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Northoff G, Hirjak D. Is depression a global brain disorder with topographic dynamic reorganization? Transl Psychiatry 2024; 14:278. [PMID: 38969642 PMCID: PMC11226458 DOI: 10.1038/s41398-024-02995-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/16/2024] [Revised: 06/20/2024] [Accepted: 06/27/2024] [Indexed: 07/07/2024] Open
Abstract
Major depressive disorder (MDD) is characterized by a multitude of psychopathological symptoms including affective, cognitive, perceptual, sensorimotor, and social. The neuronal mechanisms underlying such co-occurrence of psychopathological symptoms remain yet unclear. Rather than linking and localizing single psychopathological symptoms to specific regions or networks, this perspective proposes a more global and dynamic topographic approach. We first review recent findings on global brain activity changes during both rest and task states in MDD showing topographic reorganization with a shift from unimodal to transmodal regions. Next, we single out two candidate mechanisms that may underlie and mediate such abnormal uni-/transmodal topography, namely dynamic shifts from shorter to longer timescales and abnormalities in the excitation-inhibition balance. Finally, we show how such topographic shift from unimodal to transmodal regions relates to the various psychopathological symptoms in MDD including their co-occurrence. This amounts to what we describe as 'Topographic dynamic reorganization' which extends our earlier 'Resting state hypothesis of depression' and complements other models of MDD.
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Affiliation(s)
- Georg Northoff
- Mind, Brain Imaging and Neuroethics Research Unit, The Royal's Institute of Mental Health Research, University of Ottawa, Ottawa, ON, Canada.
| | - Dusan Hirjak
- Department of Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, University of Heidelberg, Mannheim, Germany.
- German Centre for Mental Health (DZPG), Partner Site Mannheim, Mannheim, Germany.
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Yang C, Biswal B, Cui Q, Jing X, Ao Y, Wang Y. Frequency-dependent alterations of global signal topography in patients with major depressive disorder. Psychol Med 2024:1-10. [PMID: 38362834 DOI: 10.1017/s0033291724000254] [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] [Indexed: 02/17/2024]
Abstract
BACKGROUND Major depressive disorder (MDD) is associated not only with disorders in multiple brain networks but also with frequency-specific brain activities. The abnormality of spatiotemporal networks in patients with MDD remains largely unclear. METHODS We investigated the alterations of the global spatiotemporal network in MDD patients using a large-sample multicenter resting-state functional magnetic resonance imaging dataset. The spatiotemporal characteristics were measured by the variability of global signal (GS) and its correlation with local signals (GSCORR) at multiple frequency bands. The association between these indicators and clinical scores was further assessed. RESULTS The GS fluctuations were reduced in patients with MDD across the full frequency range (0-0.1852 Hz). The GSCORR was also reduced in the MDD group, especially in the relatively higher frequency range (0.0728-0.1852 Hz). Interestingly, these indicators showed positive correlations with depressive scores in the MDD group and relative negative correlations in the control group. CONCLUSION The GS and its spatiotemporal effects on local signals were weakened in patients with MDD, which may impair inter-regional synchronization and related functions. Patients with severe depression may use the compensatory mechanism to make up for the functional impairments.
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Affiliation(s)
- Chengxiao Yang
- Institute of Brain and Psychological Sciences, Sichuan Normal University, Chengdu 610066, China
| | - Bharat Biswal
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, China
| | - Qian Cui
- School of Public Affairs and Administration, University of Electronic Science and Technology of China, Chengdu 611731, China
| | - Xiujuan Jing
- Institute of Brain and Psychological Sciences, Sichuan Normal University, Chengdu 610066, China
| | - Yujia Ao
- Faculty of Medicine, University of Ottawa, Ottawa, Ontario, Canada
| | - Yifeng Wang
- Institute of Brain and Psychological Sciences, Sichuan Normal University, Chengdu 610066, China
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An Z, Tang K, Xie Y, Tong C, Liu J, Tao Q, Feng Y. Aberrant resting-state co-activation network dynamics in major depressive disorder. Transl Psychiatry 2024; 14:1. [PMID: 38172115 PMCID: PMC10764934 DOI: 10.1038/s41398-023-02722-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/06/2023] [Revised: 12/04/2023] [Accepted: 12/19/2023] [Indexed: 01/05/2024] Open
Abstract
Major depressive disorder (MDD) is a globally prevalent and highly disabling disease characterized by dysfunction of large-scale brain networks. Previous studies have found that static functional connectivity is not sufficient to reflect the complicated and time-varying properties of the brain. The underlying dynamic interactions between brain functional networks of MDD remain largely unknown, and it is also unclear whether neuroimaging-based dynamic properties are sufficiently robust to discriminate individuals with MDD from healthy controls since the diagnosis of MDD mainly depends on symptom-based criteria evaluated by clinical observation. Resting-state functional magnetic resonance imaging (fMRI) data of 221 MDD patients and 215 healthy controls were shared by REST-meta-MDD consortium. We investigated the spatial-temporal dynamics of MDD using co-activation pattern analysis and made individual diagnoses using support vector machine (SVM). We found that MDD patients exhibited aberrant dynamic properties (such as dwell time, occurrence rate, transition probability, and entropy of Markov trajectories) in some transient networks including subcortical network (SCN), activated default mode network (DMN), de-activated SCN-cerebellum network, a joint network, activated attention network (ATN), and de-activated DMN-ATN, where some dynamic properties were indicative of depressive symptoms. The trajectories of other networks to deactivated DMN-ATN were more accessible in MDD patients. Subgroup analyses also showed subtle dynamic changes in first-episode drug-naïve (FEDN) MDD patients. Finally, SVM achieved preferable accuracies of 84.69%, 76.77%, and 88.10% in discriminating patients with MDD, FEDN MDD, and recurrent MDD from healthy controls with their dynamic metrics. Our findings reveal that MDD is characterized by aberrant dynamic fluctuations of brain network and the feasibility of discriminating MDD patients using dynamic properties, which provide novel insights into the neural mechanism of MDD.
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Affiliation(s)
- Ziqi An
- School of Biomedical Engineering, Southern Medical University, Guangzhou, China
| | - Kai Tang
- School of Biomedical Engineering, Southern Medical University, Guangzhou, China
| | - Yuanyao Xie
- School of Biomedical Engineering, Southern Medical University, Guangzhou, China
| | - Chuanjun Tong
- School of Biomedical Engineering, Southern Medical University, Guangzhou, China
- Institute of Neuroscience, CAS Key Laboratory of Primate Neurobiology, Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai, China
| | - Jiaming Liu
- School of Biomedical Engineering, Southern Medical University, Guangzhou, China
- Department of Radiology, Shunde Hospital, Southern Medical University (The First People's Hospital of Shunde), Foshan, China
| | - Quan Tao
- Department of Rehabilitation Medicine, Zhujiang Hospital, Southern Medical University, Guangzhou, China
| | - Yanqiu Feng
- School of Biomedical Engineering, Southern Medical University, Guangzhou, China.
- Department of Radiology, Shunde Hospital, Southern Medical University (The First People's Hospital of Shunde), Foshan, China.
- Key Laboratory of Mental Health of the Ministry of Education, Guangdong-Hong Kong-Macao Greater Bay Area Center for Brain Science and Brain-Inspired Intelligence, Guangdong Province Key Laboratory of Psychiatric Disorders, Department of Neurobiology, School of Basic Medical Sciences, Southern Medical University, Guangzhou, 510515, China.
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4
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Zhou YM, Yuan JJ, Xu YQ, Gou YH, Zhu YYX, Chen C, Huang XX, Ma XM, Pi M, Yang ZX. Fecal microbiota as a predictor of acupuncture responses in patients with postpartum depressive disorder. Front Cell Infect Microbiol 2023; 13:1228940. [PMID: 38053532 PMCID: PMC10694210 DOI: 10.3389/fcimb.2023.1228940] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2023] [Accepted: 10/31/2023] [Indexed: 12/07/2023] Open
Abstract
Background There are several clinical and molecular predictors of responses to antidepressant therapy. However, these markers are either too subjective or complex for clinical use. The gut microbiota could provide an easily accessible set of biomarkers to predict therapeutic efficacy, but its value in predicting therapy responses to acupuncture in patients with depression is unknown. Here we analyzed the predictive value of the gut microbiota in patients with postpartum depressive disorder (PPD) treated with acupuncture. Methods Seventy-nine PPD patients were enrolled: 55 were treated with acupuncture and 24 did not received any treatment. The 17-item Hamilton depression rating scale (HAMD-17) was used to assess patients at baseline and after eight weeks. Patients receiving acupuncture treatment were divided into an acupuncture-responsive group or non-responsive group according to HAMD-17 scores changes. Baseline fecal samples were obtained from the patients receiving acupuncture and were analyzed by high-throughput 16S ribosomal RNA sequencing to characterize the gut microbiome. Results 47.27% patients responded to acupuncture treatment and 12.5% patients with no treatment recovered after 8-week follow-up. There was no significant difference in α-diversity between responders and non-responders. The β-diversity of non-responders was significantly higher than responders. Paraprevotella and Desulfovibrio spp. were significantly enriched in acupuncture responders, and these organisms had an area under the curve of 0.76 and 0.66 for predicting responder patients, respectively. Conclusions Paraprevotella and Desulfovibrioare may be useful predictive biomarkers to predict PPD patients likely to respond to acupuncture. Larger studies and validation in independent cohorts are now needed to validate our findings.
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Affiliation(s)
- Yu-Mei Zhou
- Department of Acupuncture, The Fourth Clinical Medical College of Guangzhou University of Chinese Medicine, Shenzhen, Guangdong, China
| | - Jin-Jun Yuan
- Department of Acupuncture, The Fourth Clinical Medical College of Guangzhou University of Chinese Medicine, Shenzhen, Guangdong, China
| | - Yu-Qin Xu
- Department of Acupuncture, The Fourth Clinical Medical College of Guangzhou University of Chinese Medicine, Shenzhen, Guangdong, China
| | - Yan-Hua Gou
- Department of Acupuncture, The Fourth Clinical Medical College of Guangzhou University of Chinese Medicine, Shenzhen, Guangdong, China
| | - Yannas Y. X. Zhu
- Department of Acupuncture, The Fourth Clinical Medical College of Guangzhou University of Chinese Medicine, Shenzhen, Guangdong, China
| | - Chen Chen
- Department of Acupuncture and Tuina, Shenzhen Maternal and Child Health Care Hospital, Shenzhen, China
| | - Xing-Xian Huang
- Department of Acupuncture, The Fourth Clinical Medical College of Guangzhou University of Chinese Medicine, Shenzhen, Guangdong, China
| | - Xiao-Ming Ma
- Department of Acupuncture, The Fourth Clinical Medical College of Guangzhou University of Chinese Medicine, Shenzhen, Guangdong, China
| | - Min- Pi
- Department of Acupuncture, The Fourth Clinical Medical College of Guangzhou University of Chinese Medicine, Shenzhen, Guangdong, China
| | - Zhuo-Xin Yang
- Department of Acupuncture, The Fourth Clinical Medical College of Guangzhou University of Chinese Medicine, Shenzhen, Guangdong, China
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Wang L, Ma Q, Sun X, Xu Z, Zhang J, Liao X, Wang X, Wei D, Chen Y, Liu B, Huang CC, Zheng Y, Wu Y, Chen T, Cheng Y, Xu X, Gong Q, Si T, Qiu S, Lin CP, Cheng J, Tang Y, Wang F, Qiu J, Xie P, Li L, He Y, Xia M, Zhang Y, Li L, Cheng J, Gong Q, Li L, Lin CP, Qiu J, Qiu S, Si T, Tang Y, Wang F, Xie P, Xu X, Xia M. Frequency-resolved connectome alterations in major depressive disorder: A multisite resting fMRI study. J Affect Disord 2023; 328:47-57. [PMID: 36781144 DOI: 10.1016/j.jad.2023.01.104] [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: 08/22/2022] [Revised: 01/24/2023] [Accepted: 01/27/2023] [Indexed: 02/13/2023]
Abstract
BACKGROUND Functional connectome studies have revealed widespread connectivity alterations in major depressive disorder (MDD). However, the low frequency bandpass filtering (0.01-0.08 Hz or 0.01-0.1 Hz) in most studies have impeded our understanding on whether and how these alterations are affected by frequency of interest. METHODS Here, we performed frequency-resolved (0.01-0.06 Hz, 0.06-0.16 Hz and 0.16-0.24 Hz) connectome analyses using a large-sample resting-state functional MRI dataset of 1002 MDD patients and 924 healthy controls from seven independent centers. RESULTS We reported significant frequency-dependent connectome alterations in MDD in left inferior parietal, inferior temporal, precentral, and fusiform cortices and bilateral precuneus. These frequency-dependent connectome alterations are mainly derived by abnormalities of medium- and long-distance connections and are brain network-dependent. Moreover, the connectome alteration of left precuneus in high frequency band (0.16-0.24 Hz) is significantly associated with illness duration. LIMITATIONS Multisite harmonization model only removed linear site effects. Neurobiological underpinning of alterations in higher frequency (0.16-0.24 Hz) should be further examined by combining fMRI data with respiration, heartbeat and blood flow recordings in future studies. CONCLUSIONS These results highlight the frequency-dependency of connectome alterations in MDD and the benefit of examining connectome alteration in MDD under a wider frequency band.
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Affiliation(s)
- Lei Wang
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China; Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China; IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | - Qing Ma
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China; State Key Laboratory of Medical Neurobiology, Fudan University, Shanghai, China; MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China
| | - Xiaoyi Sun
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China; Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China; IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China; School of Systems Science, Beijing Normal University, Beijing, China
| | - Zhilei Xu
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China; Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China; IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | - Jiaying Zhang
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China; Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China; IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | - Xuhong Liao
- School of Systems Science, Beijing Normal University, Beijing, China
| | - Xiaoqin Wang
- Key Laboratory of Cognition and Personality (SWU), Ministry of Education, Chongqing, China; Department of Psychology, Southwest University, Chongqing, China
| | - Dongtao Wei
- Key Laboratory of Cognition and Personality (SWU), Ministry of Education, Chongqing, China; Department of Psychology, Southwest University, Chongqing, China
| | - Yuan Chen
- Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Bangshan Liu
- Department of Psychiatry, National Clinical Research Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha, Hunan, China; Mental Health Institute of Central South University, China National Technology Institute on Mental Disorders, Hunan Key Laboratory of Psychiatry and Mental Health, Hunan Medical Center for Mental Health, Changsha, Hunan, China
| | - Chu-Chung Huang
- Shanghai Key Laboratory of Brain Functional Genomics (Ministry of Education), Institute of Cognitive Neuroscience, School of Psychology and Cognitive Science, East China Normal University, Shanghai, China
| | - Yanting Zheng
- Department of Radiology, The First Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Yankun Wu
- Peking University Sixth Hospital, Peking University Institute of Mental Health, NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Peking University, Beijing, China
| | - Taolin Chen
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital, Sichuan University, Chengdu, China
| | - Yuqi Cheng
- Department of Psychiatry, First Affiliated Hospital of Kunming Medical University, Kunming, China
| | - Xiufeng Xu
- Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, Sichuan, China
| | - Qiyong Gong
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital, Sichuan University, Chengdu, China; Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, Sichuan, China
| | - Tianmei Si
- Peking University Sixth Hospital, Peking University Institute of Mental Health, NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Peking University, Beijing, China
| | - Shijun Qiu
- Department of Radiology, The First Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Ching-Po Lin
- Department of Psychosis Studies, Institute of Psychiatry, Psychology & Neuroscience, King's College London, De Crespigny Park, London, UK; Institute of Neuroscience, National Yang-Ming Chiao-Tung University, Taipei, Taiwan
| | - Jingliang Cheng
- Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Yanqing Tang
- Department of Psychiatry, The First Affiliated Hospital of China Medical University, Shenyang, China
| | - Fei Wang
- Department of Psychiatry, The First Affiliated Hospital of China Medical University, Shenyang, China
| | - Jiang Qiu
- Key Laboratory of Cognition and Personality (SWU), Ministry of Education, Chongqing, China; Department of Psychology, Southwest University, Chongqing, China
| | - Peng Xie
- Chongqing Key Laboratory of Neurobiology, Chongqing, China; Department of Neurology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Lingjiang Li
- Department of Psychiatry, National Clinical Research Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha, Hunan, China; Mental Health Institute of Central South University, China National Technology Institute on Mental Disorders, Hunan Key Laboratory of Psychiatry and Mental Health, Hunan Medical Center for Mental Health, Changsha, Hunan, China
| | - Yong He
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China; Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China; IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China; Chinese Institute for Brain Research, Beijing, China
| | | | - Mingrui Xia
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China; Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China; IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China.
| | - Yihe Zhang
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China; Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China; IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China.
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Volumetric MRI Findings in Mild Traumatic Brain Injury (mTBI) and Neuropsychological Outcome. Neuropsychol Rev 2023; 33:5-41. [PMID: 33656702 DOI: 10.1007/s11065-020-09474-0] [Citation(s) in RCA: 15] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2019] [Accepted: 12/20/2020] [Indexed: 10/22/2022]
Abstract
Region of interest (ROI) volumetric assessment has become a standard technique in quantitative neuroimaging. ROI volume is thought to represent a coarse proxy for making inferences about the structural integrity of a brain region when compared to normative values representative of a healthy sample, adjusted for age and various demographic factors. This review focuses on structural volumetric analyses that have been performed in the study of neuropathological effects from mild traumatic brain injury (mTBI) in relation to neuropsychological outcome. From a ROI perspective, the probable candidate structures that are most likely affected in mTBI represent the target regions covered in this review. These include the corpus callosum, cingulate, thalamus, pituitary-hypothalamic area, basal ganglia, amygdala, and hippocampus and associated structures including the fornix and mammillary bodies, as well as whole brain and cerebral cortex along with the cerebellum. Ventricular volumetrics are also reviewed as an indirect assessment of parenchymal change in response to injury. This review demonstrates the potential role and limitations of examining structural changes in the ROIs mentioned above in relation to neuropsychological outcome. There is also discussion and review of the role that post-traumatic stress disorder (PTSD) may play in structural outcome in mTBI. As emphasized in the conclusions, structural volumetric findings in mTBI are likely just a single facet of what should be a multimodality approach to image analysis in mTBI, with an emphasis on how the injury damages or disrupts neural network integrity. The review provides an historical context to quantitative neuroimaging in neuropsychology along with commentary about future directions for volumetric neuroimaging research in mTBI.
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Kim JH, De Asis-Cruz J, Cook KM, Limperopoulos C. Gestational age-related changes in the fetal functional connectome: in utero evidence for the global signal. Cereb Cortex 2023; 33:2302-2314. [PMID: 35641159 PMCID: PMC9977380 DOI: 10.1093/cercor/bhac209] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2021] [Revised: 05/06/2022] [Accepted: 05/06/2022] [Indexed: 11/13/2022] Open
Abstract
The human brain begins to develop in the third gestational week and rapidly grows and matures over the course of pregnancy. Compared to fetal structural neurodevelopment, less is known about emerging functional connectivity in utero. Here, we investigated gestational age (GA)-associated in vivo changes in functional brain connectivity during the second and third trimesters in a large dataset of 110 resting-state functional magnetic resonance imaging scans from a cohort of 95 healthy fetuses. Using representational similarity analysis, a multivariate analytical technique that reveals pair-wise similarity in high-order space, we showed that intersubject similarity of fetal functional connectome patterns was strongly related to between-subject GA differences (r = 0.28, P < 0.01) and that GA sensitivity of functional connectome was lateralized, especially at the frontal area. Our analysis also revealed a subnetwork of connections that were critical for predicting age (mean absolute error = 2.72 weeks); functional connectome patterns of individual fetuses reliably predicted their GA (r = 0.51, P < 0.001). Lastly, we identified the primary principal brain network that tracked fetal brain maturity. The main network showed a global synchronization pattern resembling global signal in the adult brain.
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Affiliation(s)
- Jung-Hoon Kim
- Developing Brain Institue, Children’s National Hospital, 111 Michigan Avenue, N.W., Washington, DC, 20010, USA
| | - Josepheen De Asis-Cruz
- Developing Brain Institue, Children’s National Hospital, 111 Michigan Avenue, N.W., Washington, DC, 20010, USA
| | - Kevin M Cook
- Developing Brain Institue, Children’s National Hospital, 111 Michigan Avenue, N.W., Washington, DC, 20010, USA
| | - Catherine Limperopoulos
- Corresponding author: Developing Brain Institute, Children’s National, 111 Michigan Ave. N.W., Washington D.C. 20010.
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Tan T, Xu Z, Gao C, Shen T, Li L, Chen Z, Chen L, Xu M, Chen B, Liu J, Zhang Z, Yuan Y. Influence and interaction of resting state functional magnetic resonance and tryptophan hydroxylase-2 methylation on short-term antidepressant drug response. BMC Psychiatry 2022; 22:218. [PMID: 35337298 PMCID: PMC8957120 DOI: 10.1186/s12888-022-03860-z] [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: 11/07/2021] [Accepted: 03/11/2022] [Indexed: 12/26/2022] Open
Abstract
BACKGROUND Most antidepressants have been developed on the basis of the monoamine deficiency hypothesis of depression, in which neuronal serotonin (5-HT) plays a key role. 5-HT biosynthesis is regulated by the rate-limiting enzyme tryptophan hydroxylase-2 (TPH2). TPH2 methylation is correlated with antidepressant effects. Resting-state functional MRI (rs-fMRI) is applied for detecting abnormal brain functional activity in patients with different antidepressant effects. We will investigate the effect of the interaction between rs-fMRI and TPH2 DNA methylation on the early antidepressant effects. METHODS A total of 300 patients with major depressive disorder (MDD) and 100 healthy controls (HCs) were enrolled, of which 60 patients with MDD were subjected to rs-fMRI. Antidepressant responses was assessed by a 50% reduction in 17-item Hamilton Rating Scale for Depression (HAMD-17) scores at baseline and after two weeks of medication. The RESTPlus software in MATLAB was used to analyze the rs-fMRI data. The amplitude of low-frequency fluctuation (ALFF), regional homogeneity (ReHo), fractional ALFF (fALFF), and functional connectivity (FC) were used, and the above results were used as regions of interest (ROIs) to extract the average value of brain ROIs regions in the RESTPlus software. Generalized linear model analysis was performed to analyze the association between abnormal activity found in rs-fMRI and the effect of TPH2 DNA methylation on antidepressant responses. RESULTS Two hundred ninety-one patients with MDD and 100 HCs were included in the methylation statistical analysis, of which 57 patients were included in the further rs-fMRI analysis (3 patients were excluded due to excessive head movement). 57 patients were divided into the responder group (n = 36) and the non-responder group (n = 21). Rs-fMRI results showed that the ALFF of the left inferior frontal gyrus (IFG) was significantly different between the two groups. The results showed that TPH2-1-43 methylation interacted with ALFF of left IFG to affect the antidepressant responses (p = 0.041, false discovery rate (FDR) corrected p = 0.149). CONCLUSIONS Our study demonstrated that the differences in the ALFF of left IFG between the two groups and its association with TPH2 methylation affect short-term antidepressant drug responses.
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Affiliation(s)
- Tingting Tan
- grid.452290.80000 0004 1760 6316Department of Psychosomatics and Psychiatry, School of Medicine, Zhongda Hospital, Southeast University, Nanjing, 210009 People’s Republic of China ,grid.263826.b0000 0004 1761 0489Key Laboratory of Developmental Genes and Human Diseases, Ministry of Education, School of Medicine, Southeast University, Nanjing, 210009 People’s Republic of China
| | - Zhi Xu
- Department of Psychosomatics and Psychiatry, School of Medicine, Zhongda Hospital, Southeast University, Nanjing, 210009, People's Republic of China. .,Key Laboratory of Developmental Genes and Human Diseases, Ministry of Education, School of Medicine, Southeast University, Nanjing, 210009, People's Republic of China.
| | - Chenjie Gao
- grid.452290.80000 0004 1760 6316Department of Psychosomatics and Psychiatry, School of Medicine, Zhongda Hospital, Southeast University, Nanjing, 210009 People’s Republic of China ,grid.263826.b0000 0004 1761 0489Key Laboratory of Developmental Genes and Human Diseases, Ministry of Education, School of Medicine, Southeast University, Nanjing, 210009 People’s Republic of China
| | - Tian Shen
- grid.452290.80000 0004 1760 6316Department of Psychosomatics and Psychiatry, School of Medicine, Zhongda Hospital, Southeast University, Nanjing, 210009 People’s Republic of China ,grid.89957.3a0000 0000 9255 8984Department of Psychiatric Rehabilitation, Wuxi Mental Health Center, Nanjing Medical University, WuXi, 214123 People’s Republic of China
| | - Lei Li
- grid.263826.b0000 0004 1761 0489School of Medicine, Southeast University, Nanjing, 210009 People’s Republic of China
| | - Zimu Chen
- grid.452290.80000 0004 1760 6316Department of Psychosomatics and Psychiatry, School of Medicine, Zhongda Hospital, Southeast University, Nanjing, 210009 People’s Republic of China ,grid.263826.b0000 0004 1761 0489Key Laboratory of Developmental Genes and Human Diseases, Ministry of Education, School of Medicine, Southeast University, Nanjing, 210009 People’s Republic of China
| | - Lei Chen
- grid.452290.80000 0004 1760 6316Department of Psychosomatics and Psychiatry, School of Medicine, Zhongda Hospital, Southeast University, Nanjing, 210009 People’s Republic of China ,Department of Psychology and Psychiatry, School of Medicine, Jinling Hospital, Nanjing University, Nanjing, 210018 People’s Republic of China
| | - Min Xu
- grid.263826.b0000 0004 1761 0489Department of Anatomy, Medical School, Southeast University, Nanjing, 210009 People’s Republic of China
| | - Bingwei Chen
- grid.263826.b0000 0004 1761 0489Department of Epidemiology and Biostatistics, School of Public Health, Southeast University, Nanjing, 210009 People’s Republic of China
| | - Jiacheng Liu
- grid.452290.80000 0004 1760 6316Department of Nuclear Medicine, School of Medicine, Zhongda Hospital, Southeast University, Nanjing, 210009 People’s Republic of China
| | - Zhijun Zhang
- grid.452290.80000 0004 1760 6316Department of Neurology, School of Medicine, Zhongda Hospital, Southeast University, Nanjing, 210009 People’s Republic of China
| | - Yonggui Yuan
- grid.452290.80000 0004 1760 6316Department of Psychosomatics and Psychiatry, School of Medicine, Zhongda Hospital, Southeast University, Nanjing, 210009 People’s Republic of China ,grid.263826.b0000 0004 1761 0489Key Laboratory of Developmental Genes and Human Diseases, Ministry of Education, School of Medicine, Southeast University, Nanjing, 210009 People’s Republic of China
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Niu H, Li W, Wang G, Hu Q, Hao R, Li T, Zhang F, Cheng T. Performances of whole-brain dynamic and static functional connectivity fingerprinting in machine learning-based classification of major depressive disorder. Front Psychiatry 2022; 13:973921. [PMID: 35958666 PMCID: PMC9360427 DOI: 10.3389/fpsyt.2022.973921] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/20/2022] [Accepted: 07/08/2022] [Indexed: 11/13/2022] Open
Abstract
BACKGROUND Alterations in static and dynamic functional connectivity during resting state have been widely reported in major depressive disorder (MDD). The objective of this study was to compare the performances of whole-brain dynamic and static functional connectivity combined with machine learning approach in differentiating MDD patients from healthy controls at the individual subject level. Given the dynamic nature of brain activity, we hypothesized that dynamic connectivity would outperform static connectivity in the classification. METHODS Seventy-one MDD patients and seventy-one well-matched healthy controls underwent resting-state functional magnetic resonance imaging scans. Whole-brain dynamic and static functional connectivity patterns were calculated and utilized as classification features. Linear kernel support vector machine was employed to design the classifier and a leave-one-out cross-validation strategy was used to assess classifier performance. RESULTS Experimental results of dynamic functional connectivity-based classification showed that MDD patients could be discriminated from healthy controls with an excellent accuracy of 100% irrespective of whether or not global signal regression (GSR) was performed (permutation test with P < 0.0002). Brain regions with the most discriminating dynamic connectivity were mainly and reliably located within the default mode network, cerebellum, and subcortical network. In contrast, the static functional connectivity-based classifiers exhibited unstable classification performances, i.e., a low accuracy of 38.0% without GSR (P = 0.9926) while a high accuracy of 96.5% with GSR (P < 0.0002); moreover, there was a considerable variability in the distribution of brain regions with static connectivity most informative for classification. CONCLUSION These findings suggest the superiority of dynamic functional connectivity in machine learning-based classification of depression, which may be helpful for a better understanding of the neural basis of MDD as well as for the development of effective computer-aided diagnosis tools in clinical settings.
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Affiliation(s)
- Heng Niu
- Department of MRI, Shanxi Cardiovascular Hospital, Taiyuan, China
| | - Weirong Li
- Department of Neurology, Shanxi Cardiovascular Hospital, Taiyuan, China
| | - Guiquan Wang
- Department of Neurology, Shanxi Cardiovascular Hospital, Taiyuan, China
| | - Qiong Hu
- Department of Neurology, Shanxi Cardiovascular Hospital, Taiyuan, China
| | - Rui Hao
- Department of Neurology, Shanxi Cardiovascular Hospital, Taiyuan, China
| | - Tianliang Li
- Department of Ultrasound, Shanxi Cardiovascular Hospital, Taiyuan, China
| | - Fan Zhang
- Department of Medical Imaging, Shanxi Traditional Chinese Medical Hospital, Taiyuan, China
| | - Tao Cheng
- Department of Neurology, Shanxi Cardiovascular Hospital, Taiyuan, China
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10
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Wang X, Liao W, Han S, Li J, Wang Y, Zhang Y, Zhao J, Chen H. Frequency-specific altered global signal topography in drug-naïve first-episode patients with adolescent-onset schizophrenia. Brain Imaging Behav 2021; 15:1876-1885. [PMID: 33188473 DOI: 10.1007/s11682-020-00381-9] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/08/2023]
Abstract
Adolescent-onset schizophrenia (AOS) is a severe neuropsychiatric disease associated with frequency-specific abnormalities across distributed neural systems in a slow rhythm. Recently, functional magnetic resonance imaging (fMRI) studies have determined that the global signal. (GS) is an important source of the local neuronal activity in 0.01-0.1 Hz frequency band. However, it remains unknown whether the effects follow a specific spatially preferential pattern in different frequency bands in schizophrenia. To address this issue, resting-state fMRI data from 39 drug-naïve AOS patients and 31 healthy controls (HCs) were used to assess the changes in GS topography patterns in the slow-4 (0.027-0.073 Hz) and slow-5 bands (0.01-0.027 Hz). Results revealed that GS mainly affects the default mode network (DMN) in slow-4 and sensory regions in the slow-5 band respectively, and GS has a stronger driving effect in the slow-5 band. Moreover, significant frequency-by-group interaction was observed in the frontoparietal network. Compared with HCs, patients with AOS exhibited altered GS topography mainly located in the DMN. Our findings demonstrated that the influence of the GS on brain networks altered in a frequency-specific way in schizophrenia.
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Affiliation(s)
- Xiao Wang
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 610054, People's Republic of China.,MOE Key Lab for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu, 610054, People's Republic of China
| | - Wei Liao
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 610054, People's Republic of China.,MOE Key Lab for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu, 610054, People's Republic of China
| | - Shaoqiang Han
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 610054, People's Republic of China.,MOE Key Lab for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu, 610054, People's Republic of China
| | - Jiao Li
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 610054, People's Republic of China.,MOE Key Lab for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu, 610054, People's Republic of China
| | - Yifeng Wang
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 610054, People's Republic of China.,MOE Key Lab for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu, 610054, People's Republic of China
| | - Yan Zhang
- Key Laboratory for Mental Health of Hunan Province, Mental Health Institute, the Second Xiangya Hospital of Central South University, Changsha, China
| | - Jingping Zhao
- Mental Health Institute, the Second Xiangya Hospital of Central South University, 139, Middle Renmin Road, Changsha, 410011, Hunan, China
| | - Huafu Chen
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 610054, People's Republic of China. .,Radiology department of the First Affiliated Hospital, the Third Military Medical University, Chongqing, 400038, China.
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11
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Ao Y, Ouyang Y, Yang C, Wang Y. Global Signal Topography of the Human Brain: A Novel Framework of Functional Connectivity for Psychological and Pathological Investigations. Front Hum Neurosci 2021; 15:644892. [PMID: 33841119 PMCID: PMC8026854 DOI: 10.3389/fnhum.2021.644892] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2020] [Accepted: 03/01/2021] [Indexed: 11/15/2022] Open
Abstract
The global signal (GS), which was once regarded as a nuisance of functional magnetic resonance imaging, has been proven to convey valuable neural information. This raised the following question: what is a GS represented in local brain regions? In order to answer this question, the GS topography was developed to measure the correlation between global and local signals. It was observed that the GS topography has an intrinsic structure characterized by higher GS correlation in sensory cortices and lower GS correlation in higher-order cortices. The GS topography could be modulated by individual factors, attention-demanding tasks, and conscious states. Furthermore, abnormal GS topography has been uncovered in patients with schizophrenia, major depressive disorder, bipolar disorder, and epilepsy. These findings provide a novel insight into understanding how the GS and local brain signals coactivate to organize information in the human brain under various brain states. Future directions were further discussed, including the local-global confusion embedded in the GS correlation, the integration of spatial information conveyed by the GS, and temporal information recruited by the connection analysis. Overall, a unified psychopathological framework is needed for understanding the GS topography.
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Affiliation(s)
- Yujia Ao
- Institute of Brain and Psychological Sciences, Sichuan Normal University, Chengdu, China
| | - Yujie Ouyang
- Institute of Brain and Psychological Sciences, Sichuan Normal University, Chengdu, China
| | - Chengxiao Yang
- Institute of Brain and Psychological Sciences, Sichuan Normal University, Chengdu, China
| | - Yifeng Wang
- Institute of Brain and Psychological Sciences, Sichuan Normal University, Chengdu, China
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12
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Alenko A, Markos Y, Fikru C, Tadesse E, Gedefaw L. Association of serum cortisol level with severity of depression and improvement in newly diagnosed patients with major depressive disorder in Jimma medical center, Southwest Ethiopia. PLoS One 2020; 15:e0240668. [PMID: 33064754 PMCID: PMC7567351 DOI: 10.1371/journal.pone.0240668] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2020] [Accepted: 09/30/2020] [Indexed: 12/23/2022] Open
Abstract
BACKGROUND Major Depressive Disorder (MDD) is the leading psychiatric disorder in low- and middle-income countries, and is to be the second leading cause of burden of disease by 2020. Cortisol plays a significant role in pathophysiology of MDD. Depression can alter serum cortisol level. However, the change in serum cortisol level and its association with depressive symptom severity and improvement among patients with MDD is not well studied. OBJECTIVE To outline change in serum cortisol levels and its association with severity and improvement of depressive symptoms in newly diagnosed patients with MDD. METHOD Hospital based longitudinal study was conducted among 34 newly diagnosed patients who met DSM-V criteria of MDD. Venous blood sample was performed twice; pre- and post- 8 weeks of treatment. Serum cortisol concentration was measured using an extracted radioimmunoassay. The 17-item Hamilton Depression Scale (HAM-D) was used to rate depression at baseline and after 8 weeks of treatment. Paired t-test was done to look the mean difference of serum cortisol level and HAM-D, before and after treatment. Pearson correlation was done to look the association between serum cortisol levels, HAM-D scores and, sociodemographic and clinical factors. Statistical significance was set at p<0.05. RESULTS There is no significant difference in cortisol concentrations at baseline and end line (t (33) = 2.02, p = 0.052). However, there is significant difference in HAM-D total score (t (33) = 5.67, p<0.001). Baseline and end line serum cortisol levels were significantly correlated (r = .561, p = .001). Monthly family income is correlated with baseline HAM-D total score (r = -0.373, p = .030). There is no significant relationship between baseline serum cortisol level and HAM-D score. There is also no significant relationship between end line serum cortisol level and HAM-D score. CONCLUSIONS The symptoms of MDD were reduced following treatment but there is no significant difference in serum cortisol levels. Baseline and end line serum cortisol levels were significantly correlated. We recommend further research based on large sample.
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Affiliation(s)
- Arefayne Alenko
- Department of Psychiatry, Jimma University, Institute of Health, Faculty of Medical Science, Jimma, Ethiopia
| | - Yohannes Markos
- Department of Biomedical Sciences, Jimma University, Institute of Health, Faculty of Medical Science, Jimma, Ethiopia
| | - Chaltu Fikru
- Department of Epidemiology, Jimma University, Institute of Health, Faculty of Public Health, Jimma, Ethiopia
| | - Eyasu Tadesse
- Department of Biomedical Sciences, Jimma University, Institute of Health, Faculty of Medical Science, Jimma, Ethiopia
| | - Lealem Gedefaw
- Department of Laboratory Sciences, Jimma University, Institute of Health, Faculty of Medical Science, Jimma, Ethiopia
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13
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Klöbl M, Gryglewski G, Rischka L, Godbersen GM, Unterholzner J, Reed MB, Michenthaler P, Vanicek T, Winkler-Pjrek E, Hahn A, Kasper S, Lanzenberger R. Predicting Antidepressant Citalopram Treatment Response via Changes in Brain Functional Connectivity After Acute Intravenous Challenge. Front Comput Neurosci 2020; 14:554186. [PMID: 33123000 PMCID: PMC7573155 DOI: 10.3389/fncom.2020.554186] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2020] [Accepted: 08/31/2020] [Indexed: 01/30/2023] Open
Abstract
Introduction: The early and therapy-specific prediction of treatment success in major depressive disorder is of paramount importance due to high lifetime prevalence, and heterogeneity of response to standard medication and symptom expression. Hence, this study assessed the predictability of long-term antidepressant effects of escitalopram based on the short-term influence of citalopram on functional connectivity. Methods: Twenty nine subjects suffering from major depression were scanned twice with resting-state functional magnetic resonance imaging under the influence of intravenous citalopram and placebo in a randomized, double-blinded cross-over fashion. Symptom factors were identified for the Hamilton depression rating scale (HAM-D) and Beck's depression inventory (BDI) taken before and after a median of seven weeks of escitalopram therapy. Predictors were calculated from whole-brain functional connectivity, fed into robust regression models, and cross-validated. Results: Significant predictive power could be demonstrated for one HAM-D factor describing insomnia and the total score (r = 0.45-0.55). Remission and response could furthermore be predicted with an area under the receiver operating characteristic curve of 0.73 and 0.68, respectively. Functional regions with high influence on the predictor were located especially in the ventral attention, fronto-parietal, and default mode networks. Conclusion: It was shown that medication-specific antidepressant symptom improvements can be predicted using functional connectivity measured during acute pharmacological challenge as an easily assessable imaging marker. The regions with high influence have previously been related to major depression as well as the response to selective serotonin reuptake inhibitors, corroborating the advantages of the current approach of focusing on treatment-specific symptom improvements.
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Affiliation(s)
- Manfred Klöbl
- Department of Psychiatry and Psychotherapy, Medical University of Vienna, Vienna, Austria
| | - Gregor Gryglewski
- Department of Psychiatry and Psychotherapy, Medical University of Vienna, Vienna, Austria
| | - Lucas Rischka
- Department of Psychiatry and Psychotherapy, Medical University of Vienna, Vienna, Austria
| | | | - Jakob Unterholzner
- Department of Psychiatry and Psychotherapy, Medical University of Vienna, Vienna, Austria
| | - Murray Bruce Reed
- Department of Psychiatry and Psychotherapy, Medical University of Vienna, Vienna, Austria
| | - Paul Michenthaler
- Department of Psychiatry and Psychotherapy, Medical University of Vienna, Vienna, Austria
| | - Thomas Vanicek
- Department of Psychiatry and Psychotherapy, Medical University of Vienna, Vienna, Austria
| | - Edda Winkler-Pjrek
- Department of Psychiatry and Psychotherapy, Medical University of Vienna, Vienna, Austria
| | - Andreas Hahn
- Department of Psychiatry and Psychotherapy, Medical University of Vienna, Vienna, Austria
| | - Siegfried Kasper
- Department of Psychiatry and Psychotherapy, Medical University of Vienna, Vienna, Austria
| | - Rupert Lanzenberger
- Department of Psychiatry and Psychotherapy, Medical University of Vienna, Vienna, Austria
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14
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Ensemble Learning for Early‐Response Prediction of Antidepressant Treatment in Major Depressive Disorder. J Magn Reson Imaging 2019; 52:161-171. [DOI: 10.1002/jmri.27029] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2019] [Revised: 11/30/2019] [Accepted: 12/02/2019] [Indexed: 01/07/2023] Open
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15
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Zhu J, Zhang Y, Zhang B, Yang Y, Wang Y, Zhang C, Zhao W, Zhu DM, Yu Y. Abnormal coupling among spontaneous brain activity metrics and cognitive deficits in major depressive disorder. J Affect Disord 2019; 252:74-83. [PMID: 30981059 DOI: 10.1016/j.jad.2019.04.030] [Citation(s) in RCA: 22] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/30/2018] [Revised: 03/07/2019] [Accepted: 04/07/2019] [Indexed: 01/08/2023]
Abstract
BACKGROUND A variety of functional metrics derived from resting-state functional magnetic resonance imaging (rs-fMRI) have been employed to explore spontaneous brain activity changes in major depressive disorder (MDD) and have enjoyed significant success in unraveling the neurobiological mechanisms underlying this disorder. However, it is unclear whether spatial and temporal coupling relationships among these rs-fMRI metrics are altered in MDD. METHODS 50 patients with MDD and 36 well-matched healthy controls underwent rs-fMRI scans. A dynamic analysis was applied to compute multiple frequently used metrics including fractional amplitude of low frequency fluctuations, regional homogeneity, voxel-mirrored homotopic connectivity, degree centrality and global signal connectivity. Kendall's W was used to calculate volume-wise (across voxels) and voxel-wise (across time windows) concordance among these metrics. Inter-group differences in the concordance and their associations with clinical and cognitive variables were tested. RESULTS Compared to healthy controls, patients with MDD showed decreased whole gray matter volume-wise concordance. Despite similar spatial distributions, quantitative comparison analysis revealed that MDD patients exhibited reduced voxel-wise concordance in multiple cortical and subcortical regions. Moreover, the lower concordance was associated with worse performances in prospective memory and sustained attention in the MDD group. LIMITATIONS The study design of fairly modest sample size did not allow us to perform a full analysis of the potential effects of medication and illness duration. CONCLUSIONS Our findings suggest that spatial and temporal decoupling of multiple resting-state brain activity metrics may help elucidate the neural mechanisms of cognitive deficits in depression.
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Affiliation(s)
- Jiajia Zhu
- Department of Radiology, The First Affiliated Hospital of Anhui Medical University, Hefei 230022, China
| | - Yu Zhang
- Department of Sleep Disorders, Hefei Fourth People's Hospital, Hefei 230022, China; Anhui Mental Health Center, Hefei 230022, China
| | - Biao Zhang
- Department of Radiology, The First Affiliated Hospital of Anhui Medical University, Hefei 230022, China
| | - Ying Yang
- Department of Radiology, The First Affiliated Hospital of Anhui Medical University, Hefei 230022, China
| | - Yajun Wang
- Department of Radiology, The First Affiliated Hospital of Anhui Medical University, Hefei 230022, China
| | - Cun Zhang
- Department of Radiology, The First Affiliated Hospital of Anhui Medical University, Hefei 230022, China
| | - Wenming Zhao
- Department of Radiology, The First Affiliated Hospital of Anhui Medical University, Hefei 230022, China
| | - Dao-Min Zhu
- Department of Sleep Disorders, Hefei Fourth People's Hospital, Hefei 230022, China; Anhui Mental Health Center, Hefei 230022, China.
| | - Yongqiang Yu
- Department of Radiology, The First Affiliated Hospital of Anhui Medical University, Hefei 230022, China.
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Zhu J, Zhu DM, Qian Y, Li X, Yu Y. Altered spatial and temporal concordance among intrinsic brain activity measures in schizophrenia. J Psychiatr Res 2018; 106:91-98. [PMID: 30300826 DOI: 10.1016/j.jpsychires.2018.09.015] [Citation(s) in RCA: 18] [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: 06/15/2018] [Revised: 09/18/2018] [Accepted: 09/28/2018] [Indexed: 01/10/2023]
Abstract
Various data-driven voxel-wise measures derived from resting-state functional magnetic resonance imaging (rs-fMRI) have been developed to characterize spontaneous brain activity. These measures have been widely applied to explore brain functional changes in schizophrenia and have enjoyed significant success in unraveling the neural mechanisms of this disorder. However, their spatial and temporal coupling alterations in schizophrenia remain largely unknown. To address this issue, 88 schizophrenia patients and 116 gender- and age-matched healthy controls underwent rs-fMRI examinations. Kendall's W was used to calculate volume-wise (across voxels) and voxel-wise (across time windows) concordance among multiple commonly used measures, including fractional amplitude of low frequency fluctuations, regional homogeneity, voxel-mirrored homotopic connectivity, degree centrality and global signal connectivity. Inter-group differences in the concordance were investigated. Results revealed that whole gray matter volume-wise concordance was reduced in schizophrenia patients relative to healthy controls. Although two groups showed similar spatial distributions of the voxel-wise concordance, quantitative comparison analysis revealed that schizophrenia patients exhibited decreased voxel-wise concordance in gray matter areas spanning the bilateral frontal, parietal, occipital, temporal and insular cortices. In addition, these concordance changes were negatively correlated with onset age in schizophrenia patients. Our findings suggest that the concordance approaches may provide new insights into the neural mechanisms of schizophrenia and have the potential to be extended to neuropsychiatric disorders.
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Affiliation(s)
- Jiajia Zhu
- Department of Radiology, The First Affiliated Hospital of Anhui Medical University, Hefei, 230022, China
| | - Dao-Min Zhu
- Department of Sleep Disorders, Hefei Fourth People's Hospital, Hefei, 230022, China; Anhui Mental Health Center, Hefei, 230022, China
| | - Yinfeng Qian
- Department of Radiology, The First Affiliated Hospital of Anhui Medical University, Hefei, 230022, China
| | - Xiaohu Li
- Department of Radiology, The First Affiliated Hospital of Anhui Medical University, Hefei, 230022, China
| | - Yongqiang Yu
- Department of Radiology, The First Affiliated Hospital of Anhui Medical University, Hefei, 230022, China.
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