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Dziego CA, Zanesco AP, Bornkessel-Schlesewsky I, Schlesewsky M, Stanley EA, Jha AP. Mindfulness Training in High-Demand Cohorts Alters Resting-State Electroencephalography: An Exploratory Investigation of Individual Alpha Frequency, Aperiodic 1/ f Activity, and Microstates. BIOLOGICAL PSYCHIATRY GLOBAL OPEN SCIENCE 2024; 4:100383. [PMID: 39416660 PMCID: PMC11480290 DOI: 10.1016/j.bpsgos.2024.100383] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2024] [Revised: 07/31/2024] [Accepted: 08/05/2024] [Indexed: 10/19/2024] Open
Abstract
Background Mindfulness training (MT) programs have demonstrated utility as cognitive training tools, but there is little consensus on the neurophysiological processes that may underlie its benefits. It has been posited that intrinsic brain activity recorded at rest reflects the functional connectivity of large-scale brain networks and may provide insight into neuroplastic changes that support MT. In the current study, we indexed changes in several resting-state electroencephalography (EEG) parameters to investigate the neurophysiological underpinnings of MT. Methods Resting-state EEG data were collected from active-duty U.S. military personnel (N = 80) at 2 testing sessions: before (time [T] 1) and after (T2) engaging in an 8-week MT or active comparison intervention (positivity training). We examined longitudinal and/or groupwise differences in several EEG parameters through parameterization of power spectra (individual alpha frequency and 1/f activity) and microstate analysis. Results While no significant group × time differences were observed in individual alpha frequency, significant group × time effects were observed in several EEG parameters from T1 to T2. Compared with MT, positivity training was associated with a steepening of the 1/f slope and higher 1/f intercepts together with decreased duration and increased global field power of microstates. Conclusions Taken together, these results suggest that the effects of interventions may be differentiated in resting-state brain activity in a sample of military personnel. Such findings provide insight into the neural underpinnings of MT-related brain changes, but more research is required to elucidate how these may relate to task-related neural and performance changes with MT and whether results generalize to other mindfulness interventions in alternative cohorts and contexts.
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Affiliation(s)
- Chloe A. Dziego
- Cognitive Neuroscience Laboratory, Australian Research Centre for Interactive and Virtual Environments, University of South Australia, Adelaide, South Australia, Australia
| | | | - Ina Bornkessel-Schlesewsky
- Cognitive Neuroscience Laboratory, Australian Research Centre for Interactive and Virtual Environments, University of South Australia, Adelaide, South Australia, Australia
| | - Matthias Schlesewsky
- Cognitive Neuroscience Laboratory, Australian Research Centre for Interactive and Virtual Environments, University of South Australia, Adelaide, South Australia, Australia
| | - Elizabeth A. Stanley
- Edmund A. Walsh School of Foreign Service, Georgetown University, Washington, DC
| | - Amishi P. Jha
- Department of Psychology, University of Miami, Coral Gables, Florida
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Faghiri A, Yang K, Faria A, Ishizuka K, Sawa A, Adali T, Calhoun V. Frequency modulation increases the specificity of time-resolved connectivity: A resting-state fMRI study. Netw Neurosci 2024; 8:734-761. [PMID: 39355435 PMCID: PMC11349031 DOI: 10.1162/netn_a_00372] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2023] [Accepted: 03/06/2024] [Indexed: 10/03/2024] Open
Abstract
Representing data using time-resolved networks is valuable for analyzing functional data of the human brain. One commonly used method for constructing time-resolved networks from data is sliding window Pearson correlation (SWPC). One major limitation of SWPC is that it applies a high-pass filter to the activity time series. Therefore, if we select a short window (desirable to estimate rapid changes in connectivity), we will remove important low-frequency information. Here, we propose an approach based on single sideband modulation (SSB) in communication theory. This allows us to select shorter windows to capture rapid changes in the time-resolved functional network connectivity (trFNC). We use simulation and real resting-state functional magnetic resonance imaging (fMRI) data to demonstrate the superior performance of SSB+SWPC compared to SWPC. We also compare the recurring trFNC patterns between individuals with the first episode of psychosis (FEP) and typical controls (TC) and show that FEPs stay more in states that show weaker connectivity across the whole brain. A result exclusive to SSB+SWPC is that TCs stay more in a state with negative connectivity between subcortical and cortical regions. Based on all the results, we argue that SSB+SWPC is more sensitive for capturing temporal variation in trFNC.
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Affiliation(s)
- 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, USA
| | - Kun Yang
- Department of Psychiatry, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Andreia Faria
- Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Koko Ishizuka
- Department of Psychiatry, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Akira Sawa
- Departments of Psychiatry, Neuroscience, Biomedical Engineering, Genetic Medicine, and Pharmacology, Johns Hopkins University School of Medicine, Baltimore, MD
- Department of Mental Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD
| | - Tülay Adali
- Deptartment of CSEE, University of Maryland, Baltimore County, Baltimore, MD, USA
| | - Vince 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, USA
- School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA, USA
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Zhao S, Cao Y, Yang W, Yu J, Xu C, Dai W, Li S, Pan G, Luo B. DOCTer: a novel EEG-based diagnosis framework for disorders of consciousness. J Neural Eng 2024; 21:056021. [PMID: 39255823 DOI: 10.1088/1741-2552/ad7904] [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: 06/01/2024] [Accepted: 09/10/2024] [Indexed: 09/12/2024]
Abstract
Objective. Accurately diagnosing patients with disorders of consciousness (DOC) is challenging and prone to errors. Recent studies have demonstrated that EEG (electroencephalography), a non-invasive technique of recording the spontaneous electrical activity of brains, offers valuable insights for DOC diagnosis. However, some challenges remain: (1) the EEG signals have not been fully used; and (2) the data scale in most existing studies is limited. In this study, our goal is to differentiate between minimally conscious state (MCS) and unresponsive wakefulness syndrome (UWS) using resting-state EEG signals, by proposing a new deep learning framework.Approach. We propose DOCTer, an end-to-end framework for DOC diagnosis based on EEG. It extracts multiple pertinent features from the raw EEG signals, including time-frequency features and microstates. Meanwhile, it takes clinical characteristics of patients into account, and then combines all the features together for the diagnosis. To evaluate its effectiveness, we collect a large-scale dataset containing 409 resting-state EEG recordings from 128 UWS and 187 MCS cases.Main results. Evaluated on our dataset, DOCTer achieves the state-of-the-art performance, compared to other methods. The temporal/spectral features contributes the most to the diagnosis task. The cerebral integrity is important for detecting the consciousness level. Meanwhile, we investigate the influence of different EEG collection duration and number of channels, in order to help make the appropriate choices for clinics.Significance. The DOCTer framework significantly improves the accuracy of DOC diagnosis, helpful for developing appropriate treatment programs. Findings derived from the large-scale dataset provide valuable insights for clinics.
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Affiliation(s)
- Sha Zhao
- College of Computer Science and Technology, Zhejiang University, Hangzhou, Zhejiang, People's Republic of China
- The State Key Laboratory of Brain-machine Intelligence, Zhejiang University, Hangzhou, Zhejiang, People's Republic of China
| | - Yue Cao
- School of Software Technology, Zhejiang University, Hangzhou, Zhejiang, People's Republic of China
- The State Key Laboratory of Brain-machine Intelligence, Zhejiang University, Hangzhou, Zhejiang, People's Republic of China
| | - Wei Yang
- College of Computer Science and Technology, Zhejiang University, Hangzhou, Zhejiang, People's Republic of China
- The State Key Laboratory of Brain-machine Intelligence, Zhejiang University, Hangzhou, Zhejiang, People's Republic of China
| | - Jie Yu
- Department of Neurology, First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, People's Republic of China
| | - Chuan Xu
- Department of Neurology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, People's Republic of China
| | - Wei Dai
- Stanford University, 450 Jane Stanford Way, Stanford, CA 94305, United States of America
| | - Shijian Li
- College of Computer Science and Technology, Zhejiang University, Hangzhou, Zhejiang, People's Republic of China
- The State Key Laboratory of Brain-machine Intelligence, Zhejiang University, Hangzhou, Zhejiang, People's Republic of China
| | - Gang Pan
- College of Computer Science and Technology, Zhejiang University, Hangzhou, Zhejiang, People's Republic of China
- The State Key Laboratory of Brain-machine Intelligence, Zhejiang University, Hangzhou, Zhejiang, People's Republic of China
- MOE Frontier Science Center for Brain Science and Brain-Machine Integration, Zhejiang University, Hangzhou, People's Republic of China
| | - Benyan Luo
- The State Key Laboratory of Brain-machine Intelligence, Zhejiang University, Hangzhou, Zhejiang, People's Republic of China
- Department of Neurology, First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, People's Republic of China
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Liu S, Yang S, Feng K, Wang C, Wang L. A Study on the Effects of Repetitive Transcranial Magnetic Stimulation on EEG Microstate in Patients With Parkinson's Disease. IEEE Trans Neural Syst Rehabil Eng 2024; 32:3369-3377. [PMID: 38917289 DOI: 10.1109/tnsre.2024.3418846] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/27/2024]
Abstract
Repetitive transcranial magnetic stimulation (rTMS) is a non-invasive neuromodulation technology that can modulate cerebral cortical excitability. Electroencephalography (EEG) microstate analysis is an important tool for studying dynamic changes in brain functional activity. This study explores the pathophysiological changes in Parkinson's disease (PD) patients by analyzing the EEG microstate of PD patients, and analyzes the impact of rTMS on the clinical symptoms of PD patients. In a trial, 25 patients with PD and 18 healthy subjects of the same age were included. The clinical scale (the third part of Movement Disorder Society-Sponsored Revision of the Unified Parkinson's Disease Rating Scale (UPDRS-III) and Montreal Cognitive Assessment (MoCA)) scores of each patient were evaluated and the microstate characteristic parameters of all subjects were calculated. 10 Hz rTMS was used to stimulate the bilateral primary motor cortex (M1) of PD patients. After two weeks of treatment (10 times), the clinical scale score of each patient was re-evaluated and the microstate characteristic parameters were calculated. At the baseline, the occurrence, duration and coverage of microstate C in PD patients were significantly higher than those in healthy controls (P <0.05),and were significantly negatively correlated with the MoCA score (P <0.05). The duration and coverage of microstate D in PD patients were significantly lower than those in healthy controls (P <0.05), and were significantly negatively correlated with UPDRS-III score (P <0.05). After rTMS treatment in the PD group, the scale score of UPDRS-III was significantly reduced (P <0.05) and the scale score of MoCA was significantly increased. Moreover, the occurrence and coverage of microstate B were significantly increased (p <0.05). The occurrence, duration and coverage of microstate C were significantly reduced (P <0.05). The occurrence, duration and coverage of microstate D were significantly increased (P <0.05). This study shows that abnormal brain functional activity of PD patients can change microstate characteristic parameters, and these changes are significantly related to the decline of motor and cognitive functions. Furthermore, rTMS can improve the motor and cognitive functions and adjust the microstate characteristic parameters of PD patients. EEG microstate analysis can reflect the therapeutic effect of rTMS on PD patients.
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Zhao Z, Ran X, Wang J, Lv S, Qiu M, Niu Y, Wang C, Xu Y, Gao Z, Ren W, Zhou X, Fan X, Song J, Yu Y. Common and differential EEG microstate of major depressive disorder patients with and without response to rTMS treatment. J Affect Disord 2024; 367:777-787. [PMID: 39265862 DOI: 10.1016/j.jad.2024.09.040] [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: 07/17/2024] [Revised: 08/31/2024] [Accepted: 09/08/2024] [Indexed: 09/14/2024]
Abstract
OBJECTIVE Repetitive transcranial magnetic stimulation (rTMS) has recently emerged as a novel treatment option for patients with major depressive disorder (MDD), but clinical observations reveal variability in patient's responses to rTMS. Therefore, it is clinically significant to investigate the baseline neuroimaging differences between patients with (Responder) and without (NonResponder) response to rTMS treatment and predict rTMS treatment outcomes based on baseline neuroimaging data. METHOD Baseline resting-state EEG data and Beck Depression Inventory (BDI) were collected from 74 rTMS Responder, 43 NonResponder, and 47 matched healthy controls (HC). EEG microstate analysis was applied to analyze common and differential microstate characteristics of Responder and NonResponder. In addition, the microstate temporal parameters were sent to four machine learning models to classify Responder from NonResponder. RESULT There exists some common and differential EEG microstate characteristics for Responder and NonResponder. Specifically, compared to the HC group, both Responder and NonResponder exhibited a significant increase in the occurrence of microstate A. Only Responder showed an increase in the coverage of microstate A, occurrence of microstate D, transition probability (TP) from A to D, D to A, and C to A, and a decrease in the duration of microstates B and E, TP from A to B and C to B compared to HC. Only NonResponder exhibited a significant decrease in the duration of microstate D, TP from C to D, and an increase in the occurrence of microstate E, TP from C to E compared to HC. The primary differences between the Responder and NonResponder are that Responder had higher parameters for microstate D, TP from other microstates to D, and lower parameters for microstate E, TP from other microstates to E compared to NonResponder. Baseline parameters of microstate D showed significant correlation with Beck Depression Inventory (BDI) reduction rate. Additionally, these microstate features were sent to four machine learning models to predict rTMS treatment response and classification results indicate that an excellent predicting performance (accuracy = 97.35 %, precision = 96.31 %, recall = 100 %, F1 score = 98.06 %) was obtained when using AdaBoost model. These results suggest that baseline resting-state EEG microstate parameters could serve as robust indicators for predicting the effectiveness of rTMS treatment. CONCLUSION This study reveals significant baseline EEG microstate differences between rTMS Responder, NonResponder, and healthy controls. Microstates D and E in baseline EEG can serve as potential biomarkers for predicting rTMS treatment outcomes in MDD patients. These findings may aid in identifying patients likely to respond to rTMS, optimizing treatment plans and reducing trial-and-error approaches in therapy selection.
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Affiliation(s)
- Zongya Zhao
- School of Medical Engineering, School of Mathematical Medicine, Xinxiang Medical University, Xinxiang, People's Republic of China; The Second Affiliated Hospital of Xinxiang Medical University, Henan Collaborative Innovation Center of Prevention and Treatment of Mental Disorder, People's Republic of China; Engineering Technology Research Center of Neurosense and Control of Henan Province, Xinxiang, People's Republic of China; Henan International Joint Laboratory of Neural Information Analysis and Drug Intelligent Design, Xinxiang, People's Republic of China; Henan Engineering Research Center of Medical VR Intelligent Sensing Feedback, Xinxiang, People's Republic of China; Henan Engineering Research Center of Physical Diagnostics and Treatment Technology for the Mental and Neurological Diseases, People's Republic of China.
| | - Xiangying Ran
- School of Medical Engineering, School of Mathematical Medicine, Xinxiang Medical University, Xinxiang, People's Republic of China; Engineering Technology Research Center of Neurosense and Control of Henan Province, Xinxiang, People's Republic of China; Henan International Joint Laboratory of Neural Information Analysis and Drug Intelligent Design, Xinxiang, People's Republic of China; Henan Engineering Research Center of Medical VR Intelligent Sensing Feedback, Xinxiang, People's Republic of China
| | - Junming Wang
- School of Medical Engineering, School of Mathematical Medicine, Xinxiang Medical University, Xinxiang, People's Republic of China; Engineering Technology Research Center of Neurosense and Control of Henan Province, Xinxiang, People's Republic of China; Henan International Joint Laboratory of Neural Information Analysis and Drug Intelligent Design, Xinxiang, People's Republic of China; Henan Engineering Research Center of Medical VR Intelligent Sensing Feedback, Xinxiang, People's Republic of China
| | - Shiyang Lv
- School of Medical Engineering, School of Mathematical Medicine, Xinxiang Medical University, Xinxiang, People's Republic of China; Engineering Technology Research Center of Neurosense and Control of Henan Province, Xinxiang, People's Republic of China; Henan International Joint Laboratory of Neural Information Analysis and Drug Intelligent Design, Xinxiang, People's Republic of China; Henan Engineering Research Center of Medical VR Intelligent Sensing Feedback, Xinxiang, People's Republic of China
| | - Mengyue Qiu
- School of Medical Engineering, School of Mathematical Medicine, Xinxiang Medical University, Xinxiang, People's Republic of China; Engineering Technology Research Center of Neurosense and Control of Henan Province, Xinxiang, People's Republic of China; Henan International Joint Laboratory of Neural Information Analysis and Drug Intelligent Design, Xinxiang, People's Republic of China; Henan Engineering Research Center of Medical VR Intelligent Sensing Feedback, Xinxiang, People's Republic of China
| | - Yanxiang Niu
- Institute of Disaster and Emergency Medicine, Tianjin University, Tianjin, People's Republic of China
| | - Chang Wang
- School of Medical Engineering, School of Mathematical Medicine, Xinxiang Medical University, Xinxiang, People's Republic of China; Engineering Technology Research Center of Neurosense and Control of Henan Province, Xinxiang, People's Republic of China; Henan International Joint Laboratory of Neural Information Analysis and Drug Intelligent Design, Xinxiang, People's Republic of China; Henan Engineering Research Center of Medical VR Intelligent Sensing Feedback, Xinxiang, People's Republic of China
| | - Yongtao Xu
- School of Medical Engineering, School of Mathematical Medicine, Xinxiang Medical University, Xinxiang, People's Republic of China; Engineering Technology Research Center of Neurosense and Control of Henan Province, Xinxiang, People's Republic of China; Henan International Joint Laboratory of Neural Information Analysis and Drug Intelligent Design, Xinxiang, People's Republic of China; Henan Engineering Research Center of Medical VR Intelligent Sensing Feedback, Xinxiang, People's Republic of China
| | - Zhixian Gao
- School of Medical Engineering, School of Mathematical Medicine, Xinxiang Medical University, Xinxiang, People's Republic of China; Engineering Technology Research Center of Neurosense and Control of Henan Province, Xinxiang, People's Republic of China; Henan International Joint Laboratory of Neural Information Analysis and Drug Intelligent Design, Xinxiang, People's Republic of China; Henan Engineering Research Center of Medical VR Intelligent Sensing Feedback, Xinxiang, People's Republic of China
| | - Wu Ren
- School of Medical Engineering, School of Mathematical Medicine, Xinxiang Medical University, Xinxiang, People's Republic of China; Engineering Technology Research Center of Neurosense and Control of Henan Province, Xinxiang, People's Republic of China; Henan International Joint Laboratory of Neural Information Analysis and Drug Intelligent Design, Xinxiang, People's Republic of China; Henan Engineering Research Center of Medical VR Intelligent Sensing Feedback, Xinxiang, People's Republic of China
| | - Xuezhi Zhou
- School of Medical Engineering, School of Mathematical Medicine, Xinxiang Medical University, Xinxiang, People's Republic of China; Engineering Technology Research Center of Neurosense and Control of Henan Province, Xinxiang, People's Republic of China; Henan International Joint Laboratory of Neural Information Analysis and Drug Intelligent Design, Xinxiang, People's Republic of China; Henan Engineering Research Center of Medical VR Intelligent Sensing Feedback, Xinxiang, People's Republic of China
| | - Xiaofeng Fan
- School of Medical Engineering, School of Mathematical Medicine, Xinxiang Medical University, Xinxiang, People's Republic of China; Engineering Technology Research Center of Neurosense and Control of Henan Province, Xinxiang, People's Republic of China; Henan International Joint Laboratory of Neural Information Analysis and Drug Intelligent Design, Xinxiang, People's Republic of China; Henan Engineering Research Center of Medical VR Intelligent Sensing Feedback, Xinxiang, People's Republic of China
| | - Jinggui Song
- Henan Engineering Research Center of Physical Diagnostics and Treatment Technology for the Mental and Neurological Diseases, People's Republic of China
| | - Yi Yu
- School of Medical Engineering, School of Mathematical Medicine, Xinxiang Medical University, Xinxiang, People's Republic of China; Engineering Technology Research Center of Neurosense and Control of Henan Province, Xinxiang, People's Republic of China; Henan International Joint Laboratory of Neural Information Analysis and Drug Intelligent Design, Xinxiang, People's Republic of China; Henan Engineering Research Center of Medical VR Intelligent Sensing Feedback, Xinxiang, People's Republic of China.
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Chenot Q, Hamery C, Truninger M, Langer N, De Boissezon X, Scannella S. Investigating the relationship between resting-state EEG microstates and executive functions: A null finding. Cortex 2024; 178:1-17. [PMID: 38954985 DOI: 10.1016/j.cortex.2024.05.019] [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: 12/04/2023] [Revised: 05/21/2024] [Accepted: 05/23/2024] [Indexed: 07/04/2024]
Abstract
Recent advances in cognitive neurosciences suggest that intrinsic brain networks dynamics are associated with cognitive functioning. Despite this emerging perspective, limited research exists to validate this hypothesis. This Registered Report aimed to specifically test the relationship between intrinsic brain spatio-temporal dynamics and executive functions. Resting-state EEG microstates were used to assess brain spatio-temporal dynamics, while a comprehensive battery of nine cognitive function tasks was employed to evaluate executive functions in 140 participants. We hypothesized that microstates (class C and D) metrics would correlate with an executive functions composite score. Contrary to expectations, our hypotheses were not supported by the data. We however observed a small, non-significant trend with a negative correlation between microstate D occurrences and executive functions scores (r = -.18, 95% CI [-.33, -.01]) which however did not meet the adjusted threshold for significance. In light of the inconclusive or minor effect sizes observed, the assertion that intrinsic brain networks dynamics - as measured by resting-state EEG microstate metrics - are a reliable signature of executive functioning remains unsupported.
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Affiliation(s)
- Quentin Chenot
- Fédération ENAC ISAE-SUPAERO ONERA, Université de Toulouse, France.
| | - Caroline Hamery
- Fédération ENAC ISAE-SUPAERO ONERA, Université de Toulouse, France
| | - Moritz Truninger
- Methods of Plasticity Research, Department of Psychology, University of Zurich, Zurich, Switzerland
| | - Nicolas Langer
- Methods of Plasticity Research, Department of Psychology, University of Zurich, Zurich, Switzerland
| | - Xavier De Boissezon
- UMR 1214-Inserm, UPS-ToNIC, Toulouse NeuroImaging Center, Université de Toulouse, Hôpital Purpan, Pavillon Baudot, Toulouse, France; Department of Rehabilitation and Physical Medicine, Pôle Neurosciences, Centre Hospitalier Universitaire de Toulouse CHU, Toulouse, France
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Bagdasarov A, Brunet D, Michel CM, Gaffrey MS. Microstate Analysis of Continuous Infant EEG: Tutorial and Reliability. Brain Topogr 2024; 37:496-513. [PMID: 38430283 PMCID: PMC11199263 DOI: 10.1007/s10548-024-01043-5] [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: 07/12/2023] [Accepted: 02/16/2024] [Indexed: 03/03/2024]
Abstract
Microstate analysis of resting-state EEG is a unique data-driven method for identifying patterns of scalp potential topographies, or microstates, that reflect stable but transient periods of synchronized neural activity evolving dynamically over time. During infancy - a critical period of rapid brain development and plasticity - microstate analysis offers a unique opportunity for characterizing the spatial and temporal dynamics of brain activity. However, whether measurements derived from this approach (e.g., temporal properties, transition probabilities, neural sources) show strong psychometric properties (i.e., reliability) during infancy is unknown and key information for advancing our understanding of how microstates are shaped by early life experiences and whether they relate to individual differences in infant abilities. A lack of methodological resources for performing microstate analysis of infant EEG has further hindered adoption of this cutting-edge approach by infant researchers. As a result, in the current study, we systematically addressed these knowledge gaps and report that most microstate-based measurements of brain organization and functioning except for transition probabilities were stable with four minutes of video-watching resting-state data and highly internally consistent with just one minute. In addition to these results, we provide a step-by-step tutorial, accompanying website, and open-access data for performing microstate analysis using a free, user-friendly software called Cartool. Taken together, the current study supports the reliability and feasibility of using EEG microstate analysis to study infant brain development and increases the accessibility of this approach for the field of developmental neuroscience.
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Affiliation(s)
- Armen Bagdasarov
- Department of Psychology & Neuroscience, Duke University, Reuben-Cooke Building, 417 Chapel Drive, Durham, NC, 27708, USA.
| | - Denis Brunet
- Department of Basic Neurosciences, University of Geneva, Campus Biotech, 9 Chemin des Mines, Geneva, 1202, Switzerland
- Center for Biomedical Imaging (CIBM) Lausanne, EPFL AVP CP CIBM Station 6, Lausanne, 1015, Switzerland
| | - Christoph M Michel
- Department of Basic Neurosciences, University of Geneva, Campus Biotech, 9 Chemin des Mines, Geneva, 1202, Switzerland
- Center for Biomedical Imaging (CIBM) Lausanne, EPFL AVP CP CIBM Station 6, Lausanne, 1015, Switzerland
| | - Michael S Gaffrey
- Department of Psychology & Neuroscience, Duke University, Reuben-Cooke Building, 417 Chapel Drive, Durham, NC, 27708, USA
- Children's Wisconsin, 9000 W. Wisconsin Avenue, Milwaukee, WI, 53226, USA
- Medical College of Wisconsin, Division of Pediatric Psychology and Developmental Medicine, Department of Pediatrics, 8701 Watertown Plank Road, Milwaukee, WI, 53226, USA
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Kleinert T, Nash K. Trait Aggression is Reflected by a Lower Temporal Stability of EEG Resting Networks. Brain Topogr 2024; 37:514-523. [PMID: 36400856 PMCID: PMC11199292 DOI: 10.1007/s10548-022-00929-6] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2022] [Accepted: 11/12/2022] [Indexed: 11/19/2022]
Abstract
Trait aggression can lead to catastrophic consequences for individuals and society. However, it remains unclear how aggressive people differ from others regarding basic, task-independent brain characteristics. We used EEG microstate analysis to investigate how the temporal organization of neural resting networks might help explain inter-individual differences in aggression. Microstates represent whole-brain networks, which are stable for short timeframes (40-120 ms) before quickly transitioning into other microstate types. Recent research demonstrates that the general temporal stability of microstates across types predicts higher levels of self-control and inhibitory control, and lower levels of risk-taking preferences. Given that these outcomes are inversely related to aggression, we investigated whether microstate stability at rest would predict lower levels of trait aggression. As males show higher levels of aggression than females, and males and females express aggression differently, we also tested for possible gender-differences. As hypothesized, people with higher levels of trait aggression showed lower microstate stability. This effect was moderated by gender, with men showing stronger associations compared to women. These findings support the notion that temporal dynamics of sub-second resting networks predict complex human traits. Furthermore, they provide initial indications of gender-differences in the functional significance of EEG microstates.
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Affiliation(s)
- Tobias Kleinert
- Department of Psychology, University of Alberta, Edmonton, AB, T6G 2E9, Canada.
- Department of Ergonomics, Leibniz Research Centre for Working Environment and Human Factors, Ardeystr. 67, 44139, Dortmund, Germany.
| | - Kyle Nash
- Department of Psychology, University of Alberta, Edmonton, AB, T6G 2E9, Canada
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Nagabhushan Kalburgi S, Kleinert T, Aryan D, Nash K, Schiller B, Koenig T. MICROSTATELAB: The EEGLAB Toolbox for Resting-State Microstate Analysis. Brain Topogr 2024; 37:621-645. [PMID: 37697212 PMCID: PMC11199309 DOI: 10.1007/s10548-023-01003-5] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2023] [Accepted: 08/22/2023] [Indexed: 09/13/2023]
Abstract
Microstate analysis is a multivariate method that enables investigations of the temporal dynamics of large-scale neural networks in EEG recordings of human brain activity. To meet the enormously increasing interest in this approach, we provide a thoroughly updated version of the first open source EEGLAB toolbox for the standardized identification, visualization, and quantification of microstates in resting-state EEG data. The toolbox allows scientists to (i) identify individual, mean, and grand mean microstate maps using topographical clustering approaches, (ii) check data quality and detect outlier maps, (iii) visualize, sort, and label individual, mean, and grand mean microstate maps according to published maps, (iv) compare topographical similarities of group and grand mean microstate maps and quantify shared variances, (v) obtain the temporal dynamics of the microstate classes in individual EEGs, (vi) export quantifications of these temporal dynamics of the microstates for statistical tests, and finally, (vii) test for topographical differences between groups and conditions using topographic analysis of variance (TANOVA). Here, we introduce the toolbox in a step-by-step tutorial, using a sample dataset of 34 resting-state EEG recordings that are publicly available to follow along with this tutorial. The goals of this manuscript are (a) to provide a standardized, freely available toolbox for resting-state microstate analysis to the scientific community, (b) to allow researchers to use best practices for microstate analysis by following a step-by-step tutorial, and (c) to improve the methodological standards of microstate research by providing previously unavailable functions and recommendations on critical decisions required in microstate analyses.
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Affiliation(s)
| | - Tobias Kleinert
- Laboratory for Biological Psychology, Clinical Psychology, and Psychotherapy, Albert-Ludwigs-University of Freiburg, Stefan-Meier-Straße 8, 79104, Freiburg, Germany
- Department of Ergonomics, Leibniz Research Centre for Working Environment and Human Factors, Ardeystr. 67, 44139, Dortmund, Germany
| | - Delara Aryan
- Children's Hospital Los Angeles, The Saban Research Institute, Los Angeles, CA, 90027, USA
| | - Kyle Nash
- Department of Psychology, University of Alberta, Edmonton, AB, T6G 2E9, Canada
| | - Bastian Schiller
- Laboratory for Biological Psychology, Clinical Psychology, and Psychotherapy, Albert-Ludwigs-University of Freiburg, Stefan-Meier-Straße 8, 79104, Freiburg, Germany
| | - Thomas Koenig
- Children's Hospital Los Angeles, The Saban Research Institute, Los Angeles, CA, 90027, USA.
- Translational Research Center, University Hospital of Psychiatry and Psychotherapy, University of Bern, CH-3000, Bern, Switzerland.
- Department of Neurobiology, Care Sciences and Society, Center for Alzheimer Research, Division of Clinical Geriatrics, Karolinska Institutet, Huddinge, Sweden.
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10
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Jiang H, Zhao S, Wu Q, Cao Y, Zhou W, Gong Y, Shao C, Chi A. Dragon boat exercise reshapes the temporal-spatial dynamics of the brain. PeerJ 2024; 12:e17623. [PMID: 38952974 PMCID: PMC11216202 DOI: 10.7717/peerj.17623] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2023] [Accepted: 06/02/2024] [Indexed: 07/03/2024] Open
Abstract
Although exercise training has been shown to enhance neurological function, there is a shortage of research on how exercise training affects the temporal-spatial synchronization properties of functional networks, which are crucial to the neurological system. This study recruited 23 professional and 24 amateur dragon boat racers to perform simulated paddling on ergometers while recording EEG. The spatiotemporal dynamics of the brain were analyzed using microstates and omega complexity. Temporal dynamics results showed that microstate D, which is associated with attentional networks, appeared significantly altered, with significantly higher duration, occurrence, and coverage in the professional group than in the amateur group. The transition probabilities of microstate D exhibited a similar pattern. The spatial dynamics results showed the professional group had lower brain complexity than the amateur group, with a significant decrease in omega complexity in the α (8-12 Hz) and β (13-30 Hz) bands. Dragon boat training may strengthen the attentive network and reduce the complexity of the brain. This study provides evidence that dragon boat exercise improves the efficiency of the cerebral functional networks on a spatiotemporal scale.
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Affiliation(s)
- Hongke Jiang
- Department of Physical Education, Shanghai Maritime University, Shanghai, China
| | - Shanguang Zhao
- Department of Physical Education, Shanghai Maritime University, Shanghai, China
| | - Qianqian Wu
- School of Physical Education, Shaanxi Normal University, Xian, China
| | - Yingying Cao
- School of Physical Education, Shaanxi Normal University, Xian, China
| | - Wu Zhou
- School of Physical Education, Shaanxi Normal University, Xian, China
| | - Youwu Gong
- Department of Physical Education, Shanghai Maritime University, Shanghai, China
| | - Changzhuan Shao
- Department of Physical Education, Shanghai Maritime University, Shanghai, China
| | - Aiping Chi
- School of Physical Education, Shaanxi Normal University, Xian, China
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11
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Li J, Li X, Chen F, Li W, Chen J, Zhang B. Studying the Alzheimer's disease continuum using EEG and fMRI in single-modality and multi-modality settings. Rev Neurosci 2024; 35:373-386. [PMID: 38157429 DOI: 10.1515/revneuro-2023-0098] [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: 08/28/2023] [Accepted: 12/01/2023] [Indexed: 01/03/2024]
Abstract
Alzheimer's disease (AD) is a biological, clinical continuum that covers the preclinical, prodromal, and clinical phases of the disease. Early diagnosis and identification of the stages of Alzheimer's disease (AD) are crucial in clinical practice. Ideally, biomarkers should reflect the underlying process (pathological or otherwise), be reproducible and non-invasive, and allow repeated measurements over time. However, the currently known biomarkers for AD are not suitable for differentiating the stages and predicting the trajectory of disease progression. Some objective parameters extracted using electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) are widely applied to diagnose the stages of the AD continuum. While electroencephalography (EEG) has a high temporal resolution, fMRI has a high spatial resolution. Combined EEG and fMRI (EEG-fMRI) can overcome single-modality drawbacks and obtain multi-dimensional information simultaneously, and it can help explore the hemodynamic changes associated with the neural oscillations that occur during information processing. This technique has been used in the cognitive field in recent years. This review focuses on the different techniques available for studying the AD continuum, including EEG and fMRI in single-modality and multi-modality settings, and the possible future directions of AD diagnosis using EEG-fMRI.
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Affiliation(s)
- Jing Li
- Department of Radiology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, Jiangsu, 210008, China
- Institute of Medical Imaging and Artificial Intelligence, Nanjing University, Nanjing, Jiangsu, 210008, China
- Medical Imaging Center, The Affiliated Drum Tower Hospital, Medical School of Nanjing University, Nanjing, Jiangsu, 210008, China
| | - Xin Li
- Department of Radiology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, Jiangsu, 210008, China
- Institute of Medical Imaging and Artificial Intelligence, Nanjing University, Nanjing, Jiangsu, 210008, China
- Medical Imaging Center, The Affiliated Drum Tower Hospital, Medical School of Nanjing University, Nanjing, Jiangsu, 210008, China
| | - Futao Chen
- Department of Radiology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, Jiangsu, 210008, China
- Institute of Medical Imaging and Artificial Intelligence, Nanjing University, Nanjing, Jiangsu, 210008, China
- Medical Imaging Center, The Affiliated Drum Tower Hospital, Medical School of Nanjing University, Nanjing, Jiangsu, 210008, China
| | - Weiping Li
- Department of Radiology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, Jiangsu, 210008, China
- Institute of Medical Imaging and Artificial Intelligence, Nanjing University, Nanjing, Jiangsu, 210008, China
- Medical Imaging Center, The Affiliated Drum Tower Hospital, Medical School of Nanjing University, Nanjing, Jiangsu, 210008, China
| | - Jiu Chen
- Department of Radiology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, Jiangsu, 210008, China
- Institute of Medical Imaging and Artificial Intelligence, Nanjing University, Nanjing, Jiangsu, 210008, China
- Medical Imaging Center, The Affiliated Drum Tower Hospital, Medical School of Nanjing University, Nanjing, Jiangsu, 210008, China
| | - Bing Zhang
- Department of Radiology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, Jiangsu, 210008, China
- Institute of Medical Imaging and Artificial Intelligence, Nanjing University, Nanjing, Jiangsu, 210008, China
- Medical Imaging Center, The Affiliated Drum Tower Hospital, Medical School of Nanjing University, Nanjing, Jiangsu, 210008, China
- Jiangsu Key Laboratory of Molecular Medicine, Nanjing, Jiangsu, 210008, China
- Institute of Brain Science, Nanjing University, Nanjing, Jiangsu, 210008, China
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12
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Lao J, Zeng Y, Wu Z, Lin G, Wang Q, Yang M, Zhang S, Xu D, Zhang M, Yao K, Liang S, Liu Q, Li J, Zhong X, Ning Y. Abnormalities in Electroencephalographic Microstates in Patients with Late-Life Depression. Neuropsychiatr Dis Treat 2024; 20:1201-1210. [PMID: 38860214 PMCID: PMC11164213 DOI: 10.2147/ndt.s456486] [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: 12/24/2023] [Accepted: 05/24/2024] [Indexed: 06/12/2024] Open
Abstract
Background Late-life depression (LLD) is characterized by disrupted brain networks. Resting-state networks in the brain are composed of both stable and transient topological structures known as microstates, which reflect the dynamics of the neural activities. However, the specific pattern of EEG microstate in LLD remains unclear. Methods Resting-state EEG were recorded for 31 patients with episodic LLD (eLLD), 20 patients with remitted LLD (rLLD) and 32 healthy controls (HCs) using a 64-channel cap. The clinical data of the patients were collected and the 17-Item Hamilton Rating Scale for Depression (HAMD) was used for symptom assessment. Duration, occurrence, time coverage and syntax of the four microstate classes (A-D) were calculated. Group differences in EEG microstates and the relationship between microstates parameters and clinical features were analyzed. Results Compared with NC and patients with rLLD, patients with eLLD showed increased duration and time coverage of microstate class D. Besides, a decrease in occurrence of microstate C and transition probability between microstate B and C was observed. In addition, the time coverage of microstate D was positively correlated with the total score of HAMD, core symptoms, and miscellaneous items. Conclusion These findings suggest that disrupted EEG microstates may be associated with the pathophysiology of LLD and may serve as potential state markers for the monitoring of the disease.
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Affiliation(s)
- Jingyi Lao
- Geriatric Neuroscience Center, The Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou, People’s Republic of China
| | - Yijie Zeng
- Geriatric Neuroscience Center, The Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou, People’s Republic of China
| | - Zhangying Wu
- Geriatric Neuroscience Center, The Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou, People’s Republic of China
| | - Gaohong Lin
- Geriatric Neuroscience Center, The Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou, People’s Republic of China
| | - Qiang Wang
- Geriatric Neuroscience Center, The Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou, People’s Republic of China
| | - Mingfeng Yang
- Geriatric Neuroscience Center, The Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou, People’s Republic of China
| | - Si Zhang
- Geriatric Neuroscience Center, The Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou, People’s Republic of China
| | - Danyan Xu
- Geriatric Neuroscience Center, The Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou, People’s Republic of China
| | - Min Zhang
- Geriatric Neuroscience Center, The Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou, People’s Republic of China
| | - Kexin Yao
- Geriatric Neuroscience Center, The Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou, People’s Republic of China
| | - Shuang Liang
- Geriatric Neuroscience Center, The Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou, People’s Republic of China
| | - Qin Liu
- Geriatric Neuroscience Center, The Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou, People’s Republic of China
| | - Jiafu Li
- Geriatric Neuroscience Center, The Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou, People’s Republic of China
| | - Xiaomei Zhong
- Geriatric Neuroscience Center, The Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou, People’s Republic of China
| | - Yuping Ning
- Geriatric Neuroscience Center, The Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou, People’s Republic of China
- The First School of Clinical Medicine, Southern Medical University, Guangzhou, People’s Republic of China
- Guangdong Engineering Technology Research Center for Translational Medicine of Mental Disorders, Guangzhou, People’s Republic of China
- Key Laboratory of Neurogenetics and Channelopathies of Guangdong Province and the Ministry of Education of China, Guangzhou, People’s Republic of China
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13
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Foster M, Scheinost D. Brain states as wave-like motifs. Trends Cogn Sci 2024; 28:492-503. [PMID: 38582654 DOI: 10.1016/j.tics.2024.03.004] [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: 05/27/2023] [Revised: 02/29/2024] [Accepted: 03/11/2024] [Indexed: 04/08/2024]
Abstract
There is ample evidence of wave-like activity in the brain at multiple scales and levels. This emerging literature supports the broader adoption of a wave perspective of brain activity. Specifically, a brain state can be described as a set of recurring, sequential patterns of propagating brain activity, namely a wave. We examine a collective body of experimental work investigating wave-like properties. Based on these works, we consider brain states as waves using a scale-agnostic framework across time and space. Emphasis is placed on the sequentiality and periodicity associated with brain activity. We conclude by discussing the implications, prospects, and experimental opportunities of this framework.
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Affiliation(s)
- Maya Foster
- Department of Biomedical Engineering, Yale University, New Haven, CT, USA.
| | - Dustin Scheinost
- Department of Biomedical Engineering, Yale University, New Haven, CT, USA; Department of Radiology and Biomedical Engineering, Yale School of Medicine, New Haven, CT, USA
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14
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Bardel B, Ayache SS, Lefaucheur JP. The contribution of EEG to assess and treat motor disorders in multiple sclerosis. Clin Neurophysiol 2024; 162:174-200. [PMID: 38643612 DOI: 10.1016/j.clinph.2024.03.024] [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: 12/18/2023] [Revised: 03/18/2024] [Accepted: 03/21/2024] [Indexed: 04/23/2024]
Abstract
OBJECTIVE Electroencephalography (EEG) can highlight significant changes in spontaneous electrical activity of the brain produced by altered brain network connectivity linked to inflammatory demyelinating lesions and neuronal loss occurring in multiple sclerosis (MS). In this review, we describe the main EEG findings reported in the literature to characterize motor network alteration in term of local activity or functional connectivity changes in patients with MS (pwMS). METHODS A comprehensive literature search was conducted to include articles with quantitative analyses of resting-state EEG recordings (spectrograms or advanced methods for assessing spatial and temporal dynamics, such as coherence, theory of graphs, recurrent quantification, microstates) or dynamic EEG recordings during a motor task, with or without connectivity analyses. RESULTS In this systematic review, we identified 26 original articles using EEG in the evaluation of MS-related motor disorders. Various resting or dynamic EEG parameters could serve as diagnostic biomarkers of motor control impairment to differentiate pwMS from healthy subjects or be related to a specific clinical condition (fatigue) or neuroradiological aspects (lesion load). CONCLUSIONS We highlight some key EEG patterns in pwMS at rest and during movement, both suggesting an alteration or disruption of brain connectivity, more specifically involving sensorimotor networks. SIGNIFICANCE Some of these EEG biomarkers of motor disturbance could be used to design future therapeutic strategies in MS based on neuromodulation approaches, or to predict the effects of motor training and rehabilitation in pwMS.
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Affiliation(s)
- Benjamin Bardel
- Univ Paris Est Creteil, Excitabilité Nerveuse et Thérapeutique (ENT), EA 4391, F-94010 Creteil, France; AP-HP, Henri Mondor University Hospital, Department of Clinical Neurophysiology, DMU FIxIT, F-94010 Creteil, France
| | - Samar S Ayache
- Univ Paris Est Creteil, Excitabilité Nerveuse et Thérapeutique (ENT), EA 4391, F-94010 Creteil, France; AP-HP, Henri Mondor University Hospital, Department of Clinical Neurophysiology, DMU FIxIT, F-94010 Creteil, France; Gilbert and Rose-Marie Chagoury School of Medicine, Department of Neurology, 4504 Byblos, Lebanon; Institut de la Colonne Vertébrale et des NeuroSciences (ICVNS), Centre Médico-Chirurgical Bizet, F-75116 Paris, France
| | - Jean-Pascal Lefaucheur
- Univ Paris Est Creteil, Excitabilité Nerveuse et Thérapeutique (ENT), EA 4391, F-94010 Creteil, France; AP-HP, Henri Mondor University Hospital, Department of Clinical Neurophysiology, DMU FIxIT, F-94010 Creteil, France.
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15
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Wan W, Gu Z, Peng CK, Cui X. Beyond Frequency Bands: Complementary-Ensemble-Empirical-Mode-Decomposition-Enhanced Microstate Sequence Non-Randomness Analysis for Aiding Diagnosis and Cognitive Prediction of Dementia. Brain Sci 2024; 14:487. [PMID: 38790465 PMCID: PMC11118442 DOI: 10.3390/brainsci14050487] [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: 04/02/2024] [Revised: 05/01/2024] [Accepted: 05/09/2024] [Indexed: 05/26/2024] Open
Abstract
Exploring the spatiotemporal dynamic patterns of multi-channel electroencephalography (EEG) is crucial for interpreting dementia and related cognitive decline. Spatiotemporal patterns of EEG can be described through microstate analysis, which provides a discrete approximation of the continuous electric field patterns generated by the brain cortex. Here, we propose a novel microstate spatiotemporal dynamic indicator, termed the microstate sequence non-randomness index (MSNRI). The essence of the method lies in initially generating a sequence of microstate transition patterns through state space compression of EEG data using microstate analysis. Following this, we assess the non-randomness of these microstate patterns using information-based similarity analysis. The results suggest that this MSNRI metric is a potential marker for distinguishing between health control (HC) and frontotemporal dementia (FTD) (HC vs. FTD: 6.958 vs. 5.756, p < 0.01), as well as between HC and populations with Alzheimer's disease (AD) (HC vs. AD: 6.958 vs. 5.462, p < 0.001). Healthy individuals exhibit more complex macroscopic structures and non-random spatiotemporal patterns of microstates, whereas dementia disorders lead to more random spatiotemporal patterns. Additionally, we extend the proposed method by integrating the Complementary Ensemble Empirical Mode Decomposition (CEEMD) method to explore spatiotemporal dynamic patterns of microstates at specific frequency scales. Moreover, we assessed the effectiveness of this innovative method in predicting cognitive scores. The results demonstrate that the incorporation of CEEMD-enhanced microstate dynamic indicators significantly improved the prediction accuracy of Mini-Mental State Examination (MMSE) scores (R2 = 0.940). The CEEMD-enhanced MSNRI method not only aids in the exploration of large-scale neural changes in populations with dementia but also offers a robust tool for characterizing the dynamics of EEG microstate transitions and their impact on cognitive function.
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Affiliation(s)
- Wang Wan
- State Key Laboratory of Digital Medical Engineering, School of Biological Science & Medical Engineering, Southeast University, Nanjing 210096, China; (W.W.); (Z.G.)
- Center for Nonlinear Dynamics in Medicine, Southeast University, Nanjing 210096, China;
| | - Zhongze Gu
- State Key Laboratory of Digital Medical Engineering, School of Biological Science & Medical Engineering, Southeast University, Nanjing 210096, China; (W.W.); (Z.G.)
| | - Chung-Kang Peng
- Center for Nonlinear Dynamics in Medicine, Southeast University, Nanjing 210096, China;
- Key Laboratory of Child Development and Learning Science, Ministry of Education, School of Biological Science & Medical Engineering, Southeast University, Nanjing 210096, China
| | - Xingran Cui
- Center for Nonlinear Dynamics in Medicine, Southeast University, Nanjing 210096, China;
- Key Laboratory of Child Development and Learning Science, Ministry of Education, School of Biological Science & Medical Engineering, Southeast University, Nanjing 210096, China
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16
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Deiber MP, Piguet C, Berchio C, Michel CM, Perroud N, Ros T. Resting-State EEG Microstates and Power Spectrum in Borderline Personality Disorder: A High-Density EEG Study. Brain Topogr 2024; 37:397-409. [PMID: 37776472 PMCID: PMC11026215 DOI: 10.1007/s10548-023-01005-3] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2023] [Accepted: 08/30/2023] [Indexed: 10/02/2023]
Abstract
Borderline personality disorder (BPD) is a debilitating psychiatric condition characterized by emotional dysregulation, unstable sense of self, and impulsive, potentially self-harming behavior. In order to provide new neurophysiological insights on BPD, we complemented resting-state EEG frequency spectrum analysis with EEG microstates (MS) analysis to capture the spatiotemporal dynamics of large-scale neural networks. High-density EEG was recorded at rest in 16 BPD patients and 16 age-matched neurotypical controls. The relative power spectrum and broadband MS spatiotemporal parameters were compared between groups and their inter-correlations were examined. Compared to controls, BPD patients showed similar global spectral power, but exploratory univariate analyses on single channels indicated reduced relative alpha power and enhanced relative delta power at parietal electrodes. In terms of EEG MS, BPD patients displayed similar MS topographies as controls, indicating comparable neural generators. However, the MS temporal dynamics were significantly altered in BPD patients, who demonstrated opposite prevalence of MS C (lower than controls) and MS E (higher than controls). Interestingly, MS C prevalence correlated positively with global alpha power and negatively with global delta power, while MS E did not correlate with any measures of spectral power. Taken together, these observations suggest that BPD patients exhibit a state of cortical hyperactivation, represented by decreased posterior alpha power, together with an elevated presence of MS E, consistent with symptoms of elevated arousal and/or vigilance. This is the first study to investigate resting-state MS patterns in BPD, with findings of elevated MS E and the suggestion of reduced posterior alpha power indicating a disorder-specific neurophysiological signature previously unreported in a psychiatric population.
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Affiliation(s)
- Marie-Pierre Deiber
- Department of Psychiatry, University Hospitals of Geneva, Chemin du Petit-Bel-Air 2, 1226 Thônex, Geneva, Switzerland.
- Department of Psychiatry, Faculty of Medicine, University of Geneva, Geneva, Switzerland.
| | - Camille Piguet
- Department of Psychiatry, Faculty of Medicine, University of Geneva, Geneva, Switzerland
- Department of Pediatrics, University Hospitals of Geneva, Geneva, Switzerland
| | - Cristina Berchio
- Department of Pediatrics, University of Geneva, Geneva, Switzerland
| | - Christoph M Michel
- Functional Brain Mapping Laboratory, Department of Fundamental Neuroscience, University of Geneva, Geneva, Switzerland
- Center for Biomedical Imaging, CIBM, Lausanne, Switzerland
| | - Nader Perroud
- Department of Psychiatry, Faculty of Medicine, University of Geneva, Geneva, Switzerland
- Division of Psychiatric Specialties, Department of Psychiatry, University Hospitals of Geneva, Geneva, Switzerland
| | - Tomas Ros
- Department of Psychiatry, Faculty of Medicine, University of Geneva, Geneva, Switzerland
- Center for Biomedical Imaging, CIBM, Lausanne, Switzerland
- Department of Neuroscience, University of Geneva, Geneva, Switzerland
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17
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Wu G, Zhao X, Luo X, Li H, Chen Y, Dang C, Sun L. Microstate dynamics and spectral components as markers of persistent and remittent attention-deficit/hyperactivity disorder. Clin Neurophysiol 2024; 161:147-156. [PMID: 38484486 DOI: 10.1016/j.clinph.2024.02.027] [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: 12/16/2023] [Revised: 02/06/2024] [Accepted: 02/26/2024] [Indexed: 04/28/2024]
Abstract
OBJECTIVE We leveraged microstate characteristics and power features to examine temporal and spectral deviations underlying persistent and remittent attention-deficit/hyperactivity disorder (ADHD). METHODS 50 young adults with childhood ADHD (28 persisters, 22 remitters) and 28 demographically similar healthy controls (HC) were compared on microstates features and frequency principal components (f-PCs) of eye-closed resting state. Support vector machine model with sequential forward selection (SVM-SFS) was utilized to discriminate three groups. RESULTS Four microstates and four comparable f-PCs were identified. Compared to HC, ADHD persisters showed prolonged duration in microstate C, elevated power of the delta component (D), and compromised amplitude of the two alpha components (A1 and A2). Remitters showed increased duration and coverage of microstate C, together with decreased activity of D, relatively intact amplitude of A1, and amplitude reduction in A2. The SVM-SFS algorithm achieved an accuracy of 93.59% in classifying persisters, remitters and controls. The most discriminative features selected were those exhibiting group differences. CONCLUSIONS We found widespread anomalies in ADHD persisters in brain dynamics and intrinsic EEG components. Meanwhile, the neural features in remitters exhibited multiple patterns. SIGNIFICANCE This study underlines the use of microstate dynamics and spectral components as potential markers of persistent and remittent ADHD.
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Affiliation(s)
- GuiSen Wu
- Peking University Sixth Hospital, Institute of Mental Health, Beijing 100191, China; NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Beijing, China
| | - XiXi Zhao
- The National Clinical Research Center for Mental Disorders & Beijing Key Laboratory of Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, China; Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing, China
| | - XiangSheng Luo
- Peking University Sixth Hospital, Institute of Mental Health, Beijing 100191, China; NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Beijing, China
| | - Hui Li
- Peking University Sixth Hospital, Institute of Mental Health, Beijing 100191, China; NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Beijing, China
| | - YanBo Chen
- Peking University Sixth Hospital, Institute of Mental Health, Beijing 100191, China; NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Beijing, China
| | - Chen Dang
- Peking University Sixth Hospital, Institute of Mental Health, Beijing 100191, China; NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Beijing, China
| | - Li Sun
- Peking University Sixth Hospital, Institute of Mental Health, Beijing 100191, China; NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Beijing, China.
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18
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Berchio C, Kumar SS, Micali N. EEG Spatial-temporal Dynamics of Resting-state Activity in Young Women with Anorexia Nervosa: Preliminary Evidence. Brain Topogr 2024; 37:447-460. [PMID: 37615798 DOI: 10.1007/s10548-023-01001-7] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2023] [Accepted: 08/14/2023] [Indexed: 08/25/2023]
Abstract
The aim of this study was to provide preliminary evidence on temporal dynamics of resting-state brain networks in youth with anorexia nervosa (AN) using electroencephalography (EEG). Resting-state EEG data were collected in 18 young women with AN and 18 healthy controls (HC). Between-group differences in brain networks were assessed using microstates analyses. Five microstates were identified across all subjects (A, B, C, D, E). Using a single set of maps representative of the whole dataset, group differences were identified for microstates A, C, and E. A common-for-all template revealed a relatively high degree of consistency in results for reduced time coverage of microstate C, but also an increased presence of microstate class E. AN and HC had different microstate transition probabilities, largely involving microstate A. Using LORETA, for microstate D, we found that those with AN had augmented activations in the left frontal inferior operculum, left insula, and bilateral paracentral lobule, compared with HC. For microstate E, AN had augmented activations in the para-hippocampal gyrus, caudate, pallidum, cerebellum, and cerebellar vermis. Our findings suggest altered microstates in young women with AN associated with integration of sensory and bodily signals, monitoring of internal/external mental states, and self-referential processes. Future research should examine how EEG-derived microstates could be applied to develop diagnostic and prognostic models of AN.
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Affiliation(s)
- Cristina Berchio
- Group of Psychiatric Neuroscience, Department of Translational Biomedicine and Neuroscience, University of Bari Aldo Moro, Piazza Giulio Cesare, 11, 70121, Bari, Italy.
| | - Samika S Kumar
- Department of Psychology, University of Cambridge, Cambridge, UK
- Section on Functional Imaging Methods, Laboratory of Brain and Cognition, National Institute of Mental Health, Bethesda, MD, USA
| | - Nadia Micali
- Great Ormond Street Institute of Child Health, University College London, London, UK
- Mental Health Services in the Capital Region of Denmark, Eating Disorders Research Unit, Psychiatric Centre Ballerup, Ballerup, Denmark
- Institute of biological Psychiatry, Psykiatrisk Center Sct. Hans, Region Hovedstaden, Denmark
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19
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Ding X, Cao F, Li M, Yang Z, Tang Y. Electroencephalography Microstate Class D is a Brain Marker of Subjective Sleep Quality for College Students with High Habitual Sleep Efficiency. Brain Topogr 2024; 37:370-376. [PMID: 37382840 DOI: 10.1007/s10548-023-00978-5] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2022] [Accepted: 06/12/2023] [Indexed: 06/30/2023]
Abstract
Subjective sleep quality is an individual's subjective sleep feeling, and its effective evaluation is the premise of improving sleep quality. However, people with autism or mental disorders often experience difficulties in verbally expressing their subjective sleep quality. To solve the above problem, this study provides a non-verbal and convenient brain feature to assess subjective sleep quality. Reportedly, microstates are often used to characterize the patterns of functional brain activity in humans. The occurrence frequency of microstate class D is an important feature in the insomnia population. We therefore hypothesize that the occurrence frequency of microstate class D is a physiological indicator of subjective sleep quality. To test this hypothesis, we recruited college students from China as participants [N = 61, mean age = 20.84 years]. The Chinese version of the Pittsburgh Sleep Quality Index scale was used to measure subjective sleep quality and habitual sleep efficiency, and the state characteristics of the brain at this time were assessed using closed eyes resting-state brain microstate class D. The occurrence frequency of EEG microstate class D was positively associated with subjective sleep quality (r = 0.32, p < 0.05). Further analysis of the moderating effect showed that the occurrence frequency of microstate class D was significantly and positively correlated with subjective sleep quality in the high habitual sleep efficiency group. However, the relationship was not significant in the low sleep efficiency group (βsimple = 0.63, p < 0.001). This study shows that the occurrence frequency of microstate class D is a physiological indicator of assessing subjective sleep quality levels in the high sleep efficiency group. This study provides brain features for assessing subjective sleep quality of people with autism and mental disorders who cannot effectively describe their subjective feelings.
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Affiliation(s)
- Xiaoqian Ding
- College of Psychology, Liaoning Normal University, Dalian, 116029, China
| | - Fengzhi Cao
- College of Psychology, Liaoning Normal University, Dalian, 116029, China
| | - Menghan Li
- College of Psychology, Liaoning Normal University, Dalian, 116029, China
| | - Zirong Yang
- Department of Gastroenterology, Affiliated Zhongshan Hospital of Dalian University, Dalian, 116001, China
| | - Yiyuan Tang
- College of Health Solutions, Arizona State University, Phoenix, AZ, 85004, USA.
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Hermans T, Khazaei M, Raeisi K, Croce P, Tamburro G, Dereymaeker A, De Vos M, Zappasodi F, Comani S. Microstate Analysis Reflects Maturation of the Preterm Brain. Brain Topogr 2024; 37:461-474. [PMID: 37823945 PMCID: PMC11026208 DOI: 10.1007/s10548-023-01008-0] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2023] [Accepted: 09/11/2023] [Indexed: 10/13/2023]
Abstract
Preterm neonates are at risk of long-term neurodevelopmental impairments due to disruption of natural brain development. Electroencephalography (EEG) analysis can provide insights into brain development of preterm neonates. This study aims to explore the use of microstate (MS) analysis to evaluate global brain dynamics changes during maturation in preterm neonates with normal neurodevelopmental outcome.The dataset included 135 EEGs obtained from 48 neonates at varying postmenstrual ages (26.4 to 47.7 weeks), divided into four age groups. For each recording we extracted a 5-minute epoch during quiet sleep (QS) and during non-quiet sleep (NQS), resulting in eight groups (4 age group x 2 sleep states). We compared MS maps and corresponding (map-specific) MS metrics across groups using group-level maps. Additionally, we investigated individual map metrics.Four group-level MS maps accounted for approximately 70% of the global variance and showed non-random syntax. MS topographies and transitions changed significantly when neonates reached 37 weeks. For both sleep states and all MS maps, MS duration decreased and occurrence increased with age. The same relationships were found using individual maps, showing strong correlations (Pearson coefficients up to 0.74) between individual map metrics and post-menstrual age. Moreover, the Hurst exponent of the individual MS sequence decreased with age.The observed changes in MS metrics with age might reflect the development of the preterm brain, which is characterized by formation of neural networks. Therefore, MS analysis is a promising tool for monitoring preterm neonatal brain maturation, while our study can serve as a valuable reference for investigating EEGs of neonates with abnormal neurodevelopmental outcomes.
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Affiliation(s)
- Tim Hermans
- Department of Electrical Engineering (ESAT), STADIUS Center for Dynamical Systems, Signal Processing and Data Analytics, KU Leuven, Leuven, Belgium
| | - Mohammad Khazaei
- Department of Neuroscience Imaging and Clinical Sciences, G. d'Annunzio University of Chieti-Pescara, Chieti, Italy
| | - Khadijeh Raeisi
- Department of Neuroscience Imaging and Clinical Sciences, G. d'Annunzio University of Chieti-Pescara, Chieti, Italy
| | - Pierpaolo Croce
- Department of Neuroscience Imaging and Clinical Sciences, G. d'Annunzio University of Chieti-Pescara, Chieti, Italy
- Behavioral Imaging and Neural Dynamics Center, G. d'Annunzio University of Chieti-Pescara, Chieti, Italy
| | - Gabriella Tamburro
- Department of Neuroscience Imaging and Clinical Sciences, G. d'Annunzio University of Chieti-Pescara, Chieti, Italy
- Behavioral Imaging and Neural Dynamics Center, G. d'Annunzio University of Chieti-Pescara, Chieti, Italy
| | - Anneleen Dereymaeker
- Department of Development and Regeneration, KU Leuven, Leuven, Belgium
- Neonatal Intensive Care Unit, UZ Leuven, Leuven, Belgium
| | - Maarten De Vos
- Department of Electrical Engineering (ESAT), STADIUS Center for Dynamical Systems, Signal Processing and Data Analytics, KU Leuven, Leuven, Belgium
- Department of Development and Regeneration, KU Leuven, Leuven, Belgium
| | - Filippo Zappasodi
- Department of Neuroscience Imaging and Clinical Sciences, G. d'Annunzio University of Chieti-Pescara, Chieti, Italy
- Behavioral Imaging and Neural Dynamics Center, G. d'Annunzio University of Chieti-Pescara, Chieti, Italy
- Institute for Advanced Biomedical Technologies, G. d'Annunzio University of Chieti-Pescara, Chieti, Italy
| | - Silvia Comani
- Department of Neuroscience Imaging and Clinical Sciences, G. d'Annunzio University of Chieti-Pescara, Chieti, Italy.
- Behavioral Imaging and Neural Dynamics Center, G. d'Annunzio University of Chieti-Pescara, Chieti, Italy.
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21
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von Wegner F, Wiemers M, Hermann G, Tödt I, Tagliazucchi E, Laufs H. Complexity Measures for EEG Microstate Sequences: Concepts and Algorithms. Brain Topogr 2024; 37:296-311. [PMID: 37751054 PMCID: PMC10884068 DOI: 10.1007/s10548-023-01006-2] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2023] [Accepted: 08/31/2023] [Indexed: 09/27/2023]
Abstract
EEG microstate sequence analysis quantifies properties of ongoing brain electrical activity which is known to exhibit complex dynamics across many time scales. In this report we review recent developments in quantifying microstate sequence complexity, we classify these approaches with regard to different complexity concepts, and we evaluate excess entropy as a yet unexplored quantity in microstate research. We determined the quantities entropy rate, excess entropy, Lempel-Ziv complexity (LZC), and Hurst exponents on Potts model data, a discrete statistical mechanics model with a temperature-controlled phase transition. We then applied the same techniques to EEG microstate sequences from wakefulness and non-REM sleep stages and used first-order Markov surrogate data to determine which time scales contributed to the different complexity measures. We demonstrate that entropy rate and LZC measure the Kolmogorov complexity (randomness) of microstate sequences, whereas excess entropy and Hurst exponents describe statistical complexity which attains its maximum at intermediate levels of randomness. We confirmed the equivalence of entropy rate and LZC when the LZ-76 algorithm is used, a result previously reported for neural spike train analysis (Amigó et al., Neural Comput 16:717-736, https://doi.org/10.1162/089976604322860677 , 2004). Surrogate data analyses prove that entropy-based quantities and LZC focus on short-range temporal correlations, whereas Hurst exponents include short and long time scales. Sleep data analysis reveals that deeper sleep stages are accompanied by a decrease in Kolmogorov complexity and an increase in statistical complexity. Microstate jump sequences, where duplicate states have been removed, show higher randomness, lower statistical complexity, and no long-range correlations. Regarding the practical use of these methods, we suggest that LZC can be used as an efficient entropy rate estimator that avoids the estimation of joint entropies, whereas entropy rate estimation via joint entropies has the advantage of providing excess entropy as the second parameter of the same linear fit. We conclude that metrics of statistical complexity are a useful addition to microstate analysis and address a complexity concept that is not yet covered by existing microstate algorithms while being actively explored in other areas of brain research.
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Affiliation(s)
- Frederic von Wegner
- School of Biomedical Sciences, University of New South Wales (UNSW), Wallace Wurth, Kensington, NSW, 2052, Australia.
| | - Milena Wiemers
- Department of Neurology and Clinical Neurophysiology, Lüneburg Hospital, Bögelstrasse 1, 21339, Lüneburg, Germany
| | - Gesine Hermann
- Department of Neurology, Christian-Albrechts University, Arnold-Heller-Strasse 3, 24105, Kiel, Germany
| | - Inken Tödt
- Institute of Sexual Medicine & Forensic Psychiatry and Psychotherapy, Christian-Albrechts University, Schwanenweg 24, 24105, Kiel, Germany
| | - Enzo Tagliazucchi
- Department of Physics, University of Buenos Aires, 1428, Buenos Aires, Argentina
| | - Helmut Laufs
- Department of Neurology, Christian-Albrechts University, Arnold-Heller-Strasse 3, 24105, Kiel, Germany
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22
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Michel CM, Brechet L, Schiller B, Koenig T. Current State of EEG/ERP Microstate Research. Brain Topogr 2024; 37:169-180. [PMID: 38349451 PMCID: PMC10884048 DOI: 10.1007/s10548-024-01037-3] [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: 01/22/2024] [Accepted: 01/31/2024] [Indexed: 02/23/2024]
Abstract
The analysis of EEG microstates for investigating rapid whole-brain network dynamics during rest and tasks has become a standard practice in the EEG research community, leading to a substantial increase in publications across various affective, cognitive, social and clinical neuroscience domains. Recognizing the growing significance of this analytical method, the authors aim to provide the microstate research community with a comprehensive discussion on methodological standards, unresolved questions, and the functional relevance of EEG microstates. In August 2022, a conference was hosted in Bern, Switzerland, which brought together many researchers from 19 countries. During the conference, researchers gave scientific presentations and engaged in roundtable discussions aiming at establishing steps toward standardizing EEG microstate analysis methods. Encouraged by the conference's success, a special issue was launched in Brain Topography to compile the current state-of-the-art in EEG microstate research, encompassing methodological advancements, experimental findings, and clinical applications. The call for submissions for the special issue garnered 48 contributions from researchers worldwide, spanning reviews, meta-analyses, tutorials, and experimental studies. Following a rigorous peer-review process, 33 papers were accepted whose findings we will comprehensively discuss in this Editorial.
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Affiliation(s)
- Christoph M Michel
- Functional Brain Mapping Lab, Department of Basic Neurosciences, Medical Faculty, University of Geneva, Geneva, Switzerland.
- Center for Biomedical Imaging (CIBM), Lausanne, Geneva, Switzerland.
| | - Lucie Brechet
- Department of Readaptation and Geriatrics, Medical Faculty, University of Geneva, Geneva, Switzerland
| | - Bastian Schiller
- Laboratory for Biological Psychology, Clinical Psychology, and Psychotherapy, Albert-Ludwigs-University of Freiburg, Freiburg, Germany
| | - Thomas Koenig
- Translational Research Center, University Hospital of Psychiatry, University of Bern, Bern, Switzerland
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23
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Bapat R, Pathak A, Banerjee A. Metastability indexes global changes in the dynamic working point of the brain following brain stimulation. Front Neurorobot 2024; 18:1336438. [PMID: 38440318 PMCID: PMC10909933 DOI: 10.3389/fnbot.2024.1336438] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2023] [Accepted: 02/01/2024] [Indexed: 03/06/2024] Open
Abstract
Several studies have shown that coordination among neural ensembles is a key to understand human cognition. A well charted path is to identify coordination states associated with cognitive functions from spectral changes in the oscillations of EEG or MEG. A growing number of studies suggest that the tendency to switch between coordination states, sculpts the dynamic repertoire of the brain and can be indexed by a measure known as metastability. In this article, we characterize perturbations in the metastability of global brain network dynamics following Transcranial Magnetic Stimulation that could quantify the duration for which information processing is altered. Thus allowing researchers to understand the network effects of brain stimulation, standardize stimulation protocols and design experimental tasks. We demonstrate the effect empirically using publicly available datasets and use a digital twin (a whole brain connectome model) to understand the dynamic principles that generate such observations. We observed a significant reduction in metastability, concurrent with an increase in coherence following single-pulse TMS reflecting the existence of a window where neural coordination is altered. The reduction in complexity was validated by an additional measure based on the Lempel-Ziv complexity of microstate labeled EEG data. Interestingly, higher frequencies in the EEG signal showed faster recovery in metastability than lower frequencies. The digital twin shed light on how the phase resetting introduced by the single-pulse TMS in local cortical networks can propagate globally, giving rise to changes in metastability and coherence.
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Affiliation(s)
| | | | - Arpan Banerjee
- Cognitive Brain Dynamics Lab, National Brain Research Centre, Manesar, Haryana, India
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D’Andrea A, Croce P, O’Byrne J, Jerbi K, Pascarella A, Raffone A, Pizzella V, Marzetti L. Mindfulness meditation styles differently modulate source-level MEG microstate dynamics and complexity. Front Neurosci 2024; 18:1295615. [PMID: 38370436 PMCID: PMC10869546 DOI: 10.3389/fnins.2024.1295615] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2023] [Accepted: 01/15/2024] [Indexed: 02/20/2024] Open
Abstract
Background The investigation of mindfulness meditation practice, classically divided into focused attention meditation (FAM), and open monitoring meditation (OMM) styles, has seen a long tradition of theoretical, affective, neurophysiological and clinical studies. In particular, the high temporal resolution of magnetoencephalography (MEG) or electroencephalography (EEG) has been exploited to fill the gap between the personal experience of meditation practice and its neural correlates. Mounting evidence, in fact, shows that human brain activity is highly dynamic, transiting between different brain states (microstates). In this study, we aimed at exploring MEG microstates at source-level during FAM, OMM and in the resting state, as well as the complexity and criticality of dynamic transitions between microstates. Methods Ten right-handed Theravada Buddhist monks with a meditative expertise of minimum 2,265 h participated in the experiment. MEG data were acquired during a randomized block design task (6 min FAM, 6 min OMM, with each meditative block preceded and followed by 3 min resting state). Source reconstruction was performed using eLORETA on individual cortical space, and then parcellated according to the Human Connect Project atlas. Microstate analysis was then applied to parcel level signals in order to derive microstate topographies and indices. In addition, from microstate sequences, the Hurst exponent and the Lempel-Ziv complexity (LZC) were computed. Results Our results show that the coverage and occurrence of specific microstates are modulated either by being in a meditative state or by performing a specific meditation style. Hurst exponent values in both meditation conditions are reduced with respect to the value observed during rest, LZC shows significant differences between OMM, FAM, and REST, with a progressive increase from REST to FAM to OMM. Discussion Importantly, we report changes in brain criticality indices during meditation and between meditation styles, in line with a state-like effect of meditation on cognitive performance. In line with previous reports, we suggest that the change in cognitive state experienced in meditation is paralleled by a shift with respect to critical points in brain dynamics.
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Affiliation(s)
- Antea D’Andrea
- Department of Neuroscience, Imaging and Clinical Sciences, University of Chieti-Pescara, Chieti, Abruzzo, Italy
| | - Pierpaolo Croce
- Department of Neuroscience, Imaging and Clinical Sciences, University of Chieti-Pescara, Chieti, Abruzzo, Italy
| | - Jordan O’Byrne
- Department of Psychology, University of Montreal, Montreal, QC, Canada
| | - Karim Jerbi
- Department of Psychology, University of Montreal, Montreal, QC, Canada
| | - Annalisa Pascarella
- Institute for the Applications of Calculus “M. Picone”, National Research Council, Rome, Lazio, Italy
| | - Antonino Raffone
- Department of Psychology, Sapienza University of Rome, Rome, Lazio, Italy
| | - Vittorio Pizzella
- Department of Neuroscience, Imaging and Clinical Sciences, University of Chieti-Pescara, Chieti, Abruzzo, Italy
- Institute for Advanced Biomedical Technologies, University of Chieti-Pescara, Chieti, Abruzzo, Italy
| | - Laura Marzetti
- Department of Neuroscience, Imaging and Clinical Sciences, University of Chieti-Pescara, Chieti, Abruzzo, Italy
- Institute for Advanced Biomedical Technologies, University of Chieti-Pescara, Chieti, Abruzzo, Italy
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25
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Czoch A, Kaposzta Z, Mukli P, Stylianou O, Eke A, Racz FS. Resting-state fractal brain connectivity is associated with impaired cognitive performance in healthy aging. GeroScience 2024; 46:473-489. [PMID: 37458934 PMCID: PMC10828136 DOI: 10.1007/s11357-023-00836-z] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2023] [Accepted: 05/20/2023] [Indexed: 01/31/2024] Open
Abstract
Aging affects cognitive functions even in the absence of ongoing pathologies. The neurophysiological basis of age-related cognitive decline (CD), however, is not completely understood. Alterations in both functional brain connectivity and in the fractal scaling of neuronal dynamics have been linked to aging and cognitive performance. Recently, fractal connectivity (FrC) has been proposed - combining the two concepts - for capturing long-term interactions among brain regions. FrC was shown to be influenced by increased mental workload; however, no prior studies investigated how resting-state FrC relates to cognitive performance and plausible CD in healthy aging. We recruited 19 healthy elderly (HE) and 24 young control (YC) participants, who underwent resting-state electroencephalography (EEG) measurements and comprehensive cognitive evaluation using 7 tests of the Cambridge Neurophysiological Test Automated Battery. FrC networks were reconstructed from EEG data using the recently introduced multiple-resampling cross-spectral analysis (MRCSA). Elderly individuals could be characterized with increased response latency and reduced performance in 4-4 tasks, respectively, with both reaction time and accuracy being affected in two tasks. Auto- and cross-spectral exponents - characterizing regional fractal dynamics and FrC, respectively, - were found reduced in HE when compared to YC over most of the cortex. Additionally, fractal scaling of frontoparietal connections expressed an inverse relationship with task performance in visual memory and sustained attention domains in elderly, but not in young individuals. Our results confirm that the fractal nature of brain connectivity - as captured by MRCSA - is affected in healthy aging. Furthermore, FrC appears as a sensitive neurophysiological marker of age-related CD.
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Affiliation(s)
- Akos Czoch
- Department of Physiology, Semmelweis University, Budapest, Hungary
| | - Zalan Kaposzta
- Department of Physiology, Semmelweis University, Budapest, Hungary
| | - Peter Mukli
- Department of Physiology, Semmelweis University, Budapest, Hungary
- Oklahoma Center for Geroscience and Healthy Brain Aging, University of Oklahoma Health Sciences Center, Oklahoma City, OK, USA
- Vascular Cognitive Impairment and Neurodegeneration Program, Department of Neurosurgery, University of Oklahoma Health Sciences Center, Oklahoma City, OK, USA
- International Training Program in Geroscience, Doctoral School of Basic and Translational Medicine/Department of Public Health, Semmelweis University, Budapest, Hungary
| | - Orestis Stylianou
- Department of Physiology, Semmelweis University, Budapest, Hungary
- Institute of Translational Medicine, Semmelweis University, Budapest, Hungary
- Berlin Institute of Health at Charité, University Hospital Berlin, Berlin, Germany
- Department of Neurology With Experimental Neurology, Charité-University Hospital Berlin, Corporate Member of Freie Universität Berlin and Humboldt Universität Zu Berlin, Berlin, Germany
| | - Andras Eke
- Department of Physiology, Semmelweis University, Budapest, Hungary
- Department of Radiology and Biomedical Imaging, Yale University School of Medicine, New Haven, CT, USA
| | - Frigyes Samuel Racz
- Department of Physiology, Semmelweis University, Budapest, Hungary.
- Department of Neurology, Dell Medical School, The University of Texas at Austin, Austin, TX, USA.
- Mulva Clinic for the Neurosciences, Dell Medical School, The University of Texas at Austin, Austin, TX, USA.
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26
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Zhang H, Yang X, Yao L, Liu Q, Lu Y, Chen X, Wang T. EEG microstates analysis after TMS in patients with subacute stroke during the resting state. Cereb Cortex 2024; 34:bhad480. [PMID: 38112223 PMCID: PMC10793572 DOI: 10.1093/cercor/bhad480] [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: 09/01/2023] [Revised: 11/21/2023] [Accepted: 11/22/2023] [Indexed: 12/21/2023] Open
Abstract
To investigate whether intermittent theta burst stimulation over the cerebellum induces changes in resting-state electroencephalography microstates in patients with subacute stroke and its correlation with cognitive and emotional function. Twenty-four stroke patients and 17 healthy controls were included in this study. Patients and healthy controls were assessed at baseline, including resting-state electroencephalography and neuropsychological scales. Fifteen patients received lateral cerebellar intermittent theta burst stimulation as well as routine rehabilitation training (intermittent theta burst stimulation-RRT group), whereas 9 patients received only conventional rehabilitation training (routine rehabilitation training group). After 2 wk, baseline data were recorded again in both groups. Stroke patients exhibited reduced parameters in microstate D and increased parameters in microstate C compared with healthy controls. However, after the administration of intermittent theta burst stimulation over the lateral cerebellum, significant alterations were observed in the majority of metrics for both microstates D and C. Lateral cerebellar intermittent theta burst stimulation combined with conventional rehabilitation has a stronger tendency to improve emotional and cognitive function in patients with subacute stroke than conventional rehabilitation. The improvement of mood and cognitive function was significantly associated with microstates C and D. We identified electroencephalography microstate spatiotemporal dynamics associated with clinical improvement following a course of intermittent theta burst stimulation therapy.
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Affiliation(s)
- Hongmei Zhang
- Department of Rehabilitation Medicine, The Second Affiliated Hospital of Kunming Medical University, Kunming 650101, Yunnan, China
| | - Xue Yang
- Department of Rehabilitation Medicine, The Second Affiliated Hospital of Kunming Medical University, Kunming 650101, Yunnan, China
| | - Liqing Yao
- Department of Rehabilitation Medicine, The Second Affiliated Hospital of Kunming Medical University, Kunming 650101, Yunnan, China
| | - Qian Liu
- Department of Rehabilitation Medicine, The Second Affiliated Hospital of Kunming Medical University, Kunming 650101, Yunnan, China
| | - Yihuan Lu
- Department of Rehabilitation Medicine, The Second Affiliated Hospital of Kunming Medical University, Kunming 650101, Yunnan, China
| | - Xueting Chen
- Department of Rehabilitation Medicine, The Second Affiliated Hospital of Kunming Medical University, Kunming 650101, Yunnan, China
| | - Tianling Wang
- Department of Rehabilitation Medicine, The Second Affiliated Hospital of Kunming Medical University, Kunming 650101, Yunnan, China
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27
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Zhou DD, Li HZ, Wang W, Kuang L. Changes in oscillatory patterns of microstate sequence in patients with first-episode psychosis. Sci Data 2024; 11:38. [PMID: 38182586 PMCID: PMC10770397 DOI: 10.1038/s41597-023-02892-8] [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: 07/29/2023] [Accepted: 12/27/2023] [Indexed: 01/07/2024] Open
Abstract
We aimed to utilize chaos game representation (CGR) for the investigation of microstate sequences and explore its potential as neurobiomarkers for psychiatric disorders. We applied our proposed method to a public dataset including 82 patients with first-episode psychosis (FEP) and 61 control subjects. Two time series were constructed: one using the microstate spacing distance in CGR and the other using complex numbers representing the microstate coordinates in CGR. Power spectral features of both time series and frequency matrix CGR (FCGR) were compared between groups and employed in a machine learning application. The four canonical microstates (A, B, C, and D) were identified using both shared and separate templates. Our results showed the microstate oscillatory pattern exhibited alterations in the FEP group. Using oscillatory features improved machine learning performance compared with classical features and FCGR. This study opens up new avenues for exploring the use of CGR in analyzing EEG microstate sequences. Features derived from microstate sequence CGR offer fine-grained neurobiomarkers for psychiatric disorders.
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Affiliation(s)
- Dong-Dong Zhou
- Mental Health Center, University-Town Hospital of Chongqing Medical University, Chongqing, China.
| | - Hong-Zhi Li
- Mental Health Center, University-Town Hospital of Chongqing Medical University, Chongqing, China
| | - Wo Wang
- Mental Health Center, University-Town Hospital of Chongqing Medical University, Chongqing, China
| | - Li Kuang
- Mental Health Center, University-Town Hospital of Chongqing Medical University, Chongqing, China.
- Department of Psychiatry, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China.
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28
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Zhu M, Gong Q. EEG spectral and microstate analysis originating residual inhibition of tinnitus induced by tailor-made notched music training. Front Neurosci 2023; 17:1254423. [PMID: 38148944 PMCID: PMC10750374 DOI: 10.3389/fnins.2023.1254423] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2023] [Accepted: 11/21/2023] [Indexed: 12/28/2023] Open
Abstract
Tailor-made notched music training (TMNMT) is a promising therapy for tinnitus. Residual inhibition (RI) is one of the few interventions that can temporarily inhibit tinnitus, which is a useful technique that can be applied to tinnitus research and explore tinnitus mechanisms. In this study, RI effect of TMNMT in tinnitus was investigated mainly using behavioral tests, EEG spectral and microstate analysis. To our knowledge, this study is the first to investigate RI effect of TMNMT. A total of 44 participants with tinnitus were divided into TMNMT group (22 participants; ECnm, NMnm, RInm represent that EEG recordings with eyes closed stimuli-pre, stimuli-ing, stimuli-post by TMNMT music, respectively) and Placebo control group (22 participants; ECpb, PBpb, RIpb represent that EEG recordings with eyes closed stimuli-pre, stimuli-ing, stimuli-post by Placebo music, respectively) in a single-blind manner. Behavioral tests, EEG spectral analysis (covering delta, theta, alpha, beta, gamma frequency bands) and microstate analysis (involving four microstate classes, A to D) were employed to evaluate RI effect of TMNMT. The results of the study showed that TMNMT had a stronger inhibition ability and longer inhibition time according to the behavioral tests compared to Placebo. Spectral analysis showed that RI effect of TMNMT increased significantly the power spectral density (PSD) of delta, theta bands and decreased significantly the PSD of alpha2 band, and microstate analysis showed that RI effect of TMNMT had shorter duration (microstate B, microstate C), higher Occurrence (microstate A, microstate C, microstate D), Coverage (microstate A) and transition probabilities (microstate A to microstate B, microstate A to microstate D and microstate D to microstate A). Meanwhile, RI effect of Placebo decreased significantly the PSD of alpha2 band, and microstate analysis showed that RI effect of Placebo had shorter duration (microstate C, microstate D), higher occurrence (microstate B, microstate C), lower coverage (microstate C, microstate D), higher transition probabilities (microstate A to microstate B, microstate B to microstate A). It was also found that the intensity of tinnitus symptoms was significant positively correlated with the duration of microstate B in five subgroups (ECnm, NMnm, RInm, ECpb, PBpb). Our study provided valuable experimental evidence and practical applications for the effectiveness of TMNMT as a novel music therapy for tinnitus. The observed stronger residual inhibition (RI) ability of TMNMT supported its potential applications in tinnitus treatment. Furthermore, the temporal dynamics of EEG microstates serve as novel functional and trait markers of synchronous brain activity that contribute to a deep understanding of the neural mechanism underlying TMNMT treatment for tinnitus.
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Affiliation(s)
- Min Zhu
- Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing, China
| | - Qin Gong
- Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing, China
- School of Medicine, Shanghai University, Shanghai, China
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Guan S, Jiang R, Chen DY, Michael A, Meng C, Biswal B. Multifractal long-range dependence pattern of functional magnetic resonance imaging in the human brain at rest. Cereb Cortex 2023; 33:11594-11608. [PMID: 37851793 DOI: 10.1093/cercor/bhad393] [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: 09/12/2023] [Revised: 10/01/2023] [Accepted: 10/02/2023] [Indexed: 10/20/2023] Open
Abstract
Long-range dependence is a prevalent phenomenon in various biological systems that characterizes the long-memory effect of temporal fluctuations. While recent research suggests that functional magnetic resonance imaging signal has fractal property, it remains unknown about the multifractal long-range dependence pattern of resting-state functional magnetic resonance imaging signals. The current study adopted the multifractal detrended fluctuation analysis on highly sampled resting-state functional magnetic resonance imaging scans to investigate long-range dependence profile associated with the whole-brain voxels as specific functional networks. Our findings revealed the long-range dependence's multifractal properties. Moreover, long-term persistent fluctuations are found for all stations with stronger persistency in whole-brain regions. Subsets with large fluctuations contribute more to the multifractal spectrum in the whole brain. Additionally, we found that the preprocessing with band-pass filtering provided significantly higher reliability for estimating long-range dependence. Our validation analysis confirmed that the optimal pipeline of long-range dependence analysis should include band-pass filtering and removal of daily temporal dependence. Furthermore, multifractal long-range dependence characteristics in healthy control and schizophrenia are different significantly. This work has provided an analytical pipeline for the multifractal long-range dependence in the resting-state functional magnetic resonance imaging signal. The findings suggest differential long-memory effects in the intrinsic functional networks, which may offer a neural marker finding for understanding brain function and pathology.
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Affiliation(s)
- Sihai Guan
- College of Electronic and Information, Southwest Minzu University, Chengdu 610041, China
- Key Laboratory of Electronic and Information Engineering, State Ethnic Affairs Commission, Chengdu 610041, China
| | - Runzhou Jiang
- School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, China
- Medical Equipment Department, Xiangyang No.1 People's Hospital, Xiangyang 441000, China
| | - Donna Y Chen
- Department of Biomedical Engineering, New Jersey Institute of Technology, Newark, NJ 07102, United States
| | - Andrew Michael
- Duke Institute for Brain Sciences, Duke University, Durham, NC 27708, United States
| | - Chun Meng
- School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, China
| | - Bharat Biswal
- School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, China
- Department of Biomedical Engineering, New Jersey Institute of Technology, Newark, NJ 07102, United States
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Wen Y, Li H, Huang Y, Qiao D, Ren T, Lei L, Li G, Yang C, Xu Y, Han M, Liu Z. Dynamic network characteristics of adolescents with major depressive disorder: Attention network mediates the association between anhedonia and attentional deficit. Hum Brain Mapp 2023; 44:5749-5769. [PMID: 37683097 PMCID: PMC10619388 DOI: 10.1002/hbm.26474] [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: 11/29/2022] [Revised: 08/10/2023] [Accepted: 08/17/2023] [Indexed: 09/10/2023] Open
Abstract
Attention deficit is a critical symptom that impairs social functioning in adolescents with major depressive disorder (MDD). In this study, we aimed to explore the dynamic neural network activity associated with attention deficits and its relationship with clinical outcomes in adolescents with MDD. We included 188 adolescents with MDD and 94 healthy controls. By combining psychophysics, resting-state electroencephalography (EEG), and functional magnetic resonance imaging (fMRI) techniques, we aimed to identify dynamic network features through the investigation of EEG microstate characteristics and related temporal network features in adolescents with MDD. At baseline, microstate analysis revealed that the occurrence of Microstate C in the patient group was lower than that in healthy controls, whereas the duration and coverage of Microstate D increased in the MDD group. Mediation analysis revealed that the probability of transition from Microstate C to D mediated anhedonia and attention deficits in the MDD group. fMRI results showed that the temporal variability of the dorsal attention network (DAN) was significantly weaker in patients with MDD than in healthy controls. Importantly, the temporal variability of DAN mediated the relationship between anhedonia and attention deficits in the patient group. After acute-stage treatment, the response prediction group (RP) showed improvement in Microstates C and D compared to the nonresponse prediction group (NRP). For resting-state fMRI data, the temporal variability of DAN was significantly higher in the RP group than in the NRP group. Overall, this study enriches our understanding of the neural mechanisms underlying attention deficits in patients with MDD and provides novel clinical biomarkers.
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Affiliation(s)
- Yujiao Wen
- Department of PsychiatryThe First Hospital of Shanxi Medical UniversityTaiyuanChina
| | - Hong Li
- Department of PsychiatryThe First Hospital of Shanxi Medical UniversityTaiyuanChina
| | - Yangxi Huang
- Department of PsychiatryThe First Hospital of Shanxi Medical UniversityTaiyuanChina
| | - Dan Qiao
- Department of PsychiatryThe First Hospital of Shanxi Medical UniversityTaiyuanChina
| | - Tian Ren
- Department of PsychiatryThe First Hospital of Shanxi Medical UniversityTaiyuanChina
| | - Lei Lei
- Department of PsychiatryThe First Hospital of Shanxi Medical UniversityTaiyuanChina
| | - Gaizhi Li
- Department of PsychiatryThe First Hospital of Shanxi Medical UniversityTaiyuanChina
| | - Chunxia Yang
- Department of PsychiatryThe First Hospital of Shanxi Medical UniversityTaiyuanChina
| | - Yifan Xu
- Department of PsychiatryThe First Hospital of Shanxi Medical UniversityTaiyuanChina
| | - Min Han
- Department of PsychiatryThe First Hospital of Shanxi Medical UniversityTaiyuanChina
| | - Zhifen Liu
- Department of PsychiatryThe First Hospital of Shanxi Medical UniversityTaiyuanChina
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31
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Catrambone V, Valenza G. Microstates of the cortical brain-heart axis. Hum Brain Mapp 2023; 44:5846-5857. [PMID: 37688575 PMCID: PMC10619395 DOI: 10.1002/hbm.26480] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2022] [Revised: 08/04/2023] [Accepted: 08/24/2023] [Indexed: 09/11/2023] Open
Abstract
Electroencephalographic (EEG) microstates are brain states with quasi-stable scalp topography. Whether such states extend to the body level, that is, the peripheral autonomic nerves, remains unknown. We hypothesized that microstates extend at the brain-heart axis level as a functional state of the central autonomic network. Thus, we combined the EEG and heartbeat dynamics series to estimate the directional information transfer originating in the cortex targeting the sympathovagal and parasympathetic activity oscillations and vice versa for the afferent functional direction. Data were from two groups of participants: 36 healthy volunteers who were subjected to cognitive workload induced by mental arithmetic, and 26 participants who underwent physical stress induced by a cold pressure test. All participants were healthy at the time of the study. Based on statistical testing and goodness-of-fit evaluations, we demonstrated the existence of microstates of the functional brain-heart axis, with emphasis on the cerebral cortex, since the microstates are derived from EEG. Such nervous-system microstates are spatio-temporal quasi-stable states that exclusively refer to the efferent brain-to-heart direction. We demonstrated brain-heart microstates that could be associated with specific experimental conditions as well as brain-heart microstates that are non-specific to tasks.
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Affiliation(s)
- Vincenzo Catrambone
- Neurocardiovascular Intelligence Laboratory, Bioengineering and Robotics Research Center E. Piaggio, & Department of Information Engineering, School of EngineeringUniversity of PisaPisaItaly
| | - Gaetano Valenza
- Neurocardiovascular Intelligence Laboratory, Bioengineering and Robotics Research Center E. Piaggio, & Department of Information Engineering, School of EngineeringUniversity of PisaPisaItaly
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32
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Eqlimi E, Bockstael A, Schönwiesner M, Talsma D, Botteldooren D. Time course of EEG complexity reflects attentional engagement during listening to speech in noise. Eur J Neurosci 2023; 58:4043-4069. [PMID: 37814423 DOI: 10.1111/ejn.16159] [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: 03/02/2023] [Revised: 08/31/2023] [Accepted: 09/13/2023] [Indexed: 10/11/2023]
Abstract
Auditory distractions are recognized to considerably challenge the quality of information encoding during speech comprehension. This study explores electroencephalography (EEG) microstate dynamics in ecologically valid, noisy settings, aiming to uncover how these auditory distractions influence the process of information encoding during speech comprehension. We examined three listening scenarios: (1) speech perception with background noise (LA), (2) focused attention on the background noise (BA), and (3) intentional disregard of the background noise (BUA). Our findings showed that microstate complexity and unpredictability increased when attention was directed towards speech compared with tasks without speech (LA > BA & BUA). Notably, the time elapsed between the recurrence of microstates increased significantly in LA compared with both BA and BUA. This suggests that coping with background noise during speech comprehension demands more sustained cognitive effort. Additionally, a two-stage time course for both microstate complexity and alpha-to-theta power ratio was observed. Specifically, in the early epochs, a lower level was observed, which gradually increased and eventually reached a steady level in the later epochs. The findings suggest that the initial stage is primarily driven by sensory processes and information gathering, while the second stage involves higher level cognitive engagement, including mnemonic binding and memory encoding.
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Affiliation(s)
- Ehsan Eqlimi
- WAVES Research Group, Department of Information Technology, Ghent University, Ghent, Belgium
| | - Annelies Bockstael
- WAVES Research Group, Department of Information Technology, Ghent University, Ghent, Belgium
| | | | - Durk Talsma
- Department of Experimental Psychology, Ghent University, Ghent, Belgium
| | - Dick Botteldooren
- WAVES Research Group, Department of Information Technology, Ghent University, Ghent, Belgium
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Jin H, Ranasinghe KG, Prabhu P, Dale C, Gao Y, Kudo K, Vossel K, Raj A, Nagarajan SS, Jiang F. Dynamic functional connectivity MEG features of Alzheimer's disease. Neuroimage 2023; 281:120358. [PMID: 37699440 PMCID: PMC10865998 DOI: 10.1016/j.neuroimage.2023.120358] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2023] [Revised: 08/14/2023] [Accepted: 08/31/2023] [Indexed: 09/14/2023] Open
Abstract
Dynamic resting state functional connectivity (RSFC) characterizes time-varying fluctuations of functional brain network activity. While many studies have investigated static functional connectivity, it has been unclear whether features of dynamic functional connectivity are associated with neurodegenerative diseases. Popular sliding-window and clustering methods for extracting dynamic RSFC have various limitations that prevent extracting reliable features to address this question. Here, we use a novel and robust time-varying dynamic network (TVDN) approach to extract the dynamic RSFC features from high resolution magnetoencephalography (MEG) data of participants with Alzheimer's disease (AD) and matched controls. The TVDN algorithm automatically and adaptively learns the low-dimensional spatiotemporal manifold of dynamic RSFC and detects dynamic state transitions in data. We show that amongst all the functional features we investigated, the dynamic manifold features are the most predictive of AD. These include: the temporal complexity of the brain network, given by the number of state transitions and their dwell times, and the spatial complexity of the brain network, given by the number of eigenmodes. These dynamic features have higher sensitivity and specificity in distinguishing AD from healthy subjects than the existing benchmarks do. Intriguingly, we found that AD patients generally have higher spatial complexity but lower temporal complexity compared with healthy controls. We also show that graph theoretic metrics of dynamic component of TVDN are significantly different in AD versus controls, while static graph metrics are not statistically different. These results indicate that dynamic RSFC features are impacted in neurodegenerative disease like Alzheimer's disease, and may be crucial to understanding the pathophysiological trajectory of these diseases.
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Affiliation(s)
- Huaqing Jin
- Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, CA, USA
| | - Kamalini G Ranasinghe
- Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, CA, USA; Memory and Aging Center, University of California San Francisco, San Francisco, CA, USA
| | - Pooja Prabhu
- Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, CA, USA
| | - Corby Dale
- Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, CA, USA
| | - Yijing Gao
- Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, CA, USA
| | - Kiwamu Kudo
- Medical Imaging Business Center, Ricoh Company, Ltd., Kanazawa, 920-0177, Japan
| | - Keith Vossel
- Department of Neurology, University of California Los Angeles, Los Angeles, CA, USA
| | - Ashish Raj
- Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, CA, USA
| | - Srikantan S Nagarajan
- Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, CA, USA.
| | - Fei Jiang
- Department of Epidemiology and Biostatistics, University of California San Francisco, San Francisco, CA, USA.
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34
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Churchill NW, Roudaia E, Jean Chen J, Gilboa A, Sekuler A, Ji X, Gao F, Lin Z, Masellis M, Goubran M, Rabin JS, Lam B, Cheng I, Fowler R, Heyn C, Black SE, MacIntosh BJ, Graham SJ, Schweizer TA. Persistent post-COVID headache is associated with suppression of scale-free functional brain dynamics in non-hospitalized individuals. Brain Behav 2023; 13:e3212. [PMID: 37872889 PMCID: PMC10636408 DOI: 10.1002/brb3.3212] [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: 04/10/2023] [Revised: 07/14/2023] [Accepted: 07/20/2023] [Indexed: 10/25/2023] Open
Abstract
INTRODUCTION Post-acute coronavirus disease 2019 (COVID-19) syndrome (PACS) is a growing concern, with headache being a particularly debilitating symptom with high prevalence. The long-term effects of COVID-19 and post-COVID headache on brain function remain poorly understood, particularly among non-hospitalized individuals. This study focused on the power-law scaling behavior of functional brain dynamics, indexed by the Hurst exponent (H). This measure is suppressed during physiological and psychological distress and was thus hypothesized to be reduced in individuals with post-COVID syndrome, with greatest reductions among those with persistent headache. METHODS Resting-state blood oxygenation level-dependent (BOLD) functional magnetic resonance imaging data were collected for 57 individuals who had COVID-19 (32 with no headache, 14 with ongoing headache, 11 recovered) and 17 controls who had cold and flu-like symptoms but tested negative for COVID-19. Individuals were assessed an average of 4-5 months after COVID testing, in a cross-sectional, observational study design. RESULTS No significant differences in H values were found between non-headache COVID-19 and control groups., while those with ongoing headache had significantly reduced H values, and those who had recovered from headache had elevated H values, relative to non-headache groups. Effects were greatest in temporal, sensorimotor, and insular brain regions. Reduced H in these regions was also associated with decreased BOLD activity and local functional connectivity. CONCLUSIONS These findings provide new insights into the neurophysiological mechanisms that underlie persistent post-COVID headache, with reduced BOLD scaling as a potential biomarker that is specific to this debilitating condition.
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Affiliation(s)
- Nathan W. Churchill
- Neuroscience Research Program, St. Michael's HospitalTorontoOntarioCanada
- Keenan Research Centre for Biomedical Science, St. Michael's HospitalTorontoOntarioCanada
- Physics DepartmentToronto Metropolitan UniversityTorontoOntarioCanada
| | - Eugenie Roudaia
- Rotman Research InstituteBaycrest Academy for Research and EducationTorontoOntarioCanada
| | - J. Jean Chen
- Rotman Research InstituteBaycrest Academy for Research and EducationTorontoOntarioCanada
- Department of Medical BiophysicsUniversity of TorontoTorontoOntarioCanada
- Institute of Biomedical EngineeringUniversity of TorontoTorontoOntarioCanada
| | - Asaf Gilboa
- Rotman Research InstituteBaycrest Academy for Research and EducationTorontoOntarioCanada
- Department of PsychologyUniversity of TorontoTorontoOntarioCanada
| | - Allison Sekuler
- Rotman Research InstituteBaycrest Academy for Research and EducationTorontoOntarioCanada
- Department of PsychologyUniversity of TorontoTorontoOntarioCanada
- Department of Psychology, Neuroscience & BehaviourMcMaster UniversityHamiltonOntarioCanada
| | - Xiang Ji
- LC Campbell Cognitive Neurology Research Group, Sunnybrook Health Sciences CentreTorontoOntarioCanada
| | - Fuqiang Gao
- Hurvitz Brain Sciences ProgramSunnybrook Research InstituteTorontoOntarioCanada
| | - Zhongmin Lin
- Department of Medical BiophysicsUniversity of TorontoTorontoOntarioCanada
- Physical Sciences PlatformSunnybrook Research InstituteTorontoOntarioCanada
| | - Mario Masellis
- Rotman Research InstituteBaycrest Academy for Research and EducationTorontoOntarioCanada
- Hurvitz Brain Sciences ProgramSunnybrook Research InstituteTorontoOntarioCanada
- Division of Neurology, Department of Medicine, Sunnybrook Health Sciences CentreUniversity of TorontoTorontoOntarioCanada
| | - Maged Goubran
- Department of Medical BiophysicsUniversity of TorontoTorontoOntarioCanada
- Hurvitz Brain Sciences ProgramSunnybrook Research InstituteTorontoOntarioCanada
- Physical Sciences PlatformSunnybrook Research InstituteTorontoOntarioCanada
- Harquail Centre for NeuromodulationSunnybrook Research InstituteTorontoOntarioCanada
| | - Jennifer S. Rabin
- Division of Neurology, Department of Medicine, Sunnybrook Health Sciences CentreUniversity of TorontoTorontoOntarioCanada
- Harquail Centre for NeuromodulationSunnybrook Research InstituteTorontoOntarioCanada
- Rehabilitation Sciences InstituteUniversity of TorontoTorontoOntarioCanada
| | - Benjamin Lam
- Rotman Research InstituteBaycrest Academy for Research and EducationTorontoOntarioCanada
- Hurvitz Brain Sciences ProgramSunnybrook Research InstituteTorontoOntarioCanada
- Division of Neurology, Department of Medicine, Sunnybrook Health Sciences CentreUniversity of TorontoTorontoOntarioCanada
| | - Ivy Cheng
- Evaluative Clinical SciencesSunnybrook Research InstituteTorontoOntarioCanada
- Integrated Community ProgramSunnybrook Research InstituteTorontoOntarioCanada
- Department of MedicineUniversity of TorontoTorontoOntarioCanada
| | - Robert Fowler
- Department of MedicineUniversity of TorontoTorontoOntarioCanada
- Emergency & Critical Care Research ProgramSunnybrook Research InstituteTorontoOntarioCanada
| | - Chris Heyn
- Hurvitz Brain Sciences ProgramSunnybrook Research InstituteTorontoOntarioCanada
- Department of Medical ImagingUniversity of TorontoTorontoOntarioCanada
| | - Sandra E. Black
- Rotman Research InstituteBaycrest Academy for Research and EducationTorontoOntarioCanada
- Hurvitz Brain Sciences ProgramSunnybrook Research InstituteTorontoOntarioCanada
- Division of Neurology, Department of Medicine, Sunnybrook Health Sciences CentreUniversity of TorontoTorontoOntarioCanada
| | - Bradley J. MacIntosh
- Department of Medical BiophysicsUniversity of TorontoTorontoOntarioCanada
- Hurvitz Brain Sciences ProgramSunnybrook Research InstituteTorontoOntarioCanada
- Physical Sciences PlatformSunnybrook Research InstituteTorontoOntarioCanada
- Computational Radiology & Artificial Intelligence Unit, Division of Radiology and Nuclear MedicineOslo University HospitalOsloNorway
| | - Simon J. Graham
- Department of Medical BiophysicsUniversity of TorontoTorontoOntarioCanada
- Hurvitz Brain Sciences ProgramSunnybrook Research InstituteTorontoOntarioCanada
- Physical Sciences PlatformSunnybrook Research InstituteTorontoOntarioCanada
| | - Tom A. Schweizer
- Neuroscience Research Program, St. Michael's HospitalTorontoOntarioCanada
- Keenan Research Centre for Biomedical Science, St. Michael's HospitalTorontoOntarioCanada
- Faculty of Medicine (Neurosurgery)University of TorontoTorontoOntarioCanada
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Bejia I, Labidi J, Warniez A, Bayot M, Bourriez JL, Derambure P, Lebouvier T, Pasquier F, Delval A, Betrouni N. Multi-approach comparative study of EEG patterns associated with the most common forms of dementia. Neurobiol Aging 2023; 130:30-39. [PMID: 37433259 DOI: 10.1016/j.neurobiolaging.2023.06.008] [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: 12/13/2022] [Revised: 05/30/2023] [Accepted: 06/10/2023] [Indexed: 07/13/2023]
Abstract
Electroencephalography's (EEG) sensitivity in discriminating dementia syndromes remains unclear. This study aimed to investigate EEG markers in patients with major cognitive disorders. The studied population included 4 groups of patients: Alzheimer's disease with associated vascular lesions, Alzheimer's disease without vascular lesions (AD-V), Lewy body disease and vascular dementia (VaD); and completed by a control group composed by cognitively unimpaired patients. EEGs were analysed quantitatively using spectral analysis, functional connectivity and micro-states. By comparison to the controls, expected slowing and alterations of functional connectivity were detected in patients with dementia. Among these patients, an overall increase in power in the alpha band was observed in the VaD group, mainly when compared to the 2 AD groups, while the Alzheimer's disease without vascular lesions group exhibited increased power in the beta-2 band and higher functional connectivity in the same frequency band. Micro-state analyses revealed differences in temporal dynamics for the VaD group. A number of EEG modifications reported as markers of some syndromes were found, but others were not reproduced.
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Affiliation(s)
- Ines Bejia
- Univ. Lille, INSERM, U1172 - LilNCog - Lille Neuroscience & Cognition, F-59000 Lille, France
| | - Jordan Labidi
- CHU Lille, Clinical Neurophysiology Department, F-59000 Lille, France
| | - Aude Warniez
- Univ. Lille, INSERM, U1172 - LilNCog - Lille Neuroscience & Cognition, F-59000 Lille, France
| | - Madli Bayot
- Univ. Lille, INSERM, U1172 - LilNCog - Lille Neuroscience & Cognition, F-59000 Lille, France; CHU Lille, Clinical Neurophysiology Department, F-59000 Lille, France
| | | | - Philippe Derambure
- Univ. Lille, INSERM, U1172 - LilNCog - Lille Neuroscience & Cognition, F-59000 Lille, France; CHU Lille, Clinical Neurophysiology Department, F-59000 Lille, France
| | - Thibaut Lebouvier
- Univ. Lille, INSERM, U1172 - LilNCog - Lille Neuroscience & Cognition, F-59000 Lille, France; CHU Lille, Centre Mémoire de Ressources et de Recherche (CMRR), F-59000 Lille, France
| | - Florence Pasquier
- Univ. Lille, INSERM, U1172 - LilNCog - Lille Neuroscience & Cognition, F-59000 Lille, France; CHU Lille, Centre Mémoire de Ressources et de Recherche (CMRR), F-59000 Lille, France
| | - Arnaud Delval
- Univ. Lille, INSERM, U1172 - LilNCog - Lille Neuroscience & Cognition, F-59000 Lille, France; CHU Lille, Clinical Neurophysiology Department, F-59000 Lille, France
| | - Nacim Betrouni
- Univ. Lille, INSERM, U1172 - LilNCog - Lille Neuroscience & Cognition, F-59000 Lille, France.
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36
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Clawson W, Waked B, Madec T, Ghestem A, Quilichini PP, Battaglia D, Bernard C. Perturbed Information Processing Complexity in Experimental Epilepsy. J Neurosci 2023; 43:6573-6587. [PMID: 37550052 PMCID: PMC10513075 DOI: 10.1523/jneurosci.0383-23.2023] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2023] [Revised: 07/07/2023] [Accepted: 07/20/2023] [Indexed: 08/09/2023] Open
Abstract
Comorbidities, such as cognitive deficits, which often accompany epilepsies, constitute a basal state, while seizures are rare and transient events. This suggests that neural dynamics, in particular those supporting cognitive function, are altered in a permanent manner in epilepsy. Here, we test the hypothesis that primitive processes of information processing at the core of cognitive function (i.e., storage and sharing of information) are altered in the hippocampus and the entorhinal cortex in experimental epilepsy in adult, male Wistar rats. We find that information storage and sharing are organized into substates across the stereotypic states of slow and theta oscillations in both epilepsy and control conditions. However, their internal composition and organization through time are disrupted in epilepsy, partially losing brain state selectivity compared with controls, and shifting toward a regimen of disorder. We propose that the alteration of information processing at this algorithmic level of computation, the theoretical intermediate level between structure and function, may be a mechanism behind the emergent and widespread comorbidities associated with epilepsy, and perhaps other disorders.SIGNIFICANCE STATEMENT Comorbidities, such as cognitive deficits, which often accompany epilepsies, constitute a basal state, while seizures are rare and transient events. This suggests that neural dynamics, in particular those supporting cognitive function, are altered in a permanent manner in epilepsy. Here, we show that basic processes of information processing at the core of cognitive function (i.e., storage and sharing of information) are altered in the hippocampus and the entorhinal cortex (two regions involved in memory processes) in experimental epilepsy. Such disruption of information processing at the algorithmic level itself could underlie the general performance impairments in epilepsy.
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Affiliation(s)
- Wesley Clawson
- Aix Marseille Université, INSERM, INS, Institut de Neurosciences des Systèmes, Marseille, France
- Allen Discovery Center, Tufts University, Medford, Massachusetts
| | - Benjamin Waked
- Aix Marseille Université, INSERM, INS, Institut de Neurosciences des Systèmes, Marseille, France
| | - Tanguy Madec
- Aix Marseille Université, INSERM, INS, Institut de Neurosciences des Systèmes, Marseille, France
| | - Antoine Ghestem
- Aix Marseille Université, INSERM, INS, Institut de Neurosciences des Systèmes, Marseille, France
| | - Pascale P Quilichini
- Aix Marseille Université, INSERM, INS, Institut de Neurosciences des Systèmes, Marseille, France
| | - Demian Battaglia
- Aix Marseille Université, INSERM, INS, Institut de Neurosciences des Systèmes, Marseille, France
- University of Strasbourg Institute for Advanced Studies, Strasbourg, France
| | - Christophe Bernard
- Aix Marseille Université, INSERM, INS, Institut de Neurosciences des Systèmes, Marseille, France
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37
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Zhou DD, Peng XY, Zhao L, Ma LL, Hu JH, Jiang ZH, He XQ, Wang W, Chen R, Kuang L. Neurophysiological biomarkers for depression classification: Utilizing microstate k-mers and a bag-of-words model. J Psychiatr Res 2023; 165:197-204. [PMID: 37517240 DOI: 10.1016/j.jpsychires.2023.07.021] [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: 04/03/2023] [Revised: 06/30/2023] [Accepted: 07/16/2023] [Indexed: 08/01/2023]
Abstract
Microstates are analogous to characters in a language, and short fragments consisting of several microstates (k-mers) are analogous to words. We aimed to investigate whether microstate k-mers could be used as neurophysiological biomarkers to differentiate between depressed patients and normal controls. We utilized a bag-of-words model to process microstate sequences, using k-mers with a k range of 1-10 as terms, and the term frequency (TF) with or without inverse-document-frequency (IDF) as features. We performed nested cross-validation on Dataset 1 (27 patients and 26 controls) and Dataset 2 (34 patients and 30 controls) separately and then trained on one dataset and tested on the other. The best area under the curve (AUC) of 81.5% was achieved for the model with L1 regularization using the TF of 4-mers as features in Dataset 1, and the best AUC of 88.9% was achieved for the model with L1 regularization using the TF of 9-mers as features in Dataset 2. When Dataset 1 was used as the training set, the best AUC of predicting Dataset 2 was 74.1% for the model with L2 regularization using the TF-IDF of 9-mers as features, while the best AUC of predicting Dataset 1 was 70.2% for the model with L1 regularization using the TF of 8-mers as features. Our study provided novel insights into the potential of microstate k-mers as neurophysiological biomarkers for individual-level classification of depression. These may facilitate further exploration of microstate sequences using natural language processing techniques.
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Affiliation(s)
- Dong-Dong Zhou
- Mental Health Center, University-Town Hospital of Chongqing Medical University, Chongqing, China
| | - Xin-Yu Peng
- Department of Psychiatry, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Lin Zhao
- Department of Psychiatry, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Ling-Li Ma
- Department of Psychiatry, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Jin-Hui Hu
- Mental Health Center, University-Town Hospital of Chongqing Medical University, Chongqing, China
| | - Zheng-Hao Jiang
- Mental Health Center, University-Town Hospital of Chongqing Medical University, Chongqing, China
| | - Xiao-Qing He
- Mental Health Center, University-Town Hospital of Chongqing Medical University, Chongqing, China
| | - Wo Wang
- Mental Health Center, University-Town Hospital of Chongqing Medical University, Chongqing, China
| | - Ran Chen
- Mental Health Center, University-Town Hospital of Chongqing Medical University, Chongqing, China.
| | - Li Kuang
- Mental Health Center, University-Town Hospital of Chongqing Medical University, Chongqing, China; Department of Psychiatry, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China.
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Naik S, Adibpour P, Dubois J, Dehaene-Lambertz G, Battaglia D. Event-related variability is modulated by task and development. Neuroimage 2023; 276:120208. [PMID: 37268095 DOI: 10.1016/j.neuroimage.2023.120208] [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: 03/02/2023] [Revised: 05/11/2023] [Accepted: 05/30/2023] [Indexed: 06/04/2023] Open
Abstract
In carefully designed experimental paradigms, cognitive scientists interpret the mean event-related potentials (ERP) in terms of cognitive operations. However, the huge signal variability from one trial to the next, questions the representability of such mean events. We explored here whether this variability is an unwanted noise, or an informative part of the neural response. We took advantage of the rapid changes in the visual system during human infancy and analyzed the variability of visual responses to central and lateralized faces in 2-to 6-month-old infants compared to adults using high-density electroencephalography (EEG). We observed that neural trajectories of individual trials always remain very far from ERP components, only moderately bending their direction with a substantial temporal jitter across trials. However, single trial trajectories displayed characteristic patterns of acceleration and deceleration when approaching ERP components, as if they were under the active influence of steering forces causing transient attraction and stabilization. These dynamic events could only partly be accounted for by induced microstate transitions or phase reset phenomena. Importantly, these structured modulations of response variability, both between and within trials, had a rich sequential organization, which in infants, was modulated by the task difficulty and age. Our approaches to characterize Event Related Variability (ERV) expand on classic ERP analyses and provide the first evidence for the functional role of ongoing neural variability in human infants.
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Affiliation(s)
- Shruti Naik
- Cognitive Neuroimaging Unit U992, NeuroSpin Center, F-91190 Gif/Yvette, France
| | - Parvaneh Adibpour
- Cognitive Neuroimaging Unit U992, NeuroSpin Center, F-91190 Gif/Yvette, France
| | - Jessica Dubois
- Cognitive Neuroimaging Unit U992, NeuroSpin Center, F-91190 Gif/Yvette, France; Université de Paris, NeuroDiderot, Inserm, F-75019 Paris, France
| | | | - Demian Battaglia
- Institute for System Neuroscience U1106, Aix-Marseille Université, F-13005 Marseille, France; University of Strasbourg Institute for Advanced Studies (USIAS), F-67000 Strasbourg, France.
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Catrambone V, Valenza G. Towards the definition of Microstates of the Cortical Brain-Heart Axis. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2023; 2023:1-4. [PMID: 38082600 DOI: 10.1109/embc40787.2023.10340338] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/18/2023]
Abstract
Brain microstates are defined as states with quasi-stable scalp activity topography and have been widely studied in literature. Whether those states are brain-specific or extend to the body level is unknown yet. We investigate the extension of cortical microstates to the peripheral autonomic nerve, specifically at the brain-heart axis level as a functional state of the central autonomic network. To achieve this, we combined Electroencephalographic (EEG) and heart rate variability (HRV) series from 36 healthy volunteers undergoing a cognitive workload elicitation after a resting state. Our results showed the existence of microstates at the functional brain-heart axis with spatio-temporal and quasi-stable states that exclusively pertained to the efferent direction from the brain to the heart. Some of the identified microstates are specific for neural or cardiovascular frequency bands, while others topographies are recurrent over the EEG and HRV spectra. Furthermore, some of the identified brain-heart microstates were associated with specific experimental conditions, while others were nonspecific to tasks. Our findings support the hypothesis that EEG microstates extend to the brain-heart axis level and may be exploited in future neuroscience and clinical research.
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Artoni F, Maillard J, Britz J, Brunet D, Lysakowski C, Tramèr MR, Michel CM. Microsynt: exploring the syntax of EEG microstates. Neuroimage 2023:120196. [PMID: 37286153 DOI: 10.1016/j.neuroimage.2023.120196] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2023] [Revised: 05/16/2023] [Accepted: 05/25/2023] [Indexed: 06/09/2023] Open
Abstract
Microstates represent electroencephalographic (EEG) activity as a sequence of switching, transient, metastable states. Growing evidence suggests the useful information on brain states is to be found in the higher-order temporal structure of these sequences. Instead of focusing on transition probabilities, here we propose "Microsynt", a method designed to highlight higher-order interactions that form a preliminary step towards understanding the syntax of microstate sequences of any length and complexity. Microsynt extracts an optimal vocabulary of "words" based on the length and complexity of the full sequence of microstates. Words are then sorted into classes of entropy and their representativeness within each class is statistically compared with surrogate and theoretical vocabularies. We applied the method on EEG data previously collected from healthy subjects undergoing propofol anaesthesia, and compared their "fully awake" (BASE) and "fully unconscious" (DEEP) conditions. Results show that microstate sequences, even at rest, are not random but tend to behave in a more predictable way, favoring simpler sub-sequences, or "words". Contrary to high-entropy words, lowest-entropy binary microstate loops are prominent and favored on average 10 times more than what is theoretically expected. Progressing from BASE to DEEP, the representation of low-entropy words increases while that of high-entropy words decreases. During the awake state, sequences of microstates tend to be attracted towards "A - B - C" microstate hubs, and most prominently A - B binary loops. Conversely, with full unconsciousness, sequences of microstates are attracted towards "C - D - E" hubs, and most prominently C - E binary loops, confirming the putative relation of microstates A and B to externally-oriented cognitive processes and microstate C and E to internally-generated mental activity. Microsynt can form a syntactic signature of microstate sequences that can be used to reliably differentiate two or more conditions.
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Affiliation(s)
- Fiorenzo Artoni
- Functional Brain Mapping Laboratory, Department of Basic Neurosciences, University of Geneva, Campus Biotech, Switzerland; CIBM Center for Biomedical Imaging, Switzerland.
| | - Julien Maillard
- Division of Anesthesiology, Department of Anesthesiology, Clinical Pharmacology, Intensive Care and Emergency Medicine, Geneva University Hospitals, Geneva, Switzerland
| | - Juliane Britz
- Department of Psychology, University of Fribourg, Fribourg, Switzerland; CIBM Center for Biomedical Imaging, Switzerland
| | - Denis Brunet
- Functional Brain Mapping Laboratory, Department of Basic Neurosciences, University of Geneva, Campus Biotech, Switzerland; CIBM Center for Biomedical Imaging, Switzerland
| | - Christopher Lysakowski
- Division of Anesthesiology, Department of Anesthesiology, Clinical Pharmacology, Intensive Care and Emergency Medicine, Geneva University Hospitals, Geneva, Switzerland
| | - Martin R Tramèr
- Division of Anesthesiology, Department of Anesthesiology, Clinical Pharmacology, Intensive Care and Emergency Medicine, Geneva University Hospitals, Geneva, Switzerland
| | - Christoph M Michel
- Functional Brain Mapping Laboratory, Department of Basic Neurosciences, University of Geneva, Campus Biotech, Switzerland; CIBM Center for Biomedical Imaging, Switzerland
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Lassi M, Fabbiani C, Mazzeo S, Burali R, Vergani AA, Giacomucci G, Moschini V, Morinelli C, Emiliani F, Scarpino M, Bagnoli S, Ingannato A, Nacmias B, Padiglioni S, Micera S, Sorbi S, Grippo A, Bessi V, Mazzoni A. Degradation of EEG microstates patterns in subjective cognitive decline and mild cognitive impairment: Early biomarkers along the Alzheimer's Disease continuum? Neuroimage Clin 2023; 38:103407. [PMID: 37094437 PMCID: PMC10149415 DOI: 10.1016/j.nicl.2023.103407] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2022] [Revised: 03/29/2023] [Accepted: 04/14/2023] [Indexed: 04/26/2023]
Abstract
Alzheimer's disease (AD) pathological changes may begin up to decades earlier than the appearance of the first symptoms of cognitive decline. Subjective cognitive decline (SCD) could be the first pre-clinical sign of possible AD, which might be followed by mild cognitive impairment (MCI), the initial stage of clinical cognitive decline. However, the neural correlates of these prodromic stages are not completely clear yet. Recent studies suggest that EEG analysis tools characterizing the cortical activity as a whole, such as microstates and cortical regions connectivity, might support a characterization of SCD and MCI conditions. Here we test this approach by performing a broad set of analyses to identify the prominent EEG markers differentiating SCD (n = 57), MCI (n = 46) and healthy control subjects (HC, n = 19). We found that the salient differences were in the temporal structure of the microstates patterns, with MCI being associated with less complex sequences due to the altered transition probability, frequency and duration of canonic microstate C. Spectral content of EEG, network connectivity, and spatial arrangement of microstates were instead largely similar in the three groups. Interestingly, comparing properties of EEG microstates in different cerebrospinal fluid (CSF) biomarkers profiles, we found that canonic microstate C displayed significant differences in topography in AD-like profile. These results show that the progression of dementia might be associated with a degradation of the cortical organization captured by microstates analysis, and that this leads to altered transitions between cortical states. Overall, our approach paves the way for the use of non-invasive EEG recordings in the identification of possible biomarkers of progression to AD from its prodromal states.
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Affiliation(s)
- Michael Lassi
- The BioRobotics Institute and Department of Excellence in Robotics and AI, Sant'Anna School of Advanced Studies, viale Rinaldo Piaggio 34, 56025 Pisa, Italy
| | - Carlo Fabbiani
- IRCCS Fondazione Don Carlo Gnocchi, via di Scandicci, 269, 50143 Florence, Italy
| | - Salvatore Mazzeo
- IRCCS Fondazione Don Carlo Gnocchi, via di Scandicci, 269, 50143 Florence, Italy; Department of Neuroscience, Psychology, Drug Research and Child Health, University of Florence, Careggi University Hospital, viale Gaetano Pieraccini, 6, 50139 Florence, Italy
| | - Rachele Burali
- IRCCS Fondazione Don Carlo Gnocchi, via di Scandicci, 269, 50143 Florence, Italy
| | - Alberto Arturo Vergani
- The BioRobotics Institute and Department of Excellence in Robotics and AI, Sant'Anna School of Advanced Studies, viale Rinaldo Piaggio 34, 56025 Pisa, Italy
| | - Giulia Giacomucci
- Department of Neuroscience, Psychology, Drug Research and Child Health, University of Florence, Careggi University Hospital, viale Gaetano Pieraccini, 6, 50139 Florence, Italy
| | - Valentina Moschini
- Department of Neuroscience, Psychology, Drug Research and Child Health, University of Florence, Careggi University Hospital, viale Gaetano Pieraccini, 6, 50139 Florence, Italy
| | - Carmen Morinelli
- Dipartimento Neuromuscolo-scheletrico e degli organi di senso, Careggi University Hospital, 50134 Florence, Italy
| | - Filippo Emiliani
- Department of Neuroscience, Psychology, Drug Research and Child Health, University of Florence, Careggi University Hospital, viale Gaetano Pieraccini, 6, 50139 Florence, Italy
| | - Maenia Scarpino
- IRCCS Fondazione Don Carlo Gnocchi, via di Scandicci, 269, 50143 Florence, Italy
| | - Silvia Bagnoli
- Department of Neuroscience, Psychology, Drug Research and Child Health, University of Florence, Careggi University Hospital, viale Gaetano Pieraccini, 6, 50139 Florence, Italy
| | - Assunta Ingannato
- Department of Neuroscience, Psychology, Drug Research and Child Health, University of Florence, Careggi University Hospital, viale Gaetano Pieraccini, 6, 50139 Florence, Italy
| | - Benedetta Nacmias
- IRCCS Fondazione Don Carlo Gnocchi, via di Scandicci, 269, 50143 Florence, Italy; Department of Neuroscience, Psychology, Drug Research and Child Health, University of Florence, Careggi University Hospital, viale Gaetano Pieraccini, 6, 50139 Florence, Italy
| | - Sonia Padiglioni
- Regional Referral Centre for Relational Criticalities - Tuscany Region, 50139 Florence, Italy
| | - Silvestro Micera
- The BioRobotics Institute and Department of Excellence in Robotics and AI, Sant'Anna School of Advanced Studies, viale Rinaldo Piaggio 34, 56025 Pisa, Italy; Bertarelli Foundation Chair in Translational Neuroengineering, Centre for Neuroprosthetics and Institute of Bioengineering, School of Engineering, École Polytechnique Fédérale de Lausanne (EPFL), 1015 Lausanne, Switzerland
| | - Sandro Sorbi
- IRCCS Fondazione Don Carlo Gnocchi, via di Scandicci, 269, 50143 Florence, Italy; Department of Neuroscience, Psychology, Drug Research and Child Health, University of Florence, Careggi University Hospital, viale Gaetano Pieraccini, 6, 50139 Florence, Italy
| | - Antonello Grippo
- IRCCS Fondazione Don Carlo Gnocchi, via di Scandicci, 269, 50143 Florence, Italy
| | - Valentina Bessi
- Department of Neuroscience, Psychology, Drug Research and Child Health, University of Florence, Careggi University Hospital, viale Gaetano Pieraccini, 6, 50139 Florence, Italy
| | - Alberto Mazzoni
- The BioRobotics Institute and Department of Excellence in Robotics and AI, Sant'Anna School of Advanced Studies, viale Rinaldo Piaggio 34, 56025 Pisa, Italy.
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Zhang C, Wang X, Ding Z, Zhou H, Liu P, Xue X, Wang L, Jiang Y, Chen J, Shen W, Yang S, Wang F. Study on tinnitus-related electroencephalogram microstates in patients with vestibular schwannomas. Front Neurosci 2023; 17:1159019. [PMID: 37090804 PMCID: PMC10118047 DOI: 10.3389/fnins.2023.1159019] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2023] [Accepted: 03/16/2023] [Indexed: 04/08/2023] Open
Abstract
Tinnitus is closely associated with cognition functioning. In order to clarify the central reorganization of tinnitus in patients with vestibular schwannoma (VS), this study explored the aberrant dynamics of electroencephalogram (EEG) microstates and their correlations with tinnitus features in VS patients. Clinical and EEG data were collected from 98 VS patients, including 76 with tinnitus and 22 without tinnitus. Microstates were clustered into four categories. Our EEG microstate analysis revealed that VS patients with tinnitus exhibited an increased frequency of microstate C compared to those without tinnitus. Furthermore, correlation analysis demonstrated that the Tinnitus Handicap Inventory (THI) score was negatively associated with the duration of microstate A and positively associated with the frequency of microstate C. These findings suggest that the time series and syntax characteristics of EEG microstates differ significantly between VS patients with and without tinnitus, potentially reflecting abnormal allocation of neural resources and transition of functional brain activity. Our results provide a foundation for developing diverse treatments for tinnitus in VS patients.
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Affiliation(s)
- Chi Zhang
- The First Medical Center, Chinese PLA General Hospital, Beijing, China
- Zhan Tan Temple Outpatient Department, Central Medical Branch of PLA General Hospital, Beijing, China
- College of Otolaryngology Head and Neck Surgery, Chinese PLA General Hospital, Beijing, China
| | - Xiaoguang Wang
- Zhan Tan Temple Outpatient Department, Central Medical Branch of PLA General Hospital, Beijing, China
| | - Zhiwei Ding
- The First Medical Center, Chinese PLA General Hospital, Beijing, China
- College of Otolaryngology Head and Neck Surgery, Chinese PLA General Hospital, Beijing, China
- Medical School of Chinese PLA, Beijing, China
| | - Hanwen Zhou
- The First Medical Center, Chinese PLA General Hospital, Beijing, China
- College of Otolaryngology Head and Neck Surgery, Chinese PLA General Hospital, Beijing, China
- Medical School of Chinese PLA, Beijing, China
| | - Peng Liu
- The First Medical Center, Chinese PLA General Hospital, Beijing, China
- College of Otolaryngology Head and Neck Surgery, Chinese PLA General Hospital, Beijing, China
- Medical School of Chinese PLA, Beijing, China
| | - Xinmiao Xue
- The First Medical Center, Chinese PLA General Hospital, Beijing, China
- College of Otolaryngology Head and Neck Surgery, Chinese PLA General Hospital, Beijing, China
- Medical School of Chinese PLA, Beijing, China
| | - Li Wang
- The First Medical Center, Chinese PLA General Hospital, Beijing, China
- College of Otolaryngology Head and Neck Surgery, Chinese PLA General Hospital, Beijing, China
- Medical School of Chinese PLA, Beijing, China
| | - Yuke Jiang
- The First Medical Center, Chinese PLA General Hospital, Beijing, China
- College of Otolaryngology Head and Neck Surgery, Chinese PLA General Hospital, Beijing, China
- Medical School of Chinese PLA, Beijing, China
| | - Jiyue Chen
- The First Medical Center, Chinese PLA General Hospital, Beijing, China
- College of Otolaryngology Head and Neck Surgery, Chinese PLA General Hospital, Beijing, China
- Medical School of Chinese PLA, Beijing, China
| | - Weidong Shen
- The First Medical Center, Chinese PLA General Hospital, Beijing, China
- College of Otolaryngology Head and Neck Surgery, Chinese PLA General Hospital, Beijing, China
- The Sixth Medical Center, Chinese PLA General Hospital, Beijing, China
| | - Shiming Yang
- The First Medical Center, Chinese PLA General Hospital, Beijing, China
- College of Otolaryngology Head and Neck Surgery, Chinese PLA General Hospital, Beijing, China
- The Sixth Medical Center, Chinese PLA General Hospital, Beijing, China
| | - Fangyuan Wang
- The First Medical Center, Chinese PLA General Hospital, Beijing, China
- College of Otolaryngology Head and Neck Surgery, Chinese PLA General Hospital, Beijing, China
- The Sixth Medical Center, Chinese PLA General Hospital, Beijing, China
- *Correspondence: Fangyuan Wang,
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Gschwandtner U, Bogaarts G, Roth V, Fuhr P. Prediction of cognitive decline in Parkinson's disease (PD) patients with electroencephalography (EEG) connectivity characterized by time-between-phase-crossing (TBPC). Sci Rep 2023; 13:5093. [PMID: 36991083 PMCID: PMC10060251 DOI: 10.1038/s41598-023-32345-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2022] [Accepted: 03/26/2023] [Indexed: 03/31/2023] Open
Abstract
The aim of the study is to identify the dynamic change pattern of EEG to predict cognitive decline in patients with Parkinson's disease. Here we demonstrate that the quantification of synchrony-pattern changes across the scalp, measured using electroencephalography (EEG), offers an alternative approach of observing an individual's functional brain organization. This method, called "Time-Between-Phase-Crossing" (TBPC), is based on the same phenomenon as the phase-lag-index (PLI); it also considers intermittent changes in the signals of phase differences between pairs of EEG signals, but additionally analyzes dynamic connectivity changes. We used data from 75 non-demented Parkinson's disease patients and 72 healthy controls, who were followed over a period of 3 years. Statistics were calculated using connectome-based modeling (CPM) and receiver operating characteristic (ROC). We show that TBPC profiles, via the use of intermittent changes in signals of analytic phase differences of pairs of EEG signals, can be used to predict cognitive decline in Parkinson's disease (p < 0.05).
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Affiliation(s)
- Ute Gschwandtner
- Departments of Neurology and of Clinical Research, University Hospital of Basel, Basel, Switzerland.
| | - Guy Bogaarts
- Department of Mathematics and Computer Science, University of Basel, Basel, Switzerland
- Departments of Neurology and of Clinical Research, University Hospital of Basel, Basel, Switzerland
| | - Volker Roth
- Department of Mathematics and Computer Science, University of Basel, Basel, Switzerland
| | - Peter Fuhr
- Departments of Neurology and of Clinical Research, University Hospital of Basel, Basel, Switzerland
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Thirioux B, Langbour N, Bokam P, Renaudin L, Wassouf I, Harika-Germaneau G, Jaafari N. Microstates imbalance is associated with a functional dysregulation of the resting-state networks in obsessive-compulsive disorder: a high-density electrical neuroimaging study using the TESS method. Cereb Cortex 2023; 33:2593-2611. [PMID: 35739579 DOI: 10.1093/cercor/bhac229] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2022] [Revised: 05/13/2022] [Accepted: 05/14/2022] [Indexed: 11/14/2022] Open
Abstract
The dysfunctional patterns of microstates dynamics in obsessive-compulsive disorder (OCD) remain uncertain. Using high-density electrical neuroimaging (EEG) at rest, we explored microstates deterioration in OCD and whether abnormal microstates patterns are associated with a dysregulation of the resting-state networks interplay. We used EEG microstates analyses, TESS method for sources reconstruction, and General Linear Models to test for the effect of disease severity on neural responses. OCD patients exhibited an increased contribution and decreased duration of microstates C and D, respectively. Activity was decreased in the Salience Network (SN), associated with microstate C, but increased in the Default Mode Network (DMN) and Executive Control Network (ECN), respectively, associated with microstates E and D. The hyperactivity of the right angular gyrus in the ECN correlated with the symptoms severity. The imbalance between microstates C and D invalidates the hypothesis that this electrophysiological pattern is specific to psychosis. Demonstrating that the SN-ECN dysregulation manifests as abnormalities in microstates C and D, we confirm that the SN deterioration in OCD is accompanied by a failure of the DMN to deactivate and aberrant compensatory activation mechanisms in the ECN. These abnormalities explain typical OCD clinical features but also detachment from reality, shared with psychosis.
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Affiliation(s)
- Bérangère Thirioux
- Unité de Recherche Clinique Pierre Deniker, Centre Hospitalier Henri Laborit, 86021 Poitiers, France
- CNRS 7295, Centre de Recherches sur la Cognition et l'Apprentissage, Université de Poitiers, 86021 Poitiers, France
| | - Nicolas Langbour
- Unité de Recherche Clinique Pierre Deniker, Centre Hospitalier Henri Laborit, 86021 Poitiers, France
- CNRS 7295, Centre de Recherches sur la Cognition et l'Apprentissage, Université de Poitiers, 86021 Poitiers, France
| | - Prasanth Bokam
- Unité de Recherche Clinique Pierre Deniker, Centre Hospitalier Henri Laborit, 86021 Poitiers, France
| | - Léa Renaudin
- Unité de Recherche Clinique Pierre Deniker, Centre Hospitalier Henri Laborit, 86021 Poitiers, France
| | - Issa Wassouf
- Unité de Recherche Clinique Pierre Deniker, Centre Hospitalier Henri Laborit, 86021 Poitiers, France
- CNRS 7295, Centre de Recherches sur la Cognition et l'Apprentissage, Université de Poitiers, 86021 Poitiers, France
| | - Ghina Harika-Germaneau
- Unité de Recherche Clinique Pierre Deniker, Centre Hospitalier Henri Laborit, 86021 Poitiers, France
- CNRS 7295, Centre de Recherches sur la Cognition et l'Apprentissage, Université de Poitiers, 86021 Poitiers, France
- Faculté de Médecine et de Pharmacie, Université de Poitiers, 86021 Poitiers, France
| | - Nematollah Jaafari
- Unité de Recherche Clinique Pierre Deniker, Centre Hospitalier Henri Laborit, 86021 Poitiers, France
- CNRS 7295, Centre de Recherches sur la Cognition et l'Apprentissage, Université de Poitiers, 86021 Poitiers, France
- Faculté de Médecine et de Pharmacie, Université de Poitiers, 86021 Poitiers, France
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Ding X, Li X, Xu M, He Z, Jiang H. The effect of repetitive transcranial magnetic stimulation on electroencephalography microstates of patients with heroin-addiction. Psychiatry Res Neuroimaging 2023; 329:111594. [PMID: 36724624 DOI: 10.1016/j.pscychresns.2023.111594] [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: 11/11/2022] [Revised: 12/28/2022] [Accepted: 01/10/2023] [Indexed: 01/30/2023]
Abstract
The effects of transcranial magnetic stimulation in treating substance use disorders are gaining attention; however, most existing studies used subjective measures to examine the treatment effects. Objective electroencephalography (EEG)-based microstate analysis is important for measuring the efficacy of transcranial magnetic stimulation in patients with heroin addiction. We investigated dynamic brain activity changes in individuals with heroin addiction after transcranial magnetic stimulation using microstate indicators. Thirty-two patients received intermittent theta-burst stimulation (iTBS) over the left dorsolateral prefrontal cortex. Resting-state EEG data were collected pre-intervention and 10 days post-intervention. The feature values of the significantly different microstate classes were computed using a K-means clustering algorithm. Four EEG microstate classes (A-D) were noted. There were significant increases in the duration, occurrence, and contribution of microstate class A after the iTBS intervention. K-means classification accuracy reached 81.5%. The EEG microstate is an effective improvement indicator in patients with heroin addiction treated with iTBS. Microstates were examined using machine learning; this method effectively classified the pre- and post-intervention cohorts among patients with heroin addiction and healthy individuals. Using EEG microstate to measure heroin addiction and further exploring the effect of iTBS in patients with heroin addiction merit clinical investigation.
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Affiliation(s)
- Xiaobin Ding
- School of Psychology, Northwest Normal University, Lanzhou 730000, China
| | - Xiaoyan Li
- School of Psychology, Northwest Normal University, Lanzhou 730000, China.
| | - Ming Xu
- School of Psychology, Northwest Normal University, Lanzhou 730000, China
| | - Zijing He
- School of Psychology, Northwest Normal University, Lanzhou 730000, China
| | - Heng Jiang
- School of Psychology, Northwest Normal University, Lanzhou 730000, China
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Zhou Y, Gong L, Yang Y, Tan L, Ruan L, Chen X, Luo H, Ruan J. Spatio-temporal dynamics of resting-state brain networks are associated with migraine disability. J Headache Pain 2023; 24:13. [PMID: 36800935 PMCID: PMC9940435 DOI: 10.1186/s10194-023-01551-y] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2022] [Accepted: 02/13/2023] [Indexed: 02/21/2023] Open
Abstract
OBJECTIVE The changes in resting-state functional networks and their correlations with clinical traits remain to be clarified in migraine. Here we aim to investigate the brain spatio-temporal dynamics of resting-state networks and their possible correlations with the clinical traits in migraine. METHODS Twenty Four migraine patients without aura and 26 healthy controls (HC) were enrolled. Each included subject underwent a resting-state EEG and echo planar imaging examination. The disability of migraine patients was evaluated by Migraine Disability Assessment (MIDAS). After data acquisition, EEG microstates (Ms) combining functional connectivity (FC) analysis based on Schafer 400-seven network atlas were performed. Then, the correlation between obtained parameters and clinical traits was investigated. RESULTS Compared with HC group, the brain temporal dynamics depicted by microstates showed significantly increased activity in functional networks involving MsB and decreased activity in functional networks involving MsD; The spatial dynamics were featured by decreased intra-network FC within the executive control network( ECN) and inter-network FC between dorsal attention network (DAN) and ECN (P < 0.05); Moreover, correlation analysis showed that the MIDAS score was positively correlated with the coverage and duration of MsC, and negatively correlated with the occurrence of MsA; The FC within default mode network (DMN), and the inter-FC of ECN- visual network (VN), ECN- limbic network, VN-limbic network was negatively correlated with MIDAS. However, the FC of DMN-ECN was positively correlated with MIDAS; Furthermore, significant interactions between the temporal and spatial dynamics were also obtained. CONCLUSIONS Our study confirmed the notion that altered spatio-temporal dynamics exist in migraine patients during resting-state. And the temporal dynamics, the spatial changes and the clinical traits such as migraine disability interact with each other. The spatio-temporal dynamics obtained from EEG microstate and fMRI FC analyses may be potential biomarkers for migraine and with a huge potential to change future clinical practice in migraine.
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Affiliation(s)
- Yan Zhou
- Department of Neurology, Jianyang People's Hospital, Jianyang, 641400, China
- Department of Neurology, The Affiliated Hospital of Southwest Medical University, Luzhou, 646000, China
- Laboratory of Neurological Diseases and Brain Function, Luzhou, 646000, China
| | - Liusheng Gong
- Department of Neurology, Jianyang People's Hospital, Jianyang, 641400, China
- Department of Neurology, The Affiliated Hospital of Southwest Medical University, Luzhou, 646000, China
| | - Yushu Yang
- Department of Neurology, The Affiliated Hospital of Southwest Medical University, Luzhou, 646000, China
- Laboratory of Neurological Diseases and Brain Function, Luzhou, 646000, China
| | - Linjie Tan
- Department of Neurology, The Affiliated Hospital of Southwest Medical University, Luzhou, 646000, China
- Laboratory of Neurological Diseases and Brain Function, Luzhou, 646000, China
| | - Lili Ruan
- Department of Neurology, The Affiliated Hospital of Southwest Medical University, Luzhou, 646000, China
- Laboratory of Neurological Diseases and Brain Function, Luzhou, 646000, China
| | - Xiu Chen
- Department of Neurology, The Affiliated Hospital of Southwest Medical University, Luzhou, 646000, China
- Laboratory of Neurological Diseases and Brain Function, Luzhou, 646000, China
| | - Hua Luo
- Department of Neurology, The Affiliated Hospital of Southwest Medical University, Luzhou, 646000, China
- Laboratory of Neurological Diseases and Brain Function, Luzhou, 646000, China
| | - Jianghai Ruan
- Department of Neurology, The Affiliated Hospital of Southwest Medical University, Luzhou, 646000, China.
- Laboratory of Neurological Diseases and Brain Function, Luzhou, 646000, China.
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47
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Lin R, Li Q, Liu Z, Zhong S, Sun Q, Guo H, Cao H, Zhang X, Hu Y, Zhou J, Wang X. Abnormalities in electroencephalographic microstates among violent patients with schizophrenia. Front Psychiatry 2023; 14:1082481. [PMID: 36846235 PMCID: PMC9950110 DOI: 10.3389/fpsyt.2023.1082481] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/28/2022] [Accepted: 01/30/2023] [Indexed: 02/12/2023] Open
Abstract
Schizophrenia is often associated with a remarkably increased risk of violence, which has become a public health concern and brought a great economic burden. Recent studies have reported changes in the electroencephalograms (EEG) of patients with schizophrenia. The evidence for an association between EEG and violence in patients with schizophrenia is not conclusive. This study aimed to investigate EEG microstates in violent patients with schizophrenia. Forty-three violent patients with schizophrenia (the VS group) and 51 non-violent patients with schizophrenia (the NVS group) were included, and their EEG microstates were recorded using 21-Channel EEG recordings. The two groups were compared for differences of four microstate classes (A-D) with regards to three microstate parameters (duration, occurrence, and coverage). Compared with the NVS group, the VS group exhibited increased duration, occurrence, and coverage of microstate class A and decreased occurrence of microstate class B. The VS group also had lower probabilities of transitions from "B to C" and from "C to B", as compared with the NVS group. In addition, the MOAS score was positively correlated with the duration, occurrence, and coverage of microstate A. The present study found an abnormal pattern of EEG microstates in violent patients with schizophrenia, which might help clinicians identify patients with schizophrenia who might engaged in violence as well as develop intervention strategies at an early stage.
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Affiliation(s)
- Ruoheng Lin
- National Center for Mental and Psychological Diseases Clinical Medical Research Center, The Second Xiangya Hospital of Central South University, Changsha, China.,Department of Psychiatry, The Second Xiangya Hospital of Central South University, Changsha, China
| | - Qiguang Li
- Xi'an Mental Health Center, Xi'an, China
| | - Ziwei Liu
- School of Medicine, Hunan Normal University, Changsha, China
| | - Shaoling Zhong
- Department of Community Mental Health, The Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou, China
| | - Qiaoling Sun
- National Center for Mental and Psychological Diseases Clinical Medical Research Center, The Second Xiangya Hospital of Central South University, Changsha, China.,Department of Psychiatry, The Second Xiangya Hospital of Central South University, Changsha, China
| | - Huijuan Guo
- National Center for Mental and Psychological Diseases Clinical Medical Research Center, The Second Xiangya Hospital of Central South University, Changsha, China.,Department of Psychiatry, The Second Xiangya Hospital of Central South University, Changsha, China
| | - Hui Cao
- Department of Psychiatry, The Second People's Hospital of Hunan, Changsha, China
| | - Xiangbin Zhang
- Department of Neurology, Xiangya Hospital, Central South University, Changsha, China
| | - Yuhang Hu
- Medicine School, Changsha Social Work College, Changsha, China
| | - Jiansong Zhou
- National Center for Mental and Psychological Diseases Clinical Medical Research Center, The Second Xiangya Hospital of Central South University, Changsha, China.,Department of Psychiatry, The Second Xiangya Hospital of Central South University, Changsha, China
| | - Xiaoping Wang
- National Center for Mental and Psychological Diseases Clinical Medical Research Center, The Second Xiangya Hospital of Central South University, Changsha, China.,Department of Psychiatry, The Second Xiangya Hospital of Central South University, Changsha, China
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48
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Martolini C, Amadeo MB, Campus C, Cappagli G, Gori M. Effects of audio-motor training on spatial representations in long-term late blindness. Neuropsychologia 2022; 176:108391. [DOI: 10.1016/j.neuropsychologia.2022.108391] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2021] [Revised: 08/16/2022] [Accepted: 10/01/2022] [Indexed: 11/15/2022]
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49
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Zhang C, Yang Y, Han S, Xu L, Chen X, Geng X, Bie L, He J. The temporal dynamics of Large-Scale brain network changes in disorders of consciousness: A Microstate-Based study. CNS Neurosci Ther 2022; 29:296-305. [PMID: 36317719 PMCID: PMC9804064 DOI: 10.1111/cns.14003] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2022] [Revised: 09/27/2022] [Accepted: 09/30/2022] [Indexed: 11/07/2022] Open
Abstract
BACKGROUND AND OBJECTIVES The resting-state brain is composed of several discrete networks, which remain stable for 10-100 ms. These functional microstates are considered the building blocks of spontaneous consciousness. Electroencephalography (EEG) microstate analysis may provide insight into the altered brain dynamics underlying consciousness recovery in patients with disorders of consciousness (DOC). We aimed to analyze microstates in the resting-state EEG source space in patients with DOC, the relationship between state-specific features and consciousness levels, and the corresponding patterns of microstates and functional networks. METHODS We obtained resting-state EEG data from 84 patients with DOC (27 in a minimally conscious state [MCS] and 57 in a vegetative state [VS] or with unresponsive wakefulness syndrome). We conducted a microstate analysis of the resting-state (EEG) source space and developed a state-transition analysis protocol for patients with DOC. RESULTS We identified seven microstates with distinct spatial distributions of cortical activation. Multivariate pattern analyses revealed that different functional connectivity patterns were associated with source-level microstates. There were significant differences in the microstate properties, including spatial activation patterns, temporal dynamics, state shifts, and connectivity construction, between the MCS and VS groups. DISCUSSION Our findings suggest that consciousness depends on complex dynamics within the brain and may originate from the anterior cortex.
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Affiliation(s)
- Chunyun Zhang
- Department of NeurosurgeryThe First Hospital of Jilin UniversityChangchunChina
| | - Yi Yang
- Department of NeurosurgeryBeijing Tiantan Hospital, Capital Medical UniversityBeijingChina,Chinese Institute for Brain ResearchBeijingChina,Beijing Institute of Brain DisordersBeijingChina,China National Clinical Research Center for Neurological DiseasesBeijingChina
| | - Shuai Han
- Department of NeurosurgeryThe First Hospital of Jilin UniversityChangchunChina
| | - Long Xu
- Department of NeurosurgeryBeijing Tiantan Hospital, Capital Medical UniversityBeijingChina
| | - Xueling Chen
- Department of NeurosurgeryBeijing Tiantan Hospital, Capital Medical UniversityBeijingChina
| | - Xiaoli Geng
- Department of NeurosurgeryBeijing Tiantan Hospital, Capital Medical UniversityBeijingChina
| | - Li Bie
- Department of NeurosurgeryThe First Hospital of Jilin UniversityChangchunChina
| | - Jianghong He
- Department of NeurosurgeryBeijing Tiantan Hospital, Capital Medical UniversityBeijingChina,China National Clinical Research Center for Neurological DiseasesBeijingChina
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50
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Task-dependent fractal patterns of information processing in working memory. Sci Rep 2022; 12:17866. [PMID: 36284105 PMCID: PMC9596406 DOI: 10.1038/s41598-022-21375-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2022] [Accepted: 09/27/2022] [Indexed: 01/20/2023] Open
Abstract
We applied detrended fluctuation analysis, power spectral density, and eigenanalysis of detrended cross-correlations to investigate fMRI data representing a diurnal variation of working memory in four visual tasks: two verbal and two nonverbal. We show that the degree of fractal scaling is regionally dependent on the engagement in cognitive tasks. A particularly apparent difference was found between memorisation in verbal and nonverbal tasks. Furthermore, the detrended cross-correlations between brain areas were predominantly indicative of differences between resting state and other tasks, between memorisation and retrieval, and between verbal and nonverbal tasks. The fractal and spectral analyses presented in our study are consistent with previous research related to visuospatial and verbal information processing, working memory (encoding and retrieval), and executive functions, but they were found to be more sensitive than Pearson correlations and showed the potential to obtain other subtler results. We conclude that regionally dependent cognitive task engagement can be distinguished based on the fractal characteristics of BOLD signals and their detrended cross-correlation structure.
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