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Li J, Long Z, Sheng W, Du L, Qiu J, Chen H, Liao W. Transcriptomic Similarity Informs Neuromorphic Deviations in Depression Biotypes. Biol Psychiatry 2024; 95:414-425. [PMID: 37573006 DOI: 10.1016/j.biopsych.2023.08.003] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/14/2023] [Revised: 07/31/2023] [Accepted: 08/01/2023] [Indexed: 08/14/2023]
Abstract
BACKGROUND Major depressive disorder (MDD) is complicated by population heterogeneity, motivating the investigation of biotypes through imaging-derived phenotypes. However, neuromorphic heterogeneity in MDD remains unclear, and how the correlated gene expression (CGE) connectome constrains these neuromorphic anomalies in MDD biotypes has not yet been studied. METHODS Here, we related cortical thickness deviations in MDD biotypes to a pattern of CGE connectome. Cortical thickness was estimated from 3-dimensional T1-weighted magnetic resonance images in 2 independent cohorts (discovery cohort: N = 425; replication cohort: N = 217). The transcriptional activity was measured according to Allen Human Brain Atlas. A density peak-based clustering algorithm was used to identify MDD biotypes. RESULTS We found that patients with MDD were clustered into 2 replicated biotypes based on single-patient regional deviations from healthy control participants across 2 datasets. Biotype 1 mainly exhibited cortical thinning across the brain, whereas biotype 2 mainly showed cortical thickening in the brain. Using brainwide gene expression data, we found that deviations of transcriptionally connected neighbors predicted regional deviation for both biotypes. Furthermore, putative CGE-informed epicenters of biotype 1 were concentrated on the cognitive control circuit, whereas biotype 2 epicenters were located in the social perception circuit. The patterns of epicenter likelihood were separately associated with depression- and anxiety-response maps, suggesting that epicenters of MDD biotypes may be associated with clinical efficacies. CONCLUSIONS Our findings linked the CGE connectome and neuromorphic deviations to identify distinct epicenters in MDD biotypes, providing insight into how microscale gene expressions informed MDD biotypes.
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Affiliation(s)
- Jiao Li
- Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, P.R. China; MOE Key Laboratory for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu, P.R. China
| | - Zhiliang Long
- Key Laboratory of Cognition and Personality, Faculty of Psychology, Southwest University, Chongqing, P.R. China
| | - Wei Sheng
- Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, P.R. China; MOE Key Laboratory for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu, P.R. China
| | - Lian Du
- Department of Psychiatry, The First Affiliated Hospital of Chongqing Medical University, Chongqing, P.R. China
| | - Jiang Qiu
- Key Laboratory of Cognition and Personality, Faculty of Psychology, Southwest University, Chongqing, P.R. China
| | - Huafu Chen
- Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, P.R. China; MOE Key Laboratory for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu, P.R. China
| | - Wei Liao
- Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, P.R. China; MOE Key Laboratory for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu, P.R. China.
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Dunlop K, Grosenick L, Downar J, Vila-Rodriguez F, Gunning FM, Daskalakis ZJ, Blumberger DM, Liston C. Dimensional and Categorical Solutions to Parsing Depression Heterogeneity in a Large Single-Site Sample. Biol Psychiatry 2024:S0006-3223(24)00055-6. [PMID: 38280408 DOI: 10.1016/j.biopsych.2024.01.012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/07/2023] [Revised: 12/21/2023] [Accepted: 01/13/2024] [Indexed: 01/29/2024]
Abstract
BACKGROUND Recent studies have reported significant advances in modeling the biological basis of heterogeneity in major depressive disorder, but investigators have also identified important technical challenges, including scanner-related artifacts, a propensity for multivariate models to overfit, and a need for larger samples with more extensive clinical phenotyping. The goals of the current study were to evaluate dimensional and categorical solutions to parsing heterogeneity in depression that are stable and generalizable in a large, single-site sample. METHODS We used regularized canonical correlation analysis to identify data-driven brain-behavior dimensions that explain individual differences in depression symptom domains in a large, single-site dataset comprising clinical assessments and resting-state functional magnetic resonance imaging data for 328 patients with major depressive disorder and 461 healthy control participants. We examined the stability of clinical loadings and model performance in held-out data. Finally, hierarchical clustering on these dimensions was used to identify categorical depression subtypes. RESULTS The optimal regularized canonical correlation analysis model yielded 3 robust and generalizable brain-behavior dimensions that explained individual differences in depressed mood and anxiety, anhedonia, and insomnia. Hierarchical clustering identified 4 depression subtypes, each with distinct clinical symptom profiles, abnormal resting-state functional connectivity patterns, and antidepressant responsiveness to repetitive transcranial magnetic stimulation. CONCLUSIONS Our results define dimensional and categorical solutions to parsing neurobiological heterogeneity in major depressive disorder that are stable, generalizable, and capable of predicting treatment outcomes, each with distinct advantages in different contexts. They also provide additional evidence that regularized canonical correlation analysis and hierarchical clustering are effective tools for investigating associations between functional connectivity and clinical symptoms.
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Affiliation(s)
- Katharine Dunlop
- Centre for Depression and Suicide Studies, St Michael's Hospital, Toronto, Ontario, Canada; Keenan Research Centre for Biomedical Science, St. Michael's Hospital, Toronto, Ontario, Canada; Department of Psychiatry and Institute of Medical Sciences, University of Toronto, Toronto, Ontario, Canada
| | - Logan Grosenick
- Department of Psychiatry, Weill Cornell Medicine, New York, New York
| | - Jonathan Downar
- Department of Psychiatry and Institute of Medical Sciences, University of Toronto, Toronto, Ontario, Canada
| | - Fidel Vila-Rodriguez
- Non-Invasive Neurostimulation Therapies Laboratory, Department of Psychiatry, University of British Columbia, Vancouver, British Columbia, Canada
| | - Faith M Gunning
- Institute of Geriatric Psychiatry, Weill Cornell Medicine, White Plains, New York
| | - Zafiris J Daskalakis
- Department of Psychiatry, University of California San Diego, San Diego, California
| | - Daniel M Blumberger
- Department of Psychiatry and Institute of Medical Sciences, University of Toronto, Toronto, Ontario, Canada; Department of Psychiatry, Weill Cornell Medicine, New York, New York; Temerty Centre for Therapeutic Brain Intervention and Campbell Family Research Institute, Centre for Addiction and Mental Health, Toronto, Ontario, Canada
| | - Conor Liston
- Department of Psychiatry, Weill Cornell Medicine, New York, New York; Feil Family Brain and Mind Research Institute, Weill Cornell Medicine, New York, New York.
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3
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Han S, Zheng R, Li S, Zhou B, Jiang Y, Fang K, Wei Y, Pang J, Li H, Zhang Y, Chen Y, Cheng J. Resolving heterogeneity in depression using individualized structural covariance network analysis. Psychol Med 2023; 53:5312-5321. [PMID: 35959558 DOI: 10.1017/s0033291722002380] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
BACKGROUND Elucidating individual aberrance is a critical first step toward precision medicine for heterogeneous disorders such as depression. The neuropathology of depression is related to abnormal inter-regional structural covariance indicating a brain maturational disruption. However, most studies focus on group-level structural covariance aberrance and ignore the interindividual heterogeneity. For that reason, we aimed to identify individualized structural covariance aberrance with the help of individualized differential structural covariance network (IDSCN) analysis. METHODS T1-weighted anatomical images of 195 first-episode untreated patients with depression and matched healthy controls (n = 78) were acquired. We obtained IDSCN for each patient and identified subtypes of depression based on shared differential edges. RESULTS As a result, patients with depression demonstrated tremendous heterogeneity in the distribution of differential structural covariance edges. Despite this heterogeneity, altered edges within subcortical-cerebellum network were often shared by most of the patients. Two robust neuroanatomical subtypes were identified. Specifically, patients in subtype 1 often shared decreased motor network-related edges. Patients in subtype 2 often shared decreased subcortical-cerebellum network-related edges. Functional annotation further revealed that differential edges in subtype 2 were mainly implicated in reward/motivation-related functional terms. CONCLUSIONS In conclusion, we investigated individualized differential structural covariance and identified that decreased edges within subcortical-cerebellum network are often shared by patients with depression. The identified two subtypes provide new insights into taxonomy and facilitate potential clues to precision diagnosis and treatment of depression.
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Affiliation(s)
- Shaoqiang Han
- Department of Magnetic Resonance Imaging, the First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
- Key Laboratory for Functional Magnetic Resonance Imaging and Molecular Imaging of Henan Province, Zhengzhou, China
- Engineering Technology Research Center for Detection and Application of Brain Function of Henan Province, Zhengzhou, China
- Engineering Research Center of Medical Imaging Intelligent Diagnosis and Treatment of Henan Province, Zhengzhou, China
- Key Laboratory of Magnetic Resonance and Brain Function of Henan Province, Zhengzhou, China
- Key Laboratory of Brain Function and Cognitive Magnetic Resonance Imaging of Zhengzhou, Zhengzhou, China
- Key Laboratory of Imaging Intelligence Research Medicine of Henan Province, Zhengzhou, China
- Henan Engineering Research Center of Brain Function Development and Application, Zhengzhou, China
| | - Ruiping Zheng
- Department of Magnetic Resonance Imaging, the First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
- Key Laboratory for Functional Magnetic Resonance Imaging and Molecular Imaging of Henan Province, Zhengzhou, China
- Engineering Technology Research Center for Detection and Application of Brain Function of Henan Province, Zhengzhou, China
- Engineering Research Center of Medical Imaging Intelligent Diagnosis and Treatment of Henan Province, Zhengzhou, China
- Key Laboratory of Magnetic Resonance and Brain Function of Henan Province, Zhengzhou, China
- Key Laboratory of Brain Function and Cognitive Magnetic Resonance Imaging of Zhengzhou, Zhengzhou, China
- Key Laboratory of Imaging Intelligence Research Medicine of Henan Province, Zhengzhou, China
- Henan Engineering Research Center of Brain Function Development and Application, Zhengzhou, China
| | - Shuying Li
- Department of Psychiatry, the First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Bingqian Zhou
- Department of Magnetic Resonance Imaging, the First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
- Key Laboratory for Functional Magnetic Resonance Imaging and Molecular Imaging of Henan Province, Zhengzhou, China
- Engineering Technology Research Center for Detection and Application of Brain Function of Henan Province, Zhengzhou, China
- Engineering Research Center of Medical Imaging Intelligent Diagnosis and Treatment of Henan Province, Zhengzhou, China
- Key Laboratory of Magnetic Resonance and Brain Function of Henan Province, Zhengzhou, China
- Key Laboratory of Brain Function and Cognitive Magnetic Resonance Imaging of Zhengzhou, Zhengzhou, China
- Key Laboratory of Imaging Intelligence Research Medicine of Henan Province, Zhengzhou, China
- Henan Engineering Research Center of Brain Function Development and Application, Zhengzhou, China
| | - Yu Jiang
- Department of Magnetic Resonance Imaging, the First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
- Key Laboratory for Functional Magnetic Resonance Imaging and Molecular Imaging of Henan Province, Zhengzhou, China
- Engineering Technology Research Center for Detection and Application of Brain Function of Henan Province, Zhengzhou, China
- Engineering Research Center of Medical Imaging Intelligent Diagnosis and Treatment of Henan Province, Zhengzhou, China
- Key Laboratory of Magnetic Resonance and Brain Function of Henan Province, Zhengzhou, China
- Key Laboratory of Brain Function and Cognitive Magnetic Resonance Imaging of Zhengzhou, Zhengzhou, China
- Key Laboratory of Imaging Intelligence Research Medicine of Henan Province, Zhengzhou, China
- Henan Engineering Research Center of Brain Function Development and Application, Zhengzhou, China
| | - Keke Fang
- Department of Pharmacy, Affiliated Cancer Hospital of Zhengzhou University, Henan Cancer Hospital, Zhengzhou, China
| | - Yarui Wei
- Department of Magnetic Resonance Imaging, the First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
- Key Laboratory for Functional Magnetic Resonance Imaging and Molecular Imaging of Henan Province, Zhengzhou, China
- Engineering Technology Research Center for Detection and Application of Brain Function of Henan Province, Zhengzhou, China
- Engineering Research Center of Medical Imaging Intelligent Diagnosis and Treatment of Henan Province, Zhengzhou, China
- Key Laboratory of Magnetic Resonance and Brain Function of Henan Province, Zhengzhou, China
- Key Laboratory of Brain Function and Cognitive Magnetic Resonance Imaging of Zhengzhou, Zhengzhou, China
- Key Laboratory of Imaging Intelligence Research Medicine of Henan Province, Zhengzhou, China
- Henan Engineering Research Center of Brain Function Development and Application, Zhengzhou, China
| | - Jianyue Pang
- Department of Psychiatry, the First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Hengfen Li
- Department of Psychiatry, the First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Yong Zhang
- Department of Magnetic Resonance Imaging, the First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
- Key Laboratory for Functional Magnetic Resonance Imaging and Molecular Imaging of Henan Province, Zhengzhou, China
- Engineering Technology Research Center for Detection and Application of Brain Function of Henan Province, Zhengzhou, China
- Engineering Research Center of Medical Imaging Intelligent Diagnosis and Treatment of Henan Province, Zhengzhou, China
- Key Laboratory of Magnetic Resonance and Brain Function of Henan Province, Zhengzhou, China
- Key Laboratory of Brain Function and Cognitive Magnetic Resonance Imaging of Zhengzhou, Zhengzhou, China
- Key Laboratory of Imaging Intelligence Research Medicine of Henan Province, Zhengzhou, China
- Henan Engineering Research Center of Brain Function Development and Application, Zhengzhou, China
| | - Yuan Chen
- Department of Magnetic Resonance Imaging, the First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
- Key Laboratory for Functional Magnetic Resonance Imaging and Molecular Imaging of Henan Province, Zhengzhou, China
- Engineering Technology Research Center for Detection and Application of Brain Function of Henan Province, Zhengzhou, China
- Engineering Research Center of Medical Imaging Intelligent Diagnosis and Treatment of Henan Province, Zhengzhou, China
- Key Laboratory of Magnetic Resonance and Brain Function of Henan Province, Zhengzhou, China
- Key Laboratory of Brain Function and Cognitive Magnetic Resonance Imaging of Zhengzhou, Zhengzhou, China
- Key Laboratory of Imaging Intelligence Research Medicine of Henan Province, Zhengzhou, China
- Henan Engineering Research Center of Brain Function Development and Application, Zhengzhou, China
| | - Jingliang Cheng
- Department of Magnetic Resonance Imaging, the First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
- Key Laboratory for Functional Magnetic Resonance Imaging and Molecular Imaging of Henan Province, Zhengzhou, China
- Engineering Technology Research Center for Detection and Application of Brain Function of Henan Province, Zhengzhou, China
- Engineering Research Center of Medical Imaging Intelligent Diagnosis and Treatment of Henan Province, Zhengzhou, China
- Key Laboratory of Magnetic Resonance and Brain Function of Henan Province, Zhengzhou, China
- Key Laboratory of Brain Function and Cognitive Magnetic Resonance Imaging of Zhengzhou, Zhengzhou, China
- Key Laboratory of Imaging Intelligence Research Medicine of Henan Province, Zhengzhou, China
- Henan Engineering Research Center of Brain Function Development and Application, Zhengzhou, China
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Chen X, Dai Z, Lin Y. Biotypes of major depressive disorder identified by a multiview clustering framework. J Affect Disord 2023; 329:257-272. [PMID: 36863463 DOI: 10.1016/j.jad.2023.02.118] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/24/2022] [Revised: 02/11/2023] [Accepted: 02/22/2023] [Indexed: 03/04/2023]
Abstract
BACKGROUND The advances in resting-state functional magnetic resonance imaging techniques motivate parsing heterogeneity in major depressive disorder (MDD) through neurophysiological subtypes (i.e., biotypes). Based on graph theories, researchers have observed the functional organization of the human brain as a complex system with modular structures and have found wide-spread but variable MDD-related abnormality regarding the modules. The evidence implies the possibility of identifying biotypes using high-dimensional functional connectivity (FC) data in ways that suit the potentially multifaceted biotypes taxonomy. METHODS We proposed a multiview biotype discovery framework that involves theory-driven feature subspace partition (i.e., "view") and independent subspace clustering. Six views were defined using intra- and intermodule FC regarding three MDD focal modules (i.e., the sensory-motor system, default mode network, and subcortical network). For robust biotypes, the framework was applied to a large multisite sample (805 MDD participants and 738 healthy controls). RESULTS Two biotypes were stably obtained in each view, respectively characterized by significantly increased and decreased FC compared to healthy controls. These view-specific biotypes promoted the diagnosis of MDD and showed different symptom profiles. By integrating the view-specific biotypes into biotype profiles, a broad spectrum in the neural heterogeneity of MDD and its separation from symptom-based subtypes was further revealed. LIMITATIONS The power of clinical effects is limited and the cross-sectional nature cannot predict the treatment effects of the biotypes. CONCLUSIONS Our findings not only contribute to the understanding of heterogeneity in MDD, but also provide a novel subtyping framework that could transcend current diagnostic boundaries and data modality.
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Affiliation(s)
- Xitian Chen
- Department of Psychology, Sun Yat-sen University, Guangzhou 510006, China
| | - Zhengjia Dai
- Department of Psychology, Sun Yat-sen University, Guangzhou 510006, China.
| | - Ying Lin
- Department of Psychology, Sun Yat-sen University, Guangzhou 510006, China.
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5
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Hou Z, Jiang W, Li F, Liu X, Hou Z, Yin Y, Zhang H, Zhang H, Xie C, Zhang Z, Kong Y, Yuan Y. Linking individual variability in functional brain connectivity to polygenic risk in major depressive disorder. J Affect Disord 2023; 329:55-63. [PMID: 36842648 DOI: 10.1016/j.jad.2023.02.104] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/20/2022] [Revised: 02/19/2023] [Accepted: 02/20/2023] [Indexed: 02/28/2023]
Abstract
BACKGROUND Major depressive disorder (MDD) is a highly heterogeneous disease, which brings great difficulties to clinical diagnosis and therapy. Its mechanism is still unknown. Prior neuroimaging studies mainly focused on mean differences between patients and healthy controls (HC), largely ignoring individual differences between patients. METHODS This study included 112 MDD patients and 93 HC subjects. Resting-state functional MRI data were obtained to examine the patterns of individual variability of brain functional connectivity (IVFC). The genetic risk of pathways including dopamine, 5-hydroxytryptamine (5-HT), norepinephrine (NE), hypothalamic-pituitary-adrenal (HPA) axis, and synaptic plasticity was assessed by multilocus genetic profile scores (MGPS), respectively. RESULTS The IVFC pattern of the MDD group was similar but higher than that in HCs. The inter-network functional connectivity in the default mode network contributed to altered IVFC in MDD. 5-HT, NE, and HPA pathway genes affected IVFC in MDD patients. The age of onset, duration, severity, and treatment response, were correlated with IVFC. IVFC in the left ventromedial prefrontal cortex had a mediating effect between MGPS of the 5-HT pathway and baseline depression severity. LIMITATIONS Environmental factors and differences in locations of functional areas across individuals were not taken into account. CONCLUSIONS This study found MDD patients had significantly different inter-individual functional connectivity variations than healthy people, and genetic risk might affect clinical manifestations through brain function heterogeneity.
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Affiliation(s)
- Zhuoliang Hou
- Department of Psychosomatics and Psychiatry, ZhongDa Hospital, School of Medical, Southeast University, Nanjing 210009, China
| | - Wenhao Jiang
- Department of Psychosomatics and Psychiatry, ZhongDa Hospital, School of Medical, Southeast University, Nanjing 210009, China
| | - Fan Li
- Jiangsu Provincial Joint International Research Laboratory of Medical Information Processing, School of Computer Science and Engineering, Southeast University, Nanjing 210096, China
| | - Xiaoyun Liu
- Department of Psychosomatics and Psychiatry, ZhongDa Hospital, School of Medical, Southeast University, Nanjing 210009, China
| | - Zhenghua Hou
- Department of Psychosomatics and Psychiatry, ZhongDa Hospital, School of Medical, Southeast University, Nanjing 210009, China
| | - Yingying Yin
- Department of Psychosomatics and Psychiatry, ZhongDa Hospital, School of Medical, Southeast University, Nanjing 210009, China
| | - Haisan Zhang
- Departments of Clinical Magnetic Resonance Imaging, The Second Affiliated Hospital of Xinxiang Medical University, Xinxiang 453002, China
| | - Hongxing Zhang
- Departments of Psychiatry, The Second Affiliated Hospital of Xinxiang Medical University, Xinxiang 453002, China; School of Psychology, Xinxiang Medical University, Xinxiang 453003, China
| | - Chunming Xie
- Department of Neurology, ZhongDa Hospital, School of Medicine, Southeast University, Nanjing 210009, China
| | - Zhijun Zhang
- Department of Neurology, ZhongDa Hospital, School of Medicine, Southeast University, Nanjing 210009, China
| | - Youyong Kong
- Jiangsu Provincial Joint International Research Laboratory of Medical Information Processing, School of Computer Science and Engineering, Southeast University, Nanjing 210096, China.
| | - Yonggui Yuan
- Department of Psychosomatics and Psychiatry, ZhongDa Hospital, School of Medical, Southeast University, Nanjing 210009, China.
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Huang MH, Fan SY, Lin IM. EEG coherences of the fronto-limbic circuit between patients with major depressive disorder and healthy controls. J Affect Disord 2023; 331:112-120. [PMID: 36958482 DOI: 10.1016/j.jad.2023.03.055] [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: 01/28/2023] [Revised: 03/07/2023] [Accepted: 03/18/2023] [Indexed: 03/25/2023]
Abstract
BACKGROUND Imaging studies found that patients with major depressive disorder (MDD) showed abnormal functional connectivity in the fronto-limbic circuit, including the prefrontal cortex (PFC), anterior cingulate cortex (ACC), and limbic system (amygdala). This study used electroencephalography (EEG) coherence as an indicator of functional connectivity in the fronto-limbic circuit and examined the group differences between the MDD group and healthy controls (HC group), and the associations between EEG coherence and depressive symptoms. METHODS 125 and 132 participants in the MDD and HC groups have measured the symptoms of depression and anxiety, and delta, theta, alpha, and beta1-beta4 EEG coherences in the fronto-limbic circuit and examined the differences between the two groups, and the associations between the EEG coherence and depressive symptoms were examined. RESULTS Lower theta, alpha, beta1, beta3, and beta4 coherence in the fronto-limbic circuit and higher beta2 coherence between the PFC and limbic system in the MDD group than in the HC group. Negative correlations between delta, theta, beta1, beta3, and beta4 coherence and total depression, cognitive depression, and somatic depression; positive correlations between beta2 coherences in the PFC and limbic system, and total depression and cognitive depression scores in the MDD group. LIMITATIONS Whether low EEG coherence in the fronto-limbic circuit is applicable to other subtypes of MDD requires further study. CONCLUSIONS Low EEG coherences in the fronto-limbic circuit were related to depressive symptoms, and increased functional connectivity in the fronto-limbic circuit can be applied by neurofeedback in future studies.
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Affiliation(s)
- Min-Han Huang
- Department of Psychology, College of Humanities and Social Sciences, Kaohsiung Medical University, Kaohsiung, Taiwan; Department of Medical Research, Kaohsiung Medical University Hospital, Kaohsiung, Taiwan
| | - Sheng-Yu Fan
- Institute of Gerontology, College of Medicine, National Cheng Kung University, Tainan, Taiwan
| | - I-Mei Lin
- Department of Psychology, College of Humanities and Social Sciences, Kaohsiung Medical University, Kaohsiung, Taiwan; Institute of Gerontology, College of Medicine, National Cheng Kung University, Tainan, Taiwan.
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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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Chen J, Patil KR, Yeo BTT, Eickhoff SB. Leveraging Machine Learning for Gaining Neurobiological and Nosological Insights in Psychiatric Research. Biol Psychiatry 2023; 93:18-28. [PMID: 36307328 DOI: 10.1016/j.biopsych.2022.07.025] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/13/2022] [Revised: 07/06/2022] [Accepted: 07/28/2022] [Indexed: 11/18/2022]
Abstract
Much attention is currently devoted to developing diagnostic classifiers for mental disorders. Complementing these efforts, we highlight the potential of machine learning to gain biological insights into the psychopathology and nosology of mental disorders. Studies to this end have mainly used brain imaging data, which can be obtained noninvasively from large cohorts and have repeatedly been argued to reveal potentially intermediate phenotypes. This may become particularly relevant in light of recent efforts to identify magnetic resonance imaging-derived biomarkers that yield insight into pathophysiological processes as well as to refine the taxonomy of mental illness. In particular, the accuracy of machine learning models may be used as dependent variables to identify features relevant to pathophysiology. Moreover, such approaches may help disentangle the dimensional (within diagnosis) and often overlapping (across diagnoses) symptomatology of psychiatric illness. We also point out a multiview perspective that combines data from different sources, bridging molecular and system-level information. Finally, we summarize recent efforts toward a data-driven definition of subtypes or disease entities through unsupervised and semisupervised approaches. The latter, blending unsupervised and supervised concepts, may represent a particularly promising avenue toward dissecting heterogeneous categories. Finally, we raise several technical and conceptual aspects related to the reviewed approaches. In particular, we discuss common pitfalls pertaining to flawed input data or analytic procedures that would likely lead to unreliable outputs.
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Affiliation(s)
- Ji Chen
- Department of Psychology and Behavioral Sciences, Zhejiang University, Hangzhou, China; Department of Psychiatry, The Fourth Affiliated Hospital, Zhejiang University School of Medicine, Yiwu, Zhejiang, China; Institute of Neuroscience and Medicine, Brain & Behaviour (INM-7), Research Centre Jülich, Jülich, Germany.
| | - Kaustubh R Patil
- Institute of Neuroscience and Medicine, Brain & Behaviour (INM-7), Research Centre Jülich, Jülich, Germany; Institute of Systems Neuroscience, Medical Faculty, Heinrich-Heine-universität Düsseldorf, Düsseldorf, Germany
| | - B T Thomas Yeo
- Centre for Sleep and Cognition & Centre for Translational MR Research, Yong Loo Lin School of Medicine, National University of Singapore, Singapore; Department of Electrical and Computer Engineering, National University of Singapore, Singapore; Integrative Sciences & Engineering Programme, National University of Singapore, Singapore; Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts
| | - Simon B Eickhoff
- Institute of Neuroscience and Medicine, Brain & Behaviour (INM-7), Research Centre Jülich, Jülich, Germany; Institute of Systems Neuroscience, Medical Faculty, Heinrich-Heine-universität Düsseldorf, Düsseldorf, Germany
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9
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Pilmeyer J, Huijbers W, Lamerichs R, Jansen JFA, Breeuwer M, Zinger S. Functional MRI in major depressive disorder: A review of findings, limitations, and future prospects. J Neuroimaging 2022; 32:582-595. [PMID: 35598083 PMCID: PMC9540243 DOI: 10.1111/jon.13011] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2022] [Revised: 05/04/2022] [Accepted: 05/04/2022] [Indexed: 02/02/2023] Open
Abstract
Objective diagnosis and prognosis in major depressive disorder (MDD) remains a challenge due to the absence of biomarkers based on physiological parameters or medical tests. Numerous studies have been conducted to identify functional magnetic resonance imaging‐based biomarkers of depression that either objectively differentiate patients with depression from healthy subjects, predict personalized treatment outcome, or characterize biological subtypes of depression. While there are some findings of consistent functional biomarkers, there is still lack of robust data acquisition and analysis methodology. According to current findings, primarily, the anterior cingulate cortex, prefrontal cortex, and default mode network play a crucial role in MDD. Yet, there are also less consistent results and the involvement of other regions or networks remains ambiguous. We further discuss image acquisition, processing, and analysis limitations that might underlie these inconsistencies. Finally, the current review aims to address and discuss possible remedies and future opportunities that could improve the search for consistent functional imaging biomarkers of depression. Novel acquisition techniques, such as multiband and multiecho imaging, and neural network‐based cleaning approaches can enhance the signal quality in limbic and frontal regions. More comprehensive analyses, such as directed or dynamic functional features or the identification of biological depression subtypes, can improve objective diagnosis or treatment outcome prediction and mitigate the heterogeneity of MDD. Overall, these improvements in functional MRI imaging techniques, processing, and analysis could advance the search for biomarkers and ultimately aid patients with MDD and their treatment course.
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Affiliation(s)
- Jesper Pilmeyer
- Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands.,Department of Research and Development, Epilepsy Centre Kempenhaeghe, Heeze, The Netherlands
| | - Willem Huijbers
- Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands.,Philips Research, Eindhoven, The Netherlands
| | - Rolf Lamerichs
- Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands.,Department of Research and Development, Epilepsy Centre Kempenhaeghe, Heeze, The Netherlands.,Philips Research, Eindhoven, The Netherlands
| | - Jacobus F A Jansen
- Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands.,Department of Radiology and Nuclear Medicine, Maastricht University Medical Centre, Maastricht, The Netherlands.,School for Mental Health and Neuroscience, Maastricht University, Maastricht, The Netherlands
| | - Marcel Breeuwer
- Philips Healthcare, Best, The Netherlands.,Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands
| | - Svitlana Zinger
- Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands.,Department of Research and Development, Epilepsy Centre Kempenhaeghe, Heeze, The Netherlands
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10
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Abstract
BACKGROUND In this modern era, depression is one of the most prevalent mental disorders from which millions of individuals are affected today. The symptoms of depression are heterogeneous and often coincide with other disorders such as bipolar disorder, Parkinson's, schizophrenia, etc. It is a serious mental illness that may lead to other health problems if left untreated. Currently, identifying individuals with depression is totally based on the expertise of the clinician's experience. In order to assist clinicians in identifying the characteristics and classifying depressed people, different types of data modalities and machine learning techniques have been incorporated by researchers in this field. This study aims to find the answers to some important questions related to the trend of publications, data modality, machine learning models, dataset usage, pre-processing techniques and feature extraction and selection techniques that are prevalent and guide the direction of future research on depression diagnosis. METHODS This systematic review was conducted using a broad range of articles from two major databases: IEEE Xplore and PubMed. Studies ranging from the years 2011 to April 2021 were retrieved from the databases resulting in a total of 590 articles (53 articles from the IEEE Xplore database and 537 articles from the PubMed database). Out of those, the articles which satisfied the defined inclusion criteria were investigated for further analysis. RESULTS A total of 135 articles were identified and analysed for this review. High growth in the number of publications has been observed in recent years. Furthermore, significant diversity in the use of data modalities and machine learning classifiers has also been noted in this study. fMRI data with an SVM classifier was found to be the most popular choice among researchers. In most of the studies, data scarcity and small sample size, particularly for neuroimaging data are major concerns. The use of identical data pre-processing tools for similar data modalities can be seen. This study also provides statistical analysis of the current framework with respect to the modality, machine learning classifier, sample size and accuracy by applying one-way ANOVA and the Tukey - Kramer test. CONCLUSION The results indicate that an effective fusion of machine learning techniques with a potential data modality has a promising future for assisting clinicians in automatic depression diagnosis.
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Affiliation(s)
- Sweta Bhadra
- Department of CS & IT, Cotton University, Guwahati, India
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11
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Guo XJ, Wu P, Jia X, Dong YM, Zhao CM, Chen NN, Zhang ZY, Miao YT, Yun KM, Gao CR, Ren Y. Mapping the structure of depression biomarker research: A bibliometric analysis. Front Psychiatry 2022; 13:943996. [PMID: 36186850 PMCID: PMC9523516 DOI: 10.3389/fpsyt.2022.943996] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/14/2022] [Accepted: 08/29/2022] [Indexed: 11/24/2022] Open
Abstract
BACKGROUND Depression is a common mental disorder and the diagnosis is still based on the descriptions of symptoms. Biomarkers can reveal disease characteristics for diagnosis, prognosis, and treatment. In recent years, many biomarkers relevant to the mechanisms of depression have been identified. This study uses bibliometric methods and visualization tools to analyse the literature on depression biomarkers and its hot topics, and research frontiers to provide references for future research. METHODS Scientific publications related to depression biomarkers published between 2009 and 2022 were obtained from the Web of Science database. The BICOMB software was used to extract high-frequency keywords and to construct binary word-document and co-word matrices. gCLUTO was used for bicluster and visual analyses of high-frequency keywords. Further graphical visualizations were generated using R, CiteSpace and VOSviewer software. RESULTS A total of 14,403 articles related to depression biomarkers were identified. The United States (34.81%) and China (15.68%), which together account for more than half of all publications, can be considered the research base for the field. Among institutions, the University of California, University of London, and Harvard University are among the top in terms of publication number. Three authors (Maes M, Penninx B.W.J.H., and Berk M) emerged as eminent researchers in the field. Finally, eight research hotspots for depression biomarkers were identified using reference co-citation analysis. CONCLUSION This study used bibliometric methods to characterize the body of literature and subject knowledge in the field of depression biomarker research. Among the core biomarkers of depression, functional magnetic resonance imaging (fMRI), cytokines, and oxidative stress are relatively well established; however, research on machine learning, metabolomics, and microRNAs holds potential for future development. We found "microRNAs" and "gut microbiota" to be the most recent burst terms in the study of depression biomarkers and the likely frontiers of future research.
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Affiliation(s)
- Xiang-Jie Guo
- Department of Forensic Medicine, Shanxi Medical University, Taiyuan, China
| | - Peng Wu
- Department of Forensic Medicine, Shanxi Medical University, Taiyuan, China
| | - Xiao Jia
- College of Pharmacy, Nankai University, Tianjin, China
| | - Yi-Ming Dong
- Department of Forensic Medicine, Shanxi Medical University, Taiyuan, China
| | - Chun-Mei Zhao
- Department of Forensic Medicine, Shanxi Medical University, Taiyuan, China
| | - Nian-Nian Chen
- Department of Forensic Medicine, Shanxi Medical University, Taiyuan, China
| | - Zhi-Yong Zhang
- Department of Health Statistics, School of Public Health, Shanxi Medical University, Taiyuan, China
| | - Yu-Ting Miao
- Department of Psychology, School of Humanities and Social Sciences, Shanxi Medical University, Taiyuan, China
| | - Ke-Ming Yun
- Department of Forensic Medicine, Shanxi Medical University, Taiyuan, China
| | - Cai-Rong Gao
- Department of Forensic Medicine, Shanxi Medical University, Taiyuan, China
| | - Yan Ren
- Department of Psychiatry, Shanxi Bethune Hospital, Shanxi Academy of Medical Sciences, Tongji Shanxi Hospital, Third Hospital of Shanxi Medical University, Taiyuan, China.,Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
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12
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Buch AM, Liston C. Dissecting diagnostic heterogeneity in depression by integrating neuroimaging and genetics. Neuropsychopharmacology 2021; 46:156-175. [PMID: 32781460 PMCID: PMC7688954 DOI: 10.1038/s41386-020-00789-3] [Citation(s) in RCA: 98] [Impact Index Per Article: 32.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/28/2020] [Revised: 07/07/2020] [Accepted: 07/16/2020] [Indexed: 12/12/2022]
Abstract
Depression is a heterogeneous and etiologically complex psychiatric syndrome, not a unitary disease entity, encompassing a broad spectrum of psychopathology arising from distinct pathophysiological mechanisms. Motivated by a need to advance our understanding of these mechanisms and develop new treatment strategies, there is a renewed interest in investigating the neurobiological basis of heterogeneity in depression and rethinking our approach to diagnosis for research purposes. Large-scale genome-wide association studies have now identified multiple genetic risk variants implicating excitatory neurotransmission and synapse function and underscoring a highly polygenic inheritance pattern that may be another important contributor to heterogeneity in depression. Here, we review various sources of phenotypic heterogeneity and approaches to defining and studying depression subtypes, including symptom-based subtypes and biology-based approaches to decomposing the depression syndrome. We review "dimensional," "categorical," and "hybrid" approaches to parsing phenotypic heterogeneity in depression and defining subtypes using functional neuroimaging. Next, we review recent progress in neuroimaging genetics (correlating neuroimaging patterns of brain function with genetic data) and its potential utility for generating testable hypotheses concerning molecular and circuit-level mechanisms. We discuss how genetic variants and transcriptomic profiles may confer risk for depression by modulating brain structure and function. We conclude by highlighting several promising areas for future research into the neurobiological underpinnings of heterogeneity, including efforts to understand sexually dimorphic mechanisms, the longitudinal dynamics of depressive episodes, and strategies for developing personalized treatments and facilitating clinical decision-making.
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Affiliation(s)
- Amanda M Buch
- Department of Psychiatry and Brain and Mind Research Institute, Weill Cornell Medicine, 413 East 69th Street, Box 240, New York, NY, 10021, USA
| | - Conor Liston
- Department of Psychiatry and Brain and Mind Research Institute, Weill Cornell Medicine, 413 East 69th Street, Box 240, New York, NY, 10021, USA.
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13
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Liang S, Deng W, Li X, Greenshaw AJ, Wang Q, Li M, Ma X, Bai TJ, Bo QJ, Cao J, Chen GM, Chen W, Cheng C, Cheng YQ, Cui XL, Duan J, Fang YR, Gong QY, Guo WB, Hou ZH, Hu L, Kuang L, Li F, Li KM, Liu YS, Liu ZN, Long YC, Luo QH, Meng HQ, Peng DH, Qiu HT, Qiu J, Shen YD, Shi YS, Si TM, Wang CY, Wang F, Wang K, Wang L, Wang X, Wang Y, Wu XP, Wu XR, Xie CM, Xie GR, Xie HY, Xie P, Xu XF, Yang H, Yang J, Yu H, Yao JS, Yao SQ, Yin YY, Yuan YG, Zang YF, Zhang AX, Zhang H, Zhang KR, Zhang ZJ, Zhao JP, Zhou RB, Zhou YT, Zou CJ, Zuo XN, Yan CG, Li T. Biotypes of major depressive disorder: Neuroimaging evidence from resting-state default mode network patterns. NEUROIMAGE-CLINICAL 2020; 28:102514. [PMID: 33396001 PMCID: PMC7724374 DOI: 10.1016/j.nicl.2020.102514] [Citation(s) in RCA: 42] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/07/2020] [Revised: 11/20/2020] [Accepted: 11/22/2020] [Indexed: 02/05/2023]
Abstract
BACKGROUND Major depressive disorder (MDD) is heterogeneous disorder associated with aberrant functional connectivity within the default mode network (DMN). This study focused on data-driven identification and validation of potential DMN-pattern-based MDD subtypes to parse heterogeneity of the disorder. METHODS The sample comprised 1397 participants including 690 patients with MDD and 707 healthy controls (HC) registered from multiple sites based on the REST-meta-MDD Project in China. Baseline resting-state functional magnetic resonance imaging (rs-fMRI) data was recorded for each participant. Discriminative features were selected from DMN between patients and HC. Patient subgroups were defined by K-means and principle component analysis in the multi-site datasets and validated in an independent single-site dataset. Statistical significance of resultant clustering were confirmed. Demographic and clinical variables were compared between identified patient subgroups. RESULTS Two MDD subgroups with differing functional connectivity profiles of DMN were identified in the multi-site datasets, and relatively stable in different validation samples. The predominant dysfunctional connectivity profiles were detected among superior frontal cortex, ventral medial prefrontal cortex, posterior cingulate cortex and precuneus, whereas one subgroup exhibited increases of connectivity (hyperDMN MDD) and another subgroup showed decreases of connectivity (hypoDMN MDD). The hyperDMN subgroup in the discovery dataset had age-related severity of depressive symptoms. Patient subgroups had comparable demographic and clinical symptom variables. CONCLUSIONS Findings suggest the existence of two neural subtypes of MDD associated with different dysfunctional DMN connectivity patterns, which may provide useful evidence for parsing heterogeneity of depression and be valuable to inform the search for personalized treatment strategies.
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Affiliation(s)
- Sugai Liang
- Mental Health Center & Psychiatric Laboratory, State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu 610041, Sichuan, China; West China Brain Research Centre, West China Hospital, Sichuan University, Chengdu 610041, Sichuan, China
| | - Wei Deng
- Mental Health Center & Psychiatric Laboratory, State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu 610041, Sichuan, China; West China Brain Research Centre, West China Hospital, Sichuan University, Chengdu 610041, Sichuan, China
| | - Xiaojing Li
- Mental Health Center & Psychiatric Laboratory, State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu 610041, Sichuan, China; West China Brain Research Centre, West China Hospital, Sichuan University, Chengdu 610041, Sichuan, China
| | - Andrew J Greenshaw
- Department of Psychiatry, University of Alberta, Edmonton T6G 2B7, AB, Canada
| | - Qiang Wang
- Mental Health Center & Psychiatric Laboratory, State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu 610041, Sichuan, China; West China Brain Research Centre, West China Hospital, Sichuan University, Chengdu 610041, Sichuan, China
| | - Mingli Li
- Mental Health Center & Psychiatric Laboratory, State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu 610041, Sichuan, China; West China Brain Research Centre, West China Hospital, Sichuan University, Chengdu 610041, Sichuan, China
| | - Xiaohong Ma
- Mental Health Center & Psychiatric Laboratory, State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu 610041, Sichuan, China; West China Brain Research Centre, West China Hospital, Sichuan University, Chengdu 610041, Sichuan, China
| | - Tong-Jian Bai
- Anhui Medical University, Hefei 230032, Anhui, China
| | - Qi-Jing Bo
- Beijing Anding Hospital, Capital Medical University, Beijing 100069, China
| | - Jun Cao
- Department of Psychiatry, The First Affiliated Hospital of Chongqing Medical University, Chongqing 400016, China
| | - Guan-Mao Chen
- The First Affiliated Hospital of Jinan University, Guangzhou 510630, Guangdong, China
| | - Wei Chen
- Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou 310058, Zhejiang, China
| | - Chang Cheng
- The Second Xiangya Hospital of Central South University, Changsha 410083, Hunan, China
| | - Yu-Qi Cheng
- First Affiliated Hospital of Kunming Medical University, Kunming 650211, Yunnan, China
| | - Xi-Long Cui
- The Second Xiangya Hospital of Central South University, Changsha 410083, Hunan, China
| | - Jia Duan
- Department of Psychiatry, First Affiliated Hospital, China Medical University, Shenyang 110001, Liaoning, China
| | - Yi-Ru Fang
- Department of Psychiatry, Shanghai Jiao Tong University School of Medicine, Shanghai 200240, China
| | - Qi-Yong Gong
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu 610041, Sichuan, China; Psychoradiology Research Unit of Chinese Academy of Medical Sciences, West China Hospital of Sichuan University, Chengdu 610040, Sichuan, China
| | - Wen-Bin Guo
- The Second Xiangya Hospital of Central South University, Changsha 410083, Hunan, China
| | - Zheng-Hua Hou
- Department of Psychosomatics and Psychiatry, Zhongda Hospital, School of Medicine, Southeast University, Nanjing 210096, Jiangsu, China
| | - Lan Hu
- Department of Psychiatry, The First Affiliated Hospital of Chongqing Medical University, Chongqing 400016, China
| | - Li Kuang
- Department of Psychiatry, The First Affiliated Hospital of Chongqing Medical University, Chongqing 400016, China
| | - Feng Li
- Beijing Anding Hospital, Capital Medical University, Beijing 100069, China
| | - Kai-Ming Li
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu 610041, Sichuan, China
| | - Yan-Song Liu
- Department of Clinical Psychology, Suzhou Psychiatric Hospital, The Affiliated Guangji Hospital of Soochow University, Suzhou 215031, Jiangsu, China
| | - Zhe-Ning Liu
- The Institute of Mental Health, Second Xiangya Hospital of Central South University, Changsha 410083, Hunan, China
| | - Yi-Cheng Long
- Faculty of Psychology, Southwest University, Chongqing 400715, China
| | - Qing-Hua Luo
- Department of Psychiatry, The First Affiliated Hospital of Chongqing Medical University, Chongqing 400016, China
| | - Hua-Qing Meng
- Department of Clinical Psychology, Suzhou Psychiatric Hospital, The Affiliated Guangji Hospital of Soochow University, Suzhou 215031, Jiangsu, China
| | - Dai-Hui Peng
- Department of Psychiatry, Shanghai Jiao Tong University School of Medicine, Shanghai 200240, China
| | - Hai-Tang Qiu
- Department of Psychiatry, The First Affiliated Hospital of Chongqing Medical University, Chongqing 400016, China
| | - Jiang Qiu
- Faculty of Psychology, Southwest University, Chongqing 400715, China
| | - Yue-Di Shen
- Department of Diagnostics, Affiliated Hospital, Hangzhou Normal University Medical School, Hangzhou 311121, Zhejiang, China
| | - Yu-Shu Shi
- Department of Radiology, The First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou 310058, Zhejiang, China
| | - Tian-Mei Si
- National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital) & Key Laboratory of Mental Health, Ministry of Health (Peking University), Beijing 100191, China
| | - Chuan-Yue Wang
- Beijing Anding Hospital, Capital Medical University, Beijing 100069, China
| | - Fei Wang
- Department of Psychiatry, First Affiliated Hospital, China Medical University, Shenyang 110001, Liaoning, China
| | - Kai Wang
- Beijing Anding Hospital, Capital Medical University, Beijing 100069, China
| | - Li Wang
- National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital) & Key Laboratory of Mental Health, Ministry of Health (Peking University), Beijing 100191, China
| | - Xiang Wang
- Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou 310058, Zhejiang, China
| | - Ying Wang
- The First Affiliated Hospital of Jinan University, Guangzhou 510630, Guangdong, China
| | - Xiao-Ping Wu
- Xi'an Central Hospital, Xi'an 710032, Shaanxi, China
| | - Xin-Ran Wu
- Faculty of Psychology, Southwest University, Chongqing 400715, China
| | - Chun-Ming Xie
- Department of Neurology, Affiliated Zhongda Hospital of Southeast University, Nanjing 210096, Jiangsu, China
| | - Guang-Rong Xie
- The Second Xiangya Hospital of Central South University, Changsha 410083, Hunan, China
| | - Hai-Yan Xie
- Department of Psychiatry, The Fourth Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou 310058, Zhejiang, China
| | - Peng Xie
- Institute of Neuroscience, Chongqing Medical University, Chongqing 400016, China; Chongqing Key Laboratory of Neurobiology, Chongqing 400016, China; Department of Neurology, The First Affiliated Hospital of Chongqing Medical University, Chongqing 400016, China
| | - Xiu-Feng Xu
- First Affiliated Hospital of Kunming Medical University, Kunming 650211, Yunnan, China
| | - Hong Yang
- Department of Radiology, The First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou 310058, Zhejiang, China
| | - Jian Yang
- The First Affiliated Hospital of Xi'an Jiaotong University, 710049 Shaanxi, China
| | - Hua Yu
- Mental Health Center & Psychiatric Laboratory, State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu 610041, Sichuan, China; West China Brain Research Centre, West China Hospital, Sichuan University, Chengdu 610041, Sichuan, China
| | - Jia-Shu Yao
- Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou 310058, Zhejiang, China
| | - Shu-Qiao Yao
- The Second Xiangya Hospital of Central South University, Changsha 410083, Hunan, China
| | - Ying-Ying Yin
- Department of Psychosomatics and Psychiatry, Zhongda Hospital, School of Medicine, Southeast University, Nanjing 210096, Jiangsu, China
| | - Yong-Gui Yuan
- Department of Psychosomatics and Psychiatry, Zhongda Hospital, School of Medicine, Southeast University, Nanjing 210096, Jiangsu, China
| | - Yu-Feng Zang
- Center for Cognition and Brain Disorders, Institutes of Psychological Sciences, Hangzhou Normal University, Hangzhou 311121, Zhejiang, China; Zhejiang Key Laboratory for Research in Assessment of Cognitive Impairments, Hangzhou 311121, Zhejiang, China
| | - Ai-Xia Zhang
- The First Affiliated Hospital of Xi'an Jiaotong University, 710049 Shaanxi, China
| | - Hong Zhang
- Xi'an Central Hospital, Xi'an 710032, Shaanxi, China
| | - Ke-Rang Zhang
- First Hospital of Shanxi Medical University, Taiyuan 030607, Shanxi, China
| | - Zhi-Jun Zhang
- Department of Neurology, Affiliated Zhongda Hospital of Southeast University, Nanjing 210096, Jiangsu, China
| | - Jing-Ping Zhao
- The Institute of Mental Health, Second Xiangya Hospital of Central South University, Changsha 410083, Hunan, China
| | - Ru-Bai Zhou
- Department of Psychiatry, Shanghai Jiao Tong University School of Medicine, Shanghai 200240, China
| | - Yi-Ting Zhou
- Mental Health Center & Psychiatric Laboratory, State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu 610041, Sichuan, China; West China Brain Research Centre, West China Hospital, Sichuan University, Chengdu 610041, Sichuan, China
| | - Chao-Jie Zou
- First Affiliated Hospital of Kunming Medical University, Kunming 650211, Yunnan, China
| | - Xi-Nian Zuo
- CAS Key Laboratory of Behavioral Science, Institute of Psychology, Beijing 100101, China; Department of Psychology, University of Chinese Academy of Sciences, Beijing 100101, China; Magnetic Resonance Imaging Research Center and Research Center for Lifespan Development of Mind and Brain (CLIMB), Institute of Psychology, Chinese Academy of Sciences, Beijing 100101, 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; Magnetic Resonance Imaging Research Center and Research Center for Lifespan Development of Mind and Brain (CLIMB), Institute of Psychology, Chinese Academy of Sciences, Beijing 100101, China.
| | - Tao Li
- Mental Health Center & Psychiatric Laboratory, State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu 610041, Sichuan, China; West China Brain Research Centre, West China Hospital, Sichuan University, Chengdu 610041, Sichuan, China.
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14
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Multilayer MEG functional connectivity as a potential marker for suicidal thoughts in major depressive disorder. NEUROIMAGE-CLINICAL 2020; 28:102378. [PMID: 32836087 PMCID: PMC7451429 DOI: 10.1016/j.nicl.2020.102378] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/02/2019] [Revised: 06/18/2020] [Accepted: 08/06/2020] [Indexed: 11/23/2022]
Abstract
Major depressive disorder (MDD) is highly heterogeneous in its clinical presentation. The present exploratory study used magnetoencephalography (MEG) to investigate electrophysiological intrinsic connectivity differences between healthy volunteers and unmedicated participants with treatment-resistant MDD. The study examined canonical frequency bands from delta through gamma. In addition to group comparisons, correlational studies were conducted to determine whether connectivity was related to five symptom factors: depressed mood, tension, negative cognition, suicidal thoughts, and amotivation. The MDD and healthy volunteer groups did not differ significantly at baseline when corrected across all frequencies and clusters, although evidence of generalized slowing in MDD was observed. Notably, however, electrophysiological connectivity was strongly related to suicidal thoughts, particularly as coupling of low frequency power fluctuations (delta and theta) with alpha and beta power. This analysis revealed hub areas underlying this symptom cluster, including left hippocampus, left anterior insula, and bilateral dorsolateral prefrontal cortex. No other symptom cluster demonstrated a relationship with neurophysiological connectivity, suggesting a specificity to these results as markers of suicidal ideation.
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15
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Lynch CJ, Gunning FM, Liston C. Causes and Consequences of Diagnostic Heterogeneity in Depression: Paths to Discovering Novel Biological Depression Subtypes. Biol Psychiatry 2020; 88:83-94. [PMID: 32171465 DOI: 10.1016/j.biopsych.2020.01.012] [Citation(s) in RCA: 72] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/26/2019] [Revised: 12/13/2019] [Accepted: 01/18/2020] [Indexed: 12/17/2022]
Abstract
Depression is a highly heterogeneous syndrome that bears only modest correlations with its biological substrates, motivating a renewed interest in rethinking our approach to diagnosing depression for research purposes and new efforts to discover subtypes of depression anchored in biology. Here, we review the major causes of diagnostic heterogeneity in depression, with consideration of both clinical symptoms and behaviors (symptomatology and trajectory of depressive episodes) and biology (genetics and sexually dimorphic factors). Next, we discuss the promise of using data-driven strategies to discover novel subtypes of depression based on functional neuroimaging measures, including dimensional, categorical, and hybrid approaches to parsing diagnostic heterogeneity and understanding its biological basis. The merits of using resting-state functional magnetic resonance imaging functional connectivity techniques for subtyping are considered along with a set of technical challenges and potential solutions. We conclude by identifying promising future directions for defining neurobiologically informed depression subtypes and leveraging them in the future for predicting treatment outcomes and informing clinical decision making.
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Affiliation(s)
- Charles J Lynch
- Brain and Mind Research Institute and Department of Psychiatry, Weill Cornell Medicine, New York, New York
| | - Faith M Gunning
- Brain and Mind Research Institute and Department of Psychiatry, Weill Cornell Medicine, New York, New York
| | - Conor Liston
- Brain and Mind Research Institute and Department of Psychiatry, Weill Cornell Medicine, New York, New York.
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16
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Liang S, Wang Q, Kong X, Deng W, Yang X, Li X, Zhang Z, Zhang J, Zhang C, Li XM, Ma X, Shao J, Greenshaw AJ, Li T. White Matter Abnormalities in Major Depression Biotypes Identified by Diffusion Tensor Imaging. Neurosci Bull 2019; 35:867-876. [PMID: 31062333 PMCID: PMC6754492 DOI: 10.1007/s12264-019-00381-w] [Citation(s) in RCA: 22] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2018] [Accepted: 02/25/2019] [Indexed: 02/05/2023] Open
Abstract
Identifying data-driven biotypes of major depressive disorder (MDD) has promise for the clarification of diagnostic heterogeneity. However, few studies have focused on white-matter abnormalities for MDD subtyping. This study included 116 patients with MDD and 118 demographically-matched healthy controls assessed by diffusion tensor imaging and neurocognitive evaluation. Hierarchical clustering was applied to the major fiber tracts, in conjunction with tract-based spatial statistics, to reveal white-matter alterations associated with MDD. Clinical and neurocognitive differences were compared between identified subgroups and healthy controls. With fractional anisotropy extracted from 20 fiber tracts, cluster analysis revealed 3 subgroups based on the patterns of abnormalities. Patients in each subgroup versus healthy controls showed a stepwise pattern of white-matter alterations as follows: subgroup 1 (25.9% of patient sample), widespread white-matter disruption; subgroup 2 (43.1% of patient sample), intermediate and more localized abnormalities in aspects of the corpus callosum and left cingulate; and subgroup 3 (31.0% of patient sample), possible mild alterations, but no statistically significant tract disruption after controlling for family-wise error. The neurocognitive impairment in each subgroup accompanied the white-matter alterations: subgroup 1, deficits in sustained attention and delayed memory; subgroup 2, dysfunction in delayed memory; and subgroup 3, no significant deficits. Three subtypes of white-matter abnormality exist in individuals with major depression, those having widespread abnormalities suffering more neurocognitive impairments, which may provide evidence for parsing the heterogeneity of the disorder and help optimize type-specific treatment approaches.
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Affiliation(s)
- Sugai Liang
- Mental Health Centre, West China Hospital, Sichuan University, Chengdu, 610041, China
- Huaxi Brain Research Centre, West China Hospital, Sichuan University, Chengdu, 610041, China
| | - Qiang Wang
- Mental Health Centre, West China Hospital, Sichuan University, Chengdu, 610041, China
| | - Xiangzhen Kong
- Language and Genetics Department, Max Planck Institute for Psycholinguistics, 6525 XD, Nijmegen, Netherlands
| | - Wei Deng
- Mental Health Centre, West China Hospital, Sichuan University, Chengdu, 610041, China
- Huaxi Brain Research Centre, West China Hospital, Sichuan University, Chengdu, 610041, China
| | - Xiao Yang
- Mental Health Centre, West China Hospital, Sichuan University, Chengdu, 610041, China
- Huaxi Brain Research Centre, West China Hospital, Sichuan University, Chengdu, 610041, China
| | - Xiaojing Li
- Mental Health Centre, West China Hospital, Sichuan University, Chengdu, 610041, China
| | - Zhong Zhang
- Big Data Research Center, School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, 611731, China
| | - Jian Zhang
- Mental Health Centre, West China Hospital, Sichuan University, Chengdu, 610041, China
| | - Chengcheng Zhang
- Mental Health Centre, West China Hospital, Sichuan University, Chengdu, 610041, China
| | - Xin-Min Li
- Department of Psychiatry, University of Alberta, Edmonton, T6G 2B7, Canada
| | - Xiaohong Ma
- Mental Health Centre, West China Hospital, Sichuan University, Chengdu, 610041, China
| | - Junming Shao
- Big Data Research Center, School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, 611731, China
| | - Andrew J Greenshaw
- Department of Psychiatry, University of Alberta, Edmonton, T6G 2B7, Canada
| | - Tao Li
- Mental Health Centre, West China Hospital, Sichuan University, Chengdu, 610041, China.
- Huaxi Brain Research Centre, West China Hospital, Sichuan University, Chengdu, 610041, China.
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Data-driven biological subtypes of depression: systematic review of biological approaches to depression subtyping. Mol Psychiatry 2019; 24:888-900. [PMID: 30824865 DOI: 10.1038/s41380-019-0385-5] [Citation(s) in RCA: 121] [Impact Index Per Article: 24.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/11/2018] [Revised: 02/11/2019] [Accepted: 02/14/2019] [Indexed: 12/20/2022]
Abstract
Research into major depressive disorder (MDD) is complicated by population heterogeneity, which has motivated the search for more homogeneous subtypes through data-driven computational methods to identify patterns in data. In addition, data on biological differences could play an important role in identifying clinically useful subtypes. This systematic review aimed to summarize evidence for biological subtypes of MDD from data-driven studies. We undertook a systematic literature search of PubMed, PsycINFO, and Embase (December 2018). We included studies that identified (1) data-driven subtypes of MDD based on biological variables, or (2) data-driven subtypes based on clinical features (e.g., symptom patterns) and validated these with biological variables post-hoc. Twenty-nine publications including 24 separate analyses in 20 unique samples were identified, including a total of ~ 4000 subjects. Five out of six biochemical studies indicated that there might be depression subtypes with and without disturbed neurotransmitter levels, and one indicated there might be an inflammatory subtype. Seven symptom-based studies identified subtypes, which were mainly determined by severity and by weight gain vs. loss. Two studies compared subtypes based on medication response. These symptom-based subtypes were associated with differences in biomarker profiles and functional connectivity, but results have not sufficiently been replicated. Four out of five neuroimaging studies found evidence for groups with structural and connectivity differences, but results were inconsistent. The single genetic study found a subtype with a distinct pattern of SNPs, but this subtype has not been replicated in an independent test sample. One study combining all aforementioned types of data discovered a subtypes with different levels of functional connectivity, childhood abuse, and treatment response, but the sample size was small. Although the reviewed work provides many leads for future research, the methodological differences across studies and lack of replication preclude definitive conclusions about the existence of clinically useful and generalizable biological subtypes.
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Peng D, Yao Z. Neuroimaging Advance in Depressive Disorder. ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2019; 1180:59-83. [DOI: 10.1007/978-981-32-9271-0_3] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/10/2023]
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