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Torres-Carmona E, Ueno F, Iwata Y, Nakajima S, Song J, Mar W, Abdolizadeh A, Agarwal SM, de Luca V, Remington G, Gerretsen P, Graff-Guerrero A. Elevated intrinsic cortical curvature in treatment-resistant schizophrenia: Evidence of structural deformation in functional connectivity areas and comparison with alternate indices of structure. Schizophr Res 2024; 269:103-113. [PMID: 38761434 DOI: 10.1016/j.schres.2024.05.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/17/2023] [Revised: 04/14/2024] [Accepted: 05/02/2024] [Indexed: 05/20/2024]
Abstract
BACKGROUND Research suggests structural and connectivity abnormalities in patients with treatment-resistant schizophrenia (TRS) compared to first-line responders and healthy-controls. However, measures of these abnormalities are often influenced by external factors like nicotine and antipsychotics, limiting their clinical utility. Intrinsic-cortical-curvature (ICC) presents a millimetre-scale measure of brain gyrification, highly sensitive to schizophrenia differences, and associated with TRS-like traits in early stages of the disorder. Despite this evidence, ICC in TRS remains unexplored. This study investigates ICC as a marker for treatment resistance in TRS, alongside structural indices for comparison. METHODS We assessed ICC in anterior cingulate, dorsolateral prefrontal, temporal, and parietal cortices of 38 first-line responders, 30 clozapine-resistant TRS, 37 clozapine-responsive TRS, and 52 healthy-controls. For comparative purposes, Fold and Curvature indices were also analyzed. RESULTS Adjusting for age, sex, nicotine-use, and chlorpromazine equivalence, principal findings indicate ICC elevations in the left hemisphere dorsolateral prefrontal (p < 0.001, η2partial = 0.142) and temporal cortices (LH p = 0.007, η2partial = 0.060; RH p = 0.011, η2partial = 0.076) of both TRS groups, and left anterior cingulate cortex of clozapine-resistant TRS (p = 0.026, η2partial = 0.065), compared to healthy-controls. Elevations that correlated with reduced cognition (p = 0.001) and negative symptomology (p < 0.034) in clozapine-resistant TRS. Fold and Curvature indices only detected group differences in the right parietal cortex, showing interactions with age, sex, and nicotine use. ICC showed interactions with age. CONCLUSION ICC elevations were found among patients with TRS, and correlated with symptom severity. ICCs relative independence from sex, nicotine-use, and antipsychotics, may support ICC's potential as a viable marker for TRS, though age interactions should be considered.
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Affiliation(s)
- Edgardo Torres-Carmona
- Research Imaging Centre, Centre for Addiction and Mental Health (CAMH), Toronto, ON, Canada; Institute of Medical Sciences, University of Toronto, Toronto, ON, Canada
| | - Fumihiko Ueno
- Research Imaging Centre, Centre for Addiction and Mental Health (CAMH), Toronto, ON, Canada
| | - Yusuke Iwata
- Research Imaging Centre, Centre for Addiction and Mental Health (CAMH), Toronto, ON, Canada; Department of Neuropsychiatry, Keio University, Minato, Tokyo, Japan
| | - Shinichiro Nakajima
- Research Imaging Centre, Centre for Addiction and Mental Health (CAMH), Toronto, ON, Canada; Department of Neuropsychiatry, Keio University, Minato, Tokyo, Japan
| | - Jianmeng Song
- Research Imaging Centre, Centre for Addiction and Mental Health (CAMH), Toronto, ON, Canada; Institute of Medical Sciences, University of Toronto, Toronto, ON, Canada
| | - Wanna Mar
- Research Imaging Centre, Centre for Addiction and Mental Health (CAMH), Toronto, ON, Canada
| | - Ali Abdolizadeh
- Research Imaging Centre, Centre for Addiction and Mental Health (CAMH), Toronto, ON, Canada
| | - Sri Mahavir Agarwal
- Department of Psychiatry, University of Toronto, Toronto, ON, Canada; Campbell Institute Research Program, CAMH, Toronto, ON, Canada
| | - Vincenzo de Luca
- Department of Psychiatry, University of Toronto, Toronto, ON, Canada; Campbell Institute Research Program, CAMH, Toronto, ON, Canada
| | - Gary Remington
- Department of Psychiatry, University of Toronto, Toronto, ON, Canada; Campbell Institute Research Program, CAMH, Toronto, ON, Canada
| | - Philip Gerretsen
- Research Imaging Centre, Centre for Addiction and Mental Health (CAMH), Toronto, ON, Canada; Department of Psychiatry, University of Toronto, Toronto, ON, Canada; Campbell Institute Research Program, CAMH, Toronto, ON, Canada
| | - Ariel Graff-Guerrero
- Research Imaging Centre, Centre for Addiction and Mental Health (CAMH), Toronto, ON, Canada; Department of Psychiatry, University of Toronto, Toronto, ON, Canada; Campbell Institute Research Program, CAMH, Toronto, ON, Canada.
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2
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Wiafe SL, Asante NO, Calhoun VD, Faghiri A. Studying time-resolved functional connectivity via communication theory: on the complementary nature of phase synchronization and sliding window Pearson correlation. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.06.12.598720. [PMID: 38915498 PMCID: PMC11195172 DOI: 10.1101/2024.06.12.598720] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/26/2024]
Abstract
Time-resolved functional connectivity (trFC) assesses the time-resolved coupling between brain regions using functional magnetic resonance imaging (fMRI) data. This study aims to compare two techniques used to estimate trFC, to investigate their similarities and differences when applied to fMRI data. These techniques are the sliding window Pearson correlation (SWPC), an amplitude-based approach, and phase synchronization (PS), a phase-based technique. To accomplish our objective, we used resting-state fMRI data from the Human Connectome Project (HCP) with 827 subjects (repetition time: 0.7s) and the Function Biomedical Informatics Research Network (fBIRN) with 311 subjects (repetition time: 2s), which included 151 schizophrenia patients and 160 controls. Our simulations reveal distinct strengths in two connectivity methods: SWPC captures high-magnitude, low-frequency connectivity, while PS detects low-magnitude, high-frequency connectivity. Stronger correlations between SWPC and PS align with pronounced fMRI oscillations. For fMRI data, higher correlations between SWPC and PS occur with matched frequencies and smaller SWPC window sizes (~30s), but larger windows (~88s) sacrifice clinically relevant information. Both methods identify a schizophrenia-associated brain network state but show different patterns: SWPC highlights low anti-correlations between visual, subcortical, auditory, and sensory-motor networks, while PS shows reduced positive synchronization among these networks. In sum, our findings underscore the complementary nature of SWPC and PS, elucidating their respective strengths and limitations without implying the superiority of one over the other.
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Affiliation(s)
- Sir-Lord Wiafe
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, and Emory University, Atlanta, GA 30303, USA
| | - Nana O. Asante
- ETH Zürich, Zürich, Rämistrasse 101, Switzerland
- Ashesi University, 1 University Avenue Berekuso, Ghana
| | - Vince D. Calhoun
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, and Emory University, Atlanta, GA 30303, USA
| | - Ashkan Faghiri
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, and Emory University, Atlanta, GA 30303, USA
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3
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Arjun, Rajpoot AS, Panicker MR. Subject independent emotion recognition using EEG signals employing attention driven neural networks. Biomed Signal Process Control 2022. [DOI: 10.1016/j.bspc.2022.103547] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/31/2023]
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4
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Topoisomerase IIIβ Deficiency Induces Neuro-Behavioral Changes and Brain Connectivity Alterations in Mice. Int J Mol Sci 2021; 22:ijms222312806. [PMID: 34884616 PMCID: PMC8657541 DOI: 10.3390/ijms222312806] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2021] [Revised: 11/19/2021] [Accepted: 11/24/2021] [Indexed: 11/17/2022] Open
Abstract
Topoisomerase IIIβ (Top3β), the only dual-activity topoisomerase in mammals that can change topology of both DNA and RNA, is known to be associated with neurodevelopment and mental dysfunction in humans. However, there is no report showing clear associations of Top3β with neuropsychiatric phenotypes in mice. Here, we investigated the effect of Top3β on neuro-behavior using newly generated Top3β deficient (Top3β-/-) mice. We found that Top3β-/- mice showed decreased anxiety and depression-like behaviors. The lack of Top3β was also associated with changes in circadian rhythm. In addition, a clear expression of Top3β was demonstrated in the central nervous system of mice. Positron emission tomography/computed tomography (PET/CT) analysis revealed significantly altered connectivity between many brain regions in Top3β-/- mice, including the connectivity between the olfactory bulb and the cerebellum, the connectivity between the amygdala and the olfactory bulb, and the connectivity between the globus pallidus and the optic nerve. These connectivity alterations in brain regions are known to be linked to neurodevelopmental as well as psychiatric and behavioral disorders in humans. Therefore, we conclude that Top3β is essential for normal brain function and behavior in mice and that Top3β could be an interesting target to study neuropsychiatric disorders in humans.
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5
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Hadjiabadi D, Lovett-Barron M, Raikov IG, Sparks FT, Liao Z, Baraban SC, Leskovec J, Losonczy A, Deisseroth K, Soltesz I. Maximally selective single-cell target for circuit control in epilepsy models. Neuron 2021; 109:2556-2572.e6. [PMID: 34197732 PMCID: PMC8448204 DOI: 10.1016/j.neuron.2021.06.007] [Citation(s) in RCA: 23] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2020] [Revised: 04/19/2021] [Accepted: 06/04/2021] [Indexed: 12/21/2022]
Abstract
Neurological and psychiatric disorders are associated with pathological neural dynamics. The fundamental connectivity patterns of cell-cell communication networks that enable pathological dynamics to emerge remain unknown. Here, we studied epileptic circuits using a newly developed computational pipeline that leveraged single-cell calcium imaging of larval zebrafish and chronically epileptic mice, biologically constrained effective connectivity modeling, and higher-order motif-focused network analysis. We uncovered a novel functional cell type that preferentially emerged in the preseizure state, the superhub, that was unusually richly connected to the rest of the network through feedforward motifs, critically enhancing downstream excitation. Perturbation simulations indicated that disconnecting superhubs was significantly more effective in stabilizing epileptic circuits than disconnecting hub cells that were defined traditionally by connection count. In the dentate gyrus of chronically epileptic mice, superhubs were predominately modeled adult-born granule cells. Collectively, these results predict a new maximally selective and minimally invasive cellular target for seizure control.
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Affiliation(s)
- Darian Hadjiabadi
- Department of Bioengineering, Stanford University, Stanford, CA 94305, USA; Department of Neurosurgery, Stanford University, Stanford, CA 94305, USA.
| | - Matthew Lovett-Barron
- Department of Bioengineering, Stanford University, Stanford, CA 94305, USA; Neurobiology Section, Division of Biological Sciences, University of California, San Diego, La Jolla, CA 92093, USA
| | | | - Fraser T Sparks
- Department of Neuroscience, Columbia University, New York, NY 10027, USA; Mortimer B. Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY 10027, USA; The Kavli Institute for Brain Science, Columbia University, New York, NY 10027, USA
| | - Zhenrui Liao
- Department of Neuroscience, Columbia University, New York, NY 10027, USA; Mortimer B. Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY 10027, USA; The Kavli Institute for Brain Science, Columbia University, New York, NY 10027, USA
| | - Scott C Baraban
- Department of Neurological Surgery and Weill Institute for Neuroscience, University of California, San Francisco, San Francisco, CA 94143, USA
| | - Jure Leskovec
- Department of Computer Science, Stanford University, Stanford, CA 94305, USA; Chan Zuckerberg Biohub, San Francisco, CA 94158, USA
| | - Attila Losonczy
- Department of Neuroscience, Columbia University, New York, NY 10027, USA; Mortimer B. Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY 10027, USA; The Kavli Institute for Brain Science, Columbia University, New York, NY 10027, USA
| | - Karl Deisseroth
- Department of Bioengineering, Stanford University, Stanford, CA 94305, USA; Howard Hughes Medical Institute, Stanford University, Stanford, CA 94305, USA
| | - Ivan Soltesz
- Department of Neurosurgery, Stanford University, Stanford, CA 94305, USA
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6
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Hearne LJ, Mill RD, Keane BP, Repovš G, Anticevic A, Cole MW. Activity flow underlying abnormalities in brain activations and cognition in schizophrenia. SCIENCE ADVANCES 2021; 7:7/29/eabf2513. [PMID: 34261649 PMCID: PMC8279516 DOI: 10.1126/sciadv.abf2513] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/14/2020] [Accepted: 05/28/2021] [Indexed: 05/03/2023]
Abstract
Cognitive dysfunction is a core feature of many brain disorders, including schizophrenia (SZ), and has been linked to aberrant brain activations. However, it is unclear how these activation abnormalities emerge. We propose that aberrant flow of brain activity across functional connectivity (FC) pathways leads to altered activations that produce cognitive dysfunction in SZ. We tested this hypothesis using activity flow mapping, an approach that models the movement of task-related activity between brain regions as a function of FC. Using functional magnetic resonance imaging data from SZ individuals and healthy controls during a working memory task, we found that activity flow models accurately predict aberrant cognitive activations across multiple brain networks. Within the same framework, we simulated a connectivity-based clinical intervention, predicting specific treatments that normalized brain activations and behavior in patients. Our results suggest that dysfunctional task-evoked activity flow is a large-scale network mechanism contributing to cognitive dysfunction in SZ.
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Affiliation(s)
- Luke J Hearne
- Center for Molecular and Behavioral Neuroscience, Rutgers University, Newark, NJ, USA.
| | - Ravi D Mill
- Center for Molecular and Behavioral Neuroscience, Rutgers University, Newark, NJ, USA
| | - Brian P Keane
- University Behavioral Health Care, Department of Psychiatry, and Center for Cognitive Science, Rutgers University, Piscataway, NJ, USA
- Departments of Psychiatry and Neuroscience, University of Rochester Medical Center, Rochester, NY, USA
| | - Grega Repovš
- Department of Psychology, University of Ljubljana, Aškerčeva 2, Ljubljana SI-1000, Slovenia
| | - Alan Anticevic
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA
| | - Michael W Cole
- Center for Molecular and Behavioral Neuroscience, Rutgers University, Newark, NJ, USA
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7
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Winterer JM, Ofosu K, Borchers F, Hadzidiakos D, Lammers-Lietz F, Spies C, Winterer G, Zacharias N. Neurocognitive disorders in the elderly: altered functional resting-state hyperconnectivities in postoperative delirium patients. Transl Psychiatry 2021; 11:213. [PMID: 33846284 PMCID: PMC8041755 DOI: 10.1038/s41398-021-01304-y] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/21/2020] [Revised: 02/03/2021] [Accepted: 03/02/2021] [Indexed: 01/11/2023] Open
Abstract
Postoperative delirium (POD) represents a confusional state during days/weeks after surgery and is particularly frequent in elderly patients. Hardly any fMRI studies were conducted to understand the underlying pathophysiology of POD patients. This prospective observational cohort study aims to examine changes of specific resting-state functional connectivity networks across different time points (pre- and 3-5 months postoperatively) in delirious patients compared to no-POD patients. Two-hundred eighty-three elderly surgical patients underwent preoperative resting-state fMRI (46 POD). One-hundred seventy-eight patients completed postoperative scans (19 POD). For functional connectivity analyses, three functional connectivity networks with seeds located in the orbitofrontal cortex (OFC), nucleus accumbens (NAcc), and hippocampus were investigated. The relationship of POD and connectivity changes between both time points (course connectivity) were examined (ANOVA). Preoperatively, delirious patients displayed hyperconnectivities across the examined functional connectivity networks. In POD patients, connectivities within NAcc and OFC networks demonstrated a decrease in course connectivity [max. F = 9.03, p = 0.003; F = 4.47, p = 0.036, resp.]. The preoperative hyperconnectivity in the three networks in the patients at risk for developing POD could possibly indicate existing compensation mechanisms for subtle brain dysfunction. The observed pathophysiology of network function in POD patients at least partially involves dopaminergic pathways.
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Affiliation(s)
- Jeanne M. Winterer
- grid.6363.00000 0001 2218 4662Department 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 ,grid.484013.aDepartment of Anesthesiology, Charité (CVK, CCM)–Charité-Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin and Berlin Institute of Health, Berlin, Germany ,Pharmaimage Biomarker Solutions GmbH, Berlin, Germany
| | - Kwaku Ofosu
- grid.484013.aDepartment of Anesthesiology, Charité (CVK, CCM)–Charité-Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin and Berlin Institute of Health, Berlin, Germany
| | - Friedrich Borchers
- grid.484013.aDepartment of Anesthesiology, Charité (CVK, CCM)–Charité-Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin and Berlin Institute of Health, Berlin, Germany
| | - Daniel Hadzidiakos
- grid.484013.aDepartment of Anesthesiology, Charité (CVK, CCM)–Charité-Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin and Berlin Institute of Health, Berlin, Germany
| | - Florian Lammers-Lietz
- grid.484013.aDepartment of Anesthesiology, Charité (CVK, CCM)–Charité-Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin and Berlin Institute of Health, Berlin, Germany
| | - Claudia Spies
- grid.484013.aDepartment of Anesthesiology, Charité (CVK, CCM)–Charité-Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin and Berlin Institute of Health, Berlin, Germany
| | - Georg Winterer
- Department of Anesthesiology, Charité (CVK, CCM)-Charité-Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin and Berlin Institute of Health, Berlin, Germany. .,Pharmaimage Biomarker Solutions GmbH, Berlin, Germany.
| | - Norman Zacharias
- grid.484013.aDepartment of Anesthesiology, Charité (CVK, CCM)–Charité-Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin and Berlin Institute of Health, Berlin, Germany ,Pharmaimage Biomarker Solutions GmbH, Berlin, Germany
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8
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Du J, Palaniyappan L, Liu Z, Cheng W, Gong W, Zhu M, Wang J, Zhang J, Feng J. The genetic determinants of language network dysconnectivity in drug-naïve early stage schizophrenia. NPJ SCHIZOPHRENIA 2021; 7:18. [PMID: 33658499 PMCID: PMC7930279 DOI: 10.1038/s41537-021-00141-8] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/09/2020] [Accepted: 01/12/2021] [Indexed: 01/31/2023]
Abstract
Schizophrenia is a neurocognitive illness of synaptic and brain network-level dysconnectivity that often reaches a persistent chronic stage in many patients. Subtle language deficits are a core feature even in the early stages of schizophrenia. However, the primacy of language network dysconnectivity and language-related genetic variants in the observed phenotype in early stages of illness remains unclear. This study used two independent schizophrenia dataset consisting of 138 and 53 drug-naïve first-episode schizophrenia (FES) patients, and 112 and 56 healthy controls, respectively. A brain-wide voxel-level functional connectivity analysis was conducted to investigate functional dysconnectivity and its relationship with illness duration. We also explored the association between critical language-related genetic (such as FOXP2) mutations and the altered functional connectivity in patients. We found elevated functional connectivity involving Broca's area, thalamus and temporal cortex that were replicated in two FES datasets. In particular, Broca's area - anterior cingulate cortex dysconnectivity was more pronounced for patients with shorter illness duration, while thalamic dysconnectivity was predominant in those with longer illness duration. Polygenic risk scores obtained from FOXP2-related genes were strongly associated with functional dysconnectivity identified in patients with shorter illness duration. Our results highlight the criticality of language network dysconnectivity, involving the Broca's area in early stages of schizophrenia, and the role of language-related genes in this aberration, providing both imaging and genetic evidence for the association between schizophrenia and the determinants of language.
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Affiliation(s)
- Jingnan Du
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Key Laboratory of Computational Neuroscience and Brain Inspired Intelligence (Fudan University), Ministry of Education, Shanghai, China
| | - Lena Palaniyappan
- Department of Psychiatry and Robarts Research Institute, University of Western Ontario, London, ON, Canada
- Lawson Health Research Institute, London, ON, Canada
| | - Zhaowen Liu
- Psychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
- Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Wei Cheng
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China
- Key Laboratory of Computational Neuroscience and Brain Inspired Intelligence (Fudan University), Ministry of Education, Shanghai, China
| | - Weikang Gong
- Centre for Functional MRI of the Brain (FMRIB), Nuffield Department of Clinical Neurosciences, Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford, UK
| | - Mengmeng Zhu
- State Key Laboratory of Cognitive Neuroscience and Learning and IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | - Jijun Wang
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Jie Zhang
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China.
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
- Key Laboratory of Computational Neuroscience and Brain Inspired Intelligence (Fudan University), Ministry of Education, Shanghai, China.
| | - Jianfeng Feng
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China.
- Key Laboratory of Computational Neuroscience and Brain Inspired Intelligence (Fudan University), Ministry of Education, Shanghai, China.
- Department of Computer Science, University of Warwick, Coventry, UK.
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9
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Gifford G, Crossley N, Morgan S, Kempton MJ, Dazzan P, Modinos G, Azis M, Samson C, Bonoldi I, Quinn B, Smart SE, Antoniades M, Bossong MG, Broome MR, Perez J, Howes OD, Stone JM, Allen P, Grace AA, McGuire P. Integrated metastate functional connectivity networks predict change in symptom severity in clinical high risk for psychosis. Hum Brain Mapp 2021; 42:439-451. [PMID: 33048435 PMCID: PMC7775992 DOI: 10.1002/hbm.25235] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2020] [Revised: 08/28/2020] [Accepted: 09/29/2020] [Indexed: 01/22/2023] Open
Abstract
The ability to identify biomarkers of psychosis risk is essential in defining effective preventive measures to potentially circumvent the transition to psychosis. Using samples of people at clinical high risk for psychosis (CHR) and Healthy controls (HC) who were administered a task fMRI paradigm, we used a framework for labelling time windows of fMRI scans as 'integrated' FC networks to provide a granular representation of functional connectivity (FC). Periods of integration were defined using the 'cartographic profile' of time windows and k-means clustering, and sub-network discovery was carried out using Network Based Statistics (NBS). There were no network differences between CHR and HC groups. Within the CHR group, using integrated FC networks, we identified a sub-network negatively associated with longitudinal changes in the severity of psychotic symptoms. This sub-network comprised brain areas implicated in bottom-up sensory processing and in integration with motor control, suggesting it may be related to the demands of the fMRI task. These data suggest that extracting integrated FC networks may be useful in the investigation of biomarkers of psychosis risk.
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Affiliation(s)
- George Gifford
- Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Nicolas Crossley
- Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK.,Department of Psychiatry, School of Medicine, Pontificia Universidad Católica de Chile, Santiago, Chile
| | - Sarah Morgan
- Department of Psychiatry, University of Cambridge, Cambridge, UK.,The Alan Turing Institute, London, UK
| | - Matthew J Kempton
- Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Paola Dazzan
- Department of Psychological Medicine, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK.,South London and Maudsley NHS Trust, Maudsley Hospital, London, UK
| | - Gemma Modinos
- Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK.,Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Matilda Azis
- Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Carly Samson
- Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Ilaria Bonoldi
- Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK.,South London and Maudsley NHS Trust, Maudsley Hospital, London, UK
| | - Beverly Quinn
- CAMEO Early Intervention in Psychosis Service, Cambridgeshire and Peterborough NHS Foundation Trust, Cambridge, UK
| | - Sophie E Smart
- Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK.,MRC Centre for Neuropsychiatric Genetics and Genomics, Division of Psychological Medicine and Clinical Neurosciences, School of Medicine, Cardiff University, Cardiff, UK
| | - Mathilde Antoniades
- Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK.,Department of Psychiatry, Icahn Medical School, Mt Sinai Hospital, New York, New York, USA
| | - Matthijs G Bossong
- Department of Psychiatry, UMC Utrecht Brain Center, Utrecht University, Utrecht, The Netherlands
| | - Matthew R Broome
- Institute for Mental Health, School of Psychology, University of Birmingham, Birmingham, UK
| | - Jesus Perez
- CAMEO Early Intervention in Psychosis Service, Cambridgeshire and Peterborough NHS Foundation Trust, Cambridge, UK
| | - Oliver D Howes
- Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK.,South London and Maudsley NHS Trust, Maudsley Hospital, London, UK
| | - James M Stone
- Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK.,South London and Maudsley NHS Trust, Maudsley Hospital, London, UK.,Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Paul Allen
- Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK.,Department of Psychology, University of Roehampton, London, UK
| | - Anthony A Grace
- Departments of Neuroscience, Psychiatry and Psychology, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
| | - Philip McGuire
- Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK.,South London and Maudsley NHS Trust, Maudsley Hospital, London, UK
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10
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Dolleman-van der Weel MJ, Witter MP. The thalamic midline nucleus reuniens: potential relevance for schizophrenia and epilepsy. Neurosci Biobehav Rev 2020; 119:422-439. [PMID: 33031816 DOI: 10.1016/j.neubiorev.2020.09.033] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2020] [Revised: 09/03/2020] [Accepted: 09/28/2020] [Indexed: 01/08/2023]
Abstract
Anatomical, electrophysiological and behavioral studies in rodents have shown that the thalamic midline nucleus reuniens (RE) is a crucial link in the communication between hippocampal formation (HIP, i.e., CA1, subiculum) and medial prefrontal cortex (mPFC), important structures for cognitive and executive functions. A common feature in neurodevelopmental and neurodegenerative brain diseases is a dysfunctional connectivity/communication between HIP and mPFC, and disturbances in the cognitive domain. Therefore, it is assumed that aberrant functioning of RE may contribute to behavioral/cognitive impairments in brain diseases characterized by cortico-thalamo-hippocampal circuit dysfunctions. In the human brain the connections of RE are largely unknown. Yet, recent studies have found important similarities in the functional connectivity of HIP-mPFC-RE in humans and rodents, making cautious extrapolating experimental findings from animal models to humans justifiable. The focus of this review is on a potential involvement of RE in schizophrenia and epilepsy.
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Affiliation(s)
- M J Dolleman-van der Weel
- Kavli Institute for Systems Neuroscience and Centre for Neural Computation, NTNU Norwegian University of Science and Technology, Trondheim NO-7491, Norway.
| | - M P Witter
- Kavli Institute for Systems Neuroscience and Centre for Neural Computation, NTNU Norwegian University of Science and Technology, Trondheim NO-7491, Norway.
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Luo Q, Pan B, Gu H, Simmonite M, Francis S, Liddle PF, Palaniyappan L. Effective connectivity of the right anterior insula in schizophrenia: The salience network and task-negative to task-positive transition. Neuroimage Clin 2020; 28:102377. [PMID: 32805679 PMCID: PMC7451428 DOI: 10.1016/j.nicl.2020.102377] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2020] [Revised: 07/20/2020] [Accepted: 08/05/2020] [Indexed: 12/30/2022]
Abstract
Triple network dysfunction theory of schizophrenia postulates that the interaction between the default-mode and the fronto-parietal executive network is disrupted by aberrant salience signals from the right anterior insula (rAI). To date, it is not clear how the proposed resting-state disruption translates to task-processing inefficiency in subjects with schizophrenia. Using a contiguous resting and 2-back task performance fMRI paradigm, we quantified the change in effective connectivity that accompanies rest-to-task state transition in 29 clinically stable patients with schizophrenia and 31 matched healthy controls. We found an aberrant task-evoked increase in the influence of the rAI to both executive (Cohen's d = 1.35, p = 2.8 × 10-6) and default-mode (Cohen's d = 1.22, p = 1.5 × 10-5) network regions occur in patients when compared to controls. In addition, the effective connectivity from middle occipital gyrus (dorsal visual cortex) to insula is also increased in patients as compared with healthy controls. Aberrant insula to executive network influence is pronounced in patients with more severe negative symptom burden. These findings suggest that control signals from rAI are abnormally elevated and directed towards both task-positive and task-negative brain regions, when task-related demands arise in schizophrenia. This aberrant, undiscriminating surge in salience signalling may disrupt contextually appropriate allocation of resources in the neuronal workspace in patients with schizophrenia.
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Affiliation(s)
- Qiang Luo
- Institute of Science and Technology for Brain-Inspired Intelligence, MOE-Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Institutes of Brain Science and Human Phenome Institute, Fudan University, Shanghai 200433, China
| | - Baobao Pan
- School of Aerospace Engineering and Applied Mechanics, Tongji University, Shanghai, China
| | - Huaguang Gu
- School of Aerospace Engineering and Applied Mechanics, Tongji University, Shanghai, China
| | - Molly Simmonite
- Translational Neuroimaging for Mental Health, Division of Psychiatry and Applied Psychology, University of Nottingham, Nottingham, UK
| | - Susan Francis
- Sir Peter Mansfield Imaging Centre (SPMIC), School of Physics and Astronomy, University of Nottingham, Nottingham, UK
| | - Peter F Liddle
- Translational Neuroimaging for Mental Health, Division of Psychiatry and Applied Psychology, University of Nottingham, Nottingham, UK
| | - Lena Palaniyappan
- Robarts Research Institute & The Brain and Mind Institute, University of Western Ontario, London, Ontario, Canada; Department of Psychiatry, University of Western Ontario, London, Ontario, Canada; Lawson Health Research Institute, London, Ontario, Canada.
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12
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Zarghami TS, Hossein-Zadeh GA, Bahrami F. Deep Temporal Organization of fMRI Phase Synchrony Modes Promotes Large-Scale Disconnection in Schizophrenia. Front Neurosci 2020; 14:214. [PMID: 32292324 PMCID: PMC7118690 DOI: 10.3389/fnins.2020.00214] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2019] [Accepted: 02/27/2020] [Indexed: 12/30/2022] Open
Abstract
Itinerant dynamics of the brain generates transient and recurrent spatiotemporal patterns in neuroimaging data. Characterizing metastable functional connectivity (FC) - particularly at rest and using functional magnetic resonance imaging (fMRI) - has shaped the field of dynamic functional connectivity (DFC). Mainstream DFC research relies on (sliding window) correlations to identify recurrent FC patterns. Recently, functional relevance of the instantaneous phase synchrony (IPS) of fMRI signals has been revealed using imaging studies and computational models. In the present paper, we identify the repertoire of whole-brain inter-network IPS states at rest. Moreover, we uncover a hierarchy in the temporal organization of IPS modes. We hypothesize that connectivity disorder in schizophrenia (SZ) is related to the (deep) temporal arrangement of large-scale IPS modes. Hence, we analyze resting-state fMRI data from 68 healthy controls (HC) and 51 SZ patients. Seven resting-state networks (and their sub-components) are identified using spatial independent component analysis. IPS is computed between subject-specific network time courses, using analytic signals. The resultant phase coupling patterns, across time and subjects, are clustered into eight IPS states. Statistical tests show that the relative expression and mean lifetime of certain IPS states have been altered in SZ. Namely, patients spend (45%) less time in a globally coherent state and a subcortical-centered state, and (40%) more time in states reflecting anticoupling within the cognitive control network, compared to the HC. Moreover, the transition profile (between states) reveals a deep temporal structure, shaping two metastates with distinct phase synchrony profiles. A metastate is a collection of states such that within-metastate transitions are more probable than across. Remarkably, metastate occupation balance is altered in SZ, in favor of the less synchronous metastate that promotes disconnection across networks. Furthermore, the trajectory of IPS patterns is less efficient, less smooth, and more restricted in SZ subjects, compared to the HC. Finally, a regression analysis confirms the diagnostic value of the defined IPS measures for SZ identification, highlighting the distinctive role of metastate proportion. Our results suggest that the proposed IPS features may be used for classification studies and for characterizing phase synchrony modes in other (clinical) populations.
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Affiliation(s)
- Tahereh S. Zarghami
- Bio-Electric Department, School of Electrical and Computer Engineering, College of Engineering, University of Tehran, Tehran, Iran
- Human Motor Control and Computational Neuroscience Laboratory, School of Electrical and Computer Engineering, College of Engineering, University of Tehran, Tehran, Iran
| | - Gholam-Ali Hossein-Zadeh
- Bio-Electric Department, School of Electrical and Computer Engineering, College of Engineering, University of Tehran, Tehran, Iran
| | - Fariba Bahrami
- Human Motor Control and Computational Neuroscience Laboratory, School of Electrical and Computer Engineering, College of Engineering, University of Tehran, Tehran, Iran
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Joo SW, Yoon W, Jo YT, Kim H, Kim Y, Lee J. Aberrant Executive Control and Auditory Networks in Recent-Onset Schizophrenia. Neuropsychiatr Dis Treat 2020; 16:1561-1570. [PMID: 32606708 PMCID: PMC7319504 DOI: 10.2147/ndt.s254208] [Citation(s) in RCA: 8] [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: 03/16/2020] [Accepted: 05/27/2020] [Indexed: 12/21/2022] Open
Abstract
PURPOSE Despite a large number of resting-state functional MRI (rsfMRI) studies in schizophrenia, current evidence on the abnormalities of functional connectivity (FC) of resting-state networks shows high variability, and the findings on recent-onset schizophrenia are insufficient compared to those on chronic schizophrenia. PATIENTS AND METHODS We performed a rsfMRI in 46 patients with recent-onset schizophrenia and 22 healthy controls. Group independent component brainmap and dual regression were performed for voxel-wise comparisons between the groups. Correlation of the symptom severity, cognitive function, duration of illness, and a total antipsychotics dose with FC was evaluated with Spearman's rho correlation. RESULTS The patient group had areas with a significantly decreased FC compared to that of the control group in which it existed in the left supplementary motor cortex and supramarginal gyrus (the executive control network) and the right postcentral gyrus (the auditory network). The patient group had a significant correlation of the total antipsychotics dose with the FC of the cluster in the left supplementary motor cortex in the executive control network. CONCLUSION Patients with recent-onset schizophrenia have decreased FC of the executive control and auditory networks compared to healthy controls.
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Affiliation(s)
- Sung Woo Joo
- Medical Corps, Republic of Korea Navy 1st Fleet, Donghae, Republic of Korea
| | - Woon Yoon
- Department of Psychiatry, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea
| | - Young Tak Jo
- Department of Psychiatry, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea
| | - Harin Kim
- Department of Psychiatry, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea
| | - Yangsik Kim
- Department of Psychiatry, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea
| | - Jungsun Lee
- Department of Psychiatry, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea
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Ovsepian SV. The dark matter of the brain. Brain Struct Funct 2019; 224:973-983. [PMID: 30659350 DOI: 10.1007/s00429-019-01835-7] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2018] [Accepted: 01/12/2019] [Indexed: 02/07/2023]
Abstract
The bulk of brain energy expenditure is allocated for maintenance of perpetual intrinsic activity of neurons and neural circuits. Long-term electrophysiological and neuroimaging studies in anesthetized and behaving animals show, however, that the great majority of nerve cells in the intact brain do not fire action potentials, i.e., are permanently silent. Herein, I review emerging data suggesting massive redundancy of nerve cells in mammalian nervous system, maintained in inhibited state at high energetic costs. Acquired in the course of evolution, these collections of dormant neurons and circuits evade routine functional undertakings, and hence, keep out of the reach of natural selection. Under penetrating stress and disease, however, they occasionally switch in active state and drive a variety of neuro-psychiatric symptoms and behavioral abnormalities. The increasing evidence for widespread occurrence of silent neurons warrants careful revision of functional models of the brain and entails unforeseen reserves for rehabilitation and plasticity.
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Affiliation(s)
- Saak V Ovsepian
- National Institute of Mental Health, Topolová 748, 250 67, Klecany, Czech Republic. .,Faculty of Medicine at Charles University, 116 36, Prague, Czech Republic. .,Institute for Biological and Medical Imaging, Helmholtz Zentrum Munich, Neuherberg, Germany. .,Munich School of Bioengineering, Technical University Munich, Munich, Germany. .,International Centre for Neurotherapeutics, Dublin City University, Dublin, Republic of Ireland.
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15
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Tarcijonas G, Sarpal DK. Neuroimaging markers of antipsychotic treatment response in schizophrenia: An overview of magnetic resonance imaging studies. Neurobiol Dis 2018; 131:104209. [PMID: 29953933 DOI: 10.1016/j.nbd.2018.06.021] [Citation(s) in RCA: 28] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2017] [Revised: 05/16/2018] [Accepted: 06/23/2018] [Indexed: 12/18/2022] Open
Abstract
Antipsychotic drugs are the primary treatment for psychosis, yet individual response to their administration remains variable. At present, no biological predictors of response exist to guide clinicians as they select treatments for patients, and our understanding of the neurobiology underlying the heterogeneity of outcomes remains limited. Magnetic Resonance Imaging (MRI) has been applied by numerous studies to examine the response to antipsychotic treatment, though a large gap remains between their results and our clinical practice. To advance patient care with precision medicine approaches, prior work must be accounted for and built upon with future studies. This review provides an overview of studies that relate treatment outcome to various MRI-related measures, including structural, spectroscopic, diffusion tensor, and functional imaging. Knowledge derived from these studies will be discussed along with future directions for the field.
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Affiliation(s)
- Goda Tarcijonas
- University of Pittsburgh School of Medicine, Pittsburgh, PA, United States
| | - Deepak K Sarpal
- University of Pittsburgh School of Medicine, Pittsburgh, PA, United States.
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16
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Kristensen TD, Mandl RC, Jepsen JR, Rostrup E, Glenthøj LB, Nordentoft M, Glenthøj BY, Ebdrup BH. Non-pharmacological modulation of cerebral white matter organization: A systematic review of non-psychiatric and psychiatric studies. Neurosci Biobehav Rev 2018; 88:84-97. [DOI: 10.1016/j.neubiorev.2018.03.013] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2017] [Revised: 03/11/2018] [Accepted: 03/12/2018] [Indexed: 10/17/2022]
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17
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Tian Y, Yang L, Xu W, Zhang H, Wang Z, Zhang H, Zheng S, Shi Y, Xu P. Predictors for drug effects with brain disease: Shed new light from EEG parameters to brain connectomics. Eur J Pharm Sci 2017; 110:26-36. [PMID: 28456573 DOI: 10.1016/j.ejps.2017.04.019] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2017] [Revised: 04/24/2017] [Accepted: 04/24/2017] [Indexed: 01/21/2023]
Abstract
Though researchers spent a lot of effort to develop treatments for neuropsychiatric disorders, the poor translation of drug efficacy data from animals to human hampered the success of these therapeutic approaches in human. Pharmaceutical industry is challenged by low clinical success rates for new drug registration. To maximize the success in drug development, biomarkers are required to act as surrogate end points and predictors of drug effects. The pathology of brain disease could be in part due to synaptic dysfunction. Electroencephalogram (EEG), generating from the result of the postsynaptic potential discharge between cells, could be a potential measure to bridge the gaps between animal and human data. Here we discuss recent progress on using relevant EEG characteristics and brain connectomics as biomarkers to monitor drug effects and measure cognitive changes on animal models and human in real-time. It is expected that the novel approach, i.e. EEG connectomics, will offer a deeper understanding on the drug efficacy at a microcirculatory level, which will be useful to support the development of new treatments for neuropsychiatric disorders.
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Affiliation(s)
- Yin Tian
- Biomedical Engineering Department, Key Laboratory of Photoelectronic Information Sensing and Transmitting Technology, High School Innovation Team of Architecture and Core Technologies of Smart Medical System, ChongQing University of Posts and Telecommunications, ChongQing 400065, China.
| | - Li Yang
- Biomedical Engineering Department, Key Laboratory of Photoelectronic Information Sensing and Transmitting Technology, High School Innovation Team of Architecture and Core Technologies of Smart Medical System, ChongQing University of Posts and Telecommunications, ChongQing 400065, China
| | - Wei Xu
- Biomedical Engineering Department, Key Laboratory of Photoelectronic Information Sensing and Transmitting Technology, High School Innovation Team of Architecture and Core Technologies of Smart Medical System, ChongQing University of Posts and Telecommunications, ChongQing 400065, China
| | - Huiling Zhang
- Biomedical Engineering Department, Key Laboratory of Photoelectronic Information Sensing and Transmitting Technology, High School Innovation Team of Architecture and Core Technologies of Smart Medical System, ChongQing University of Posts and Telecommunications, ChongQing 400065, China
| | - Zhongyan Wang
- Biomedical Engineering Department, Key Laboratory of Photoelectronic Information Sensing and Transmitting Technology, High School Innovation Team of Architecture and Core Technologies of Smart Medical System, ChongQing University of Posts and Telecommunications, ChongQing 400065, China
| | - Haiyong Zhang
- Biomedical Engineering Department, Key Laboratory of Photoelectronic Information Sensing and Transmitting Technology, High School Innovation Team of Architecture and Core Technologies of Smart Medical System, ChongQing University of Posts and Telecommunications, ChongQing 400065, China
| | - Shuxing Zheng
- Biomedical Engineering Department, Key Laboratory of Photoelectronic Information Sensing and Transmitting Technology, High School Innovation Team of Architecture and Core Technologies of Smart Medical System, ChongQing University of Posts and Telecommunications, ChongQing 400065, China
| | - Yupan Shi
- Biomedical Engineering Department, Key Laboratory of Photoelectronic Information Sensing and Transmitting Technology, High School Innovation Team of Architecture and Core Technologies of Smart Medical System, ChongQing University of Posts and Telecommunications, ChongQing 400065, China
| | - Peng Xu
- Key Laboratory for NeuroInformation of Ministry of Education, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, China
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Zhu J, Zhuo C, Xu L, Liu F, Qin W, Yu C. Altered Coupling Between Resting-State Cerebral Blood Flow and Functional Connectivity in Schizophrenia. Schizophr Bull 2017; 43:1363-1374. [PMID: 28521048 PMCID: PMC5737873 DOI: 10.1093/schbul/sbx051] [Citation(s) in RCA: 103] [Impact Index Per Article: 14.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/10/2023]
Abstract
BACKGROUND Respective changes in resting-state cerebral blood flow (CBF) and functional connectivity in schizophrenia have been reported. However, their coupling alterations in schizophrenia remain largely unknown. METHODS 89 schizophrenia patients and 90 sex- and age-matched healthy controls underwent resting-state functional MRI to calculate functional connectivity strength (FCS) and arterial spin labeling imaging to compute CBF. The CBF-FCS coupling of the whole gray matter and the CBF/FCS ratio (the amount of blood supply per unit of connectivity strength) of each voxel were compared between the 2 groups. RESULTS Whole gray matter CBF-FCS coupling was decreased in schizophrenia patients relative to healthy controls. In schizophrenia patients, the decreased CBF/FCS ratio was predominantly located in cognitive- and emotional-related brain regions, including the dorsolateral prefrontal cortex, insula, hippocampus and thalamus, whereas an increased CBF/FCS ratio was mainly identified in the sensorimotor regions, including the putamen, and sensorimotor, mid-cingulate and visual cortices. CONCLUSION These findings suggest that the neurovascular decoupling in the brain may be a possible neuropathological mechanism of schizophrenia.
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Affiliation(s)
- Jiajia Zhu
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, China
| | - Chuanjun Zhuo
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, China,Department of Psychiatry Functional Neuroimaging Laboratory, Tianjin Mental Health Center, Tianjin Anding Hospital, Tianjin, China,Tianjin Anning Hospital, Tianjin, China
| | - Lixue Xu
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, China
| | - Feng Liu
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, China
| | - Wen Qin
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, China
| | - Chunshui Yu
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, China,To whom correspondence should be addressed; Department of Radiology, Tianjin Medical University General Hospital, No. 154, Anshan Road, Heping District, Tianjin 300052, China; tel: +86-22-63062026, fax: +86-22-63062290, e-mail:
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Alamian G, Hincapié AS, Pascarella A, Thiery T, Combrisson E, Saive AL, Martel V, Althukov D, Haesebaert F, Jerbi K. Measuring alterations in oscillatory brain networks in schizophrenia with resting-state MEG: State-of-the-art and methodological challenges. Clin Neurophysiol 2017; 128:1719-1736. [DOI: 10.1016/j.clinph.2017.06.246] [Citation(s) in RCA: 27] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2017] [Revised: 05/08/2017] [Accepted: 06/19/2017] [Indexed: 02/06/2023]
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20
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Learning stable and predictive network-based patterns of schizophrenia and its clinical symptoms. NPJ SCHIZOPHRENIA 2017; 3:22. [PMID: 28560268 PMCID: PMC5441570 DOI: 10.1038/s41537-017-0022-8] [Citation(s) in RCA: 27] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/02/2017] [Revised: 04/04/2017] [Accepted: 04/20/2017] [Indexed: 01/19/2023]
Abstract
Schizophrenia is often associated with disrupted brain connectivity. However, identifying specific neuroimaging-based patterns pathognomonic for schizophrenia and related symptom severity remains a challenging open problem requiring large-scale data-driven analyses emphasizing not only statistical significance but also stability across multiple datasets, contexts and cohorts. Accurate prediction on previously unseen subjects, or generalization, is also essential for any useful biomarker of schizophrenia. In order to build a predictive model based on functional network feature patterns, we studied whole-brain fMRI functional networks, both at the voxel level and lower-resolution supervoxel level. Targeting Auditory Oddball task data on the FBIRN fMRI dataset (n = 95), we considered node-degree and link-weight network features and evaluated stability and generalization accuracy of statistically significant feature sets in discriminating patients vs. CONTROLS We also applied sparse multivariate regression (elastic net) to whole-brain functional connectivity features, for the first time, to derive stable predictive features for symptom severity. Whole-brain link-weight features achieved 74% accuracy in identifying patients and were more stable than voxel-wise node-degrees. Link-weight features predicted severity of several negative and positive symptom scales, including inattentiveness and bizarre behavior. The most-significant, stable and discriminative functional connectivity changes involved increased correlations between thalamus and primary motor/primary sensory cortex, and between precuneus (BA7) and thalamus, putamen, and Brodmann areas BA9 and BA44. Precuneus, along with BA6 and primary sensory cortex, was also involved in predicting severity of several symptoms. Overall, the proposed multi-step methodology may help identify more reliable multivariate patterns allowing for accurate prediction of schizophrenia and its symptoms severity.
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Korostil M, Remington G, McIntosh AR. Practice and Learning: Spatiotemporal Differences in Thalamo-Cortical-Cerebellar Networks Engagement across Learning Phases in Schizophrenia. Front Psychiatry 2017; 7:212. [PMID: 28167919 PMCID: PMC5256117 DOI: 10.3389/fpsyt.2016.00212] [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: 09/12/2016] [Accepted: 12/22/2016] [Indexed: 11/13/2022] Open
Abstract
BACKGROUND Understanding how practice mediates the transition of brain-behavior networks between early and later stages of learning is constrained by the common approach to analysis of fMRI data. Prior imaging studies have mostly relied on a single scan, and parametric, task-related analyses. Our experiment incorporates a multisession fMRI lexicon-learning experiment with multivariate, whole-brain analysis to further knowledge of the distributed networks supporting practice-related learning in schizophrenia (SZ). METHODS Participants with SZ were compared with healthy control (HC) participants as they learned a novel lexicon during two fMRI scans over a several day period. All participants were trained to equal task proficiency prior to scanning. Behavioral-Partial Least Squares, a multivariate analytic approach, was used to analyze the imaging data. Permutation testing was used to determine statistical significance and bootstrap resampling to determine the reliability of the findings. RESULTS With practice, HC participants transitioned to a brain-accuracy network incorporating dorsostriatal regions in late-learning stages. The SZ participants did not transition to this pattern despite comparable behavioral results. Instead, successful learners with SZ were differentiated primarily on the basis of greater engagement of perceptual and perceptual-integration brain regions. CONCLUSION There is a different spatiotemporal unfolding of brain-learning relationships in SZ. In SZ, given the same amount of practice, the movement from networks suggestive of effortful learning toward subcortically driven procedural one differs from HC participants. Learning performance in SZ is driven by varying levels of engagement in perceptual regions, which suggests perception itself is impaired and may impact downstream, "higher level" cognition.
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Affiliation(s)
- Michele Korostil
- Centre for Addiction and Mental Health, Toronto, ON, Canada
- Rotman Research Institute of Baycrest Health Sciences, Toronto, ON, Canada
- University of Toronto, Toronto, ON, Canada
| | - Gary Remington
- Centre for Addiction and Mental Health, Toronto, ON, Canada
- University of Toronto, Toronto, ON, Canada
| | - Anthony Randal McIntosh
- Rotman Research Institute of Baycrest Health Sciences, Toronto, ON, Canada
- University of Toronto, Toronto, ON, Canada
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22
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Alamian G, Hincapié AS, Combrisson E, Thiery T, Martel V, Althukov D, Jerbi K. Alterations of Intrinsic Brain Connectivity Patterns in Depression and Bipolar Disorders: A Critical Assessment of Magnetoencephalography-Based Evidence. Front Psychiatry 2017; 8:41. [PMID: 28367127 PMCID: PMC5355450 DOI: 10.3389/fpsyt.2017.00041] [Citation(s) in RCA: 24] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/30/2016] [Accepted: 02/28/2017] [Indexed: 12/21/2022] Open
Abstract
Despite being the object of a thriving field of clinical research, the investigation of intrinsic brain network alterations in psychiatric illnesses is still in its early days. Because the pathological alterations are predominantly probed using functional magnetic resonance imaging (fMRI), many questions about the electrophysiological bases of resting-state alterations in psychiatric disorders, particularly among mood disorder patients, remain unanswered. Alongside important research using electroencephalography (EEG), the specific recent contributions and future promise of magnetoencephalography (MEG) in this field are not fully recognized and valued. Here, we provide a critical review of recent findings from MEG resting-state connectivity within major depressive disorder (MDD) and bipolar disorder (BD). The clinical MEG resting-state results are compared with those previously reported with fMRI and EEG. Taken together, MEG appears to be a promising but still critically underexploited technique to unravel the neurophysiological mechanisms that mediate abnormal (both hyper- and hypo-) connectivity patterns involved in MDD and BD. In particular, a major strength of MEG is its ability to provide source-space estimations of neuromagnetic long-range rhythmic synchronization at various frequencies (i.e., oscillatory coupling). The reviewed literature highlights the relevance of probing local and interregional rhythmic synchronization to explore the pathophysiological underpinnings of each disorder. However, before we can fully take advantage of MEG connectivity analyses in psychiatry, several limitations inherent to MEG connectivity analyses need to be understood and taken into account. Thus, we also discuss current methodological challenges and outline paths for future research. MEG resting-state studies provide an important window onto perturbed spontaneous oscillatory brain networks and hence supply an important complement to fMRI-based resting-state measurements in psychiatric populations.
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Affiliation(s)
- Golnoush Alamian
- Department of Psychology, University of Montreal , Montreal, QC , Canada
| | - Ana-Sofía Hincapié
- Department of Psychology, University of Montreal, Montreal, QC, Canada; Department of Computer Science, Pontificia Universidad Católica de Chile, Santiago de Chile, Chile; Interdisciplinary Center for Neurosciences, School of Psychology, Pontificia Universidad Católica de Chile, Santiago de Chile, Chile
| | - Etienne Combrisson
- Department of Psychology, University of Montreal, Montreal, QC, Canada; Center of Research and Innovation in Sport, Mental Processes and Motor Performance, University Claude Bernard Lyon I, University of Lyon, Villeurbanne, France; Brain Dynamics and Cognition, Lyon Neuroscience Research Center, INSERM U1028, UMR 5292, University of Lyon, Villeurbanne, France
| | - Thomas Thiery
- Department of Psychology, University of Montreal , Montreal, QC , Canada
| | - Véronique Martel
- Department of Psychology, University of Montreal , Montreal, QC , Canada
| | - Dmitrii Althukov
- Department of Psychology, University of Montreal, Montreal, QC, Canada; Department of Computer Sciences, National Research Institution Higher School of Economics, Moscow, Russia; MEG Center, Moscow State University of Pedagogics and Education, Moscow, Russia
| | - Karim Jerbi
- Department of Psychology, University of Montreal, Montreal, QC, Canada; Centre de recherche de l'Institut universitaire en santé mentale de Montréal, Montreal, QC, Canada
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Steffens M, Becker B, Neumann C, Kasparbauer AM, Meyhöfer I, Weber B, Mehta MA, Hurlemann R, Ettinger U. Effects of ketamine on brain function during smooth pursuit eye movements. Hum Brain Mapp 2016; 37:4047-4060. [PMID: 27342447 PMCID: PMC6867533 DOI: 10.1002/hbm.23294] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2016] [Revised: 05/18/2016] [Accepted: 06/13/2016] [Indexed: 11/07/2022] Open
Abstract
The uncompetitive NMDA receptor antagonist ketamine has been proposed to model symptoms of psychosis. Smooth pursuit eye movements (SPEM) are an established biomarker of schizophrenia. SPEM performance has been shown to be impaired in the schizophrenia spectrum and during ketamine administration in healthy volunteers. However, the neural mechanisms mediating SPEM impairments during ketamine administration are unknown. In a counter-balanced, placebo-controlled, double-blind, within-subjects design, 27 healthy participants received intravenous racemic ketamine (100 ng/mL target plasma concentration) on one of two assessment days and placebo (intravenous saline) on the other. Participants performed a block-design SPEM task during functional magnetic resonance imaging (fMRI) at 3 Tesla field strength. Self-ratings of psychosis-like experiences were obtained using the Psychotomimetic States Inventory (PSI). Ketamine administration induced psychosis-like symptoms, during ketamine infusion, participants showed increased ratings on the PSI dimensions cognitive disorganization, delusional thinking, perceptual distortion and mania. Ketamine led to robust deficits in SPEM performance, which were accompanied by reduced blood oxygen level dependent (BOLD) signal in the SPEM network including primary visual cortex, area V5 and the right frontal eye field (FEF), compared to placebo. A measure of connectivity with V5 and FEF as seed regions, however, was not significantly affected by ketamine. These results are similar to the deviations found in schizophrenia patients. Our findings support the role of glutamate dysfunction in impaired smooth pursuit performance and the use of ketamine as a pharmacological model of psychosis, especially when combined with oculomotor biomarkers. Hum Brain Mapp 37:4047-4060, 2016. © 2016 Wiley Periodicals, Inc.
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Affiliation(s)
- M Steffens
- Department of Psychology, University of Bonn, Bonn, Germany
| | - B Becker
- Department of Psychiatry and Division of Medical Psychology, University of Bonn, Bonn, Germany
| | - C Neumann
- Department of Anesthesiology, University of Bonn, Bonn, Germany
| | | | - I Meyhöfer
- Department of Psychology, University of Bonn, Bonn, Germany
| | - B Weber
- Center for Economics and Neuroscience, University of Bonn, Bonn, Germany
- Department of Epileptology, University Hospital Bonn, Bonn, Germany
- Department of NeuroCognition/Imaging, Life&Brain Research Center, Bonn, Germany
| | - M A Mehta
- Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
| | - R Hurlemann
- Department of Psychiatry and Division of Medical Psychology, University of Bonn, Bonn, Germany
| | - U Ettinger
- Department of Psychology, University of Bonn, Bonn, Germany.
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24
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Collin G, de Nijs J, Hulshoff Pol HE, Cahn W, van den Heuvel MP. Connectome organization is related to longitudinal changes in general functioning, symptoms and IQ in chronic schizophrenia. Schizophr Res 2016; 173:166-173. [PMID: 25843919 DOI: 10.1016/j.schres.2015.03.012] [Citation(s) in RCA: 31] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/21/2014] [Revised: 03/13/2015] [Accepted: 03/15/2015] [Indexed: 11/28/2022]
Abstract
Emerging evidence suggests schizophrenia to involve widespread alterations in the macroscale wiring architecture of the human connectome. Recent findings of attenuated connectome alterations in unaffected siblings of schizophrenia patients suggest that altered connectome organization may relate to the vulnerability to develop the disorder, but whether it relates to progression of illness after disease onset is currently unknown. Here, we examined the interaction between connectome structure and longitudinal changes in general functioning, clinical symptoms and IQ in the 3years following MRI assessment in a group of chronically ill schizophrenia patients. Effects in patients were compared to associations between connectome organization and changes in subclinical symptoms and IQ in healthy controls and unaffected siblings of schizophrenia patients. Analyzing the patient sample revealed a relationship between structural connectivity-particularly among central 'brain hubs'-and progressive changes in general functioning (p=0.007), suggesting that more prominent impairments of hub connectivity may herald future functional decline. Our findings further indicate that affected local connectome organization relates to longitudinal increases in overall PANSS symptoms (p=0.013) and decreases in total IQ (p=0.003), independent of baseline symptoms and IQ. No significant associations were observed in controls and siblings, suggesting that the findings in patients represent effects of ongoing illness, as opposed to normal time-related changes. In all, our findings suggest connectome structure to have predictive value for the course of illness in schizophrenia.
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Affiliation(s)
- G Collin
- Department of Psychiatry, Brain Center Rudolf Magnus, University Medical Center Utrecht, Utrecht, The Netherlands.
| | - J de Nijs
- Department of Psychiatry, Brain Center Rudolf Magnus, University Medical Center Utrecht, Utrecht, The Netherlands
| | - H E Hulshoff Pol
- Department of Psychiatry, Brain Center Rudolf Magnus, University Medical Center Utrecht, Utrecht, The Netherlands
| | - W Cahn
- Department of Psychiatry, Brain Center Rudolf Magnus, University Medical Center Utrecht, Utrecht, The Netherlands
| | - M P van den Heuvel
- Department of Psychiatry, Brain Center Rudolf Magnus, University Medical Center Utrecht, Utrecht, The Netherlands
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25
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Brandt CL, Kaufmann T, Agartz I, Hugdahl K, Jensen J, Ueland T, Haatveit B, Skatun KC, Doan NT, Melle I, Andreassen OA, Westlye LT. Cognitive Effort and Schizophrenia Modulate Large-Scale Functional Brain Connectivity. Schizophr Bull 2015; 41:1360-9. [PMID: 25731885 PMCID: PMC4601701 DOI: 10.1093/schbul/sbv013] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
Schizophrenia (SZ) is characterized by cognitive dysfunction and disorganized thought, in addition to hallucinations and delusions, and is regarded a disorder of brain connectivity. Recent efforts have been made to characterize the underlying brain network organization and interactions. However, to which degree connectivity alterations in SZ vary across different levels of cognitive effort is unknown. Utilizing independent component analysis (ICA) and methods for delineating functional connectivity measures from functional magnetic resonance imaging (fMRI) data, we investigated the effects of cognitive effort, SZ and their interactions on between-network functional connectivity during 2 levels of cognitive load in a large and well-characterized sample of SZ patients (n = 99) and healthy individuals (n = 143). Cognitive load influenced a majority of the functional connections, including but not limited to fronto-parietal and default-mode networks, reflecting both decreases and increases in between-network synchronization. Reduced connectivity in SZ was identified in 2 large-scale functional connections across load conditions, with a particular involvement of an insular network. The results document an important role of interactions between insular, default-mode, and visual networks in SZ pathophysiology. The interplay between brain networks was robustly modulated by cognitive effort, but the reduced functional connectivity in SZ, primarily related to an insular network, was independent of cognitive load, indicating a relatively general brain network-level dysfunction.
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Affiliation(s)
- Christine Lycke Brandt
- Norwegian Centre for Mental Disorders Research, KG Jebsen Centre for Psychosis Research, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway;
| | - Tobias Kaufmann
- Norwegian Centre for Mental Disorders Research, KG Jebsen Centre for Psychosis Research, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Ingrid Agartz
- Norwegian Centre for Mental Disorders Research, KG Jebsen Centre for Psychosis Research, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway;,Department of Psychiatric Research, Diakonhjemmet Hospital, Oslo, Norway;,Department of Clinical Neuroscience, Psychiatry Section, Karolinska Institutet, Stockholm, Sweden
| | - Kenneth Hugdahl
- Norwegian Centre for Mental Disorders Research, Department of Biological and Medical Psychology, University of Bergen, Bergen, Norway;,Division of Psychiatry and Department of Radiology, Haukeland University Hospital, Bergen, Norway;,KG Jebsen Centre for Research on Neuropsychiatric Disorders, University of Bergen, Bergen, Norway
| | - Jimmy Jensen
- Norwegian Centre for Mental Disorders Research, KG Jebsen Centre for Psychosis Research, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway;,KG Jebsen Centre for Research on Neuropsychiatric Disorders, University of Bergen, Bergen, Norway
| | - Torill Ueland
- Norwegian Centre for Mental Disorders Research, KG Jebsen Centre for Psychosis Research, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway;,Centre for Psychology, Kristianstad University, Kristianstad, Sweden
| | - Beathe Haatveit
- Norwegian Centre for Mental Disorders Research, KG Jebsen Centre for Psychosis Research, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Kristina C. Skatun
- Norwegian Centre for Mental Disorders Research, KG Jebsen Centre for Psychosis Research, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Nhat Trung Doan
- Norwegian Centre for Mental Disorders Research, KG Jebsen Centre for Psychosis Research, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Ingrid Melle
- Norwegian Centre for Mental Disorders Research, KG Jebsen Centre for Psychosis Research, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Ole A. Andreassen
- Norwegian Centre for Mental Disorders Research, KG Jebsen Centre for Psychosis Research, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Lars T. Westlye
- Norwegian Centre for Mental Disorders Research, KG Jebsen Centre for Psychosis Research, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway;,Department of Psychology, University of Oslo, Oslo, Norway
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26
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Cheng W, Palaniyappan L, Li M, Kendrick KM, Zhang J, Luo Q, Liu Z, Yu R, Deng W, Wang Q, Ma X, Guo W, Francis S, Liddle P, Mayer AR, Schumann G, Li T, Feng J. Voxel-based, brain-wide association study of aberrant functional connectivity in schizophrenia implicates thalamocortical circuitry. NPJ SCHIZOPHRENIA 2015; 1:15016. [PMID: 27336032 PMCID: PMC4849447 DOI: 10.1038/npjschz.2015.16] [Citation(s) in RCA: 102] [Impact Index Per Article: 11.3] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/24/2014] [Revised: 03/08/2015] [Accepted: 03/12/2015] [Indexed: 02/05/2023]
Abstract
Background: Wernicke’s concept of ‘sejunction’ or aberrant associations among specialized brain regions is one of the earliest hypotheses attempting to explain the myriad of symptoms in psychotic disorders. Unbiased data mining of all possible brain-wide connections in large data sets is an essential first step in localizing these aberrant circuits. Methods: We analyzed functional connectivity using the largest resting-state neuroimaging data set reported to date in the schizophrenia literature (415 patients vs. 405 controls from UK, USA, Taiwan, and China). An exhaustive brain-wide association study at both regional and voxel-based levels enabled a continuous data-driven discovery of the key aberrant circuits in schizophrenia. Results: Results identify the thalamus as the key hub for altered functional networks in patients. Increased thalamus–primary somatosensory cortex connectivity was the most significant aberration in schizophrenia (P=10−18). Overall, a number of thalamic links with motor and sensory cortical regions showed increased connectivity in schizophrenia, whereas thalamo–frontal connectivity was weakened. Network changes were correlated with symptom severity and illness duration, and support vector machine analysis revealed discrimination accuracies of 73.53–80.92%. Conclusions: Widespread alterations in resting-state thalamocortical functional connectivity is likely to be a core feature of schizophrenia that contributes to the extensive sensory, motor, cognitive, and emotional impairments in this disorder. Changes in this schizophrenia-associated network could be a reliable mechanistic index to discriminate patients from healthy controls.
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Affiliation(s)
- Wei Cheng
- Centre for Computational Systems Biology, Fudan University , Shanghai, China
| | - Lena Palaniyappan
- Centre for Translational Neuroimaging, Division of Psychiatry and Applied Psychology, Institute of Mental Health, University of Nottingham , Nottingham, UK
| | - Mingli Li
- The Mental Health Center and the Psychiatric Laboratory, West China Hospital, Sichuan University , Chengdu, China
| | - Keith M Kendrick
- Key Laboratory for Neuroinformation, Ministry of Education of China, School of Life Science and Technology, University of Electronic Science and Technology of China , Chengdu, China
| | - Jie Zhang
- Centre for Computational Systems Biology, Fudan University , Shanghai, China
| | - Qiang Luo
- Centre for Computational Systems Biology, Fudan University , Shanghai, China
| | - Zening Liu
- Institute of Mental Health, Second Xiangya Hospital, Central South University , Changsha, China
| | - Rongjun Yu
- School of Psychology and Center for Studies of Psychological Application, South China Normal University , Guangzhou, China
| | - Wei Deng
- The Mental Health Center and the Psychiatric Laboratory, West China Hospital, Sichuan University , Chengdu, China
| | - Qiang Wang
- The Mental Health Center and the Psychiatric Laboratory, West China Hospital, Sichuan University , Chengdu, China
| | - Xiaohong Ma
- The Mental Health Center and the Psychiatric Laboratory, West China Hospital, Sichuan University , Chengdu, China
| | - Wanjun Guo
- The Mental Health Center and the Psychiatric Laboratory, West China Hospital, Sichuan University , Chengdu, China
| | - Susan Francis
- Sir Peter Mansfield MR Centre, University of Nottingham , Nottingham, UK
| | - Peter Liddle
- Centre for Translational Neuroimaging, Division of Psychiatry and Applied Psychology, Institute of Mental Health, University of Nottingham , Nottingham, UK
| | | | - Gunter Schumann
- Medical Research Council-Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, King's College London, De Crespigny Park , London, UK
| | - Tao Li
- The Mental Health Center and the Psychiatric Laboratory, West China Hospital, Sichuan University , Chengdu, China
| | - Jianfeng Feng
- Centre for Computational Systems Biology, Fudan University, Shanghai, China; Department of Computer Science, University of Warwick, Coventry, UK; School of Life Science and the Collaborative Innovation Center for Brain Science, Fudan University, Shanghai, China
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27
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Sarpal DK, Robinson DG, Lencz T, Argyelan M, Ikuta T, Karlsgodt K, Gallego JA, Kane JM, Szeszko PR, Malhotra AK. Antipsychotic treatment and functional connectivity of the striatum in first-episode schizophrenia. JAMA Psychiatry 2015; 72:5-13. [PMID: 25372846 PMCID: PMC4286512 DOI: 10.1001/jamapsychiatry.2014.1734] [Citation(s) in RCA: 230] [Impact Index Per Article: 25.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/14/2022]
Abstract
IMPORTANCE Previous evidence has implicated corticostriatal abnormalities in the pathophysiology of psychosis. Although the striatum is the primary target of all efficacious antipsychotics, the relationship between its functional connectivity and symptomatic reduction remains unknown. OBJECTIVE To explore the longitudinal effect of treatment with second-generation antipsychotics on functional connectivity of the striatum during the resting state in patients experiencing a first episode of psychosis. DESIGN, SETTING, AND PARTICIPANTS This prospective controlled study took place at a clinical research center and included 24 patients with first-episode psychosis and 24 healthy participants matched for age, sex, education, and handedness. Medications were administered in a double-blind randomized manner. INTERVENTIONS Patients were scanned at baseline and after 12 weeks of treatment with either risperidone or aripiprazole. Their symptoms were evaluated with the Brief Psychiatric Rating Scale at baseline and follow-up. Healthy participants were scanned twice within a 12-week interval. MAIN OUTCOMES AND MEASURES Functional connectivity of striatal regions was examined via functional magnetic resonance imaging using a seed-based approach. Changes in functional connectivity of these seeds were compared with reductions in ratings of psychotic symptoms. RESULTS Patients had a median exposure of 1 day to antipsychotic medication prior to being scanned (mean [SD] = 4.5 [6.1]). Eleven patients were treated with aripiprazole and 13 patients were treated with risperidone. As psychosis improved, we observed an increase in functional connectivity between striatal seed regions and the anterior cingulate, dorsolateral prefrontal cortex, and limbic regions such as the hippocampus and anterior insula (P < .05, corrected for multiple comparisons). Conversely, a negative relationship was observed between reduction in psychosis and functional connectivity of striatal regions with structures within the parietal lobe (P < .05, corrected for multiple comparisons). CONCLUSIONS AND RELEVANCE Our results indicated that corticostriatal functional dysconnectivity in psychosis is a state-dependent phenomenon. Increased functional connectivity of the striatum with prefrontal and limbic regions may be a biomarker for improvement in symptoms associated with antipsychotic treatment.
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Affiliation(s)
- Deepak K. Sarpal
- Department of Psychiatry, The Zucker Hillside Hospital, North Shore-LIJ Health System, Glen Oaks, NY
| | - Delbert G. Robinson
- Department of Psychiatry, The Zucker Hillside Hospital, North Shore-LIJ Health System, Glen Oaks, NY,Center for Psychiatric Neuroscience, Feinstein Institute for Medical Research, Manhasset, NY,Department of Psychiatry, Hofstra North Shore-LIJ School of Medicine, Hempstead, NY
| | - Todd Lencz
- Department of Psychiatry, The Zucker Hillside Hospital, North Shore-LIJ Health System, Glen Oaks, NY,Center for Psychiatric Neuroscience, Feinstein Institute for Medical Research, Manhasset, NY,Department of Psychiatry, Hofstra North Shore-LIJ School of Medicine, Hempstead, NY
| | - Miklos Argyelan
- Department of Psychiatry, The Zucker Hillside Hospital, North Shore-LIJ Health System, Glen Oaks, NY
| | - Toshikazu Ikuta
- Department of Communication Sciences and Disorders, School of Applied Sciences, University of Mississippi, University, MS
| | - Katherine Karlsgodt
- Department of Psychiatry, The Zucker Hillside Hospital, North Shore-LIJ Health System, Glen Oaks, NY,Center for Psychiatric Neuroscience, Feinstein Institute for Medical Research, Manhasset, NY
| | - Juan A. Gallego
- Department of Psychiatry, The Zucker Hillside Hospital, North Shore-LIJ Health System, Glen Oaks, NY,Center for Psychiatric Neuroscience, Feinstein Institute for Medical Research, Manhasset, NY,Department of Psychiatry, Hofstra North Shore-LIJ School of Medicine, Hempstead, NY
| | - John M. Kane
- Department of Psychiatry, The Zucker Hillside Hospital, North Shore-LIJ Health System, Glen Oaks, NY,Center for Psychiatric Neuroscience, Feinstein Institute for Medical Research, Manhasset, NY,Department of Psychiatry, Hofstra North Shore-LIJ School of Medicine, Hempstead, NY
| | - Philip R. Szeszko
- Department of Psychiatry, The Zucker Hillside Hospital, North Shore-LIJ Health System, Glen Oaks, NY,Center for Psychiatric Neuroscience, Feinstein Institute for Medical Research, Manhasset, NY,Department of Psychiatry, Hofstra North Shore-LIJ School of Medicine, Hempstead, NY
| | - Anil K. Malhotra
- Department of Psychiatry, The Zucker Hillside Hospital, North Shore-LIJ Health System, Glen Oaks, NY,Center for Psychiatric Neuroscience, Feinstein Institute for Medical Research, Manhasset, NY,Department of Psychiatry, Hofstra North Shore-LIJ School of Medicine, Hempstead, NY
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28
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Yu Q, Erhardt EB, Sui J, Du Y, He H, Hjelm D, Cetin MS, Rachakonda S, Miller RL, Pearlson G, Calhoun VD. Assessing dynamic brain graphs of time-varying connectivity in fMRI data: application to healthy controls and patients with schizophrenia. Neuroimage 2014; 107:345-355. [PMID: 25514514 DOI: 10.1016/j.neuroimage.2014.12.020] [Citation(s) in RCA: 150] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2014] [Revised: 11/12/2014] [Accepted: 12/07/2014] [Indexed: 01/08/2023] Open
Abstract
Graph theory-based analysis has been widely employed in brain imaging studies, and altered topological properties of brain connectivity have emerged as important features of mental diseases such as schizophrenia. However, most previous studies have focused on graph metrics of stationary brain graphs, ignoring that brain connectivity exhibits fluctuations over time. Here we develop a new framework for accessing dynamic graph properties of time-varying functional brain connectivity in resting-state fMRI data and apply it to healthy controls (HCs) and patients with schizophrenia (SZs). Specifically, nodes of brain graphs are defined by intrinsic connectivity networks (ICNs) identified by group independent component analysis (ICA). Dynamic graph metrics of the time-varying brain connectivity estimated by the correlation of sliding time-windowed ICA time courses of ICNs are calculated. First- and second-level connectivity states are detected based on the correlation of nodal connectivity strength between time-varying brain graphs. Our results indicate that SZs show decreased variance in the dynamic graph metrics. Consistent with prior stationary functional brain connectivity works, graph measures of identified first-level connectivity states show lower values in SZs. In addition, more first-level connectivity states are disassociated with the second-level connectivity state which resembles the stationary connectivity pattern computed by the entire scan. Collectively, the findings provide new evidence about altered dynamic brain graphs in schizophrenia, which may underscore the abnormal brain performance in this mental illness.
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Affiliation(s)
- Qingbao Yu
- The Mind Research Network, Albuquerque, NM 87106, USA.
| | - Erik B Erhardt
- Department of Mathematics and Statistics, University of New Mexico, Albuquerque, NM 87113, USA
| | - Jing Sui
- The Mind Research Network, Albuquerque, NM 87106, USA; Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
| | - Yuhui Du
- The Mind Research Network, Albuquerque, NM 87106, USA; School of Information and Communication Engineering, North University of China, Taiyuan 030051, China
| | - Hao He
- The Mind Research Network, Albuquerque, NM 87106, USA; Department of Electrical and Computer Engineering, University of New Mexico, Albuquerque, NM 87106, USA
| | - Devon Hjelm
- The Mind Research Network, Albuquerque, NM 87106, USA; Department of Computer Science, University of New Mexico, Albuquerque, NM 87106, USA
| | - Mustafa S Cetin
- The Mind Research Network, Albuquerque, NM 87106, USA; Department of Computer Science, University of New Mexico, Albuquerque, NM 87106, USA
| | | | | | - Godfrey Pearlson
- Olin Neuropsychiatry Research Center, Hartford, CT 06106, USA; Department of Psychiatry, Yale University, New Haven, CT 06520, USA; Department of Neurobiology, Yale University, New Haven, CT 06520, USA
| | - Vince D Calhoun
- The Mind Research Network, Albuquerque, NM 87106, USA; Department of Electrical and Computer Engineering, University of New Mexico, Albuquerque, NM 87106, USA; Olin Neuropsychiatry Research Center, Hartford, CT 06106, USA; Department of Psychiatry, Yale University, New Haven, CT 06520, USA.
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29
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Fornito A, Bullmore ET. Reconciling abnormalities of brain network structure and function in schizophrenia. Curr Opin Neurobiol 2014; 30:44-50. [PMID: 25238608 DOI: 10.1016/j.conb.2014.08.006] [Citation(s) in RCA: 103] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2014] [Accepted: 08/22/2014] [Indexed: 12/30/2022]
Abstract
Schizophrenia is widely regarded as a disorder of abnormal brain connectivity. Magnetic resonance imaging (MRI) suggests that patients show robust reductions of structural connectivity. However, corresponding changes in functional connectivity do not always follow, with increased functional connectivity being reported in many cases. Here, we consider different methodological and mechanistic accounts that might reconcile these apparently contradictory findings and argue that increased functional connectivity in schizophrenia likely represents a pathophysiological dysregulation of brain activity arising from abnormal neurodevelopmental wiring of structural connections linking putative hub regions of association cortex to other brain areas. Elucidating the pathophysiological significance of connectivity abnormalities in schizophrenia will be contingent on better understanding how network structure shapes and constrains function.
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Affiliation(s)
- Alex Fornito
- Monash Clinical and Imaging Neuroscience, School of Psychological Sciences and Monash Biomedical Imaging, Monash University, Victoria, Australia.
| | - Edward T Bullmore
- Brain Mapping Unit, Department of Psychiatry, and Behavioural and Clinical Neuroscience Institute, University of Cambridge, Cambridge, UK; Alternative Discovery and Development, GlaxoSmithKline, Cambridge, UK
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30
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31
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Yu Q, Sui J, Kiehl KA, Pearlson G, Calhoun VD. State-related functional integration and functional segregation brain networks in schizophrenia. Schizophr Res 2013; 150:450-8. [PMID: 24094882 PMCID: PMC3839349 DOI: 10.1016/j.schres.2013.09.016] [Citation(s) in RCA: 31] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/16/2013] [Revised: 09/16/2013] [Accepted: 09/17/2013] [Indexed: 01/20/2023]
Abstract
Altered topological properties of brain connectivity networks have emerged as important features of schizophrenia. The aim of this study was to investigate how the state-related modulations to graph measures of functional integration and functional segregation brain networks are disrupted in schizophrenia. Firstly, resting state and auditory oddball discrimination (AOD) fMRI data of healthy controls (HCs) and schizophrenia patients (SZs) were decomposed into spatially independent components (ICs) by group independent component analysis (ICA). Then, weighted positive and negative functional integration (inter-component networks) and functional segregation (intra-component networks) brain networks were built in each subject. Subsequently, connectivity strength, clustering coefficient, and global efficiency of all brain networks were statistically compared between groups (HCs and SZs) in each state and between states (rest and AOD) within group. We found that graph measures of negative functional integration brain network and several positive functional segregation brain networks were altered in schizophrenia during AOD task. The metrics of positive functional integration brain network and one positive functional segregation brain network were higher during the resting state than during the AOD task only in HCs. These findings imply that state-related characteristics of both functional integration and functional segregation brain networks are impaired in schizophrenia which provides new insight into the altered brain performance in this brain disorder.
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Affiliation(s)
- Qingbao Yu
- The Mind Research Network, Albuquerque, NM 87106, USA.
| | - Jing Sui
- The Mind Research Network, Albuquerque, NM 87106, USA
| | - Kent A. Kiehl
- The Mind Research Network, Albuquerque, NM 87106, USA,Dept. of Psychology, University of New Mexico, Albuquerque, NM 87106, USA
| | - Godfrey Pearlson
- Olin Neuropsychiatry Research Center, Hartford, CT 06106, USA,Dept. of Psychiatry, Yale University, New Haven, CT 06520, USA,Dept. of Neurobiology, Yale University, New Haven, CT 06520, USA
| | - Vince D. Calhoun
- The Mind Research Network, Albuquerque, NM 87106, USA,Olin Neuropsychiatry Research Center, Hartford, CT 06106, USA,Dept. of Psychiatry, Yale University, New Haven, CT 06520, USA,Dept. of ECE, University of New Mexico, Albuquerque, NM 87106, USA
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