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Nhu NT, Chen DYT, Yang YCSH, Lo YC, Kang JH. Associations Between Brain-Gut Axis and Psychological Distress in Fibromyalgia: A Microbiota and Magnetic Resonance Imaging Study. THE JOURNAL OF PAIN 2024; 25:934-945. [PMID: 37866648 DOI: 10.1016/j.jpain.2023.10.015] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/21/2023] [Revised: 10/03/2023] [Accepted: 10/16/2023] [Indexed: 10/24/2023]
Abstract
An altered brain-gut axis is suspected to be one of the pathomechanisms in fibromyalgia (FM). This cross-sectional study investigated the associations among altered microbiota, psychological distress, and brain functional connectivity (FC) in FM. We recruited 25 FM patients and 25 healthy people in the present study. Psychological distress was measured using standardized questionnaires. Microbiota analysis was performed on the participants' stools. Functional magnetic resonance imaging data were acquired, and seed-based resting-state FC (rs-FC) analysis was conducted with the salience network nodes as seeds. Linear regression and mediation analyses evaluated microbiota, symptoms, and rs-FCs associations. We found altered microbiota diversity in FM, of which Phascolarctobacterium and Lachnoclostridium taxa increased the most and Faecalibacterium taxon decreased the most compared to controls. The Phascolarctobacterium abundance significantly predicted Beck depression inventory (BDI-II) scores in FM (β = 6.83; P = .033). Rs-FCs from salience network nodes were reduced in FM, of which rs-FCs from the right lateral rostral prefrontal cortex (RPFC) to the lateral occipital cortex, superior division right (RPFC-sLOC) could be predicted by BDI-II scores in patients (β = -.0064; P = .0054). In addition, the BDI-II score was a mediator in the association between Phascolarctobacterium abundance and rs-FCs of RPFC-sLOC (ab = -.06; 95% CI: -.16 to -9.10-3). In conclusion, microbial dysbiosis might be associated with altered neural networks mediated by psychological distress in FM, emphasizing the critical role of the brain-gut axis in FM's non-pain symptoms and supporting further analysis of mechanism-targeted therapies to reduce FM symptoms. PERSPECTIVE: Our study suggests microbial dysbiosis might be associated with psychological distress and the altered salience network, supporting the role of brain-gut axis dysfunction in fibromyalgia pathomechanisms. Further targeting therapies for microbial dysbiosis should be investigated to manage fibromyalgia patients in the future.
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Affiliation(s)
- Nguyen Thanh Nhu
- International PhD program in Medicine, College of Medicine, Taipei Medical University, Taipei, Taiwan; Faculty of Medicine, Can Tho University of Medicine and Pharmacy, Can Tho, Vietnam
| | - David Yen-Ting Chen
- Department of Medical Imaging, Taipei Medical University - Shuang-Ho Hospital, New Taipei City, Taiwan; Department of Radiology, School of Medicine, College of Medicine, Taipei Medical University, Taipei, Taiwan
| | - Yu-Chen S H Yang
- Joint Biobank, Office of Human Research, Taipei Medical University, Taipei, Taiwan
| | - Yu-Chun Lo
- Ph.D. Program in Medical Neuroscience, College of Medical Science and Technology, Taipei Medical University, Taipei, Taiwan
| | - Jiunn-Horng Kang
- International PhD program in Medicine, College of Medicine, Taipei Medical University, Taipei, Taiwan; Department of Physical Medicine and Rehabilitation, School of Medicine, College of Medicine, Taipei Medical University, Taipei, Taiwan; Department of Physical Medicine and Rehabilitation, Taipei Medical University Hospital, Taipei, Taiwan; Graduate Institute of Nanomedicine and Medical Engineering, College of Biomedical Engineering, Taipei Medical University, Taipei, Taiwan; TMU Research Center for Digestive Medicine, Taipei Medical University, Taipei, Taiwan
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Broeders TAA, Linsen F, Louter TS, Nawijn L, Penninx BWJH, van Tol MJ, van der Wee NJA, Veltman DJ, van der Werf YD, Schoonheim MM, Vinkers CH. Dynamic reconfigurations of brain networks in depressive and anxiety disorders: The influence of antidepressants. Psychiatry Res 2024; 334:115774. [PMID: 38341928 DOI: 10.1016/j.psychres.2024.115774] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/11/2023] [Revised: 01/30/2024] [Accepted: 02/04/2024] [Indexed: 02/13/2024]
Abstract
Major Depressive Disorder (MDD) and anxiety disorders are highly comorbid recurrent psychiatric disorders. Reduced dynamic reconfiguration of brain regions across subnetworks may play a critical role underlying these deficits, with indications of normalization after treatment with antidepressants. This study investigated dynamic reconfigurations in controls and individuals with a current MDD and/or anxiety disorder including antidepressant users and non-users in a large sample (N = 207) of adults. We quantified the number of subnetworks a region switched to (promiscuity) as well as the total number of switches (flexibility). Average whole-brain (i.e., global) values and subnetwork-specific values were compared between diagnosis and antidepressant groups. No differences in reconfiguration dynamics were found between individuals with a current MDD (N = 49), anxiety disorder (N = 46), comorbid MDD and anxiety disorder (N = 55), or controls (N = 57). Global and sensorimotor network (SMN) promiscuity and flexibility were higher in antidepressant users (N = 49, regardless of diagnosis) compared to non-users (N = 101) and controls. Dynamic reconfigurations were considerably higher in antidepressant users relative to non-users and controls, but not significantly altered in individuals with a MDD and/or anxiety disorder. The increase in antidepressant users was apparent across the whole brain and in the SMN when investigating subnetworks. These findings help disentangle how antidepressants improve symptoms.
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Affiliation(s)
- T A A Broeders
- Department of Anatomy & Neurosciences, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands.
| | - F Linsen
- Department of Anatomy & Neurosciences, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands; Department of Psychiatry, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - T S Louter
- Department of Anatomy & Neurosciences, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands; Department of Psychiatry, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - L Nawijn
- Department of Psychiatry, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - B W J H Penninx
- Department of Psychiatry, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - M J van Tol
- Department of Neuroscience, University Medical Center Groningen, Groningen, The Netherlands
| | - N J A van der Wee
- Department of Psychiatry, Leiden University Medical Center, Leiden, The Netherlands
| | - D J Veltman
- Department of Anatomy & Neurosciences, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Y D van der Werf
- Department of Psychiatry, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - M M Schoonheim
- Department of Anatomy & Neurosciences, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - C H Vinkers
- Department of Anatomy & Neurosciences, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands; Department of Psychiatry, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands; Amsterdam Public Health, Mental Health program, Amsterdam, The Netherlands; GGZ inGeest Mental Health Care, Amsterdam, The Netherlands
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Jirsaraie RJ, Palma AM, Small SL, Sandman CA, Davis EP, Baram TZ, Stern H, Glynn LM, Yassa MA. Prenatal Exposure to Maternal Mood Entropy Is Associated With a Weakened and Inflexible Salience Network in Adolescence. BIOLOGICAL PSYCHIATRY. COGNITIVE NEUROSCIENCE AND NEUROIMAGING 2024; 9:207-216. [PMID: 37611745 PMCID: PMC10881896 DOI: 10.1016/j.bpsc.2023.08.002] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/08/2023] [Revised: 08/04/2023] [Accepted: 08/07/2023] [Indexed: 08/25/2023]
Abstract
BACKGROUND Fetal exposure to maternal mood dysregulation influences child cognitive and emotional development, which may have long-lasting implications for mental health. However, the neurobiological alterations associated with this dimension of adversity have yet to be explored. Here, we tested the hypothesis that fetal exposure to entropy, a novel index of dysregulated maternal mood, would predict the integrity of the salience network, which is involved in emotional processing. METHODS A sample of 138 child-mother pairs (70 females) participated in this prospective longitudinal study. Maternal negative mood level and entropy (an index of variable and unpredictable mood) were assessed 5 times during pregnancy. Adolescents engaged in a functional magnetic resonance imaging task that was acquired between 2 resting-state scans. Changes in network integrity were analyzed using mixed-effect and latent growth curve models. The amplitude of low frequency fluctuations was analyzed to corroborate findings. RESULTS Prenatal maternal mood entropy, but not mood level, was associated with salience network integrity. Both prenatal negative mood level and entropy were associated with the amplitude of low frequency fluctuations of the salience network. Latent class analysis yielded 2 profiles based on changes in network integrity across all functional magnetic resonance imaging sequences. The profile that exhibited little variation in network connectivity (i.e., inflexibility) consisted of adolescents who were exposed to higher negative maternal mood levels and more entropy. CONCLUSIONS These findings suggest that fetal exposure to maternal mood dysregulation is associated with a weakened and inflexible salience network. More broadly, they identify maternal mood entropy as a novel marker of early adversity that exhibits long-lasting associations with offspring brain development.
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Affiliation(s)
- Robert J Jirsaraie
- Center for the Neurobiology of Learning and Memory, University of California, Irvine, Irvine, California; Department of Neurobiology and Behavior, University of California, Irvine, Irvine, California
| | - Anton M Palma
- Department of Statistics, University of California, Irvine, Irvine, California
| | - Steven L Small
- School of Behavioral and Brain Sciences, University of Texas at Dallas, Dallas, Texas
| | - Curt A Sandman
- Department of Psychiatry and Human Behavior, University of California, Irvine, Irvine, California
| | - Elysia Poggi Davis
- Department of Psychology, University of Denver, Denver, Colorado; Department of Pediatrics, University of California, Irvine, Irvine, California
| | - Tallie Z Baram
- Center for the Neurobiology of Learning and Memory, University of California, Irvine, Irvine, California; Department of Pediatrics, University of California, Irvine, Irvine, California; Department of Anatomy and Neurobiology, University of California, Irvine, Irvine, California
| | - Hal Stern
- Department of Statistics, University of California, Irvine, Irvine, California
| | - Laura M Glynn
- Department of Psychology, Chapman University, Orange, California.
| | - Michael A Yassa
- Center for the Neurobiology of Learning and Memory, University of California, Irvine, Irvine, California; Department of Neurobiology and Behavior, University of California, Irvine, Irvine, California; Department of Psychiatry and Human Behavior, University of California, Irvine, Irvine, California; Department of Anatomy and Neurobiology, University of California, Irvine, Irvine, California.
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Wang H, Zhu R, Tian S, Shao J, Dai Z, Xue L, Sun Y, Chen Z, Yao Z, Lu Q. Classification of bipolar disorders using the multilayer modularity in dynamic minimum spanning tree from resting state fMRI. Cogn Neurodyn 2023; 17:1609-1619. [PMID: 37974586 PMCID: PMC10640554 DOI: 10.1007/s11571-022-09907-x] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2021] [Revised: 07/19/2022] [Accepted: 10/28/2022] [Indexed: 12/04/2022] Open
Abstract
The diagnosis of bipolar disorders (BD) mainly depends on the clinical history and behavior observation, while only using clinical tools often limits the diagnosis accuracy. The study aimed to create a novel BD diagnosis framework using multilayer modularity in the dynamic minimum spanning tree (MST). We collected 45 un-medicated BD patients and 47 healthy controls (HC). The sliding window approach was utilized to construct dynamic MST via resting-state functional magnetic resonance imaging (fMRI) data. Firstly, we used three null models to explore the effectiveness of multilayer modularity in dynamic MST. Furthermore, the module allegiance exacted from dynamic MST was applied to train a classifier to discriminate BD patients. Finally, we explored the influence of the FC estimator and MST scale on the performance of the model. The findings indicated that multilayer modularity in the dynamic MST was not a random process in the human brain. And the model achieved an accuracy of 83.70% for identifying BD patients. In addition, we found the default mode network, subcortical network (SubC), and attention network played a key role in the classification. These findings suggested that the multilayer modularity in dynamic MST could highlight the difference between HC and BD patients, which opened up a new diagnostic tool for BD patients. Supplementary Information The online version contains supplementary material available at 10.1007/s11571-022-09907-x.
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Affiliation(s)
- Huan Wang
- School of Biological Sciences & Medical Engineering, Southeast University, No.2 Sipailou, Nanjing, 210096 Jiangsu Province China
- Child Development and Learning Science, Key Laboratory of Ministry of Education, Nanjing, China
| | - Rongxin Zhu
- Department of Psychiatry, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, 210029 China
| | - Shui Tian
- School of Biological Sciences & Medical Engineering, Southeast University, No.2 Sipailou, Nanjing, 210096 Jiangsu Province China
- Child Development and Learning Science, Key Laboratory of Ministry of Education, Nanjing, China
| | - Junneng Shao
- School of Biological Sciences & Medical Engineering, Southeast University, No.2 Sipailou, Nanjing, 210096 Jiangsu Province China
- Child Development and Learning Science, Key Laboratory of Ministry of Education, Nanjing, China
| | - Zhongpeng Dai
- School of Biological Sciences & Medical Engineering, Southeast University, No.2 Sipailou, Nanjing, 210096 Jiangsu Province China
- Child Development and Learning Science, Key Laboratory of Ministry of Education, Nanjing, China
| | - Li Xue
- School of Biological Sciences & Medical Engineering, Southeast University, No.2 Sipailou, Nanjing, 210096 Jiangsu Province China
- Child Development and Learning Science, Key Laboratory of Ministry of Education, Nanjing, China
| | - Yurong Sun
- School of Biological Sciences & Medical Engineering, Southeast University, No.2 Sipailou, Nanjing, 210096 Jiangsu Province China
- Child Development and Learning Science, Key Laboratory of Ministry of Education, Nanjing, China
| | - Zhilu Chen
- Department of Psychiatry, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, 210029 China
| | - Zhijian Yao
- School of Biological Sciences & Medical Engineering, Southeast University, No.2 Sipailou, Nanjing, 210096 Jiangsu Province China
- Department of Psychiatry, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, 210029 China
- Nanjing Brain Hospital, Medical School of Nanjing University, Nanjing, 210093 China
| | - Qing Lu
- School of Biological Sciences & Medical Engineering, Southeast University, No.2 Sipailou, Nanjing, 210096 Jiangsu Province China
- Child Development and Learning Science, Key Laboratory of Ministry of Education, Nanjing, China
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5
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Ten Doesschate F, Bruin W, Zeidman P, Abbott CC, Argyelan M, Dols A, Emsell L, van Eijndhoven PFP, van Exel E, Mulders PCR, Narr K, Tendolkar I, Rhebergen D, Sienaert P, Vandenbulcke M, Verdijk J, van Verseveld M, Bartsch H, Oltedal L, van Waarde JA, van Wingen GA. Effective resting-state connectivity in severe unipolar depression before and after electroconvulsive therapy. Brain Stimul 2023; 16:1128-1134. [PMID: 37517467 DOI: 10.1016/j.brs.2023.07.054] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2023] [Revised: 07/06/2023] [Accepted: 07/27/2023] [Indexed: 08/01/2023] Open
Abstract
BACKGROUND Electroconvulsive therapy (ECT) is one of the most effective treatments for severe depressive disorders. A recent multi-center study found no consistent changes in correlation-based (undirected) resting-state connectivity after ECT. Effective (directed) connectivity may provide more insight into the working mechanism of ECT. OBJECTIVE We investigated whether there are consistent changes in effective resting-state connectivity. METHODS This multi-center study included data from 189 patients suffering from severe unipolar depression and 59 healthy control participants. Longitudinal data were available for 81 patients and 24 healthy controls. We used dynamic causal modeling for resting-state functional magnetic resonance imaging to determine effective connectivity in the default mode, salience and central executive networks before and after a course of ECT. Bayesian general linear models were used to examine differences in baseline and longitudinal effective connectivity effects associated with ECT and its effectiveness. RESULTS Compared to controls, depressed patients showed many differences in effective connectivity at baseline, which varied according to the presence of psychotic features and later treatment outcome. Additionally, effective connectivity changed after ECT, which was related to ECT effectiveness. Notably, treatment effectiveness was associated with decreasing and increasing effective connectivity from the posterior default mode network to the left and right insula, respectively. No effects were found using correlation-based (undirected) connectivity. CONCLUSIONS A beneficial response to ECT may depend on how brain regions influence each other in networks important for emotion and cognition. These findings further elucidate the working mechanisms of ECT and may provide directions for future non-invasive brain stimulation research.
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Affiliation(s)
- Freek Ten Doesschate
- Department of Psychiatry, Rijnstate Hospital, Arnhem, the Netherlands; Amsterdam UMC Location University of Amsterdam, Department of Psychiatry, Amsterdam, the Netherlands; Amsterdam Neuroscience, Amsterdam, the Netherlands.
| | - Willem Bruin
- Amsterdam UMC Location University of Amsterdam, Department of Psychiatry, Amsterdam, the Netherlands; Amsterdam Neuroscience, Amsterdam, the Netherlands
| | - Peter Zeidman
- Wellcome Centre for Human Neuroimaging, 12 Queen Square, London, WC1N 3AR, UK
| | - Christopher C Abbott
- Department of Psychiatry, University of New Mexico School of Medicine, Albuquerque, NM, USA
| | - Miklos Argyelan
- Center for Psychiatric Neuroscience at the Feinstein Institute for Medical Research, New York, NY, USA
| | - Annemieke Dols
- GGZ inGeest Specialized Mental Health Care, Department of Old Age Psychiatry, Oldenaller 1, 1081 HJ, Amsterdam, the Netherlands; Amsterdam UMC, Vrije Universiteit, Psychiatry, Amsterdam Public Health Research Institute, the Netherlands; Amsterdam UMC, Vrije Universiteit, Psychiatry, Amsterdam Neuroscience, the Netherlands
| | - Louise Emsell
- Katholieke Universiteit Leuven, University Psychiatric Center Katholieke Universiteit Leuven, Leuven, Belgium
| | - Philip F P van Eijndhoven
- Donders Institute for Brain, Cognition and Behavior, Department of Psychiatry, Nijmegen, the Netherlands; Department of Psychiatry, Radboud University Medical Centre, Huispost 961, P.O. Box 9101, 6500 HB, Nijmegen, the Netherlands
| | - Eric van Exel
- GGZ inGeest Specialized Mental Health Care, Department of Old Age Psychiatry, Oldenaller 1, 1081 HJ, Amsterdam, the Netherlands; Amsterdam UMC, Vrije Universiteit, Psychiatry, Amsterdam Public Health Research Institute, the Netherlands; Amsterdam UMC, Vrije Universiteit, Psychiatry, Amsterdam Neuroscience, the Netherlands
| | - Peter C R Mulders
- Donders Institute for Brain, Cognition and Behavior, Department of Psychiatry, Nijmegen, the Netherlands; Department of Psychiatry, Radboud University Medical Centre, Huispost 961, P.O. Box 9101, 6500 HB, Nijmegen, the Netherlands
| | - Katherine Narr
- Departments of Neurology, Psychiatry, and Biobehavioral Sciences, University of California, Los Angeles, CA, USA
| | - Indira Tendolkar
- Donders Institute for Brain, Cognition and Behavior, Department of Psychiatry, Nijmegen, the Netherlands; Department of Psychiatry, Radboud University Medical Centre, Huispost 961, P.O. Box 9101, 6500 HB, Nijmegen, the Netherlands
| | - Didi Rhebergen
- Amsterdam UMC, Vrije Universiteit, Psychiatry, Amsterdam Public Health Research Institute, the Netherlands; Amsterdam UMC, Vrije Universiteit, Psychiatry, Amsterdam Neuroscience, the Netherlands
| | - Pascal Sienaert
- Academic Center for ECT and Neuromodulation (AcCENT), University Psychiatric Center KU Leuven (Catholic University of Leuven), Leuven, Belgium
| | - Mathieu Vandenbulcke
- Katholieke Universiteit Leuven, University Psychiatric Center Katholieke Universiteit Leuven, Leuven, Belgium
| | - Joey Verdijk
- Department of Psychiatry, Rijnstate Hospital, Arnhem, the Netherlands
| | | | - Hauke Bartsch
- Mohn Medical Imaging and Visualization Center, Department of Radiology, Haukeland University Hospital, Bergen, Norway
| | - Leif Oltedal
- Mohn Medical Imaging and Visualization Center, Department of Radiology, Haukeland University Hospital, Bergen, Norway; Department of Clinical Medicine, University of Bergen, Bergen, Norway
| | | | - Guido A van Wingen
- Amsterdam UMC Location University of Amsterdam, Department of Psychiatry, Amsterdam, the Netherlands; Amsterdam Neuroscience, Amsterdam, the Netherlands
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Ke M, Wang C, Liu G. Multilayer brain network modeling and dynamic analysis of juvenile myoclonic epilepsy. Front Behav Neurosci 2023; 17:1123534. [PMID: 36969802 PMCID: PMC10036585 DOI: 10.3389/fnbeh.2023.1123534] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2022] [Accepted: 02/15/2023] [Indexed: 03/12/2023] Open
Abstract
Objective: It is indisputable that the functional connectivity of the brain network in juvenile myoclonic epilepsy (JME) patients is abnormal. As a mathematical extension of the traditional network model, the multilayer network can fully capture the fluctuations of brain imaging data with time, and capture subtle abnormal dynamic changes. This study assumed that the dynamic structure of JME patients is abnormal and used the multilayer network framework to analyze the change brain community structure in JME patients from the perspective of dynamic analysis.Methods: First, functional magnetic resonance imaging (fMRI) data were obtained from 35 JME patients and 34 healthy control subjects. In addition, the communities of the two groups were explored with the help of a multilayer network model and a multilayer community detection algorithm. Finally, differences were described by metrics that are specific to the multilayer network.Results: Compared with healthy controls, JME patients had a significantly lower modularity degree of the brain network. Furthermore, from the level of the functional network, the integration of the default mode network (DMN) and visual network (VN) in JME patients showed a significantly higher trend, and the flexibility of the attention network (AN) also increased significantly. At the node level, the integration of seven nodes of the DMN was significantly increased, the integration of five nodes of the VN was significantly increased, and the flexibility of three nodes of the AN was significantly increased. Moreover, through division of the core-peripheral system, we found that the left insula and left cuneus were core regions specific to the JME group, while most of the peripheral systems specific to the JME group were distributed in the prefrontal cortex and hippocampus. Finally, we found that the flexibility of the opercular part of the inferior frontal gyrus was significantly correlated with the severity of JME symptoms.Conclusion: Our findings indicate that the dynamic community structure of JME patients is indeed abnormal. These results provide a new perspective for the study of dynamic changes in communities in JME patients.
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Affiliation(s)
- Ming Ke
- School of Computer and Communication, Lanzhou University of Technology, Lanzhou, China
- *Correspondence: Ming Ke Guangyao Liu
| | - Changliang Wang
- School of Computer and Communication, Lanzhou University of Technology, Lanzhou, China
| | - Guangyao Liu
- Department of Magnetic Resonance, Lanzhou University Second Hospital, Lanzhou, China
- *Correspondence: Ming Ke Guangyao Liu
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Yang C, Xiao K, Ao Y, Cui Q, Jing X, Wang Y. The thalamus is the causal hub of intervention in patients with major depressive disorder: Evidence from the Granger causality analysis. Neuroimage Clin 2023; 37:103295. [PMID: 36549233 PMCID: PMC9795532 DOI: 10.1016/j.nicl.2022.103295] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2022] [Revised: 12/13/2022] [Accepted: 12/14/2022] [Indexed: 12/23/2022]
Abstract
Major depressive disorder (MDD) is the leading mental disorder and afflicts more than 350 million people worldwide. The underlying neural mechanisms of MDD remain unclear, hindering the accurate treatment. Recent brain imaging studies have observed functional abnormalities in multiple brain regions in patients with MDD, identifying core brain regions is the key to locating potential therapeutic targets for MDD. The Granger causality analysis (GCA) measures directional effects between brain regions and, therefore, can track causal hubs as potential intervention targets for MDD. We reviewed literature employing GCA to investigate abnormal brain connections in patients with MDD. The total degree of effective connections in the thalamus (THA) is more than twice that in traditional targets such as the superior frontal gyrus and anterior cingulate cortex. Altered causal connections in patients with MDD mainly included enhanced bottom-up connections from the thalamus to various cortical and subcortical regions and reduced top-down connections from these regions to the THA, indicating excessive uplink sensory information and insufficient downlink suppression information for negative emotions. We suggest that the thalamus is the most crucial causal hub for MDD, which may serve as the downstream target for non-invasive brain stimulation and medication approaches in MDD treatment.
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Affiliation(s)
- Chengxiao Yang
- Institute of Brain and Psychological Sciences, Sichuan Normal University, Chengdu, China
| | - Kunchen Xiao
- Institute of Brain and Psychological Sciences, Sichuan Normal University, Chengdu, China
| | - Yujia Ao
- Institute of Brain and Psychological Sciences, Sichuan Normal University, Chengdu, China
| | - Qian Cui
- School of Public Affairs and Administration, University of Electronic Science and Technology of China, Chengdu, 611731, China
| | - Xiujuan Jing
- Tianfu College of Southwestern University of Finance and Economics, Chengdu 610052, China
| | - Yifeng Wang
- Institute of Brain and Psychological Sciences, Sichuan Normal University, Chengdu, China.
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Belleau EL, Bolton TAW, Kaiser RH, Clegg R, Cárdenas E, Goer F, Pechtel P, Beltzer M, Vitaliano G, Olson DP, Teicher MH, Pizzagalli DA. Resting state brain dynamics: Associations with childhood sexual abuse and major depressive disorder. Neuroimage Clin 2022; 36:103164. [PMID: 36044792 PMCID: PMC9449675 DOI: 10.1016/j.nicl.2022.103164] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2022] [Revised: 07/23/2022] [Accepted: 08/22/2022] [Indexed: 01/07/2023]
Abstract
Early life stress (ELS) and major depressive disorder (MDD) share neural network abnormalities. However, it is unclear how ELS and MDD may separately and/or jointly relate to brain networks, and whether neural differences exist between depressed individuals with vs without ELS. Moreover, prior work evaluated static versus dynamic network properties, a critical gap considering brain networks show changes in coordinated activity over time. Seventy-one unmedicated females with and without childhood sexual abuse (CSA) histories and/or MDD completed a resting state scan and a stress task in which cortisol and affective ratings were collected. Recurring functional network co-activation patterns (CAPs) were examined and time in CAP (number of times each CAP is expressed) and transition frequencies (transitioning between different CAPs) were computed. The effects of MDD and CSA on CAP metrics were examined and CAP metrics were correlated with depression and stress-related variables. Results showed that MDD, but not CSA, related to CAP metrics. Specifically, individuals with MDD (N = 35) relative to HCs (N = 36), spent more time in a posterior default mode (DMN)-frontoparietal network (FPN) CAP and transitioned more frequently between posterior DMN-FPN and prototypical DMN CAPs. Across groups, more time spent in a posterior DMN-FPN CAP and greater DMN-FPN and prototypical DMN CAP transition frequencies were linked to higher rumination. Imbalances between the DMN and the FPN appear central to MDD and might contribute to MDD-related cognitive dysfunction, including rumination. Unexpectedly, CSA did not modulate such dysfunctions, a finding that needs to be replicated by future studies with larger sample sizes.
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Affiliation(s)
- Emily L Belleau
- Center for Depression, Anxiety and Stress Research, McLean Hospital, Belmont, MA, USA; Department of Psychiatry, Harvard Medical School, Boston, MA, USA.
| | - Thomas A W Bolton
- Department of Clinical Neurosciences, Neurosurgery Service and Gamma Knife Center, Centre Hospitalier Universitaire Vaudois (CHUV), Lausanne, Switzerland
| | - Roselinde H Kaiser
- Department of Psychology and Neuroscience, University of Colorado Boulder, Boulder, USA
| | - Rachel Clegg
- Center for Depression, Anxiety and Stress Research, McLean Hospital, Belmont, MA, USA
| | - Emilia Cárdenas
- Center for Depression, Anxiety and Stress Research, McLean Hospital, Belmont, MA, USA
| | - Franziska Goer
- Center for Depression, Anxiety and Stress Research, McLean Hospital, Belmont, MA, USA
| | - Pia Pechtel
- Center for Depression, Anxiety and Stress Research, McLean Hospital, Belmont, MA, USA
| | - Miranda Beltzer
- Center for Depression, Anxiety and Stress Research, McLean Hospital, Belmont, MA, USA
| | - Gordana Vitaliano
- Department of Psychiatry, Harvard Medical School, Boston, MA, USA; McLean Imaging Center, McLean Hospital, Belmont, MA, USA
| | - David P Olson
- Department of Psychiatry, Harvard Medical School, Boston, MA, USA; McLean Imaging Center, McLean Hospital, Belmont, MA, USA
| | - Martin H Teicher
- Department of Psychiatry, Harvard Medical School, Boston, MA, USA
| | - Diego A Pizzagalli
- Center for Depression, Anxiety and Stress Research, McLean Hospital, Belmont, MA, USA; Department of Psychiatry, Harvard Medical School, Boston, MA, USA; McLean Imaging Center, McLean Hospital, Belmont, MA, USA
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9
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Kabbara A, Robert G, Khalil M, Verin M, Benquet P, Hassan M. An electroencephalography connectome predictive model of major depressive disorder severity. Sci Rep 2022; 12:6816. [PMID: 35473962 PMCID: PMC9042869 DOI: 10.1038/s41598-022-10949-8] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2021] [Accepted: 04/05/2022] [Indexed: 11/21/2022] Open
Abstract
Emerging evidence showed that major depressive disorder (MDD) is associated with disruptions of brain structural and functional networks, rather than impairment of isolated brain region. Thus, connectome-based models capable of predicting the depression severity at the individual level can be clinically useful. Here, we applied a machine-learning approach to predict the severity of depression using resting-state networks derived from source-reconstructed Electroencephalography (EEG) signals. Using regression models and three independent EEG datasets (N = 328), we tested whether resting state functional connectivity could predict individual depression score. On the first dataset, results showed that individuals scores could be reasonably predicted (r = 0.6, p = 4 × 10-18) using intrinsic functional connectivity in the EEG alpha band (8-13 Hz). In particular, the brain regions which contributed the most to the predictive network belong to the default mode network. We further tested the predictive potential of the established model by conducting two external validations on (N1 = 53, N2 = 154). Results showed statistically significant correlations between the predicted and the measured depression scale scores (r1 = 0.52, r2 = 0.44, p < 0.001). These findings lay the foundation for developing a generalizable and scientifically interpretable EEG network-based markers that can ultimately support clinicians in a biologically-based characterization of MDD.
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Affiliation(s)
- Aya Kabbara
- Lebanese Association for Scientific Research, Tripoli, Lebanon
- MINDig, F-35000, Rennes, France
| | - Gabriel Robert
- Academic Department of Psychiatry, Centre Hospitalier Guillaume Régnier, Rennes, France
- Empenn, U1228, IRISA, UMR 6074, Rennes, France
- Comportement et Noyaux Gris Centraux, EA 4712, CHU Rennes, Université de Rennes 1, 35000, Rennes, France
| | - Mohamad Khalil
- Azm Center for Research in Biotechnology and Its Applications, EDST, Tripoli, Lebanon
- CRSI Research Center, Faculty of Engineering, Lebanese University, Beirut, Lebanon
| | - Marc Verin
- Comportement et Noyaux Gris Centraux, EA 4712, CHU Rennes, Université de Rennes 1, 35000, Rennes, France
- Univ Rennes, Inserm, LTSI-U1099, F-35000, Rennes, France
| | - Pascal Benquet
- Univ Rennes, Inserm, LTSI-U1099, F-35000, Rennes, France
| | - Mahmoud Hassan
- MINDig, F-35000, Rennes, France.
- School of Science and Engineering, Reykjavik University, Reykjavik, Iceland.
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10
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Daws RE, Timmermann C, Giribaldi B, Sexton JD, Wall MB, Erritzoe D, Roseman L, Nutt D, Carhart-Harris R. Increased global integration in the brain after psilocybin therapy for depression. Nat Med 2022; 28:844-851. [DOI: 10.1038/s41591-022-01744-z] [Citation(s) in RCA: 34] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2021] [Accepted: 02/14/2022] [Indexed: 12/13/2022]
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11
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Liu J, Zhu Q, Zhu L, Yang Y, Zhang Y, Liu X, Zhang L, Jia Y, Peng Q, Wang J, Sun P, Fan W, Wang J. Altered brain network in first-episode, drug-naive patients with major depressive disorder. J Affect Disord 2022; 297:1-7. [PMID: 34656674 DOI: 10.1016/j.jad.2021.10.012] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/31/2021] [Revised: 10/06/2021] [Accepted: 10/11/2021] [Indexed: 11/25/2022]
Abstract
BACKGROUND Resting-state functional magnetic resonance imaging has been widely used for the assessment of brain functional network, yet with inconsistent results. The present study aimed to investigate intranetwork and internetwork connectivity differences between patients with major depressive disorder (MDD) and healthy controls at the integrity, network and edge levels of 8 well-defined resting state networks. METHODS Thirty patients with MDD and sixty-three healthy control subjects were recruited in this study. RESULTS Compared with healthy controls, patients with MDD showed increased node degree in the right amygdala and putamen, increased connectivity strength in the deep gray matter network (DGN) and increased functional connectivity in intranetwork and internetwork. Meanwhile, MDD showed decreased connectivity strength in visual network-DGN pair. LIMITATIONS The sample size was small, and all patients in this study were of Asian ethnicity, especially Han individuals. CONCLUSIONS These findings demonstrate that MDD cases and healthy controls may have divergent intranetwork and internetwork connectivity at an early stage without confounding influence of medication. These differences may underlie cognitive and behavioral alterations in patients with MDD. And these differences may help with the discrimination of patients and healthy people at an early stage of MDD. More studies in the future are warranted to assist in the diagnosis of this burdensome disease.
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Affiliation(s)
- Jia Liu
- Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China; Hubei Province Key Laboratory of Molecular Imaging, Wuhan, China
| | - Qing Zhu
- Department of Neurology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Licheng Zhu
- Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China; Hubei Province Key Laboratory of Molecular Imaging, Wuhan, China
| | - Yun Yang
- Hubei Province Key Laboratory of Molecular Imaging, Wuhan, China; Department of Ultrasound, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China; Hubei Province Clinical Research Center for Medical Imaging, China
| | - Yiran Zhang
- Department of Neurology, Huashan Hospital, Fudan University, Shanghai, China
| | - Xiaoming Liu
- Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China; Hubei Province Key Laboratory of Molecular Imaging, Wuhan, China
| | - Lan Zhang
- Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China; Hubei Province Key Laboratory of Molecular Imaging, Wuhan, China
| | - Yuxi Jia
- Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China; Hubei Province Key Laboratory of Molecular Imaging, Wuhan, China
| | - Qinmu Peng
- School of Electronic Information and Communications, Huazhong University of Science and Technology, Wuhan, China
| | - Jiazheng Wang
- MSC Clinical and Technical Solutions, Philips Healthcare, Beijing, China
| | - Peng Sun
- MSC Clinical and Technical Solutions, Philips Healthcare, Wuhan, China
| | - Wenliang Fan
- Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China; Hubei Province Key Laboratory of Molecular Imaging, Wuhan, China.
| | - Jing Wang
- Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China; Hubei Province Key Laboratory of Molecular Imaging, Wuhan, China.
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12
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Kuburi S, Di Passa AM, Tassone VK, Mahmood R, Lalovic A, Ladha KS, Dunlop K, Rizvi S, Demchenko I, Bhat V. Neuroimaging Correlates of Treatment Response with Psychedelics in Major Depressive Disorder: A Systematic Review. CHRONIC STRESS 2022; 6:24705470221115342. [PMID: 35936944 PMCID: PMC9350516 DOI: 10.1177/24705470221115342] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/05/2022] [Accepted: 07/07/2022] [Indexed: 12/03/2022]
Abstract
Preliminary evidence supports the use of psychedelics for major depressive
disorder (MDD). However, less attention has been given to the neural mechanisms
behind their effects. We conducted a systematic review examining the
neuroimaging correlates of antidepressant response following psychedelic
interventions for MDD. Through MEDLINE, Embase, and APA PsycINFO, 187 records
were identified and 42 articles were screened. Six published studies and one
conference abstract were included. Five ongoing trials were included from
subjective outcomesTrials.gov. Our search covered several psychedelics, though
included studies were specific to psilocybin, ayahuasca, and lysergic acid
diethylamide. Three psilocybin studies noted amygdala activity and functional
connectivity (FC) alterations that correlated with treatment response. Two
psilocybin studies reported that FC changes in the medial and ventromedial
prefrontal cortices correlated with treatment response. Two trials from a single
study reported global decreases in brain network modularity which correlated
with antidepressant response. One ayahuasca study reported increased activity in
the limbic regions following treatment. Preliminary evidence suggests that the
default mode and limbic networks may be a target for future research on the
neural mechanisms of psychedelics. More data is required to corroborate these
initial findings as the evidence summarized in this review is based on four
datasets.
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Affiliation(s)
- Sarah Kuburi
- Interventional Psychiatry Program, Mental Health and Addictions Service, St. Michael’s Hospital, 193 Yonge Street 6-013, M5B 1M8, Toronto, Ontario, Canada
| | - Anne-Marie Di Passa
- Interventional Psychiatry Program, Mental Health and Addictions Service, St. Michael’s Hospital, 193 Yonge Street 6-013, M5B 1M8, Toronto, Ontario, Canada
| | - Vanessa K. Tassone
- Interventional Psychiatry Program, Mental Health and Addictions Service, St. Michael’s Hospital, 193 Yonge Street 6-013, M5B 1M8, Toronto, Ontario, Canada
| | - Raesham Mahmood
- Institute of Medical Science, Faculty of Medicine, University of Toronto, 1 King's College Circle, Medical Sciences Building, M5S 1A8, Toronto, Ontario, Canada
| | - Aleksandra Lalovic
- Department of Psychiatry, Faculty of Medicine, University of Toronto, 250 College Street, M5T 1R8, Toronto, Ontario, Canada
- Li Ka Shing Knowledge Institute, St. Michael’s Hospital, 209 Victoria Street, M5B 1T8, Toronto, Ontario, Canada
| | - Karim S. Ladha
- Li Ka Shing Knowledge Institute, St. Michael’s Hospital, 209 Victoria Street, M5B 1T8, Toronto, Ontario, Canada
- Department of Anesthesiology and Pain Medicine, University of Toronto, 250 College Street, M5T 1R8, Toronto, Ontario, Canada
- Department of Anesthesia, St. Michael's Hospital, 193 Yonge Street 6-013, M5B 1M8, Toronto, Ontario, Canada
| | - Katharine Dunlop
- Interventional Psychiatry Program, Mental Health and Addictions Service, St. Michael’s Hospital, 193 Yonge Street 6-013, M5B 1M8, Toronto, Ontario, Canada
- Department of Psychiatry, Faculty of Medicine, University of Toronto, 250 College Street, M5T 1R8, Toronto, Ontario, Canada
- Center for Depression and Suicide Studies, St. Michael’s Hospital, Unity Health Toronto, 30 Bond Street, M5B 1W8, Toronto, ON, Canada
- Keenan Research Center for Biomedical Science, St. Michael’s Hospital, Unity Health Toronto, 30 Bond Street, M5B 1W8, Toronto, Ontario, Canada
| | - Sakina Rizvi
- Department of Psychiatry, Faculty of Medicine, University of Toronto, 250 College Street, M5T 1R8, Toronto, Ontario, Canada
- Li Ka Shing Knowledge Institute, St. Michael’s Hospital, 209 Victoria Street, M5B 1T8, Toronto, Ontario, Canada
| | - Ilya Demchenko
- Interventional Psychiatry Program, Mental Health and Addictions Service, St. Michael’s Hospital, 193 Yonge Street 6-013, M5B 1M8, Toronto, Ontario, Canada
| | - Venkat Bhat
- Interventional Psychiatry Program, Mental Health and Addictions Service, St. Michael’s Hospital, 193 Yonge Street 6-013, M5B 1M8, Toronto, Ontario, Canada
- Institute of Medical Science, Faculty of Medicine, University of Toronto, 1 King's College Circle, Medical Sciences Building, M5S 1A8, Toronto, Ontario, Canada
- Department of Psychiatry, Faculty of Medicine, University of Toronto, 250 College Street, M5T 1R8, Toronto, Ontario, Canada
- Li Ka Shing Knowledge Institute, St. Michael’s Hospital, 209 Victoria Street, M5B 1T8, Toronto, Ontario, Canada
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13
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Serotonin 2A receptor polymorphism rs3803189 mediated by dynamics of default mode network: a potential biomarker for antidepressant early response. J Affect Disord 2021; 283:130-138. [PMID: 33548906 DOI: 10.1016/j.jad.2021.01.047] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/23/2020] [Revised: 01/15/2021] [Accepted: 01/20/2021] [Indexed: 01/10/2023]
Abstract
BACKGROUND Serotonin 2A receptors (HTR2A) play a crucial role in the therapeutic response to antidepressant. The activity of serotonergic system could modulate the connectivity of the default mode network (DMN) in human brain. Our research investigated the influence of the single nucleotide polymorphism (SNP) of HTR2A on the early treatment response of antidepressant and their relation to dynamic changes of DMN for the first time. METHODS A total of 134 major depressive disorder patients and 95 healthy controls from two independent datasets were enrolled. All subjects have genotyped candidate HTR2A polymorphisms, dynamic brain parameters flexibility and integration were calculated according to the resting-state functional magnetic resonance imaging (rs-fMRI) at baseline. Patients received selective serotonin reuptake inhibitors (SSRIs) treatment with conventional dose in the next two weeks. RESULTS We found the correlation of the risk-associated variant belonged to HTR2A polymorphism rs3803189 with the achievements of antidepressant early response, and also with the stronger dynamic changes of DMN. Further mediation analysis indicated that the bond between rs3803189 and antidepressant early response was mediated by the integration between the right angular gyrus (AG.R) and the subcortical network (SCN), which were validated over both the main and replication datasets. LIMITATIONS Except the AG.R-SCN circuit, other factors which influence the relationship between rs3803189 and antidepressant therapy deserve to be explored further. Besides, heterogeneity of samples limited the power of the current result. CONCLUSIONS Our findings provided a potential biomarker for individual treatment sensitivity and produced positive effects on revealing the complicated gene-brain-disorder relationship.
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14
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Yang Z, Telesford QK, Franco AR, Lim R, Gu S, Xu T, Ai L, Castellanos FX, Yan CG, Colcombe S, Milham MP. Measurement reliability for individual differences in multilayer network dynamics: Cautions and considerations. Neuroimage 2021; 225:117489. [PMID: 33130272 PMCID: PMC7829665 DOI: 10.1016/j.neuroimage.2020.117489] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2020] [Accepted: 10/21/2020] [Indexed: 01/16/2023] Open
Abstract
Multilayer network models have been proposed as an effective means of capturing the dynamic configuration of distributed neural circuits and quantitatively describing how communities vary over time. Beyond general insights into brain function, a growing number of studies have begun to employ these methods for the study of individual differences. However, test-retest reliabilities for multilayer network measures have yet to be fully quantified or optimized, potentially limiting their utility for individual difference studies. Here, we systematically evaluated the impact of multilayer community detection algorithms, selection of network parameters, scan duration, and task condition on test-retest reliabilities of multilayer network measures (i.e., flexibility, integration, and recruitment). A key finding was that the default method used for community detection by the popular generalized Louvain algorithm can generate erroneous results. Although available, an updated algorithm addressing this issue is yet to be broadly adopted in the neuroimaging literature. Beyond the algorithm, the present work identified parameter selection as a key determinant of test-retest reliability; however, optimization of these parameters and expected reliabilities appeared to be dataset-specific. Once parameters were optimized, consistent with findings from the static functional connectivity literature, scan duration was a much stronger determinant of reliability than scan condition. When the parameters were optimized and scan duration was sufficient, both passive (i.e., resting state, Inscapes, and movie) and active (i.e., flanker) tasks were reliable, although reliability in the movie watching condition was significantly higher than in the other three tasks. The minimal data requirement for achieving reliable measures for the movie watching condition was 20 min, and 30 min for the other three tasks. Our results caution the field against the use of default parameters without optimization based on the specific datasets to be employed - a process likely to be limited for most due to the lack of test-retest samples to enable parameter optimization.
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Affiliation(s)
- Zhen Yang
- Center for Biomedical Imaging and Neuromodulation, The Nathan S. Kline Institute for Psychiatric Research, 140 Old Orangeburg Rd, Orangeburg, NY 10962, United States; Department of Psychiatry, NYU Grossman School of Medicine, 550 1st Avenue, New York, NY 10016, United States.
| | - Qawi K Telesford
- Center for Biomedical Imaging and Neuromodulation, The Nathan S. Kline Institute for Psychiatric Research, 140 Old Orangeburg Rd, Orangeburg, NY 10962, United States
| | - Alexandre R Franco
- Center for Biomedical Imaging and Neuromodulation, The Nathan S. Kline Institute for Psychiatric Research, 140 Old Orangeburg Rd, Orangeburg, NY 10962, United States; Department of Psychiatry, NYU Grossman School of Medicine, 550 1st Avenue, New York, NY 10016, United States; Center for the Developing Brain, The Child Mind Institute, 101 East 56th Street, New York, NY 10022, United States
| | - Ryan Lim
- Center for Biomedical Imaging and Neuromodulation, The Nathan S. Kline Institute for Psychiatric Research, 140 Old Orangeburg Rd, Orangeburg, NY 10962, United States
| | - Shi Gu
- University of Electronic Science and Technology of China, Chengdu, Sichuan, China
| | - Ting Xu
- Center for the Developing Brain, The Child Mind Institute, 101 East 56th Street, New York, NY 10022, United States
| | - Lei Ai
- Center for the Developing Brain, The Child Mind Institute, 101 East 56th Street, New York, NY 10022, United States
| | - Francisco X Castellanos
- Center for Biomedical Imaging and Neuromodulation, The Nathan S. Kline Institute for Psychiatric Research, 140 Old Orangeburg Rd, Orangeburg, NY 10962, United States; Department of Child and Adolescent Psychiatry, NYU Grossman School of Medicine, New York, NY 10016, United States
| | - Chao-Gan Yan
- CAS Key Laboratory of Behavioral Science, Institute of Psychology, Beijing, China
| | - Stan Colcombe
- Center for Biomedical Imaging and Neuromodulation, The Nathan S. Kline Institute for Psychiatric Research, 140 Old Orangeburg Rd, Orangeburg, NY 10962, United States; Department of Psychiatry, NYU Grossman School of Medicine, 550 1st Avenue, New York, NY 10016, United States
| | - Michael P Milham
- Center for Biomedical Imaging and Neuromodulation, The Nathan S. Kline Institute for Psychiatric Research, 140 Old Orangeburg Rd, Orangeburg, NY 10962, United States; Center for the Developing Brain, The Child Mind Institute, 101 East 56th Street, New York, NY 10022, United States.
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15
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Zhang H, Palaniyappan L, Wu Y, Cong E, Wu C, Ding L, Jin F, Qiu M, Huang Y, Wu Y, Wang J, Ying S, Peng D. The concurrent disturbance of dynamic functional and structural brain connectome in major depressive disorder: the prefronto-insular pathway. J Affect Disord 2020; 274:1084-1090. [PMID: 32663936 DOI: 10.1016/j.jad.2020.05.148] [Citation(s) in RCA: 8] [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: 04/12/2020] [Revised: 05/17/2020] [Accepted: 05/22/2020] [Indexed: 01/30/2023]
Abstract
BACKGROUND Robust evidence has shown that abnormal function networks, particularly the salience network (SN), are observed in depressed patients. Although white matter structural connectivity may predict time-varying functional connectivity, including symptom phenotype, in psychiatric disorders, there is still a gap in elucidating the concurrent dynamic functional and structural connectivity profiles of the SN in depressed patients. METHODS We measured static and dynamic functional connectivity (FC) of the SN using resting-state fMRI BOLD time series in 76 subjects (21 with major depressive disorder (MDD), 27 with bipolar depression (BD), and 28 healthy controls (HC)). Hamilton Depression Scale total score was used to measure depression severity. Furthermore, we investigated the concurrent structural connectivity using diffusion kurtosis imaging (DKI)-based tractography. RESULTS Our findings suggested that in the presence of MDD, both structural and dynamic (but not static) FC were reduced in the SN, particularly affecting the left prefronto-insular pathways (L.aPFC-L.insula). MDD patients showed decreased connectivity variability within the SN compared with HC. The aberrant dynamic FC in the prefronto-insular pathways of the SN related to severity of depressive symptoms in MDD. Furthermore, compared with BD patients, those with MDD showed significantly decreased dynamic FC in the left prefronto-parietal system (L.aPFC-lateral parietal cortex). LIMITATIONS The generalizability of our findings is, to some extent, constrained by the small sample size. CONCLUSIONS The integrity of SN connectivity, particularly the prefronto-insular pathway, appears to be a crucial signature of MDD. The perturbed dynamic interaction of SN with prefrontal regions may underlie the clinical severity in depressed patients.
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Affiliation(s)
- Huifeng Zhang
- Division of Mood Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Lena Palaniyappan
- Robarts Research Institute and Department of Psychiatry, University of Western Ontario, London, ON, Canada; Lawson Health Research Institute, London, ON, Canada; Institute of Science and Technology for Brain Inspired Intelligence, Fudan University, Shanghai
| | - Yan Wu
- Division of Mood Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Enchao Cong
- Division of Mood Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Chuangxin Wu
- Division of Mood Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Lei Ding
- Division of Mood Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Feng Jin
- Division of Mood Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Meihui Qiu
- Department of Medical Psychology, Xinhua Hospital Affiliated to Shanghai Jiao Tong University school of Medicine, Shanghai, China
| | - Yueqi Huang
- Department of Medical Psychology, Hangzhou Seventh People's Hospital, Hangzhou, China
| | - Ye Wu
- Department of Radiology and Biomedical Research Imaging Center (BRIC), University of North Carolina at Chapel Hill, NC, U.S.A
| | - Jinhong Wang
- Department of Medical Imaging, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Shihui Ying
- Department of Mathematics, School of Science, Shanghai University, Shanghai, China.
| | - Daihui Peng
- Division of Mood Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
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16
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Yu Z, Qin J, Xiong X, Xu F, Wang J, Hou F, Yang A. Abnormal topology of brain functional networks in unipolar depression and bipolar disorder using optimal graph thresholding. Prog Neuropsychopharmacol Biol Psychiatry 2020; 96:109758. [PMID: 31493423 DOI: 10.1016/j.pnpbp.2019.109758] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/12/2019] [Revised: 08/09/2019] [Accepted: 09/03/2019] [Indexed: 12/27/2022]
Abstract
Two popular debilitating illness, unipolar depression (UD) and bipolar disorder (BD), have the similar symptoms and tight association on the psychopathological level, leading to a clinical challenge to distinguish them. In order to figure out the underlying common and different mechanism of both mood disorders, resting-state functional magnetic resonance imaging (rs-fMRI) data derived from 36 UD patients, 42 BD patients (specially type I, BD-I) and 45 healthy controls (HC) were analyzed retrospectively in this study. Functional brain networks were firstly constructed on both group and individual levels with a density 0.2, which was determined by a network thresholding approach based on modular similarity. Then we investigated the alterations of modular structure and other topological properties of the functional brain network, including global network characteristics and nodal network measures. The results demonstrated that the functional brain networks of UD and BD-I groups preserved the modularity and small-worldness property. However, compared with HC, reduced number of modules was observed in both patients' groups with shared alterations occurring in hippocampus, para hippocampal gyrus, amygdala and superior parietal gyrus and distinct changes of modular composition mainly in the caudate regions of basal ganglia. Additionally, for the network characteristics, compared to HC, significantly decreased global efficiency and small-worldness were observed in BD-I. For the nodal metrics, significant decrease of local efficiency was found in several regions in both UD and BD-I, while a UD-specified increase of participant coefficient was found in the right paracentral lobule and the right thalamus. These findings may contribute to throw light on the neuropathological mechanisms underlying the two disorders and further help to explore objective biomarkers for the correct diagnosis of UD and BD.
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Affiliation(s)
- Zhinan Yu
- Key Laboratory of Biomedical Functional Materials, School of Science, China Pharmaceutical University, Nanjing 210009, China
| | - Jiaolong Qin
- Key Laboratory of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education, School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing 210094, China
| | - Xinyuan Xiong
- School of Software Institute, Nanjing University, Nanjing 210093, China
| | - Fengguo Xu
- Key Laboratory of Drug Quality Control & Pharmacovigilance, China Pharmaceutical University, Nanjing 210009, China
| | - Jun Wang
- School of Geographic and Biologic Information, Nanjing University of Posts and Telecommunications, Nanjing 210003, China
| | - Fengzhen Hou
- Key Laboratory of Biomedical Functional Materials, School of Science, China Pharmaceutical University, Nanjing 210009, China.
| | - Albert Yang
- Division of Interdisciplinary Medicine and Biotechnology, Department of Medicine, Beth Israel Deaconess Medical Center/Harvard Medical School, Boston, MA, USA
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17
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Long Y, Cao H, Yan C, Chen X, Li L, Castellanos FX, Bai T, Bo Q, Chen G, Chen N, Chen W, Cheng C, Cheng Y, Cui X, Duan J, Fang Y, Gong Q, Guo W, Hou Z, Hu L, Kuang L, Li F, Li K, Li T, Liu Y, Luo Q, Meng H, Peng D, Qiu H, Qiu J, Shen Y, Shi Y, Si T, Wang C, Wang F, Wang K, Wang L, Wang X, Wang Y, Wu X, Wu X, Xie C, Xie G, Xie H, Xie P, Xu X, Yang H, Yang J, Yao J, Yao S, Yin Y, Yuan Y, Zhang A, Zhang H, Zhang K, Zhang L, Zhang Z, Zhou R, Zhou Y, Zhu J, Zou C, Zang Y, Zhao J, Kin-Yuen Chan C, Pu W, Liu Z. Altered resting-state dynamic functional brain networks in major depressive disorder: Findings from the REST-meta-MDD consortium. NEUROIMAGE-CLINICAL 2020; 26:102163. [PMID: 31953148 PMCID: PMC7229351 DOI: 10.1016/j.nicl.2020.102163] [Citation(s) in RCA: 53] [Impact Index Per Article: 13.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/10/2019] [Revised: 11/17/2019] [Accepted: 01/02/2020] [Indexed: 02/05/2023]
Abstract
BACKGROUND Major depressive disorder (MDD) is known to be characterized by altered brain functional connectivity (FC) patterns. However, whether and how the features of dynamic FC would change in patients with MDD are unclear. In this study, we aimed to characterize dynamic FC in MDD using a large multi-site sample and a novel dynamic network-based approach. METHODS Resting-state functional magnetic resonance imaging (fMRI) data were acquired from a total of 460 MDD patients and 473 healthy controls, as a part of the REST-meta-MDD consortium. Resting-state dynamic functional brain networks were constructed for each subject by a sliding-window approach. Multiple spatio-temporal features of dynamic brain networks, including temporal variability, temporal clustering and temporal efficiency, were then compared between patients and healthy subjects at both global and local levels. RESULTS The group of MDD patients showed significantly higher temporal variability, lower temporal correlation coefficient (indicating decreased temporal clustering) and shorter characteristic temporal path length (indicating increased temporal efficiency) compared with healthy controls (corrected p < 3.14×10-3). Corresponding local changes in MDD were mainly found in the default-mode, sensorimotor and subcortical areas. Measures of temporal variability and characteristic temporal path length were significantly correlated with depression severity in patients (corrected p < 0.05). Moreover, the observed between-group differences were robustly present in both first-episode, drug-naïve (FEDN) and non-FEDN patients. CONCLUSIONS Our findings suggest that excessive temporal variations of brain FC, reflecting abnormal communications between large-scale bran networks over time, may underlie the neuropathology of MDD.
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Affiliation(s)
- Yicheng Long
- Department of Psychiatry, The Second Xiangya Hospital, Central South University, 139 Middle Renmin Road, Changsha, Hunan 410011, China; Mental Health Institute, Central South University, Changsha, Hunan 410011, China; Department of Psychiatry, Yale University School of Medicine, New Haven, CT 06511, USA
| | - Hengyi Cao
- Department of Psychology, Yale University, 2 Hillhouse Avenue, New Haven, CT 06511, USA.
| | - Chaogan Yan
- CAS Key Laboratory of Behavioral Science, Institute of Psychology, Beijing 100101, China; Department of Psychology, University of Chinese Academy of Sciences, Beijing 100049, China; Magnetic Resonance Imaging Research Center and Research Center for Lifespan Development of Mind and Brain, Institute of Psychology, Chinese Academy of Sciences, Beijing 100101, China; Department of Child and Adolescent Psychiatry, NYU School of Medicine, New York, NY 10016, USA
| | - Xiao Chen
- CAS Key Laboratory of Behavioral Science, Institute of Psychology, Beijing 100101, China; Department of Psychology, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Le Li
- CAS Key Laboratory of Behavioral Science, Institute of Psychology, Beijing 100101, China; Department of Psychology, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Francisco Xavier Castellanos
- Department of Child and Adolescent Psychiatry, NYU School of Medicine, New York, NY 10016, USA; Nathan Kline Institute for Psychiatric Research, Orangeburg, NY 10962, USA
| | - Tongjian Bai
- The first affiliated hospital of Anhui Medical University, Hefei, Anhui 230022, China
| | - Qijing Bo
- Beijing Anding Hospital, Capital Medical University, Beijing 100088, China
| | - Guanmao Chen
- The First Affiliated Hospital of Jinan University, Guangzhou, Guangdong 510630, China
| | - Ningxuan Chen
- CAS Key Laboratory of Behavioral Science, Institute of Psychology, Beijing 100101, China; Department of Psychology, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Wei Chen
- Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang 310016, China
| | - Chang Cheng
- Department of Psychiatry, The Second Xiangya Hospital, Central South University, 139 Middle Renmin Road, Changsha, Hunan 410011, China
| | - Yuqi Cheng
- Department of Psychiatry, First Affiliated Hospital of Kunming Medical University, Kunming, Yunnan 650032, China
| | - Xilong Cui
- Department of Psychiatry, The Second Xiangya Hospital, Central South University, 139 Middle Renmin Road, Changsha, Hunan 410011, China
| | - Jia Duan
- Department of Psychiatry, First Affiliated Hospital, China Medical University, Shenyang, Liaoning 110001, China
| | - Yiru Fang
- Clinical Research Center and Division of Mood Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai 200030, China
| | - Qiyong Gong
- Huaxi MR Research Center, West China Hospital, Sichuan University, Chengdu, Sichuan 610041, China
| | - Wenbin Guo
- Department of Psychiatry, The Second Xiangya Hospital, Central South University, 139 Middle Renmin Road, Changsha, Hunan 410011, China
| | - Zhenghua Hou
- Department of Psychosomatics and Psychiatry, School of Medicine, Zhongda Hospital, Southeast University, Nanjing, Jiangsu 210096, China
| | - Lan Hu
- Department of Psychiatry, The First Affiliated Hospital of Chongqing Medical University, Chongqing 400016, China
| | - Li Kuang
- Mental Health Center, West China Hospital, Sichuan University, Chengdu, Sichuan 610041, China
| | - Feng Li
- Beijing Anding Hospital, Capital Medical University, Beijing 100088, China
| | - Kaiming Li
- Huaxi MR Research Center, West China Hospital, Sichuan University, Chengdu, Sichuan 610041, China
| | - Tao Li
- Mental Health Center, West China Hospital, Sichuan University, Chengdu, Sichuan 610041, China
| | - Yansong Liu
- Department of Clinical Psychology, Suzhou Psychiatric Hospital, The Affiliated Guangji Hospital of Soochow University, Suzhou, Jiangsu 215137, China
| | - Qinghua Luo
- Department of Psychiatry, The First Affiliated Hospital of Chongqing Medical University, Chongqing 400016, China
| | - Huaqing Meng
- Department of Psychiatry, The First Affiliated Hospital of Chongqing Medical University, Chongqing 400016, China
| | - Daihui Peng
- Clinical Research Center and Division of Mood Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai 200030, China
| | - Haitang Qiu
- Department of Psychiatry, The First Affiliated Hospital of Chongqing Medical University, Chongqing 400016, China
| | - Jiang Qiu
- Faculty of Psychology, Southwest University, Chongqing 400716, China
| | - Yuedi Shen
- Department of Diagnostics, Affiliated Hospital, Medical School, Hangzhou Normal University, Hangzhou, Zhejiang 311121, China
| | - Yushu Shi
- Department of Radiology, The First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, Zhejiang 310058, China
| | - Tianmei Si
- National Clinical Research Center for Mental Disorders, Peking University Sixth Hospital, Beijing 100191, China
| | - Chuanyue Wang
- Beijing Anding Hospital, Capital Medical University, Beijing 100088, China
| | - Fei Wang
- Department of Psychiatry, First Affiliated Hospital, China Medical University, Shenyang, Liaoning 110001, China
| | - Kai Wang
- The first affiliated hospital of Anhui Medical University, Hefei, Anhui 230022, China
| | - Li Wang
- National Clinical Research Center for Mental Disorders, Peking University Sixth Hospital, Beijing 100191, China
| | - Xiang Wang
- Department of Psychiatry, The Second Xiangya Hospital, Central South University, 139 Middle Renmin Road, Changsha, Hunan 410011, China
| | - Ying Wang
- The First Affiliated Hospital of Jinan University, Guangzhou, Guangdong 510630, China
| | - Xiaoping Wu
- Xi'an Central Hospital, Xi'an, Shaanxi 710003, China
| | - Xinran Wu
- Department of Radiology, The First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, Zhejiang 310058, China
| | - Chunming Xie
- Department of Neurology, Affiliated Zhongda Hospital of Southeast University, Nanjing, Jiangsu 210009, China
| | - Guangrong Xie
- Department of Psychiatry, The Second Xiangya Hospital, Central South University, 139 Middle Renmin Road, Changsha, Hunan 410011, China
| | - Haiyan Xie
- Department of Psychiatry, The Fourth Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, Zhejiang 310058, 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
| | - Xiufeng Xu
- Department of Psychiatry, First Affiliated Hospital of Kunming Medical University, Kunming, Yunnan 650032, China
| | - Hong Yang
- Department of Radiology, The First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, Zhejiang 310058, China
| | - Jian Yang
- The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, Shaanxi 710061, China
| | - Jiashu Yao
- Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang 310016, China
| | - Shuqiao Yao
- Department of Psychiatry, The Second Xiangya Hospital, Central South University, 139 Middle Renmin Road, Changsha, Hunan 410011, China
| | - Yingying Yin
- Department of Psychosomatics and Psychiatry, School of Medicine, Zhongda Hospital, Southeast University, Nanjing, Jiangsu 210096, China
| | - Yonggui Yuan
- Department of Psychosomatics and Psychiatry, School of Medicine, Zhongda Hospital, Southeast University, Nanjing, Jiangsu 210096, China
| | - Aixia Zhang
- First Hospital of Shanxi Medical University, Taiyuan, Shanxi 030001, China
| | - Hong Zhang
- Xi'an Central Hospital, Xi'an, Shaanxi 710003, China
| | - Kerang Zhang
- First Hospital of Shanxi Medical University, Taiyuan, Shanxi 030001, China
| | - Lei Zhang
- CAS Key Laboratory of Behavioral Science, Institute of Psychology, Beijing 100101, China; Department of Psychology, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Zhijun Zhang
- Xi'an Central Hospital, Xi'an, Shaanxi 710003, China
| | - Rubai Zhou
- Clinical Research Center and Division of Mood Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai 200030, China
| | - Yiting Zhou
- Mental Health Center, West China Hospital, Sichuan University, Chengdu, Sichuan 610041, China
| | - Junjuan Zhu
- Clinical Research Center and Division of Mood Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai 200030, China
| | - Chaojie Zou
- Department of Psychiatry, First Affiliated Hospital of Kunming Medical University, Kunming, Yunnan 650032, China
| | - Yufeng Zang
- Center for Cognition and Brain Disorders, Institutes of Psychological Sciences, Hangzhou Normal University, Hangzhou, Zhejiang 311121, China; Zhejiang Key Laboratory for Research in Assessment of Cognitive Impairments, Hangzhou, Zhejiang 311121, China
| | - Jingping Zhao
- Department of Psychiatry, The Second Xiangya Hospital, Central South University, 139 Middle Renmin Road, Changsha, Hunan 410011, China
| | | | - Weidan Pu
- Medical Psychological Institute, Second Xiangya Hospital, Central South University, Changsha, Hunan 410011, China
| | - Zhening Liu
- Department of Psychiatry, The Second Xiangya Hospital, Central South University, 139 Middle Renmin Road, Changsha, Hunan 410011, China; Mental Health Institute, Central South University, Changsha, Hunan 410011, China.
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18
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Shao J, Dai Z, Zhu R, Wang X, Tao S, Bi K, Tian S, Wang H, Sun Y, Yao Z, Lu Q. Early identification of bipolar from unipolar depression before manic episode: Evidence from dynamic rfMRI. Bipolar Disord 2019; 21:774-784. [PMID: 31407477 DOI: 10.1111/bdi.12819] [Citation(s) in RCA: 26] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/01/2023]
Abstract
OBJECTIVE Misdiagnosis of bipolar disorder (BD) as unipolar disorder (UD) may cause improper treatment strategy to be chosen, especially in the early stages of disease. The aim of this study was to characterize alterations in specific brain networks for depressed patients who transformed into BD (tBD) from UD. METHOD The module allegiance from resting-fMRI by applying a multilayer modular method was estimated in 99 patients (33 tBD, 33 BD, 33 UD) and 33 healthy controls (HC). A classification model was trained on tBD and UD patients. HC was used to explore the functional declination patterns of BD, tBD, and UD. RESULTS Based on our classification model, difference mainly reflected in default-mode network (DMN). Compared with HC, both BD and tBD focused on the difference of somatomotor network (SMN), while UD on the abnormity of DMN. The patterns of brain network between patients with BD and tBD were well-overlapped, except for cognitive control network (CCN). CONCLUSION The functional declination of internal interaction in DMN was suggested to be useful for the identification of BD from UD in the early stage. The higher recruitment of DMN may predispose patients to depressive states, while higher recruitment of SMN makes them more sensitive to external stimuli and prone to mania. Furthermore, CCN may be a critical network for identifying different stages of BD, suggesting that the onset of mania in depressed patients is accompanied by CCN related cognitive impairments.
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Affiliation(s)
- Junneng Shao
- School of Biological Sciences & Medical Engineering, Southeast University, Nanjing, China.,Child Development and Learning Science, Key Laboratory of Ministry of Education, Nanjing, China
| | - Zhongpeng Dai
- School of Biological Sciences & Medical Engineering, Southeast University, Nanjing, China.,Child Development and Learning Science, Key Laboratory of Ministry of Education, Nanjing, China
| | - Rongxin Zhu
- Department of Psychiatry, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China.,Nanjing Brain Hospital, Medical School of Nanjing University, Nanjing, China
| | - Xinyi Wang
- School of Biological Sciences & Medical Engineering, Southeast University, Nanjing, China.,Child Development and Learning Science, Key Laboratory of Ministry of Education, Nanjing, China
| | - Shiwan Tao
- Department of Psychiatry, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China.,Nanjing Brain Hospital, Medical School of Nanjing University, Nanjing, China
| | - Kun Bi
- School of Biological Sciences & Medical Engineering, Southeast University, Nanjing, China.,Child Development and Learning Science, Key Laboratory of Ministry of Education, Nanjing, China
| | - Shui Tian
- School of Biological Sciences & Medical Engineering, Southeast University, Nanjing, China.,Child Development and Learning Science, Key Laboratory of Ministry of Education, Nanjing, China
| | - Huan Wang
- School of Biological Sciences & Medical Engineering, Southeast University, Nanjing, China.,Child Development and Learning Science, Key Laboratory of Ministry of Education, Nanjing, China
| | - Yurong Sun
- School of Biological Sciences & Medical Engineering, Southeast University, Nanjing, China.,Child Development and Learning Science, Key Laboratory of Ministry of Education, Nanjing, China
| | - Zhijian Yao
- Department of Psychiatry, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China.,Nanjing Brain Hospital, Medical School of Nanjing University, Nanjing, China
| | - Qing Lu
- School of Biological Sciences & Medical Engineering, Southeast University, Nanjing, China.,Child Development and Learning Science, Key Laboratory of Ministry of Education, Nanjing, China
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19
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Association between dynamic resting-state functional connectivity and ketamine plasma levels in visual processing networks. Sci Rep 2019; 9:11484. [PMID: 31391479 PMCID: PMC6685940 DOI: 10.1038/s41598-019-46702-x] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2018] [Accepted: 06/26/2019] [Indexed: 12/25/2022] Open
Abstract
Numerous studies demonstrate ketamine’s influence on resting-state functional connectivity (rsFC). Seed-based and static rsFC estimation methods may oversimplify FC. These limitations can be addressed with whole-brain, dynamic rsFC estimation methods. We assessed data from 27 healthy subjects who underwent two 3 T resting-state fMRI scans, once under subanesthetic, intravenous esketamine and once under placebo, in a randomized, cross-over manner. We aimed to isolate only highly robust effects of esketamine on dynamic rsFC by using eight complementary methodologies derived from two dynamic rsFC estimation methods, two functionally defined atlases and two statistical measures. All combinations revealed a negative influence of esketamine on dynamic rsFC within the left visual network and inter-hemispherically between visual networks (p < 0.05, corrected), hereby suggesting that esketamine’s influence on dynamic rsFC is highly stable in visual processing networks. Our findings may be reflective of ketamine’s role as a model for psychosis, a disorder associated with alterations to visual processing and impaired inter-hemispheric connectivity. Ketamine is a highly effective antidepressant and studies have shown changes to sensory processing in depression. Dynamic rsFC in sensory processing networks might be a promising target for future investigations of ketamine’s antidepressant properties. Mechanistically, sensitivity of visual networks for esketamine’s effects may result from their high expression of NMDA-receptors.
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20
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Geng X, Xu J, Liu B, Shi Y. Multivariate Classification of Major Depressive Disorder Using the Effective Connectivity and Functional Connectivity. Front Neurosci 2018; 12:38. [PMID: 29515348 PMCID: PMC5825897 DOI: 10.3389/fnins.2018.00038] [Citation(s) in RCA: 40] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2017] [Accepted: 01/16/2018] [Indexed: 12/29/2022] Open
Abstract
Major depressive disorder (MDD) is a mental disorder characterized by at least 2 weeks of low mood, which is present across most situations. Diagnosis of MDD using rest-state functional magnetic resonance imaging (fMRI) data faces many challenges due to the high dimensionality, small samples, noisy and individual variability. To our best knowledge, no studies aim at classification with effective connectivity and functional connectivity measures between MDD patients and healthy controls. In this study, we performed a data-driving classification analysis using the whole brain connectivity measures which included the functional connectivity from two brain templates and effective connectivity measures created by the default mode network (DMN), dorsal attention network (DAN), frontal-parietal network (FPN), and silence network (SN). Effective connectivity measures were extracted using spectral Dynamic Causal Modeling (spDCM) and transformed into a vectorial feature space. Linear Support Vector Machine (linear SVM), non-linear SVM, k-Nearest Neighbor (KNN), and Logistic Regression (LR) were used as the classifiers to identify the differences between MDD patients and healthy controls. Our results showed that the highest accuracy achieved 91.67% (p < 0.0001) when using 19 effective connections and 89.36% when using 6,650 functional connections. The functional connections with high discriminative power were mainly located within or across the whole brain resting-state networks while the discriminative effective connections located in several specific regions, such as posterior cingulate cortex (PCC), ventromedial prefrontal cortex (vmPFC), dorsal cingulate cortex (dACC), and inferior parietal lobes (IPL). To further compare the discriminative power of functional connections and effective connections, a classification analysis only using the functional connections from those four networks was conducted and the highest accuracy achieved 78.33% (p < 0.0001). Our study demonstrated that the effective connectivity measures might play a more important role than functional connectivity in exploring the alterations between patients and health controls and afford a better mechanistic interpretability. Moreover, our results showed a diagnostic potential of the effective connectivity for the diagnosis of MDD patients with high accuracies allowing for earlier prevention or intervention.
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Affiliation(s)
- Xiangfei Geng
- Tianjin Key Laboratory of Cognitive Computing and Application, School of Computer Science and Technology, Tianjin University, Tianjin, China
| | - Junhai Xu
- Tianjin Key Laboratory of Cognitive Computing and Application, School of Computer Science and Technology, Tianjin University, Tianjin, China
- Laboratory of Neural Imaging, Keck School of Medicine, USC Stevens Neuroimaging and Informatics Institute, University of Southern California, Los Angeles, CA, United States
| | - Baolin Liu
- Tianjin Key Laboratory of Cognitive Computing and Application, School of Computer Science and Technology, Tianjin University, Tianjin, China
- State Key Laboratory of Intelligent Technology and Systems, National Laboratory for Information Science and Technology, Tsinghua University, Beijing, China
| | - Yonggang Shi
- Laboratory of Neural Imaging, Keck School of Medicine, USC Stevens Neuroimaging and Informatics Institute, University of Southern California, Los Angeles, CA, United States
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21
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Khambhati AN, Mattar MG, Wymbs NF, Grafton ST, Bassett DS. Beyond modularity: Fine-scale mechanisms and rules for brain network reconfiguration. Neuroimage 2017; 166:385-399. [PMID: 29138087 DOI: 10.1016/j.neuroimage.2017.11.015] [Citation(s) in RCA: 33] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2017] [Revised: 10/18/2017] [Accepted: 11/07/2017] [Indexed: 11/15/2022] Open
Abstract
The human brain is in constant flux, as distinct areas engage in transient communication to support basic behaviors as well as complex cognition. The collection of interactions between cortical and subcortical areas forms a functional brain network whose topology evolves with time. Despite the nontrivial dynamics that are germane to this networked system, experimental evidence demonstrates that functional interactions organize into putative brain systems that facilitate different facets of cognitive computation. We hypothesize that such dynamic functional networks are organized around a set of rules that constrain their spatial architecture - which brain regions may functionally interact - and their temporal architecture - how these interactions fluctuate over time. To objectively uncover these organizing principles, we apply an unsupervised machine learning approach called non-negative matrix factorization to time-evolving, resting state functional networks in 20 healthy subjects. This machine learning approach automatically groups temporally co-varying functional interactions into subgraphs that represent putative topological modes of dynamic functional architecture. We find that subgraphs are stratified based on both the underlying modular organization and the topographical distance of their strongest interactions: while many subgraphs are largely contained within modules, others span between modules and are expressed differently over time. The relationship between dynamic subgraphs and modular architecture is further highlighted by the ability of time-varying subgraph expression to explain inter-individual differences in module reorganization. Collectively, these results point to the critical role that subgraphs play in constraining the topography and topology of functional brain networks. More broadly, this machine learning approach opens a new door for understanding the architecture of dynamic functional networks during both task and rest states, and for probing alterations of that architecture in disease.
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Affiliation(s)
- Ankit N Khambhati
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Marcelo G Mattar
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Psychology, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Nicholas F Wymbs
- Department of Physical Medicine and Rehabilitation, Johns Hopkins Medical Institution, Baltimore, MD 21205, USA
| | - Scott T Grafton
- Department of Psychological and Brain Sciences, University of California, Santa Barbara, CA 93106, USA
| | - Danielle S Bassett
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Electrical and Systems Engineering, University of Pennsylvania, Philadelphia, PA 19104 USA.
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