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de Freitas MBL, Luna LP, Beatriz M, Pinto RK, Alves CHL, Bittencourt L, Nardi AE, Oertel V, Veras AB, de Lucena DF, Alves GS. Resting-state fMRI is associated with trauma experiences, mood and psychosis in Afro-descendants with bipolar disorder and schizophrenia. Psychiatry Res Neuroimaging 2024; 340:111766. [PMID: 38408419 DOI: 10.1016/j.pscychresns.2023.111766] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/31/2023] [Revised: 11/19/2023] [Accepted: 11/26/2023] [Indexed: 02/28/2024]
Abstract
BACKGROUND Bipolar disorder (BD) and schizophrenia (SCZ) may exhibit functional abnormalities in several brain areas, including the medial temporal and prefrontal cortex and hippocampus; however, a less explored topic is how brain connectivity is linked to premorbid trauma experiences and clinical features in non-Caucasian samples of SCZ and BD. METHODS Sixty-two individuals with SCZ (n = 20), BD (n = 21), and healthy controls (HC, n = 21) from indigenous and African ethnicity were submitted to clinical screening (Di-PAD), traumata experiences (ETISR-SF), cognitive and functional MRI assessment. The item psychosis/hallucinations in SCZ patients showed a negative correlation with the global efficiency (GE) in the right dorsal attention network. The items mania, irritable mood, and racing thoughts in the Di-PAD scale had a significant negative correlation with the GE in the parietal right default mode network. CONCLUSIONS Differences in the activation of specific networks were associated with earlier disease onset, history of physical abuse, and more severe psychotic and mood symptoms in SCZ and BD subjects of indigenous and black ethnicity. Findings provide further evidence on SZ and BD's brain connectivity disturbances, and their clinical significance, in non-Caucasian samples.
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Affiliation(s)
| | - Licia P Luna
- Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins Hospital, Baltimore, MD, USA
| | - Márcia Beatriz
- Neuroradiology Service, São Domingos Hospital, São Luís, Brazil; Translational Psychiatry Research Group, Federal University of Maranhão, São Luís, Brazil
| | | | - Candida H Lopes Alves
- Translational Psychiatry Research Group, Federal University of Maranhão, São Luís, Brazil
| | - Lays Bittencourt
- Neuropsychiatry Service, Nina Rodrigues Hospital, São Luís, Brazil
| | - Antônio E Nardi
- Post-Graduation in Psychiatry and Mental Health (PROPSAM), Federal University of Rio de Janeiro, Rio de Janeiro, Brazil
| | - Viola Oertel
- Department of Psychiatry, Psychosomatics, and Psychotherapy, Frankfurt Goethe University, Germany
| | - André B Veras
- Post-Graduation in Psychiatry and Mental Health (PROPSAM), Federal University of Rio de Janeiro, Rio de Janeiro, Brazil
| | | | - Gilberto Sousa Alves
- Translational Psychiatry Research Group, Federal University of Maranhão, São Luís, Brazil; Neuropsychiatry Service, Nina Rodrigues Hospital, São Luís, Brazil; Post-Graduation in Psychiatry and Mental Health (PROPSAM), Federal University of Rio de Janeiro, Rio de Janeiro, Brazil.
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2
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Becske M, Marosi C, Molnár H, Fodor Z, Farkas K, Rácz FS, Baradits M, Csukly G. Minimum spanning tree analysis of EEG resting-state functional networks in schizophrenia. Sci Rep 2024; 14:10495. [PMID: 38714807 PMCID: PMC11076461 DOI: 10.1038/s41598-024-61316-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2023] [Accepted: 05/03/2024] [Indexed: 05/10/2024] Open
Abstract
Schizophrenia is a serious and complex mental disease, known to be associated with various subtle structural and functional deviations in the brain. Recently, increased attention is given to the analysis of brain-wide, global mechanisms, strongly altering the communication of long-distance brain areas in schizophrenia. Data of 32 patients with schizophrenia and 28 matched healthy control subjects were analyzed. Two minutes long 64-channel EEG recordings were registered during resting, eyes closed condition. Average connectivity strength was estimated with Weighted Phase Lag Index (wPLI) in lower frequencies: delta and theta, and Amplitude Envelope Correlation with leakage correction (AEC-c) in higher frequencies: alpha, beta, lower gamma and higher gamma. To analyze functional network topology Minimum Spanning Tree (MST) algorithms were applied. Results show that patients have weaker functional connectivity in delta and alpha frequency bands. Concerning network differences, the result of lower diameter, higher leaf number, and also higher maximum degree and maximum betweenness centrality in patients suggest a star-like, and more random network topology in patients with schizophrenia. Our findings are in accordance with some previous findings based on resting-state EEG (and fMRI) data, suggesting that MST network structure in schizophrenia is biased towards a less optimal, more centralized organization.
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Affiliation(s)
- Melinda Becske
- Department of Psychiatry and Psychotherapy, Semmelweis University, Balassa u. 6., Budapest, 1083, Hungary
| | - Csilla Marosi
- Department of Psychiatry and Psychotherapy, Semmelweis University, Balassa u. 6., Budapest, 1083, Hungary
| | - Hajnalka Molnár
- Department of Psychiatry and Psychotherapy, Semmelweis University, Balassa u. 6., Budapest, 1083, Hungary
| | - Zsuzsanna Fodor
- Department of Psychiatry and Psychotherapy, Semmelweis University, Balassa u. 6., Budapest, 1083, Hungary
| | - Kinga Farkas
- Department of Psychiatry and Psychotherapy, Semmelweis University, Balassa u. 6., Budapest, 1083, Hungary
| | | | - Máté Baradits
- Department of Psychiatry and Psychotherapy, Semmelweis University, Balassa u. 6., Budapest, 1083, Hungary
| | - Gábor Csukly
- Department of Psychiatry and Psychotherapy, Semmelweis University, Balassa u. 6., Budapest, 1083, Hungary.
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Xiang J, Sun Y, Wu X, Guo Y, Xue J, Niu Y, Cui X. Abnormal Spatial and Temporal Overlap of Time-Varying Brain Functional Networks in Patients with Schizophrenia. Brain Sci 2023; 14:40. [PMID: 38248255 PMCID: PMC10813230 DOI: 10.3390/brainsci14010040] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2023] [Revised: 12/25/2023] [Accepted: 12/27/2023] [Indexed: 01/23/2024] Open
Abstract
Schizophrenia (SZ) is a complex psychiatric disorder with unclear etiology and pathological features. Neuroscientists are increasingly proposing that schizophrenia is an abnormality in the dynamic organization of brain networks. Previous studies have found that the dynamic brain networks of people with SZ are abnormal in both space and time. However, little is known about the interactions and overlaps between hubs of the brain underlying spatiotemporal dynamics. In this study, we aimed to investigate different patterns of spatial and temporal overlap of hubs between SZ patients and healthy individuals. Specifically, we obtained resting-state functional magnetic resonance imaging data from the public dataset for 43 SZ patients and 49 healthy individuals. We derived a representation of time-varying functional connectivity using the Jackknife Correlation (JC) method. We employed the Betweenness Centrality (BC) method to identify the hubs of the brain's functional connectivity network. We then applied measures of temporal overlap, spatial overlap, and hierarchical clustering to investigate differences in the organization of brain hubs between SZ patients and healthy controls. Our findings suggest significant differences between SZ patients and healthy controls at the whole-brain and subnetwork levels. Furthermore, spatial overlap and hierarchical clustering analysis showed that quasi-periodic patterns were disrupted in SZ patients. Analyses of temporal overlap revealed abnormal pairwise engagement preferences in the hubs of SZ patients. These results provide new insights into the dynamic characteristics of the network organization of the SZ brain.
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Affiliation(s)
- Jie Xiang
- College of Computer Science and Technology, Taiyuan University of Technology, Taiyuan 030024, China; (J.X.); (Y.S.); (X.W.); (J.X.); (Y.N.)
| | - Yumeng Sun
- College of Computer Science and Technology, Taiyuan University of Technology, Taiyuan 030024, China; (J.X.); (Y.S.); (X.W.); (J.X.); (Y.N.)
| | - Xubin Wu
- College of Computer Science and Technology, Taiyuan University of Technology, Taiyuan 030024, China; (J.X.); (Y.S.); (X.W.); (J.X.); (Y.N.)
| | - Yuxiang Guo
- School of Software, Taiyuan University of Technology, Taiyuan 030024, China;
| | - Jiayue Xue
- College of Computer Science and Technology, Taiyuan University of Technology, Taiyuan 030024, China; (J.X.); (Y.S.); (X.W.); (J.X.); (Y.N.)
| | - Yan Niu
- College of Computer Science and Technology, Taiyuan University of Technology, Taiyuan 030024, China; (J.X.); (Y.S.); (X.W.); (J.X.); (Y.N.)
| | - Xiaohong Cui
- College of Computer Science and Technology, Taiyuan University of Technology, Taiyuan 030024, China; (J.X.); (Y.S.); (X.W.); (J.X.); (Y.N.)
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4
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Porter A, Fei S, Damme KSF, Nusslock R, Gratton C, Mittal VA. A meta-analysis and systematic review of single vs. multimodal neuroimaging techniques in the classification of psychosis. Mol Psychiatry 2023; 28:3278-3292. [PMID: 37563277 PMCID: PMC10618094 DOI: 10.1038/s41380-023-02195-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/03/2022] [Revised: 07/11/2023] [Accepted: 07/17/2023] [Indexed: 08/12/2023]
Abstract
BACKGROUND Psychotic disorders are characterized by structural and functional abnormalities in brain networks. Neuroimaging techniques map and characterize such abnormalities using unique features (e.g., structural integrity, coactivation). However, it is unclear if a specific method, or a combination of modalities, is particularly effective in identifying differences in brain networks of someone with a psychotic disorder. METHODS A systematic meta-analysis evaluated machine learning classification of schizophrenia spectrum disorders in comparison to healthy control participants using various neuroimaging modalities (i.e., T1-weighted imaging (T1), diffusion tensor imaging (DTI), resting state functional connectivity (rs-FC), or some combination (multimodal)). Criteria for manuscript inclusion included whole-brain analyses and cross-validation to provide a complete picture regarding the predictive ability of large-scale brain systems in psychosis. For this meta-analysis, we searched Ovid MEDLINE, PubMed, PsychInfo, Google Scholar, and Web of Science published between inception and March 13th 2023. Prediction results were averaged for studies using the same dataset, but parallel analyses were run that included studies with pooled sample across many datasets. We assessed bias through funnel plot asymmetry. A bivariate regression model determined whether differences in imaging modality, demographics, and preprocessing methods moderated classification. Separate models were run for studies with internal prediction (via cross-validation) and external prediction. RESULTS 93 studies were identified for quantitative review (30 T1, 9 DTI, 40 rs-FC, and 14 multimodal). As a whole, all modalities reliably differentiated those with schizophrenia spectrum disorders from controls (OR = 2.64 (95%CI = 2.33 to 2.95)). However, classification was relatively similar across modalities: no differences were seen across modalities in the classification of independent internal data, and a small advantage was seen for rs-FC studies relative to T1 studies in classification in external datasets. We found large amounts of heterogeneity across results resulting in significant signs of bias in funnel plots and Egger's tests. Results remained similar, however, when studies were restricted to those with less heterogeneity, with continued small advantages for rs-FC relative to structural measures. Notably, in all cases, no significant differences were seen between multimodal and unimodal approaches, with rs-FC and unimodal studies reporting largely overlapping classification performance. Differences in demographics and analysis or denoising were not associated with changes in classification scores. CONCLUSIONS The results of this study suggest that neuroimaging approaches have promise in the classification of psychosis. Interestingly, at present most modalities perform similarly in the classification of psychosis, with slight advantages for rs-FC relative to structural modalities in some specific cases. Notably, results differed substantially across studies, with suggestions of biased effect sizes, particularly highlighting the need for more studies using external prediction and large sample sizes. Adopting more rigorous and systematized standards will add significant value toward understanding and treating this critical population.
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Affiliation(s)
- Alexis Porter
- Department of Psychology, Northwestern University, Evanston, IL, USA.
| | - Sihan Fei
- Department of Psychology, Northwestern University, Evanston, IL, USA
| | - Katherine S F Damme
- Department of Psychology, Northwestern University, Evanston, IL, USA
- Institute for Innovations in Developmental Sciences, Northwestern University, Evanston and Chicago, IL, USA
| | - Robin Nusslock
- Department of Psychology, Northwestern University, Evanston, IL, USA
| | - Caterina Gratton
- Department of Psychology, Florida State University, Tallahassee, FL, USA
| | - Vijay A Mittal
- Department of Psychology, Northwestern University, Evanston, IL, USA
- Institute for Innovations in Developmental Sciences, Northwestern University, Evanston and Chicago, IL, USA
- Department of Psychiatry, Northwestern University, Chicago, IL, USA
- Medical Social Sciences, Northwestern University, Chicago, IL, USA
- Institute for Policy Research, Northwestern University, Chicago, IL, USA
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5
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Yang YS, Smucny J, Zhang H, Maddock RJ. Meta-analytic evidence of elevated choline, reduced N-acetylaspartate, and normal creatine in schizophrenia and their moderation by measurement quality, echo time, and medication status. Neuroimage Clin 2023; 39:103461. [PMID: 37406595 PMCID: PMC10509531 DOI: 10.1016/j.nicl.2023.103461] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2023] [Revised: 06/21/2023] [Accepted: 06/22/2023] [Indexed: 07/07/2023]
Abstract
BACKGROUND Brain metabolite abnormalities measured with magnetic resonance spectroscopy (MRS) provide insight into pathological processes in schizophrenia. Prior meta-analyses have not yet answered important questions about the influence of clinical and technical factors on neurometabolite abnormalities and brain region differences. To address these gaps, we performed an updated meta-analysis of N-acetylaspartate (NAA), choline, and creatine levels in patients with schizophrenia and assessed the moderating effects of medication status, echo time, measurement quality, and other factors. METHODS We searched citations from three earlier meta-analyses and the PubMed database after the most recent meta-analysis to identify studies for screening. In total, 113 publications reporting 366 regional metabolite datasets met our inclusion criteria and reported findings in medial prefrontal cortex (MPFC), dorsolateral prefrontal cortex, frontal white matter, hippocampus, thalamus, and basal ganglia from a total of 4445 patient and 3944 control observations. RESULTS Patients with schizophrenia had reduced NAA in five of the six brain regions, with a statistically significant sparing of the basal ganglia. Patients had elevated choline in the basal ganglia and both prefrontal cortical regions. Patient creatine levels were normal in all six regions. In some regions, the NAA and choline differences were greater in studies enrolling predominantly medicated patients compared to studies enrolling predominantly unmedicated patients. Patient NAA levels were more reduced in hippocampus and frontal white matter in studies using longer echo times than those using shorter echo times. MPFC choline and NAA abnormalities were greater in studies reporting better metabolite measurement quality. CONCLUSIONS Choline is elevated in the basal ganglia and prefrontal cortical regions, suggesting regionally increased membrane turnover or glial activation in schizophrenia. The basal ganglia are significantly spared from the well-established widespread reduction of NAA in schizophrenia suggesting a regional difference in disease-associated factors affecting NAA. The echo time findings agree with prior reports and suggest microstructural changes cause faster NAA T2 relaxation in hippocampus and frontal white matter in schizophrenia. Separating the effects of medication status and illness chronicity on NAA and choline abnormalities will require further patient-level studies. Metabolite measurement quality was shown to be a critical factor in MRS studies of schizophrenia.
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Affiliation(s)
- Yvonne S Yang
- VISN22 Mental Illness Research, Education and Clinical Center, VA Greater Los Angeles Healthcare System, 11301 Wilshire Blvd, Los Angeles, CA 90073, USA; Department of Psychiatry and Biobehavioral Sciences, University of California, Los Angeles, 760 Westwood Plaza, Los Angeles, CA 90095, USA.
| | - Jason Smucny
- Imaging Research Center, University of California, Davis, 4701 X Street, Sacramento, CA 95817, USA; Department of Psychiatry and Biobehavioral Sciences, University of California, Davis, 2230 Stockton Blvd, Sacramento, CA 95817, USA
| | - Huailin Zhang
- Department of Internal Medicine, Adventist Health White Memorial, 1720 E Cesar E Chavez Ave, Los Angeles, CA 90033, USA
| | - Richard J Maddock
- Imaging Research Center, University of California, Davis, 4701 X Street, Sacramento, CA 95817, USA; Department of Psychiatry and Biobehavioral Sciences, University of California, Davis, 2230 Stockton Blvd, Sacramento, CA 95817, USA.
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6
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Keane BP, Krekelberg B, Mill RD, Silverstein SM, Thompson JL, Serody MR, Barch DM, Cole MW. Dorsal attention network activity during perceptual organization is distinct in schizophrenia and predictive of cognitive disorganization. Eur J Neurosci 2023; 57:458-478. [PMID: 36504464 DOI: 10.1111/ejn.15889] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2022] [Revised: 12/02/2022] [Accepted: 12/07/2022] [Indexed: 12/14/2022]
Abstract
Visual shape completion is a canonical perceptual organization process that integrates spatially distributed edge information into unified representations of objects. People with schizophrenia show difficulty in discriminating completed shapes, but the brain networks and functional connections underlying this perceptual difference remain poorly understood. Also unclear is whether brain network differences in schizophrenia occur in related illnesses or vary with illness features transdiagnostically. To address these topics, we scanned (functional magnetic resonance imaging, fMRI) people with schizophrenia, bipolar disorder, or no psychiatric illness during rest and during a task in which they discriminated configurations that formed or failed to form completed shapes (illusory and fragmented condition, respectively). Multivariate pattern differences were identified on the cortical surface using 360 predefined parcels and 12 functional networks composed of such parcels. Brain activity flow mapping was used to evaluate the likely involvement of resting-state connections for shape completion. Illusory/fragmented task activation differences ('modulations') in the dorsal attention network (DAN) could distinguish people with schizophrenia from the other groups (AUCs > .85) and could transdiagnostically predict cognitive disorganization severity. Activity flow over functional connections from the DAN could predict secondary visual network modulations in each group, except in schizophrenia. The secondary visual network was strongly and similarly modulated in each group. Task modulations were dispersed over more networks in patients compared to controls. In summary, DAN activity during visual perceptual organization is distinct in schizophrenia, symptomatically relevant, and potentially related to improper attention-related feedback into secondary visual areas.
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Affiliation(s)
- Brian P Keane
- University Behavioral Health Care, Department of Psychiatry, and Center for Cognitive Science, Rutgers, The State University of New Jersey, Piscataway, New Jersey, USA
- Departments of Psychiatry and Neuroscience, University of Rochester Medical Center, Rochester, New York, USA
| | - Bart Krekelberg
- Center for Molecular and Behavioral Neuroscience, Rutgers, The State University of New Jersey, Newark, New Jersey, USA
| | - Ravi D Mill
- Center for Molecular and Behavioral Neuroscience, Rutgers, The State University of New Jersey, Newark, New Jersey, USA
| | - Steven M Silverstein
- University Behavioral Health Care, Department of Psychiatry, and Center for Cognitive Science, Rutgers, The State University of New Jersey, Piscataway, New Jersey, USA
- Departments of Psychiatry and Neuroscience, University of Rochester Medical Center, Rochester, New York, USA
- Department of Ophthalmology, University of Rochester Medical Center, Rochester, New York, USA
| | - Judy L Thompson
- Departments of Psychiatry and Neuroscience, University of Rochester Medical Center, Rochester, New York, USA
- Department of Psychiatric Rehabilitation and Counseling Professions, Rutgers, The State University of New Jersey, Piscataway, New Jersey, USA
| | - Megan R Serody
- University Behavioral Health Care, Department of Psychiatry, and Center for Cognitive Science, Rutgers, The State University of New Jersey, Piscataway, New Jersey, USA
- Departments of Psychiatry and Neuroscience, University of Rochester Medical Center, Rochester, New York, USA
| | - Deanna M Barch
- Departments of Psychological & Brain Sciences, Psychiatry, and Radiology, Washington University in St. Louis, St. Louis, Missouri, USA
| | - Michael W Cole
- Center for Molecular and Behavioral Neuroscience, Rutgers, The State University of New Jersey, Newark, New Jersey, USA
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7
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Ditzel FL, van Montfort SJT, Vernooij LM, Kant IMJ, Aarts E, Spies CD, Hendrikse J, Slooter AJC, van Dellen E. Functional brain network and trail making test changes following major surgery and postoperative delirium: a prospective, multicentre, observational cohort study. Br J Anaesth 2023; 130:e281-e288. [PMID: 36261307 DOI: 10.1016/j.bja.2022.07.054] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2022] [Revised: 07/22/2022] [Accepted: 07/31/2022] [Indexed: 01/25/2023] Open
Abstract
BACKGROUND Delirium is a frequent complication after surgery in older adults and is associated with an increased risk of long-term cognitive impairment and dementia. Disturbances in functional brain networks were previously reported during delirium. We hypothesised that alterations in functional brain networks persist after remission of postoperative delirium and that functional brain network alterations are associated with long-term cognitive impairment. METHODS In this prospective, multicentre, observational cohort study, we included older patients who underwent clinical assessments (including the Trail Making Test B [TMT-B]) and resting-state functional MRI (rs-fMRI) before and 3 months after elective surgery. Delirium was assessed on the first seven postoperative days. RESULTS Of the 554 enrolled patients, 246 remained after strict motion correction, of whom 38 (16%) developed postoperative delirium. The rs-fMRI functional connectivity strength increased 3 months after surgery in the total study population (β=0.006; 95% confidence interval [CI]: 0.001-0.011; P=0.013), but it decreased after postoperative delirium (β=-0.015; 95% CI: -0.028 to 0.002; P=0.023). No difference in TMT-B scores was found at follow-up between patients with and without postoperative delirium. Patients with decreased functional connectivity strength declined in TMT-B scores compared with those who did not (β=11.04; 95% CI: 0.85-21.2; P=0.034). CONCLUSIONS Postoperative delirium was associated with decreased brain functional connectivity strength after 3 months, suggesting that delirium has a long-lasting impact on brain networks. The decreased connectivity strength was associated with significant cognitive deterioration after major surgery. CLINICAL TRIAL REGISTRATION NCT02265263.
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Affiliation(s)
- Fienke L Ditzel
- Department of Intensive Care Medicine and University Medical Center Utrecht Brain Center, Utrecht University, Utrecht, the Netherlands.
| | - Simone J T van Montfort
- Department of Intensive Care Medicine and University Medical Center Utrecht Brain Center, Utrecht University, Utrecht, the Netherlands
| | - Lisette M Vernooij
- Department of Intensive Care Medicine and University Medical Center Utrecht Brain Center, Utrecht University, Utrecht, the Netherlands; Department of Anesthesiology, University Medical Center Utrecht, Utrecht University, Utrecht, the Netherlands
| | - Ilse M J Kant
- Department of Intensive Care Medicine and University Medical Center Utrecht Brain Center, Utrecht University, Utrecht, the Netherlands
| | - Ellen Aarts
- Department of Intensive Care Medicine and University Medical Center Utrecht Brain Center, Utrecht University, Utrecht, the Netherlands; Faculty of Psychology, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
| | - Claudia D Spies
- Department of Anaesthesiology, Charité Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin and Berlin Institute of Health, Berlin, Germany
| | - Jeroen Hendrikse
- Department of Radiology, University Medical Center Utrecht Brain Center, Utrecht University, Utrecht, the Netherlands
| | - Arjen J C Slooter
- Department of Intensive Care Medicine and University Medical Center Utrecht Brain Center, Utrecht University, Utrecht, the Netherlands; Department of Neurology, UZ Brussel and Vrije Universiteit Brussel, Brussels, Belgium
| | - Edwin van Dellen
- Department of Intensive Care Medicine and University Medical Center Utrecht Brain Center, Utrecht University, Utrecht, the Netherlands; Department of Psychiatry and UMC University Medical Center Utrecht Brain Center, Utrecht University, Utrecht, the Netherlands
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8
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Korda AI, Andreou C, Avram M, Handels H, Martinetz T, Borgwardt S. Chaos analysis of the brain topology in first-episode psychosis and clinical high risk patients. Front Psychiatry 2022; 13:965128. [PMID: 36311536 PMCID: PMC9606602 DOI: 10.3389/fpsyt.2022.965128] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/09/2022] [Accepted: 09/16/2022] [Indexed: 11/17/2022] Open
Abstract
Structural MRI studies in first-episode psychosis (FEP) and in clinical high risk (CHR) patients have consistently shown volumetric abnormalities in frontal, temporal, and cingulate cortex areas. The aim of the present study was to employ chaos analysis for the identification of brain topology differences in people with psychosis. Structural MRI were acquired from 77 FEP, 73 CHR and 44 healthy controls (HC). Chaos analysis of the gray matter distribution was performed: First, the distances of each voxel from the center of mass in the gray matter image was calculated. Next, the distances multiplied by the voxel intensity were represented as a spatial-series, which then was analyzed by extracting the Largest-Lyapunov-Exponent (lambda). The lambda brain map depicts thus how the gray matter topology changes. Between-group differences were identified by (a) comparing the lambda brain maps, which resulted in statistically significant differences in FEP and CHR compared to HC; and (b) matching the lambda series with the Morlet wavelet, which resulted in statistically significant differences in the scalograms of FEP against CHR and HC. The proposed framework using spatial-series extraction enhances the between-group differences of FEP, CHR and HC subjects, verifies diagnosis-relevant features and may potentially contribute to the identification of structural biomarkers for psychosis.
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Affiliation(s)
- Alexandra I. Korda
- Translational Psychiatry, Department of Psychiatry and Psycotherapy, University of Lübeck, Lübeck, Germany
| | - Christina Andreou
- Translational Psychiatry, Department of Psychiatry and Psycotherapy, University of Lübeck, Lübeck, Germany
| | - Mihai Avram
- Translational Psychiatry, Department of Psychiatry and Psycotherapy, University of Lübeck, Lübeck, Germany
| | - Heinz Handels
- Institute of Medical Informatics, University of Lübeck, Lübeck, Germany
| | - Thomas Martinetz
- Institute for Neuro- and Bioinformatics, University of Lübeck, Lübeck, Germany
| | - Stefan Borgwardt
- Translational Psychiatry, Department of Psychiatry and Psycotherapy, University of Lübeck, Lübeck, Germany
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9
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Blomsma N, de Rooy B, Gerritse F, van der Spek R, Tewarie P, Hillebrand A, Otte WM, Stam CJ, van Dellen E. Minimum spanning tree analysis of brain networks: A systematic review
of network size effects, sensitivity for neuropsychiatric pathology and disorder
specificity. Netw Neurosci 2022; 6:301-319. [PMID: 35733422 PMCID: PMC9207994 DOI: 10.1162/netn_a_00245] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2021] [Accepted: 03/10/2022] [Indexed: 11/05/2022] Open
Abstract
Brain network characteristics’ potential to serve as a neurological and psychiatric pathology biomarker has been hampered by the so-called thresholding problem. The minimum spanning tree (MST) is increasingly applied to overcome this problem. It is yet unknown whether this approach leads to more consistent findings across studies and converging outcomes of either disease-specific biomarkers or transdiagnostic effects. We performed a systematic review on MST analysis in neurophysiological and neuroimaging studies (N = 43) to study consistency of MST metrics between different network sizes and assessed disease specificity and transdiagnostic sensitivity of MST metrics for neurological and psychiatric conditions. Analysis of data from control groups (12 studies) showed that MST leaf fraction but not diameter decreased with increasing network size. Studies showed a broad range in metric values, suggesting that specific processing pipelines affect MST topology. Contradicting findings remain in the inconclusive literature of MST brain network studies, but some trends were seen: (1) a more linelike organization characterizes neurodegenerative disorders across pathologies, and is associated with symptom severity and disease progression; (2) neurophysiological studies in epilepsy show frequency band specific MST alterations that normalize after successful treatment; and (3) less efficient MST topology in alpha band is found across disorders associated with attention impairments. The potential of brain network characteristics to serve as biomarker of neurological and psychiatric pathology has been hampered by the so-called thresholding problem. The minimum spanning tree (MST) is increasingly applied to overcome this problem. We performed a systematic review on MST analysis in neurophysiological and neuroimaging studies and assessed disease specificity and transdiagnostic sensitivity of MST metrics for neurological and psychiatric conditions. MST leaf fraction but not diameter decreased with increasing network size. Contradicting findings remain in the literature on MST brain network studies, but some trends were seen: (1) a more linelike organization characterizes neurodegenerative disorders; (2) in epilepsy there are frequency band specific MST alterations that normalize after successful treatment; and (3) less efficient MST topology is found across disorders associated with attention impairments.
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Affiliation(s)
- Nicky Blomsma
- University Medical Center Utrecht, Department of Psychiatry, Brain Center, Heidelberglaan 100, Utrecht, the Netherlands
| | - Bart de Rooy
- University Medical Center Utrecht, Department of Psychiatry, Brain Center, Heidelberglaan 100, Utrecht, the Netherlands
| | - Frank Gerritse
- University Medical Center Utrecht, Department of Psychiatry, Brain Center, Heidelberglaan 100, Utrecht, the Netherlands
| | - Rick van der Spek
- University Medical Center Utrecht, Department of Psychiatry, Brain Center, Heidelberglaan 100, Utrecht, the Netherlands
| | - Prejaas Tewarie
- Amsterdam UMC, Vrije Universiteit Amsterdam, Department of Neurology and Department of Clinical Neurophysiology and MEG center, Amsterdam Neuroscience, De Boelelaan 1117, Amsterdam, The Netherlands
| | - Arjan Hillebrand
- Amsterdam UMC, Vrije Universiteit Amsterdam, Department of Neurology and Department of Clinical Neurophysiology and MEG center, Amsterdam Neuroscience, De Boelelaan 1117, Amsterdam, The Netherlands
| | - Wim M. Otte
- University Medical Center Utrecht, Department of Child Neurology, Brain Center, Heidelberglaan 100, Utrecht, the Netherlands
| | - Cornelis Jan Stam
- Amsterdam UMC, Vrije Universiteit Amsterdam, Department of Neurology and Department of Clinical Neurophysiology and MEG center, Amsterdam Neuroscience, De Boelelaan 1117, Amsterdam, The Netherlands
| | - Edwin van Dellen
- University Medical Center Utrecht, Department of Psychiatry, Brain Center, Heidelberglaan 100, Utrecht, the Netherlands
- University Medical Center Utrecht, Department of Intensive Care Medicine, Brain Center, Heidelberglaan 100, Utrecht, the Netherlands
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10
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Zhu Y, Zhu G, Li B, Yang Y, Zheng X, Xu Q, Li X. Abnormality of Functional Connections in the Resting State Brains of Schizophrenics. Front Hum Neurosci 2022; 16:799881. [PMID: 35355584 PMCID: PMC8959982 DOI: 10.3389/fnhum.2022.799881] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2021] [Accepted: 01/10/2022] [Indexed: 11/13/2022] Open
Abstract
To explore the change of brain connectivity in schizophrenics (SCZ), the resting-state EEG source functional connections of SCZ and healthy control (HC) were investigated in this paper. Different band single-layer networks, multilayer networks, and improved multilayer networks were constructed and their topological attributes were extracted. The topological attributes of SCZ and HC were automatically distinguished using ensemble learning methods called Ensemble Learning based on Trees and Soft voting method, and the effectiveness of different network construction methods was compared based on the classification accuracy. The results showed that the classification accuracy was 89.38% for α band network, 82.5% for multilayer network, and 86.88% for improved multilayer network. Comparing patients with SCZ to those with Alzheimer's disease (AD), the classification accuracy of improved multilayer network was the highest, which was 88.12%. The power spectrum in the α band of SCZ was significantly lower than HC, whereas there was no significant difference between SCZ and AD. This indicated that the improved multilayer network can effectively distinguish SCZ and other groups not only when their power spectrum was significantly different. The results also suggested that the improved multilayer topological attributes were regarded as biological markers in the clinical diagnosis of patients with schizophrenia and even other mental disorders.
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Affiliation(s)
- Yan Zhu
- College of Medical Instruments, Shanghai University of Medicine & Health Sciences, Shanghai, China
- College of Medical Instrument and Food Engineering, University of Shanghai for Science and Technology, Shanghai, China
| | - Geng Zhu
- College of Medical Instruments, Shanghai University of Medicine & Health Sciences, Shanghai, China
| | - Bin Li
- Shanghai Yangpu District Mental Health Center, Shanghai, China
| | - Yueqi Yang
- College of Medical Instruments, Shanghai University of Medicine & Health Sciences, Shanghai, China
- College of Medical Instrument and Food Engineering, University of Shanghai for Science and Technology, Shanghai, China
| | - Xiaohan Zheng
- College of Medical Instruments, Shanghai University of Medicine & Health Sciences, Shanghai, China
- College of Medical Instrument and Food Engineering, University of Shanghai for Science and Technology, Shanghai, China
| | - Qi Xu
- College of Medical Instruments, Shanghai University of Medicine & Health Sciences, Shanghai, China
- College of Medical Instrument and Food Engineering, University of Shanghai for Science and Technology, Shanghai, China
| | - Xiaoou Li
- College of Medical Instruments, Shanghai University of Medicine & Health Sciences, Shanghai, China
- College of Medical Instrument and Food Engineering, University of Shanghai for Science and Technology, Shanghai, China
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11
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Chopra S, Francey SM, O’Donoghue B, Sabaroedin K, Arnatkeviciute A, Cropley V, Nelson B, Graham J, Baldwin L, Tahtalian S, Yuen HP, Allott K, Alvarez-Jimenez M, Harrigan S, Pantelis C, Wood SJ, McGorry P, Fornito A. Functional Connectivity in Antipsychotic-Treated and Antipsychotic-Naive Patients With First-Episode Psychosis and Low Risk of Self-harm or Aggression: A Secondary Analysis of a Randomized Clinical Trial. JAMA Psychiatry 2021; 78:994-1004. [PMID: 34160595 PMCID: PMC8223142 DOI: 10.1001/jamapsychiatry.2021.1422] [Citation(s) in RCA: 34] [Impact Index Per Article: 11.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Abstract
IMPORTANCE Altered functional connectivity (FC) is a common finding in resting-state functional magnetic resonance imaging (rs-fMRI) studies of people with psychosis, yet how FC disturbances evolve in the early stages of illness, and how antipsychotic treatment influences these disturbances, remains unknown. OBJECTIVE To investigate longitudinal FC changes in antipsychotic-naive and antipsychotic-treated patients with first-episode psychosis (FEP). DESIGN, SETTING, AND PARTICIPANTS This secondary analysis of a triple-blind, randomized clinical trial was conducted over a 5-year recruitment period between April 2008 and December 2016 with 59 antipsychotic-naive patients with FEP receiving either a second-generation antipsychotic or a placebo pill over a treatment period of 6 months. Participants were required to have low suicidality and aggression, to have a duration of untreated psychosis of less than 6 months, and to be living in stable accommodations with social support. Both FEP groups received intensive psychosocial therapy. A healthy control group was also recruited. Participants completed rs-fMRI scans at baseline, 3 months, and 12 months. Data were analyzed from May 2019 to August 2020. INTERVENTIONS Resting-state functional MRI was used to probe brain FC. Patients received either a second-generation antipsychotic or a matched placebo tablet. Both patient groups received a manualized psychosocial intervention. MAIN OUTCOMES AND MEASURES The primary outcomes of this analysis were to investigate (1) FC differences between patients and controls at baseline; (2) FC changes in medicated and unmedicated patients between baseline and 3 months; and (3) associations between longitudinal FC changes and clinical outcomes. An additional aim was to investigate long-term FC changes at 12 months after baseline. These outcomes were not preregistered. RESULTS Data were analyzed for 59 patients (antipsychotic medication plus psychosocial treatment: 28 [47.5%]; mean [SD] age, 19.5 [3.0] years; 15 men [53.6%]; placebo plus psychosocial treatment: 31 [52.5%]; mean [SD] age, 18.8 [2.7]; 16 men [51.6%]) and 27 control individuals (mean [SD] age, 21.9 [1.9] years). At baseline, patients showed widespread functional dysconnectivity compared with controls, with reductions predominantly affecting interactions between the default mode network, limbic systems, and the rest of the brain. From baseline to 3 months, patients receiving placebo showed increased FC principally within the same systems; some of these changes correlated with improved clinical outcomes (canonical correlation analysis R = 0.901; familywise error-corrected P = .005). Antipsychotic exposure was associated with increased FC primarily between the thalamus and the rest of the brain. CONCLUSIONS AND RELEVANCE In this secondary analysis of a clinical trial, antipsychotic-naive patients with FEP showed widespread functional dysconnectivity at baseline, followed by an early normalization of default mode network and cortical limbic dysfunction in patients receiving placebo and psychosocial intervention. Antipsychotic exposure was associated with FC changes concentrated on thalamocortical networks. TRIAL REGISTRATION ACTRN12607000608460.
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Affiliation(s)
- Sidhant Chopra
- Turner Institute for Brain and Mental Health, Monash University School of Psychological Sciences, Clayton, Victoria, Australia,Monash Biomedical Imaging, Monash University, Clayton, Victoria, Australia
| | - Shona M. Francey
- Orygen, Parkville, Victoria, Australia,Centre for Youth Mental Health, The University of Melbourne, Melbourne, Victoria, Australia
| | - Brian O’Donoghue
- Orygen, Parkville, Victoria, Australia,Centre for Youth Mental Health, The University of Melbourne, Melbourne, Victoria, Australia
| | - Kristina Sabaroedin
- Turner Institute for Brain and Mental Health, Monash University School of Psychological Sciences, Clayton, Victoria, Australia,Monash Biomedical Imaging, Monash University, Clayton, Victoria, Australia
| | - Aurina Arnatkeviciute
- Turner Institute for Brain and Mental Health, Monash University School of Psychological Sciences, Clayton, Victoria, Australia
| | - Vanessa Cropley
- Melbourne Neuropsychiatry Centre, Department of Psychiatry, University of Melbourne & Melbourne Health, Melbourne, Victoria, Australia
| | - Barnaby Nelson
- Orygen, Parkville, Victoria, Australia,Centre for Youth Mental Health, The University of Melbourne, Melbourne, Victoria, Australia
| | - Jessica Graham
- Orygen, Parkville, Victoria, Australia,Centre for Youth Mental Health, The University of Melbourne, Melbourne, Victoria, Australia
| | - Lara Baldwin
- Orygen, Parkville, Victoria, Australia,Centre for Youth Mental Health, The University of Melbourne, Melbourne, Victoria, Australia
| | - Steven Tahtalian
- Melbourne Neuropsychiatry Centre, Department of Psychiatry, University of Melbourne & Melbourne Health, Melbourne, Victoria, Australia
| | - Hok Pan Yuen
- Orygen, Parkville, Victoria, Australia,Centre for Youth Mental Health, The University of Melbourne, Melbourne, Victoria, Australia
| | - Kelly Allott
- Orygen, Parkville, Victoria, Australia,Centre for Youth Mental Health, The University of Melbourne, Melbourne, Victoria, Australia
| | - Mario Alvarez-Jimenez
- Orygen, Parkville, Victoria, Australia,Centre for Youth Mental Health, The University of Melbourne, Melbourne, Victoria, Australia
| | - Susy Harrigan
- Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, Victoria, Australia,Department of Social Work, Monash University, Caulfield, Victoria, Australia
| | - Christos Pantelis
- Melbourne Neuropsychiatry Centre, Department of Psychiatry, University of Melbourne & Melbourne Health, Melbourne, Victoria, Australia
| | - Stephen J. Wood
- Orygen, Parkville, Victoria, Australia,Centre for Youth Mental Health, The University of Melbourne, Melbourne, Victoria, Australia,University of Birmingham School of Psychology, Edgbaston, United Kingdom
| | - Patrick McGorry
- Orygen, Parkville, Victoria, Australia,Centre for Youth Mental Health, The University of Melbourne, Melbourne, Victoria, Australia
| | - Alex Fornito
- Turner Institute for Brain and Mental Health, Monash University School of Psychological Sciences, Clayton, Victoria, Australia,Monash Biomedical Imaging, Monash University, Clayton, Victoria, Australia
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12
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Sato J, Hirano Y, Hirakawa N, Takahashi J, Oribe N, Kuga H, Nakamura I, Hirano S, Ueno T, Togao O, Hiwatashi A, Nakao T, Onitsuka T. Lower Hippocampal Volume in Patients with Schizophrenia and Bipolar Disorder: A Quantitative MRI Study. J Pers Med 2021; 11:jpm11020121. [PMID: 33668432 PMCID: PMC7918861 DOI: 10.3390/jpm11020121] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2020] [Revised: 02/06/2021] [Accepted: 02/09/2021] [Indexed: 01/02/2023] Open
Abstract
Since patients with schizophrenia (SZ) and bipolar disorder (BD) share many biological features, detecting biomarkers that differentiate SZ and BD patients is crucial for optimized treatments. High-resolution magnetic resonance imaging (MRI) is suitable for detecting subtle brain structural differences in patients with psychiatric disorders. In the present study, we adopted a neuroanatomically defined and manually delineated region of interest (ROI) method to evaluate the amygdalae, hippocampi, Heschl’s gyrus (HG), and planum temporale (PT), because these regions are crucial in the development of SZ and BD. ROI volumes were measured using high resolution MRI in 31 healthy subjects (HS), 23 SZ patients, and 21 BD patients. Right hippocampal volumes differed significantly among groups (HS > BD > SZ), whereas left hippocampal volumes were lower in SZ patients than in HS and BD patients (HS = BD > SZ). Volumes of the amygdalae, HG, and PT did not differ among the three groups. For clinical correlations, there were no significant associations between ROI volumes and demographics/clinical symptoms. Our study revealed significant lower hippocampal volume in patients with SZ and BD, and we suggest that the right hippocampal volume is a potential biomarker for differentiation between SZ and BD.
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Affiliation(s)
- Jinya Sato
- Department of Neuropsychiatry, Graduate School of Medical Sciences, Kyushu University, Fukuoka 812-8582, Japan; (J.S.); (N.H.); (J.T.); (N.O.); (H.K.); (I.N.); (S.H.); (T.N.)
| | - Yoji Hirano
- Department of Neuropsychiatry, Graduate School of Medical Sciences, Kyushu University, Fukuoka 812-8582, Japan; (J.S.); (N.H.); (J.T.); (N.O.); (H.K.); (I.N.); (S.H.); (T.N.)
- Institute of Industrial Science, The University of Tokyo, Tokyo 153-8505, Japan
- Correspondence: (Y.H.); (T.O.); Tel.: +81-92-642-5627 (Y.H. & T.O.)
| | - Noriaki Hirakawa
- Department of Neuropsychiatry, Graduate School of Medical Sciences, Kyushu University, Fukuoka 812-8582, Japan; (J.S.); (N.H.); (J.T.); (N.O.); (H.K.); (I.N.); (S.H.); (T.N.)
| | - Junichi Takahashi
- Department of Neuropsychiatry, Graduate School of Medical Sciences, Kyushu University, Fukuoka 812-8582, Japan; (J.S.); (N.H.); (J.T.); (N.O.); (H.K.); (I.N.); (S.H.); (T.N.)
| | - Naoya Oribe
- Department of Neuropsychiatry, Graduate School of Medical Sciences, Kyushu University, Fukuoka 812-8582, Japan; (J.S.); (N.H.); (J.T.); (N.O.); (H.K.); (I.N.); (S.H.); (T.N.)
- Hizen Psychiatric Medical Center, Division of Clinical Research, National Hospital Organization, Saga 842-0192, Japan;
| | - Hironori Kuga
- Department of Neuropsychiatry, Graduate School of Medical Sciences, Kyushu University, Fukuoka 812-8582, Japan; (J.S.); (N.H.); (J.T.); (N.O.); (H.K.); (I.N.); (S.H.); (T.N.)
- Hizen Psychiatric Medical Center, Division of Clinical Research, National Hospital Organization, Saga 842-0192, Japan;
| | - Itta Nakamura
- Department of Neuropsychiatry, Graduate School of Medical Sciences, Kyushu University, Fukuoka 812-8582, Japan; (J.S.); (N.H.); (J.T.); (N.O.); (H.K.); (I.N.); (S.H.); (T.N.)
| | - Shogo Hirano
- Department of Neuropsychiatry, Graduate School of Medical Sciences, Kyushu University, Fukuoka 812-8582, Japan; (J.S.); (N.H.); (J.T.); (N.O.); (H.K.); (I.N.); (S.H.); (T.N.)
| | - Takefumi Ueno
- Hizen Psychiatric Medical Center, Division of Clinical Research, National Hospital Organization, Saga 842-0192, Japan;
| | - Osamu Togao
- Department of Molecular Imaging and Diagnosis, Graduate School of Medical Sciences, Kyushu University, Fukuoka 812-8582, Japan;
| | - Akio Hiwatashi
- Department of Clinical Radiology, Graduate School of Medical Sciences, Kyushu University, Fukuoka 812-8582, Japan;
| | - Tomohiro Nakao
- Department of Neuropsychiatry, Graduate School of Medical Sciences, Kyushu University, Fukuoka 812-8582, Japan; (J.S.); (N.H.); (J.T.); (N.O.); (H.K.); (I.N.); (S.H.); (T.N.)
| | - Toshiaki Onitsuka
- Department of Neuropsychiatry, Graduate School of Medical Sciences, Kyushu University, Fukuoka 812-8582, Japan; (J.S.); (N.H.); (J.T.); (N.O.); (H.K.); (I.N.); (S.H.); (T.N.)
- Correspondence: (Y.H.); (T.O.); Tel.: +81-92-642-5627 (Y.H. & T.O.)
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13
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Wang F, Hujjaree K, Wang X. Electroencephalographic Microstates in Schizophrenia and Bipolar Disorder. Front Psychiatry 2021; 12:638722. [PMID: 33716831 PMCID: PMC7952514 DOI: 10.3389/fpsyt.2021.638722] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/07/2020] [Accepted: 02/08/2021] [Indexed: 12/19/2022] Open
Abstract
Schizophrenia (SCH) and bipolar disorder (BD) are characterized by many types of symptoms, damaged cognitive function, and abnormal brain connections. The microstates are considered to be the cornerstones of the mental states shown in EEG data. In our study, we investigated the use of microstates as biomarkers to distinguish patients with bipolar disorder from those with schizophrenia by analyzing EEG data measured in an eyes-closed resting state. The purpose of this article is to provide an electron directional physiological explanation for the observed brain dysfunction of schizophrenia and bipolar disorder patients. Methods: We used microstate resting EEG data to explore group differences in the duration, coverage, occurrence, and transition probability of 4 microstate maps among 20 SCH patients, 26 BD patients, and 35 healthy controls (HCs). Results: Microstate analysis revealed 4 microstates (A-D) in global clustering across SCH patients, BD patients, and HCs. The samples were chosen to be matched. We found the greater presence of microstate B in BD patients, and the less presence of microstate class A and B, the greater presence of microstate class C, and less presence of D in SCH patients. Besides, a greater frequent switching between microstates A and B and between microstates B and A in BD patients than in SCH patients and HCs and less frequent switching between microstates C and D and between microstates D and C in BD patients compared with SCH patients. Conclusion: We found abnormal features of microstate A, B in BD patients and abnormal features of microstate A, B, C, and D in SCH patients. These features may indicate the potential abnormalities of SCH patients and BD patients in distributing neural resources and influencing opportune transitions between different states of activity.
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Affiliation(s)
- Fanglan Wang
- Department of Psychiatry, National Clinical Research Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha, China
| | - Khamlesh Hujjaree
- Department of Psychiatry, National Clinical Research Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha, China
| | - Xiaoping Wang
- Department of Psychiatry, National Clinical Research Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha, China
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