, MI, 48105,University of Michigan, Department of Psychiatry, 4250 Plymouth Road, Ann Arbor, MI, 48109
| | - Elizabeth Goetter
- Massachusetts General Hospital, Department of Psychiatry, One Bowdoin Square, 6th Floor, Boston, MA 02114,Harvard Medical School, Department of Psychiatry, 25 Shattuck Street, Boston, MA 02115
| | - Lauren McSweeney
- Emory University School of Medicine, 12 Executive Park, 3 Floor, Atlanta, GA, 30029
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48
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Kato M, Ogata H, Tahara H, Shimamoto A, Takekita Y, Koshikawa Y, Nishida K, Nonen S, Higasa K, Kinoshita T. Multiple Pre-Treatment miRNAs Levels in Untreated Major Depressive Disorder Patients Predict Early Response to Antidepressants and Interact with Key Pathways. Int J Mol Sci 2022; 23:ijms23073873. [PMID: 35409234 PMCID: PMC8999364 DOI: 10.3390/ijms23073873] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2022] [Revised: 03/29/2022] [Accepted: 03/29/2022] [Indexed: 12/16/2022] Open
Abstract
Major depressive disorder (MDD) is a life-impairing disorder, and early successful treatment is important for a favorable prognosis. However, early response to antidepressants differs widely among individuals, and is difficult to predict pre-treatment. As miRNAs have been reported to play important roles in depression, identification of miRNAs associated with antidepressant treatment responses and their interacting genes and pathways will be beneficial in understanding the predictors and molecular mechanisms of depression treatment. This randomized control trial examined miRNAs correlated with the early therapeutic effect of selective serotonin reuptake inhibitors (SSRIs; paroxetine or sertraline) and mirtazapine monotherapy. Before medication, we comprehensively analyzed the miRNA expression of 92 depressed participants and identified genes and pathways interacting with miRNAs. A total of 228 miRNAs were significantly correlated with depressive symptoms improvements after 2 weeks of SSRIs treatment, with miR-483.5p showing the most robust correlation. These miRNAs are involved in 21 pathways, including TGF-β, glutamatergic synapse, long-term depression, and the mitogen-activated protein kinase (MAPK) signaling pathways. Using these miRNAs enabled us to predict SSRI response at week 2 with a 57% difference. This study shows that pre-treatment levels of miRNAs could be used to predict early responses to antidepressant administration, a knowledge of genes, and an identification of genes and pathways associated with the antidepressant response.
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Affiliation(s)
- Masaki Kato
- Department of Neuropsychiatry, Kansai Medical University, Osaka 573-1191, Japan; (H.O.); (Y.T.); (Y.K.); (K.N.); (T.K.)
- Correspondence:
| | - Haruhiko Ogata
- Department of Neuropsychiatry, Kansai Medical University, Osaka 573-1191, Japan; (H.O.); (Y.T.); (Y.K.); (K.N.); (T.K.)
| | - Hidetoshi Tahara
- Department of Cellular and Molecular Biology, Graduate School of Biomedical & Health Sciences, Hiroshima University, Hiroshima 734-8533, Japan;
| | - Akira Shimamoto
- Faculty of Pharmaceutical Sciences, Sanyo-Onoda City University, Sanyo Onoda 756-0084, Japan;
| | - Yoshiteru Takekita
- Department of Neuropsychiatry, Kansai Medical University, Osaka 573-1191, Japan; (H.O.); (Y.T.); (Y.K.); (K.N.); (T.K.)
| | - Yosuke Koshikawa
- Department of Neuropsychiatry, Kansai Medical University, Osaka 573-1191, Japan; (H.O.); (Y.T.); (Y.K.); (K.N.); (T.K.)
| | - Keiichiro Nishida
- Department of Neuropsychiatry, Kansai Medical University, Osaka 573-1191, Japan; (H.O.); (Y.T.); (Y.K.); (K.N.); (T.K.)
| | - Shinpei Nonen
- Department of Pharmacy, Hyogo University of Health Sciences, Kobe 650-8530, Japan;
| | - Koichiro Higasa
- Department of Genome Analysis, Institute of Biomedical Science, Kansai Medical University, Osaka 573-1191, Japan;
| | - Toshihiko Kinoshita
- Department of Neuropsychiatry, Kansai Medical University, Osaka 573-1191, Japan; (H.O.); (Y.T.); (Y.K.); (K.N.); (T.K.)
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49
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Klimes-Dougan B, Başgöze Z, Mueller B, Wiglesworth A, Carosella KA, Westlund Schreiner M, Bortnova A, Reigstad K, Cullen KR, Gunlicks-Stoessel M. Structural and Functional Neural Correlates of Treatment Response for Interpersonal Psychotherapy for Depressed Adolescents. J Clin Med 2022; 11:jcm11071878. [PMID: 35407493 PMCID: PMC8999886 DOI: 10.3390/jcm11071878] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2022] [Revised: 03/11/2022] [Accepted: 03/21/2022] [Indexed: 02/04/2023] Open
Abstract
Precision medicine approaches hold tremendous promise to advance current clinical practice by providing information about which individuals will benefit from which treatments. This pilot study evaluated if baseline structure and function of the salience and emotion brain regions implicated in adolescent depression, specifically the amygdala and anterior cingulate cortex (ACC), predict response to Interpersonal Psychotherapy for Depressed Adolescents (IPT-A). Adolescents (n = 15; mean age = 14.5 (1.6); 80.0% female) diagnosed with a depressive disorder completed brain scans before the start of a 16 week trial of IPT-A. Clinical measures assessing depressive symptoms were completed before, during, and after a trial of therapy. Results show that at baseline, greater ACC activation in the context of an emotion-matching task and greater amygdala-ACC resting-state functional connectivity was related to greater improvement in depression symptoms. There was minimal evidence that brain structure predicted changes in depressive symptoms. The present study is the first to evaluate neural predictors of IPT-A response. While the results are preliminary, these findings suggest some avenues for future research to pursue in the hopes that more will benefit from treatment.
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Affiliation(s)
- Bonnie Klimes-Dougan
- Department of Psychology, University of Minnesota, Minneapolis, MN 55455, USA; (A.W.); (K.A.C.)
- Correspondence: ; Tel.: +1-612-626-4347
| | - Zeynep Başgöze
- Department of Psychiatry and Behavioral Sciences, University of Minnesota, Minneapolis, MN 55454, USA; (Z.B.); (B.M.); (K.R.); (K.R.C.); (M.G.-S.)
| | - Bryon Mueller
- Department of Psychiatry and Behavioral Sciences, University of Minnesota, Minneapolis, MN 55454, USA; (Z.B.); (B.M.); (K.R.); (K.R.C.); (M.G.-S.)
| | - Andrea Wiglesworth
- Department of Psychology, University of Minnesota, Minneapolis, MN 55455, USA; (A.W.); (K.A.C.)
| | - Kathrine A. Carosella
- Department of Psychology, University of Minnesota, Minneapolis, MN 55455, USA; (A.W.); (K.A.C.)
| | | | - Ana Bortnova
- Minnesota Department of Health and Human Services, Saint Paul, MN 55101, USA;
| | - Kristina Reigstad
- Department of Psychiatry and Behavioral Sciences, University of Minnesota, Minneapolis, MN 55454, USA; (Z.B.); (B.M.); (K.R.); (K.R.C.); (M.G.-S.)
| | - Kathryn R. Cullen
- Department of Psychiatry and Behavioral Sciences, University of Minnesota, Minneapolis, MN 55454, USA; (Z.B.); (B.M.); (K.R.); (K.R.C.); (M.G.-S.)
| | - Meredith Gunlicks-Stoessel
- Department of Psychiatry and Behavioral Sciences, University of Minnesota, Minneapolis, MN 55454, USA; (Z.B.); (B.M.); (K.R.); (K.R.C.); (M.G.-S.)
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50
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Herrman H, Patel V, Kieling C, Berk M, Buchweitz C, Cuijpers P, Furukawa TA, Kessler RC, Kohrt BA, Maj M, McGorry P, Reynolds CF, Weissman MM, Chibanda D, Dowrick C, Howard LM, Hoven CW, Knapp M, Mayberg HS, Penninx BWJH, Xiao S, Trivedi M, Uher R, Vijayakumar L, Wolpert M. Time for united action on depression: a Lancet-World Psychiatric Association Commission. Lancet 2022; 399:957-1022. [PMID: 35180424 DOI: 10.1016/s0140-6736(21)02141-3] [Citation(s) in RCA: 301] [Impact Index Per Article: 150.5] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/19/2020] [Revised: 09/15/2021] [Accepted: 09/21/2021] [Indexed: 12/12/2022]
Affiliation(s)
- Helen Herrman
- Orygen, The National Centre of Excellence in Youth Mental Health, Parkville, VIC, Australia; Centre for Youth Mental Health, The University of Melbourne, Parkville, VIC, Australia.
| | - Vikram Patel
- Department of Global Health and Social Medicine, Harvard Medical School, Boston, MA, USA; Sangath, Goa, India; Department of Global Health and Population, Harvard T H Chan School of Public Health, Boston, MA, USA
| | - Christian Kieling
- Department of Psychiatry, School of Medicine, Universidade Federal do Rio Grande do Sul, Porto Alegre, Brazil; Child & Adolescent Psychiatry Division, Hospital de Clínicas de Porto Alegre, Porto Alegre, Brazil
| | - Michael Berk
- Deakin University, IMPACT Institute, Geelong, VIC, Australia
| | - Claudia Buchweitz
- Graduate Program in Psychiatry, Universidade Federal do Rio Grande do Sul, Porto Alegre, Brazil
| | - Pim Cuijpers
- Department of Clinical, Neuro and Developmental Psychology, Vrije Universiteit Amsterdam, Amsterdam, Netherlands
| | - Toshiaki A Furukawa
- Department of Health Promotion and Human Behavior, Kyoto University Graduate School of Medicine/School of Public Health, Kyoto, Japan
| | - Ronald C Kessler
- Department of Health Care Policy, Harvard Medical School, Boston, MA, USA
| | - Brandon A Kohrt
- Department of Psychiatry and Behavioral Sciences, George Washington University, Washington, DC, USA
| | - Mario Maj
- Department of Psychiatry, University of Campania L Vanvitelli, Naples, Italy
| | - Patrick McGorry
- Orygen, The National Centre of Excellence in Youth Mental Health, Parkville, VIC, Australia; Centre for Youth Mental Health, The University of Melbourne, Parkville, VIC, Australia
| | - Charles F Reynolds
- Department of Psychiatry, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
| | - Myrna M Weissman
- Columbia University Mailman School of Public Health, New York, NY, USA; Columbia University Vagelos College of Physicians and Surgeons, New York, NY, USA; New York State Psychiatric Institute, New York, NY, USA
| | - Dixon Chibanda
- Department of Psychiatry, University of Zimbabwe, Harare, Zimbabwe; Centre for Global Mental Health, The London School of Hygiene and Tropical Medicine, London, UK
| | - Christopher Dowrick
- Department of Primary Care and Mental Health, University of Liverpool, Liverpool, UK
| | - Louise M Howard
- Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Christina W Hoven
- Columbia University Mailman School of Public Health, New York, NY, USA; Columbia University Vagelos College of Physicians and Surgeons, New York, NY, USA; New York State Psychiatric Institute, New York, NY, USA
| | - Martin Knapp
- Care Policy and Evaluation Centre, London School of Economics and Political Science, London, UK
| | - Helen S Mayberg
- Departments of Neurology, Neurosurgery, Psychiatry and Neuroscience, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Brenda W J H Penninx
- Department of Psychiatry, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, Netherlands
| | - Shuiyuan Xiao
- Central South University Xiangya School of Public Health, Changsha, China
| | - Madhukar Trivedi
- Peter O'Donnell Jr Brain Institute and the Department of Psychiatry, The University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Rudolf Uher
- Department of Psychiatry, Dalhousie University, Halifax, Canada
| | - Lakshmi Vijayakumar
- Sneha, Suicide Prevention Centre and Voluntary Health Services, Chennai, India
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