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Long F, Chen Y, Zhang Q, Li Q, Wang Y, Wang Y, Li H, Zhao Y, McNamara RK, DelBello MP, Sweeney JA, Gong Q, Li F. Predicting treatment outcomes in major depressive disorder using brain magnetic resonance imaging: a meta-analysis. Mol Psychiatry 2024:10.1038/s41380-024-02710-6. [PMID: 39187625 DOI: 10.1038/s41380-024-02710-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/18/2024] [Revised: 08/18/2024] [Accepted: 08/19/2024] [Indexed: 08/28/2024]
Abstract
Recent studies have provided promising evidence that neuroimaging data can predict treatment outcomes for patients with major depressive disorder (MDD). As most of these studies had small sample sizes, a meta-analysis is warranted to identify the most robust findings and imaging modalities, and to compare predictive outcomes obtained in magnetic resonance imaging (MRI) and studies using clinical and demographic features. We conducted a literature search from database inception to July 22, 2023, to identify studies using pretreatment clinical or brain MRI features to predict treatment outcomes in patients with MDD. Two meta-analyses were conducted on clinical and MRI studies, respectively. The meta-regression was employed to explore the effects of covariates and compare the predictive performance between clinical and MRI groups, as well as across MRI modalities and intervention subgroups. Meta-analysis of 13 clinical studies yielded an area under the curve (AUC) of 0.73, while in 44 MRI studies, the AUC was 0.89. MRI studies showed a higher sensitivity than clinical studies (0.78 vs. 0.62, Z = 3.42, P = 0.001). In MRI studies, resting-state functional MRI (rsfMRI) exhibited a higher specificity than task-based fMRI (tbfMRI) (0.79 vs. 0.69, Z = -2.86, P = 0.004). No significant differences in predictive performance were found between structural and functional MRI, nor between different interventions. Of note, predictive MRI features for treatment outcomes in studies using antidepressants were predominantly located in the limbic and default mode networks, while studies of electroconvulsive therapy (ECT) were restricted mainly to the limbic network. Our findings suggest a promise for pretreatment brain MRI features to predict MDD treatment outcomes, outperforming clinical features. While tasks in tbfMRI studies differed, those studies overall had less predictive utility than rsfMRI data. Overlapping but distinct network-level measures predicted antidepressants and ECT outcomes. Future studies are needed to predict outcomes using multiple MRI features, and to clarify whether imaging features predict outcomes generally or differ depending on treatments.
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Affiliation(s)
- Fenghua Long
- Department of Radiology and Huaxi MR Research Center (HMRRC), Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital, Sichuan University, Chengdu, 610041, Sichuan Province, PR China
- Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, 610041, Sichuan, PR China
| | - Yufei Chen
- Department of Radiology and Huaxi MR Research Center (HMRRC), Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital, Sichuan University, Chengdu, 610041, Sichuan Province, PR China
- Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, 610041, Sichuan, PR China
| | - Qian Zhang
- Department of Radiology and Huaxi MR Research Center (HMRRC), Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital, Sichuan University, Chengdu, 610041, Sichuan Province, PR China
- Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, 610041, Sichuan, PR China
| | - Qian Li
- Department of Radiology and Huaxi MR Research Center (HMRRC), Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital, Sichuan University, Chengdu, 610041, Sichuan Province, PR China
- Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, 610041, Sichuan, PR China
| | - Yaxuan Wang
- Department of Radiology and Huaxi MR Research Center (HMRRC), Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital, Sichuan University, Chengdu, 610041, Sichuan Province, PR China
- Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, 610041, Sichuan, PR China
| | - Yitian Wang
- Department of Radiology and Huaxi MR Research Center (HMRRC), Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital, Sichuan University, Chengdu, 610041, Sichuan Province, PR China
- Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, 610041, Sichuan, PR China
| | - Haoran Li
- Department of Radiology and Huaxi MR Research Center (HMRRC), Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital, Sichuan University, Chengdu, 610041, Sichuan Province, PR China
- Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, 610041, Sichuan, PR China
| | - Youjin Zhao
- Department of Radiology and Huaxi MR Research Center (HMRRC), Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital, Sichuan University, Chengdu, 610041, Sichuan Province, PR China
- Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, 610041, Sichuan, PR China
| | - Robert K McNamara
- Department of Psychiatry and Behavioral Neuroscience, University of Cincinnati, Cincinnati, OH, 45219, USA
| | - Melissa P DelBello
- Department of Psychiatry and Behavioral Neuroscience, University of Cincinnati, Cincinnati, OH, 45219, USA
| | - John A Sweeney
- Department of Radiology and Huaxi MR Research Center (HMRRC), Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital, Sichuan University, Chengdu, 610041, Sichuan Province, PR China
- Department of Psychiatry and Behavioral Neuroscience, University of Cincinnati, Cincinnati, OH, 45219, USA
| | - Qiyong Gong
- Department of Radiology and Huaxi MR Research Center (HMRRC), Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital, Sichuan University, Chengdu, 610041, Sichuan Province, PR China
- Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, 610041, Sichuan, PR China
| | - Fei Li
- Department of Radiology and Huaxi MR Research Center (HMRRC), Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital, Sichuan University, Chengdu, 610041, Sichuan Province, PR China.
- Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, 610041, Sichuan, PR China.
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Prompiengchai S, Dunlop K. Breakthroughs and challenges for generating brain network-based biomarkers of treatment response in depression. Neuropsychopharmacology 2024:10.1038/s41386-024-01907-1. [PMID: 38951585 DOI: 10.1038/s41386-024-01907-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/29/2024] [Revised: 05/17/2024] [Accepted: 06/13/2024] [Indexed: 07/03/2024]
Abstract
Treatment outcomes widely vary for individuals diagnosed with major depressive disorder, implicating a need for deeper understanding of the biological mechanisms conferring a greater likelihood of response to a particular treatment. Our improved understanding of intrinsic brain networks underlying depression psychopathology via magnetic resonance imaging and other neuroimaging modalities has helped reveal novel and potentially clinically meaningful biological markers of response. And while we have made considerable progress in identifying such biomarkers over the last decade, particularly with larger, multisite trials, there are significant methodological and practical obstacles that need to be overcome to translate these markers into the clinic. The aim of this review is to review current literature on brain network structural and functional biomarkers of treatment response or selection in depression, with a specific focus on recent large, multisite trials reporting predictive accuracy of candidate biomarkers. Regarding pharmaco- and psychotherapy, we discuss candidate biomarkers, reporting that while we have identified candidate biomarkers of response to a single intervention, we need more trials that distinguish biomarkers between first-line treatments. Further, we discuss the ways prognostic neuroimaging may help to improve treatment outcomes to neuromodulation-based therapies, such as transcranial magnetic stimulation and deep brain stimulation. Lastly, we highlight obstacles and technical developments that may help to address the knowledge gaps in this area of research. Ultimately, integrating neuroimaging-derived biomarkers into clinical practice holds promise for enhancing treatment outcomes and advancing precision psychiatry strategies for depression management. By elucidating the neural predictors of treatment response and selection, we can move towards more individualized and effective depression interventions, ultimately improving patient outcomes and quality of life.
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Affiliation(s)
| | - Katharine Dunlop
- Centre for Depression and Suicide Studies, Unity Health Toronto, Toronto, ON, Canada.
- Keenan Research Centre for Biomedical Science, Unity Health Toronto, Toronto, ON, Canada.
- Department of Psychiatry and Institute of Medical Science, University of Toronto, Toronto, ON, Canada.
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3
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Al-Sharif NB, Zavaliangos-Petropulu A, Narr KL. A review of diffusion MRI in mood disorders: mechanisms and predictors of treatment response. Neuropsychopharmacology 2024:10.1038/s41386-024-01894-3. [PMID: 38902355 DOI: 10.1038/s41386-024-01894-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/08/2024] [Revised: 05/15/2024] [Accepted: 05/21/2024] [Indexed: 06/22/2024]
Abstract
By measuring the molecular diffusion of water molecules in brain tissue, diffusion MRI (dMRI) provides unique insight into the microstructure and structural connections of the brain in living subjects. Since its inception, the application of dMRI in clinical research has expanded our understanding of the possible biological bases of psychiatric disorders and successful responses to different therapeutic interventions. Here, we review the past decade of diffusion imaging-based investigations with a specific focus on studies examining the mechanisms and predictors of therapeutic response in people with mood disorders. We present a brief overview of the general application of dMRI and key methodological developments in the field that afford increasingly detailed information concerning the macro- and micro-structural properties and connectivity patterns of white matter (WM) pathways and their perturbation over time in patients followed prospectively while undergoing treatment. This is followed by a more in-depth summary of particular studies using dMRI approaches to examine mechanisms and predictors of clinical outcomes in patients with unipolar or bipolar depression receiving pharmacological, neurostimulation, or behavioral treatments. Limitations associated with dMRI research in general and with treatment studies in mood disorders specifically are discussed, as are directions for future research. Despite limitations and the associated discrepancies in findings across individual studies, evolving research strongly indicates that the field is on the precipice of identifying and validating dMRI biomarkers that could lead to more successful personalized treatment approaches and could serve as targets for evaluating the neural effects of novel treatments.
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Affiliation(s)
- Noor B Al-Sharif
- Departments of Neurology and Psychiatry and Biobehavioral Sciences, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA.
| | - Artemis Zavaliangos-Petropulu
- Departments of Neurology and Psychiatry and Biobehavioral Sciences, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
| | - Katherine L Narr
- Departments of Neurology and Psychiatry and Biobehavioral Sciences, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
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Singh P, Vasundhara B, Das N, Sharma R, Kumar A, Datusalia AK. Metabolomics in Depression: What We Learn from Preclinical and Clinical Evidences. Mol Neurobiol 2024:10.1007/s12035-024-04302-5. [PMID: 38898199 DOI: 10.1007/s12035-024-04302-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2023] [Accepted: 06/11/2024] [Indexed: 06/21/2024]
Abstract
Depression is one of the predominant common mental illnesses that affects millions of people of all ages worldwide. Random mood changes, loss of interest in routine activities, and prevalent unpleasant senses often characterize this common depreciated mental illness. Subjects with depressive disorders have a likelihood of developing cardiovascular complications, diabesity, and stroke. The exact genesis and pathogenesis of this disease are still questionable. A significant proportion of subjects with clinical depression display inadequate response to antidepressant therapies. Hence, clinicians often face challenges in predicting the treatment response. Emerging reports have indicated the association of depression with metabolic alterations. Metabolomics is one of the promising approaches that can offer fresh perspectives into the diagnosis, treatment, and prognosis of depression at the metabolic level. Despite numerous studies exploring metabolite profiles post-pharmacological interventions, a quantitative understanding of consistently altered metabolites is not yet established. The article gives a brief discussion on different biomarkers in depression and the degree to which biomarkers can improve treatment outcomes. In this review article, we have systemically reviewed the role of metabolomics in depression along with current challenges and future perspectives.
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Affiliation(s)
- Pooja Singh
- Department of Pharmacology and Toxicology, National Institute of Pharmaceutical Education and Research (NIPER), Raebareli, 226002, India
| | - Boosani Vasundhara
- Department of Pharmacology and Toxicology, National Institute of Pharmaceutical Education and Research (NIPER), Raebareli, 226002, India
| | - Nabanita Das
- Department of Pharmacology and Toxicology, National Institute of Pharmaceutical Education and Research (NIPER), Raebareli, 226002, India
| | - Ruchika Sharma
- Centre for Precision Medicine and Centre, Delhi Pharmaceutical Sciences and Research University (DPSRU), New Delhi, 110017, India
| | - Anoop Kumar
- Department of Pharmacology, Delhi Pharmaceutical Sciences and Research University (DPSRU), New Delhi, 110017, India
| | - Ashok Kumar Datusalia
- Department of Pharmacology and Toxicology, National Institute of Pharmaceutical Education and Research (NIPER), Raebareli, 226002, India.
- Department of Regulatory Toxicology, National Institute of Pharmaceutical Education and Research (NIPER), Raebareli, 226002, India.
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5
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Ayyash S, Davis AD, Alders GL, MacQueen G, Strother SC, Hassel S, Zamyadi M, Arnott SR, Harris JK, Lam RW, Milev R, Müller DJ, Kennedy SH, Rotzinger S, Frey BN, Minuzzi L, Hall GB. Assessing remission in major depressive disorder using a functional-structural data fusion pipeline: A CAN-BIND-1 study. IBRO Neurosci Rep 2024; 16:135-146. [PMID: 38293679 PMCID: PMC10826332 DOI: 10.1016/j.ibneur.2023.12.011] [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: 09/24/2023] [Accepted: 12/30/2023] [Indexed: 02/01/2024] Open
Abstract
Neural network-level changes underlying symptom remission in major depressive disorder (MDD) are often studied from a single perspective. Multimodal approaches to assess neuropsychiatric disorders are evolving, as they offer richer information about brain networks. A FATCAT-awFC pipeline was developed to integrate a computationally intense data fusion method with a toolbox, to produce a faster and more intuitive pipeline for combining functional connectivity with structural connectivity (denoted as anatomically weighted functional connectivity (awFC)). Ninety-three participants from the Canadian Biomarker Integration Network for Depression study (CAN-BIND-1) were included. Patients with MDD were treated with 8 weeks of escitalopram and adjunctive aripiprazole for another 8 weeks. Between-group connectivity (SC, FC, awFC) comparisons contrasted remitters (REM) with non-remitters (NREM) at baseline and 8 weeks. Additionally, a longitudinal study analysis was performed to compare connectivity changes across time for REM, from baseline to week-8. Association between cognitive variables and connectivity were also assessed. REM were distinguished from NREM by lower awFC within the default mode, frontoparietal, and ventral attention networks. Compared to REM at baseline, REM at week-8 revealed increased awFC within the dorsal attention network and decreased awFC within the frontoparietal network. A medium effect size was observed for most results. AwFC in the frontoparietal network was associated with neurocognitive index and cognitive flexibility for the NREM group at week-8. In conclusion, the FATCAT-awFC pipeline has the benefit of providing insight on the 'full picture' of connectivity changes for REMs and NREMs while making for an easy intuitive approach.
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Affiliation(s)
- Sondos Ayyash
- School of Biomedical Engineering, McMaster University, Hamilton, Ontario, Canada
- Department of Psychology, Neuroscience & Behaviour, McMaster University, Hamilton, Ontario, Canada
| | - Andrew D Davis
- Department of Psychiatry and Behavioural Neurosciences, McMaster University, Hamilton, Ontario, Canada
- Rotman Research Institute, Baycrest, Toronto, Ontario, Canada
| | - Gésine L Alders
- Neuroscience Graduate Program, McMaster University, Hamilton, Ontario, Canada
| | - Glenda MacQueen
- Hotchkiss Brain Institute and Mathison Centre for Mental Health Research and Education, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada
- Department of Psychiatry, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada
| | - Stephen C Strother
- Rotman Research Institute, Baycrest, Toronto, Ontario, Canada
- Institute of Medical Science, University of Toronto, Toronto, Ontario, Canada
- Department of Medical Biophysics, University of Toronto, Ontario, Canada
| | - Stefanie Hassel
- Hotchkiss Brain Institute and Mathison Centre for Mental Health Research and Education, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada
- Department of Psychiatry, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada
| | - Mojdeh Zamyadi
- Rotman Research Institute, Baycrest, Toronto, Ontario, Canada
| | | | - Jacqueline K Harris
- Department of Computer Science, University of Alberta, Edmonton, Alberta, Canada
| | - Raymond W Lam
- Department of Psychiatry, University of British Columbia, Vancouver, British Columbia, Canada
| | - Roumen Milev
- Departments of Psychiatry and Psychology, Queen's University, Providence Care Hospital, Kingston, Ontario, Canada
| | - Daniel J Müller
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, Ontario, Canada
- Department of Psychiatry, University of Toronto, Toronto, Ontario, Canada
| | - Sidney H Kennedy
- Institute of Medical Science, University of Toronto, Toronto, Ontario, Canada
- Department of Psychiatry, University of Toronto, Toronto, Ontario, Canada
- Centre for Mental Health, University Health Network, Toronto, Ontario, Canada
- Krembil Research Institute, University Health Network, Toronto, Ontario, Canada
- Centre for Depression and Suicide Studies, and Li Ka Shing Knowledge Institute, St. Michael's Hospital, Toronto, Ontario, Canada
| | - Susan Rotzinger
- Department of Psychiatry, University of Toronto, Toronto, Ontario, Canada
- Krembil Research Institute, University Health Network, Toronto, Ontario, Canada
- Centre for Depression and Suicide Studies, and Li Ka Shing Knowledge Institute, St. Michael's Hospital, Toronto, Ontario, Canada
| | - Benicio N Frey
- Department of Psychiatry and Behavioural Neurosciences, McMaster University, Hamilton, Ontario, Canada
- Neuroscience Graduate Program, McMaster University, Hamilton, Ontario, Canada
- Mood Disorders Treatment and Research Centre and Women's Health Concerns Clinic, St. Joseph's Healthcare, Hamilton, Ontario, Canada
| | - Luciano Minuzzi
- Department of Psychiatry and Behavioural Neurosciences, McMaster University, Hamilton, Ontario, Canada
- Neuroscience Graduate Program, McMaster University, Hamilton, Ontario, Canada
- Mood Disorders Treatment and Research Centre and Women's Health Concerns Clinic, St. Joseph's Healthcare, Hamilton, Ontario, Canada
| | - Geoffrey B Hall
- School of Biomedical Engineering, McMaster University, Hamilton, Ontario, Canada
- Department of Psychology, Neuroscience & Behaviour, McMaster University, Hamilton, Ontario, Canada
- Department of Psychiatry and Behavioural Neurosciences, McMaster University, Hamilton, Ontario, Canada
- Neuroscience Graduate Program, McMaster University, Hamilton, Ontario, Canada
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Lu B, Chen X, Xavier Castellanos F, Thompson PM, Zuo XN, Zang YF, Yan CG. The power of many brains: Catalyzing neuropsychiatric discovery through open neuroimaging data and large-scale collaboration. Sci Bull (Beijing) 2024; 69:1536-1555. [PMID: 38519398 DOI: 10.1016/j.scib.2024.03.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2023] [Revised: 12/12/2023] [Accepted: 02/27/2024] [Indexed: 03/24/2024]
Abstract
Recent advances in open neuroimaging data are enhancing our comprehension of neuropsychiatric disorders. By pooling images from various cohorts, statistical power has increased, enabling the detection of subtle abnormalities and robust associations, and fostering new research methods. Global collaborations in imaging have furthered our knowledge of the neurobiological foundations of brain disorders and aided in imaging-based prediction for more targeted treatment. Large-scale magnetic resonance imaging initiatives are driving innovation in analytics and supporting generalizable psychiatric studies. We also emphasize the significant role of big data in understanding neural mechanisms and in the early identification and precise treatment of neuropsychiatric disorders. However, challenges such as data harmonization across different sites, privacy protection, and effective data sharing must be addressed. With proper governance and open science practices, we conclude with a projection of how large-scale imaging resources and collaborations could revolutionize diagnosis, treatment selection, and outcome prediction, contributing to optimal brain health.
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Affiliation(s)
- Bin Lu
- CAS Key Laboratory of Behavioral Science, Institute of Psychology, Beijing 100101, China; Department of Psychology, University of Chinese Academy of Sciences, Beijing 100101, China
| | - Xiao Chen
- CAS Key Laboratory of Behavioral Science, Institute of Psychology, Beijing 100101, China; Department of Psychology, University of Chinese Academy of Sciences, Beijing 100101, China
| | - Francisco Xavier Castellanos
- Department of Child and Adolescent Psychiatry, NYU Grossman School of Medicine, New York 10016, USA; Nathan Kline Institute for Psychiatric Research, Orangeburg 10962, USA
| | - Paul M Thompson
- Imaging Genetics Center, Mark & Mary Stevens Institute for Neuroimaging & Informatics, Keck School of Medicine, University of Southern California, Los Angeles 90033, USA
| | - Xi-Nian Zuo
- Developmental Population Neuroscience Research Center, IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China; National Basic Science Data Center, Beijing 100190, China
| | - Yu-Feng Zang
- Centre for Cognition and Brain Disorders, The Affiliated Hospital of Hangzhou Normal University, Hangzhou 310004, China; Institute of Psychological Science, Hangzhou Normal University, Hangzhou 310030, China; Zhejiang Key Laboratory for Research in Assessment of Cognitive Impairment, Hangzhou 311121, China
| | - Chao-Gan Yan
- CAS Key Laboratory of Behavioral Science, Institute of Psychology, Beijing 100101, China; Department of Psychology, University of Chinese Academy of Sciences, Beijing 100101, China; International Big-Data Center for Depression Research, Institute of Psychology, Chinese Academy of Sciences, Beijing 100101, China; Magnetic Resonance Imaging Research Center, Institute of Psychology, Chinese Academy of Sciences, Beijing 100101, China.
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7
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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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8
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Gai Q, Chu T, Li Q, Guo Y, Ma H, Shi Y, Che K, Zhao F, Dong F, Li Y, Xie H, Mao N. Altered intersubject functional variability of brain white-matter in major depressive disorder and its association with gene expression profiles. Hum Brain Mapp 2024; 45:e26670. [PMID: 38553866 PMCID: PMC10980843 DOI: 10.1002/hbm.26670] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2023] [Revised: 03/02/2024] [Accepted: 03/13/2024] [Indexed: 04/02/2024] Open
Abstract
Major depressive disorder (MDD) is a clinically heterogeneous disorder. Its mechanism is still unknown. Although the altered intersubject variability in functional connectivity (IVFC) within gray-matter has been reported in MDD, the alterations to IVFC within white-matter (WM-IVFC) remain unknown. Based on the resting-state functional MRI data of discovery (145 MDD patients and 119 healthy controls [HCs]) and validation cohorts (54 MDD patients, and 78 HCs), we compared the WM-IVFC between the two groups. We further assessed the meta-analytic cognitive functions related to the alterations. The discriminant WM-IVFC values were used to classify MDD patients and predict clinical symptoms in patients. In combination with the Allen Human Brain Atlas, transcriptome-neuroimaging association analyses were further conducted to investigate gene expression profiles associated with WM-IVFC alterations in MDD, followed by a set of gene functional characteristic analyses. We found extensive WM-IVFC alterations in MDD compared to HCs, which were associated with multiple behavioral domains, including sensorimotor processes and higher-order functions. The discriminant WM-IVFC could not only effectively distinguish MDD patients from HCs with an area under curve ranging from 0.889 to 0.901 across three classifiers, but significantly predict depression severity (r = 0.575, p = 0.002) and suicide risk (r = 0.384, p = 0.040) in patients. Furthermore, the variability-related genes were enriched for synapse, neuronal system, and ion channel, and predominantly expressed in excitatory and inhibitory neurons. Our results obtained good reproducibility in the validation cohort. These findings revealed intersubject functional variability changes of brain WM in MDD and its linkage with gene expression profiles, providing potential implications for understanding the high clinical heterogeneity of MDD.
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Affiliation(s)
- Qun Gai
- Department of Radiology, Yantai Yuhuangding HospitalQingdao UniversityYantaiShandongChina
- Big Data & Artificial Intelligence LaboratoryYantai Yuhuangding HospitalYantaiShandongChina
- Shandong Provincial Key Medical and Health Laboratory of Intelligent Diagnosis and Treatment for Women's DiseasesYantai Yuhuangding HospitalYantaiShandongChina
| | - Tongpeng Chu
- Department of Radiology, Yantai Yuhuangding HospitalQingdao UniversityYantaiShandongChina
- Big Data & Artificial Intelligence LaboratoryYantai Yuhuangding HospitalYantaiShandongChina
- Shandong Provincial Key Medical and Health Laboratory of Intelligent Diagnosis and Treatment for Women's DiseasesYantai Yuhuangding HospitalYantaiShandongChina
| | - Qinghe Li
- School of Medical ImagingBinzhou Medical UniversityYantaiShandongChina
| | - Yuting Guo
- School of Medical ImagingBinzhou Medical UniversityYantaiShandongChina
| | - Heng Ma
- Department of Radiology, Yantai Yuhuangding HospitalQingdao UniversityYantaiShandongChina
| | - Yinghong Shi
- Department of Radiology, Yantai Yuhuangding HospitalQingdao UniversityYantaiShandongChina
| | - Kaili Che
- Department of Radiology, Yantai Yuhuangding HospitalQingdao UniversityYantaiShandongChina
| | - Feng Zhao
- School of Computer Science and TechnologyShandong Technology and Business UniversityYantaiShandongChina
| | - Fanghui Dong
- School of Medical ImagingBinzhou Medical UniversityYantaiShandongChina
| | - Yuna Li
- Department of Radiology, Beijing Tiantan HospitalCapital Medical UniversityBeijingChina
| | - Haizhu Xie
- Department of Radiology, Yantai Yuhuangding HospitalQingdao UniversityYantaiShandongChina
| | - Ning Mao
- Department of Radiology, Yantai Yuhuangding HospitalQingdao UniversityYantaiShandongChina
- Big Data & Artificial Intelligence LaboratoryYantai Yuhuangding HospitalYantaiShandongChina
- Shandong Provincial Key Medical and Health Laboratory of Intelligent Diagnosis and Treatment for Women's DiseasesYantai Yuhuangding HospitalYantaiShandongChina
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9
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Poirot MG, Ruhe HG, Mutsaerts HJMM, Maximov II, Groote IR, Bjørnerud A, Marquering HA, Reneman L, Caan MWA. Treatment Response Prediction in Major Depressive Disorder Using Multimodal MRI and Clinical Data: Secondary Analysis of a Randomized Clinical Trial. Am J Psychiatry 2024; 181:223-233. [PMID: 38321916 DOI: 10.1176/appi.ajp.20230206] [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: 02/08/2024]
Abstract
OBJECTIVE Response to antidepressant treatment in major depressive disorder varies substantially between individuals, which lengthens the process of finding effective treatment. The authors sought to determine whether a multimodal machine learning approach could predict early sertraline response in patients with major depressive disorder. They assessed the predictive contribution of MR neuroimaging and clinical assessments at baseline and after 1 week of treatment. METHODS This was a preregistered secondary analysis of data from the Establishing Moderators and Biosignatures of Antidepressant Response in Clinical Care (EMBARC) study, a multisite double-blind, placebo-controlled randomized clinical trial that included 296 adult outpatients with unmedicated recurrent or chronic major depressive disorder. MR neuroimaging and clinical data were collected before and after 1 week of treatment. Performance in predicting response and remission, collected after 8 weeks, was quantified using balanced accuracy (bAcc) and area under the receiver operating characteristic curve (AUROC) scores. RESULTS A total of 229 patients were included in the analyses (mean age, 38 years [SD=13]; 66% female). Internal cross-validation performance in predicting response to sertraline (bAcc=68% [SD=10], AUROC=0.73 [SD=0.03]) was significantly better than chance. External cross-validation on data from placebo nonresponders (bAcc=62%, AUROC=0.66) and placebo nonresponders who were switched to sertraline (bAcc=65%, AUROC=0.68) resulted in differences that suggest specificity for sertraline treatment compared with placebo treatment. Finally, multimodal models outperformed unimodal models. CONCLUSIONS The study results confirm that early sertraline treatment response can be predicted; that the models are sertraline specific compared with placebo; that prediction benefits from integrating multimodal MRI data with clinical data; and that perfusion imaging contributes most to these predictions. Using this approach, a lean and effective protocol could individualize sertraline treatment planning to improve psychiatric care.
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Affiliation(s)
- Maarten G Poirot
- Department of Radiology and Nuclear Medicine (Poirot, Ruhe, Marquering, Reneman) and Department of Biomedical Engineering and Physics (Poirot, Marquering, Reneman, Caan), Amsterdam UMC Location AMC, University of Amsterdam, Amsterdam; Brain Imaging, Amsterdam Neuroscience, Amsterdam (Poirot, Mutsaerts, Reneman, Caan); Department of Psychiatry, Radboudumc, Nijmegen, the Netherlands (Ruhe); Donders Institute for Brain, Cognition, and Behavior, Radboud University, Nijmegen, the Netherlands (Ruhe); Department of Radiology and Nuclear Medicine, Amsterdam UMC location, Vrije Universiteit Amsterdam, Amsterdam (Mutsaerts); Department of Health and Functioning, Western Norway University of Applied Sciences, Bergen (Maximov, Bjørnerud); Division of Radiology, Vestfold Hospital Trust, Tønsberg, Norway (Groote, Caan); Computational Radiology and Artificial Intelligence, Division of Radiology and Nuclear Medicine, Oslo University Hospital, Oslo (Groote, Bjørnerud); Department of Psychology, University of Oslo, Oslo (Bjørnerud)
| | - Henricus G Ruhe
- Department of Radiology and Nuclear Medicine (Poirot, Ruhe, Marquering, Reneman) and Department of Biomedical Engineering and Physics (Poirot, Marquering, Reneman, Caan), Amsterdam UMC Location AMC, University of Amsterdam, Amsterdam; Brain Imaging, Amsterdam Neuroscience, Amsterdam (Poirot, Mutsaerts, Reneman, Caan); Department of Psychiatry, Radboudumc, Nijmegen, the Netherlands (Ruhe); Donders Institute for Brain, Cognition, and Behavior, Radboud University, Nijmegen, the Netherlands (Ruhe); Department of Radiology and Nuclear Medicine, Amsterdam UMC location, Vrije Universiteit Amsterdam, Amsterdam (Mutsaerts); Department of Health and Functioning, Western Norway University of Applied Sciences, Bergen (Maximov, Bjørnerud); Division of Radiology, Vestfold Hospital Trust, Tønsberg, Norway (Groote, Caan); Computational Radiology and Artificial Intelligence, Division of Radiology and Nuclear Medicine, Oslo University Hospital, Oslo (Groote, Bjørnerud); Department of Psychology, University of Oslo, Oslo (Bjørnerud)
| | - Henk-Jan M M Mutsaerts
- Department of Radiology and Nuclear Medicine (Poirot, Ruhe, Marquering, Reneman) and Department of Biomedical Engineering and Physics (Poirot, Marquering, Reneman, Caan), Amsterdam UMC Location AMC, University of Amsterdam, Amsterdam; Brain Imaging, Amsterdam Neuroscience, Amsterdam (Poirot, Mutsaerts, Reneman, Caan); Department of Psychiatry, Radboudumc, Nijmegen, the Netherlands (Ruhe); Donders Institute for Brain, Cognition, and Behavior, Radboud University, Nijmegen, the Netherlands (Ruhe); Department of Radiology and Nuclear Medicine, Amsterdam UMC location, Vrije Universiteit Amsterdam, Amsterdam (Mutsaerts); Department of Health and Functioning, Western Norway University of Applied Sciences, Bergen (Maximov, Bjørnerud); Division of Radiology, Vestfold Hospital Trust, Tønsberg, Norway (Groote, Caan); Computational Radiology and Artificial Intelligence, Division of Radiology and Nuclear Medicine, Oslo University Hospital, Oslo (Groote, Bjørnerud); Department of Psychology, University of Oslo, Oslo (Bjørnerud)
| | - Ivan I Maximov
- Department of Radiology and Nuclear Medicine (Poirot, Ruhe, Marquering, Reneman) and Department of Biomedical Engineering and Physics (Poirot, Marquering, Reneman, Caan), Amsterdam UMC Location AMC, University of Amsterdam, Amsterdam; Brain Imaging, Amsterdam Neuroscience, Amsterdam (Poirot, Mutsaerts, Reneman, Caan); Department of Psychiatry, Radboudumc, Nijmegen, the Netherlands (Ruhe); Donders Institute for Brain, Cognition, and Behavior, Radboud University, Nijmegen, the Netherlands (Ruhe); Department of Radiology and Nuclear Medicine, Amsterdam UMC location, Vrije Universiteit Amsterdam, Amsterdam (Mutsaerts); Department of Health and Functioning, Western Norway University of Applied Sciences, Bergen (Maximov, Bjørnerud); Division of Radiology, Vestfold Hospital Trust, Tønsberg, Norway (Groote, Caan); Computational Radiology and Artificial Intelligence, Division of Radiology and Nuclear Medicine, Oslo University Hospital, Oslo (Groote, Bjørnerud); Department of Psychology, University of Oslo, Oslo (Bjørnerud)
| | - Inge R Groote
- Department of Radiology and Nuclear Medicine (Poirot, Ruhe, Marquering, Reneman) and Department of Biomedical Engineering and Physics (Poirot, Marquering, Reneman, Caan), Amsterdam UMC Location AMC, University of Amsterdam, Amsterdam; Brain Imaging, Amsterdam Neuroscience, Amsterdam (Poirot, Mutsaerts, Reneman, Caan); Department of Psychiatry, Radboudumc, Nijmegen, the Netherlands (Ruhe); Donders Institute for Brain, Cognition, and Behavior, Radboud University, Nijmegen, the Netherlands (Ruhe); Department of Radiology and Nuclear Medicine, Amsterdam UMC location, Vrije Universiteit Amsterdam, Amsterdam (Mutsaerts); Department of Health and Functioning, Western Norway University of Applied Sciences, Bergen (Maximov, Bjørnerud); Division of Radiology, Vestfold Hospital Trust, Tønsberg, Norway (Groote, Caan); Computational Radiology and Artificial Intelligence, Division of Radiology and Nuclear Medicine, Oslo University Hospital, Oslo (Groote, Bjørnerud); Department of Psychology, University of Oslo, Oslo (Bjørnerud)
| | - Atle Bjørnerud
- Department of Radiology and Nuclear Medicine (Poirot, Ruhe, Marquering, Reneman) and Department of Biomedical Engineering and Physics (Poirot, Marquering, Reneman, Caan), Amsterdam UMC Location AMC, University of Amsterdam, Amsterdam; Brain Imaging, Amsterdam Neuroscience, Amsterdam (Poirot, Mutsaerts, Reneman, Caan); Department of Psychiatry, Radboudumc, Nijmegen, the Netherlands (Ruhe); Donders Institute for Brain, Cognition, and Behavior, Radboud University, Nijmegen, the Netherlands (Ruhe); Department of Radiology and Nuclear Medicine, Amsterdam UMC location, Vrije Universiteit Amsterdam, Amsterdam (Mutsaerts); Department of Health and Functioning, Western Norway University of Applied Sciences, Bergen (Maximov, Bjørnerud); Division of Radiology, Vestfold Hospital Trust, Tønsberg, Norway (Groote, Caan); Computational Radiology and Artificial Intelligence, Division of Radiology and Nuclear Medicine, Oslo University Hospital, Oslo (Groote, Bjørnerud); Department of Psychology, University of Oslo, Oslo (Bjørnerud)
| | - Henk A Marquering
- Department of Radiology and Nuclear Medicine (Poirot, Ruhe, Marquering, Reneman) and Department of Biomedical Engineering and Physics (Poirot, Marquering, Reneman, Caan), Amsterdam UMC Location AMC, University of Amsterdam, Amsterdam; Brain Imaging, Amsterdam Neuroscience, Amsterdam (Poirot, Mutsaerts, Reneman, Caan); Department of Psychiatry, Radboudumc, Nijmegen, the Netherlands (Ruhe); Donders Institute for Brain, Cognition, and Behavior, Radboud University, Nijmegen, the Netherlands (Ruhe); Department of Radiology and Nuclear Medicine, Amsterdam UMC location, Vrije Universiteit Amsterdam, Amsterdam (Mutsaerts); Department of Health and Functioning, Western Norway University of Applied Sciences, Bergen (Maximov, Bjørnerud); Division of Radiology, Vestfold Hospital Trust, Tønsberg, Norway (Groote, Caan); Computational Radiology and Artificial Intelligence, Division of Radiology and Nuclear Medicine, Oslo University Hospital, Oslo (Groote, Bjørnerud); Department of Psychology, University of Oslo, Oslo (Bjørnerud)
| | - Liesbeth Reneman
- Department of Radiology and Nuclear Medicine (Poirot, Ruhe, Marquering, Reneman) and Department of Biomedical Engineering and Physics (Poirot, Marquering, Reneman, Caan), Amsterdam UMC Location AMC, University of Amsterdam, Amsterdam; Brain Imaging, Amsterdam Neuroscience, Amsterdam (Poirot, Mutsaerts, Reneman, Caan); Department of Psychiatry, Radboudumc, Nijmegen, the Netherlands (Ruhe); Donders Institute for Brain, Cognition, and Behavior, Radboud University, Nijmegen, the Netherlands (Ruhe); Department of Radiology and Nuclear Medicine, Amsterdam UMC location, Vrije Universiteit Amsterdam, Amsterdam (Mutsaerts); Department of Health and Functioning, Western Norway University of Applied Sciences, Bergen (Maximov, Bjørnerud); Division of Radiology, Vestfold Hospital Trust, Tønsberg, Norway (Groote, Caan); Computational Radiology and Artificial Intelligence, Division of Radiology and Nuclear Medicine, Oslo University Hospital, Oslo (Groote, Bjørnerud); Department of Psychology, University of Oslo, Oslo (Bjørnerud)
| | - Matthan W A Caan
- Department of Radiology and Nuclear Medicine (Poirot, Ruhe, Marquering, Reneman) and Department of Biomedical Engineering and Physics (Poirot, Marquering, Reneman, Caan), Amsterdam UMC Location AMC, University of Amsterdam, Amsterdam; Brain Imaging, Amsterdam Neuroscience, Amsterdam (Poirot, Mutsaerts, Reneman, Caan); Department of Psychiatry, Radboudumc, Nijmegen, the Netherlands (Ruhe); Donders Institute for Brain, Cognition, and Behavior, Radboud University, Nijmegen, the Netherlands (Ruhe); Department of Radiology and Nuclear Medicine, Amsterdam UMC location, Vrije Universiteit Amsterdam, Amsterdam (Mutsaerts); Department of Health and Functioning, Western Norway University of Applied Sciences, Bergen (Maximov, Bjørnerud); Division of Radiology, Vestfold Hospital Trust, Tønsberg, Norway (Groote, Caan); Computational Radiology and Artificial Intelligence, Division of Radiology and Nuclear Medicine, Oslo University Hospital, Oslo (Groote, Bjørnerud); Department of Psychology, University of Oslo, Oslo (Bjørnerud)
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10
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Videtta G, Squarcina L, Prunas C, Brambilla P, Delvecchio G. White matter integrity and medication response to antidepressants in major depressive disorder: a review of the literature. Front Psychiatry 2024; 14:1335706. [PMID: 38361831 PMCID: PMC10867229 DOI: 10.3389/fpsyt.2023.1335706] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/09/2023] [Accepted: 12/27/2023] [Indexed: 02/17/2024] Open
Abstract
Major Depressive Disorder (MDD) is a severe psychiatric disorder characterized by selective impairments in mood regulation, cognition and behavior. Although it is well-known that antidepressants can effectively treat moderate to severe depression, the biochemical effects of these medications on white matter (WM) integrity are still unclear. Therefore, the aim of the study is to review the main scientific evidence on the differences in WM integrity in responders and non-responders to antidepressant medications. A record search was performed on three datasets (PubMed, Scopus and Web of Science) and ten records matched our inclusion criteria. Overall, the reviewed studies highlighted a good efficacy of antidepressants in MDD treatment. Furthermore, there were differences in WM integrity between responders and non-responders, mainly localized in cingulate cortices, hippocampus and corpus callosum, where the former group showed higher fractional anisotropy and lower axial diffusivity values. Modifications in WM integrity might be partially explained by branching and proliferation as well as neurogenesis of axonal fibers mediated by antidepressants, which in turn may have positively affected brain metabolism and increase the quantity of the serotonergic neurotransmitter within synaptic clefts. However, the reviewed studies suffer from some limitations, including the heterogeneity in treatment duration, antidepressant administration, medical posology, and psychiatric comorbidities. Therefore, future studies are needed to reduce confounding effects of antidepressant medications and to adopt longitudinal and multimodal approaches in order to better characterize the differences in WM integrity between responders and non-responders.
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Affiliation(s)
- Giovanni Videtta
- Department of Biomedical Sciences for Health, University of Milan, Milan, Italy
| | - Letizia Squarcina
- Department of Pathophysiology and Transplantation, University of Milan, Milan, Italy
| | - Cecilia Prunas
- Department of Neurosciences and Mental Health, Fondazione IRCCS Ca’ Granda Ospedale Maggiore Policlinico, Milan, Italy
| | - Paolo Brambilla
- Department of Pathophysiology and Transplantation, University of Milan, Milan, Italy
- Department of Neurosciences and Mental Health, Fondazione IRCCS Ca’ Granda Ospedale Maggiore Policlinico, Milan, Italy
| | - Giuseppe Delvecchio
- Department of Neurosciences and Mental Health, Fondazione IRCCS Ca’ Granda Ospedale Maggiore Policlinico, Milan, Italy
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11
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Thomas PJ, Leow A, Klumpp H, Phan KL, Ajilore O. Default Mode Network Hypoalignment of Function to Structure Correlates With Depression and Rumination. BIOLOGICAL PSYCHIATRY. COGNITIVE NEUROSCIENCE AND NEUROIMAGING 2024; 9:101-111. [PMID: 37468065 DOI: 10.1016/j.bpsc.2023.06.008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/13/2023] [Revised: 06/06/2023] [Accepted: 06/30/2023] [Indexed: 07/21/2023]
Abstract
BACKGROUND Recent studies have begun to examine how signals in the brain correspond to the underlying white matter structure using tools from the field of graph signal processing to quantify brain function alignment to brain network topology. Here, we applied this framework for the first time toward a transdiagnostic cohort of individuals with internalizing psychopathologies, including mood and anxiety disorders, to uncover how such alignment within the default mode network (DMN) is related to depression and rumination symptoms. METHODS Both diffusion-weighted and resting-state functional magnetic resonance imaging were obtained from participants at baseline (n = 60 patients, n = 19 healthy control participants). Patients were randomized to 12 weeks of treatment with either a selective serotonin reuptake inhibitor or cognitive behavioral therapy, and symptom scales were readministered posttreatment (n = 46 patients at follow-up). Using graph signal processing methodology, we quantified the alignment of functional signals to their underlying white matter structural networks. RESULTS We found that signal alignment within the posterior DMN was decreased in patients with internalizing psychopathologies compared with healthy control participants and was inversely (negatively) correlated with baseline depression and rumination scales. Signal alignment within the posterior DMN was also correlated with the ratio of total within-DMN to extra-DMN functional connectivity for these regions. CONCLUSIONS These findings are consistent with previous literature regarding pathological promiscuity of posterior DMN connectivity and provide the first graph signal processing-based analyses in a transdiagnostic cohort of patients with internalizing psychopathologies.
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Affiliation(s)
- Paul J Thomas
- Department of Biomedical Engineering, University of Illinois Chicago, Chicago, Illinois
| | - Alex Leow
- Department of Biomedical Engineering, University of Illinois Chicago, Chicago, Illinois
| | - Heide Klumpp
- Department of Psychiatry & Behavioral Health, University of Illinois Chicago, Chicago, Illinois
| | - K Luan Phan
- Department of Psychiatry, The Ohio State University, Columbus, Ohio
| | - Olusola Ajilore
- Department of Psychiatry & Behavioral Health, University of Illinois Chicago, Chicago, Illinois.
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12
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Dadkhah M, Jafarzadehgharehziaaddin M, Molaei S, Akbari M, Gholizadeh N, Fathi F. Major depressive disorder: biomarkers and biosensors. Clin Chim Acta 2023:117437. [PMID: 37315724 DOI: 10.1016/j.cca.2023.117437] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2023] [Revised: 06/01/2023] [Accepted: 06/07/2023] [Indexed: 06/16/2023]
Abstract
Depressive disorders belong to highly heterogeneous psychiatric diseases. Loss of in interest in previously enjoyed activities and a depressed mood are the main characteristics of major depressive disorder (MDD). Moreover, due to significant heterogeneity in clinical presentation and lack of applicable biomarkers, diagnosis and treatment remains challenging. Identification of relevant biomarkers would allow for improved disease classification and more personalized treatment strategies. Herein, we review the current state of these biomarkers and then discuss diagnostic techniques of aimed to specifically target these analytes using state of the art biosensor technology.
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Affiliation(s)
- Masoomeh Dadkhah
- Pharmaceutical Sciences Research Center, Ardabil University of Medical Sciences, Ardabil, Iran
| | | | - Soheila Molaei
- Zoonoses Research Center, Ardabil University of Medical Sciences, Ardabil, Iran
| | - Morteza Akbari
- Immunology Research Center (IRC), Tabriz University of Medical Sciences, Tabriz, Iran
| | - Neghin Gholizadeh
- Students Research Committee, Health School, Ardabil University of Medical Sciences, Ardabil, Iran
| | - Farzaneh Fathi
- Pharmaceutical Sciences Research Center, Ardabil University of Medical Sciences, Ardabil, Iran; Biosensor Sciences and Technologies Research Center, Ardabil University of Medical Sciences, Ardabil, Iran.
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13
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Tura A, Goya-Maldonado R. Brain connectivity in major depressive disorder: a precision component of treatment modalities? Transl Psychiatry 2023; 13:196. [PMID: 37296121 DOI: 10.1038/s41398-023-02499-y] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/17/2021] [Revised: 05/15/2023] [Accepted: 05/30/2023] [Indexed: 06/12/2023] Open
Abstract
Major depressive disorder (MDD) is a very prevalent mental disorder that imposes an enormous burden on individuals, society, and health care systems. Most patients benefit from commonly used treatment methods such as pharmacotherapy, psychotherapy, electroconvulsive therapy (ECT), and repetitive transcranial magnetic stimulation (rTMS). However, the clinical decision on which treatment method to use remains generally informed and the individual clinical response is difficult to predict. Most likely, a combination of neural variability and heterogeneity in MDD still impedes a full understanding of the disorder, as well as influences treatment success in many cases. With the help of neuroimaging methods like functional magnetic resonance imaging (fMRI) and diffusion tensor imaging (DTI), the brain can be understood as a modular set of functional and structural networks. In recent years, many studies have investigated baseline connectivity biomarkers of treatment response and the connectivity changes after successful treatment. Here, we systematically review the literature and summarize findings from longitudinal interventional studies investigating the functional and structural connectivity in MDD. By compiling and discussing these findings, we recommend the scientific and clinical community to deepen the systematization of findings to pave the way for future systems neuroscience roadmaps that include brain connectivity parameters as a possible precision component of the clinical evaluation and therapeutic decision.
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Affiliation(s)
- Asude Tura
- Laboratory of Systems Neuroscience and Imaging in Psychiatry (SNIP-Lab), Department of Psychiatry and Psychotherapy, University Medical Center Göttingen (UMG), Göttingen, Germany
| | - Roberto Goya-Maldonado
- Laboratory of Systems Neuroscience and Imaging in Psychiatry (SNIP-Lab), Department of Psychiatry and Psychotherapy, University Medical Center Göttingen (UMG), Göttingen, Germany.
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14
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Zhou Y, Wei L, Gao S, Wang J, Hu Z. Characterization of diffusion magnetic resonance imaging revealing relationships between white matter disconnection and behavioral disturbances in mild cognitive impairment: a systematic review. Front Neurosci 2023; 17:1209378. [PMID: 37360170 PMCID: PMC10285107 DOI: 10.3389/fnins.2023.1209378] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2023] [Accepted: 05/23/2023] [Indexed: 06/28/2023] Open
Abstract
White matter disconnection is the primary cause of cognition and affection abnormality in mild cognitive impairment (MCI). Adequate understanding of behavioral disturbances, such as cognition and affection abnormality in MCI, can help to intervene and slow down the progression of Alzheimer's disease (AD) promptly. Diffusion MRI is a non-invasive and effective technique for studying white matter microstructure. This review searched the relevant papers published from 2010 to 2022. Sixty-nine studies using diffusion MRI for white matter disconnections associated with behavioral disturbances in MCI were screened. Fibers connected to the hippocampus and temporal lobe were associated with cognition decline in MCI. Fibers connected to the thalamus were associated with both cognition and affection abnormality. This review summarized the correspondence between white matter disconnections and behavioral disturbances such as cognition and affection, which provides a theoretical basis for the future diagnosis and treatment of AD.
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Affiliation(s)
- Yu Zhou
- College of Medical Technology and Engineering, Henan University of Science and Technology, Luoyang, China
| | - Lan Wei
- Business School, The University of Sydney, Sydney, NSW, Australia
| | - Song Gao
- College of Agricultural Equipment Engineering, Henan University of Science and Technology, Luoyang, China
| | - Jun Wang
- School of Information Engineering, Henan University of Science and Technology, Luoyang, China
| | - Zhigang Hu
- College of Medical Technology and Engineering, Henan University of Science and Technology, Luoyang, China
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15
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Abd-Alrazaq A, AlSaad R, Shuweihdi F, Ahmed A, Aziz S, Sheikh J. Systematic review and meta-analysis of performance of wearable artificial intelligence in detecting and predicting depression. NPJ Digit Med 2023; 6:84. [PMID: 37147384 PMCID: PMC10163239 DOI: 10.1038/s41746-023-00828-5] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2023] [Accepted: 04/19/2023] [Indexed: 05/07/2023] Open
Abstract
Given the limitations of traditional approaches, wearable artificial intelligence (AI) is one of the technologies that have been exploited to detect or predict depression. The current review aimed at examining the performance of wearable AI in detecting and predicting depression. The search sources in this systematic review were 8 electronic databases. Study selection, data extraction, and risk of bias assessment were carried out by two reviewers independently. The extracted results were synthesized narratively and statistically. Of the 1314 citations retrieved from the databases, 54 studies were included in this review. The pooled mean of the highest accuracy, sensitivity, specificity, and root mean square error (RMSE) was 0.89, 0.87, 0.93, and 4.55, respectively. The pooled mean of lowest accuracy, sensitivity, specificity, and RMSE was 0.70, 0.61, 0.73, and 3.76, respectively. Subgroup analyses revealed that there is a statistically significant difference in the highest accuracy, lowest accuracy, highest sensitivity, highest specificity, and lowest specificity between algorithms, and there is a statistically significant difference in the lowest sensitivity and lowest specificity between wearable devices. Wearable AI is a promising tool for depression detection and prediction although it is in its infancy and not ready for use in clinical practice. Until further research improve its performance, wearable AI should be used in conjunction with other methods for diagnosing and predicting depression. Further studies are needed to examine the performance of wearable AI based on a combination of wearable device data and neuroimaging data and to distinguish patients with depression from those with other diseases.
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Affiliation(s)
- Alaa Abd-Alrazaq
- AI Center for Precision Health, Weill Cornell Medicine-Qatar, Doha, Qatar.
| | - Rawan AlSaad
- AI Center for Precision Health, Weill Cornell Medicine-Qatar, Doha, Qatar
- College of Computing and Information Technology, University of Doha for Science and Technology, Doha, Qatar
| | - Farag Shuweihdi
- School of Medicine, Leeds Institute of Health Sciences, University of Leeds, Leeds, UK
| | - Arfan Ahmed
- AI Center for Precision Health, Weill Cornell Medicine-Qatar, Doha, Qatar
| | - Sarah Aziz
- AI Center for Precision Health, Weill Cornell Medicine-Qatar, Doha, Qatar
| | - Javaid Sheikh
- AI Center for Precision Health, Weill Cornell Medicine-Qatar, Doha, Qatar
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16
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Bingham KS, Calarco N, Dickie EW, Alexopoulos GS, Butters MA, Meyers BS, Marino P, Neufeld NH, Rothschild AJ, Whyte EM, Mulsant BH, Flint AJ, Voineskos AN. The relationship of white matter microstructure with psychomotor disturbance and relapse in remitted psychotic depression. J Affect Disord 2023; 334:317-324. [PMID: 37149056 DOI: 10.1016/j.jad.2023.04.136] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/07/2022] [Revised: 04/06/2023] [Accepted: 04/29/2023] [Indexed: 05/08/2023]
Abstract
BACKGROUND Psychomotor disturbance is common in psychotic depression and is associated with relapse. In this analysis, we examined whether white matter microstructure is associated with relapse probability in psychotic depression and, if so, whether white matter microstructure accounts for the association between psychomotor disturbance and relapse. METHODS We used tractography to characterize diffusion-weighted MRI data in 80 participants enrolled in a randomized clinical trial that compared efficacy and tolerability of sertraline plus olanzapine with sertraline plus placebo in the continuation treatment of remitted psychotic depression. Cox proportional hazard models tested the relationships between psychomotor disturbance (processing speed and CORE score) at baseline, white matter microstructure (fractional anisotropy [FA] and mean diffusivity [MD]) in 15 selected tracts at baseline, and relapse probability. RESULTS CORE was significantly associated with relapse. Higher mean MD was significantly associated with relapse in the each of the following tracts: corpus callosum, left striato-frontal, left thalamo-frontal, and right thalamo-frontal. CORE and MD were each associated with relapse in the final models. LIMITATIONS As a secondary analysis with a small sample size, this study was not powered for its aims, and is vulnerable to types I and II statistical errors. Further, the sample size was not sufficient to test the interaction of the independent variables and randomized treatment group with relapse probability. CONCLUSIONS While both psychomotor disturbance and MD were associated with psychotic depression relapse, MD did not account for the relationship between psychomotor disturbance and relapse. The mechanism by which of psychomotor disturbance increases the risk of relapse requires further investigation. CLINICAL TRIAL REGISTRATION Study of the Pharmacotherapy of Psychotic Depression II (STOP-PD II); NCT01427608. URL: https://clinicaltrials.gov/ct2/show/NCT01427608.
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Affiliation(s)
- Kathleen S Bingham
- Centre for Mental Health, University Health Network, Toronto, Canada; Centre for Addiction and Mental Health, Toronto, Canada; Department of Psychiatry, Temerty Faculty of Medicine, University of Toronto, Canada.
| | - Navona Calarco
- Centre for Addiction and Mental Health, Toronto, Canada; Department of Psychiatry, Temerty Faculty of Medicine, University of Toronto, Canada
| | - Erin W Dickie
- Centre for Addiction and Mental Health, Toronto, Canada; Department of Psychiatry, Temerty Faculty of Medicine, University of Toronto, Canada
| | - George S Alexopoulos
- Department of Psychiatry, Weill Cornell Medicine of Cornell University and New York Presbyterian Hospital, Westchester Division, New York, USA
| | - Meryl A Butters
- Department of Psychiatry, University of Pittsburgh School of Medicine and UPMC Western Psychiatric Hospital, Pittsburgh, USA
| | - Barnett S Meyers
- Department of Psychiatry, Weill Cornell Medicine of Cornell University and New York Presbyterian Hospital, Westchester Division, New York, USA
| | - Patricia Marino
- Department of Psychiatry, Weill Cornell Medicine of Cornell University and New York Presbyterian Hospital, Westchester Division, New York, USA
| | - Nicholas H Neufeld
- Centre for Addiction and Mental Health, Toronto, Canada; Department of Psychiatry, Temerty Faculty of Medicine, University of Toronto, Canada
| | - Anthony J Rothschild
- University of Massachusetts Chan Medical School and UMass Memorial Health Care, Worcester, USA
| | - Ellen M Whyte
- Department of Psychiatry, University of Pittsburgh School of Medicine and UPMC Western Psychiatric Hospital, Pittsburgh, USA
| | - Benoit H Mulsant
- Centre for Addiction and Mental Health, Toronto, Canada; Department of Psychiatry, Temerty Faculty of Medicine, University of Toronto, Canada
| | - Alastair J Flint
- Centre for Mental Health, University Health Network, Toronto, Canada; Department of Psychiatry, Temerty Faculty of Medicine, University of Toronto, Canada
| | - Aristotle N Voineskos
- Centre for Addiction and Mental Health, Toronto, Canada; Department of Psychiatry, Temerty Faculty of Medicine, University of Toronto, Canada
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17
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Xu EP, Nguyen L, Leibenluft E, Stange JP, Linke JO. A meta-analysis on the uncinate fasciculus in depression. Psychol Med 2023; 53:2721-2731. [PMID: 37051913 PMCID: PMC10235669 DOI: 10.1017/s0033291723000107] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/22/2022] [Revised: 12/29/2022] [Accepted: 01/09/2023] [Indexed: 04/14/2023]
Abstract
Aberrant microstructure of the uncinate fasciculus (UNC), a white matter (WM) tract implicated in emotion regulation, has been hypothesized as a neurobiological mechanism of depression. However, studies testing this hypothesis have yielded inconsistent results. The present meta-analysis consolidates evidence from 44 studies comparing fractional anisotropy (FA) and radial diffusivity (RD), two metrics characterizing WM microstructure, of the UNC in individuals with depression (n = 5016) to healthy individuals (n = 18 425). We conduct meta-regressions to identify demographic and clinical characteristics that contribute to cross-study heterogeneity in UNC findings. UNC FA was reduced in individuals with depression compared to healthy individuals. UNC RD was comparable between individuals with depression and healthy individuals. Comorbid anxiety explained inter-study heterogeneity in UNC findings. Depression is associated with perturbations in UNC microstructure, specifically with respect to UNC FA and not UNC RD. The association between depression and UNC microstructure appears to be moderated by anxiety. Future work should unravel the cellular mechanisms contributing to aberrant UNC microstructure in depression; clarify the relationship between UNC microstructure, depression, and anxiety; and link UNC microstructure to psychological processes, such as emotion regulation.
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Affiliation(s)
- Ellie P. Xu
- Department of Psychology, University of Southern California, Los Angeles, CA, USA
| | - Lynn Nguyen
- National Institute of Mental Health, National Institutes of Health, Bethesda, MD, USA
| | - Ellen Leibenluft
- National Institute of Mental Health, National Institutes of Health, Bethesda, MD, USA
| | - Jonathan P. Stange
- Department of Psychology, University of Southern California, Los Angeles, CA, USA
- Department of Psychiatry and Behavioral Sciences, University of Southern California, Los Angeles, CA, USA
| | - Julia O. Linke
- National Institute of Mental Health, National Institutes of Health, Bethesda, MD, USA
- Louis A. Faillace, MD, Department of Psychiatry and Behavioral Sciences, University of Texas Health Science Center at Houston, Houston, TX, USA
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18
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Breit S, Mazza E, Poletti S, Benedetti F. White matter integrity and pro-inflammatory cytokines as predictors of antidepressant response in MDD. J Psychiatr Res 2023; 159:22-32. [PMID: 36657311 DOI: 10.1016/j.jpsychires.2022.12.009] [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: 11/30/2021] [Revised: 11/12/2022] [Accepted: 12/10/2022] [Indexed: 01/13/2023]
Abstract
Major depressive disorder (MDD) is a multifactorial, serious and heterogeneous mental disorder that can lead to chronic recurrent symptoms, treatment resistance and suicidal behavior. MDD often involves immune dysregulation with high peripheral levels of inflammatory cytokines that might have an influence on the clinical course and treatment response. Moreover, patients with MDD show brain volume changes as well as white matter (WM) alterations that are already existing in the early stage of illness. Mounting evidence suggests that both neuroimaging markers, such as WM integrity and blood markers, such as inflammatory cytokines might serve as predictors of treatment response in MDD. However, the relationship between peripheral inflammation, WM structure and antidepressant response is not yet clearly understood. The aim of the present review is to elucidate the association between inflammation and WM integrity and its impact on the pathophysiology and progression of MDD as well as the role of possible novel biomarkers of treatment response to improve MDD prevention and treatment strategies.
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Affiliation(s)
- Sigrid Breit
- Psychiatry and Clinical Psychobiology, Division of Neuroscience, IRCCS Ospedale San Raffaele, Milano, Italy; Translational Research Center, University Hospital of Psychiatry and Psychotherapy, University of Bern, Bern, Switzerland.
| | - Elena Mazza
- Psychiatry and Clinical Psychobiology, Division of Neuroscience, IRCCS Ospedale San Raffaele, Milano, Italy; University Vita-Salute San Raffaele, Milano, Italy
| | - Sara Poletti
- Psychiatry and Clinical Psychobiology, Division of Neuroscience, IRCCS Ospedale San Raffaele, Milano, Italy; University Vita-Salute San Raffaele, Milano, Italy
| | - Francesco Benedetti
- Psychiatry and Clinical Psychobiology, Division of Neuroscience, IRCCS Ospedale San Raffaele, Milano, Italy; University Vita-Salute San Raffaele, Milano, Italy
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19
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A systematic review on the potential use of machine learning to classify major depressive disorder from healthy controls using resting state fMRI measures. Neurosci Biobehav Rev 2023; 144:104972. [PMID: 36436736 DOI: 10.1016/j.neubiorev.2022.104972] [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: 04/27/2022] [Revised: 09/08/2022] [Accepted: 11/21/2022] [Indexed: 11/27/2022]
Abstract
BACKGROUND Major Depressive Disorder (MDD) is a psychiatric disorder characterized by functional brain deficits, as documented by resting-state functional magnetic resonance imaging (rs-fMRI) studies. AIMS In recent years, some studies used machine learning (ML) approaches, based on rs-fMRI features, for classifying MDD from healthy controls (HC). In this context, this review aims to provide a comprehensive overview of the results of these studies. DESIGN The studies research was performed on 3 online databases, examining English-written articles published before August 5, 2022, that performed a two-class ML classification using rs-fMRI features. The search resulted in 20 eligible studies. RESULTS The reviewed studies showed good performance metrics, with better performance achieved when the dataset was restricted to a more homogeneous group in terms of disease severity. Regions within the default mode network, salience network, and central executive network were reported as the most important features in the classification algorithms. LIMITATIONS The small sample size together with the methodological and clinical heterogeneity limited the generalizability of the findings. CONCLUSIONS In conclusion, ML applied to rs-fMRI features can be a valid approach to classify MDD and HC subjects and to discover features that can be used for additional investigation of the pathophysiology of the disease.
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20
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Idlett-Ali SL, Salazar CA, Bell MS, Short EB, Rowland NC. Neuromodulation for treatment-resistant depression: Functional network targets contributing to antidepressive outcomes. Front Hum Neurosci 2023; 17:1125074. [PMID: 36936612 PMCID: PMC10018031 DOI: 10.3389/fnhum.2023.1125074] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/26/2022] [Accepted: 02/14/2023] [Indexed: 03/06/2023] Open
Abstract
Non-invasive brain stimulation is designed to target accessible brain regions that underlie many psychiatric disorders. One such method, transcranial magnetic stimulation (TMS), is commonly used in patients with treatment-resistant depression (TRD). However, for non-responders, the choice of an alternative therapy is unclear and often decided empirically without detailed knowledge of precise circuit dysfunction. This is also true of invasive therapies, such as deep brain stimulation (DBS), in which responses in TRD patients are linked to circuit activity that varies in each individual. If the functional networks affected by these approaches were better understood, a theoretical basis for selection of interventions could be developed to guide psychiatric treatment pathways. The mechanistic understanding of TMS is that it promotes long-term potentiation of cortical targets, such as dorsolateral prefrontal cortex (DLPFC), which are attenuated in depression. DLPFC is highly interconnected with other networks related to mood and cognition, thus TMS likely alters activity remote from DLPFC, such as in the central executive, salience and default mode networks. When deeper structures such as subcallosal cingulate cortex (SCC) are targeted using DBS for TRD, response efficacy has depended on proximity to white matter pathways that similarly engage emotion regulation and reward. Many have begun to question whether these networks, targeted by different modalities, overlap or are, in fact, the same. A major goal of current functional and structural imaging in patients with TRD is to elucidate neuromodulatory effects on the aforementioned networks so that treatment of intractable psychiatric conditions may become more predictable and targeted using the optimal technique with fewer iterations. Here, we describe several therapeutic approaches to TRD and review clinical studies of functional imaging and tractography that identify the diverse loci of modulation. We discuss differentiating factors associated with responders and non-responders to these stimulation modalities, with a focus on mechanisms of action for non-invasive and intracranial stimulation modalities. We advance the hypothesis that non-invasive and invasive neuromodulation approaches for TRD are likely impacting shared networks and critical nodes important for alleviating symptoms associated with this disorder. We close by describing a therapeutic framework that leverages personalized connectome-guided target identification for a stepwise neuromodulation paradigm.
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Affiliation(s)
- Shaquia L. Idlett-Ali
- Department of Neurosurgery, University of Colorado Anschutz Medical Campus, Aurora, CO, United States
- *Correspondence: Shaquia L. Idlett-Ali,
| | - Claudia A. Salazar
- Department of Neurosurgery, Medical University of South Carolina, Charleston, SC, United States
| | - Marcus S. Bell
- Department of Neurosurgery, Medical University of South Carolina, Charleston, SC, United States
| | - E. Baron Short
- Department of Psychiatry and Behavioral Sciences, Medical University of South Carolina, Charleston, SC, United States
| | - Nathan C. Rowland
- Department of Neurosurgery, Medical University of South Carolina, Charleston, SC, United States
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21
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Zhou J, Jiang X, Zhou Y, Zhu Y, Jia L, Sun T, Liu L, Sun Q, Ren L, Guo Y, Wu F, Kong L, Tang Y. Distinguishing major depressive disorder from bipolar disorder in remission: A brain structural network analysis. J Affect Disord 2022; 319:8-14. [PMID: 36058360 DOI: 10.1016/j.jad.2022.08.102] [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: 12/18/2021] [Revised: 05/18/2022] [Accepted: 08/26/2022] [Indexed: 10/14/2022]
Abstract
BACKGROUND It is challenging to differentiate major depressive disorder (MDD) from bipolar disorder (BD) in depression and remission. To exclude the potential influence of depressive episodes, we compared the white matter (WM) network between MDD and BD patients in remission to find disease-specific alterations in MDD and BD, and then distinguish these two affective disorders. METHODS We recruited 33 patients with remitted MDD (rMDD), 54 patients with remitted BD (rBD), and 60 healthy controls (HCs). Diffusion tensor imaging and high-resolution 3D T1-weighted image were acquired. Global and nodal topological parameters were used to depict the alterations of the whole-brain WM network. RESULTS We found that rMDD displayed increased global network efficiency (Eglob) and local network efficiency (Eloc) compared with HCs, whereas we found no significance between rBD and HCs. Compared with rBD and HCs, patients in the rMDD group showed increased nodal degree and nodal efficiency, and decreased nodal shortest path length in the four cerebral regions, including the right calcarine fissure (CAL.R), right cuneus (CUN.R), left lingual gyrus (LING.L), and left middle occipital gyrus (MOG.L). We did not find any rBD specific changes of nodal topological metrics. LIMITATIONS The main limitation is the possible effects of medication and BD subtypes on the results. CONCLUSIONS Our findings indicate that rMDD exhibited elevated global properties compared with HCs group, and increased nodal properties in the CAL.R, CUN.R, LING.L, and MOG.L specifically compared with rBD and HCs, which may underlie the distinction of the two affective disorders in remission.
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Affiliation(s)
- Jian Zhou
- Department of Psychiatry, The First Hospital of China Medical University, Shenyang, China; Brain Function Research Section, The First Hospital of China Medical University, Shenyang, China
| | - Xiaowei Jiang
- Department of Psychiatry, The First Hospital of China Medical University, Shenyang, China; Department of Radiology, The First Hospital of China Medical University, Shenyang, China
| | - Yifang Zhou
- Department of Psychiatry, The First Hospital of China Medical University, Shenyang, China; Brain Function Research Section, The First Hospital of China Medical University, Shenyang, China
| | - Yue Zhu
- Department of Psychiatry, The First Hospital of China Medical University, Shenyang, China; Brain Function Research Section, The First Hospital of China Medical University, Shenyang, China
| | - Linna Jia
- Department of Psychiatry, The First Hospital of China Medical University, Shenyang, China; Brain Function Research Section, The First Hospital of China Medical University, Shenyang, China
| | - Ting Sun
- Department of Psychiatry, The First Hospital of China Medical University, Shenyang, China; Brain Function Research Section, The First Hospital of China Medical University, Shenyang, China
| | - Linzi Liu
- Department of Psychiatry, The First Hospital of China Medical University, Shenyang, China; Brain Function Research Section, The First Hospital of China Medical University, Shenyang, China
| | - Qikun Sun
- Department of Radiation Oncology, The First Hospital of China Medical University, Shenyang, China
| | - Luyu Ren
- Department of Psychiatry, The First Hospital of China Medical University, Shenyang, China; Brain Function Research Section, The First Hospital of China Medical University, Shenyang, China
| | - Yanan Guo
- Department of Psychiatry, The First Hospital of China Medical University, Shenyang, China; Brain Function Research Section, The First Hospital of China Medical University, Shenyang, China
| | - Feng Wu
- Department of Psychiatry, The First Hospital of China Medical University, Shenyang, China; Brain Function Research Section, The First Hospital of China Medical University, Shenyang, China
| | - Lingtao Kong
- Department of Psychiatry, The First Hospital of China Medical University, Shenyang, China; Brain Function Research Section, The First Hospital of China Medical University, Shenyang, China
| | - Yanqing Tang
- Department of Psychiatry, The First Hospital of China Medical University, Shenyang, China; Department of Geriatric Medicine, The First Hospital of China Medical University, Shenyang, China.
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22
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Hermens DF, Jamieson D, Fitzpatrick L, Sacks DD, Iorfino F, Crouse JJ, Guastella AJ, Scott EM, Hickie IB, Lagopoulos J. Sex differences in fronto-limbic white matter tracts in youth with mood disorders. Psychiatry Clin Neurosci 2022; 76:481-489. [PMID: 35730893 DOI: 10.1111/pcn.13440] [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: 01/12/2022] [Revised: 05/22/2022] [Accepted: 06/14/2022] [Indexed: 11/29/2022]
Abstract
AIMS Patients with depression and bipolar disorder have previously been shown to have impaired white matter (WM) integrity compared with healthy controls. This study aimed to investigate potential sex differences that may provide further insight into the pathophysiology of these highly debilitating mood disorders. METHODS Participants aged 17 to 30 years (168 with depression [60% females], 107 with bipolar disorder [74% females], and 61 controls [64% females]) completed clinical assessment, self-report measures, and a neuropsychological assessment battery. Participants also underwent magnetic resonance imaging from which diffusion tensor imaging data were collected among five fronto-limbic WM tracts: cingulum bundle (cingulate gyrus and hippocampus subsections), fornix, stria terminalis, and the uncinate fasciculus. Mean fractional anisotropy (FA) scores were compared between groups using analyses of variance with sex and diagnosis as fixed factors. RESULTS Among the nine WM tracts analyzed, one revealed a significant interaction between sex and diagnosis, controlling for age. Male patients with bipolar disorder had significantly lower FA scores in the fornix compared with the other groups. Furthermore, partial correlations revealed a significant positive association between FA scores for the fornix and psychomotor speed. CONCLUSIONS Our findings suggest that males with bipolar disorder may be at increased risk of disruptions in WM integrity, especially in the fornix, which is thought to be responsible for a range of cognitive functions. More broadly, our findings suggest that sex differences may exist in WM integrity and thereby alter our understanding of the pathophysiology of mood disorders.
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Affiliation(s)
- Daniel F Hermens
- Thompson Institute, University of the Sunshine Coast, Birtinya, Queensland, Australia
| | - Daniel Jamieson
- Thompson Institute, University of the Sunshine Coast, Birtinya, Queensland, Australia
| | - Lauren Fitzpatrick
- Youth Mental Health & Technology Team, Brain and Mind Centre, University of Sydney, Camperdown, New South Wales, Australia
| | - Dashiell D Sacks
- Thompson Institute, University of the Sunshine Coast, Birtinya, Queensland, Australia
| | - Frank Iorfino
- Youth Mental Health & Technology Team, Brain and Mind Centre, University of Sydney, Camperdown, New South Wales, Australia
| | - Jacob J Crouse
- Youth Mental Health & Technology Team, Brain and Mind Centre, University of Sydney, Camperdown, New South Wales, Australia
| | - Adam J Guastella
- Youth Mental Health & Technology Team, Brain and Mind Centre, University of Sydney, Camperdown, New South Wales, Australia
| | - Elizabeth M Scott
- Youth Mental Health & Technology Team, Brain and Mind Centre, University of Sydney, Camperdown, New South Wales, Australia
| | - Ian B Hickie
- Youth Mental Health & Technology Team, Brain and Mind Centre, University of Sydney, Camperdown, New South Wales, Australia
| | - Jim Lagopoulos
- Thompson Institute, University of the Sunshine Coast, Birtinya, Queensland, Australia
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23
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Electroconvulsive therapy changes temporal dynamics of intrinsic brain activity in depressed patients. Psychiatry Res 2022; 316:114732. [PMID: 35926361 DOI: 10.1016/j.psychres.2022.114732] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/19/2021] [Revised: 07/14/2022] [Accepted: 07/16/2022] [Indexed: 11/24/2022]
Abstract
Electroconvulsive therapy (ECT) has been demonstrated to be effective in treating depressed patients. Previous neuroimaging studies have focused mainly on alterations in static brain activity and connectivity to study the effects of ECT in depressed patients. However, it remains unclear whether the temporal dynamics of brain activity are associated with mechanisms of ECT in depressed patients. We measured the dynamics of spontaneous brain activity using dynamic amplitude of low-frequency fluctuation (dALFF) in healthy controls (n = 40) and patients diagnosed with unipolar depression (UD, n = 36) or bipolar disorder (BD, n = 9) before and after ECT. Furthermore, the temporal variability of intrinsic brain activity (iBA) was quantified as the variance of dALFF across sliding window. In addition, correlation analysis was performed to investigate the relationships among dALFF, depressive symptoms, and cognitive function in depressed patients. We lack second resting-state functional magnetic resonance imaging (rs-fMRI) data for healthy controls. After ECT, patients showed decreased brain dynamics (less temporal variability) in the right dorsal anterior cingulate cortex (dACC) and the right precuneus, whereas they showed increased brain dynamics in the bilateral superior medial frontal cortex (mSFC). No significant correlation was found between the dALFF and clinical variables in depressed patients. Our findings suggest that right dACC, right precuneus, and bilateral mSFC play an important role in response to ECT depressed patients from the perspective of dynamic local brain activity, indicating that the dALFF variability may be useful in further understanding the mechanisms of ECT's antidepressant effects.
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24
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Langhein M, Seitz-Holland J, Lyall AE, Pasternak O, Chunga N, Cetin-Karayumak S, Kubicki A, Mulert C, Espinoza RT, Narr KL, Kubicki M. Association between peripheral inflammation and free-water imaging in Major Depressive Disorder before and after ketamine treatment - A pilot study. J Affect Disord 2022; 314:78-85. [PMID: 35779673 PMCID: PMC11186306 DOI: 10.1016/j.jad.2022.06.043] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [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: 05/27/2022] [Accepted: 06/20/2022] [Indexed: 10/17/2022]
Abstract
BACKGROUND Alterations in the peripheral inflammatory profile and white matter (WM) deterioration are frequent in Major Depressive Disorder (MDD). The present study applies free-water imaging to investigate the relationship between altered peripheral inflammation and WM microstructure and their predictive value in determining response to ketamine treatment in MDD. METHODS Ten individuals with MDD underwent diffusion-weighted magnetic resonance imaging and a blood-draw before and 24 h after ketamine infusion. We utilized MANCOVAs and ANCOVAs to compare tissue-specific fractional anisotropy (FAT) and free-water (FW) of the forceps and cingulum, and the ratio of pro-inflammatory interleukin(IL)-8/anti-inflammatory IL-10 between individuals with MDD and 15 healthy controls at baseline. Next, we compared all baseline measures between ketamine responders (6) and non-responders (4) and analyzed changes in imaging and blood data after ketamine infusion. RESULTS The MDD group exhibited an increased IL-8/IL-10 ratio compared to controls at baseline (p = .040), which positively correlated with average FW across regions of interest (p = .013). Ketamine responders demonstrated higher baseline FAT in the left cingulum than non-responders (p = .023). Ketamine infusion did not influence WM microstructure but decreased the IL-8/IL-10 ratio (p = .043). LIMITATIONS The small sample size and short follow-up period limit the conclusion regarding the longer-term effects of ketamine in MDD. CONCLUSIONS This pilot study provides evidence for the role of inflammation in MDD by illustrating an association between peripheral inflammation and WM microstructure. Additionally, we demonstrate that free-water diffusion-weighted imaging might be a valuable tool to determine which individuals with MDD benefit from the anti-inflammatory mediated effects of ketamine treatment.
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Affiliation(s)
- Mina Langhein
- Psychiatry Neuroimaging Laboratory, Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA; Psychiatry Neuroimaging Branch, Department of Psychiatry and Psychotherapy, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Johanna Seitz-Holland
- Psychiatry Neuroimaging Laboratory, Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA; Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Amanda E Lyall
- Psychiatry Neuroimaging Laboratory, Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA; Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Ofer Pasternak
- Psychiatry Neuroimaging Laboratory, Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA; Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Natalia Chunga
- Psychiatry Neuroimaging Laboratory, Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Suheyla Cetin-Karayumak
- Psychiatry Neuroimaging Laboratory, Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Antoni Kubicki
- Ahmanson-Lovelace Brain Mapping Center, Department of Neurology, Geffen School of Medicine at the University of California, Los Angeles, CA, USA
| | - Christoph Mulert
- Centre for Psychiatry, Justus-Liebig-University, Giessen, Germany
| | - Randall T Espinoza
- Jane and Terry Semel Institute for Neuroscience and Human Behavior, Department of Psychiatry and Biobehavioral Sciences, Geffen School of Medicine at the University of California, Los Angeles, CA, USA
| | - Katherine L Narr
- Ahmanson-Lovelace Brain Mapping Center, Department of Neurology, Geffen School of Medicine at the University of California, Los Angeles, CA, USA; Jane and Terry Semel Institute for Neuroscience and Human Behavior, Department of Psychiatry and Biobehavioral Sciences, Geffen School of Medicine at the University of California, Los Angeles, CA, USA
| | - Marek Kubicki
- Psychiatry Neuroimaging Laboratory, Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA; Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA; Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
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25
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Personalized Diagnosis and Treatment for Neuroimaging in Depressive Disorders. J Pers Med 2022; 12:jpm12091403. [PMID: 36143188 PMCID: PMC9504356 DOI: 10.3390/jpm12091403] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2022] [Revised: 08/26/2022] [Accepted: 08/26/2022] [Indexed: 01/10/2023] Open
Abstract
Depressive disorders are highly heterogeneous in nature. Previous studies have not been useful for the clinical diagnosis and prediction of outcomes of major depressive disorder (MDD) at the individual level, although they provide many meaningful insights. To make inferences beyond group-level analyses, machine learning (ML) techniques can be used for the diagnosis of subtypes of MDD and the prediction of treatment responses. We searched PubMed for relevant studies published until December 2021 that included depressive disorders and applied ML algorithms in neuroimaging fields for depressive disorders. We divided these studies into two sections, namely diagnosis and treatment outcomes, for the application of prediction using ML. Structural and functional magnetic resonance imaging studies using ML algorithms were included. Thirty studies were summarized for the prediction of an MDD diagnosis. In addition, 19 studies on the prediction of treatment outcomes for MDD were reviewed. We summarized and discussed the results of previous studies. For future research results to be useful in clinical practice, ML enabling individual inferences is important. At the same time, there are important challenges to be addressed in the future.
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26
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Gerlach AR, Karim HT, Peciña M, Ajilore O, Taylor WD, Butters MA, Andreescu C. MRI predictors of pharmacotherapy response in major depressive disorder. Neuroimage Clin 2022; 36:103157. [PMID: 36027717 PMCID: PMC9420953 DOI: 10.1016/j.nicl.2022.103157] [Citation(s) in RCA: 18] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2022] [Revised: 07/11/2022] [Accepted: 08/15/2022] [Indexed: 02/08/2023]
Abstract
Major depressive disorder is among the most prevalent psychiatric disorders, exacting a substantial personal, social, and economic toll. Antidepressant treatment typically involves an individualized trial and error approach with an inconsistent success rate. Despite a pressing need, no reliable biomarkers for predicting treatment outcome have yet been discovered. Brain MRI measures hold promise in this regard, though clinical translation remains elusive. In this review, we summarize structural MRI and functional MRI (fMRI) measures that have been investigated as predictors of treatment outcome. We broadly divide these into five categories including three structural measures: volumetric, white matter burden, and white matter integrity; and two functional measures: resting state fMRI and task fMRI. Currently, larger hippocampal volume is the most widely replicated predictor of successful treatment. Lower white matter hyperintensity burden has shown robustness in late life depression. However, both have modest discriminative power. Higher fractional anisotropy of the cingulum bundle and frontal white matter, amygdala hypoactivation and anterior cingulate cortex hyperactivation in response to negative emotional stimuli, and hyperconnectivity within the default mode network (DMN) and between the DMN and executive control network also show promise as predictors of successful treatment. Such network-focused measures may ultimately provide a higher-dimensional measure of treatment response with closer ties to the underlying neurobiology.
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Affiliation(s)
- Andrew R Gerlach
- Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA, USA
| | - Helmet T Karim
- Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA, USA; Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA, USA
| | - Marta Peciña
- Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA, USA
| | - Olusola Ajilore
- Department of Psychiatry, University of Illinois-Chicago, Chicago, IL, USA
| | - Warren D Taylor
- Department of Psychiatry and Behavioral Sciences, Vanderbilt University Medical Center, Nashville, TN, USA; Geriatric Research, Education, and Clinical Center, Veterans Affairs Tennessee Valley Health System, Nashville, TN, USA
| | - Meryl A Butters
- Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA, USA
| | - Carmen Andreescu
- Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA, USA.
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27
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Haeffel GJ, Jeronimus BF, Kaiser BN, Weaver LJ, Soyster PD, Fisher AJ, Vargas I, Goodson JT, Lu W. Folk Classification and Factor Rotations: Whales, Sharks, and the Problems With the Hierarchical Taxonomy of Psychopathology (HiTOP). Clin Psychol Sci 2022; 10:259-278. [PMID: 35425668 PMCID: PMC9004619 DOI: 10.1177/21677026211002500] [Citation(s) in RCA: 20] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
The Hierarchical Taxonomy of Psychopathology (HiTOP) uses factor analysis to group people with similar self-reported symptoms (i.e., like-goes-with-like). It is hailed as a significant improvement over other diagnostic taxonomies. However, the purported advantages and fundamental assumptions of HiTOP have received little, if any scientific scrutiny. We critically evaluated five fundamental claims about HiTOP. We conclude that HiTOP does not demonstrate a high degree of verisimilitude and has the potential to hinder progress on understanding the etiology of psychopathology. It does not lend itself to theory-building or taxonomic evolution, and it cannot account for multifinality, equifinality, or developmental and etiological processes. In its current form, HiTOP is not ready to use in clinical settings and may result in algorithmic bias against underrepresented groups. We recommend a bifurcation strategy moving forward in which the DSM is used in clinical settings while researchers focus on developing a falsifiable theory-based classification system.
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Affiliation(s)
| | | | | | | | | | | | | | | | - Wei Lu
- University of Iowa Hospitals and Clinics
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28
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A neural network approach to optimising treatments for depression using data from specialist and community psychiatric services in Australia, New Zealand and Japan. Neural Comput Appl 2022; 35:11497-11516. [PMID: 35039718 PMCID: PMC8754538 DOI: 10.1007/s00521-021-06710-3] [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: 08/28/2021] [Accepted: 10/27/2021] [Indexed: 01/10/2023]
Abstract
This study investigated the application of a recurrent neural network for optimising pharmacological treatment for depression. A clinical dataset of 458 participants from specialist and community psychiatric services in Australia, New Zealand and Japan were extracted from an existing custom-built, web-based tool called Psynary . This data, which included baseline and self-completed reviews, was used to train and refine a novel algorithm which was a fully connected network feature extractor and long short-term memory algorithm was firstly trained in isolation and then integrated and annealed using slow learning rates due to the low dimensionality of the data. The accuracy of predicting depression remission before processing patient review data was 49.8%. After processing only 2 reviews, the accuracy was 76.5%. When considering a change in medication, the precision of changing medications was 97.4% and the recall was 71.4% . The medications with predicted best results were antipsychotics (88%) and selective serotonin reuptake inhibitors (87.9%). This is the first study that has created an all-in-one algorithm for optimising treatments for all subtypes of depression. Reducing treatment optimisation time for patients suffering with depression may lead to earlier remission and hence reduce the high levels of disability associated with the condition. Furthermore, in a setting where mental health conditions are increasing strain on mental health services, the utilisation of web-based tools for remote monitoring and machine/deep learning algorithms may assist clinicians in both specialist and primary care in extending specialist mental healthcare to a larger patient community.
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Gao Y, Wang X, Xiong Z, Ren H, Liu R, Wei Y, Li D. Abnormal Fractional Amplitude of Low-Frequency Fluctuation as a Potential Imaging Biomarker for First-Episode Major Depressive Disorder: A Resting-State fMRI Study and Support Vector Machine Analysis. Front Neurol 2021; 12:751400. [PMID: 34912284 PMCID: PMC8666416 DOI: 10.3389/fneur.2021.751400] [Citation(s) in RCA: 26] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2021] [Accepted: 10/27/2021] [Indexed: 12/24/2022] Open
Abstract
Objective: Major depressive disorder (MDD) is a psychiatric disorder with serious negative health outcomes; however, there is no reliable method of diagnosis. This study explored the clinical diagnostic value of the fractional amplitude of low-frequency fluctuation (fALFF) based on the support vector machine (SVM) method for the diagnosis of MDD. Methods: A total of 198 first-episode MDD patients and 234 healthy controls were involved in this study, and all participants underwent resting-state functional magnetic resonance imaging (fMRI) scanning. Imaging data were analyzed with the fALFF and SVM methods. Results: Compared with the healthy controls, the first-episode MDD patients showed higher fALFF in the left mid cingulum, right precuneus, and left superior frontal gyrus (SFG). The increased fALFF in these three brain regions was positively correlated with the executive control reaction time (ECRT), and the increased fALFF in the left mid cingulum and left SFG was positively correlated with the 17-item Hamilton Rating Scale for Depression (HRSD-17) scores. The SVM results showed that increased fALFF in the left mid cingulum, right precuneus, and left SFG exhibited high diagnostic accuracy of 72.92% (315/432), 71.76% (310/432), and 73.84% (319/432), respectively. The highest diagnostic accuracy of 76.39% (330/432) was demonstrated for the combination of increased fALFF in the right precuneus and left SFG, along with a sensitivity of 84.34% (167/198), and a specificity of 70.51% (165/234). Conclusion: Increased fALFF in the left mid cingulum, right precuneus, and left SFG may serve as a neuroimaging marker for first-episode MDD. The use of the increased fALFF in the right precuneus and left SFG in combination showed the best diagnostic value.
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Affiliation(s)
- Yujun Gao
- Department of Psychiatry, Renmin Hospital of Wuhan University, Wuhan, China
| | - Xi Wang
- Department of Mental Health, Taihe Hospital, Hubei University of Medicine, Shiyan, China
| | - Zhenying Xiong
- Department of Psychiatry, Jiangxia District Mental Hospital, Wuhan, China
| | - Hongwei Ren
- Department of Medical Imaging, Tianyou Hospital Affiliated to Wuhan University of Science and Technology, Wuhan, China
| | - Ruoshi Liu
- Department of Neurology, The Fourth Affiliated Hospital of Harbin Medical University, Harbin, China
| | - Yafen Wei
- First Department of Neurology and Neuroscience Center, Heilongjiang Provincial Hospital, Harbin, China
| | - Dongbin Li
- First Department of Neurology and Neuroscience Center, Heilongjiang Provincial Hospital, Harbin, China.,Department of Neurology, The First Affiliated Hospital of Harbin Medical University, Harbin, China
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30
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Zhou Y, Si X, Chen Y, Chao Y, Lin CP, Li S, Zhang X, Ming D, Li Q. Hippocampus- and Thalamus-Related Fiber-Specific White Matter Reductions in Mild Cognitive Impairment. Cereb Cortex 2021; 32:3159-3174. [PMID: 34891164 DOI: 10.1093/cercor/bhab407] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2021] [Revised: 09/04/2021] [Accepted: 10/20/2021] [Indexed: 11/13/2022] Open
Abstract
Early diagnosis of mild cognitive impairment (MCI) fascinates screening high-risk Alzheimer's disease (AD). White matter is found to degenerate earlier than gray matter and functional connectivity during MCI. Although studies reveal white matter degenerates in the limbic system for MCI, how other white matter degenerates during MCI remains unclear. In our method, regions of interest with a high level of resting-state functional connectivity with hippocampus were selected as seeds to track fibers based on diffusion tensor imaging (DTI). In this way, hippocampus-temporal and thalamus-related fibers were selected, and each fiber's DTI parameters were extracted. Then, statistical analysis, machine learning classification, and Pearson's correlations with behavior scores were performed between MCI and normal control (NC) groups. Results show that: 1) the mean diffusivity of hippocampus-temporal and thalamus-related fibers are significantly higher in MCI and could be used to classify 2 groups effectively. 2) Compared with normal fibers, the degenerated fibers detected by the DTI indexes, especially for hippocampus-temporal fibers, have shown significantly higher correlations with cognitive scores. 3) Compared with the hippocampus-temporal fibers, thalamus-related fibers have shown significantly higher correlations with depression scores within MCI. Our results provide novel biomarkers for the early diagnoses of AD.
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Affiliation(s)
- Yu Zhou
- School of Microelectronics, Tianjin University, Tianjin 300072, China
| | - Xiaopeng Si
- Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin 300072, China.,Tianjin Key Laboratory of Brain Science and Neural Engineering, Tianjin University, Tianjin 300072, China.,Institute of Applied Psychology, Tianjin University, Tianjin 300350, China
| | - Yuanyuan Chen
- Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin 300072, China.,Tianjin Key Laboratory of Brain Science and Neural Engineering, Tianjin University, Tianjin 300072, China
| | - Yiping Chao
- Graduate Institute of Biomedical Engineering, Chang Gung University, Taoyuan 33302, Taiwan.,Department of Computer Science and Information Engineering, Chang Gung University, Taoyuan 33302, Taiwan
| | - Ching-Po Lin
- Institute of Neuroscience Hsinchu City, National Yang Ming Chiao Tung University, Hsinchu 30010, Taiwan
| | - Sicheng Li
- Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin 300072, China.,Tianjin Key Laboratory of Brain Science and Neural Engineering, Tianjin University, Tianjin 300072, China
| | - Xingjian Zhang
- Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin 300072, China.,Tianjin Key Laboratory of Brain Science and Neural Engineering, Tianjin University, Tianjin 300072, China
| | - Dong Ming
- Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin 300072, China.,Tianjin Key Laboratory of Brain Science and Neural Engineering, Tianjin University, Tianjin 300072, China
| | - Qiang Li
- School of Microelectronics, Tianjin University, Tianjin 300072, China
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31
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Vieira R, Coelho A, Reis J, Portugal-Nunes C, Magalhães R, Ferreira S, Moreira PS, Sousa N, Bessa JM. White Matter Microstructure Alterations Associated With Paroxetine Treatment Response in Major Depression. Front Behav Neurosci 2021; 15:693109. [PMID: 34366806 PMCID: PMC8341904 DOI: 10.3389/fnbeh.2021.693109] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2021] [Accepted: 06/16/2021] [Indexed: 12/28/2022] Open
Abstract
More than one-third of depressive patients do not achieve remission after the first antidepressant treatment. The "watch and wait" approach used to find the most effective antidepressant leads to an increased personal, social, and economic burden in society. In order to overcome this challenge, there has been a focus on studying neural biomarkers associated with antidepressant response. Diffusion tensor imaging measures have shown a promising role as predictors of antidepressant response by pointing to pretreatment differences in the white matter microstructural integrity between future responders and non-responders to different pharmacotherapies. Therefore, the aim of the present study was to explore whether response to paroxetine treatment was associated with differences in the white matter microstructure at baseline. Twenty drug-naive patients diagnosed with major depressive disorder followed a 6- to 12-week treatment with paroxetine. All patients completed magnetic resonance brain imaging and a clinical assessment at baseline and 6-12 weeks after treatment. Whole-brain tract-based spatial statistics was used to explore differences in white matter microstructural properties estimated from diffusion magnetic resonance imaging. Voxel-wise statistical analysis revealed a significant increase in fractional anisotropy and a decrease in radial diffusivity in forceps minor and superior longitudinal fasciculus in responders compared to non-responders. Thus, alterations in white matter integrity, specifically in forceps minor and the superior longitudinal fasciculus, are associated with paroxetine treatment response. These findings pave the way for personalized treatment strategies in major depression.
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Affiliation(s)
- Rita Vieira
- Life and Health Sciences Research Institute (ICVS), School of Medicine, University of Minho, Braga, Portugal.,ICVS/3B's, PT Government Associate Laboratory, Braga/Guimarães, Portugal.,Clinical Academic Center - Braga, Braga, Portugal
| | - Ana Coelho
- Life and Health Sciences Research Institute (ICVS), School of Medicine, University of Minho, Braga, Portugal.,ICVS/3B's, PT Government Associate Laboratory, Braga/Guimarães, Portugal.,Clinical Academic Center - Braga, Braga, Portugal
| | - Joana Reis
- Life and Health Sciences Research Institute (ICVS), School of Medicine, University of Minho, Braga, Portugal.,ICVS/3B's, PT Government Associate Laboratory, Braga/Guimarães, Portugal.,Clinical Academic Center - Braga, Braga, Portugal
| | - Carlos Portugal-Nunes
- Life and Health Sciences Research Institute (ICVS), School of Medicine, University of Minho, Braga, Portugal.,ICVS/3B's, PT Government Associate Laboratory, Braga/Guimarães, Portugal.,Clinical Academic Center - Braga, Braga, Portugal
| | - Ricardo Magalhães
- Life and Health Sciences Research Institute (ICVS), School of Medicine, University of Minho, Braga, Portugal.,ICVS/3B's, PT Government Associate Laboratory, Braga/Guimarães, Portugal.,Clinical Academic Center - Braga, Braga, Portugal.,NeuroSpin, Institut des Sciences du Vivant Frédéric Joliot, Commissariat à l' Énergie Atomique et aux Énergies Alternatives, Université Paris-Saclay, Gif-sur-Yvette, France
| | - Sónia Ferreira
- Life and Health Sciences Research Institute (ICVS), School of Medicine, University of Minho, Braga, Portugal.,ICVS/3B's, PT Government Associate Laboratory, Braga/Guimarães, Portugal.,Clinical Academic Center - Braga, Braga, Portugal
| | - Pedro Silva Moreira
- Life and Health Sciences Research Institute (ICVS), School of Medicine, University of Minho, Braga, Portugal.,ICVS/3B's, PT Government Associate Laboratory, Braga/Guimarães, Portugal.,Clinical Academic Center - Braga, Braga, Portugal.,Psychological Neuroscience Laboratory, CIPsi, School of Psychology, University of Minho, Braga, Portugal
| | - Nuno Sousa
- Life and Health Sciences Research Institute (ICVS), School of Medicine, University of Minho, Braga, Portugal.,ICVS/3B's, PT Government Associate Laboratory, Braga/Guimarães, Portugal.,Clinical Academic Center - Braga, Braga, Portugal
| | - João M Bessa
- Life and Health Sciences Research Institute (ICVS), School of Medicine, University of Minho, Braga, Portugal.,ICVS/3B's, PT Government Associate Laboratory, Braga/Guimarães, Portugal.,Clinical Academic Center - Braga, Braga, Portugal
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32
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Magnetic resonance imaging for individual prediction of treatment response in major depressive disorder: a systematic review and meta-analysis. Transl Psychiatry 2021; 11:168. [PMID: 33723229 PMCID: PMC7960732 DOI: 10.1038/s41398-021-01286-x] [Citation(s) in RCA: 28] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/29/2020] [Revised: 02/05/2021] [Accepted: 02/18/2021] [Indexed: 02/06/2023] Open
Abstract
No tools are currently available to predict whether a patient suffering from major depressive disorder (MDD) will respond to a certain treatment. Machine learning analysis of magnetic resonance imaging (MRI) data has shown potential in predicting response for individual patients, which may enable personalized treatment decisions and increase treatment efficacy. Here, we evaluated the accuracy of MRI-guided response prediction in MDD. We conducted a systematic review and meta-analysis of all studies using MRI to predict single-subject response to antidepressant treatment in patients with MDD. Classification performance was calculated using a bivariate model and expressed as area under the curve, sensitivity, and specificity. In addition, we analyzed differences in classification performance between different interventions and MRI modalities. Meta-analysis of 22 samples including 957 patients showed an overall area under the bivariate summary receiver operating curve of 0.84 (95% CI 0.81-0.87), sensitivity of 77% (95% CI 71-82), and specificity of 79% (95% CI 73-84). Although classification performance was higher for electroconvulsive therapy outcome prediction (n = 285, 80% sensitivity, 83% specificity) than medication outcome prediction (n = 283, 75% sensitivity, 72% specificity), there was no significant difference in classification performance between treatments or MRI modalities. Prediction of treatment response using machine learning analysis of MRI data is promising but should not yet be implemented into clinical practice. Future studies with more generalizable samples and external validation are needed to establish the potential of MRI to realize individualized patient care in MDD.
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33
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Structural brain network topology underpinning ADHD and response to methylphenidate treatment. Transl Psychiatry 2021; 11:150. [PMID: 33654073 PMCID: PMC7925571 DOI: 10.1038/s41398-021-01278-x] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/13/2020] [Revised: 01/21/2021] [Accepted: 02/12/2021] [Indexed: 02/03/2023] Open
Abstract
Behavioural disturbances in attention deficit hyperactivity disorder (ADHD) are thought to be due to dysfunction of spatially distributed, interconnected neural systems. While there is a fast-growing literature on functional dysconnectivity in ADHD, far less is known about the structural architecture underpinning these disturbances and how it may contribute to ADHD symptomology and treatment prognosis. We applied graph theoretical analyses on diffusion MRI tractography data to produce quantitative measures of global network organisation and local efficiency of network nodes. Support vector machines (SVMs) were used for comparison of multivariate graph measures of 37 children and adolescents with ADHD relative to 26 age and gender matched typically developing children (TDC). We also explored associations between graph measures and functionally-relevant outcomes such as symptom severity and prediction of methylphenidate (MPH) treatment response. We found that multivariate patterns of reduced local efficiency, predominantly in subcortical regions (SC), were able to distinguish between ADHD and TDC groups with 76% accuracy. For treatment prognosis, higher global efficiency, higher local efficiency of the right supramarginal gyrus and multivariate patterns of increased local efficiency across multiple networks at baseline also predicted greater symptom reduction after 6 weeks of MPH treatment. Our findings demonstrate that graph measures of structural topology provide valuable diagnostic and prognostic markers of ADHD, which may aid in mechanistic understanding of this complex disorder.
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34
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Xue L, Pei C, Wang X, Wang H, Tian S, Yao Z, Lu Q. Predicting Neuroimaging Biomarkers for Antidepressant Selection in Early Treatment of Depression. J Magn Reson Imaging 2021; 54:551-559. [PMID: 33634921 DOI: 10.1002/jmri.27577] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2020] [Revised: 02/09/2021] [Accepted: 02/10/2021] [Indexed: 12/23/2022] Open
Abstract
BACKGROUND Due to the biological heterogeneity, 60%-70% of patients with major depressive disorder (MDD) do not respond to or achieve remission from first-line antidepressants. Predicting neuroimaging biomarkers for early antidepressant treatment could guide initial antidepressant therapy. PURPOSE To assess for neuroimaging biomarkers for antidepressant selection in early antidepressant treatment. STUDY TYPE Prospective. SUBJECTS A total of 85 MDD patients from the major site and 33 MDD patients from an out-of-sample test site. FIELD STRENGTH/SEQUENCE A 3.0 T, T1-weighted imaging using a magnetization-prepared rapid acquisition gradient-echo sequence and diffusion tensor imaging (DTI) using an echo-planar sequence. ASSESSMENT Baseline DTI data of patients who achieved early improvement after 2-weeks of antidepressant treatment (selective serotonin reuptake inhibitors [SSRI] or serotonin-norepinephrine reuptake inhibitors [SNRI]) were analyzed. An ensemble model was constructed using data from the major site and then applied to assess the early response of patients at the out-of-sample test site. STATISTICAL TESTS Support vector machine combined with leave-one-out cross-validation were applied to construct the whole model from individual base models from different brain regions. Discriminative biomarkers were evaluated by calculating the changes in sensitivity and specificity obtained when removing a single base model from the whole model, the base model being removed changing in each run. RESULTS Training performance over MDD patients at the major site achieved 75% accuracy while performance with accuracy of 70% was achieved in the out-of-sample test site. Assessing sensitivity and specificity changes following the removal of single base models from the prominent model highlighted the functions of two neural circuitries: SSRI-related emotion regulation circuitry, centered on the hippocampus (sensitivity changes: 10%) and amygdala (sensitivity changes: 11%); and SNRI-related emotion and reward circuitry, centered on the putamen (specificity changes: 8%) and orbital part of superior frontal gyrus (specificity changes: 12%). DATA CONCLUSION These findings support future research on clinical antidepressant selection for MDD. EVIDENCE LEVEL 1 TECHNICAL EFFICACY: Stage 2.
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Affiliation(s)
- 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
| | - Cong Pei
- 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
| | - 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
| | - Huan 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
| | - Shui Tian
- 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
| | - 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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35
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Korgaonkar MS, Felmingham KL, Klimova A, Erlinger M, Williams LM, Bryant RA. White matter anisotropy and response to cognitive behavior therapy for posttraumatic stress disorder. Transl Psychiatry 2021; 11:14. [PMID: 33414363 PMCID: PMC7791115 DOI: 10.1038/s41398-020-01143-3] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/18/2020] [Revised: 11/12/2020] [Accepted: 11/30/2020] [Indexed: 12/04/2022] Open
Abstract
Trauma-focused cognitive behavior therapy (TF-CBT) is the gold standard treatment for posttraumatic stress disorder (PTSD), up to one-half of PTSD patients remain treatment non-responders. Although studies have used functional MRI to understand the neurobiology of treatment response, there is less understanding of the role of white matter brain structures in response to TF-CBT. Thirty-six treatment-seeking PTSD patients and 33 age-gender matched healthy controls completed diffusion-weighted imaging scans at baseline. Patients underwent nine sessions of TF-CBT treatment and PTSD symptom severity was assessed with the Clinician-Administered PTSD Scale before and after completing treatment. Patients were assessed to estimate the reduction in overall symptoms and also specifically fear and dysphoric symptoms of PTSD. Tract-based spatial statistical analyses were performed for the PTSD group to evaluate whole-brain correlations of fractional anisotropy (FA) with improvement in overall, fear, and dysphoric symptoms using non-parametric permutation inference testing (pFWE < 0.05). Next, we evaluated if these significant measures also characterized PTSD from controls. Greater improvement in dysphoric symptoms was found correlated with lower FA in white matter regions associated with the limbic system, frontal cortex, thalamic association and projection fibers, corpus callosum, and tracts related to the brainstem. White matter anisotropy was not found associated with either overall or fear symptoms. FA in the significant clusters was similar between PTSD and controls. White-matter related to key functional regions may also play an important role in response to TF-CBT. Our results underscore the heterogeneity of PTSD and the need to evaluate distinct symptom phenotypes in treatment studies.
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Affiliation(s)
- Mayuresh S. Korgaonkar
- grid.1013.30000 0004 1936 834XBrain Dynamics Centre, Westmead Institute for Medical Research, The University of Sydney, Sydney, NSW Australia ,grid.1013.30000 0004 1936 834XSchool of Health Sciences, Faculty of Medicine and Health, The University of Sydney, Sydney, NSW Australia
| | - Kim L. Felmingham
- grid.1008.90000 0001 2179 088XSchool of Psychological Sciences, University of Melbourne, Melbourne, VIC Australia
| | - Aleksandra Klimova
- grid.1013.30000 0004 1936 834XBrain Dynamics Centre, Westmead Institute for Medical Research, The University of Sydney, Sydney, NSW Australia
| | - May Erlinger
- grid.1013.30000 0004 1936 834XBrain Dynamics Centre, Westmead Institute for Medical Research, The University of Sydney, Sydney, NSW Australia
| | - Leanne M. Williams
- grid.168010.e0000000419368956Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA USA ,grid.280747.e0000 0004 0419 2556Sierra-Pacific Mental Illness Research, Education and Clinical Center (MIRECC) VA Palo Alto Health Care System, Palo Alto, CA USA
| | - Richard A. Bryant
- grid.1013.30000 0004 1936 834XBrain Dynamics Centre, Westmead Institute for Medical Research, The University of Sydney, Sydney, NSW Australia ,grid.1005.40000 0004 4902 0432School of Psychology, University of New South Wales, Sydney, NSW Australia
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36
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Sydnor VJ, Lyall AE, Cetin-Karayumak S, Cheung JC, Felicione JM, Akeju O, Shenton ME, Deckersbach T, Ionescu DF, Pasternak O, Cusin C, Kubicki M. Studying pre-treatment and ketamine-induced changes in white matter microstructure in the context of ketamine's antidepressant effects. Transl Psychiatry 2020; 10:432. [PMID: 33319774 PMCID: PMC7738670 DOI: 10.1038/s41398-020-01122-8] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/23/2020] [Revised: 10/23/2020] [Accepted: 11/16/2020] [Indexed: 12/28/2022] Open
Abstract
Ketamine is increasingly being used as a therapeutic for treatment-resistant depression (TRD), yet the effects of ketamine on the human brain remain largely unknown. This pilot study employed diffusion magnetic resonance imaging (dMRI) to examine relationships between ketamine treatment and white matter (WM) microstructure, with the aim of increasing the current understanding of ketamine's neural mechanisms of action in humans. Longitudinal dMRI data were acquired from 13 individuals with TRD two hours prior to (pre-infusion), and four hours following (post-infusion), an intravenous ketamine infusion. Free-water imaging was employed to quantify cerebrospinal fluid-corrected mean fractional anisotropy (FA) in 15 WM bundles pre- and post-infusion. Analyses revealed that higher pre-infusion FA in the left cingulum bundle and the left superior longitudinal fasciculus was associated with greater depression symptom improvement 24 h post-ketamine. Moreover, four hours after intravenous administration of ketamine, FA rapidly increased in numerous WM bundles in the brain; this increase was significantly associated with 24 h symptom improvement in select bundles. Overall, the results of this preliminary study suggest that WM properties, as measured by dMRI, may have a potential impact on clinical improvement following ketamine. Ketamine administration additionally appears to be associated with rapid WM diffusivity changes, suggestive of rapid changes in WM microstructure. This study thus points to pre-treatment WM structure as a potential factor associated with ketamine's clinical efficacy, and to post-treatment microstructural changes as a candidate neuroimaging marker of ketamine's cellular mechanisms.
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Affiliation(s)
- Valerie J. Sydnor
- Psychiatry Neuroimaging Laboratory, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA USA
| | - Amanda E. Lyall
- Psychiatry Neuroimaging Laboratory, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA USA ,grid.32224.350000 0004 0386 9924Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA USA
| | - Suheyla Cetin-Karayumak
- Psychiatry Neuroimaging Laboratory, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA USA
| | - Joey C. Cheung
- grid.32224.350000 0004 0386 9924Depression Clinical and Research Program (DCRP), Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA USA
| | - Julia M. Felicione
- grid.32224.350000 0004 0386 9924Athinoula A. Martinos Center for Biomedical Imaging, Charlestown, Boston, MA USA
| | - Oluwaseun Akeju
- Department of Anesthesia, Critical Care and Pain Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA USA
| | - Martha E. Shenton
- Psychiatry Neuroimaging Laboratory, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA USA ,Department of Radiology, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA USA ,grid.410370.10000 0004 4657 1992VA Boston Healthcare System, Brockton Division, Brockton, MA USA
| | - Thilo Deckersbach
- grid.32224.350000 0004 0386 9924Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA USA
| | - Dawn F. Ionescu
- grid.32224.350000 0004 0386 9924Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA USA ,grid.32224.350000 0004 0386 9924Depression Clinical and Research Program (DCRP), Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA USA
| | - Ofer Pasternak
- Psychiatry Neuroimaging Laboratory, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA USA ,Department of Radiology, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA USA
| | - Cristina Cusin
- grid.32224.350000 0004 0386 9924Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA USA ,grid.32224.350000 0004 0386 9924Depression Clinical and Research Program (DCRP), Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA USA
| | - Marek Kubicki
- Psychiatry Neuroimaging Laboratory, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA. .,Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA. .,Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
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37
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Postmortem Dissections of the Papez Circuit and Nonmotor Targets for Functional Neurosurgery. World Neurosurg 2020; 144:e866-e875. [DOI: 10.1016/j.wneu.2020.09.088] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2020] [Revised: 09/16/2020] [Accepted: 09/16/2020] [Indexed: 12/11/2022]
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38
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Chahal R, Gotlib IH, Guyer AE. Research Review: Brain network connectivity and the heterogeneity of depression in adolescence - a precision mental health perspective. J Child Psychol Psychiatry 2020; 61:1282-1298. [PMID: 32458453 PMCID: PMC7688558 DOI: 10.1111/jcpp.13250] [Citation(s) in RCA: 34] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 04/03/2020] [Indexed: 12/18/2022]
Abstract
BACKGROUND Adolescence is a period of high risk for the onset of depression, characterized by variability in symptoms, severity, and course. During adolescence, the neurocircuitry implicated in depression continues to mature, suggesting that it is an important period for intervention. Reflecting the recent emergence of 'precision mental health' - a person-centered approach to identifying, preventing, and treating psychopathology - researchers have begun to document associations between heterogeneity in features of depression and individual differences in brain circuitry, most frequently in resting-state functional connectivity (RSFC). METHODS In this review, we present emerging work examining pre- and post-treatment measures of network connectivity in depressed adolescents; these studies reveal potential intervention-specific neural markers of treatment efficacy. We also review findings from studies examining associations between network connectivity and both types of depressive symptoms and response to treatment in adults, and indicate how this work can be extended to depressed adolescents. Finally, we offer recommendations for research that we believe will advance the science of precision mental health of adolescence. RESULTS Nascent studies suggest that linking RSFC-based pathophysiological variation with effects of different types of treatment and changes in mood following specific interventions will strengthen predictions of prognosis and treatment response. Studies with larger sample sizes and direct comparisons of treatments are required to determine whether RSFC patterns are reliable neuromarkers of treatment response for depressed adolescents. Although we are not yet at the point of using RSFC to guide clinical decision-making, findings from research examining the stability and reliability of RSFC point to a favorable future for network-based clinical phenotyping. CONCLUSIONS Delineating the correspondence between specific clinical characteristics of depression (e.g., symptoms, severity, and treatment response) and patterns of network-based connectivity will facilitate the development of more tailored and effective approaches to the assessment, prevention, and treatment of depression in adolescents.
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Affiliation(s)
- Rajpreet Chahal
- Department of Psychology, Stanford University, Stanford, CA, USA
| | - Ian H. Gotlib
- Department of Psychology, Stanford University, Stanford, CA, USA
| | - Amanda E. Guyer
- Department of Human Ecology, University of California, Davis, Davis, CA, USA,Center for Mind and Brain, University of California, Davis, Davis, CA, USA
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Magnetic resonance diffusion tensor imaging in psychiatry: a narrative review of its potential role in diagnosis. Pharmacol Rep 2020; 73:43-56. [PMID: 33125677 PMCID: PMC7862529 DOI: 10.1007/s43440-020-00177-0] [Citation(s) in RCA: 26] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2020] [Revised: 10/04/2020] [Accepted: 10/14/2020] [Indexed: 12/28/2022]
Abstract
Diffusion tensor imaging (DTI) is an imaging technique that uses magnetic resonance. It measures the diffusion of water molecules in tissues, which can occur either without restriction (i.e., in an isotropic manner) or limited by some obstacles, such as cell membranes (i.e., in an anisotropic manner). Diffusion is most often measured in terms of, inter alia, fractional anisotropy (FA), mean diffusivity (MD), radial diffusivity (RD), and axial diffusivity (AD). DTI allows us to reconstruct, visualize, and evaluate certain qualities of white matter. To date, many studies have sought to associate various changes in the distribution of diffusion within the brain with mental diseases and disorders. A better understanding of white matter integrity disorders can help us recognize the causes of diseases, as well as help create objective methods of psychiatric diagnosis, identify biomarkers of mental illness, and improve pharmacotherapy. The aim of this work is to present the characteristics of DTI as well as current research on its use in schizophrenia, affective disorders, and other mental disorders.
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40
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Sasaki Y, Ito K, Fukumoto K, Kawamura H, Oyama R, Sasaki M, Baba T. Cerebral diffusion kurtosis imaging to assess the pathophysiology of postpartum depression. Sci Rep 2020; 10:15391. [PMID: 32958845 PMCID: PMC7505968 DOI: 10.1038/s41598-020-72310-1] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2020] [Accepted: 08/24/2020] [Indexed: 11/26/2022] Open
Abstract
Postpartum depression (PPD), a main cause of maternal suicide, is an important issue in perinatal mental health. Recently, cerebral diffusion tensor imaging (DTI) studies have shown reduced fractional anisotropy (FA) in major depressive disorder (MDD) patients. There are, however, no reports using diffusion kurtosis imaging (DKI) for evaluation of PPD. This was a Japanese single-institutional prospective study from 2016 to 2019 to examine the pathophysiological changes in the brain of PPD patients using DKI. The DKI data from 3.0 T MRI of patients one month after delivery were analyzed; the patients were examined for PPD by a psychiatrist. The mean kurtosis (MK), FA and mean diffusivity (MD) were calculated from the DKI data and compared between PPD and non-PPD groups using tract-based spatial statistics analysis. Of the 75 patients analyzed, eight patients (10.7%) were diagnosed as having PPD. In the PPD group, FA values in the white matter and thalamus were significantly lower and MD values in the white matter and putamen were significantly higher. The area with significant differences in MD value was more extensive (40.8%) than the area with significant differences in FA value (6.5%). These findings may reflect pathophysiological differences of PPD compared with MDD.
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Affiliation(s)
- Yuri Sasaki
- Department of Obstetrics and Gynecology, Iwate Medical University School of Medicine, 2-1-1 Idaidori, Yahaba, Shiwa, Iwate, 028-3695, Japan.
| | - Kenji Ito
- Division of Ultrahigh Field MRI, Institute for Biomedical Science, Iwate Medical University School of Medicine, Yahaba, Japan
| | - Kentaro Fukumoto
- Department of Neuropsychiatry, Iwate Medical University School of Medicine, Yahaba, Japan
| | - Hanae Kawamura
- Department of Obstetrics and Gynecology, Iwate Medical University School of Medicine, 2-1-1 Idaidori, Yahaba, Shiwa, Iwate, 028-3695, Japan
| | - Rie Oyama
- Department of Obstetrics and Gynecology, Iwate Medical University School of Medicine, 2-1-1 Idaidori, Yahaba, Shiwa, Iwate, 028-3695, Japan
| | - Makoto Sasaki
- Division of Ultrahigh Field MRI, Institute for Biomedical Science, Iwate Medical University School of Medicine, Yahaba, Japan
| | - Tsukasa Baba
- Department of Obstetrics and Gynecology, Iwate Medical University School of Medicine, 2-1-1 Idaidori, Yahaba, Shiwa, Iwate, 028-3695, Japan
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Melloni EMT, Poletti S, Dallaspezia S, Bollettini I, Vai B, Barbini B, Zanardi R, Colombo C, Benedetti F. Changes of white matter microstructure after successful treatment of bipolar depression. J Affect Disord 2020; 274:1049-1056. [PMID: 32663931 DOI: 10.1016/j.jad.2020.05.146] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/03/2020] [Revised: 04/22/2020] [Accepted: 05/27/2020] [Indexed: 01/31/2023]
Abstract
BACKGROUND Diffusion tensor imaging (DTI) measures suggest a widespread alteration of white matter (WM) microstructure in patients with bipolar disorder (BD). The chronotherapeutic combination of repeated total sleep deprivation and morning light therapy (TSD+LT) can acutely reverse depressive symptoms in approximately 60% of patients, and it has been confirmed as a model antidepressant treatment to investigate the neurobiological correlates of rapid antidepressant response. METHODS We tested if changes in DTI measures of WM microstructure could parallel antidepressant response in a sample of 44 patients with a major depressive episode in course of BD, treated with chronoterapeutics for one week. We used both a tract-wise and a voxel-wise approach for the whole-brain extraction of DTI measures of WM microstructure: axial (AD), radial (RD), and mean diffusivity (MD), and fractional anisotropy (FA). RESULTS Compared to baseline level, at one-week follow up we observed a significant increase in average FA measures paralleled by a significant decrease in MD measures of several WM tracts including cingulum, corpus callosum, corona radiata, cortico-spinal tract, internal capsule, fornix and uncinate fasciculus. The degree of change was associated to clinical response. CONCLUSIONS This is the first study to show changes of individual DTI measures of WM microstructure in response to antidepressant treatment in BD. Our results add new evidence to warrant a role for chronotherapeutics as a first-line treatment for bipolar depression and contribute identifying generalizable neuroimaging-based biomarkers of antidepressant response.
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Affiliation(s)
- Elisa M T Melloni
- Psychiatry & Clinical Psychobiology Unit, Division of Neuroscience, Scientific Institute Ospedale San Raffaele, Milano, Italy; University Vita-Salute San Raffaele, Milano, Italy.
| | - Sara Poletti
- Psychiatry & Clinical Psychobiology Unit, Division of Neuroscience, Scientific Institute Ospedale San Raffaele, Milano, Italy; University Vita-Salute San Raffaele, Milano, Italy
| | - Sara Dallaspezia
- Psychiatry & Clinical Psychobiology Unit, Division of Neuroscience, Scientific Institute Ospedale San Raffaele, Milano, Italy
| | - Irene Bollettini
- Psychiatry & Clinical Psychobiology Unit, Division of Neuroscience, Scientific Institute Ospedale San Raffaele, Milano, Italy
| | - Benedetta Vai
- Psychiatry & Clinical Psychobiology Unit, Division of Neuroscience, Scientific Institute Ospedale San Raffaele, Milano, Italy; University Vita-Salute San Raffaele, Milano, Italy; Fondazione Centro San Raffaele, Milano, Italy
| | - Barbara Barbini
- Psychiatry & Clinical Psychobiology Unit, Division of Neuroscience, Scientific Institute Ospedale San Raffaele, Milano, Italy
| | - Raffaella Zanardi
- Psychiatry & Clinical Psychobiology Unit, Division of Neuroscience, Scientific Institute Ospedale San Raffaele, Milano, Italy
| | - Cristina Colombo
- Psychiatry & Clinical Psychobiology Unit, Division of Neuroscience, Scientific Institute Ospedale San Raffaele, Milano, Italy; University Vita-Salute San Raffaele, Milano, Italy
| | - Francesco Benedetti
- Psychiatry & Clinical Psychobiology Unit, Division of Neuroscience, Scientific Institute Ospedale San Raffaele, Milano, Italy; University Vita-Salute San Raffaele, Milano, Italy
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Thomas PJ, Panchamukhi S, Nathan J, Francis J, Langenecker S, Gorka S, Leow A, Klumpp H, Phan KL, Ajilore OA. Graph theoretical measures of the uncinate fasciculus subnetwork as predictors and correlates of treatment response in a transdiagnostic psychiatric cohort. Psychiatry Res Neuroimaging 2020; 299:111064. [PMID: 32163837 PMCID: PMC7183891 DOI: 10.1016/j.pscychresns.2020.111064] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/24/2019] [Revised: 03/03/2020] [Accepted: 03/03/2020] [Indexed: 01/01/2023]
Abstract
The internalizing psychopathologies (IP) are a highly prevalent group of disorders for which little data exists to guide treatment selection. We examine whether graph theoretical metrics from white matter connectomes may serve as biomarkers of disease and predictors of treatment response. We focus on the uncinate fasciculus subnetwork, which has been previously implicated in these disorders. We compared baseline graph measures from a transdiagnostic IP cohort with controls. Patients were randomized to either SSRI or cognitive behavioral therapy and we determined if graph theory metrics change following treatment, and whether these changes correlated with treatment response. Lastly, we investigated whether baseline metrics correlated with treatment response. Several baseline nodal graph metrics differed at baseline. Of note, right amygdala betweenness centrality was increased in patients relative to controls. In addition, white matter integrity of the uncinate fasciculus was decreased at baseline in patients versus controls. The SSRI and CBT cohorts had increased left frontal superior orbital betweenness centrality and left frontal medial orbital clustering coefficient, respectively, suggesting the presence of treatment specific neural correlates of treatment response. This study provides insight on shared white matter network features of IPs and elucidates potential biomarkers of treatment response that may be modality-specific.
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Affiliation(s)
- Paul J Thomas
- Department of Psychiatry, University of Illinois at Chicago, Chicago, IL, USA; Department of Bioengineering, University of Illinois at Chicago, Chicago, IL, USA
| | | | | | - Jennifer Francis
- Department of Behavioral Sciences, Rush University, Chicago, IL, USA
| | | | - Stephanie Gorka
- Department of Psychiatry, University of Illinois at Chicago, Chicago, IL, USA
| | - Alex Leow
- Department of Psychiatry, University of Illinois at Chicago, Chicago, IL, USA; Department of Bioengineering, University of Illinois at Chicago, Chicago, IL, USA
| | - Heide Klumpp
- Department of Psychiatry, University of Illinois at Chicago, Chicago, IL, USA
| | - K Luan Phan
- Department of Psychiatry, University of Illinois at Chicago, Chicago, IL, USA
| | - Olusola A Ajilore
- Department of Psychiatry, University of Illinois at Chicago, Chicago, IL, USA.
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43
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Kang SG, Cho SE. Neuroimaging Biomarkers for Predicting Treatment Response and Recurrence of Major Depressive Disorder. Int J Mol Sci 2020; 21:ijms21062148. [PMID: 32245086 PMCID: PMC7139562 DOI: 10.3390/ijms21062148] [Citation(s) in RCA: 35] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2020] [Revised: 03/12/2020] [Accepted: 03/19/2020] [Indexed: 12/26/2022] Open
Abstract
The acute treatment duration for major depressive disorder (MDD) is 8 weeks or more. Treatment of patients with MDD without predictors of treatment response and future recurrence presents challenges and clinical problems to patients and physicians. Recently, many neuroimaging studies have been published on biomarkers for treatment response and recurrence of MDD using various methods such as brain volumetric magnetic resonance imaging (MRI), functional MRI (resting-state and affective tasks), diffusion tensor imaging, magnetic resonance spectroscopy, near-infrared spectroscopy, and molecular imaging (i.e., positron emission tomography and single photon emission computed tomography). The results have been inconsistent, and we hypothesize that this could be due to small sample size; different study design, including eligibility criteria; and differences in the imaging and analysis techniques. In the future, we suggest a more sophisticated research design, larger sample size, and a more comprehensive integration including genetics to establish biomarkers for the prediction of treatment response and recurrence of MDD.
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Intrinsic connectomes are a predictive biomarker of remission in major depressive disorder. Mol Psychiatry 2020; 25:1537-1549. [PMID: 31695168 PMCID: PMC7303006 DOI: 10.1038/s41380-019-0574-2] [Citation(s) in RCA: 94] [Impact Index Per Article: 23.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/15/2019] [Revised: 10/07/2019] [Accepted: 10/23/2019] [Indexed: 12/28/2022]
Abstract
Although major depressive disorder (MDD) is associated with altered functional coupling between disparate neural networks, the degree to which such measures are ameliorated by antidepressant treatment is unclear. It is also unclear whether functional connectivity can be used as a predictive biomarker of treatment response. Here, we used whole-brain functional connectivity analysis to identify neural signatures of remission following antidepressant treatment, and to identify connectomic predictors of treatment response. 163 MDD and 62 healthy individuals underwent functional MRI during pre-treatment baseline and 8-week follow-up sessions. Patients were randomized to escitalopram, sertraline or venlafaxine-XR antidepressants and assessed at follow-up for remission. Baseline measures of intrinsic functional connectivity between each pair of 333 regions were analyzed to identify pre-treatment connectomic features that distinguish remitters from non-remitters. We then interrogated these connectomic differences to determine if they changed post-treatment, distinguished patients from controls, and were modulated by medication type. Irrespective of medication type, remitters were distinguished from non-remitters by greater connectivity within the default mode network (DMN); specifically, between the DMN, fronto-parietal and somatomotor networks, the DMN and visual, limbic, auditory and ventral attention networks, and between the fronto-parietal and somatomotor networks with cingulo-opercular and dorsal attention networks. This baseline hypo-connectivity for non-remitters also distinguished them from controls and increased following treatment. In contrast, connectivity for remitters was higher than controls at baseline and also following remission, suggesting a trait-like connectomic characteristic. Increased functional connectivity within and between large-scale intrinsic brain networks may characterize acute recovery with antidepressants in depression.
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45
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Barredo J, Bellone JA, Edwards M, Carpenter LL, Correia S, Philip NS. White matter integrity and functional predictors of response to repetitive transcranial magnetic stimulation for posttraumatic stress disorder and major depression. Depress Anxiety 2019; 36:1047-1057. [PMID: 31475432 PMCID: PMC8015421 DOI: 10.1002/da.22952] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/12/2019] [Revised: 07/02/2019] [Accepted: 07/27/2019] [Indexed: 12/12/2022] Open
Abstract
BACKGROUND Recent evidence suggests that therapeutic repetitive transcranial magnetic stimulation (TMS) is an effective treatment for pharmacoresistant posttraumatic stress disorder (PTSD) and comorbid major depressive disorder (MDD). We recently demonstrated that response to 5 Hz TMS administered to the dorsolateral prefrontal cortex was predicted by functional connectivity of the medial prefrontal (MPFC) and subgenual anterior cingulate cortex (sgACC). This functionally-defined circuit is a novel target for treatment optimization research, however, our limited knowledge of the structural pathways that underlie this functional predisposition is a barrier to target engagement research. METHODS To investigate underlying structural elements of our previous functional connectivity findings, we submitted pre-TMS diffusion-weighted imaging data from 20 patients with PTSD and MDD to anatomically constrained tract-based probabilistic tractography (FreeSurfer's TRActs Constrained by UnderLying Anatomy). Averaged pathway fractional anisotropy (FA) was extracted from four frontal white matter tracts: the forceps minor, cingulum, anterior thalamic radiations (ATRs), and uncinate fasciculi. Tract FA statistics were treated as explanatory variables in backward regressions testing the relationship between tract integrity and functional connectivity coefficients from MPFC and sgACC predictors of symptom improvement after TMS. RESULTS FA in the ATRs was consistently associated with symptom improvement in PTSD and MDD (Bonferroni-corrected p < .05). CONCLUSION We found that structural characteristics of the ATR account for significant variance in individual-level functional predictors of post-TMS improvement. TMS optimization studies should target this circuit either in stand-alone or successive TMS stimulation protocols.
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Affiliation(s)
- Jennifer Barredo
- Department of Psychiatry and Human Behavior, Alpert Medical School, Brown University,Center for Neurorestoration and Neurotechnology, Providence VA Medical Center,Corresponding author: Jennifer Barredo PhD, 830 Chalkstone Ave, Providence RI 02908;
| | - John A. Bellone
- Department of Psychiatry and Human Behavior, Alpert Medical School, Brown University,Center for Neurorestoration and Neurotechnology, Providence VA Medical Center
| | - Melissa Edwards
- Department of Psychiatry and Human Behavior, Alpert Medical School, Brown University
| | - Linda L. Carpenter
- Department of Psychiatry and Human Behavior, Alpert Medical School, Brown University,Butler Hospital Neuromodulation Research Facility
| | - Stephen Correia
- Department of Psychiatry and Human Behavior, Alpert Medical School, Brown University,Center for Neurorestoration and Neurotechnology, Providence VA Medical Center,Butler Hospital Neuromodulation Research Facility
| | - Noah S. Philip
- Department of Psychiatry and Human Behavior, Alpert Medical School, Brown University,Center for Neurorestoration and Neurotechnology, Providence VA Medical Center,Butler Hospital Neuromodulation Research Facility
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Pillai RL, Chuan H, LaBella A, Mengru Z, Jie Y, Trivedi M, Weissman M, McGrath P, Fava M, Kurian B, Cooper C, McInnis M, Oquendo MA, Pizzagalli DA, Parsey RV, DeLorenzo C. Examining raphe-amygdala structural connectivity as a biological predictor of SSRI response. J Affect Disord 2019; 256:8-16. [PMID: 31158720 PMCID: PMC6750958 DOI: 10.1016/j.jad.2019.05.055] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/29/2018] [Revised: 04/18/2019] [Accepted: 05/27/2019] [Indexed: 12/28/2022]
Abstract
BACKGROUND Our lab has previously found that structural integrity in tracts from the raphe nucleus (RN) to the amygdala, measured by fractional anisotropy (FA), predicts remission to selective serotonin reuptake inhibitors (SSRIs) in major depressive disorder (MDD). This could potentially serve as a biomarker for remission that can guide clinical decision-making. To enhance repeatability and reproducibility, we replicated our study in a larger, more representative multi-site sample. METHODS 64 direction DTI was collected in 144 medication-free patients with MDD from the Establishing Moderators and Biosignatures of Antidepressant Response for Clinical Care (EMBARC) study. We performed probabilistic tractography between the RN and bilateral amygdala and hippocampus and calculated weighted FA in these tracts. Patients were treated with either sertraline or placebo, and their change in Hamilton Depression Rating Scale (HDRS) score reported. Pretreatment weighted FA was compared between remitters and nonremitters, and correlation between FA and percent change in HDRS score was assessed. Exploratory moderator and voxel analyses were also performed. RESULTS Contrary to our hypotheses, FA was greater in nonremitters than in remitters in RN-left and right amygdala tracts (p = 0.02 and 0.01, respectively). Pretreatment FA between the raphe and left amygdala correlated with greater, not reduced, HDRS (r = 0.18, p = 0.04). This finding was found to be greater in the placebo group. Moderator and voxel analyses yielded no significant findings. CONCLUSIONS We found greater FA in nonremitters between the RN and amygdala than in remitters, and a correlation between FA and symptom worsening, particularly with placebo. These findings may help reveal more about the nature of MDD, as well as guide research methods involving placebo response.
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Affiliation(s)
| | - Huang Chuan
- Department of Psychiatry, Stony Brook University, Stony Brook, NY, United States,Department of Radiology, Stony Brook University, Stony Brook, NY, United States,Corresponding author at: Department of Psychiatry, Stony Brook Medicine, HSC-T10-020, Stony Brook, NY 11794, United States., (C. Huang)
| | - Andrew LaBella
- Department of Biomedical Engineering, Stony Brook University, Stony Brook, NY, United States
| | - Zhang Mengru
- Department of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, NY, United States
| | - Yang Jie
- Department of Family, Population, & Preventive Medicine, Stony Brook University, Stony Brook, NY, United States
| | - Madhukar Trivedi
- Department of Psychiatry, University of Texas Southwestern Medical Center, United States
| | - Myrna Weissman
- Department of Psychiatry, College of Physicians and Surgeons, Columbia University and the New York Psychiatric Institute, United States
| | - Patrick McGrath
- Department of Psychiatry, College of Physicians and Surgeons, Columbia University and the New York Psychiatric Institute, United States
| | - Maurizio Fava
- Department of Psychiatry, Harvard Medical School, United States
| | - Benji Kurian
- Department of Psychiatry, University of Texas Southwestern Medical Center, United States
| | - Crystal Cooper
- Department of Psychiatry, University of Texas Southwestern Medical Center, United States
| | - Melvin McInnis
- Department of Psychiatry, University of Michigan, United States
| | - Maria A. Oquendo
- Department of Psychiatry, University of Pennsylvania, United States
| | | | - Ramin V. Parsey
- Department of Psychiatry, Stony Brook University, Stony Brook, NY, United States
| | - Christine DeLorenzo
- Department of Psychiatry, Stony Brook University, Stony Brook, NY, United States,Department of Psychiatry, Molecular Imaging and Neuropathology Division, Columbia University, New York, NY, United States
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Fani N, Michopoulos V, van Rooij SJ, Clendinen C, Hardy RA, Jovanovic T, Rothbaum BO, Ressler KJ, Stevens JS. Structural connectivity and risk for anhedonia after trauma: A prospective study and replication. J Psychiatr Res 2019; 116:34-41. [PMID: 31181391 PMCID: PMC7039326 DOI: 10.1016/j.jpsychires.2019.05.009] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/01/2018] [Revised: 03/22/2019] [Accepted: 05/09/2019] [Indexed: 01/22/2023]
Abstract
Anhedonia emerges in some people after psychological trauma, reflected by a loss of interest, diminished affect, and detachment. Structural abnormalities in specific neural pathways at the time of trauma may influence the development of these posttraumatic anhedonia (PTA) symptoms. In this prospective study, we determined whether white matter connectivity at around one month post-trauma predicts PTA and other PTSD symptoms at six months post-trauma. Thirty men and women aged 19-62 were recruited from the emergency department of a Level I trauma center. Participants received diffusion tensor imaging at approximately one month post-trauma and clinical assessments at one and six months post-trauma. Probabilistic tractography was used to examine connectivity of select pathways. A replication sample (N = 57) in an independent, cross-sectional dataset of traumatized women was similarly analyzed. Logistic regression results indicated that, after accounting for early PTSD symptoms (at one month) and other clinical risk factors, the integrity of the uncinate fasciculus (UF) uniquely predicted the presence of PTA at six months post-trauma (Beta = -225.6, p < .05). Together, these factors contributed to 76% of the variance in PTA. Integrity of the UF also predicted re-experiencing PTSD symptoms at six months post-trauma. These results were supported in our replication analyses. Our findings indicate that the integrity of the UF around 1 month post-trauma affects vulnerability for the development of anhedonic PTSD symptoms as well as re-experiencing symptoms. Connectivity of this amygdala-ventromedial prefrontal pathway appears to be a salient predictor of anhedonia, above and beyond clinical risk factors.
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Affiliation(s)
- Negar Fani
- Emory University School of Medicine, Department of Psychiatry and Behavioral Sciences, USA.
| | - Vasiliki Michopoulos
- Emory University School of Medicine, Department of Psychiatry and Behavioral Sciences, USA,Yerkes National Primate Research Center, Atlanta, GA, USA
| | - Sanne J.H. van Rooij
- Emory University School of Medicine, Department of Psychiatry and Behavioral Sciences, USA
| | - Cherita Clendinen
- Emory University School of Medicine, Department of Psychiatry and Behavioral Sciences, USA
| | - Raven A. Hardy
- Emory University School of Medicine, Department of Psychiatry and Behavioral Sciences, USA
| | - Tanja Jovanovic
- Emory University School of Medicine, Department of Psychiatry and Behavioral Sciences, USA
| | - Barbara O. Rothbaum
- Emory University School of Medicine, Department of Psychiatry and Behavioral Sciences, USA
| | - Kerry J. Ressler
- Emory University School of Medicine, Department of Psychiatry and Behavioral Sciences, USA,McLean Hospital, Harvard Medical School, USA
| | - Jennifer S. Stevens
- Emory University School of Medicine, Department of Psychiatry and Behavioral Sciences, USA
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Davis AD, Hassel S, Arnott SR, Harris J, Lam RW, Milev R, Rotzinger S, Zamyadi M, Frey BN, Minuzzi L, Strother SC, MacQueen GM, Kennedy SH, Hall GB. White Matter Indices of Medication Response in Major Depression: A Diffusion Tensor Imaging Study. BIOLOGICAL PSYCHIATRY: COGNITIVE NEUROSCIENCE AND NEUROIMAGING 2019; 4:913-924. [PMID: 31471185 DOI: 10.1016/j.bpsc.2019.05.016] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/25/2019] [Accepted: 05/28/2019] [Indexed: 01/17/2023]
Abstract
BACKGROUND While response to antidepressants in major depressive disorder is difficult to predict, characterizing the organization and integrity of white matter in the brain with diffusion tensor imaging (DTI) may provide the means to distinguish between antidepressant responders and nonresponders. METHODS DTI data were collected at 6 sites (Canadian Biomarker Integration Network in Depression-1 [CAN-BIND-1 study]) from 200 (127 women) depressed and 112 (71 women) healthy participants at 3 time points: at baseline, 2 weeks, and 8 weeks following initiation of selective serotonin reuptake inhibitor treatment. Therapeutic response was established by a 50% reduction of symptoms at 8 weeks. Analysis on responders, nonresponders, and control subjects yielded 4 scalar metrics: fractional anisotropy and mean, axial, and radial diffusivity. Region-of-interest analysis was carried out on 40 white matter regions using a skeletonization approach. Mixed-effects regression was incorporated to test temporal trends. RESULTS The data acquired at baseline showed that axial diffusivity in the external capsule, which overlaps the superior longitudinal fasciculus, was significantly associated with medication response. Regression analysis revealed further baseline differences of responders compared with nonresponders in the cingulum regions, sagittal stratum, and corona radiata. Additional group differences relative to control subjects were seen in the internal capsule, posterior thalamic radiation, and uncinate fasciculus. Most effect sizes were moderate (near 0.5), with a maximum of 0.76 in the cingulum-hippocampus region. No temporal changes in DTI metrics were observed over the 8-week study period. CONCLUSIONS Several DTI measures of altered white matter specifically distinguished medication responders and nonresponders at baseline and show promise for predicting treatment response in depression.
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Affiliation(s)
- Andrew D Davis
- Department of Psychology, Neuroscience & Behavior, McMaster University, Hamilton, Ontario, Canada; Imaging Research Center, St. Joseph's Healthcare Hamilton, Hamilton, Ontario, Canada
| | - Stefanie Hassel
- Department of Psychiatry, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada
| | - Stephen R Arnott
- Rotman Research Institute, Baycrest Centre, Toronto, Ontario, Canada
| | - Jacqueline Harris
- Department of Computer Science, University of Alberta, Edmonton, Alberta, Canada
| | - Raymond W Lam
- Department of Psychiatry, University of British Columbia, Vancouver, British Columbia, Canada
| | - Roumen Milev
- Department of Psychology, Queen's University, Kingston, Ontario, Canada; Department of Psychiatry, Queen's University and Providence Care Hospital, Kingston, Ontario, Canada
| | - Susan Rotzinger
- Department of Psychiatry, Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada; Department of Psychiatry, St. Michael's Hospital, Toronto, Ontario, Canada; Department of Psychiatry, Krembil Research Centre, University Health Network, Toronto, Ontario, Canada; Keenan Research Centre for Biomedical Science, Li Ka Shing Knowledge Institute, St. Michael's Hospital, Toronto, Ontario, Canada
| | - Mojdeh Zamyadi
- Rotman Research Institute, Baycrest Centre, Toronto, Ontario, Canada
| | - Benicio N Frey
- Department of Psychiatry and Behavioural Neurosciences, McMaster University, Hamilton, Ontario, Canada; Women's Health Concerns Clinic, St. Joseph's Healthcare Hamilton, Hamilton, Ontario, Canada
| | - Luciano Minuzzi
- Department of Psychiatry and Behavioural Neurosciences, McMaster University, Hamilton, Ontario, Canada; Women's Health Concerns Clinic, St. Joseph's Healthcare Hamilton, Hamilton, Ontario, Canada
| | - Stephen C Strother
- Rotman Research Institute, Baycrest Centre, Toronto, Ontario, Canada; Department of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada
| | - Glenda M MacQueen
- Department of Psychiatry, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada
| | - Sidney H Kennedy
- Department of Psychiatry, Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada; Department of Psychiatry, St. Michael's Hospital, Toronto, Ontario, Canada; Department of Psychiatry, Krembil Research Centre, University Health Network, Toronto, Ontario, Canada; Keenan Research Centre for Biomedical Science, Li Ka Shing Knowledge Institute, St. Michael's Hospital, Toronto, Ontario, Canada
| | - Geoffrey B Hall
- Department of Psychology, Neuroscience & Behavior, McMaster University, Hamilton, Ontario, Canada; Imaging Research Center, St. Joseph's Healthcare Hamilton, Hamilton, Ontario, Canada.
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Doolin K, Andrews S, Carballedo A, McCarthy H, O'Hanlon E, Tozzi L, Frodl T. Longitudinal diffusion weighted imaging of limbic regions in patients with major depressive disorder after 6 years and partial to full remission. Psychiatry Res Neuroimaging 2019; 287:75-86. [PMID: 31004996 DOI: 10.1016/j.pscychresns.2019.04.004] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/03/2018] [Revised: 04/08/2019] [Accepted: 04/11/2019] [Indexed: 12/13/2022]
Abstract
The objective of this study was to determine the effect of major depressive disorder (MDD) on white matter microstructures after a 6-year period compared to healthy controls (HC). This study included a small sample size of 26 participants, including 14 patients with MDD clinically diagnosed at baseline, and 12 HCs. MRI brain scans were conducted at baseline and follow-up, 75.32 (±2.25) months after the initial scan. Tractography of 7 regions including the fornix, cingulum, superior longitudinal fasciculus, inferior fronto-occipital fasciculus and uncinate fasciculus were conducted using ExploreDTI software. Both groups showed significant reduction in tract integrity between time points. MDD diagnosis was shown to have an effect on longitudinal FA of the left dorsal cingulum and the left parahippocampal cingulum. A significant inverse relationship was found between ΔFA [baseline FA - follow-up FA] of the right uncinate fasciculus and the left rostral cingulum with ΔHAM-D [baseline HAM-D - follow-up HAM-D] within the MDD group. These preliminary findings support the hypothesis that limbic structures including the cingulum are involved in MDD pathophysiology and may be affected even after remission. Moreover, they indicate that recovery from depression symptoms may slow the rate of WM degradation associated with aging in these regions of interest.
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Affiliation(s)
- Kelly Doolin
- Trinity College Institute of Neuroscience, Trinity College Dublin, Dublin 2, Ireland.
| | - Sinaoife Andrews
- Trinity College Institute of Neuroscience, Trinity College Dublin, Dublin 2, Ireland
| | - Angela Carballedo
- Trinity College Institute of Neuroscience, Trinity College Dublin, Dublin 2, Ireland
| | - Hazel McCarthy
- Trinity College Institute of Neuroscience, Trinity College Dublin, Dublin 2, Ireland
| | - Erik O'Hanlon
- Trinity College Institute of Neuroscience, Trinity College Dublin, Dublin 2, Ireland
| | - Leonardo Tozzi
- Department of Psychiatry and Behavioural Sciences, Stanford University, Stanford, CA 94305, USA
| | - Thomas Frodl
- Department of Psychiatry and Psychotherapy, Otto von Guericke University, 39120 Magdeburg, Germany
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50
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Wang X, Qin J, Zhu J, Bi K, Zhang S, Yan R, Zhao P, Yao Z, Lu Q. Rehabilitative compensatory mechanism of hierarchical subnetworks in major depressive disorder: A longitudinal study across multi-sites. Eur Psychiatry 2019; 58:54-62. [PMID: 30822739 DOI: 10.1016/j.eurpsy.2019.02.004] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/30/2018] [Revised: 02/16/2019] [Accepted: 02/16/2019] [Indexed: 10/27/2022] Open
Abstract
BACKGROUND Brain structural connectome comprise of a minority of efficiently interconnected rich club nodes that are regarded as 'high-order regions'. The remission of major depressive disorder (MDD) in response to selective serotonin reuptake inhibitor (SSRI) treatment could be investigated by the hierarchical structural connectomes' alterations of subnetworks. METHODS Fifty-five MDD patients who achieved remission underwent diffusion tensors imaging (DTI) scanning from 3 cohorts before and after 8-weeks antidepressant treatment. Five hierarchical subnetworks namely, rich, local, feeder, rich-feeder and feeder-local, were constructed according to the different combinations of connections and nodes as defined by rich club architecture. The critical treatment-related subnetwork pattern was explored by multivariate pattern analysis with support vector machine to differ the pre-/post-treatment patients. Then, relationships between graph metrics of discriminative subnetworks/ nodes and clinical variables were further explored. RESULTS The feeder-local subnetwork presented the most discriminative power in differing pre-/post- treatment patients, while the rich-feeder subnetwork had the highest discriminative power when comparing pre-treatment patients and controls. Furthermore, based on the feeder connection, which indicates the information transmission between the core and non-core architectures of brain networks, its topological measures were found to be significantly correlated with the reduction rate of 17-item Hamilton Rating Scale for Depression. CONCLUSION Although pathological lesion on MDD relied on abnormal core organization, disease remission was association with the compensation from non-core organization. These results suggested that the dysfunctions arising from hierarchical subnetworks are compensated by increased information interactions between core brain regions and functionally diverse regions.
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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, China
| | - Jiaolong Qin
- The Key Laboratory of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education, School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, 210094, China
| | - Jinlong Zhu
- School of Biological Sciences & Medical Engineering, Southeast University, Nanjing, 210096, China; Child Development and Learning Science, Key Laboratory of Ministry of Education, China
| | - Kun Bi
- School of Biological Sciences & Medical Engineering, Southeast University, Nanjing, 210096, China; Child Development and Learning Science, Key Laboratory of Ministry of Education, China
| | - Siqi Zhang
- School of Biological Sciences & Medical Engineering, Southeast University, Nanjing, 210096, China; Child Development and Learning Science, Key Laboratory of Ministry of Education, China
| | - Rui Yan
- Department of Psychiatry, the Affiliated Brain Hospital of Nanjing Medical University, Nanjing, 210029, China
| | - Peng Zhao
- Nanjing Drum Tower Hospital, the Affiliated Hospital of Nanjing University Medical School, Nanjing, 210008, 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, China.
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