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Gao Z, Xiao Y, Zhu F, Tao B, Zhao Q, Yu W, Sweeney JA, Gong Q, Lui S. Multilayer network analysis reveals instability of brain dynamics in untreated first-episode schizophrenia. Cereb Cortex 2024; 34:bhae402. [PMID: 39375878 DOI: 10.1093/cercor/bhae402] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2024] [Revised: 09/10/2024] [Accepted: 09/18/2024] [Indexed: 10/09/2024] Open
Abstract
Although aberrant static functional brain network activity has been reported in schizophrenia, little is known about how the dynamics of neural function are altered in first-episode schizophrenia and are modulated by antipsychotic treatment. The baseline resting-state functional magnetic resonance imaging data were acquired from 122 first-episode drug-naïve schizophrenia patients and 128 healthy controls (HCs), and 44 patients were rescanned after 1-year of antipsychotic treatment. Multilayer network analysis was applied to calculate the network switching rates between brain states. Compared to HCs, schizophrenia patients at baseline showed significantly increased network switching rates. This effect was observed mainly in the sensorimotor (SMN) and dorsal attention networks (DAN), and in temporal and parietal regions at the nodal level. Switching rates were reduced after 1-year of antipsychotic treatment at the global level and in DAN. Switching rates at baseline at the global level and in the inferior parietal lobule were correlated with the treatment-related reduction of negative symptoms. These findings suggest that instability of functional network activity plays an important role in the pathophysiology of acute psychosis in early-stage schizophrenia. The normalization of network stability after antipsychotic medication suggests that this effect may represent a systems-level mechanism for their therapeutic efficacy.
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Affiliation(s)
- Ziyang Gao
- Department of Radiology, and Functional and Molecular Imaging key Laboratory of Sichuan Province, West China Hospital of Sichuan University, Guoxuexiang 37#, Wuhou, Chengdu 610041, China
- Huaxi MR Research Center (HMRRC), West China Hospital of Sichuan University, Guoxuexiang 37#, Wuhou, Chengdu 610041, China
- Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu 610041, Guoxuexiang 37#, Wuhou, China
| | - Yuan Xiao
- Department of Radiology, and Functional and Molecular Imaging key Laboratory of Sichuan Province, West China Hospital of Sichuan University, Guoxuexiang 37#, Wuhou, Chengdu 610041, China
- Huaxi MR Research Center (HMRRC), West China Hospital of Sichuan University, Guoxuexiang 37#, Wuhou, Chengdu 610041, China
- Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu 610041, Guoxuexiang 37#, Wuhou, China
| | - Fei Zhu
- Department of Radiology, and Functional and Molecular Imaging key Laboratory of Sichuan Province, West China Hospital of Sichuan University, Guoxuexiang 37#, Wuhou, Chengdu 610041, China
- Huaxi MR Research Center (HMRRC), West China Hospital of Sichuan University, Guoxuexiang 37#, Wuhou, Chengdu 610041, China
- Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu 610041, Guoxuexiang 37#, Wuhou, China
| | - Bo Tao
- Department of Radiology, and Functional and Molecular Imaging key Laboratory of Sichuan Province, West China Hospital of Sichuan University, Guoxuexiang 37#, Wuhou, Chengdu 610041, China
- Huaxi MR Research Center (HMRRC), West China Hospital of Sichuan University, Guoxuexiang 37#, Wuhou, Chengdu 610041, China
- Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu 610041, Guoxuexiang 37#, Wuhou, China
| | - Qiannan Zhao
- Department of Radiology, and Functional and Molecular Imaging key Laboratory of Sichuan Province, West China Hospital of Sichuan University, Guoxuexiang 37#, Wuhou, Chengdu 610041, China
- Huaxi MR Research Center (HMRRC), West China Hospital of Sichuan University, Guoxuexiang 37#, Wuhou, Chengdu 610041, China
- Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu 610041, Guoxuexiang 37#, Wuhou, China
| | - Wei Yu
- Department of Radiology, and Functional and Molecular Imaging key Laboratory of Sichuan Province, West China Hospital of Sichuan University, Guoxuexiang 37#, Wuhou, Chengdu 610041, China
- Huaxi MR Research Center (HMRRC), West China Hospital of Sichuan University, Guoxuexiang 37#, Wuhou, Chengdu 610041, China
- Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu 610041, Guoxuexiang 37#, Wuhou, China
| | - John A Sweeney
- Huaxi MR Research Center (HMRRC), West China Hospital of Sichuan University, Guoxuexiang 37#, Wuhou, Chengdu 610041, China
- Department of Psychiatry and Behavioral Neuroscience, University of Cincinnati College of Medicine, 260 Stetson Street, Cincinnati, OH 45219, United States
| | - Qiyong Gong
- Department of Radiology, and Functional and Molecular Imaging key Laboratory of Sichuan Province, West China Hospital of Sichuan University, Guoxuexiang 37#, Wuhou, Chengdu 610041, China
- Huaxi MR Research Center (HMRRC), West China Hospital of Sichuan University, Guoxuexiang 37#, Wuhou, Chengdu 610041, China
- Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu 610041, Guoxuexiang 37#, Wuhou, China
| | - Su Lui
- Department of Radiology, and Functional and Molecular Imaging key Laboratory of Sichuan Province, West China Hospital of Sichuan University, Guoxuexiang 37#, Wuhou, Chengdu 610041, China
- Huaxi MR Research Center (HMRRC), West China Hospital of Sichuan University, Guoxuexiang 37#, Wuhou, Chengdu 610041, China
- Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu 610041, Guoxuexiang 37#, Wuhou, China
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He R, Alonso‐Sánchez MF, Sepulcre J, Palaniyappan L, Hinzen W. Changes in the structure of spontaneous speech predict the disruption of hierarchical brain organization in first-episode psychosis. Hum Brain Mapp 2024; 45:e70030. [PMID: 39301700 PMCID: PMC11413563 DOI: 10.1002/hbm.70030] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2024] [Revised: 04/29/2024] [Accepted: 09/04/2024] [Indexed: 09/22/2024] Open
Abstract
Psychosis implicates changes across a broad range of cognitive functions. These functions are cortically organized in the form of a hierarchy ranging from primary sensorimotor (unimodal) to higher-order association cortices, which involve functions such as language (transmodal). Language has long been documented as undergoing structural changes in psychosis. We hypothesized that these changes as revealed in spontaneous speech patterns may act as readouts of alterations in the configuration of this unimodal-to-transmodal axis of cortical organization in psychosis. Results from 29 patients with first-episodic psychosis (FEP) and 29 controls scanned with 7 T resting-state fMRI confirmed a compression of the cortical hierarchy in FEP, which affected metrics of the hierarchical distance between the sensorimotor and default mode networks, and of the hierarchical organization within the semantic network. These organizational changes were predicted by graphs representing semantic and syntactic associations between meaningful units in speech produced during picture descriptions. These findings unite psychosis, language, and the cortical hierarchy in a single conceptual scheme, which helps to situate language within the neurocognition of psychosis and opens the clinical prospect for mental dysfunction to become computationally measurable in spontaneous speech.
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Affiliation(s)
- Rui He
- Department of Translation and Language SciencesUniversitat Pompeu FabraBarcelonaSpain
| | | | - Jorge Sepulcre
- Department of Radiology and Biomedical Imaging, Yale PET Center, Yale School of MedicineYale UniversityNew HavenConnecticutUSA
| | - Lena Palaniyappan
- Douglas Mental Health University Institute, Department of PsychiatryMcGill UniversityMontrealQuebecCanada
- Department of Medical Biophysics, Schulich School of Medicine and DentistryWestern UniversityLondonOntarioCanada
- Robarts Research Institute, Schulich School of Medicine and DentistryWestern UniversityLondonOntarioCanada
| | - Wolfram Hinzen
- Department of Translation and Language SciencesUniversitat Pompeu FabraBarcelonaSpain
- Intitut Català de Recerca i Estudis Avançats (ICREA)BarcelonaSpain
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Yang B, Liu H, Jiang T, Yu S. Fluctuation in cortical excitation/inhibition modulates capability of attention across time scales ranging from hours to seconds. Cereb Cortex 2024; 34:bhae309. [PMID: 39076112 DOI: 10.1093/cercor/bhae309] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2024] [Revised: 07/04/2024] [Accepted: 07/13/2024] [Indexed: 07/31/2024] Open
Abstract
Sustained attention, as the basis of general cognitive ability, naturally varies across different time scales, spanning from hours, e.g. from wakefulness to drowsiness state, to seconds, e.g. trial-by-trail fluctuation in a task session. Whether there is a unified mechanism underneath such trans-scale variability remains unclear. Here we show that fluctuation of cortical excitation/inhibition (E/I) is a strong modulator to sustained attention in humans across time scales. First, we observed the ability to attend varied across different brain states (wakefulness, postprandial somnolence, sleep deprived), as well as within any single state with larger swings. Second, regardless of the time scale involved, we found highly attentive state was always linked to more balanced cortical E/I characterized by electroencephalography (EEG) features, while deviations from the balanced state led to temporal decline in attention, suggesting the fluctuation of cortical E/I as a common mechanism underneath trans-scale attentional variability. Furthermore, we found the variations of both sustained attention and cortical E/I indices exhibited fractal structure in the temporal domain, exhibiting features of self-similarity. Taken together, these results demonstrate that sustained attention naturally varies across different time scales in a more complex way than previously appreciated, with the cortical E/I as a shared neurophysiological modulator.
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Affiliation(s)
- Binghao Yang
- Laboratory of Brain Atlas and Brain-inspired Intelligence, Institute of Automation, Chinese Academy of Sciences, No. 95, Zhongguancun East Road, Haidian District, Beijing 100190, China
- School of Future Technology, University of Chinese Academy of Sciences, No. 19, Yuquan Road, Shijingshan District, Beijing 100049, China
- Key Laboratory of Brain Cognition and Brain-inspired Intelligence Technology, Chinese Academy of Sciences, No. 230, Yueyang Road, Shanghai 200031, China
| | - Hao Liu
- Laboratory of Brain Atlas and Brain-inspired Intelligence, Institute of Automation, Chinese Academy of Sciences, No. 95, Zhongguancun East Road, Haidian District, Beijing 100190, China
- Key Laboratory of Brain Cognition and Brain-inspired Intelligence Technology, Chinese Academy of Sciences, No. 230, Yueyang Road, Shanghai 200031, China
- School of Artificial Intelligence, University of Chinese Academy of Sciences, No. 19, Yuquan Road, Shijingshan District, Beijing 100049, China
| | - Tianzi Jiang
- Laboratory of Brain Atlas and Brain-inspired Intelligence, Institute of Automation, Chinese Academy of Sciences, No. 95, Zhongguancun East Road, Haidian District, Beijing 100190, China
- Key Laboratory of Brain Cognition and Brain-inspired Intelligence Technology, Chinese Academy of Sciences, No. 230, Yueyang Road, Shanghai 200031, China
- School of Artificial Intelligence, University of Chinese Academy of Sciences, No. 19, Yuquan Road, Shijingshan District, Beijing 100049, China
- Xiaoxiang Institute for Brain Health and Yongzhou Central Hospital, No. 151, Xiaoshui West Road, Lingling District, Yongzhou 425000, Hunan Province, China
| | - Shan Yu
- Laboratory of Brain Atlas and Brain-inspired Intelligence, Institute of Automation, Chinese Academy of Sciences, No. 95, Zhongguancun East Road, Haidian District, Beijing 100190, China
- School of Future Technology, University of Chinese Academy of Sciences, No. 19, Yuquan Road, Shijingshan District, Beijing 100049, China
- Key Laboratory of Brain Cognition and Brain-inspired Intelligence Technology, Chinese Academy of Sciences, No. 230, Yueyang Road, Shanghai 200031, China
- Lead contact. Laboratory of Brain Atlas and Brain-inspired Intelligence, Institute of Automation, Chinese Academy of Sciences, No. 95, Zhongguancun East Road, Haidian District, Beijing 100190, China
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Saberi A, Wischnewski KJ, Jung K, Lotter LD, Schaare HL, Banaschewski T, Barker GJ, Bokde ALW, Desrivières S, Flor H, Grigis A, Garavan H, Gowland P, Heinz A, Brühl R, Martinot JL, Martinot MLP, Artiges E, Nees F, Orfanos DP, Lemaitre H, Poustka L, Hohmann S, Holz N, Baeuchl C, Smolka MN, Vaidya N, Walter H, Whelan R, Schumann G, Paus T, Dukart J, Bernhardt BC, Popovych OV, Eickhoff SB, Valk SL. Adolescent maturation of cortical excitation-inhibition balance based on individualized biophysical network modeling. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.06.18.599509. [PMID: 38948771 PMCID: PMC11213014 DOI: 10.1101/2024.06.18.599509] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/02/2024]
Abstract
The balance of excitation and inhibition is a key functional property of cortical microcircuits which changes through the lifespan. Adolescence is considered a crucial period for the maturation of excitation-inhibition balance. This has been primarily observed in animal studies, yet human in vivo evidence on adolescent maturation of the excitation-inhibition balance at the individual level is limited. Here, we developed an individualized in vivo marker of regional excitation-inhibition balance in human adolescents, estimated using large-scale simulations of biophysical network models fitted to resting-state functional magnetic resonance imaging data from two independent cross-sectional (N = 752) and longitudinal (N = 149) cohorts. We found a widespread relative increase of inhibition in association cortices paralleled by a relative age-related increase of excitation, or lack of change, in sensorimotor areas across both datasets. This developmental pattern co-aligned with multiscale markers of sensorimotor-association differentiation. The spatial pattern of excitation-inhibition development in adolescence was robust to inter-individual variability of structural connectomes and modeling configurations. Notably, we found that alternative simulation-based markers of excitation-inhibition balance show a variable sensitivity to maturational change. Taken together, our study highlights an increase of inhibition during adolescence in association areas using cross sectional and longitudinal data, and provides a robust computational framework to estimate microcircuit maturation in vivo at the individual level.
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Affiliation(s)
- Amin Saberi
- Institute of Neuroscience and Medicine - Brain and Behaviour (INM-7), Research Centre Jülich, Jülich, Germany
- Institute of Systems Neuroscience, Medical Faculty and University Hospital Düsseldorf, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
- Otto Hahn Group Cognitive Neurogenetics, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
| | - Kevin J Wischnewski
- Institute of Neuroscience and Medicine - Brain and Behaviour (INM-7), Research Centre Jülich, Jülich, Germany
- Institute of Systems Neuroscience, Medical Faculty and University Hospital Düsseldorf, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
- Institute of Mathematics, Faculty of Mathematics and Natural Sciences, Heinrich Heine University Düsseldorf, Dusseldorf, Germany
| | - Kyesam Jung
- Institute of Neuroscience and Medicine - Brain and Behaviour (INM-7), Research Centre Jülich, Jülich, Germany
- Institute of Systems Neuroscience, Medical Faculty and University Hospital Düsseldorf, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Leon D Lotter
- Institute of Neuroscience and Medicine - Brain and Behaviour (INM-7), Research Centre Jülich, Jülich, Germany
- Institute of Systems Neuroscience, Medical Faculty and University Hospital Düsseldorf, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
- Max Planck School of Cognition, Stephanstrasse 1A, 04103 Leipzig, Germany
| | - H Lina Schaare
- Institute of Systems Neuroscience, Medical Faculty and University Hospital Düsseldorf, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
- Otto Hahn Group Cognitive Neurogenetics, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
| | - Tobias Banaschewski
- Department of Child and Adolescent Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Square J5, 68159 Mannheim, Germany
| | - Gareth J Barker
- Department of Neuroimaging, Institute of Psychiatry, Psychology & Neuroscience, King's College London, United Kingdom
| | - Arun L W Bokde
- Discipline of Psychiatry, School of Medicine and Trinity College Institute of Neuroscience, Trinity College Dublin, Dublin, Ireland
| | - Sylvane Desrivières
- Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology & Neuroscience, King's College London, United Kingdom
| | - Herta Flor
- Institute of Cognitive and Clinical Neuroscience, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Square J5, Mannheim, Germany
- Department of Psychology, School of Social Sciences, University of Mannheim, 68131 Mannheim, Germany
| | - Antoine Grigis
- NeuroSpin, CEA, Université Paris-Saclay, F-91191 Gif-sur-Yvette, France
| | - Hugh Garavan
- Departments of Psychiatry and Psychology, University of Vermont, 05405 Burlington, Vermont, USA
| | - Penny Gowland
- Sir Peter Mansfield Imaging Centre School of Physics and Astronomy, University of Nottingham, University Park, Nottingham, United Kingdom
| | - Andreas Heinz
- Department of Psychiatry and Psychotherapy CCM, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Berlin, Germany
- German Center for Mental Health (DZPG), site Berlin-Potsdam, Germany
| | - Rüdiger Brühl
- Physikalisch-Technische Bundesanstalt (PTB), Braunschweig and Berlin, Germany
| | - Jean-Luc Martinot
- Institut National de la Santé et de la Recherche Médicale, INSERM U A10 "Trajectoires développementales en psychiatrie"; Université Paris-Saclay, Ecole Normale supérieure Paris-Saclay, CNRS, Centre Borelli; Gif-sur-Yvette, France
| | - Marie-Laure Paillère Martinot
- Institut National de la Santé et de la Recherche Médicale, INSERM U A10 "Trajectoires développementales en psychiatrie"; Université Paris-Saclay, Ecole Normale supérieure Paris-Saclay, CNRS, Centre Borelli; Gif-sur-Yvette, France
- AP-HP. Sorbonne Université, Department of Child and Adolescent Psychiatry, Pitié-Salpêtrière Hospital, Paris, France
| | - Eric Artiges
- Institut National de la Santé et de la Recherche Médicale, INSERM U A10 "Trajectoires développementales en psychiatrie"; Université Paris-Saclay, Ecole Normale supérieure Paris-Saclay, CNRS, Centre Borelli; Gif-sur-Yvette, France
- Psychiatry Department, EPS Barthélémy Durand, Etampes, France
| | - Frauke Nees
- Department of Child and Adolescent Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Square J5, 68159 Mannheim, Germany
- Institute of Cognitive and Clinical Neuroscience, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Square J5, Mannheim, Germany
- Institute of Medical Psychology and Medical Sociology, University Medical Center Schleswig-Holstein, Kiel University, Kiel, Germany
| | | | - Herve Lemaitre
- NeuroSpin, CEA, Université Paris-Saclay, F-91191 Gif-sur-Yvette, France
- Institut des Maladies Neurodégénératives, UMR 5293, CNRS, CEA, Université de Bordeaux, 33076 Bordeaux, France
| | - Luise Poustka
- Department of Child and Adolescent Psychiatry, Center for Psychosocial Medicine, University Hospital Heidelberg, Heidelberg, Germany
| | - Sarah Hohmann
- Department of Child and Adolescent Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Square J5, 68159 Mannheim, Germany
| | - Nathalie Holz
- Department of Child and Adolescent Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Square J5, 68159 Mannheim, Germany
| | - Christian Baeuchl
- Department of Psychiatry and Psychotherapy, Technische Universität Dresden, Dresden, Germany
| | - Michael N Smolka
- Department of Psychiatry and Psychotherapy, Technische Universität Dresden, Dresden, Germany
| | - Nilakshi Vaidya
- Centre for Population Neuroscience and Stratified Medicine (PONS), Department of Psychiatry and Psychotherapy, Charité Universitätsmedizin Berlin, Germany
| | - Henrik Walter
- Department of Psychiatry and Psychotherapy CCM, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Berlin, Germany
| | - Robert Whelan
- School of Psychology and Global Brain Health Institute, Trinity College Dublin, Ireland
| | - Gunter Schumann
- Centre for Population Neuroscience and Stratified Medicine (PONS), Department of Psychiatry and Psychotherapy, Charité Universitätsmedizin Berlin, Germany
- Centre for Population Neuroscience and Precision Medicine (PONS), Institute for Science and Technology of Brain-inspired Intelligence (ISTBI), Fudan University, Shanghai, China
| | - Tomáš Paus
- Departments of Psychiatry and Neuroscience, Faculty of Medicine and Centre Hospitalier Universitaire Sainte-Justine, University of Montreal, Montreal, Quebec, Canada
| | - Juergen Dukart
- Institute of Neuroscience and Medicine - Brain and Behaviour (INM-7), Research Centre Jülich, Jülich, Germany
- Institute of Systems Neuroscience, Medical Faculty and University Hospital Düsseldorf, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Boris C Bernhardt
- Multimodal Imaging and Connectome Analysis Laboratory, McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, Canada
| | - Oleksandr V Popovych
- Institute of Neuroscience and Medicine - Brain and Behaviour (INM-7), Research Centre Jülich, Jülich, Germany
- Institute of Systems Neuroscience, Medical Faculty and University Hospital Düsseldorf, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Simon B Eickhoff
- Institute of Neuroscience and Medicine - Brain and Behaviour (INM-7), Research Centre Jülich, Jülich, Germany
- Institute of Systems Neuroscience, Medical Faculty and University Hospital Düsseldorf, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Sofie L Valk
- Institute of Neuroscience and Medicine - Brain and Behaviour (INM-7), Research Centre Jülich, Jülich, Germany
- Institute of Systems Neuroscience, Medical Faculty and University Hospital Düsseldorf, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
- Otto Hahn Group Cognitive Neurogenetics, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
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Zhang H, Wang Z, Qiao X, Peng N, Wu J, Chen Y, Cheng C. Unveiling the therapeutic potential of IHMT-337 in glioma treatment: targeting the EZH2-SLC12A5 axis. Mol Med 2024; 30:91. [PMID: 38886655 PMCID: PMC11184773 DOI: 10.1186/s10020-024-00857-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2024] [Accepted: 06/07/2024] [Indexed: 06/20/2024] Open
Abstract
Glioma is the most common malignant tumor of the central nervous system, with EZH2 playing a crucial regulatory role. This study further explores the abnormal expression of EZH2 and its mechanisms in regulating glioma progression. Additionally, it was found that IHMT-337 can potentially be a therapeutic agent for glioma. The prognosis, expression, and localization of EZH2 were determined using bioinformatics, IHC staining, Western blot (WB) analysis, and immunofluorescence (IF) localization. The effects of EZH2 on cell function were assessed using CCK-8 assays, Transwell assays, and wound healing assays. Public databases and RT-qPCR were utilized to identify downstream targets. The mechanisms regulating these downstream targets were elucidated using MS-PCR and WB analysis. The efficacy of IHMT-337 was demonstrated through IC50 measurements, WB analysis, and RT-qPCR. The effects of IHMT-337 on glioma cells in vitro were evaluated using Transwell assays, EdU incorporation assays, and flow cytometry. The potential of IHMT-337 as a treatment for glioma was assessed using a blood-brain barrier (BBB) model and an orthotopic glioma model. Our research confirms significantly elevated EZH2 expression in gliomas, correlating with patient prognosis. EZH2 facilitates glioma proliferation, migration, and invasion alongside promoting SLC12A5 DNA methylation. By regulating SLC12A5 expression, EZH2 activates the WNK1-OSR1-NKCC1 pathway, enhancing its interaction with ERM to promote glioma migration. IHMT-337 targets EZH2 in vitro to inhibit WNK1 activation, thereby suppressing glioma cell migration. Additionally, it inhibits cell proliferation and arrests the cell cycle. IHMT-337 has the potential to cross the BBB and has successfully inhibited glioma progression in vivo. This study expands our understanding of the EZH2-SLC12A5 axis in gliomas, laying a new foundation for the clinical translation of IHMT-337 and offering new insights for precision glioma therapy.
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Affiliation(s)
- Hongwei Zhang
- Anhui University of Science and Technology, Huainan, 232001, Anhui, China
- Division of Life Sciences and Medicine, Department of Neurosurgery, Centre for Leading Medicine and Advanced Technologies of IHM, The First Affiliated Hospital of USTC, University of Science and Technology of China, Hefei, 230001, Anhui, China
| | - Zixuan Wang
- Division of Life Sciences and Medicine, Department of Neurosurgery, Centre for Leading Medicine and Advanced Technologies of IHM, The First Affiliated Hospital of USTC, University of Science and Technology of China, Hefei, 230001, Anhui, China
- Dalian Medical University, Dalian, 116044, Liaoning, China
| | - Xiaolong Qiao
- Anhui University of Science and Technology, Huainan, 232001, Anhui, China
- Division of Life Sciences and Medicine, Department of Neurosurgery, Centre for Leading Medicine and Advanced Technologies of IHM, The First Affiliated Hospital of USTC, University of Science and Technology of China, Hefei, 230001, Anhui, China
| | - Nan Peng
- Division of Life Sciences and Medicine, Department of Neurosurgery, Centre for Leading Medicine and Advanced Technologies of IHM, The First Affiliated Hospital of USTC, University of Science and Technology of China, Hefei, 230001, Anhui, China
| | - Jiaxing Wu
- Division of Life Sciences and Medicine, Department of Neurosurgery, Centre for Leading Medicine and Advanced Technologies of IHM, The First Affiliated Hospital of USTC, University of Science and Technology of China, Hefei, 230001, Anhui, China
- Bengbu Medical University, Bengbu, 233000, Anhui, China
| | - Yinan Chen
- Division of Life Sciences and Medicine, Department of Neurosurgery, Centre for Leading Medicine and Advanced Technologies of IHM, The First Affiliated Hospital of USTC, University of Science and Technology of China, Hefei, 230001, Anhui, China
| | - Chuandong Cheng
- Anhui University of Science and Technology, Huainan, 232001, Anhui, China.
- Division of Life Sciences and Medicine, Department of Neurosurgery, Centre for Leading Medicine and Advanced Technologies of IHM, The First Affiliated Hospital of USTC, University of Science and Technology of China, Hefei, 230001, Anhui, China.
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6
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Corredor D, Segobin S, Hinault T, Eustache F, Dayan J, Guillery-Girard B, Naveau M. The multiscale topological organization of the functional brain network in adolescent PTSD. Cereb Cortex 2024; 34:bhae246. [PMID: 38864573 PMCID: PMC11167567 DOI: 10.1093/cercor/bhae246] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2024] [Revised: 05/17/2024] [Accepted: 05/18/2024] [Indexed: 06/13/2024] Open
Abstract
The experience of an extremely aversive event can produce enduring deleterious behavioral, and neural consequences, among which posttraumatic stress disorder (PTSD) is a representative example. Although adolescence is a period of great exposure to potentially traumatic events, the effects of trauma during adolescence remain understudied in clinical neuroscience. In this exploratory work, we aim to study the whole-cortex functional organization of 14 adolescents with PTSD using a data-driven method tailored to our population of interest. To do so, we built on the network neuroscience framework and specifically on multilayer (multisubject) community analysis to study the functional connectivity of the brain. We show, across different topological scales (the number of communities composing the cortex), a hyper-colocalization between regions belonging to occipital and pericentral regions and hypo-colocalization in middle temporal, posterior-anterior medial, and frontal cortices in the adolescent PTSD group compared to a nontrauma exposed group of adolescents. These preliminary results raise the question of an altered large-scale cortical organization in adolescent PTSD, opening an interesting line of research for future investigations.
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Affiliation(s)
- David Corredor
- Centre Cyceron, CHU de Caen, Neuropsychologie et Imagerie de la Mémoire Humaine, Normandie Université, UNICAEN, PSL Université Paris, EPHE, INSERM, U1077, Caen 14000, France
| | - Shailendra Segobin
- Centre Cyceron, CHU de Caen, Neuropsychologie et Imagerie de la Mémoire Humaine, Normandie Université, UNICAEN, PSL Université Paris, EPHE, INSERM, U1077, Caen 14000, France
| | - Thomas Hinault
- Centre Cyceron, CHU de Caen, Neuropsychologie et Imagerie de la Mémoire Humaine, Normandie Université, UNICAEN, PSL Université Paris, EPHE, INSERM, U1077, Caen 14000, France
| | - Francis Eustache
- Centre Cyceron, CHU de Caen, Neuropsychologie et Imagerie de la Mémoire Humaine, Normandie Université, UNICAEN, PSL Université Paris, EPHE, INSERM, U1077, Caen 14000, France
| | - Jacques Dayan
- Centre Cyceron, CHU de Caen, Neuropsychologie et Imagerie de la Mémoire Humaine, Normandie Université, UNICAEN, PSL Université Paris, EPHE, INSERM, U1077, Caen 14000, France
- Pôle Hospitalo-Universitaire de Psychiatrie de l’Enfant et de l’Adolescent, Centre Hospitalier Guillaume Régnier, Université Rennes 1, Rennes 35700, France
| | - Bérengère Guillery-Girard
- Centre Cyceron, CHU de Caen, Neuropsychologie et Imagerie de la Mémoire Humaine, Normandie Université, UNICAEN, PSL Université Paris, EPHE, INSERM, U1077, Caen 14000, France
| | - Mikaël Naveau
- UNICAEN, CNRS, INSERM, CEA, UAR3408 CYCERON, Normandie Université, Caen 14000, France
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7
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Metzner C, Dimulescu C, Kamp F, Fromm S, Uhlhaas PJ, Obermayer K. Exploring global and local processes underlying alterations in resting-state functional connectivity and dynamics in schizophrenia. Front Psychiatry 2024; 15:1352641. [PMID: 38414495 PMCID: PMC10897003 DOI: 10.3389/fpsyt.2024.1352641] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/08/2023] [Accepted: 01/19/2024] [Indexed: 02/29/2024] Open
Abstract
Introduction We examined changes in large-scale functional connectivity and temporal dynamics and their underlying mechanisms in schizophrenia (ScZ) through measurements of resting-state functional magnetic resonance imaging (rs-fMRI) data and computational modelling. Methods The rs-fMRI measurements from patients with chronic ScZ (n=38) and matched healthy controls (n=43), were obtained through the public schizConnect repository. Computational models were constructed based on diffusion-weighted MRI scans and fit to the experimental rs-fMRI data. Results We found decreased large-scale functional connectivity across sensory and association areas and for all functional subnetworks for the ScZ group. Additionally global synchrony was reduced in patients while metastability was unaltered. Perturbations of the computational model revealed that decreased global coupling and increased background noise levels both explained the experimentally found deficits better than local changes to the GABAergic or glutamatergic system. Discussion The current study suggests that large-scale alterations in ScZ are more likely the result of global rather than local network changes.
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Affiliation(s)
- Christoph Metzner
- Neural Information Processing Group, Institute of Software Engineering and Theoretical Computer Science, Technische Universität Berlin, Berlin, Germany
- Department of Child and Adolescent Psychiatry, Charité – Universitätsmedizin Berlin, Berlin, Germany
- School of Physics, Engineering and Computer Science, University of Hertfordshire, Hatfield, United Kingdom
| | - Cristiana Dimulescu
- Neural Information Processing Group, Institute of Software Engineering and Theoretical Computer Science, Technische Universität Berlin, Berlin, Germany
- Bernstein Center for Computational Neuroscience Berlin, Berlin, Germany
| | - Fabian Kamp
- Neural Information Processing Group, Institute of Software Engineering and Theoretical Computer Science, Technische Universität Berlin, Berlin, Germany
- Max Planck School of Cognition, Max Planck Institute for Human Cognitive and Brain Science, Leipzig, Germany
- Center for Lifespan Psychology, Max Planck Institute for Human Development, Berlin, Germany
| | - Sophie Fromm
- Neural Information Processing Group, Institute of Software Engineering and Theoretical Computer Science, Technische Universität Berlin, Berlin, Germany
- Department of Psychiatry and Psychotherapy, Charité – Universitätsmedizin Berlin, Berlin, Germany
| | - Peter J. Uhlhaas
- Department of Child and Adolescent Psychiatry, Charité – Universitätsmedizin Berlin, Berlin, Germany
- Institute of Neuroscience and Psychology, University of Glasgow, Glasgow, United Kingdom
| | - Klaus Obermayer
- Neural Information Processing Group, Institute of Software Engineering and Theoretical Computer Science, Technische Universität Berlin, Berlin, Germany
- Bernstein Center for Computational Neuroscience Berlin, Berlin, Germany
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8
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Thakkar KN, Silverstein SM, Fattal J, Bao J, Slate R, Roberts D, Brascamp JW. Stronger tilt aftereffects in individuals diagnosed with schizophrenia spectrum disorders but not bipolar disorder. Schizophr Res 2024; 264:345-353. [PMID: 38218020 PMCID: PMC10923089 DOI: 10.1016/j.schres.2023.12.029] [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: 03/09/2023] [Revised: 12/04/2023] [Accepted: 12/25/2023] [Indexed: 01/15/2024]
Abstract
An altered use of context and experience to interpret incoming information has been posited to explain schizophrenia symptoms. The visual system can serve as a model system for examining how context and experience guide perception and the neural mechanisms underlying putative alterations. The influence of prior experience on current perception is evident in visual aftereffects, the perception of the "opposite" of a previously viewed stimulus. Aftereffects are associated with neural adaptation and concomitant change in strength of lateral inhibitory connections in visually responsive neurons. In a previous study, we observed stronger aftereffects related to orientation (tilt aftereffects) but not luminance (negative afterimages) in individuals diagnosed with schizophrenia, which we interpreted as potentially suggesting altered cortical (but not subcortical) adaptability and local changes in excitatory-inhibitory interactions. Here, we tested whether stronger tilt aftereffects were specific to individuals with schizophrenia or extended to individuals with bipolar disorder. We measured tilt aftereffects and negative afterimages in 32 individuals with bipolar disorder, and compared aftereffect strength to a previously reported group of 36 individuals with schizophrenia and 22 healthy controls. We observed stronger tilt aftereffects, but not negative afterimages, in individuals with schizophrenia as compared to both controls and individuals with bipolar disorder, who did not differ from each other. These results mitigate concerns that stronger tilt aftereffects in schizophrenia are a consequence of medication or of the psychosocial consequences of a severe mental illness.
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Affiliation(s)
- Katharine N Thakkar
- Department of Psychology, Michigan State University, East Lansing, MI, United States of America; Division of Psychiatry and Behavioral Medicine, Michigan State University, Grand Rapids, MI, United States of America.
| | - Steven M Silverstein
- Department of Psychiatry, University of Rochester Medical Center, Rochester, NY, United States of America
| | - Jessica Fattal
- Department of Psychology, Michigan State University, East Lansing, MI, United States of America
| | - Jacqueline Bao
- Department of Psychology, Michigan State University, East Lansing, MI, United States of America; Department of Psychology and Neuroscience, Duke University, Durham, NC, United States of America
| | - Rachael Slate
- Department of Psychology, Michigan State University, East Lansing, MI, United States of America
| | - Dominic Roberts
- Department of Psychology, Michigan State University, East Lansing, MI, United States of America
| | - Jan W Brascamp
- Department of Psychology, Michigan State University, East Lansing, MI, United States of America
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9
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Han L, Chan MY, Agres PF, Winter-Nelson E, Zhang Z, Wig GS. Measures of resting-state brain network segregation and integration vary in relation to data quantity: implications for within and between subject comparisons of functional brain network organization. Cereb Cortex 2024; 34:bhad506. [PMID: 38385891 PMCID: PMC10883417 DOI: 10.1093/cercor/bhad506] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2023] [Revised: 12/05/2023] [Accepted: 12/16/2023] [Indexed: 02/23/2024] Open
Abstract
Measures of functional brain network segregation and integration vary with an individual's age, cognitive ability, and health status. Based on these relationships, these measures are frequently examined to study and quantify large-scale patterns of network organization in both basic and applied research settings. However, there is limited information on the stability and reliability of the network measures as applied to functional time-series; these measurement properties are critical to understand if the measures are to be used for individualized characterization of brain networks. We examine measurement reliability using several human datasets (Midnight Scan Club and Human Connectome Project [both Young Adult and Aging]). These datasets include participants with multiple scanning sessions, and collectively include individuals spanning a broad age range of the adult lifespan. The measurement and reliability of measures of resting-state network segregation and integration vary in relation to data quantity for a given participant's scan session; notably, both properties asymptote when estimated using adequate amounts of clean data. We demonstrate how this source of variability can systematically bias interpretation of differences and changes in brain network organization if appropriate safeguards are not included. These observations have important implications for cross-sectional, longitudinal, and interventional comparisons of functional brain network organization.
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Affiliation(s)
- Liang Han
- Center for Vital Longevity and School of Behavioral and Brain Sciences, University of Texas at Dallas, Dallas, TX 75235, United States
| | - Micaela Y Chan
- Center for Vital Longevity and School of Behavioral and Brain Sciences, University of Texas at Dallas, Dallas, TX 75235, United States
| | - Phillip F Agres
- Center for Vital Longevity and School of Behavioral and Brain Sciences, University of Texas at Dallas, Dallas, TX 75235, United States
| | - Ezra Winter-Nelson
- Center for Vital Longevity and School of Behavioral and Brain Sciences, University of Texas at Dallas, Dallas, TX 75235, United States
| | - Ziwei Zhang
- Center for Vital Longevity and School of Behavioral and Brain Sciences, University of Texas at Dallas, Dallas, TX 75235, United States
| | - Gagan S Wig
- Center for Vital Longevity and School of Behavioral and Brain Sciences, University of Texas at Dallas, Dallas, TX 75235, United States
- Department of Psychiatry, University of Texas Southwestern Medical Center, Dallas, TX 75390, United States
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10
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Diehl GW, Redish AD. Measuring excitation-inhibition balance through spectral components of local field potentials. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.01.24.577086. [PMID: 38328057 PMCID: PMC10849740 DOI: 10.1101/2024.01.24.577086] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/09/2024]
Abstract
The balance between excitation and inhibition is critical to brain functioning, and dysregulation of this balance is a hallmark of numerous psychiatric conditions. Measuring this excitation-inhibition (E:I) balance in vivo has remained difficult, but theoretical models have proposed that characteristics of local field potentials (LFP) may provide an accurate proxy. To establish a conclusive link between LFP and E:I balance, we recorded single units and LFP from the prefrontal cortex (mPFC) of rats during decision making. Dynamic measures of synaptic coupling strength facilitated direct quantification of E:I balance and revealed a strong inverse relationship to broadband spectral power of LFP. These results provide a critical link between LFP and underlying network properties, opening the door for non-invasive recordings to measure E:I balance in clinical settings.
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Affiliation(s)
- Geoffrey W Diehl
- Department of Neuroscience, University of Minnesota, Minneapolis, MN, United States
| | - A David Redish
- Department of Neuroscience, University of Minnesota, Minneapolis, MN, United States
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11
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Holmes A, Levi PT, Chen YC, Chopra S, Aquino KM, Pang JC, Fornito A. Disruptions of Hierarchical Cortical Organization in Early Psychosis and Schizophrenia. BIOLOGICAL PSYCHIATRY. COGNITIVE NEUROSCIENCE AND NEUROIMAGING 2023; 8:1240-1250. [PMID: 37683727 DOI: 10.1016/j.bpsc.2023.08.008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/07/2023] [Revised: 07/27/2023] [Accepted: 08/14/2023] [Indexed: 09/10/2023]
Abstract
BACKGROUND The cerebral cortex is organized hierarchically along an axis that spans unimodal sensorimotor to transmodal association areas. This hierarchy is often characterized using low-dimensional embeddings, termed gradients, of interregional functional coupling estimates measured with resting-state functional magnetic resonance imaging. Such analyses may offer insights into the pathophysiology of schizophrenia, which has been frequently linked to dysfunctional interactions between association and sensorimotor areas. METHODS To examine disruptions of hierarchical cortical function across distinct stages of psychosis, we applied diffusion map embedding to 2 independent functional magnetic resonance imaging datasets: one comprising 114 patients with early psychosis and 48 control participants, and the other comprising 50 patients with established schizophrenia and 121 control participants. Then, we analyzed the primary sensorimotor-to-association and secondary visual-to-sensorimotor gradients of each participant in both datasets. RESULTS There were no significant differences in regional gradient scores between patients with early psychosis and control participants. Patients with established schizophrenia showed significant differences in the secondary, but not primary, gradient compared with control participants. Gradient differences in schizophrenia were characterized by lower within-network dispersion in the dorsal attention (false discovery rate [FDR]-corrected p [pFDR] < .001), visual (pFDR = .003), frontoparietal (pFDR = .018), and limbic (pFDR = .020) networks and lower between-network dispersion between the visual network and other networks (pFDR < .001). CONCLUSIONS These findings indicate that differences in cortical hierarchical function occur along the secondary visual-to-sensorimotor axis rather than the primary sensorimotor-to-association axis as previously thought. The absence of differences in early psychosis suggests that visual-sensorimotor abnormalities may emerge as the illness progresses.
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Affiliation(s)
- Alexander Holmes
- Turner Institute for Brain and Mental Health, School of Psychological Science, Monash University, Melbourne, Victoria, Australia.
| | - Priscila T Levi
- Turner Institute for Brain and Mental Health, School of Psychological Science, Monash University, Melbourne, Victoria, Australia
| | - Yu-Chi Chen
- Turner Institute for Brain and Mental Health, School of Psychological Science, Monash University, Melbourne, Victoria, Australia; Brain and Mind Centre, University of Sydney, Sydney, New South Wales, Australia
| | - Sidhant Chopra
- Department of Psychology, Yale University, New Haven, Connecticut
| | - Kevin M Aquino
- School of Physics, University of Sydney, Sydney, New South Wales, Australia
| | - James C Pang
- Turner Institute for Brain and Mental Health, School of Psychological Science, Monash University, Melbourne, Victoria, Australia
| | - Alex Fornito
- Turner Institute for Brain and Mental Health, School of Psychological Science, Monash University, Melbourne, Victoria, Australia
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12
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Yang B, Zhang H, Jiang T, Yu S. Natural brain state change with E/I balance shifting toward inhibition is associated with vigilance impairment. iScience 2023; 26:107963. [PMID: 37822500 PMCID: PMC10562778 DOI: 10.1016/j.isci.2023.107963] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2023] [Revised: 07/25/2023] [Accepted: 09/15/2023] [Indexed: 10/13/2023] Open
Abstract
The delicate balance between cortical excitation and inhibition (E/I) plays a pivotal role in brain state changes. While previous studies have associated cortical hyperexcitability with brain state changes induced by sleep deprivation, whether cortical hypoexcitability is also linked to brain state changes and, if so, how it could affect cognitive performance remain unknown. Here, we address these questions by examining the brain state change occurring after meals, i.e., postprandial somnolence, and comparing it with that induced by sleep deprivation. By analyzing features representing network excitability based on electroencephalogram (EEG) signals, we confirmed cortical hyperexcitability under sleep deprivation but revealed hypoexcitability under postprandial somnolence. In addition, we found that both sleep deprivation and postprandial somnolence adversely affected the level of vigilance. These results indicate that cortical E/I balance toward inhibition is associated with brain state changes, and deviation from the balanced state, regardless of its direction, could impair cognitive performance.
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Affiliation(s)
- Binghao Yang
- Brainnetome Center, Laboratory of Brain Atlas and Brain-inspired Intelligence, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
- School of Future Technology, University of Chinese Academy of Sciences, Beijing 101408, China
| | - Haoran Zhang
- Brainnetome Center, Laboratory of Brain Atlas and Brain-inspired Intelligence, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
- School of Future Technology, University of Chinese Academy of Sciences, Beijing 101408, China
| | - Tianzi Jiang
- Brainnetome Center, Laboratory of Brain Atlas and Brain-inspired Intelligence, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 101408, China
- Research Center for Augmented Intelligence, Zhejiang Lab, Hangzhou 311121, China
| | - Shan Yu
- Brainnetome Center, Laboratory of Brain Atlas and Brain-inspired Intelligence, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
- School of Future Technology, University of Chinese Academy of Sciences, Beijing 101408, China
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13
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Jin H, Verma P, Jiang F, Nagarajan SS, Raj A. Bayesian inference of a spectral graph model for brain oscillations. Neuroimage 2023; 279:120278. [PMID: 37516373 PMCID: PMC10840584 DOI: 10.1016/j.neuroimage.2023.120278] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2023] [Revised: 05/22/2023] [Accepted: 07/12/2023] [Indexed: 07/31/2023] Open
Abstract
The relationship between brain functional connectivity and structural connectivity has caught extensive attention of the neuroscience community, commonly inferred using mathematical modeling. Among many modeling approaches, spectral graph model (SGM) is distinctive as it has a closed-form solution of the wide-band frequency spectra of brain oscillations, requiring only global biophysically interpretable parameters. While SGM is parsimonious in parameters, the determination of SGM parameters is non-trivial. Prior works on SGM determine the parameters through a computational intensive annealing algorithm, which only provides a point estimate with no confidence intervals for parameter estimates. To fill this gap, we incorporate the simulation-based inference (SBI) algorithm and develop a Bayesian procedure for inferring the posterior distribution of the SGM parameters. Furthermore, using SBI dramatically reduces the computational burden for inferring the SGM parameters. We evaluate the proposed SBI-SGM framework on the resting-state magnetoencephalography recordings from healthy subjects and show that the proposed procedure has similar performance to the annealing algorithm in recovering power spectra and the spatial distribution of the alpha frequency band. In addition, we also analyze the correlations among the parameters and their uncertainty with the posterior distribution which cannot be done with annealing inference. These analyses provide a richer understanding of the interactions among biophysical parameters of the SGM. In general, the use of simulation-based Bayesian inference enables robust and efficient computations of generative model parameter uncertainties and may pave the way for the use of generative models in clinical translation applications.
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Affiliation(s)
- Huaqing Jin
- Department of Radiology and Biomedical Imaging University of California San Francisco, San Francisco, CA, USA
| | - Parul Verma
- Department of Radiology and Biomedical Imaging University of California San Francisco, San Francisco, CA, USA
| | - Fei Jiang
- Department of Epidemiology and Biostatistics University of California San Francisco, San Francisco, CA, USA
| | - Srikantan S Nagarajan
- Department of Radiology and Biomedical Imaging University of California San Francisco, San Francisco, CA, USA.
| | - Ashish Raj
- Department of Radiology and Biomedical Imaging University of California San Francisco, San Francisco, CA, USA.
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14
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Derome M, Kozuharova P, Diaconescu AO, Denève S, Jardri R, Allen P. Functional connectivity and glutamate levels of the medial prefrontal cortex in schizotypy are related to sensory amplification in a probabilistic reasoning task. Neuroimage 2023; 278:120280. [PMID: 37460012 DOI: 10.1016/j.neuroimage.2023.120280] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2023] [Revised: 07/04/2023] [Accepted: 07/13/2023] [Indexed: 07/27/2023] Open
Abstract
The circular inference (CI) computational model assumes a corruption of sensory data by prior information and vice versa, leading at the extremes to 'see what we expect' (through prior amplification) and/or to 'expect what we see' (through sensory amplification). Although a CI mechanism has been reported in a schizophrenia population, it has not been investigated in individuals experiencing psychosis-like experiences, such as people with high schizotypy traits. Furthermore, the neurobiological basis of CI, such as the link between hierarchical amplifications, excitatory neurotransmission, and resting state functional connectivity (RSFC), remains untested. The participants included in the present study consisted of a subsample of those recruited in a study previously published by our group, Kozhuharova et al. (2021b). We included 36 participants with High (n=18) and Low (n=18) levels of schizotypy who completed a probabilistic reasoning task (the Fisher task) for which individual confidence levels were obtained and fitted to the CI model. Participants also underwent a 1H-Magnetic Resonance Spectroscopy (MRS) scan to measure medial prefrontal cortex (mPFC) glutamate metabolite levels, and a functional Magnetic Resonance Imaging (fMRI) scan to measure RSFC of the medial prefrontal cortex (mPFC). People with high levels of schizotypy exhibited changes in CI parameters, altered cortical excitatory neurotransmission and RSFC that were all associated with sensory amplification. Our findings capture a multimodal signature of CI that is observable in people early in the psychosis spectrum.
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Affiliation(s)
- Mélodie Derome
- School of Psychology, University of Roehampton, Whitelands College, Hollybourne Avenue, London SW154JD, UK; Lille Neuroscience & Cognition Centre (LiNC), Plasticity & Subjectivity Team, Univ Lille, INSERM U-1172, CHU Lille, FR 59037, France; Combined Universities Brain Imaging Centre, Royal Holloway University, London TW200EX, UK
| | - Petya Kozuharova
- School of Psychology, University of Roehampton, Whitelands College, Hollybourne Avenue, London SW154JD, UK
| | - Andreea O Diaconescu
- Department of Psychiatry, Brain and Therapeutics, Krembil Centre for Neuroinformatics, CAMH, Toronto M5S2S1, Canada; Department of Psychiatry, University of Toronto, Toronto, ON MS5, Canada
| | - Sophie Denève
- Laboratoire de Neurosciences Cognitives et Computationnelles (LNC²), ENS, INSERM U-960, PSL Research University, Paris, FR 75006, France
| | - Renaud Jardri
- School of Psychology, University of Roehampton, Whitelands College, Hollybourne Avenue, London SW154JD, UK; Laboratoire de Neurosciences Cognitives et Computationnelles (LNC²), ENS, INSERM U-960, PSL Research University, Paris, FR 75006, France.
| | - Paul Allen
- School of Psychology, University of Roehampton, Whitelands College, Hollybourne Avenue, London SW154JD, UK; Combined Universities Brain Imaging Centre, Royal Holloway University, London TW200EX, UK; Department of Neuroimaging, Institute of Psychiatry, Psychology & Neuroscience, King's College London, SE58AF, UK.
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15
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Eo J, Kang J, Youn T, Park HJ. Neuropharmacological computational analysis of longitudinal electroencephalograms in clozapine-treated patients with schizophrenia using hierarchical dynamic causal modeling. Neuroimage 2023; 275:120161. [PMID: 37172662 DOI: 10.1016/j.neuroimage.2023.120161] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2023] [Revised: 04/15/2023] [Accepted: 05/09/2023] [Indexed: 05/15/2023] Open
Abstract
The hierarchical characteristics of the brain are prominent in the pharmacological treatment of psychiatric diseases, primarily targeting cellular receptors that extend upward to intrinsic connectivity within a region, interregional connectivity, and, consequently, clinical observations such as an electroencephalogram (EEG). To understand the long-term effects of neuropharmacological intervention on neurobiological properties at different hierarchical levels, we explored long-term changes in neurobiological parameters of an N-methyl-D-aspartate canonical microcircuit model (CMM-NMDA) in the default mode network (DMN) and auditory hallucination network (AHN) using dynamic causal modeling of longitudinal EEG in clozapine-treated patients with schizophrenia. The neurobiological properties of the CMM-NMDA model associated with symptom improvement in schizophrenia were found across hierarchical levels, from a reduced membrane capacity of the deep pyramidal cell and intrinsic connectivity with the inhibitory population in DMN and intrinsic and extrinsic connectivity in AHN. The medication duration mainly affects the intrinsic connectivity and NMDA time constant in DMN. Virtual perturbation analysis specified the contribution of each parameter to the cross-spectral density (CSD) of the EEG, particularly intrinsic connectivity and membrane capacitances for CSD frequency shifts and progression. It further reveals that excitatory and inhibitory connectivity complements frequency-specific CSD changes, notably the alpha frequency band in DMN. Positive and negative synergistic interactions exist between neurobiological properties primarily within the same region in patients treated with clozapine. The current study shows how computational neuropharmacology helps explore the multiscale link between neurobiological properties and clinical observations and understand the long-term mechanism of neuropharmacological intervention reflected in clinical EEG.
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Affiliation(s)
- Jinseok Eo
- Graduate School of Medical Science, Brain Korea 21 Project, Department of Nuclear Medicine, Department of Psychiatry, Yonsei University College of Medicine, Seoul, Republic of Korea; Center for Systems and Translational Brain Science, Institute of Human Complexity and Systems Science, Yonsei University, Seoul, Republic of Korea
| | - Jiyoung Kang
- Department of Scientific Computing, Pukyong National University, Busan, Republic of Korea; Center for Systems and Translational Brain Science, Institute of Human Complexity and Systems Science, Yonsei University, Seoul, Republic of Korea
| | - Tak Youn
- Department of Psychiatry and Electroconvulsive Therapy Center, Dongguk University International Hospital, Goyang, Republic of Korea; Institute of Buddhism and Medicine, Dongguk University, Seoul, Republic of Korea
| | - Hae-Jeong Park
- Graduate School of Medical Science, Brain Korea 21 Project, Department of Nuclear Medicine, Department of Psychiatry, Yonsei University College of Medicine, Seoul, Republic of Korea; Center for Systems and Translational Brain Science, Institute of Human Complexity and Systems Science, Yonsei University, Seoul, Republic of Korea; Department of Cognitive Science, Yonsei University, Seoul, Republic of Korea.
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16
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Jin H, Verma P, Jiang F, Nagarajan S, Raj A. Bayesian Inference of a Spectral Graph Model for Brain Oscillations. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.03.01.530704. [PMID: 36909647 PMCID: PMC10002745 DOI: 10.1101/2023.03.01.530704] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/07/2023]
Abstract
The relationship between brain functional connectivity and structural connectivity has caught extensive attention of the neuroscience community, commonly inferred using mathematical modeling. Among many modeling approaches, spectral graph model (SGM) is distinctive as it has a closed-form solution of the wide-band frequency spectra of brain oscillations, requiring only global biophysically interpretable parameters. While SGM is parsimonious in parameters, the determination of SGM parameters is non-trivial. Prior works on SGM determine the parameters through a computational intensive annealing algorithm, which only provides a point estimate with no confidence intervals for parameter estimates. To fill this gap, we incorporate the simulation-based inference (SBI) algorithm and develop a Bayesian procedure for inferring the posterior distribution of the SGM parameters. Furthermore, using SBI dramatically reduces the computational burden for inferring the SGM parameters. We evaluate the proposed SBI-SGM framework on the resting-state magnetoencephalography recordings from healthy subjects and show that the proposed procedure has similar performance to the annealing algorithm in recovering power spectra and the spatial distribution of the alpha frequency band. In addition, we also analyze the correlations among the parameters and their uncertainty with the posterior distribution which can not be done with annealing inference. These analyses provide a richer understanding of the interactions among biophysical parameters of the SGM. In general, the use of simulation-based Bayesian inference enables robust and efficient computations of generative model parameter uncertainties and may pave the way for the use of generative models in clinical translation applications.
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Affiliation(s)
- Huaqing Jin
- Department of Radiology and Biomedical Imaging, University of California San Francisco, USA San Francisco, CA
| | - Parul Verma
- Department of Radiology and Biomedical Imaging, University of California San Francisco, USA San Francisco, CA
| | - Fei Jiang
- Department of Epidemiology and Biostatistics, University of California San Francisco, USA San Francisco, CA
| | - Srikantan Nagarajan
- Department of Radiology and Biomedical Imaging, University of California San Francisco, USA San Francisco, CA
| | - Ashish Raj
- Department of Radiology and Biomedical Imaging, University of California San Francisco, USA San Francisco, CA
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17
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Dong D, Yao D, Wang Y, Hong SJ, Genon S, Xin F, Jung K, He H, Chang X, Duan M, Bernhardt BC, Margulies DS, Sepulcre J, Eickhoff SB, Luo C. Compressed sensorimotor-to-transmodal hierarchical organization in schizophrenia. Psychol Med 2023; 53:771-784. [PMID: 34100349 DOI: 10.1017/s0033291721002129] [Citation(s) in RCA: 33] [Impact Index Per Article: 33.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Abstract
BACKGROUND Schizophrenia has been primarily conceptualized as a disorder of high-order cognitive functions with deficits in executive brain regions. Yet due to the increasing reports of early sensory processing deficit, recent models focus more on the developmental effects of impaired sensory process on high-order functions. The present study examined whether this pathological interaction relates to an overarching system-level imbalance, specifically a disruption in macroscale hierarchy affecting integration and segregation of unimodal and transmodal networks. METHODS We applied a novel combination of connectome gradient and stepwise connectivity analysis to resting-state fMRI to characterize the sensorimotor-to-transmodal cortical hierarchy organization (96 patients v. 122 controls). RESULTS We demonstrated compression of the cortical hierarchy organization in schizophrenia, with a prominent compression from the sensorimotor region and a less prominent compression from the frontal-parietal region, resulting in a diminished separation between sensory and fronto-parietal cognitive systems. Further analyses suggested reduced differentiation related to atypical functional connectome transition from unimodal to transmodal brain areas. Specifically, we found hypo-connectivity within unimodal regions and hyper-connectivity between unimodal regions and fronto-parietal and ventral attention regions along the classical sensation-to-cognition continuum (voxel-level corrected, p < 0.05). CONCLUSIONS The compression of cortical hierarchy organization represents a novel and integrative system-level substrate underlying the pathological interaction of early sensory and cognitive function in schizophrenia. This abnormal cortical hierarchy organization suggests cascading impairments from the disruption of the somatosensory-motor system and inefficient integration of bottom-up sensory information with attentional demands and executive control processes partially account for high-level cognitive deficits characteristic of schizophrenia.
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Affiliation(s)
- Debo Dong
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, School of Life Science and Technology, University of Electronic Science and Technology of China, China
| | - Dezhong Yao
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, School of Life Science and Technology, University of Electronic Science and Technology of China, China
- Research Unit of NeuroInformation, Chinese Academy of Medical Sciences, 2019RU035, Chengdu, China
| | - Yulin Wang
- Faculty of Psychological and Educational Sciences, Department of Experimental and Applied Psychology, Vrije Universiteit Brussel, Belgium
- Faculty of Psychology and Educational Sciences, Department of Data Analysis, Ghent University, Belgium
| | - Seok-Jun Hong
- Center for the Developing Brain, Child Mind Institute, NY, USA
- Department of Biomedical Engineering, Center for Neuroscience Imaging Research, Institute for Basic Science, Sungkyunkwan University, South Korea
| | - Sarah Genon
- Institute for Systems Neuroscience, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
- Institute of Neuroscience and Medicine, Brain & Behaviour (INM-7), Research Centre Jülich, Jülich, Germany
| | - Fei Xin
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, School of Life Science and Technology, University of Electronic Science and Technology of China, China
| | - Kyesam Jung
- Institute for Systems Neuroscience, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
- Institute of Neuroscience and Medicine, Brain & Behaviour (INM-7), Research Centre Jülich, Jülich, Germany
| | - Hui He
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, School of Life Science and Technology, University of Electronic Science and Technology of China, China
- Department of Psychiatry, The Fourth People's Hospital of Chengdu, Chengdu, China
| | - Xuebin Chang
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, School of Life Science and Technology, University of Electronic Science and Technology of China, China
| | - Mingjun Duan
- Department of Psychiatry, The Fourth People's Hospital of Chengdu, Chengdu, China
| | - Boris C Bernhardt
- Multimodal Imaging and Connectome Analysis Lab, McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada
| | - Daniel S Margulies
- Centre National de la Recherche Scientifique (CNRS) UMR 7225, Institut du Cerveau et de la Moelle épinière, Paris, France
| | - Jorge Sepulcre
- Department of Radiology, Gordon Center for Medical Imaging, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
- Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Charlestown, MA, USA
| | - Simon B Eickhoff
- Institute for Systems Neuroscience, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
- Institute of Neuroscience and Medicine, Brain & Behaviour (INM-7), Research Centre Jülich, Jülich, Germany
| | - Cheng Luo
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, School of Life Science and Technology, University of Electronic Science and Technology of China, China
- Department of Neurology, Brain Disorders and Brain Function Key Laboratory, First Affiliated Hospital of Hainan Medical University, Haikou, China
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18
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Vinogradov S, Chafee MV, Lee E, Morishita H. Psychosis spectrum illnesses as disorders of prefrontal critical period plasticity. Neuropsychopharmacology 2023; 48:168-185. [PMID: 36180784 PMCID: PMC9700720 DOI: 10.1038/s41386-022-01451-w] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/29/2022] [Revised: 08/17/2022] [Accepted: 08/21/2022] [Indexed: 01/05/2023]
Abstract
Emerging research on neuroplasticity processes in psychosis spectrum illnesses-from the synaptic to the macrocircuit levels-fill key gaps in our models of pathophysiology and open up important treatment considerations. In this selective narrative review, we focus on three themes, emphasizing alterations in spike-timing dependent and Hebbian plasticity that occur during adolescence, the critical period for prefrontal system development: (1) Experience-dependent dysplasticity in psychosis emerges from activity decorrelation within neuronal ensembles. (2) Plasticity processes operate bidirectionally: deleterious environmental and experiential inputs shape microcircuits. (3) Dysregulated plasticity processes interact across levels of scale and time and include compensatory mechanisms that have pathogenic importance. We present evidence that-given the centrality of progressive dysplastic changes, especially in prefrontal cortex-pharmacologic or neuromodulatory interventions will need to be supplemented by corrective learning experiences for the brain if we are to help people living with these illnesses to fully thrive.
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Affiliation(s)
- Sophia Vinogradov
- Department of Psychiatry & Behavioral Science, University of Minnesota Medical School, Minneapolis, MN, USA.
| | - Matthew V Chafee
- Department of Neuroscience, University of Minnesota Medical School, Minneapolis, MN, USA
| | - Erik Lee
- Masonic Institute for the Developing Brain, University of Minnesota Medical School, Minneapolis, MN, USA
- University of Minnesota Informatics Institute, University of Minnesota, Minneapolis, MN, USA
| | - Hirofumi Morishita
- Department of Psychiatry, Neuroscience, & Ophthalmology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
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19
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Vohryzek J, Cabral J, Castaldo F, Sanz-Perl Y, Lord LD, Fernandes HM, Litvak V, Kringelbach ML, Deco G. Dynamic sensitivity analysis: Defining personalised strategies to drive brain state transitions via whole brain modelling. Comput Struct Biotechnol J 2022; 21:335-345. [PMID: 36582443 PMCID: PMC9792354 DOI: 10.1016/j.csbj.2022.11.060] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2022] [Revised: 11/28/2022] [Accepted: 11/28/2022] [Indexed: 12/03/2022] Open
Abstract
Traditionally, in neuroimaging, model-free analyses are used to find significant differences between brain states via signal detection theory. Depending on the a priori assumptions about the underlying data, different spatio-temporal features can be analysed. Alternatively, model-based techniques infer features from the data and compare significance from model parameters. However, to assess transitions from one brain state to another remains a challenge in current paradigms. Here, we introduce a "Dynamic Sensitivity Analysis" framework that quantifies transitions between brain states in terms of stimulation ability to rebalance spatio-temporal brain activity towards a target state such as healthy brain dynamics. In practice, it means building a whole-brain model fitted to the spatio-temporal description of brain dynamics, and applying systematic stimulations in-silico to assess the optimal strategy to drive brain dynamics towards a target state. Further, we show how Dynamic Sensitivity Analysis extends to various brain stimulation paradigms, ultimately contributing to improving the efficacy of personalised clinical interventions.
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Key Words
- Brain State
- Brain stimulation
- Deep Brain Stimulation, DBS
- Magnetic Resonance Imaging, MRI
- Non-Invasive Brain Stimulations, NIBS
- Position Emission Tomography, PET
- Probability Metastable Substates, PMS
- Spatio-temporal dynamics
- Transcranial Magnetic Stimulation, TMS
- Transition Probability Matrix, TPM
- Whole-brain models
- diffusion Magnetic Resonance Imaging, dMRI
- dynamic Functional Connectivity, dFC
- functional Magnetic Resonance Imaging, fMRI
- static Functional Connectivity, sFC
- transcranial Alternating Current Stimulation, tACS
- transcranial Direct Stimulation, tDCS
- transcranial Electric Stimulation, tES
- transcranial Random Noise Stimulation, tRNS
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Affiliation(s)
- Jakub Vohryzek
- Centre for Brain and Cognition, Computational Neuroscience Group, Department of Information and Communication Technologies, Universitat Pompeu Fabra, Barcelona, Spain
- Centre for Eudaimonia and Human Flourishing, Linacre College, University of Oxford, UK
| | - Joana Cabral
- Centre for Eudaimonia and Human Flourishing, Linacre College, University of Oxford, UK
- Life and Health Sciences Research Institute (ICVS), School of Medicine, University of Minho, Braga, Portugal
| | - Francesca Castaldo
- Wellcome Centre for Human Neuroimaging, University College London, Queen Square Institute of Neurology, London, UK
| | - Yonatan Sanz-Perl
- Centre for Brain and Cognition, Computational Neuroscience Group, Department of Information and Communication Technologies, Universitat Pompeu Fabra, Barcelona, Spain
- National Scientific and Technical Research Council (CONICET), Buenos Aires, Argentina
| | - Louis-David Lord
- Centre for Eudaimonia and Human Flourishing, Linacre College, University of Oxford, UK
| | - Henrique M. Fernandes
- Centre for Eudaimonia and Human Flourishing, Linacre College, University of Oxford, UK
- Centre for Music in the Brain, Aarhus University, Aarhus, Denmark
| | - Vladimir Litvak
- Wellcome Centre for Human Neuroimaging, University College London, Queen Square Institute of Neurology, London, UK
| | - Morten L. Kringelbach
- Centre for Eudaimonia and Human Flourishing, Linacre College, University of Oxford, UK
- Centre for Music in the Brain, Aarhus University, Aarhus, Denmark
- Department of Psychiatry, University of Oxford, Oxford, UK
| | - Gustavo Deco
- Centre for Brain and Cognition, Computational Neuroscience Group, Department of Information and Communication Technologies, Universitat Pompeu Fabra, Barcelona, Spain
- Institució Catalana de la Recerca i Estudis Avançats (ICREA), Barcelona, Spain
- Department of Neuropsychology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
- School of Psychological Sciences, Monash University, Melbourne, Australia
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20
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Raj A, Verma P, Nagarajan S. Structure-function models of temporal, spatial, and spectral characteristics of non-invasive whole brain functional imaging. Front Neurosci 2022; 16:959557. [PMID: 36110093 PMCID: PMC9468900 DOI: 10.3389/fnins.2022.959557] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2022] [Accepted: 07/27/2022] [Indexed: 11/29/2022] Open
Abstract
We review recent advances in using mathematical models of the relationship between the brain structure and function that capture features of brain dynamics. We argue the need for models that can jointly capture temporal, spatial, and spectral features of brain functional activity. We present recent work on spectral graph theory based models that can accurately capture spectral as well as spatial patterns across multiple frequencies in MEG reconstructions.
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Affiliation(s)
- Ashish Raj
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, CA, United States
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21
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Bouttier V, Duttagupta S, Denève S, Jardri R. Circular inference predicts nonuniform overactivation and dysconnectivity in brain-wide connectomes. Schizophr Res 2022; 245:59-67. [PMID: 33618940 DOI: 10.1016/j.schres.2020.12.045] [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: 08/24/2020] [Revised: 12/22/2020] [Accepted: 12/26/2020] [Indexed: 12/17/2022]
Abstract
Schizophrenia is a severe mental disorder whose neural basis remains difficult to ascertain. Among the available pathophysiological theories, recent work has pointed towards subtle perturbations in the excitation-inhibition (E/I) balance within different neural circuits. Computational approaches have suggested interesting mechanisms that can account for both E/I imbalances and psychotic symptoms. Based on hierarchical neural networks propagating information through a message-passing algorithm, it was hypothesized that changes in the E/I ratio could cause a "circular belief propagation" in which bottom-up and top-down information reverberate. This circular inference (CI) was proposed to account for the clinical features of schizophrenia. Under this assumption, this paper examined the impact of CI on network dynamics in light of brain imaging findings related to psychosis. Using brain-inspired graphical models, we show that CI causes overconfidence and overactivation most specifically at the level of connector hubs (e.g., nodes with many connections allowing integration across networks). By also measuring functional connectivity in these graphs, we provide evidence that CI is able to predict specific changes in modularity known to be associated with schizophrenia. Altogether, these findings suggest that the CI framework may facilitate behavioral and neural research on the multifaceted nature of psychosis.
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Affiliation(s)
- Vincent Bouttier
- Univ Lille, INSERM U1172, CHU Lille, Lille Neurosciences & Cognition Centre (LiNC), Plasticity & SubjectivitY team, 59037 Lille, France; Group for Neural Theory, Laboratoire de Neurosciences Cognitives et Computationnelles (LNC(2)), Ecole Normale Supérieure, INSERM U960, PSL University, 75005 Paris, France.
| | - Suhrit Duttagupta
- Group for Neural Theory, Laboratoire de Neurosciences Cognitives et Computationnelles (LNC(2)), Ecole Normale Supérieure, INSERM U960, PSL University, 75005 Paris, France
| | - Sophie Denève
- Group for Neural Theory, Laboratoire de Neurosciences Cognitives et Computationnelles (LNC(2)), Ecole Normale Supérieure, INSERM U960, PSL University, 75005 Paris, France
| | - Renaud Jardri
- Univ Lille, INSERM U1172, CHU Lille, Lille Neurosciences & Cognition Centre (LiNC), Plasticity & SubjectivitY team, 59037 Lille, France; Group for Neural Theory, Laboratoire de Neurosciences Cognitives et Computationnelles (LNC(2)), Ecole Normale Supérieure, INSERM U960, PSL University, 75005 Paris, France.
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22
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Yuan L, Ma X, Li D, Li Z, Ouyang L, Fan L, Yang Z, Zhang Z, Li C, He Y, Chen X. Abnormal Brain Network Interaction Associated With Positive Symptoms in Drug-Naive Patients With First-Episode Schizophrenia. Front Psychiatry 2022; 13:870709. [PMID: 35656348 PMCID: PMC9152123 DOI: 10.3389/fpsyt.2022.870709] [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: 02/07/2022] [Accepted: 04/14/2022] [Indexed: 11/23/2022] Open
Abstract
Positive symptoms are marked features of schizophrenia, and emerging evidence has suggested that abnormalities of the brain network underlying these symptoms may play a crucial role in the pathophysiology of the disease. We constructed two brain functional networks based on the positive and negative correlations between positive symptom scores and brain connectivity in drug-naive patients with first-episode schizophrenia (FES, n = 45) by using a machine-learning approach (connectome-based predictive modeling, CPM). The accuracy of the model was r = 0.47 (p = 0.002). The positively and negatively associated network strengths were then compared among FES subjects, individuals at genetic high risk (GHR, n = 41) for schizophrenia, and healthy controls (HCs, n = 48). The results indicated that the positively associated network contained more cross-subnetwork connections (96.02% of 176 edges), with a focus on the default-mode network (DMN)-salience network (SN) and the DMN-frontoparietal task control (FPT) network. The negatively associated network had fewer cross-subnetwork connections (71.79% of 117 edges) and focused on the sensory/somatomotor hand (SMH)-Cingulo opercular task control (COTC) network, the DMN, and the visual network with significantly decreased connectivity in the COTC-SMH network in FES (FES < GHR, p = 0.01; FES < HC, p = 0.01). Additionally, the connectivity strengths of the right supplementary motor area (SMA) (p < 0.001) and the right precentral gyrus (p < 0.0001) were reduced in FES. To the best of our knowledge, this is the first study to generate two brain networks associated with positive symptoms by utilizing CPM in FES. Abnormal segregation, interactions of brain subnetworks, and impaired SMA might lead to salience attribution abnormalities and, thus, as a result, induce positive symptoms in schizophrenia.
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Affiliation(s)
- Liu Yuan
- Department of Psychiatry, National Clinical Research Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha, China.,Mental Health Institute of Central South University, China National Technology Institute on Mental Disorders, Hunan Technology Institute of Psychiatry, Hunan Key Laboratory of Psychiatry and Mental Health, Changsha, China
| | - Xiaoqian Ma
- Department of Psychiatry, National Clinical Research Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha, China.,Mental Health Institute of Central South University, China National Technology Institute on Mental Disorders, Hunan Technology Institute of Psychiatry, Hunan Key Laboratory of Psychiatry and Mental Health, Changsha, China
| | - David Li
- Department of Psychiatry, National Clinical Research Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha, China.,Mental Health Institute of Central South University, China National Technology Institute on Mental Disorders, Hunan Technology Institute of Psychiatry, Hunan Key Laboratory of Psychiatry and Mental Health, Changsha, China
| | - Zongchang Li
- Department of Psychiatry, National Clinical Research Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha, China.,Mental Health Institute of Central South University, China National Technology Institute on Mental Disorders, Hunan Technology Institute of Psychiatry, Hunan Key Laboratory of Psychiatry and Mental Health, Changsha, China
| | - Lijun Ouyang
- Department of Psychiatry, National Clinical Research Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha, China.,Mental Health Institute of Central South University, China National Technology Institute on Mental Disorders, Hunan Technology Institute of Psychiatry, Hunan Key Laboratory of Psychiatry and Mental Health, Changsha, China
| | - Lejia Fan
- Department of Psychiatry, National Clinical Research Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha, China.,Mental Health Institute of Central South University, China National Technology Institute on Mental Disorders, Hunan Technology Institute of Psychiatry, Hunan Key Laboratory of Psychiatry and Mental Health, Changsha, China
| | - Zihao Yang
- Department of Psychiatry, National Clinical Research Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha, China.,Mental Health Institute of Central South University, China National Technology Institute on Mental Disorders, Hunan Technology Institute of Psychiatry, Hunan Key Laboratory of Psychiatry and Mental Health, Changsha, China
| | - Zhenmei Zhang
- Department of Psychiatry, National Clinical Research Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha, China.,Mental Health Institute of Central South University, China National Technology Institute on Mental Disorders, Hunan Technology Institute of Psychiatry, Hunan Key Laboratory of Psychiatry and Mental Health, Changsha, China
| | - Chunwang Li
- Department of Radiology, Hunan Children's Hospital, Changsha, China
| | - Ying He
- Department of Psychiatry, National Clinical Research Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha, China.,Mental Health Institute of Central South University, China National Technology Institute on Mental Disorders, Hunan Technology Institute of Psychiatry, Hunan Key Laboratory of Psychiatry and Mental Health, Changsha, China
| | - Xiaogang Chen
- Department of Psychiatry, National Clinical Research Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha, China.,Mental Health Institute of Central South University, China National Technology Institute on Mental Disorders, Hunan Technology Institute of Psychiatry, Hunan Key Laboratory of Psychiatry and Mental Health, Changsha, China
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23
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Aberrant maturation and connectivity of prefrontal cortex in schizophrenia-contribution of NMDA receptor development and hypofunction. Mol Psychiatry 2022; 27:731-743. [PMID: 34163013 PMCID: PMC8695640 DOI: 10.1038/s41380-021-01196-w] [Citation(s) in RCA: 30] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/24/2020] [Revised: 06/02/2021] [Accepted: 06/10/2021] [Indexed: 02/06/2023]
Abstract
The neurobiology of schizophrenia involves multiple facets of pathophysiology, ranging from its genetic basis over changes in neurochemistry and neurophysiology, to the systemic level of neural circuits. Although the precise mechanisms associated with the neuropathophysiology remain elusive, one essential aspect is the aberrant maturation and connectivity of the prefrontal cortex that leads to complex symptoms in various stages of the disease. Here, we focus on how early developmental dysfunction, especially N-methyl-D-aspartate receptor (NMDAR) development and hypofunction, may lead to the dysfunction of both local circuitry within the prefrontal cortex and its long-range connectivity. More specifically, we will focus on an "all roads lead to Rome" hypothesis, i.e., how NMDAR hypofunction during development acts as a convergence point and leads to local gamma-aminobutyric acid (GABA) deficits and input-output dysconnectivity in the prefrontal cortex, which eventually induce cognitive and social deficits. Many outstanding questions and hypothetical mechanisms are listed for future investigations of this intriguing hypothesis that may lead to a better understanding of the aberrant maturation and connectivity associated with the prefrontal cortex.
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24
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Qin Y, Liu X, Guo X, Liu M, Li H, Xu S. Low-Frequency Repetitive Transcranial Magnetic Stimulation Restores Dynamic Functional Connectivity in Subcortical Stroke. Front Neurol 2021; 12:771034. [PMID: 34950102 PMCID: PMC8689061 DOI: 10.3389/fneur.2021.771034] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2021] [Accepted: 10/27/2021] [Indexed: 01/09/2023] Open
Abstract
Background and Purpose: Strokes consistently result in brain network dysfunction. Previous studies have focused on the resting-state characteristics over the study period, while dynamic recombination remains largely unknown. Thus, we explored differences in dynamics between brain networks in patients who experienced subcortical stroke and the effects of low-frequency repetitive transcranial magnetic stimulation (LF-rTMS) on dynamic functional connectivity (dFC). Methods: A total of 41 patients with subcortical stroke were randomly divided into the LF-rTMS (n = 23) and the sham stimulation groups (n = 18). Resting-state functional MRI data were collected before (1 month after stroke) and after (3 months after stroke) treatment; a total of 20 age- and sex-matched healthy controls were also included. An independent component analysis, sliding window approach, and k-means clustering were used to identify different functional networks, estimate dFC matrices, and analyze dFC states before treatment. We further assessed the effect of LF-rTMS on dFCs in patients with subcortical stroke. Results: Compared to healthy controls, patients with stroke spent significantly more time in state I [p = 0.043, effect size (ES) = 0.64] and exhibited shortened stay in state II (p = 0.015, ES = 0.78); the dwell time gradually returned to normal after LF-rTMS treatment (p = 0.015, ES = 0.55). Changes in dwell time before and after LF-rTMS treatment were positively correlated with changes in the Fugl-Meyer Assessment for Upper Extremity (pr = 0.48, p = 0.028). Moreover, patients with stroke had decreased dFCs between the sensorimotor and cognitive control domains, yet connectivity within the cognitive control network increased. These abnormalities were partially improved after LF-rTMS treatment. Conclusion: Abnormal changes were noted in temporal and spatial characteristics of sensorimotor domains and cognitive control domains of patients who experience subcortical stroke; LF-rTMS can promote the partial recovery of dFC. These findings offer new insight into the dynamic neural mechanisms underlying effect of functional recombination and rTMS in subcortical stroke. Registration: http://www.chictr.org.cn/index.aspx, Unique.identifier: ChiCTR1800019452.
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Affiliation(s)
- Yin Qin
- Fuzong Clinical Medical College of Fujian Medical University, Fuzhou, Fujian, China
- Department of Rehabilitation Medicine, The 900th Hospital of Joint Logistic Support Force, PLA, Fuzhou, China
| | - Xiaoying Liu
- Fuzong Clinical Medical College of Fujian Medical University, Fuzhou, Fujian, China
- Department of Rehabilitation Medicine, The 900th Hospital of Joint Logistic Support Force, PLA, Fuzhou, China
| | - Xiaoping Guo
- Fuzong Clinical Medical College of Fujian Medical University, Fuzhou, Fujian, China
- Department of Rehabilitation Medicine, The 900th Hospital of Joint Logistic Support Force, PLA, Fuzhou, China
| | - Minhua Liu
- Fuzong Clinical Medical College of Fujian Medical University, Fuzhou, Fujian, China
- Department of Rehabilitation Medicine, The 900th Hospital of Joint Logistic Support Force, PLA, Fuzhou, China
| | - Hui Li
- Department of Radiology, The 900th Hospital of Joint Logistic Support Force, PLA, Fuzhou, China
| | - Shangwen Xu
- Department of Radiology, The 900th Hospital of Joint Logistic Support Force, PLA, Fuzhou, China
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25
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Resolving heterogeneity in schizophrenia through a novel systems approach to brain structure: individualized structural covariance network analysis. Mol Psychiatry 2021; 26:7719-7731. [PMID: 34316005 DOI: 10.1038/s41380-021-01229-4] [Citation(s) in RCA: 46] [Impact Index Per Article: 15.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/17/2021] [Revised: 06/15/2021] [Accepted: 07/05/2021] [Indexed: 12/12/2022]
Abstract
Reliable mapping of system-level individual differences is a critical first step toward precision medicine for complex disorders such as schizophrenia. Disrupted structural covariance indicates a system-level brain maturational disruption in schizophrenia. However, most studies examine structural covariance at the group level. This prevents subject-level inferences. Here, we introduce a Network Template Perturbation approach to construct individual differential structural covariance network (IDSCN) using regional gray-matter volume. IDSCN quantifies how structural covariance between two nodes in a patient deviates from the normative covariance in healthy subjects. We analyzed T1 images from 1287 subjects, including 107 first-episode (drug-naive) patients and 71 controls in the discovery datasets and established robustness in 213 first-episode (drug-naive), 294 chronic, 99 clinical high-risk patients, and 494 controls from the replication datasets. Patients with schizophrenia were highly variable in their altered structural covariance edges; the number of altered edges was related to severity of hallucinations. Despite this variability, a subset of covariance edges, including the left hippocampus-bilateral putamen/globus pallidus edges, clustered patients into two distinct subgroups with opposing changes in covariance compared to controls, and significant differences in their anxiety and depression scores. These subgroup differences were stable across all seven datasets with meaningful genetic associations and functional annotation for the affected edges. We conclude that the underlying physiology of affective symptoms in schizophrenia involves the hippocampus and putamen/pallidum, predates disease onset, and is sufficiently consistent to resolve morphological heterogeneity throughout the illness course. The two schizophrenia subgroups identified thus have implications for the nosology and clinical treatment.
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26
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Dimitriadis SI, Lancaster TM, Perry G, Tansey KE, Jones DK, Singh KD, Zammit S, Smith GD, Hall J, O'Donovan MC, Owen MJ, Linden DE. Global Brain Flexibility During Working Memory Is Reduced in a High-Genetic-Risk Group for Schizophrenia. BIOLOGICAL PSYCHIATRY. COGNITIVE NEUROSCIENCE AND NEUROIMAGING 2021; 6:1176-1184. [PMID: 33524599 PMCID: PMC7613444 DOI: 10.1016/j.bpsc.2021.01.007] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/24/2020] [Revised: 01/07/2021] [Accepted: 01/09/2021] [Indexed: 10/22/2022]
Abstract
BACKGROUND Altered functional brain connectivity has been proposed as an intermediate phenotype between genetic risk loci and clinical expression of schizophrenia. Genetic high-risk groups of healthy subjects are particularly suited for the investigation of this proposition because they can be tested in the absence of medication or other secondary effects of schizophrenia. METHODS Here, we applied dynamic functional connectivity analysis to functional magnetic resonance imaging data to reveal the reconfiguration of brain networks during a cognitive task. We recruited healthy carriers of common risk variants using the recall-by-genotype design. We assessed 197 individuals: 99 individuals (52 female, 47 male) with low polygenic risk scores (schizophrenia risk profile scores [SCZ-PRSs]) and 98 individuals (52 female, 46 male) with high SCZ-PRSs from both tails of the SCZ-PRS distribution from a genotyped population cohort, the Avon Longitudinal Study of Parents and Children (N = 8169). We compared groups both on conventional brain activation profiles, using the general linear model of the experiment, and on the neural flexibility index, which quantifies how frequent a brain region's community affiliation changes over experimental time. RESULTS Behavioral performance and standard brain activation profiles did not differ significantly between groups. High SCZ-PRS was associated with reduced flexibility index and network modularity across n-back levels. The whole-brain flexibility index and that of the frontoparietal working memory network was associated with n-back performance. We identified a dynamic network phenotype related to high SCZ-PRS. CONCLUSIONS Such neurophysiological markers can become important for the elucidation of biological mechanisms of schizophrenia and, particularly, the associated cognitive deficit.
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Affiliation(s)
- Stavros I Dimitriadis
- Neuroscience and Mental Health Research Institute, Cardiff University, Cardiff, United Kingdom; Cardiff University Brain Research Imaging Centre, School of Psychology, Cardiff University, Cardiff, United Kingdom; MRC Centre for Neuropsychiatric Genetics and Genomics, Institute of Psychological Medicine and Clinical Neurosciences, Cardiff School of Medicine, Cardiff University, Cardiff, United Kingdom; Neuroinformatics Group, School of Psychology, Cardiff University, Cardiff, United Kingdom.
| | - Thomas M Lancaster
- Neuroscience and Mental Health Research Institute, Cardiff University, Cardiff, United Kingdom; Cardiff University Brain Research Imaging Centre, School of Psychology, Cardiff University, Cardiff, United Kingdom; School of Psychology, Bath University, Bath, United Kingdom
| | - Gavin Perry
- Cardiff University Brain Research Imaging Centre, School of Psychology, Cardiff University, Cardiff, United Kingdom
| | - Katherine E Tansey
- MRC Integrative Epidemiology Unit, University of Bristol, United Kingdom
| | - Derek K Jones
- Cardiff University Brain Research Imaging Centre, School of Psychology, Cardiff University, Cardiff, United Kingdom
| | - Krish D Singh
- Cardiff University Brain Research Imaging Centre, School of Psychology, Cardiff University, Cardiff, United Kingdom
| | - Stanley Zammit
- MRC Centre for Neuropsychiatric Genetics and Genomics, Institute of Psychological Medicine and Clinical Neurosciences, Cardiff School of Medicine, Cardiff University, Cardiff, United Kingdom; Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, United Kingdom
| | - George Davey Smith
- MRC Integrative Epidemiology Unit, University of Bristol, United Kingdom; Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, United Kingdom
| | - Jeremy Hall
- Neuroscience and Mental Health Research Institute, Cardiff University, Cardiff, United Kingdom; MRC Centre for Neuropsychiatric Genetics and Genomics, Institute of Psychological Medicine and Clinical Neurosciences, Cardiff School of Medicine, Cardiff University, Cardiff, United Kingdom
| | - Michael C O'Donovan
- Neuroscience and Mental Health Research Institute, Cardiff University, Cardiff, United Kingdom; MRC Centre for Neuropsychiatric Genetics and Genomics, Institute of Psychological Medicine and Clinical Neurosciences, Cardiff School of Medicine, Cardiff University, Cardiff, United Kingdom
| | - Michael J Owen
- Neuroscience and Mental Health Research Institute, Cardiff University, Cardiff, United Kingdom; MRC Centre for Neuropsychiatric Genetics and Genomics, Institute of Psychological Medicine and Clinical Neurosciences, Cardiff School of Medicine, Cardiff University, Cardiff, United Kingdom
| | - David E Linden
- Neuroscience and Mental Health Research Institute, Cardiff University, Cardiff, United Kingdom; Cardiff University Brain Research Imaging Centre, School of Psychology, Cardiff University, Cardiff, United Kingdom; MRC Centre for Neuropsychiatric Genetics and Genomics, Institute of Psychological Medicine and Clinical Neurosciences, Cardiff School of Medicine, Cardiff University, Cardiff, United Kingdom; School for Mental Health and Neuroscience, Faculty of Health, Medicine and Life Sciences, Maastricht University, Maastricht, the Netherlands
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27
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Liu Y, Ouyang P, Zheng Y, Mi L, Zhao J, Ning Y, Guo W. A Selective Review of the Excitatory-Inhibitory Imbalance in Schizophrenia: Underlying Biology, Genetics, Microcircuits, and Symptoms. Front Cell Dev Biol 2021; 9:664535. [PMID: 34746116 PMCID: PMC8567014 DOI: 10.3389/fcell.2021.664535] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2021] [Accepted: 09/27/2021] [Indexed: 12/29/2022] Open
Abstract
Schizophrenia is a chronic disorder characterized by specific positive and negative primary symptoms, social behavior disturbances and cognitive deficits (e.g., impairment in working memory and cognitive flexibility). Mounting evidence suggests that altered excitability and inhibition at the molecular, cellular, circuit and network level might be the basis for the pathophysiology of neurodevelopmental and neuropsychiatric disorders such as schizophrenia. In the past decades, human and animal studies have identified that glutamate and gamma-aminobutyric acid (GABA) neurotransmissions are critically involved in several cognitive progresses, including learning and memory. The purpose of this review is, by analyzing emerging findings relating to the balance of excitatory and inhibitory, ranging from animal models of schizophrenia to clinical studies in patients with early onset, first-episode or chronic schizophrenia, to discuss how the excitatory-inhibitory imbalance may relate to the pathophysiology of disease phenotypes such as cognitive deficits and negative symptoms, and highlight directions for appropriate therapeutic strategies.
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Affiliation(s)
- Yi Liu
- National Clinical Research Center on Mental Disorders and Department of Psychiatry, The Second Xiangya Hospital, Central South University, Changsha, China.,Department of Psychiatry, The Affiliated Brain Hospital, Guangzhou Medical University, Guangzhou, China
| | - Pan Ouyang
- National Clinical Research Center on Mental Disorders and Department of Psychiatry, The Second Xiangya Hospital, Central South University, Changsha, China
| | - Yingjun Zheng
- Department of Psychiatry, The Affiliated Brain Hospital, Guangzhou Medical University, Guangzhou, China
| | - Lin Mi
- Department of Psychiatry, The Affiliated Brain Hospital, Guangzhou Medical University, Guangzhou, China
| | - Jingping Zhao
- National Clinical Research Center on Mental Disorders and Department of Psychiatry, The Second Xiangya Hospital, Central South University, Changsha, China
| | - Yuping Ning
- Department of Psychiatry, The Affiliated Brain Hospital, Guangzhou Medical University, Guangzhou, China.,The First School of Clinical Medical University, Guangzhou, China
| | - Wenbin Guo
- National Clinical Research Center on Mental Disorders and Department of Psychiatry, The Second Xiangya Hospital, Central South University, Changsha, China
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28
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Overbeek G, Gawne TJ, Reid MA, Kraguljac NV, Lahti AC. A multimodal neuroimaging study investigating resting-state connectivity, glutamate and GABA at 7 T in first-episode psychosis. J Psychiatry Neurosci 2021; 46:E702-E710. [PMID: 34933941 PMCID: PMC8695527 DOI: 10.1503/jpn.210107] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/04/2021] [Revised: 10/05/2021] [Accepted: 10/18/2021] [Indexed: 12/12/2022] Open
Abstract
BACKGROUND The major excitatory and inhibitory neurometabolites in the brain, glutamate and γ-aminobutyric acid (GABA), respectively, are related to the functional MRI signal. Disruption of resting-state functional MRI signals has been reported in psychosis spectrum disorders, but few studies have investigated the role of these metabolites in this context. METHODS We included 19 patients with first-episode psychosis and 21 healthy controls in this combined magnetic resonance spectroscopy (MRS) and resting-state functional connectivity study. All imaging was performed on a Siemens Magnetom 7 T MRI scanner. Both the MRS voxel and the seed for functional connectivity analysis were located in the dorsal anterior cingulate cortex (ACC). We used multiple regressions to test for an interaction between ACC brain connectivity, diagnosis and neurometabolites. RESULTS ACC brain connectivity was altered in first-episode psychosis. The relationship between ACC glutamate and ACC functional connectivity differed between patients with first-episode psychosis and healthy controls in the precuneus, retrosplenial cortex, supramarginal gyrus and angular gyrus. As well, the relationship between ACC GABA and ACC functional connectivity differed between groups in the caudate, putamen and supramarginal gyrus. LIMITATIONS We used a small sample size. As well, although they were not chronically medicated, all participants were medicated during the study. CONCLUSION We demonstrated a link between the major excitatory and inhibitory brain metabolites and resting-state functional connectivity in healthy participants, as well as an alteration in this relationship in patients with first-episode psychosis. Combining data from different imaging modalities may help our mechanistic understanding of the relationship between major neurometabolites and brain network dynamics, and shed light on the pathophysiology of first-episode psychosis.
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Affiliation(s)
- Gregory Overbeek
- From the Department of Psychiatry and Behavioral Neurobiology, University of Alabama at Birmingham (Overbeek, Kraguljac, Lahti); the Department of Optometry and Vision Science, University of Alabama at Birmingham (Gawne); and the Department of Electrical and Computer Engineering, Auburn University, Auburn AL (Reid)
| | - Timothy J Gawne
- From the Department of Psychiatry and Behavioral Neurobiology, University of Alabama at Birmingham (Overbeek, Kraguljac, Lahti); the Department of Optometry and Vision Science, University of Alabama at Birmingham (Gawne); and the Department of Electrical and Computer Engineering, Auburn University, Auburn AL (Reid)
| | - Meredith A Reid
- From the Department of Psychiatry and Behavioral Neurobiology, University of Alabama at Birmingham (Overbeek, Kraguljac, Lahti); the Department of Optometry and Vision Science, University of Alabama at Birmingham (Gawne); and the Department of Electrical and Computer Engineering, Auburn University, Auburn AL (Reid)
| | - Nina V Kraguljac
- From the Department of Psychiatry and Behavioral Neurobiology, University of Alabama at Birmingham (Overbeek, Kraguljac, Lahti); the Department of Optometry and Vision Science, University of Alabama at Birmingham (Gawne); and the Department of Electrical and Computer Engineering, Auburn University, Auburn AL (Reid)
| | - Adrienne C Lahti
- From the Department of Psychiatry and Behavioral Neurobiology, University of Alabama at Birmingham (Overbeek, Kraguljac, Lahti); the Department of Optometry and Vision Science, University of Alabama at Birmingham (Gawne); and the Department of Electrical and Computer Engineering, Auburn University, Auburn AL (Reid)
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29
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Deco G, Kringelbach ML, Arnatkeviciute A, Oldham S, Sabaroedin K, Rogasch NC, Aquino KM, Fornito A. Dynamical consequences of regional heterogeneity in the brain's transcriptional landscape. SCIENCE ADVANCES 2021; 7:eabf4752. [PMID: 34261652 PMCID: PMC8279501 DOI: 10.1126/sciadv.abf4752] [Citation(s) in RCA: 59] [Impact Index Per Article: 19.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/29/2020] [Accepted: 06/01/2021] [Indexed: 05/02/2023]
Abstract
Brain regions vary in their molecular and cellular composition, but how this heterogeneity shapes neuronal dynamics is unclear. Here, we investigate the dynamical consequences of regional heterogeneity using a biophysical model of whole-brain functional magnetic resonance imaging (MRI) dynamics in humans. We show that models in which transcriptional variations in excitatory and inhibitory receptor (E:I) gene expression constrain regional heterogeneity more accurately reproduce the spatiotemporal structure of empirical functional connectivity estimates than do models constrained by global gene expression profiles or MRI-derived estimates of myeloarchitecture. We further show that regional transcriptional heterogeneity is essential for yielding both ignition-like dynamics, which are thought to support conscious processing, and a wide variance of regional-activity time scales, which supports a broad dynamical range. We thus identify a key role for E:I heterogeneity in generating complex neuronal dynamics and demonstrate the viability of using transcriptomic data to constrain models of large-scale brain function.
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Affiliation(s)
- Gustavo Deco
- Center for Brain and Cognition, Computational Neuroscience Group, Department of Information and Communication Technologies, Universitat Pompeu Fabra, Roc Boronat 138, Barcelona 08018, Spain
- Institució Catalana de la Recerca i Estudis Avançats (ICREA), Passeig Lluís Companys 23, Barcelona 08010, Spain
- Department of Neuropsychology, Max Planck Institute for Human Cognitive and Brain Sciences, 04103 Leipzig, Germany
- Turner Institute for Brain and Mental Health, School of Psychological Sciences, and Monash Biomedical Imaging, Monash University, Clayton, Melbourne, VIC, Australia
| | - Morten L Kringelbach
- Department of Psychiatry, University of Oxford, Oxford, UK
- Center for Music in the Brain, Department of Clinical Medicine, Aarhus University, Aarhus, Denmark
| | - Aurina Arnatkeviciute
- Turner Institute for Brain and Mental Health, School of Psychological Sciences, and Monash Biomedical Imaging, Monash University, Clayton, Melbourne, VIC, Australia
| | - Stuart Oldham
- Turner Institute for Brain and Mental Health, School of Psychological Sciences, and Monash Biomedical Imaging, Monash University, Clayton, Melbourne, VIC, Australia
| | - Kristina Sabaroedin
- Turner Institute for Brain and Mental Health, School of Psychological Sciences, and Monash Biomedical Imaging, Monash University, Clayton, Melbourne, VIC, Australia
| | - Nigel C Rogasch
- Turner Institute for Brain and Mental Health, School of Psychological Sciences, and Monash Biomedical Imaging, Monash University, Clayton, Melbourne, VIC, Australia
- Discipline of Psychiatry, Adelaide Medical School, University of Adelaide, and Hopwood Centre for Neurobiology, Lifelong Health Theme, South Australian Health and Medical Research Institute, Adelaide, SA, Australia
| | - Kevin M Aquino
- Turner Institute for Brain and Mental Health, School of Psychological Sciences, and Monash Biomedical Imaging, Monash University, Clayton, Melbourne, VIC, Australia
- School of Physics, University of Sydney, New South Wales, 2006 Australia
| | - Alex Fornito
- Turner Institute for Brain and Mental Health, School of Psychological Sciences, and Monash Biomedical Imaging, Monash University, Clayton, Melbourne, VIC, Australia.
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30
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Hipólito I, Ramstead MJD, Convertino L, Bhat A, Friston K, Parr T. Markov blankets in the brain. Neurosci Biobehav Rev 2021; 125:88-97. [PMID: 33607182 PMCID: PMC8373616 DOI: 10.1016/j.neubiorev.2021.02.003] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2020] [Revised: 01/18/2021] [Accepted: 02/01/2021] [Indexed: 01/19/2023]
Abstract
Recent characterisations of self-organising systems depend upon the presence of a 'Markov blanket': a statistical boundary that mediates the interactions between the inside and outside of a system. We leverage this idea to provide an analysis of partitions in neuronal systems. This is applicable to brain architectures at multiple scales, enabling partitions into single neurons, brain regions, and brain-wide networks. This treatment is based upon the canonical micro-circuitry used in empirical studies of effective connectivity, so as to speak directly to practical applications. The notion of effective connectivity depends upon the dynamic coupling between functional units, whose form recapitulates that of a Markov blanket at each level of analysis. The nuance afforded by partitioning neural systems in this way highlights certain limitations of 'modular' perspectives of brain function that only consider a single level of description.
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Affiliation(s)
- Inês Hipólito
- Humboldt-Universität zu Berlin, Department of Philosophy & Berlin School of Mind and Brain, Germany; Wellcome Centre for Human Neuroimaging, University College London, United Kingdom.
| | - Maxwell J D Ramstead
- Wellcome Centre for Human Neuroimaging, University College London, United Kingdom; Division of Social and Transcultural Psychiatry, Department of Psychiatry, McGill University, Montreal, Quebec, Canada; Culture, Mind, and Brain Program, McGill University, Montreal, Quebec, Canada
| | - Laura Convertino
- Wellcome Centre for Human Neuroimaging, University College London, United Kingdom; Institute of Cognitive Neuroscience (ICN), University College London, London, United Kingdom
| | - Anjali Bhat
- Wellcome Centre for Human Neuroimaging, University College London, United Kingdom
| | - Karl Friston
- Wellcome Centre for Human Neuroimaging, University College London, United Kingdom
| | - Thomas Parr
- Wellcome Centre for Human Neuroimaging, University College London, United Kingdom
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31
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McCutcheon RA, Pillinger T, Rogdaki M, Bustillo J, Howes OD. Glutamate connectivity associations converge upon the salience network in schizophrenia and healthy controls. Transl Psychiatry 2021; 11:322. [PMID: 34045446 PMCID: PMC8159959 DOI: 10.1038/s41398-021-01455-y] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/23/2021] [Revised: 05/04/2021] [Accepted: 05/14/2021] [Indexed: 11/27/2022] Open
Abstract
Alterations in cortical inter-areal functional connectivity, and aberrant glutamatergic signalling are implicated in the pathophysiology of schizophrenia but the relationship between the two is unclear. We used multimodal imaging to identify areas of convergence between the two systems. Two separate cohorts were examined, comprising 195 participants in total. All participants received resting state functional MRI to characterise functional brain networks and proton magnetic resonance spectroscopy (1H-MRS) to measure glutamate concentrations in the frontal cortex. Study A investigated the relationship between frontal cortex glutamate concentrations and network connectivity in individuals with schizophrenia and healthy controls. Study B also used 1H-MRS, and scanned individuals with schizophrenia and healthy controls before and after a challenge with the glutamatergic modulator riluzole, to investigate the relationship between changes in glutamate concentrations and changes in network connectivity. In both studies the network based statistic was used to probe associations between glutamate and connectivity, and glutamate associated networks were then characterised in terms of their overlap with canonical functional networks. Study A involved 76 individuals with schizophrenia and 82 controls, and identified a functional network negatively associated with glutamate concentrations that was concentrated within the salience network (p < 0.05) and did not differ significantly between patients and controls (p > 0.85). Study B involved 19 individuals with schizophrenia and 17 controls and found that increases in glutamate concentrations induced by riluzole were linked to increases in connectivity localised to the salience network (p < 0.05), and the relationship did not differ between patients and controls (p > 0.4). Frontal cortex glutamate concentrations are associated with inter-areal functional connectivity of a network that localises to the salience network. Changes in network connectivity in response to glutamate modulation show an opposite effect compared to the relationship observed at baseline, which may complicate pharmacological attempts to simultaneously correct glutamatergic and connectivity aberrations.
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Affiliation(s)
- Robert A McCutcheon
- Department of Psychosis Studies, Institute of Psychiatry, Psychology & Neuroscience, Kings College London, London, SE5 8AF, UK. .,Psychiatric Imaging Group, MRC London Institute of Medical Sciences, Hammersmith Hospital, London, W12 0NN, UK. .,Institute of Clinical Sciences, Faculty of Medicine, Imperial College London, London, W12 0NN, UK. .,South London and Maudsley NHS Foundation Trust, London, UK.
| | - Toby Pillinger
- Department of Psychosis Studies, Institute of Psychiatry, Psychology & Neuroscience, Kings College London, London, SE5 8AF, UK.,Psychiatric Imaging Group, MRC London Institute of Medical Sciences, Hammersmith Hospital, London, W12 0NN, UK.,Institute of Clinical Sciences, Faculty of Medicine, Imperial College London, London, W12 0NN, UK.,South London and Maudsley NHS Foundation Trust, London, UK
| | - Maria Rogdaki
- Department of Psychosis Studies, Institute of Psychiatry, Psychology & Neuroscience, Kings College London, London, SE5 8AF, UK.,Psychiatric Imaging Group, MRC London Institute of Medical Sciences, Hammersmith Hospital, London, W12 0NN, UK.,Institute of Clinical Sciences, Faculty of Medicine, Imperial College London, London, W12 0NN, UK.,South London and Maudsley NHS Foundation Trust, London, UK
| | - Juan Bustillo
- Department of Psychiatry, University of New Mexico, Albuquerque, NM, USA.,Department of Neurosciences, University of New Mexico, Albuquerque, NM, USA
| | - Oliver D Howes
- Department of Psychosis Studies, Institute of Psychiatry, Psychology & Neuroscience, Kings College London, London, SE5 8AF, UK.,Psychiatric Imaging Group, MRC London Institute of Medical Sciences, Hammersmith Hospital, London, W12 0NN, UK.,Institute of Clinical Sciences, Faculty of Medicine, Imperial College London, London, W12 0NN, UK.,South London and Maudsley NHS Foundation Trust, London, UK
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32
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Northoff G, Gomez-Pilar J. Overcoming Rest-Task Divide-Abnormal Temporospatial Dynamics and Its Cognition in Schizophrenia. Schizophr Bull 2021; 47:751-765. [PMID: 33305324 PMCID: PMC8661394 DOI: 10.1093/schbul/sbaa178] [Citation(s) in RCA: 29] [Impact Index Per Article: 9.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Abstract
Schizophrenia is a complex psychiatric disorder exhibiting alterations in spontaneous and task-related cerebral activity whose relation (termed "state dependence") remains unclear. For unraveling their relationship, we review recent electroencephalographic (and a few functional magnetic resonance imaging) studies in schizophrenia that assess and compare both rest/prestimulus and task states, ie, rest/prestimulus-task modulation. Results report reduced neural differentiation of task-related activity from rest/prestimulus activity across different regions, neural measures, cognitive domains, and imaging modalities. Together, the findings show reduced rest/prestimulus-task modulation, which is mediated by abnormal temporospatial dynamics of the spontaneous activity. Abnormal temporospatial dynamics, in turn, may lead to abnormal prediction, ie, predictive coding, which mediates cognitive changes and psychopathological symptoms, including confusion of internally and externally oriented cognition. In conclusion, reduced rest/prestimulus-task modulation in schizophrenia provides novel insight into the neuronal mechanisms that connect task-related changes to cognitive abnormalities and psychopathological symptoms.
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Affiliation(s)
- Georg Northoff
- Mental Health Center/7th Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
- Mind, Brain Imaging and Neuroethics, Institute of Mental Health Research, Royal Ottawa Healthcare Group, University of Ottawa, Ottawa ON, Canada
| | - Javier Gomez-Pilar
- Biomedical Engineering Group, University of Valladolid, Valladolid, Spain
- Centro de Investigación Biomédica en Red en Bioingeniería, Biomateriales y Nanomedicina, Valladolid, Spain
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33
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Klein PC, Ettinger U, Schirner M, Ritter P, Rujescu D, Falkai P, Koutsouleris N, Kambeitz-Ilankovic L, Kambeitz J. Brain Network Simulations Indicate Effects of Neuregulin-1 Genotype on Excitation-Inhibition Balance in Cortical Dynamics. Cereb Cortex 2021; 31:2013-2025. [PMID: 33279967 DOI: 10.1093/cercor/bhaa339] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2020] [Revised: 09/01/2020] [Accepted: 10/11/2020] [Indexed: 11/14/2022] Open
Abstract
Neuregulin-1 (NRG1) represents an important factor for multiple processes including neurodevelopment, brain functioning or cognitive functions. Evidence from animal research suggests an effect of NRG1 on the excitation-inhibition (E/I) balance in cortical circuits. However, direct evidence for the importance of NRG1 in E/I balance in humans is still lacking. In this work, we demonstrate the application of computational, biophysical network models to advance our understanding of the interaction between cortical activity observed in neuroimaging and the underlying neurobiology. We employed a biophysical neuronal model to simulate large-scale brain dynamics and to investigate the role of polymorphisms in the NRG1 gene (rs35753505, rs3924999) in n = 96 healthy adults. Our results show that G/G-carriers (rs3924999) exhibit a significant difference in global coupling (P = 0.048) and multiple parameters determining E/I-balance such as excitatory synaptic coupling (P = 0.047), local excitatory recurrence (P = 0.032) and inhibitory synaptic coupling (P = 0.028). This indicates that NRG1 may be related to excitatory recurrence or excitatory synaptic coupling potentially resulting in altered E/I-balance. Moreover, we suggest that computational modeling is a suitable tool to investigate specific biological mechanisms in health and disease.
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Affiliation(s)
- Pedro Costa Klein
- Department of Psychiatry, University of Cologne, Faculty of Medicine and University Hospital Cologne, 50937, Germany
| | - Ulrich Ettinger
- Department of Psychology, University of Bonn, Bonn, 53111, Germany
| | - Michael Schirner
- Charité Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Dept. of Neurology, 10117, Germany.,Bernstein Focus State Dependencies of Learning & Bernstein Center for Computational Neuroscience, Berlin 10115, Germany
| | - Petra Ritter
- Charité Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Dept. of Neurology, 10117, Germany.,Bernstein Focus State Dependencies of Learning & Bernstein Center for Computational Neuroscience, Berlin 10115, Germany
| | - Dan Rujescu
- University Clinic for Psychiatry, Psychotherapy and Psychosomatic, Martin-Luther-University, Halle-Wittenberg, 06112, Germany
| | - Peter Falkai
- Department of Psychiatry, Ludwig Maximilians Universität München, 80336, Germany
| | | | - Lana Kambeitz-Ilankovic
- Department of Psychiatry, University of Cologne, Faculty of Medicine and University Hospital Cologne, 50937, Germany.,Department of Psychiatry, Ludwig Maximilians Universität München, 80336, Germany
| | - Joseph Kambeitz
- Department of Psychiatry, University of Cologne, Faculty of Medicine and University Hospital Cologne, 50937, Germany
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34
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Li R, Wang H, Wang L, Zhang L, Zou T, Wang X, Liao W, Zhang Z, Lu G, Chen H. Shared and distinct global signal topography disturbances in subcortical and cortical networks in human epilepsy. Hum Brain Mapp 2021; 42:412-426. [PMID: 33073893 PMCID: PMC7776006 DOI: 10.1002/hbm.25231] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2020] [Revised: 08/08/2020] [Accepted: 09/29/2020] [Indexed: 01/21/2023] Open
Abstract
Epilepsy is a common brain network disorder associated with disrupted large-scale excitatory and inhibitory neural interactions. Recent resting-state fMRI evidence indicates that global signal (GS) fluctuations that have commonly been ignored are linked to neural activity. However, the mechanisms underlying the altered global pattern of fMRI spontaneous fluctuations in epilepsy remain unclear. Here, we quantified GS topography using beta weights obtained from a multiple regression model in a large group of epilepsy with different subtypes (98 focal temporal epilepsy; 116 generalized epilepsy) and healthy population (n = 151). We revealed that the nonuniformly distributed GS topography across association and sensory areas in healthy controls was significantly shifted in patients. Particularly, such shifts of GS topography disturbances were more widespread and bilaterally distributed in the midbrain, cerebellum, visual cortex, and medial and orbital cortex in generalized epilepsy, whereas in focal temporal epilepsy, these networks spread beyond the temporal areas but mainly remain lateralized. Moreover, we found that these abnormal GS topography patterns were likely to evolve over the course of a longer epilepsy disease. Our study demonstrates that epileptic processes can potentially affect global excitation/inhibition balance and shift the normal GS topological distribution. These progressive topographical GS disturbances in subcortical-cortical networks may underlie pathophysiological mechanisms of global fluctuations in human epilepsy.
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Affiliation(s)
- Rong Li
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of ChinaChengduChina
- MOE Key Laboratory for Neuroinformation, High‐Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan ProvinceUniversity of Electronic Science and Technology of ChinaChengduChina
| | - Hongyu Wang
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of ChinaChengduChina
- MOE Key Laboratory for Neuroinformation, High‐Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan ProvinceUniversity of Electronic Science and Technology of ChinaChengduChina
| | - Liangcheng Wang
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of ChinaChengduChina
- MOE Key Laboratory for Neuroinformation, High‐Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan ProvinceUniversity of Electronic Science and Technology of ChinaChengduChina
| | - Leiyao Zhang
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of ChinaChengduChina
- MOE Key Laboratory for Neuroinformation, High‐Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan ProvinceUniversity of Electronic Science and Technology of ChinaChengduChina
| | - Ting Zou
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of ChinaChengduChina
- MOE Key Laboratory for Neuroinformation, High‐Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan ProvinceUniversity of Electronic Science and Technology of ChinaChengduChina
| | - Xuyang Wang
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of ChinaChengduChina
- MOE Key Laboratory for Neuroinformation, High‐Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan ProvinceUniversity of Electronic Science and Technology of ChinaChengduChina
| | - Wei Liao
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of ChinaChengduChina
- MOE Key Laboratory for Neuroinformation, High‐Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan ProvinceUniversity of Electronic Science and Technology of ChinaChengduChina
| | - Zhiqiang Zhang
- Department of Medical ImagingJinling Hospital, Nanjing University School of MedicineNanjingChina
| | - Guangming Lu
- Department of Medical ImagingJinling Hospital, Nanjing University School of MedicineNanjingChina
| | - Huafu Chen
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of ChinaChengduChina
- MOE Key Laboratory for Neuroinformation, High‐Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan ProvinceUniversity of Electronic Science and Technology of ChinaChengduChina
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All roads lead to the motor cortex: psychomotor mechanisms and their biochemical modulation in psychiatric disorders. Mol Psychiatry 2021; 26:92-102. [PMID: 32555423 DOI: 10.1038/s41380-020-0814-5] [Citation(s) in RCA: 95] [Impact Index Per Article: 31.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/03/2020] [Revised: 06/01/2020] [Accepted: 06/05/2020] [Indexed: 02/08/2023]
Abstract
Psychomotor abnormalities have been abundantly observed in psychiatric disorders like major depressive disorder (MDD), bipolar disorder (BD), and schizophrenia (SCH). Although early psychopathological descriptions highlighted the truly psychomotor nature of these abnormalities, more recent investigations conceive them rather in purely motor terms. This has led to an emphasis of dopamine-based abnormalities in subcortical-cortical circuits including substantia nigra, basal ganglia, thalamus, and motor cortex. Following recent findings in MDD, BD, and SCH, we suggest a concept of psychomotor symptoms in the literal sense of the term by highlighting three specifically psychomotor (rather than motor) mechanisms including their biochemical modulation. These include: (i) modulation of dopamine- and substantia nigra-based subcortical-cortical motor circuit by primarily non-motor subcortical raphe nucleus and serotonin via basal ganglia and thalamus (as well as by other neurotransmitters like glutamate and GABA); (ii) modulation of motor cortex and motor network by non-motor cortical networks like default-mode network and sensory networks; (iii) global activity in cortex may also shape regional distribution of neural activity in motor cortex. We demonstrate that these three psychomotor mechanisms and their underlying biochemical modulation are operative in both healthy subjects as well as in MDD, BD, and SCH subjects; the only difference consists in the fact that these mechanisms are abnormally balanced and thus manifest in extreme values in psychiatric disorders. We conclude that psychomotor mechanisms operate in a dimensional and cross-nosological way as their degrees of expression are related to levels of psychomotor activity (across different disorders) rather than to the diagnostic categories themselves. Psychomotor mechanisms and their biochemical modulation can be considered paradigmatic examples of a dimensional approach as suggested in RDoC and the recently introduced spatiotemporal psychopathology.
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Deco G, Kringelbach ML. Turbulent-like Dynamics in the Human Brain. Cell Rep 2020; 33:108471. [PMID: 33296654 PMCID: PMC7725672 DOI: 10.1016/j.celrep.2020.108471] [Citation(s) in RCA: 42] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2020] [Revised: 09/07/2020] [Accepted: 11/11/2020] [Indexed: 12/11/2022] Open
Abstract
Turbulence facilitates fast energy/information transfer across scales in physical systems. These qualities are important for brain function, but it is currently unknown if the dynamic intrinsic backbone of the brain also exhibits turbulence. Using large-scale neuroimaging empirical data from 1,003 healthy participants, we demonstrate turbulent-like human brain dynamics. Furthermore, we build a whole-brain model with coupled oscillators to demonstrate that the best fit to the data corresponds to a region of maximally developed turbulent-like dynamics, which also corresponds to maximal sensitivity to the processing of external stimulations (information capability). The model shows the economy of anatomy by following the exponential distance rule of anatomical connections as a cost-of-wiring principle. This establishes a firm link between turbulent-like brain activity and optimal brain function. Overall, our results reveal a way of analyzing and modeling whole-brain dynamics that suggests a turbulent-like dynamic intrinsic backbone facilitating large-scale network communication.
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Affiliation(s)
- Gustavo Deco
- Center for Brain and Cognition, Computational Neuroscience Group, Department of Information and Communication Technologies, Universitat Pompeu Fabra, Roc Boronat 138, Barcelona, 08018, Spain; Institució Catalana de la Recerca i Estudis Avançats (ICREA), Passeig Lluís Companys 23, Barcelona, 08010, Spain; Department of Neuropsychology, Max Planck Institute for Human Cognitive and Brain Sciences, 04103 Leipzig, Germany; School of Psychological Sciences, Monash University, Melbourne, Clayton, VIC 3800, Australia
| | - Morten L Kringelbach
- Centre for Eudaimonia and Human Flourishing, University of Oxford, Oxford, UK; Department of Psychiatry, University of Oxford, Oxford, UK; Center for Music in the Brain, Department of Clinical Medicine, Aarhus University, Aarhus, Denmark.
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Wang Y, Wang C, Miao P, Liu J, Wei Y, Wu L, Wang K, Cheng J. An imbalance between functional segregation and integration in patients with pontine stroke: A dynamic functional network connectivity study. NEUROIMAGE-CLINICAL 2020; 28:102507. [PMID: 33395996 PMCID: PMC7714678 DOI: 10.1016/j.nicl.2020.102507] [Citation(s) in RCA: 25] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/01/2020] [Revised: 10/24/2020] [Accepted: 11/12/2020] [Indexed: 11/04/2022]
Abstract
Pontine stroke patients show abnormal time-varying brain activity. Pontine stroke patients exist aberrant functional segregation and integration. The alterations of dFNC may lead to a poor functional recovery after stroke.
Background Previous studies on brain functional connectivity have revealed the neural physiopathology in patients with pontine stroke (PS). However, those studies focused only on the static features of intrinsic fluctuations, rather than on the time-varying effects throughout the entire scan. In the present study, we sought to explore the underlying mechanism of PS using the dynamic functional network connectivity (dFNC) method. Methods Resting-state functional magnetic resonance imaging (fMRI) data were collected from 58 patients with PS and 52 healthy controls (HC). Independent component analysis (ICA), the sliding window method, and k-means clustering analysis were performed to extract different functional networks, to calculate dFNC matrices, and to estimate distinct dynamic connectivity states. Additionally, temporal features were compared between the two groups in each state to explore the brain’s preference for different dynamic connectivity states in PS, and global and local efficiency were compared among states to explore the differences of topologic organization across different dFNC states. The correlations between clinical scales and the temporal features that differed between the two groups also were calculated. Results The dFNC analyses suggested four recurring states; in two of these states, the PS group showed a different duration from that of the HC group. Patients with PS spent significantly more time in a sparsely connected state (State 1), which was characterized by relatively low levels of connectivity within and between all brain networks. In contrast, patients with PS spent significantly less time in a highly segregated state (State 2), which was characterized by high levels of positive connectivities within primary perceptional domains and within higher cognitive control domains, and by high levels of negative inter-functional connectivities (inter-FCs) among primary perceptional and higher cognitive control domains. Additionally, the dwell time in State 2 was positively correlated with HC group’s long-term memory scores in the Rey Auditory Verbal Learning Test (RAVLT-L), whereas there was no correlation between the State-2 dwell time and RAVLT-L scores in the PS group. Furthermore, the sparsely connected state and the highly segregated state mentioned above had the highest global efficiency and the highest local efficiency among the four states, respectively. Conclusions In summary, we observed a preference in the aberrant brain for dynamic connectivity states with different network topologic organization in patients with PS, indicating abnormal functional segregation and integration of the whole brain and confirming the imperfection of functional network connectivity in patients with PS. These findings provide new evidence for the dynamic neural mechanisms underlying clinical symptoms in patients with PS.
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Affiliation(s)
- Yingying Wang
- Department of MRI, Henan Key Laboratory of Magnetic Resonance Function and Molecular Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Caihong Wang
- Department of MRI, Henan Key Laboratory of Magnetic Resonance Function and Molecular Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China.
| | - Peifang Miao
- Department of MRI, Henan Key Laboratory of Magnetic Resonance Function and Molecular Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Jingchun Liu
- Department of Radiology, Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, China
| | - Ying Wei
- Department of MRI, Henan Key Laboratory of Magnetic Resonance Function and Molecular Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Luobing Wu
- Department of MRI, Henan Key Laboratory of Magnetic Resonance Function and Molecular Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Kaiyu Wang
- GE Healthcare MR Research, Beijing, China
| | - Jingliang Cheng
- Department of MRI, Henan Key Laboratory of Magnetic Resonance Function and Molecular Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China.
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van Leeuwen TM, Sauer A, Jurjut AM, Wibral M, Uhlhaas PJ, Singer W, Melloni L. Perceptual Gains and Losses in Synesthesia and Schizophrenia. Schizophr Bull 2020; 47:722-730. [PMID: 33150444 PMCID: PMC8084450 DOI: 10.1093/schbul/sbaa162] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/29/2022]
Abstract
Individual differences in perception are widespread. Considering inter-individual variability, synesthetes experience stable additional sensations; schizophrenia patients suffer perceptual deficits in, eg, perceptual organization (alongside hallucinations and delusions). Is there a unifying principle explaining inter-individual variability in perception? There is good reason to believe perceptual experience results from inferential processes whereby sensory evidence is weighted by prior knowledge about the world. Perceptual variability may result from different precision weighting of sensory evidence and prior knowledge. We tested this hypothesis by comparing visibility thresholds in a perceptual hysteresis task across medicated schizophrenia patients (N = 20), synesthetes (N = 20), and controls (N = 26). Participants rated the subjective visibility of stimuli embedded in noise while we parametrically manipulated the availability of sensory evidence. Additionally, precise long-term priors in synesthetes were leveraged by presenting either synesthesia-inducing or neutral stimuli. Schizophrenia patients showed increased visibility thresholds, consistent with overreliance on sensory evidence. In contrast, synesthetes exhibited lowered thresholds exclusively for synesthesia-inducing stimuli suggesting high-precision long-term priors. Additionally, in both synesthetes and schizophrenia patients explicit, short-term priors-introduced during the hysteresis experiment-lowered thresholds but did not normalize perception. Our results imply that perceptual variability might result from differences in the precision afforded to prior beliefs and sensory evidence, respectively.
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Affiliation(s)
- Tessa M van Leeuwen
- Department of Neurophysiology, Max Planck Institute for Brain Research, Frankfurt am Main, Germany,Ernst Strüngmann Institute (ESI) for Neuroscience in Cooperation with Max Planck Society, Frankfurt am Main, Germany,Radboud University, Donders Institute for Brain, Cognition and Behaviour, Nijmegen, the Netherlands
| | - Andreas Sauer
- Department of Neurophysiology, Max Planck Institute for Brain Research, Frankfurt am Main, Germany,Ernst Strüngmann Institute (ESI) for Neuroscience in Cooperation with Max Planck Society, Frankfurt am Main, Germany
| | - Anna-Maria Jurjut
- Department of Neurophysiology, Max Planck Institute for Brain Research, Frankfurt am Main, Germany
| | - Michael Wibral
- Magnetoencephalography Unit, Brain Imaging Center, Johann Wolfgang Goethe University, Frankfurt am Main, Germany
| | - Peter J Uhlhaas
- Department of Neurophysiology, Max Planck Institute for Brain Research, Frankfurt am Main, Germany,Institute of Neuroscience and Psychology, University of Glasgow, Scotland,Department of Child and Adolescent Psychiatry, Charité Universitätsmedizin, Berlin, Germany
| | - Wolf Singer
- Department of Neurophysiology, Max Planck Institute for Brain Research, Frankfurt am Main, Germany,Ernst Strüngmann Institute (ESI) for Neuroscience in Cooperation with Max Planck Society, Frankfurt am Main, Germany,Frankfurt Institute for Advanced Studies, Johann Wolfgang Goethe University, Frankfurt am Main, Germany
| | - Lucia Melloni
- Department of Neurophysiology, Max Planck Institute for Brain Research, Frankfurt am Main, Germany,Department of Neurology, New York University School of Medicine, New York, NY,Department of Neuroscience, Max Planck Institute for Empirical Aesthetics, Frankfurt am Main, Germany,To whom correspondence should be addressed; Max-Planck-Institute for Empirical Aesthetics, Department of Neuroscience, Grüneburgweg 14, 60322 Frankfurt am Main, Germany. tel: +49 (0)69-8300479-330, fax: +49 69 8300 479 399, e-mail:
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Scalabrini A, Vai B, Poletti S, Damiani S, Mucci C, Colombo C, Zanardi R, Benedetti F, Northoff G. All roads lead to the default-mode network-global source of DMN abnormalities in major depressive disorder. Neuropsychopharmacology 2020; 45:2058-2069. [PMID: 32740651 PMCID: PMC7547732 DOI: 10.1038/s41386-020-0785-x] [Citation(s) in RCA: 102] [Impact Index Per Article: 25.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/07/2020] [Revised: 07/21/2020] [Accepted: 07/24/2020] [Indexed: 12/18/2022]
Abstract
Major depressive disorder (MDD) is a psychiatric disorder characterized by abnormal resting state functional connectivity (rsFC) in various neural networks and especially in default-mode network (DMN). However, inconsistent findings, i.e., increased and decreased DMN rsFC, have been reported, which raise the question for the source of DMN changes in MDD. Testing whether the DMN abnormalities in MDD can be traced to either a local, i.e., intra-network, or a global, i.e., inter-network, source, we conducted a novel sequence of rsFC analyses, i.e., global FC, intra-network FC, and inter-network FC. Moreover, all analyses were conducted without global signal regression (non-GSR) and with GSR in order to identify the impact of specifically the global component of functional connectivity on within-network functional connectivity within specifically the DMN. In MDD our findings demonstrate (i) increased representation of global signal correlation (GSCORR) in DMN regions, as confirmed independently by degree of centrality (DC) and by an independent DMN template, (ii) increased within-network DMN rsFC, (iii) highly increased inter-network rsFC of both lower- and higher order non-DMN networks with DMN, (iv) high accuracy in classifying MDD vs. healthy subjects by using GSCORR as predictor. Further supporting the global, i.e., non-DMN source of within-network rsFC of the DMN, all results were obtained only when including the global signal, i.e., non-GSR, but not when conducting GSR. Together, we show for the first time increased global signal representation within rsFC of DMN as stemming from inter-network sources as distinguished from local sources, i.e., within- or intra-DMN.
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Affiliation(s)
- Andrea Scalabrini
- Department of Psychological, Health and Territorial Sciences (DiSPuTer), G. d'Annunzio University of Chieti-Pescara, Via dei Vestini 33, 66100, Chieti (CH), Italy. .,Psychiatry & Clinical Psychobiology Unit, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milan, Italy.
| | - Benedetta Vai
- grid.18887.3e0000000417581884Psychiatry & Clinical Psychobiology Unit, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milan, Italy ,grid.15496.3fDepartment of Clinical Neurosciences, University Vita-Salute San Raffaele, Milan, Italy
| | - Sara Poletti
- grid.18887.3e0000000417581884Psychiatry & Clinical Psychobiology Unit, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milan, Italy ,grid.15496.3fDepartment of Clinical Neurosciences, University Vita-Salute San Raffaele, Milan, Italy
| | - Stefano Damiani
- grid.8982.b0000 0004 1762 5736Department of Brain and Behavioral Science, University of Pavia, 27100 Pavia, Italy
| | - Clara Mucci
- grid.412451.70000 0001 2181 4941Department of Psychological, Health and Territorial Sciences (DiSPuTer), G. d’Annunzio University of Chieti-Pescara, Via dei Vestini 33, 66100 Chieti (CH), Italy
| | - Cristina Colombo
- grid.15496.3fDepartment of Clinical Neurosciences, University Vita-Salute San Raffaele, Milan, Italy ,grid.18887.3e0000000417581884Department of Clinical Neurosciences, IRCCS San Raffaele Scientific Institute, Milan, Italy ,grid.28046.380000 0001 2182 2255The Royal’s Institute of Mental Health Research, University of Ottawa, Ottawa, ON Canada ,grid.28046.380000 0001 2182 2255Brain and Mind Research Institute, Centre for Neural Dynamics, Faculty of Medicine, University of Ottawa, 145 Carling Avenue, Rm. 6435, Ottawa, ON K1Z 7K4 Canada
| | - Raffaella Zanardi
- grid.18887.3e0000000417581884Department of Clinical Neurosciences, IRCCS San Raffaele Scientific Institute, Milan, Italy ,grid.28046.380000 0001 2182 2255The Royal’s Institute of Mental Health Research, University of Ottawa, Ottawa, ON Canada ,grid.28046.380000 0001 2182 2255Brain and Mind Research Institute, Centre for Neural Dynamics, Faculty of Medicine, University of Ottawa, 145 Carling Avenue, Rm. 6435, Ottawa, ON K1Z 7K4 Canada
| | - Francesco Benedetti
- grid.18887.3e0000000417581884Psychiatry & Clinical Psychobiology Unit, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milan, Italy ,grid.15496.3fDepartment of Clinical Neurosciences, University Vita-Salute San Raffaele, Milan, Italy
| | - Georg Northoff
- Department of Clinical Neurosciences, IRCCS San Raffaele Scientific Institute, Milan, Italy. .,The Royal's Institute of Mental Health Research, University of Ottawa, Ottawa, ON, Canada. .,Brain and Mind Research Institute, Centre for Neural Dynamics, Faculty of Medicine, University of Ottawa, 145 Carling Avenue, Rm. 6435, Ottawa, ON, K1Z 7K4, Canada. .,Mental Health Centre, Zhejiang University School of Medicine, Tianmu Road 305, Hangzhou, 310013, Zhejiang Province, China. .,Centre for Cognition and Brain Disorders, Hangzhou Normal University, Tianmu Road 305, Hangzhou, 310013, Zhejiang Province, China.
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Wengler K, Goldberg AT, Chahine G, Horga G. Distinct hierarchical alterations of intrinsic neural timescales account for different manifestations of psychosis. eLife 2020; 9:e56151. [PMID: 33107431 PMCID: PMC7591251 DOI: 10.7554/elife.56151] [Citation(s) in RCA: 41] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2020] [Accepted: 10/05/2020] [Indexed: 12/14/2022] Open
Abstract
Hierarchical perceptual-inference models of psychosis may provide a holistic framework for understanding psychosis in schizophrenia including heterogeneity in clinical presentations. Particularly, hypothesized alterations at distinct levels of the perceptual-inference hierarchy may explain why hallucinations and delusions tend to cluster together yet sometimes manifest in isolation. To test this, we used a recently developed resting-state fMRI measure of intrinsic neural timescale (INT), which reflects the time window of neural integration and captures hierarchical brain gradients. In analyses examining extended sensory hierarchies that we first validated, we found distinct hierarchical INT alterations for hallucinations versus delusions in the auditory and somatosensory systems, thus providing support for hierarchical perceptual-inference models of psychosis. Simulations using a large-scale biophysical model suggested local elevations of excitation-inhibition ratio at different hierarchical levels as a potential mechanism. More generally, our work highlights the robustness and utility of INT for studying hierarchical processes relevant to basic and clinical neuroscience.
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Affiliation(s)
- Kenneth Wengler
- Department of Psychiatry, Columbia UniversityNew YorkUnited States
- New York State Psychiatric InstituteNew YorkUnited States
| | | | - George Chahine
- Department of Psychiatry, Yale UniversityNew HavenUnited States
| | - Guillermo Horga
- Department of Psychiatry, Columbia UniversityNew YorkUnited States
- New York State Psychiatric InstituteNew YorkUnited States
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Dong D, Luo C, Guell X, Wang Y, He H, Duan M, Eickhoff SB, Yao D. Compression of Cerebellar Functional Gradients in Schizophrenia. Schizophr Bull 2020; 46:1282-1295. [PMID: 32144421 PMCID: PMC7505192 DOI: 10.1093/schbul/sbaa016] [Citation(s) in RCA: 55] [Impact Index Per Article: 13.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/06/2023]
Abstract
Our understanding of cerebellar involvement in brain disorders has evolved from motor processing to high-level cognitive and affective processing. Recent neuroscience progress has highlighted hierarchy as a fundamental principle for the brain organization. Despite substantial research on cerebellar dysfunction in schizophrenia, there is a need to establish a neurobiological framework to better understand the co-occurrence and interaction of low- and high-level functional abnormalities of cerebellum in schizophrenia. To help to establish such a framework, we investigated the abnormalities in the distribution of sensorimotor-supramodal hierarchical processing topography in the cerebellum and cerebellar-cerebral circuits in schizophrenia using a novel gradient-based resting-state functional connectivity (FC) analysis (96 patients with schizophrenia vs 120 healthy controls). We found schizophrenia patients showed a compression of the principal motor-to-supramodal gradient. Specifically, there were increased gradient values in sensorimotor regions and decreased gradient values in supramodal regions, resulting in a shorter distance (compression) between the sensorimotor and supramodal poles of this gradient. This pattern was observed in intra-cerebellar, cerebellar-cerebral, and cerebral-cerebellar FC. Further investigation revealed hyper-connectivity between sensorimotor and cognition areas within cerebellum, between cerebellar sensorimotor and cerebral cognition areas, and between cerebellar cognition and cerebral sensorimotor areas, possibly contributing to the observed compressed pattern. These findings present a novel mechanism that may underlie the co-occurrence and interaction of low- and high-level functional abnormalities of cerebellar and cerebro-cerebellar circuits in schizophrenia. Within this framework of abnormal motor-to-supramodal organization, a cascade of impairments stemming from disrupted low-level sensorimotor system may in part account for high-level cognitive cerebellar dysfunction in schizophrenia.
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Affiliation(s)
- Debo Dong
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, Center for Information in Medicine, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Cheng Luo
- Department of Psychiatry, The Fourth People’s Hospital of Chengdu, Chengdu, China
- High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu, China
| | - Xavier Guell
- McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA
- Massachusetts General Hospital and Harvard Medical School, Boston, MA
| | - Yulin Wang
- Faculty of Psychological and Educational Sciences, Department of Experimental and Applied Psychology, Vrije Universiteit Brussel, Brussels, Belgium
- Faculty of Psychology and Educational Sciences, Department of Data Analysis, Ghent University, Ghent, Belgium
| | - Hui He
- Department of Psychiatry, The Fourth People’s Hospital of Chengdu, Chengdu, China
- High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu, China
| | - Mingjun Duan
- Department of Psychiatry, The Fourth People’s Hospital of Chengdu, Chengdu, China
| | - Simon B Eickhoff
- Institute for Systems Neuroscience, Medical Faculty, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
- Institute of Neuroscience and Medicine, Brain & Behaviour (INM-7), Research Centre Jülich, Jülich, Germany
| | - Dezhong Yao
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, Center for Information in Medicine, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
- Research Unit of NeuroInformation, Chinese Academy of Medical Sciences, Chengdu, China
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Reduced serial dependence suggests deficits in synaptic potentiation in anti-NMDAR encephalitis and schizophrenia. Nat Commun 2020; 11:4250. [PMID: 32843635 PMCID: PMC7447775 DOI: 10.1038/s41467-020-18033-3] [Citation(s) in RCA: 37] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2019] [Accepted: 07/31/2020] [Indexed: 01/06/2023] Open
Abstract
A mechanistic understanding of core cognitive processes, such as working memory, is crucial to addressing psychiatric symptoms in brain disorders. We propose a combined psychophysical and biophysical account of two symptomatologically related diseases, both linked to hypofunctional NMDARs: schizophrenia and autoimmune anti-NMDAR encephalitis. We first quantified shared working memory alterations in a delayed-response task. In both patient groups, we report a markedly reduced influence of previous stimuli on working memory contents, despite preserved memory precision. We then simulated this finding with NMDAR-dependent synaptic alterations in a microcircuit model of prefrontal cortex. Changes in cortical excitation destabilized within-trial memory maintenance and could not account for disrupted serial dependence in working memory. Rather, a quantitative fit between data and simulations supports alterations of an NMDAR-dependent memory mechanism operating on longer timescales, such as short-term potentiation. Stein, Barbosa et al. show that anti-NMDAR encephalitis and schizophrenia are characterized by reduced serial dependence in spatial working memory. Cortical network simulations show that this can be parsimoniously explained by a reduction in NMDAR-dependent short-term synaptic potentiation in these diseases.
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Transcriptomics Inform Hierarchical Neuroimaging Features Relevant for Psychosis Spectrum Symptoms. Biol Psychiatry 2020; 88:212-214. [PMID: 32674843 DOI: 10.1016/j.biopsych.2020.05.016] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/14/2020] [Accepted: 05/16/2020] [Indexed: 11/22/2022]
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Molina JL, Voytek B, Thomas ML, Joshi YB, Bhakta SG, Talledo JA, Swerdlow NR, Light GA. Memantine Effects on Electroencephalographic Measures of Putative Excitatory/Inhibitory Balance in Schizophrenia. BIOLOGICAL PSYCHIATRY. COGNITIVE NEUROSCIENCE AND NEUROIMAGING 2020; 5:562-568. [PMID: 32340927 PMCID: PMC7286803 DOI: 10.1016/j.bpsc.2020.02.004] [Citation(s) in RCA: 47] [Impact Index Per Article: 11.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/09/2020] [Revised: 02/07/2020] [Accepted: 02/11/2020] [Indexed: 12/20/2022]
Abstract
BACKGROUND Abnormalities in cortical excitation and inhibition (E/I) balance are thought to underlie sensory and information processing deficits in schizophrenia. Deficits in early auditory information processing mediate both neurocognitive and functional impairment and appear to be normalized by acute pharmacologic challenge with the NMDA antagonist memantine (MEM). METHODS Thirty-six subjects with a diagnosis of schizophrenia and 31 healthy control subjects underwent electroencephalographic recordings. Subjects ingested either placebo or MEM (10 or 20 mg) in a double-blind, within-subject, crossover, randomized design. The aperiodic, 1/f-like scaling property of the neural power spectra, which is thought to index relative E/I balance, was estimated using a robust linear regression algorithm. RESULTS Patients with schizophrenia had greater aperiodic components compared with healthy control subjects (p < .01, d = 0.64), which was normalized after 20 mg MEM. Analysis revealed a significant dose × diagnosis interaction (p < .0001, d = 0.82). Furthermore, the MEM effect (change in aperiodic component in MEM vs. placebo conditions) was associated with baseline attention and vigilance (r = .54, p < .05) and MEM-induced enhancements in gamma power (r = -.60, p < .01). CONCLUSIONS Findings confirmed E/I balance abnormalities in schizophrenia that were normalized with acute MEM administration and suggest that neurocognitive profiles may predict treatment response based on E/I sensitivity. These data provide proof-of-concept evidence for the utility of E/I balance indices as metrics of acute pharmacologic sensitivity for central nervous system therapeutics.
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Affiliation(s)
- Juan L Molina
- Department of Psychiatry, University of California, San Diego, La Jolla, California
| | - Bradley Voytek
- Department of Cognitive Sciences, Halicioğlu Data Science Institute, and Neurosciences Graduate Program, University of California, San Diego, La Jolla, California
| | - Michael L Thomas
- Department of Psychiatry, University of California, San Diego, La Jolla, California; Department of Psychology, Colorado State University, Fort Collins, Colorado
| | - Yash B Joshi
- Department of Psychiatry, University of California, San Diego, La Jolla, California; VA Desert Pacific Mental Illness Research, Education and Clinical Center, VA San Diego Healthcare System, San Diego, California
| | - Savita G Bhakta
- Department of Psychiatry, University of California, San Diego, La Jolla, California
| | - Jo A Talledo
- Department of Psychiatry, University of California, San Diego, La Jolla, California
| | - Neal R Swerdlow
- Department of Psychiatry, University of California, San Diego, La Jolla, California.
| | - Gregory A Light
- Department of Psychiatry, University of California, San Diego, La Jolla, California; VA Desert Pacific Mental Illness Research, Education and Clinical Center, VA San Diego Healthcare System, San Diego, California
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Wang XJ. Macroscopic gradients of synaptic excitation and inhibition in the neocortex. Nat Rev Neurosci 2020; 21:169-178. [PMID: 32029928 PMCID: PMC7334830 DOI: 10.1038/s41583-020-0262-x] [Citation(s) in RCA: 127] [Impact Index Per Article: 31.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 01/06/2020] [Indexed: 12/15/2022]
Abstract
With advances in connectomics, transcriptome and neurophysiological technologies, the neuroscience of brain-wide neural circuits is poised to take off. A major challenge is to understand how a vast diversity of functions is subserved by parcellated areas of mammalian neocortex composed of repetitions of a canonical local circuit. Areas of the cerebral cortex differ from each other not only in their input-output patterns but also in their biological properties. Recent experimental and theoretical work has revealed that such variations are not random heterogeneities; rather, synaptic excitation and inhibition display systematic macroscopic gradients across the entire cortex, and they are abnormal in mental illness. Quantitative differences along these gradients can lead to qualitatively novel behaviours in non-linear neural dynamical systems, by virtue of a phenomenon mathematically described as bifurcation. The combination of macroscopic gradients and bifurcations, in tandem with biological evolution, development and plasticity, provides a generative mechanism for functional diversity among cortical areas, as a general principle of large-scale cortical organization.
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Affiliation(s)
- Xiao-Jing Wang
- Center for Neural Science, New York University, New York, NY, USA.
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Abstract
IMPORTANCE Schizophrenia is a common, severe mental illness that most clinicians will encounter regularly during their practice. This report provides an overview of the clinical characteristics, epidemiology, genetics, neuroscience, and psychopharmacology of schizophrenia to provide a basis to understand the disorder and its treatment. This educational review is integrated with a clinical case to highlight how recent research findings can inform clinical understanding. OBSERVATIONS The first theme considered is the role of early-life environmental and genetic risk factors in altering neurodevelopmental trajectories to predispose an individual to the disorder and leading to the development of prodromal symptoms. The second theme is the role of cortical excitatory-inhibitory imbalance in the development of the cognitive and negative symptoms of the disorder. The third theme considers the role of psychosocial stressors, psychological factors, and subcortical dopamine dysfunction in the onset of the positive symptoms of the disorder. The final theme considers the mechanisms underlying treatment for schizophrenia and common adverse effects of treatment. CONCLUSIONS AND RELEVANCE Schizophrenia has a complex presentation with a multifactorial cause. Nevertheless, advances in neuroscience have identified roles for key circuits, particularly involving frontal, temporal, and mesostriatal brain regions, in the development of positive, negative, and cognitive symptoms. Current pharmacological treatments operate using the same mechanism, blockade of dopamine D2 receptor, which contribute to their adverse effects. However, the circuit mechanisms discussed herein identify novel potential treatment targets that may be of particular benefit in symptom domains not well served by existing medications.
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Affiliation(s)
- Robert A McCutcheon
- Department of Psychosis Studies, Institute of Psychiatry, Psychology & Neuroscience, Kings College London, London, United Kingdom.,Psychiatric Imaging Group, Medical Research Council, London Institute of Medical Sciences, Hammersmith Hospital, London, United Kingdom.,Institute of Clinical Sciences, Faculty of Medicine, Imperial College London, London, United Kingdom
| | - Tiago Reis Marques
- Department of Psychosis Studies, Institute of Psychiatry, Psychology & Neuroscience, Kings College London, London, United Kingdom.,Psychiatric Imaging Group, Medical Research Council, London Institute of Medical Sciences, Hammersmith Hospital, London, United Kingdom.,Institute of Clinical Sciences, Faculty of Medicine, Imperial College London, London, United Kingdom
| | - Oliver D Howes
- Department of Psychosis Studies, Institute of Psychiatry, Psychology & Neuroscience, Kings College London, London, United Kingdom.,Psychiatric Imaging Group, Medical Research Council, London Institute of Medical Sciences, Hammersmith Hospital, London, United Kingdom.,Institute of Clinical Sciences, Faculty of Medicine, Imperial College London, London, United Kingdom
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Spalatro AV, Amianto F, Huang Z, D’Agata F, Bergui M, Abbate Daga G, Fassino S, Northoff G. Neuronal variability of Resting State activity in Eating Disorders: increase and decoupling in Ventral Attention Network and relation with clinical symptoms. Eur Psychiatry 2020; 55:10-17. [DOI: 10.1016/j.eurpsy.2018.08.005] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/19/2018] [Revised: 08/14/2018] [Accepted: 08/27/2018] [Indexed: 01/25/2023] Open
Abstract
AbstractBackground:Despite the great number of resting state functional connectivity studies on Eating Disorders (ED), no biomarkers could be detected yet. Therefore, we here focus on a different measure of resting state activity that is neuronal variability. The objective of this study was to investigate neuronal variability in the resting state of women with ED and to correlate possible differences with clinical and psychopathological indices.Methods:58 women respectively 25 with Anorexia Nervosa (AN), 16 with Bulimia Nervosa (BN) and 17 matched healthy controls (CN) were enrolled for the study. All participants were tested with a battery of psychometric tests and underwent a functional Magnetic Resonance Imaging (fMRI) resting state scanning. We investigated topographical patterns of variability measured by the Standard Deviation (SD) of the Blood-Oxygen-Level-Dependent (BOLD) signal (as a measure of neuronal variability) in the resting-state and their relationship to clinical and psychopathological indices.Results:Neuronal variability was increased in both anorectic and bulimic subjects specifically in the Ventral Attention Network (VAN) compared to healthy controls. No significant differences were found in the other networks. Significant correlations were found between neuronal variability of VAN and various clinical and psychopathological indices.Conclusions:We here show increased neuronal variability of VAN in ED patients. As the VAN is relevant for switching between endogenous and exogenous stimuli, our results showing increased neuronal variability suggest unstable balance between body attention and attention to external world. These results offer new perspective on the neurobiological basis of ED. Clinical and therapeutic implication will be discussed.
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Ji JL, Diehl C, Schleifer C, Tamminga CA, Keshavan MS, Sweeney JA, Clementz BA, Hill SK, Pearlson G, Yang G, Creatura G, Krystal JH, Repovs G, Murray J, Winkler A, Anticevic A. Schizophrenia Exhibits Bi-directional Brain-Wide Alterations in Cortico-Striato-Cerebellar Circuits. Cereb Cortex 2019; 29:4463-4487. [PMID: 31157363 PMCID: PMC6917525 DOI: 10.1093/cercor/bhy306] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2018] [Revised: 10/17/2018] [Indexed: 01/05/2023] Open
Abstract
Distributed neural dysconnectivity is considered a hallmark feature of schizophrenia (SCZ), yet a tension exists between studies pinpointing focal disruptions versus those implicating brain-wide disturbances. The cerebellum and the striatum communicate reciprocally with the thalamus and cortex through monosynaptic and polysynaptic connections, forming cortico-striatal-thalamic-cerebellar (CSTC) functional pathways that may be sensitive to brain-wide dysconnectivity in SCZ. It remains unknown if the same pattern of alterations persists across CSTC systems, or if specific alterations exist along key functional elements of these networks. We characterized connectivity along major functional CSTC subdivisions using resting-state functional magnetic resonance imaging in 159 chronic patients and 162 matched controls. Associative CSTC subdivisions revealed consistent brain-wide bi-directional alterations in patients, marked by hyper-connectivity with sensory-motor cortices and hypo-connectivity with association cortex. Focusing on the cerebellar and striatal components, we validate the effects using data-driven k-means clustering of voxel-wise dysconnectivity and support vector machine classifiers. We replicate these results in an independent sample of 202 controls and 145 patients, additionally demonstrating that these neural effects relate to cognitive performance across subjects. Taken together, these results from complementary approaches implicate a consistent motif of brain-wide alterations in CSTC systems in SCZ, calling into question accounts of exclusively focal functional disturbances.
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Affiliation(s)
- Jie Lisa Ji
- Department of Psychiatry, Yale University School of Medicine, 300 George Street, New Haven, CT, USA
| | - Caroline Diehl
- Department of Psychiatry, Yale University School of Medicine, 300 George Street, New Haven, CT, USA
| | - Charles Schleifer
- Department of Psychiatry, Yale University School of Medicine, 300 George Street, New Haven, CT, USA
| | - Carol A Tamminga
- Department of Psychiatry and Pediatrics, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Matcheri S Keshavan
- Department of Psychiatry, Beth Israel Deaconess Medical Center, Boston, MA, USA
- Department of Psychiatry, Harvard Medical School, Boston, MA, USA
| | - John A Sweeney
- Department of Psychiatry, University of Cincinnati, Cincinnati, OH, USA
| | - Brett A Clementz
- Department of Psychology, BioImaging Research Center, University of Georgia, Athens, GA, USA
- Department of Neuroscience, BioImaging Research Center, University of Georgia, Athens, GA, USA
| | - S Kristian Hill
- Department of Psychology, Rosalind Franklin University of Medicine and Science, Chicago, IL, USA
| | - Godfrey Pearlson
- Department of Psychiatry, Yale University School of Medicine, 300 George Street, New Haven, CT, USA
| | - Genevieve Yang
- Department of Psychiatry, Yale University School of Medicine, 300 George Street, New Haven, CT, USA
| | - Gina Creatura
- Department of Psychiatry, Yale University School of Medicine, 300 George Street, New Haven, CT, USA
| | - John H Krystal
- Department of Psychiatry, Yale University School of Medicine, 300 George Street, New Haven, CT, USA
| | - Grega Repovs
- Department of Psychology, University of Ljubljana, Ljubljana, Slovenia
| | - John Murray
- Department of Psychiatry, Yale University School of Medicine, 300 George Street, New Haven, CT, USA
| | - Anderson Winkler
- Nuffield Department of Clinical Neurosciences, Oxford University, John Radcliffe Hospital, Oxford University, Headington, Oxford, UK
| | - Alan Anticevic
- Department of Psychiatry, Yale University School of Medicine, 300 George Street, New Haven, CT, USA
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Jia W, Zhu H, Ni Y, Su J, Xu R, Jia H, Wan X. Disruptions of frontoparietal control network and default mode network linking the metacognitive deficits with clinical symptoms in schizophrenia. Hum Brain Mapp 2019; 41:1445-1458. [PMID: 31789478 PMCID: PMC7267896 DOI: 10.1002/hbm.24887] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2019] [Revised: 11/20/2019] [Accepted: 11/21/2019] [Indexed: 12/29/2022] Open
Abstract
The metacognitive deficit in awareness of one's own mental states is a core feature of schizophrenia (SZ). The previous studies suggested that the metacognitive deficit associates with clinical symptoms. However, the neural mechanisms underlying the relationship remain largely unknown. We here investigated the neural activities associated with the metacognitive deficit and the neural signatures associated with clinical symptoms in 38 patients with SZ using functional magnetic resonance imaging with a perceptual decision-making task accompanied with metacognition, in comparison to 38 age, gender, and education matched healthy control subjects. The metacognitive deficit in patients with SZ was associated with reduced regional activity in both the frontoparietal control network (FPCN) and the default mode network. Critically, the anticorrelational balance between the two disrupted networks was substantially altered during metacognition, and the extent of alteration positively scaled with negative symptoms. Conversely, decoupling between the two networks was impaired when metacognitive monitoring was not required, and the strength of excessive neural activity positively scaled with positive symptoms. Thus, disruptions of the FPCN and the default mode network underlie the metacognitive deficit, and alternations of network balance between the two networks correlate with clinical symptoms in SZ. These findings implicate that rebalancing these networks holds important clinical potential in developing more efficacious therapeutic treatments.
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Affiliation(s)
- Wenbin Jia
- State Key Laboratory of Cognitive Neuroscience and Learning and IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | - Hong Zhu
- National Clinical Research Center for Mental Disorders & Beijing Key Laboratory of Mental Disorders and Beijing Anding Hospital, Capital Medical University, Beijing, China
| | - Yinmei Ni
- State Key Laboratory of Cognitive Neuroscience and Learning and IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | - Jie Su
- State Key Laboratory of Cognitive Neuroscience and Learning and IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | - Rui Xu
- National Clinical Research Center for Mental Disorders & Beijing Key Laboratory of Mental Disorders and Beijing Anding Hospital, Capital Medical University, Beijing, China
| | - Hongxiao Jia
- National Clinical Research Center for Mental Disorders & Beijing Key Laboratory of Mental Disorders and Beijing Anding Hospital, Capital Medical University, Beijing, China
| | - Xiaohong Wan
- State Key Laboratory of Cognitive Neuroscience and Learning and IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
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50
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Dienel SJ, Lewis DA. Alterations in cortical interneurons and cognitive function in schizophrenia. Neurobiol Dis 2019; 131:104208. [PMID: 29936230 PMCID: PMC6309598 DOI: 10.1016/j.nbd.2018.06.020] [Citation(s) in RCA: 173] [Impact Index Per Article: 34.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2018] [Revised: 05/31/2018] [Accepted: 06/20/2018] [Indexed: 12/18/2022] Open
Abstract
Certain clinical features of schizophrenia, such as working memory disturbances, appear to emerge from altered gamma oscillatory activity in the prefrontal cortex (PFC). Given the essential role of GABA neurotransmission in both working memory and gamma oscillations, understanding the cellular substrate for their disturbances in schizophrenia requires evidence from in vivo neuroimaging studies, which provide a means to link markers of GABA neurotransmission to gamma oscillations and working memory, and from postmortem studies, which provide insight into GABA neurotransmission at molecular and cellular levels of resolution. Here, we review findings from both types of studies which converge on the notions that 1) inhibitory GABA signaling in the PFC, especially between parvalbumin positive GABAergic basket cells and excitatory pyramidal cells, is required for gamma oscillatory activity and working memory function; and 2) disturbances in this signaling contribute to altered gamma oscillations and working memory in schizophrenia. Because the PFC is only one node in a distributed cortical network that mediates working memory, we also review evidence of GABA abnormalities in other cortical regions in schizophrenia.
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Affiliation(s)
- Samuel J Dienel
- Medical Scientist Training Program, University of Pittsburgh, United States; Translational Neuroscience Program, Department of Psychiatry, School of Medicine, University of Pittsburgh, United States
| | - David A Lewis
- Translational Neuroscience Program, Department of Psychiatry, School of Medicine, University of Pittsburgh, United States; Department of Neuroscience, Dietrich School of Arts and Sciences, University of Pittsburgh, United States.
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