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Zhang J, Yang Y, Liu T, Shi Z, Pei G, Wang L, Wu J, Funahashi S, Suo D, Wang C, Yan T. Functional connectivity in people at clinical and familial high risk for schizophrenia. Psychiatry Res 2023; 328:115464. [PMID: 37690192 DOI: 10.1016/j.psychres.2023.115464] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/28/2023] [Revised: 09/03/2023] [Accepted: 09/04/2023] [Indexed: 09/12/2023]
Abstract
Patients diagnosed with schizophrenia (SZ) exhibit compromised functional connectivity within extensive brain networks. However, the precise development of this impairment during disease progression in the clinical high-risk (CHR) population and their relatives remains unclear. Our study leveraged data from 128 resting electroencephalography (EEG) channels acquired from 30 SZ patients, 21 CHR individuals, 17 unaffected healthy relatives (RSs; those at heightened SZ risk due to family history), and 31 healthy controls (HCs). These data were harnessed to establish functional connectivity patterns. By calculating the geometric distance between EEG sequences, we unveiled local and global nonlinear relationships within the entire brain. The process of dimensionality reduction led to low-dimensional representations, providing insights into high-dimensional EEG data. Our findings indicated that CHR participants exhibited aberrant functional connectivity across hemispheres, whereas RS individuals showcased anomalies primarily concentrated within hemispheres. In the realm of low-dimensional analysis, RS participants' third-dimensional occipital lobe values lay between those of the CHR individuals and HCs, significantly correlating with scale scores. This low-dimensional approach facilitated the visualization of brain states, potentially offering enhanced comprehension of brain structure, function, and early-stage functional impairment, such as occipital visual deficits, in RS individuals before cognitive decline onset.
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Affiliation(s)
- Jian Zhang
- School of Medical Technology, Beijing Institute of Technology, 5 South Zhongguancun St, Haidian District, Beijing 100081, China
| | - Yaxin Yang
- School of Life Science, Beijing Institute of Technology, 5 South Zhongguancun St, Haidian District, Beijing 100081, China
| | - Tiantian Liu
- School of Medical Technology, Beijing Institute of Technology, 5 South Zhongguancun St, Haidian District, Beijing 100081, China
| | - Zhongyan Shi
- School of Medical Technology, Beijing Institute of Technology, 5 South Zhongguancun St, Haidian District, Beijing 100081, China
| | - Guangying Pei
- School of Medical Technology, Beijing Institute of Technology, 5 South Zhongguancun St, Haidian District, Beijing 100081, China
| | - Li Wang
- School of Medical Technology, Beijing Institute of Technology, 5 South Zhongguancun St, Haidian District, Beijing 100081, China
| | - Jinglong Wu
- School of Medical Technology, Beijing Institute of Technology, 5 South Zhongguancun St, Haidian District, Beijing 100081, China
| | - Shintaro Funahashi
- Advanced Research Institute of Multidisciplinary Science, Beijing Institute of Technology, 5 South Zhongguancun St, Haidian District, Beijing 100081, China
| | - Dingjie Suo
- School of Medical Technology, Beijing Institute of Technology, 5 South Zhongguancun St, Haidian District, Beijing 100081, China
| | - Changming Wang
- Department of Neurosurgery, Xuanwu Hospital, Capital Medical University, Beijing, 100053, China
| | - Tianyi Yan
- School of Medical Technology, Beijing Institute of Technology, 5 South Zhongguancun St, Haidian District, Beijing 100081, China.
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2
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Ippolito G, Bertaccini R, Tarasi L, Di Gregorio F, Trajkovic J, Battaglia S, Romei V. The Role of Alpha Oscillations among the Main Neuropsychiatric Disorders in the Adult and Developing Human Brain: Evidence from the Last 10 Years of Research. Biomedicines 2022; 10:biomedicines10123189. [PMID: 36551945 PMCID: PMC9775381 DOI: 10.3390/biomedicines10123189] [Citation(s) in RCA: 41] [Impact Index Per Article: 20.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2022] [Revised: 12/03/2022] [Accepted: 12/05/2022] [Indexed: 12/14/2022] Open
Abstract
Alpha oscillations (7-13 Hz) are the dominant rhythm in both the resting and active brain. Accordingly, translational research has provided evidence for the involvement of aberrant alpha activity in the onset of symptomatological features underlying syndromes such as autism, schizophrenia, major depression, and Attention Deficit and Hyperactivity Disorder (ADHD). However, findings on the matter are difficult to reconcile due to the variety of paradigms, analyses, and clinical phenotypes at play, not to mention recent technical and methodological advances in this domain. Herein, we seek to address this issue by reviewing the literature gathered on this topic over the last ten years. For each neuropsychiatric disorder, a dedicated section will be provided, containing a concise account of the current models proposing characteristic alterations of alpha rhythms as a core mechanism to trigger the associated symptomatology, as well as a summary of the most relevant studies and scientific contributions issued throughout the last decade. We conclude with some advice and recommendations that might improve future inquiries within this field.
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Affiliation(s)
- Giuseppe Ippolito
- Centro Studi e Ricerche in Neuroscienze Cognitive, Dipartimento di Psicologia, Alma Mater Studiorum—Università di Bologna, 47521 Cesena, Italy
| | - Riccardo Bertaccini
- Centro Studi e Ricerche in Neuroscienze Cognitive, Dipartimento di Psicologia, Alma Mater Studiorum—Università di Bologna, 47521 Cesena, Italy
| | - Luca Tarasi
- Centro Studi e Ricerche in Neuroscienze Cognitive, Dipartimento di Psicologia, Alma Mater Studiorum—Università di Bologna, 47521 Cesena, Italy
| | - Francesco Di Gregorio
- UO Medicina Riabilitativa e Neuroriabilitazione, Azienda Unità Sanitaria Locale, 40133 Bologna, Italy
| | - Jelena Trajkovic
- Centro Studi e Ricerche in Neuroscienze Cognitive, Dipartimento di Psicologia, Alma Mater Studiorum—Università di Bologna, 47521 Cesena, Italy
| | - Simone Battaglia
- Centro Studi e Ricerche in Neuroscienze Cognitive, Dipartimento di Psicologia, Alma Mater Studiorum—Università di Bologna, 47521 Cesena, Italy
- Dipartimento di Psicologia, Università di Torino, 10124 Torino, Italy
| | - Vincenzo Romei
- Centro Studi e Ricerche in Neuroscienze Cognitive, Dipartimento di Psicologia, Alma Mater Studiorum—Università di Bologna, 47521 Cesena, Italy
- Correspondence:
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3
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Bagherzadeh S, Shahabi MS, Shalbaf A. Detection of schizophrenia using hybrid of deep learning and brain effective connectivity image from electroencephalogram signal. Comput Biol Med 2022; 146:105570. [DOI: 10.1016/j.compbiomed.2022.105570] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2022] [Revised: 04/14/2022] [Accepted: 04/25/2022] [Indexed: 02/06/2023]
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Korda A, Ventouras E, Asvestas P, Toumaian M, Matsopoulos G, Smyrnis N. Convolutional neural network propagation on electroencephalographic scalograms for detection of schizophrenia. Clin Neurophysiol 2022; 139:90-105. [DOI: 10.1016/j.clinph.2022.04.010] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2021] [Revised: 02/11/2022] [Accepted: 04/01/2022] [Indexed: 11/28/2022]
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5
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Zandbagleh A, Mirzakuchaki S, Daliri MR, Premkumar P, Sanei S. Classification of Low and High Schizotypy Levels via Evaluation of Brain Connectivity. Int J Neural Syst 2022; 32:2250013. [PMID: 35236254 DOI: 10.1142/s0129065722500137] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Schizotypy is a latent cluster of personality traits that denote a vulnerability for schizophrenia or a type of spectrum disorder. The aim of the study is to investigate parametric effective brain connectivity features for classifying high versus low schizotypy (LS) status. Electroencephalography (EEG) signals are recorded from 13 high schizotypy (HS) and 11 LS participants during an emotional auditory odd-ball task. The brain connectivity signals for machine learning are taken after the settlement of event-related potentials. A multivariate autoregressive (MVAR)-based connectivity measure is estimated from the EEG signals using the directed transfer functions (DTFs) method. The values of DTF power in five standard frequency bands are used as features. The support vector machines (SVMs) revealed significant differences between HS and LS. The accuracy, specificity, and sensitivity of the results using SVM are as high as 89.21%, 90.3%, and 88.2%, respectively. Our results demonstrate that the effective brain connectivity in prefrontal/parietal and prefrontal/frontal brain regions considerably changes according to schizotypal status. These findings prove that the brain connectivity indices offer valuable biomarkers for detecting schizotypal personality. Further monitoring of the changes in DTF following the diagnosis of schizotypy may lead to the early identification of schizophrenia and other spectrum disorders.
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Affiliation(s)
- Ahmad Zandbagleh
- School of Electrical Engineering, Iran University of Science and Technology, Tehran, Iran
| | - Sattar Mirzakuchaki
- School of Electrical Engineering, Iran University of Science and Technology, Tehran, Iran
| | - Mohammad Reza Daliri
- School of Electrical Engineering, Iran University of Science and Technology, Tehran, Iran
| | - Preethi Premkumar
- Division of Psychology, School of Applied Sciences, London Southbank University, London, UK
| | - Saeid Sanei
- School of Science and Technology, Nottingham Trent University, Clifton Lane, Nottingham, UK
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A survey of brain network analysis by electroencephalographic signals. Cogn Neurodyn 2022; 16:17-41. [PMID: 35126769 PMCID: PMC8807775 DOI: 10.1007/s11571-021-09689-8] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2020] [Revised: 04/25/2021] [Accepted: 05/31/2021] [Indexed: 02/03/2023] Open
Abstract
Brain network analysis is one efficient tool in exploring human brain diseases and can differentiate the alterations from comparative networks. The alterations account for time, mental states, tasks, individuals, and so forth. Furthermore, the changes determine the segregation and integration of functional networks that lead to network reorganization (or reconfiguration) to extend the neuroplasticity of the brain. Exploring related brain networks should be of interest that may provide roadmaps for brain research and clinical diagnosis. Recent electroencephalogram (EEG) studies have revealed the secrets of the brain networks and diseases (or disorders) within and between subjects and have provided instructive and promising suggestions and methods. This review summarized the corresponding algorithms that had been used to construct functional or effective networks on the scalp and cerebral cortex. We reviewed EEG network analysis that unveils more cognitive functions and neural disorders of the human and then explored the relationship between brain science and artificial intelligence which may fuel each other to accelerate their advances, and also discussed some innovations and future challenges in the end.
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Tarasi L, Trajkovic J, Diciotti S, di Pellegrino G, Ferri F, Ursino M, Romei V. Predictive waves in the autism-schizophrenia continuum: A novel biobehavioral model. Neurosci Biobehav Rev 2021; 132:1-22. [PMID: 34774901 DOI: 10.1016/j.neubiorev.2021.11.006] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2021] [Revised: 10/29/2021] [Accepted: 11/07/2021] [Indexed: 12/14/2022]
Abstract
The brain is a predictive machine. Converging data suggests a diametric predictive strategy from autism spectrum disorders (ASD) to schizophrenic spectrum disorders (SSD). Whereas perceptual inference in ASD is rigidly shaped by incoming sensory information, the SSD population is prone to overestimate the precision of their priors' models. Growing evidence considers brain oscillations pivotal biomarkers to understand how top-down predictions integrate bottom-up input. Starting from the conceptualization of ASD and SSD as oscillopathies, we introduce an integrated perspective that ascribes the maladjustments of the predictive mechanism to dysregulation of neural synchronization. According to this proposal, disturbances in the oscillatory profile do not allow the appropriate trade-off between descending predictive signal, overweighted in SSD, and ascending prediction errors, overweighted in ASD. These opposing imbalances both result in an ill-adapted reaction to external challenges. This approach offers a neuro-computational model capable of linking predictive coding theories with electrophysiological findings, aiming to increase knowledge on the neuronal foundations of the two spectra features and stimulate hypothesis-driven rehabilitation/research perspectives.
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Affiliation(s)
- Luca Tarasi
- Centro Studi e Ricerche in Neuroscienze Cognitive, Dipartimento di Psicologia, Alma Mater Studiorum - Università di Bologna, Campus di Cesena, 47521 Cesena, Italy.
| | - Jelena Trajkovic
- Centro Studi e Ricerche in Neuroscienze Cognitive, Dipartimento di Psicologia, Alma Mater Studiorum - Università di Bologna, Campus di Cesena, 47521 Cesena, Italy
| | - Stefano Diciotti
- Department of Electrical, Electronic, and Information Engineering "Guglielmo Marconi", University of Bologna, Cesena, Italy; Alma Mater Research Institute for Human-Centered Artificial Intelligence, University of Bologna, Bologna, Italy
| | - Giuseppe di Pellegrino
- Centro Studi e Ricerche in Neuroscienze Cognitive, Dipartimento di Psicologia, Alma Mater Studiorum - Università di Bologna, Campus di Cesena, 47521 Cesena, Italy
| | - Francesca Ferri
- Department of Neuroscience, Imaging and Clinical Sciences, "G. d'Annunzio" University of Chieti-Pescara, Chieti, Italy
| | - Mauro Ursino
- Department of Electrical, Electronic, and Information Engineering "Guglielmo Marconi", University of Bologna, Cesena, Italy
| | - Vincenzo Romei
- Centro Studi e Ricerche in Neuroscienze Cognitive, Dipartimento di Psicologia, Alma Mater Studiorum - Università di Bologna, Campus di Cesena, 47521 Cesena, Italy; IRCCS Fondazione Santa Lucia, 00179 Rome, Italy.
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Trajkovic J, Di Gregorio F, Ferri F, Marzi C, Diciotti S, Romei V. Resting state alpha oscillatory activity is a valid and reliable marker of schizotypy. Sci Rep 2021; 11:10379. [PMID: 34001914 PMCID: PMC8129121 DOI: 10.1038/s41598-021-89690-7] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2020] [Accepted: 04/30/2021] [Indexed: 12/20/2022] Open
Abstract
Schizophrenia is among the most debilitating neuropsychiatric disorders. However, clear neurophysiological markers that would identify at-risk individuals represent still an unknown. The aim of this study was to investigate possible alterations in the resting alpha oscillatory activity in normal population high on schizotypy trait, a physiological condition known to be severely altered in patients with schizophrenia. Direct comparison of resting-state EEG oscillatory activity between Low and High Schizotypy Group (LSG and HSG) has revealed a clear right hemisphere alteration in alpha activity of the HSG. Specifically, HSG shows a significant slowing down of right hemisphere posterior alpha frequency and an altered distribution of its amplitude, with a tendency towards a reduction in the right hemisphere in comparison to LSG. Furthermore, altered and reduced connectivity in the right fronto-parietal network within the alpha range was found in the HSG. Crucially, a trained pattern classifier based on these indices of alpha activity was able to successfully differentiate HSG from LSG on tested participants further confirming the specific importance of right hemispheric alpha activity and intrahemispheric functional connectivity. By combining alpha activity and connectivity measures with a machine learning predictive model optimized in a nested stratified cross-validation loop, current research offers a promising clinical tool able to identify individuals at-risk of developing psychosis (i.e., high schizotypy individuals).
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Affiliation(s)
- Jelena Trajkovic
- Centro Studi e Ricerche in Neuroscienze Cognitive, Dipartimento di Psicologia, Alma Mater Studiorum - Università di Bologna, Campus di Cesena, 47521, Cesena, Italy
| | - Francesco Di Gregorio
- UO Medicina Riabilitativa e Neuroriabilitazione, Azienda Unità Sanitaria Locale, 40139, Bologna, Italy
| | - Francesca Ferri
- Department of Neuroscience, Imaging and Clinical Sciences, "G. d'Annunzio" University of Chieti-Pescara, Chieti, Italy.,Institute for Advanced Biomedical Technologies, "G. d'Annunzio" University of Chieti-Pescara, Chieti, Italy
| | - Chiara Marzi
- Department of Electrical, Electronic, and Information Engineering "Guglielmo Marconi", University of Bologna, Cesena, Italy
| | - Stefano Diciotti
- Department of Electrical, Electronic, and Information Engineering "Guglielmo Marconi", University of Bologna, Cesena, Italy.,Alma Mater Research Institute for Human-Centered Artificial Intelligence, University of Bologna, Bologna, Italy
| | - Vincenzo Romei
- Centro Studi e Ricerche in Neuroscienze Cognitive, Dipartimento di Psicologia, Alma Mater Studiorum - Università di Bologna, Campus di Cesena, 47521, Cesena, Italy. .,IRCCS Fondazione Santa Lucia, 00179, Rome, Italy.
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9
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Perrottelli A, Giordano GM, Brando F, Giuliani L, Mucci A. EEG-Based Measures in At-Risk Mental State and Early Stages of Schizophrenia: A Systematic Review. Front Psychiatry 2021; 12:653642. [PMID: 34017273 PMCID: PMC8129021 DOI: 10.3389/fpsyt.2021.653642] [Citation(s) in RCA: 31] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/14/2021] [Accepted: 04/06/2021] [Indexed: 12/17/2022] Open
Abstract
Introduction: Electrophysiological (EEG) abnormalities in subjects with schizophrenia have been largely reported. In the last decades, research has shifted to the identification of electrophysiological alterations in the prodromal and early phases of the disorder, focusing on the prediction of clinical and functional outcome. The identification of neuronal aberrations in subjects with a first episode of psychosis (FEP) and in those at ultra high-risk (UHR) or clinical high-risk (CHR) to develop a psychosis is crucial to implement adequate interventions, reduce the rate of transition to psychosis, as well as the risk of irreversible functioning impairment. The aim of the review is to provide an up-to-date synthesis of the electrophysiological findings in the at-risk mental state and early stages of schizophrenia. Methods: A systematic review of English articles using Pubmed, Scopus, and PsychINFO was undertaken in July 2020. Additional studies were identified by hand-search. Electrophysiological studies that included at least one group of FEP or subjects at risk to develop psychosis, compared to healthy controls (HCs), were considered. The heterogeneity of the studies prevented a quantitative synthesis. Results: Out of 319 records screened, 133 studies were included in a final qualitative synthesis. Included studies were mainly carried out using frequency analysis, microstates and event-related potentials. The most common findings included an increase in delta and gamma power, an impairment in sensory gating assessed through P50 and N100 and a reduction of Mismatch Negativity and P300 amplitude in at-risk mental state and early stages of schizophrenia. Progressive changes in some of these electrophysiological measures were associated with transition to psychosis and disease course. Heterogeneous data have been reported for indices evaluating synchrony, connectivity, and evoked-responses in different frequency bands. Conclusions: Multiple EEG-indices were altered during at-risk mental state and early stages of schizophrenia, supporting the hypothesis that cerebral network dysfunctions appear already before the onset of the disorder. Some of these alterations demonstrated association with transition to psychosis or poor functional outcome. However, heterogeneity in subjects' inclusion criteria, clinical measures and electrophysiological methods prevents drawing solid conclusions. Large prospective studies are needed to consolidate findings concerning electrophysiological markers of clinical and functional outcome.
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Affiliation(s)
- Andrea Perrottelli
- Department of Psychiatry, University of Campania "Luigi Vanvitelli", Naples, Italy
| | | | - Francesco Brando
- Department of Psychiatry, University of Campania "Luigi Vanvitelli", Naples, Italy
| | - Luigi Giuliani
- Department of Psychiatry, University of Campania "Luigi Vanvitelli", Naples, Italy
| | - Armida Mucci
- Department of Psychiatry, University of Campania "Luigi Vanvitelli", Naples, Italy
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Shalbaf A, Bagherzadeh S, Maghsoudi A. Transfer learning with deep convolutional neural network for automated detection of schizophrenia from EEG signals. Phys Eng Sci Med 2020; 43:1229-1239. [DOI: 10.1007/s13246-020-00925-9] [Citation(s) in RCA: 31] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2020] [Accepted: 09/08/2020] [Indexed: 12/17/2022]
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11
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Resolving the Connectome, Spectrally-Specific Functional Connectivity Networks and Their Distinct Contributions to Behavior. eNeuro 2020; 7:ENEURO.0101-20.2020. [PMID: 32826259 PMCID: PMC7484267 DOI: 10.1523/eneuro.0101-20.2020] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2020] [Revised: 07/17/2020] [Accepted: 07/22/2020] [Indexed: 12/18/2022] Open
Abstract
The resting human brain exhibits spontaneous patterns of activity that reflect features of the underlying neural substrate. Examination of interareal coupling of resting-state oscillatory activity has revealed that the brain’s resting activity is composed of functional networks, whose topographies differ depending on oscillatory frequency, suggesting a role for carrier frequency as a means of creating multiplexed, or functionally segregated, communication channels between brain areas. Using canonical correlation analysis (CCA), we examined spectrally resolved resting-state connectivity patterns derived from magnetoencephalography (MEG) recordings to determine the relationship between connectivity intrinsic to different frequency channels and a battery of over a hundred behavioral and demographic indicators, in a group of 89 young healthy participants. We demonstrate that each of the classical frequency bands in the range 1–40 Hz (δ, θ, α, β, and γ) delineates a subnetwork that is behaviorally relevant, spatially distinct, and whose expression is either negatively or positively predictive of individual traits, with the strongest link in the α-band being negative and networks oscillating at different frequencies, such as θ, β, and γ carrying positive function.
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12
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Hu DK, Li LY, Lopour BA, Martin EA. Schizotypy dimensions are associated with altered resting state alpha connectivity. Int J Psychophysiol 2020; 155:175-183. [PMID: 32599002 DOI: 10.1016/j.ijpsycho.2020.06.012] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2020] [Revised: 05/27/2020] [Accepted: 06/22/2020] [Indexed: 11/16/2022]
Abstract
The disconnection hypothesis of schizophrenia says that symptoms are explained by dysfunctional connections across a wide range of brain networks. Despite some support for this hypothesis, there have been mixed findings. One reason for these may be the multidimensional nature of schizophrenia symptoms. In order to clarify the relationship between symptoms and brain networks, the current study included individuals at risk for schizophrenia-spectrum disorders who either report extreme levels of positive schizotypy traits (perceptual aberrations and magical ideation, or "PerMag"; n = 23), or an extreme negative schizotypy trait (social anhedonia, or "SocAnh"; n = 19), as well as a control group (n = 18). Resting-state alpha electroencephalography was collected, and functional networks for each subject were measured using the phase-lag index to calculate the connectivity between channel pairs based on the symmetry of instantaneous phase differences over time. Furthermore, graph theory measures were introduced to identify network features exclusive to schizotypy groups. We found that the PerMag group exhibited a smaller difference in node strength and clustering coefficient in frontal/occipital and central/occipital regional comparisons compared to controls, suggesting a more widespread network. The SocAnh group exhibited a larger difference in degree in the central/occipital regional comparison relative to controls, suggesting a localized occipital focus in the connectivity network. Regional differences in functional connectivity suggest that different schizotypy dimensions are manifested at the network level by different forms of disconnections. Taken together, these findings lend further support to the disconnection hypothesis and suggest that altered connectivity networks may serve as a potential biomarker for schizophrenia risk.
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Affiliation(s)
- Derek K Hu
- Department of Biomedical Engineering, University of California, Irvine, Irvine, CA, USA
| | - Lilian Y Li
- Department of Psychological Science, University of California, Irvine, Irvine, CA, USA
| | - Beth A Lopour
- Department of Biomedical Engineering, University of California, Irvine, Irvine, CA, USA
| | - Elizabeth A Martin
- Department of Psychological Science, University of California, Irvine, Irvine, CA, USA.
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13
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Koshiyama D, Miyakoshi M, Tanaka-Koshiyama K, Joshi YB, Molina JL, Sprock J, Braff DL, Light GA. Neurophysiologic Characterization of Resting State Connectivity Abnormalities in Schizophrenia Patients. Front Psychiatry 2020; 11:608154. [PMID: 33329160 PMCID: PMC7729083 DOI: 10.3389/fpsyt.2020.608154] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/19/2020] [Accepted: 11/04/2020] [Indexed: 12/24/2022] Open
Abstract
Background: Patients with schizophrenia show abnormal spontaneous oscillatory activity in scalp-level electroencephalographic (EEG) responses across multiple frequency bands. While oscillations play an essential role in the transmission of information across neural networks, few studies have assessed the frequency-specific dynamics across cortical source networks at rest. Identification of the neural sources and their dynamic interactions may improve our understanding of core pathophysiologic abnormalities associated with the neuropsychiatric disorders. Methods: A novel multivector autoregressive modeling approach for assessing effective connectivity among cortical sources was developed and applied to resting-state EEG recordings obtained from n = 139 schizophrenia patients and n = 126 healthy comparison subjects. Results: Two primary abnormalities in resting-state networks were detected in schizophrenia patients. The first network involved the middle frontal and fusiform gyri and a region near the calcarine sulcus. The second network involved the cingulate gyrus and the Rolandic operculum (a region that includes the auditory cortex). Conclusions: Schizophrenia patients show widespread patterns of hyper-connectivity across a distributed network of the frontal, temporal, and occipital brain regions. Results highlight a novel approach for characterizing alterations in connectivity in the neuropsychiatric patient populations. Further mechanistic characterization of network functioning is needed to clarify the pathophysiology of neuropsychiatric and neurological diseases.
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Affiliation(s)
- Daisuke Koshiyama
- Department of Psychiatry, University of California, San Diego, La Jolla, CA, United States
| | - Makoto Miyakoshi
- Swartz Center for Neural Computation, University of California, San Diego, La Jolla, CA, United States
| | | | - Yash B Joshi
- Department of Psychiatry, University of California, San Diego, La Jolla, CA, United States
| | - Juan L Molina
- Department of Psychiatry, University of California, San Diego, La Jolla, CA, United States
| | - Joyce Sprock
- Department of Psychiatry, University of California, San Diego, La Jolla, CA, United States
| | - David L Braff
- Department of Psychiatry, University of California, San Diego, La Jolla, CA, United States
| | - Gregory A Light
- Department of Psychiatry, University of California, San Diego, La Jolla, CA, United States.,VISN-22 Mental Illness, Research, Education and Clinical Center, VA San Diego Healthcare System, San Diego, CA, United States
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