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Murphy M, Jiang C, Wang LA, Kozhemiako N, Wang Y, Wang J, Pan JQ, Purcell SM. Electroencephalographic Microstates During Sleep and Wake in Schizophrenia. BIOLOGICAL PSYCHIATRY GLOBAL OPEN SCIENCE 2024; 4:100371. [PMID: 39296796 PMCID: PMC11408315 DOI: 10.1016/j.bpsgos.2024.100371] [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: 04/23/2024] [Revised: 07/25/2024] [Accepted: 07/26/2024] [Indexed: 09/21/2024] Open
Abstract
Background Aberrant functional connectivity is a hallmark of schizophrenia. The precise nature and mechanism of dysconnectivity in schizophrenia remains unclear, but evidence suggests that dysconnectivity is different in wake versus sleep. Microstate analysis uses electroencephalography (EEG) to investigate large-scale patterns of coordinated brain activity by clustering EEG data into a small set of recurring spatial patterns, or microstates. We hypothesized that this technique would allow us to probe connectivity between brain networks at a fine temporal resolution and uncover previously unknown sleep-specific dysconnectivity. Methods We studied microstates during sleep in patients with schizophrenia by analyzing high-density EEG sleep data from 114 patients with schizophrenia and 79 control participants. We used a polarity-insensitive k-means analysis to extract a set of 6 microstate topographies. Results These 6 states included 4 widely reported canonical microstates. In patients and control participants, falling asleep was characterized by a shift from microstates A, B, and C to microstates D, E, and F. Microstate F was decreased in patients during wake, and microstate E was decreased in patients during sleep. The complexity of microstate transitions was greater in patients than control participants during wake, but this reversed during sleep. Conclusions Our findings reveal behavioral state-dependent patterns of cortical dysconnectivity in schizophrenia. Furthermore, these findings are largely unrelated to previous sleep-related EEG markers of schizophrenia such as decreased sleep spindles. Therefore, these findings are driven by previously undescribed sleep-related pathology in schizophrenia.
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Affiliation(s)
- Michael Murphy
- Department of Psychiatry, McLean Hospital, Harvard Medical School, Belmont, Massachusetts
| | - Chenguang Jiang
- Affiliated Wuxi Mental Health Center of Jiangnan University, Wuxi, Jiangsu, China
| | - Lei A Wang
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, Massachusetts
| | - Nataliia Kozhemiako
- Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts
| | - Yining Wang
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, Massachusetts
| | - Jun Wang
- Affiliated Wuxi Mental Health Center of Jiangnan University, Wuxi, Jiangsu, China
| | - Jen Q Pan
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, Massachusetts
| | - Shaun M Purcell
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, Massachusetts
- Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts
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Man H, Gong A, Song X, Zhang Y, Zhou Y, Fu Y. Decoding the Preparation Stage of Target Shooting under Audiovisual Restricted Conditions: Investigating Neural Mechanisms Using Microstate Analysis. Brain Topogr 2024; 37:1118-1138. [PMID: 38990422 DOI: 10.1007/s10548-024-01066-y] [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: 01/12/2024] [Accepted: 07/02/2024] [Indexed: 07/12/2024]
Abstract
Shooting is a fine sport that is greatly influenced by mental state, and the neural activity of brain in the preparation stage of shooting has a direct influence on the level of shooting. In order to explore the brain neural mechanism in the preparation stage of pistol shooting under audiovisual restricted conditions, and to reveal the intrinsic relationship between brain activity and shooting behavior indicators, the electroencephalography (EEG) signals and seven shooting behaviors including shooting performance, gun holding stability, and firing stability, were experimentally captured from 30 shooters, these shooters performed pistol shooting under three conditions, normal, dim, and noisy. Using EEG microstates combined with standardized low-resolution brain electromagnetic tomography (sLORETA) traceability analysis method, we investigated the difference between the microstates characteristics under audiovisual restricted conditions and normal condition, the relationship between the microstates characteristics and the behavioral indicators during the shooting preparation stage under different conditions. The experimental results showed that microstate 1 corresponded to microstate A, microstate 2 corresponded to microstate B, and microstate 4 corresponded to microstate D; Microstate 3 was a unique template, which was localized in the occipital lobe, its function was to generate the "vision for action"; The dim condition significantly reduced the shooter's performance, whereas the noisy condition had less effect on the shooter's performance; In audiovisual restricted conditions, the microstate characteristics were significantly different from those in the normal condition. Microstate 4' parameters decreased significantly while microstate 3' parameters increased significantly under restricted visual and auditory conditions; Dim condition required more shooting skills from the shooter; There was a significant relationship between characteristics of microstates and indicators of shooting behavior; It was concluded that in order to obtain good shooting performance, shooters should improve attention and concentrate on the adjustment of collimator and target's center leveling relation, but the focus was slightly different in the three conditions; Microstates that are more important for accomplishing the task have less variation in their characteristics over time; Similar conclusions to previous studies were obtained at the same time, i.e., increased visual attention prior to shooting is detrimental to shooting performance, and there is a high positive correlation with microstate D for task completion. The experimental results further reveal the brain neural mechanism in the shooting preparation stage, and the extracted neural markers can be used as effective functional indicators for monitoring the brain state in the shooting preparation stage of pistols.
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Affiliation(s)
- Huijie Man
- College of information Engineering, Engineering University of PAP, Xi'an, 710086, China
| | - Anmin Gong
- College of information Engineering, Engineering University of PAP, Xi'an, 710086, China.
- School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, 710049, China.
| | - Xiaoou Song
- College of information Engineering, Engineering University of PAP, Xi'an, 710086, China
| | - Yijing Zhang
- Center for Psychological Sciences, Zhejiang University, Hangzhou, 200234, China
| | - Yalan Zhou
- College of information Engineering, Engineering University of PAP, Xi'an, 710086, China
| | - Yunfa Fu
- School of Information Engineering and Automation, Kunming University of Science and Technology, Kunming, 650000, China.
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Valt C, Tavella A, Berchio C, Seebold D, Sportelli L, Rampino A, Salisbury DF, Bertolino A, Pergola G. MEG Microstates: An Investigation of Underlying Brain Sources and Potential Neurophysiological Processes. Brain Topogr 2024; 37:993-1009. [PMID: 39115626 PMCID: PMC11408537 DOI: 10.1007/s10548-024-01073-z] [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: 03/19/2024] [Accepted: 07/22/2024] [Indexed: 09/18/2024]
Abstract
Microstates are transient scalp configurations of brain activity measured by electroencephalography (EEG). The application of microstate analysis in magnetoencephalography (MEG) data remains challenging. In one MEG dataset (N = 113), we aimed to identify MEG microstates at rest, explore their brain sources, and relate them to changes in brain activity during open-eyes (ROE) or closed-eyes resting state (RCE) and an auditory Mismatch Negativity (MMN) task. In another dataset of simultaneously recorded EEG-MEG data (N = 21), we investigated the association between MEG and EEG microstates. Six MEG microstates (mMS) provided the best clustering of resting-state activity, each linked to different brain sources: mMS 1-2: left/right occipito-parietal; mMS 3: fronto-temporal; mMS 4: centro-medial; mMS 5-6: left/right fronto-parietal. Increases in occipital alpha power in RCE relative to ROE correlated with greater mMS 1-2 time coverage (τbs < 0.20, ps > .002), while the lateralization of deviance detection in MMN was associated with mMS 5-6 time coverage (τbs < 0.16, ps > .012). No temporal correlation was found between EEG and MEG microstates (ps > .05), despite some overlap in brain sources and global explained variance between mMS 2-3 and EEG microstates B-C (rs > 0.60, ps < .002). Hence, the MEG signal can be decomposed into microstates, but mMS brain activity clustering captures phenomena different from EEG microstates. Source reconstruction and task-related modulations link mMS to large-scale networks and localized activities. Thus, mMSs offer insights into brain dynamics and task-specific processes, complementing EEG microstates in studying physiological and dysfunctional brain activity.
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Affiliation(s)
- Christian Valt
- Department of Translational Biomedicine and Neuroscience, University of Bari Aldo Moro, Bari, Italy.
| | - Angelantonio Tavella
- Department of Translational Biomedicine and Neuroscience, University of Bari Aldo Moro, Bari, Italy
- Department of Mental Health, ASL Bari, Bari, Italy
| | - Cristina Berchio
- Department of Translational Biomedicine and Neuroscience, University of Bari Aldo Moro, Bari, Italy
| | - Dylan Seebold
- Clinical Neurophysiology Research Laboratory, Western Psychiatric Hospital, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
| | - Leonardo Sportelli
- Department of Translational Biomedicine and Neuroscience, University of Bari Aldo Moro, Bari, Italy
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD, USA
| | - Antonio Rampino
- Department of Translational Biomedicine and Neuroscience, University of Bari Aldo Moro, Bari, Italy
- Psychiatric Unit, Bari University Hospital, Bari, Italy
| | - Dean F Salisbury
- Clinical Neurophysiology Research Laboratory, Western Psychiatric Hospital, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
| | - Alessandro Bertolino
- Department of Translational Biomedicine and Neuroscience, University of Bari Aldo Moro, Bari, Italy
- Psychiatric Unit, Bari University Hospital, Bari, Italy
| | - Giulio Pergola
- Department of Translational Biomedicine and Neuroscience, University of Bari Aldo Moro, Bari, Italy.
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD, USA.
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
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Ma S, Yan X, Billington J, Merat N, Markkula G. Cognitive load during driving: EEG microstate metrics are sensitive to task difficulty and predict safety outcomes. ACCIDENT; ANALYSIS AND PREVENTION 2024; 207:107769. [PMID: 39236441 DOI: 10.1016/j.aap.2024.107769] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/14/2024] [Revised: 07/25/2024] [Accepted: 09/02/2024] [Indexed: 09/07/2024]
Abstract
Engaging in phone conversations or other cognitively challenging tasks while driving detrimentally impacts cognitive functions and has been associated with increased risk of accidents. Existing EEG methods have been shown to differentiate between load and no load, but not between different levels of cognitive load. Furthermore, it has not been investigated whether EEG measurements of load can be used to predict safety outcomes in critical events. EEG microstates analysis, categorizing EEG signals into a concise set of prototypical functional states, has been used in other task contexts with good results, but has not been applied in the driving context. Here, this gap is addressed by means of a driving simulation experiment. Three phone use conditions (no phone use, hands-free, and handheld), combined with two task difficulty levels (single- or double-digit addition and subtraction), were tested before and during a rear-end collision conflict. Both conventional EEG spectral power and EEG microstates were analyzed. The results showed that different levels of cognitive load influenced EEG microstates differently, while EEG spectral power remained unaffected. A distinct EEG pattern emerged when drivers engaged in phone tasks while driving, characterized by a simultaneous increase and decrease in two of the EEG microstates, suggesting a heightened focus on auditory information, potentially at a cost to attention reorientation ability. The increase and decrease in these two microstates follow a monotonic sequence from baseline to hands-free simple, hands-free complex, handheld simple, and finally handheld complex, showing sensitivity to task difficulty. This pattern was found both before and after the lead vehicle braked. Furthermore, EEG microstates prior to the lead vehicle braking improved predictions of safety outcomes in terms of minimum time headway after the lead vehicle braked, clearly suggesting that these microstates measure brain states which are indicative of impaired driving. Additionally, EEG microstates are more predictive of safety outcomes than task difficulty, highlighting individual differences in task effects. These findings enhance our understanding of the neural dynamics involved in distracted driving and can be used in methods for evaluating the cognitive load induced by in-vehicle systems.
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Affiliation(s)
- Siwei Ma
- MOT Key Laboratory of Transport Industry of Big Data Application Technologies for Comprehensive Transport, Beijing Jiaotong University, Beijing 100044, PR China.
| | - Xuedong Yan
- MOT Key Laboratory of Transport Industry of Big Data Application Technologies for Comprehensive Transport, Beijing Jiaotong University, Beijing 100044, PR China.
| | - Jac Billington
- School of Psychology, University of Leeds, Leeds LS2 9JT, UK.
| | - Natasha Merat
- Institute for Transport Studies, University of Leeds, Leeds LS2 9JT, UK.
| | - Gustav Markkula
- Institute for Transport Studies, University of Leeds, Leeds LS2 9JT, UK.
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Yan Y, Gao M, Geng Z, Wu Y, Xiao G, Wang L, Pang X, Yang C, Zhou S, Li H, Hu P, Wu X, Wang K. Abnormal EEG microstates in Alzheimer's disease: predictors of β-amyloid deposition degree and disease classification. GeroScience 2024; 46:4779-4792. [PMID: 38727873 PMCID: PMC11336126 DOI: 10.1007/s11357-024-01181-5] [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: 11/23/2023] [Accepted: 04/23/2024] [Indexed: 08/22/2024] Open
Abstract
Electroencephalography (EEG) microstates are used to study cognitive processes and brain disease-related changes. However, dysfunctional patterns of microstate dynamics in Alzheimer's disease (AD) remain uncertain. To investigate microstate changes in AD using EEG and assess their association with cognitive function and pathological changes in cerebrospinal fluid (CSF). We enrolled 56 patients with AD and 38 age- and sex-matched healthy controls (HC). All participants underwent various neuropsychological assessments and resting-state EEG recordings. Patients with AD also underwent CSF examinations to assess biomarkers related to the disease. Stepwise regression was used to analyze the relationship between changes in microstate patterns and CSF biomarkers. Receiver operating characteristics analysis was used to assess the potential of these microstate patterns as diagnostic predictors for AD. Compared with HC, patients with AD exhibited longer durations of microstates C and D, along with a decreased occurrence of microstate B. These microstate pattern changes were associated with Stroop Color Word Test and Activities of Daily Living scale scores (all P < 0.05). Mean duration, occurrences of microstate B, and mean occurrence were correlated with CSF Aβ 1-42 levels, while duration of microstate C was correlated with CSF Aβ 1-40 levels in AD (all P < 0.05). EEG microstates are used to predict AD classification with moderate accuracy. Changes in EEG microstate patterns in patients with AD correlate with cognition and disease severity, relate to Aβ deposition, and may be useful predictors for disease classification.
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Affiliation(s)
- Yibing Yan
- Department of Neurology, The First Affiliated Hospital of Anhui Medical University, the School of Mental Health and Psychological Sciences, Anhui Medical University, 218 Jixi Road, Hefei, 230032, Anhui, China
- Anhui Province Key Laboratory of Cognition and Neuropsychiatric Disorders, Hefei, 230022, China
| | - Manman Gao
- Department of Neurology, The First Affiliated Hospital of Anhui Medical University, the School of Mental Health and Psychological Sciences, Anhui Medical University, 218 Jixi Road, Hefei, 230032, Anhui, China
- Anhui Province Key Laboratory of Cognition and Neuropsychiatric Disorders, Hefei, 230022, China
| | - Zhi Geng
- Department of Neurology, The First Affiliated Hospital of Anhui Medical University, the School of Mental Health and Psychological Sciences, Anhui Medical University, 218 Jixi Road, Hefei, 230032, Anhui, China
- Anhui Province Key Laboratory of Cognition and Neuropsychiatric Disorders, Hefei, 230022, China
| | - Yue Wu
- Department of Sleep Psychology, The Second Hospital of Anhui Medical University, Anhui Medical University, Hefei, 230032, China
| | - Guixian Xiao
- Department of Sleep Psychology, The Second Hospital of Anhui Medical University, Anhui Medical University, Hefei, 230032, China
| | - Lu Wang
- Department of Neurology, The First Affiliated Hospital of Anhui Medical University, the School of Mental Health and Psychological Sciences, Anhui Medical University, 218 Jixi Road, Hefei, 230032, Anhui, China
- Anhui Province Key Laboratory of Cognition and Neuropsychiatric Disorders, Hefei, 230022, China
- Anhui Provincial Institute of Translational Medicine, Anhui Medical University, Hefei, 230032, China
| | - Xuerui Pang
- Department of Neurology, The First Affiliated Hospital of Anhui Medical University, the School of Mental Health and Psychological Sciences, Anhui Medical University, 218 Jixi Road, Hefei, 230032, Anhui, China
- Anhui Province Key Laboratory of Cognition and Neuropsychiatric Disorders, Hefei, 230022, China
| | - Chaoyi Yang
- Department of Neurology, The First Affiliated Hospital of Anhui Medical University, the School of Mental Health and Psychological Sciences, Anhui Medical University, 218 Jixi Road, Hefei, 230032, Anhui, China
- Anhui Province Key Laboratory of Cognition and Neuropsychiatric Disorders, Hefei, 230022, China
| | - Shanshan Zhou
- Department of Neurology, The First Affiliated Hospital of Anhui Medical University, the School of Mental Health and Psychological Sciences, Anhui Medical University, 218 Jixi Road, Hefei, 230032, Anhui, China
- Anhui Province Key Laboratory of Cognition and Neuropsychiatric Disorders, Hefei, 230022, China
- Anhui Provincial Institute of Translational Medicine, Anhui Medical University, Hefei, 230032, China
| | - Hongru Li
- Department of Neurology, The First Affiliated Hospital of Anhui Medical University, the School of Mental Health and Psychological Sciences, Anhui Medical University, 218 Jixi Road, Hefei, 230032, Anhui, China
- Anhui Province Key Laboratory of Cognition and Neuropsychiatric Disorders, Hefei, 230022, China
| | - Panpan Hu
- Department of Neurology, The First Affiliated Hospital of Anhui Medical University, the School of Mental Health and Psychological Sciences, Anhui Medical University, 218 Jixi Road, Hefei, 230032, Anhui, China.
- Anhui Province Key Laboratory of Cognition and Neuropsychiatric Disorders, Hefei, 230022, China.
- Collaborative Innovation Center of Neuropsychiatric Disorders and Mental Health, Hefei, 230022, China.
- Anhui Provincial Institute of Translational Medicine, Anhui Medical University, Hefei, 230032, China.
| | - Xingqi Wu
- Department of Neurology, The First Affiliated Hospital of Anhui Medical University, the School of Mental Health and Psychological Sciences, Anhui Medical University, 218 Jixi Road, Hefei, 230032, Anhui, China.
- Anhui Province Key Laboratory of Cognition and Neuropsychiatric Disorders, Hefei, 230022, China.
- Anhui Provincial Institute of Translational Medicine, Anhui Medical University, Hefei, 230032, China.
| | - Kai Wang
- Department of Neurology, The First Affiliated Hospital of Anhui Medical University, the School of Mental Health and Psychological Sciences, Anhui Medical University, 218 Jixi Road, Hefei, 230032, Anhui, China.
- Anhui Province Key Laboratory of Cognition and Neuropsychiatric Disorders, Hefei, 230022, China.
- Institute of Artificial Intelligence, Hefei Comprehensive National Science Center, Hefei, 230088, China.
- Collaborative Innovation Center of Neuropsychiatric Disorders and Mental Health, Hefei, 230022, China.
- Anhui Provincial Institute of Translational Medicine, Anhui Medical University, Hefei, 230032, China.
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Lu HY, Ma ZZ, Zhang JP, Wu JJ, Zheng MX, Hua XY, Xu JG. Altered Resting-State Electroencephalogram Microstate Characteristics in Stroke Patients. J Integr Neurosci 2024; 23:176. [PMID: 39344234 DOI: 10.31083/j.jin2309176] [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: 04/13/2024] [Revised: 06/24/2024] [Accepted: 06/29/2024] [Indexed: 10/01/2024] Open
Abstract
BACKGROUND Stroke remains a leading cause of disability globally and movement impairment is the most common complication in stroke patients. Resting-state electroencephalography (EEG) microstate analysis is a non-invasive approach of whole-brain imaging based on the spatiotemporal pattern of the entire cerebral cortex. The present study aims to investigate microstate alterations in stroke patients. METHODS Resting-state EEG data collected from 24 stroke patients and 19 healthy controls matched by age and gender were subjected to microstate analysis. For four classic microstates labeled as class A, B, C and D, their temporal characteristics (duration, occurrence and coverage) and transition probabilities (TP) were extracted and compared between the two groups. Furthermore, we explored their correlations with clinical outcomes including the Fugl-Meyer assessment (FMA) and the action research arm test (ARAT) scores in stroke patients. Finally, we analyzed the relationship between the temporal characteristics and spectral power in frequency bands. False discovery rate (FDR) method was applied for correction of multiple comparisons. RESULTS Microstate analysis revealed that the stroke group had lower occurrence of microstate A which was regarded as the sensorimotor network (SMN) compared with the control group (p = 0.003, adjusted p = 0.036, t = -2.959). The TP from microstate A to microstate D had a significant positive correlation with the Fugl-Meyer assessment of lower extremity (FMA-LE) scores (p = 0.049, r = 0.406), but this finding did not survive FDR adjustment (adjusted p = 0.432). Additionally, the occurrence and the coverage of microstate B were negatively correlated with the power of delta band in the stroke group, which did not pass adjustment (p = 0.033, adjusted p = 0.790, r = -0.436; p = 0.026, adjusted p = 0.790, r = -0.454, respectively). CONCLUSIONS Our results confirm the abnormal temporal dynamics of brain activity in stroke patients. The study provides further electrophysiological evidence for understanding the mechanism of brain motor functional reorganization after stroke.
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Affiliation(s)
- Hao-Yu Lu
- School of Rehabilitation Science, Shanghai University of Traditional Chinese Medicine, 201203 Shanghai, China
| | - Zhen-Zhen Ma
- Department of Rehabilitation Medicine, Longhua Hospital, Shanghai University of Traditional Chinese Medicine, 200032 Shanghai, China
| | - Jun-Peng Zhang
- School of Rehabilitation Science, Shanghai University of Traditional Chinese Medicine, 201203 Shanghai, China
| | - Jia-Jia Wu
- Department of Rehabilitation Medicine, Yueyang Hospital of Integrated Traditional Chinese and Western Medicine, Shanghai University of Traditional Chinese Medicine, 200437 Shanghai, China
| | - Mou-Xiong Zheng
- Department of Traumatology and Orthopedics, Yueyang Hospital of Integrated Traditional Chinese and Western Medicine, Shanghai University of Traditional Chinese Medicine, 200437 Shanghai, China
| | - Xu-Yun Hua
- Department of Traumatology and Orthopedics, Yueyang Hospital of Integrated Traditional Chinese and Western Medicine, Shanghai University of Traditional Chinese Medicine, 200437 Shanghai, China
| | - Jian-Guang Xu
- School of Rehabilitation Science, Shanghai University of Traditional Chinese Medicine, 201203 Shanghai, China
- Department of Rehabilitation Medicine, Yueyang Hospital of Integrated Traditional Chinese and Western Medicine, Shanghai University of Traditional Chinese Medicine, 200437 Shanghai, China
- Engineering Research Center of Traditional Chinese Medicine Intelligent Rehabilitation, Ministry of Education, 201203 Shanghai, 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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Li Y, Wan X, Zhang Y, Song W. Modulation of electroencephalogram brain activity dynamics by 10 Hz parietal repetitive transcranial magnetic stimulation: Implications for recovery of the minimally conscious state. Neurosci Lett 2024; 842:137986. [PMID: 39260738 DOI: 10.1016/j.neulet.2024.137986] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2024] [Revised: 09/02/2024] [Accepted: 09/08/2024] [Indexed: 09/13/2024]
Abstract
BACKGROUND AND OBJECTIVES Despite the fact that the parietal cortex is associated with consciousness, the underlying mechanisms of parietal repetitive transcranial magnetic stimulation (rTMS) have not yet been specifically investigated. The present study aims to examine the effects of parietal rTMS on patients with disorders of consciousness (DoC) and identify a novel potential target. METHODS Twenty minimally conscious state (MCS) patients were stochastically assigned to a real or sham rTMS group in a controlled trial. The real rTMS group was administered over the parietal cortex, with a frequency of 10 Hz and a rest motor threshold of 90 %. The sham rTMS group was identical to the real rTMS group without magnetic stimulation over the cortex. Pre- and post-treatment resting-state electrophysiological (EEG) data and coma recovery scale-revised (CRS-R) score were gathered. Microstate analyses were calculated to evaluate the brain activity dynamics. RESULTS The real rTMS treatment improved the CRS-R scores. There were notable alterations in the mean microstate duration (MMD) of microstate B in the real rTMS group. The sham rTMS group did not exhibit such changes in CRS-R score or EEG results, which were not statistically significant. Furthermore, the MMD and RTC of microstate E were found to be negatively correlated with baseline CRS-R scores. CONCLUSION Parietal rTMS can induce behavioral improvement and brain activity dynamics in patients with MCS. EEG microstates can be used as a valuable method to study neurophysiological mechanisms behind MCS. And the parietal cortex represents an alternative for rTMS therapy protocols.
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Affiliation(s)
- Yanhua Li
- Department of Rehabilitation Medicine, Xuan Wu Hospital, Capital Medical University, Beijing, China
| | - Xiaoping Wan
- Department of Rehabilitation Medicine, Xuan Wu Hospital, Capital Medical University, Beijing, China
| | - Ye Zhang
- Department of Rehabilitation Medicine, Xuan Wu Hospital, Capital Medical University, Beijing, China
| | - Weiqun Song
- Department of Rehabilitation Medicine, Xuan Wu Hospital, Capital Medical University, Beijing, China.
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9
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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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10
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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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11
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Tomescu MI, Van der Donck S, Perisanu EM, Berceanu AI, Alaerts K, Boets B, Carcea I. Social functioning predicts individual changes in EEG microstates following intranasal oxytocin administration: A double-blind, cross-over randomized clinical trial. Psychophysiology 2024; 61:e14581. [PMID: 38594888 DOI: 10.1111/psyp.14581] [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: 08/03/2023] [Revised: 03/20/2024] [Accepted: 03/21/2024] [Indexed: 04/11/2024]
Abstract
Oxytocin (OXT) modulates social behaviors. However, the administration of exogenous OXT in humans produces inconsistent behavioral changes, affecting future consideration of OXT as a treatment for autism and other disorders with social symptoms. Inter-individual variability in social functioning traits might play a key role in how OXT changes brain activity and, therefore, behavior. Here, we investigated if inter-individual variability might dictate how single-dose intranasal OXT administration (IN-OXT) changes spontaneous neural activity during the eyes-open resting state. We used a double-blinded, randomized, placebo-controlled, cross-over design on 30 typically developing young adult men to investigate the dynamics of EEG microstates corresponding to activity in defined neural networks. We confirmed previous reports that, at the group level, IN-OXT increases the representation of the attention and salience microstates. Furthermore, we identified a decreased representation of microstates associated with the default mode network. Using multivariate partial least square statistical analysis, we found that social functioning traits associated with IN-OXT-induced changes in microstate dynamics in specific spectral bands. Correlation analysis further revealed that the higher the social functioning, the more IN-OXT increased the appearance of the visual network-associated microstate, and suppressed the appearance of a default mode network-related microstate. The lower the social functioning, the more IN-OXT increases the appearance of the salience microstate. The effects we report on the salience microstate support the hypothesis that OXT regulates behavior by enhancing social salience. Moreover, our findings indicate that social functioning traits modulate responses to IN-OXT and could partially explain the inconsistent reports on IN-OXT effects.
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Affiliation(s)
- Miralena I Tomescu
- Faculty of Educational Sciences, Department of Psychology, University "Stefan cel Mare" of Suceava, Bucharest, Romania
- CINETic Center, National University of Theatre and Film "I.L. Caragiale" Bucharest, Bucharest, Romania
- Faculty of Psychology and Educational Sciences, Department of Psychology, University of Bucharest, Bucharest, Romania
| | - Stephanie Van der Donck
- Center for Developmental Psychiatry, Department of Neurosciences, KU Leuven, Leuven, Belgium
- Leuven Autism Research (LAuRes), KU Leuven, Leuven, Belgium
| | - Emanuela M Perisanu
- Institute of Cardiovascular Diseases, Timisoara, Romania
- Faculty of Medicine, University of Sibiu, Sibiu, Romania
| | - Alexandru I Berceanu
- CINETic Center, National University of Theatre and Film "I.L. Caragiale" Bucharest, Bucharest, Romania
| | - Kaat Alaerts
- Neuromodulation Laboratory, Research Group for Neurorehabilitation, KU Leuven, Leuven, Belgium
| | - Bart Boets
- Center for Developmental Psychiatry, Department of Neurosciences, KU Leuven, Leuven, Belgium
- Leuven Autism Research (LAuRes), KU Leuven, Leuven, Belgium
| | - Ioana Carcea
- CINETic Center, National University of Theatre and Film "I.L. Caragiale" Bucharest, Bucharest, Romania
- Department of Pharmacology, Physiology, and Neuroscience, Rutgers Brain Health Institute, Rutgers, The State University of New Jersey, Newark, New Jersey, USA
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12
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Liu H, Bai Y, Zheng Q, Liu J, Zhu J, Ni G. Electrophysiological correlation of auditory selective spatial attention in the "cocktail party" situation. Hum Brain Mapp 2024; 45:e26793. [PMID: 39037186 PMCID: PMC11261592 DOI: 10.1002/hbm.26793] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2024] [Revised: 07/04/2024] [Accepted: 07/09/2024] [Indexed: 07/23/2024] Open
Abstract
The auditory system can selectively attend to the target source in complex environments, the phenomenon known as the "cocktail party" effect. However, the spatiotemporal dynamics of electrophysiological activity associated with auditory selective spatial attention (ASSA) remain largely unexplored. In this study, single-source and multiple-source paradigms were designed to simulate different auditory environments, and microstate analysis was introduced to reveal the electrophysiological correlates of ASSA. Furthermore, cortical source analysis was employed to reveal the neural activity regions of these microstates. The results showed that five microstates could explain the spatiotemporal dynamics of ASSA, ranging from MS1 to MS5. Notably, MS2 and MS3 showed significantly lower partial properties in multiple-source situations than in single-source situations, whereas MS4 had shorter durations and MS5 longer durations in multiple-source situations than in single-source situations. MS1 had insignificant differences between the two situations. Cortical source analysis showed that the activation regions of these microstates initially transferred from the right temporal cortex to the temporal-parietal cortex, and subsequently to the dorsofrontal cortex. Moreover, the neural activity of the single-source situations was greater than that of the multiple-source situations in MS2 and MS3, correlating with the N1 and P2 components, with the greatest differences observed in the superior temporal gyrus and inferior parietal lobule. These findings suggest that these specific microstates and their associated activation regions may serve as promising substrates for decoding ASSA in complex environments.
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Affiliation(s)
- Hongxing Liu
- Academy of Medical Engineering and Translational MedicineTianjin UniversityTianjinChina
- State Key Laboratory of Advanced Medical Materials and DevicesTianjin UniversityTianjinChina
| | - Yanru Bai
- Academy of Medical Engineering and Translational MedicineTianjin UniversityTianjinChina
- State Key Laboratory of Advanced Medical Materials and DevicesTianjin UniversityTianjinChina
- Haihe Laboratory of Brain‐computer Interaction and Human‐machine IntegrationTianjinChina
| | - Qi Zheng
- Academy of Medical Engineering and Translational MedicineTianjin UniversityTianjinChina
- State Key Laboratory of Advanced Medical Materials and DevicesTianjin UniversityTianjinChina
| | - Jihan Liu
- Academy of Medical Engineering and Translational MedicineTianjin UniversityTianjinChina
- State Key Laboratory of Advanced Medical Materials and DevicesTianjin UniversityTianjinChina
| | - Jianing Zhu
- Academy of Medical Engineering and Translational MedicineTianjin UniversityTianjinChina
- State Key Laboratory of Advanced Medical Materials and DevicesTianjin UniversityTianjinChina
| | - Guangjian Ni
- Academy of Medical Engineering and Translational MedicineTianjin UniversityTianjinChina
- State Key Laboratory of Advanced Medical Materials and DevicesTianjin UniversityTianjinChina
- Haihe Laboratory of Brain‐computer Interaction and Human‐machine IntegrationTianjinChina
- Tianjin Key Laboratory of Brain Science and NeuroengineeringTianjinChina
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13
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Li Y, Li J, Wang P, Yu D, Chen Z, Shi Z, Wu Y, Qi W, Lu W, Shi H. Electroencephalography microstate alterations reflect potential double-edged cognitive adaptation in Ménière's disease. CNS Neurosci Ther 2024; 30:e14896. [PMID: 39107944 PMCID: PMC11303265 DOI: 10.1111/cns.14896] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2024] [Revised: 07/11/2024] [Accepted: 07/19/2024] [Indexed: 08/10/2024] Open
Abstract
PURPOSE To explore the microstate characteristics and underlying brain network activity of Ménière's disease (MD) patients based on high-density electroencephalography (EEG), elucidate the association between microstate dynamics and clinical manifestation, and explore the potential of EEG microstate features as future neurobiomarkers for MD. METHODS Thirty-two patients diagnosed with MD and 29 healthy controls (HC) matched for demographic characteristics were included in the study. Dysfunction and subjective symptom severity were assessed by neuropsychological questionnaires, pure tone audiometry, and vestibular function tests. Resting-state EEG recordings were obtained using a 256-channel EEG system, and the electric field topographies were clustered into four dominant microstate classes (A, B, C, and D). The dynamic parameters of each microstate were analyzed and utilized as input for a support vector machine (SVM) classifier to identify significant microstate signatures associated with MD. The clinical significance was further explored through Spearman correlation analysis. RESULTS MD patients exhibited an increased presence of microstate class C and a decreased frequency of transitions between microstate class A and B, as well as between class A and D. The transitions from microstate class A to C were also elevated. Further analysis revealed a positive correlation between equilibrium scores and the transitions from microstate class A to C under somatosensory challenging conditions. Conversely, transitions between class A and B were negatively correlated with vertigo symptoms. No significant correlations were detected between these characteristics and auditory test results or emotional scores. Utilizing the microstate features identified via sequential backward selection, the linear SVM classifier achieved a sensitivity of 86.21% and a specificity of 90.61% in distinguishing MD patients from HC. CONCLUSIONS We identified several EEG microstate characteristics in MD patients that facilitate postural control yet exacerbate subjective symptoms, and effectively discriminate MD from HC. The microstate features may offer a new approach for optimizing cognitive compensation strategies and exploring potential neurobiological markers in MD.
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Affiliation(s)
- Yi‐Ni Li
- Department of Otolaryngology‐Head and Neck SurgeryShanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of MedicineShanghaiChina
| | - Jie Li
- Department of Otolaryngology‐Head and Neck SurgeryShanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of MedicineShanghaiChina
| | - Peng‐Jun Wang
- Department of Otolaryngology‐Head and Neck SurgeryShanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of MedicineShanghaiChina
| | - Dong‐Zhen Yu
- Department of Otolaryngology‐Head and Neck SurgeryShanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of MedicineShanghaiChina
| | - Zheng‐Nong Chen
- Department of Otolaryngology‐Head and Neck SurgeryShanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of MedicineShanghaiChina
| | - Zheng‐Yu Shi
- Department of Otolaryngology‐Head and Neck SurgeryShanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of MedicineShanghaiChina
| | - Ya‐Qin Wu
- Department of Otolaryngology‐Head and Neck SurgeryShanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of MedicineShanghaiChina
| | - Wei‐Dong Qi
- Department of Otolaryngology Head and Neck SurgeryHuashan Hospital Fudan UniversityShanghaiChina
| | - Wen Lu
- Department of Otolaryngology‐Head and Neck SurgeryShanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of MedicineShanghaiChina
| | - Hai‐Bo Shi
- Department of Otolaryngology‐Head and Neck SurgeryShanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of MedicineShanghaiChina
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14
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Cheng J, Wang Y, Tang Y, Lin L, Gao J, Wang Z. EEG microstates are associated with the improvement of obsessive-compulsive symptoms after transcranial direct current stimulation. J Psychiatr Res 2024; 176:360-367. [PMID: 38941759 DOI: 10.1016/j.jpsychires.2024.06.034] [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: 10/03/2023] [Revised: 06/07/2024] [Accepted: 06/24/2024] [Indexed: 06/30/2024]
Abstract
BACKGROUND Transcranial direct current stimulation (tDCS) is a safe, accessible, and promising therapeutic approach for obsessive-compulsive disorder (OCD). AIMS This study aimed to evaluate the effect of tDCS on electroencephalography (EEG) microstates and identify potential biomarkers to predict efficacy. METHODS A total of 24 individuals diagnosed with OCD underwent ten sessions of tDCS targeting the orbitofrontal cortex, while 27 healthy individuals were included as controls. Microstates A, B, C, and D were extracted before and after tDCS. A comparative analysis of microstate metrics was performed between the OCD and the healthy control groups, as well as within the OCD group before and after tDCS. Multiple linear regression analysis was performed to identify potential biomarkers of tDCS. RESULTS Comparison to healthy controls, the OCD group exhibited a significantly reduced duration of microstate A and increased occurrence of microstate D. The transition between microstates A and C was significantly different between patients with OCD and healthy controls and was no longer observed following tDCS. Multiple linear regression analysis revealed that the duration of microstate C was associated with an improvement OCD symptom after tDCS. CONCLUSIONS The results revealed an aberrant large-scale EEG brain network that could be modulated by tDCS. In particular, the duration of EEG microstate C may be a neurophysiological characteristic associated with the therapeutic effects of tDCS on OCD.
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Affiliation(s)
- Jiayue Cheng
- Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, PR China
| | - Yang Wang
- Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, PR China
| | - Yingying Tang
- Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, PR China
| | - Liangjun Lin
- Suzhou Guangji Hospital, The Affiliated Guangji Hospital of Soochow University, Suzhou, 215004, PR China
| | - Jian Gao
- Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, PR China
| | - Zhen Wang
- Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, PR China; Institute of Psychological and Behavioral Science, Shanghai Jiao Tong University, Shanghai, PR China; Shanghai Intelligent Psychological Evaluation and Intervention Engineering Technology Research Center, Shanghai, PR China.
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15
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Jiang Y, Zheng M. EEG microstates are associated with music training experience. Front Hum Neurosci 2024; 18:1434110. [PMID: 39118820 PMCID: PMC11306160 DOI: 10.3389/fnhum.2024.1434110] [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: 05/17/2024] [Accepted: 07/15/2024] [Indexed: 08/10/2024] Open
Abstract
Background Music training facilitates the development of individual cognitive functions and influences brain plasticity. A comprehensive understanding of the pathways and processes through which music affects the human brain, as well as the neurobiological mechanisms underlying human brain perception of music, is necessary to fully harness the plasticity that music offers for brain development. Aims To investigate the resting-state electroencephalogram (EEG) activity of individuals with and without music training experience, and explore the microstate patterns of EEG signals. Method In this study, an analysis of electroencephalogram (EEG) microstates from 57 participants yielded temporal parameters(mean duration, time coverage, occurrence, and transition probability)of four classic microstate categories (Categories A, B, C, and D) for two groups: those with music training experience and those without. Statistical analysis was conducted on these parameters between groups. Results The results indicate that compared to individuals without music training experience, participants with music training experience exhibit significantly longer mean durations of microstate A, which is associated with speech processing. Additionally, they show a greater time coverage of microstate B, which is associated with visual processing. Transition probabilities from microstate A to microstate B were greater in participants with music training experience compared to those without. Conversely, transition probabilities from microstate A to microstate C and from microstate C to microstate D were greater in participants without music training experience. Conclusion Our study found differences in characteristic parameters of certain microstates between individuals with and without music training experience. This suggests distinct brain activity patterns during tasks related to speech, vision, and attention regulation among individuals with varying levels of music training experience. These findings support an association between music training experience and specific neural activities. Furthermore, they endorse the hypothesis of music training experience influencing brain activity during resting states. Additionally, they imply a facilitative role of music training in tasks related to speech, vision, and attention regulation, providing initial evidence for further empirical investigation into the cognitive processes influenced by music training.
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Affiliation(s)
- Yihe Jiang
- Key Laboratory of Cognition and Personality (Ministry of Education), Southwest University, Chongqing, China
- School of Psychology, Southwest University, Chongqing, China
| | - Maoping Zheng
- School of Music, Southwest University, Chongqing, China
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16
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Khoo SY, Lai WH, On SH, On YY, Adam BM, Law WC, Ng BHS, Fong AYY, Anselm ST. Resting-state electroencephalography (EEG) microstates of healthy individuals following mild sleep deprivation. Sci Rep 2024; 14:16820. [PMID: 39039219 PMCID: PMC11263689 DOI: 10.1038/s41598-024-67902-0] [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: 03/22/2024] [Accepted: 07/17/2024] [Indexed: 07/24/2024] Open
Abstract
Mild sleep deprivation is widespread in many societies worldwide. Electroencephalography (EEG) microstate analysis provides information on spatial and temporal characteristics of resting brain network, serving as an indicator of neurophysiological activities at rest. This study seeks to investigate potential neural markers in EEG following mild sleep deprivation of a single night using EEG microstate analysis. Six-minute resting EEG was conducted on thirty healthy adults within 6 hours of waking in the morning and after at least 18 h of sleep deprivation. Translated and validated Malay language Karolinska Sleepiness Scale was used to assess the participants' degree of sleepiness. Microstate characteristics analysis was conducted on the final 24 subjects based on four standard microstate maps. Microstate C shows a significant increase in mean duration, coverage and occurrence, while microstate D has significantly higher occurrence after sleep deprivation. This study demonstrates notable changes in resting state EEG microstates following mild sleep deprivation. Present findings deepen our understanding of the brain's spatiotemporal dynamics under this condition and suggest the potential utility of neural markers in this domain as components of composite markers for sleep deprivation.
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Affiliation(s)
- Sing Yee Khoo
- Faculty of Medicine and Health Sciences, Universiti Malaysia Sarawak, Jalan Datuk Mohammad Musa, 94300, Kota Samarahan, Sarawak, Malaysia.
- Clinical Research Centre, Institutes for Clinical Research, National Institutes of Health, Sarawak General Hospital, Jalan Hospital, 93586, Kuching, Sarawak, Malaysia.
| | - Wei Hong Lai
- Clinical Research Centre, Institutes for Clinical Research, National Institutes of Health, Sarawak General Hospital, Jalan Hospital, 93586, Kuching, Sarawak, Malaysia
| | - Shin Hui On
- Yong Loo Lin School of Medicine, National University of Singapore, 21 Lower Kent Ridge Road, Singapore, 119077, Singapore
| | - Yue Yuan On
- School of Biological Sciences, Nanyang Technological University, 50 Nanyang Ave, Singapore, 639798, Singapore
| | - Bujang Mohamad Adam
- Clinical Research Centre, Institutes for Clinical Research, National Institutes of Health, Sarawak General Hospital, Jalan Hospital, 93586, Kuching, Sarawak, Malaysia
| | - Wan Chung Law
- Neurology Department, Sarawak General Hospital, Jalan Hospital, Ministry of Health, 93586, Kuching, Sarawak, Malaysia
| | - Benjamin Han Sim Ng
- Neurology Department, Sibu General Hospital, Ministry of Health, KM 5 ½, Jalan Ulu Oya, Pekan Sibu, 96000, Sibu, Sarawak, Malaysia
| | - Alan Yean Yip Fong
- Clinical Research Centre, Institutes for Clinical Research, National Institutes of Health, Sarawak General Hospital, Jalan Hospital, 93586, Kuching, Sarawak, Malaysia
| | - Su Ting Anselm
- Faculty of Medicine and Health Sciences, Universiti Malaysia Sarawak, Jalan Datuk Mohammad Musa, 94300, Kota Samarahan, Sarawak, Malaysia
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17
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Raghuraman L, Joshi SH. Application of EEG in the Diagnosis and Classification of Migraine: A Scoping Review. Cureus 2024; 16:e64961. [PMID: 39171023 PMCID: PMC11336234 DOI: 10.7759/cureus.64961] [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: 10/01/2023] [Accepted: 07/19/2024] [Indexed: 08/23/2024] Open
Abstract
Migraine is a chronic debilitating disease affecting a significant number of people, more often women than men. The gold standard for diagnosis is the International Classification of Headache Disorders-3 (ICHD-3). Authors have identified multiple tight spots in the present method of diagnosis. An alternative method of diagnosis has always been coveted. Electroencephalogram (EEG) is one of the most researched of such alternatives. The visually evoked potential is the most studied; auditory evoked potentials and transcranial direct current stimulation are also being studied. Cortical hyperexcitability and habituation deficit to sensory stimuli are some of the consistent findings. Alpha oscillations are among the most frequently studied bands; spectral analysis of EEG waves has often shown more reliable and consistent results than features read off the EEG directly. EEG microstate is a novel and promising method showing characteristic identifiable features that may help diagnose Migraine patients. An alternative to the ICHD-3 criterion for diagnosing Migraines would be instrumental in promptly diagnosing the disease. EEG is one of the most explored alternatives within which enumerable features can be used to identify Migraines, of which the most promising are EEG microstates.
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Affiliation(s)
- Lakshana Raghuraman
- Medicine, Jawaharlal Nehru Medical College, Datta Meghe Institute of Higher Education and Research, Wardha, IND
| | - Shiv H Joshi
- Community Medicine, Jawaharlal Nehru Medical College, Datta Meghe Institute of Higher Education and Research, Wardha, IND
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18
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Tomescu MI, Papasteri C, Sofonea A, Berceanu AI, Carcea I. Personality Moderates Intra-Individual Variability in EEG Microstates and Spontaneous Thoughts. Brain Topogr 2024; 37:524-535. [PMID: 38038786 PMCID: PMC11199214 DOI: 10.1007/s10548-023-01019-x] [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: 05/31/2023] [Accepted: 10/26/2023] [Indexed: 12/02/2023]
Abstract
Variability in brain activity that persists after accounting for overt behavioral and physiological states is often considered noise and controlled as a covariate in research. However, studying intra-individual variability in brain function can provide valuable insights into the dynamic nature of the brain. To explore this, we conducted a study on 43 participants analyzing the EEG microstate dynamics and self-reported spontaneous mental activity during five-minute resting-state recordings on two separate days with a twenty days average delay between recordings. Our results showed that the associations between EEG microstates and spontaneous cognition significantly changed from one day to another. Moreover, microstate changes were associated with changes in spontaneous cognition. Specifically, inter-day changes in Verbal thoughts about Others and future Planning were positively related to bottom-up sensory network-related microstate changes and negatively associated with top-down, attention, and salience network-related microstates. In addition, we find that personality traits are related to inter-day changes in microstates and spontaneous thoughts. Specifically, extraversion, neuroticism, agreeableness, and openness to experience moderated the relationship between inter-day changes in EEG microstates and spontaneous thoughts. Our study provides valuable information on the dynamic changes in the EEG microstate-spontaneous cognition organization, which could be essential for developing interventions and treatments for neuropsychiatric disorders.
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Affiliation(s)
- Miralena I Tomescu
- Department of Psychology, Faculty of Educational Sciences, University "Stefan cel Mare" of Suceava, Suceava, Romania.
- Departement of Research and Development, CINETic Center, National University of Theatre and Film "I.L. Caragiale", Bucharest, Romania.
- Department of Cognitive Sciences, Faculty of Psychology and Educational Sciences, University of Bucharest, Bucharest, Romania.
| | - Claudiu Papasteri
- Departement of Research and Development, CINETic Center, National University of Theatre and Film "I.L. Caragiale", Bucharest, Romania
- Department of Cognitive Sciences, Faculty of Psychology and Educational Sciences, University of Bucharest, Bucharest, Romania
| | - Alexandra Sofonea
- Departement of Research and Development, CINETic Center, National University of Theatre and Film "I.L. Caragiale", Bucharest, Romania
| | - Alexandru I Berceanu
- Departement of Research and Development, CINETic Center, National University of Theatre and Film "I.L. Caragiale", Bucharest, Romania
| | - Ioana Carcea
- Departement of Research and Development, CINETic Center, National University of Theatre and Film "I.L. Caragiale", Bucharest, Romania
- Department of Pharmacology, Physiology, and Neuroscience, Rutgers Brain Health Institute, New Jersey, NJ, USA
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19
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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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20
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Murphy M, Carrión RE, Rubio J, Malhotra AK. Peak alpha frequency and electroencephalographic microstates are correlated with aggression in schizophrenia. J Psychiatr Res 2024; 175:60-67. [PMID: 38704982 PMCID: PMC11374487 DOI: 10.1016/j.jpsychires.2024.04.051] [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: 02/09/2024] [Revised: 03/28/2024] [Accepted: 04/25/2024] [Indexed: 05/07/2024]
Abstract
Large scale retrospective studies have shown an association between schizophrenia and risk of violence. Overall, this increase in risk is small and does not justify or support stigmatizing public perceptions or media depictions of people with schizophrenia. Nonetheless, in some situations, some symptoms of schizophrenia can increase the risk of violent behavior. Prediction of this behavior would allow high impact preventive interventions. However, to date the neurobiological correlates of violent behavior in schizophrenia are not well understood, precluding the development of prognostic biomarkers. We used electroencephalography to measure alpha activity and microstates from 31 patients with schizophrenia and 18 age matched controls. Participants also completed multiple assessments of current aggressive tendencies and their lifetime history of aggressive acts. We found that individual alpha peak frequency was negatively correlated with aggression scores in both patients and controls (largest Spearman's r = -0.45). Furthermore, this result could be replicated in data taken from a single frontal channel suggesting that this may be possible to obtain in routine clinical settings (largest Spearman's r = -0.40). We also found that transitions between microstates corresponding to auditory and visual networks were inversely correlated with aggression scores. Finally, we found that, within patients, aggression was correlated with the degree of randomness between microstate transitions. This suggests that aggression is related to inappropriate switching between large scale brain networks and subsequent failure to appropriately integrate complicated environmental and internal stimuli. By elucidating some of the electrophysiological correlates of aggression, these data facilitate the development of prognostic biomarkers.
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Affiliation(s)
- Michael Murphy
- McLean Hospital, Belmont, MA, USA; Harvard Medical School, Boston, MA, USA.
| | - Ricardo E Carrión
- Institute of Behavioral Science, Feinstein Institutes for Medical Research, Manhasset, NY, USA; Department of Psychiatry, Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, Hempstead, NY, USA; Northwell, New Hyde Park, NY, USA
| | - Jose Rubio
- Institute of Behavioral Science, Feinstein Institutes for Medical Research, Manhasset, NY, USA; Department of Psychiatry, Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, Hempstead, NY, USA; Northwell, New Hyde Park, NY, USA
| | - Anil K Malhotra
- Institute of Behavioral Science, Feinstein Institutes for Medical Research, Manhasset, NY, USA; Department of Psychiatry, Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, Hempstead, NY, USA; Northwell, New Hyde Park, NY, USA
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21
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Schiller B, Sperl MFJ, Kleinert T, Nash K, Gianotti LRR. EEG Microstates in Social and Affective Neuroscience. Brain Topogr 2024; 37:479-495. [PMID: 37523005 PMCID: PMC11199304 DOI: 10.1007/s10548-023-00987-4] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2023] [Accepted: 07/05/2023] [Indexed: 08/01/2023]
Abstract
Social interactions require both the rapid processing of multifaceted socio-affective signals (e.g., eye gaze, facial expressions, gestures) and their integration with evaluations, social knowledge, and expectations. Researchers interested in understanding complex social cognition and behavior face a "black box" problem: What are the underlying mental processes rapidly occurring between perception and action and why are there such vast individual differences? In this review, we promote electroencephalography (EEG) microstates as a powerful tool for both examining socio-affective states (e.g., processing whether someone is in need in a given situation) and identifying the sources of heterogeneity in socio-affective traits (e.g., general willingness to help others). EEG microstates are identified by analyzing scalp field maps (i.e., the distribution of the electrical field on the scalp) over time. This data-driven, reference-independent approach allows for identifying, timing, sequencing, and quantifying the activation of large-scale brain networks relevant to our socio-affective mind. In light of these benefits, EEG microstates should become an indispensable part of the methodological toolkit of laboratories working in the field of social and affective neuroscience.
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Affiliation(s)
- Bastian Schiller
- Laboratory for Biological Psychology, Clinical Psychology, and Psychotherapy, Albert-Ludwigs-University of Freiburg, Freiburg, Germany.
- Freiburg Brain Imaging Center, University Medical Center, Albert-Ludwigs-University of Freiburg, Freiburg, Germany.
| | - Matthias F J Sperl
- Department of Clinical Psychology and Psychotherapy, University of Giessen, Giessen, Germany
- Center for Mind, Brain and Behavior, Universities of Marburg and Giessen (Research Campus Central Hessen), Marburg, Germany
| | - Tobias Kleinert
- Laboratory for Biological Psychology, Clinical Psychology, and Psychotherapy, Albert-Ludwigs-University of Freiburg, Freiburg, Germany
- Department of Ergonomics, Leibniz Research Centre for Working Environment and Human Factors (IfADo), Dortmund, Germany
| | - Kyle Nash
- Department of Psychology, University of Alberta, Edmonton, Canada.
| | - Lorena R R Gianotti
- Department of Social Neuroscience and Social Psychology, Institute of Psychology, University of Bern, Bern, Switzerland.
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22
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Penalver-Andres JA, Buetler KA, Koenig T, Müri RM, Marchal-Crespo L. Resting-State Functional Networks Correlate with Motor Performance in a Complex Visuomotor Task: An EEG Microstate Pilot Study on Healthy Individuals. Brain Topogr 2024; 37:590-607. [PMID: 36566448 PMCID: PMC11199229 DOI: 10.1007/s10548-022-00934-9] [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: 07/10/2022] [Accepted: 12/05/2022] [Indexed: 12/26/2022]
Abstract
Developing motor and cognitive skills is needed to achieve expert (motor) performance or functional recovery from a neurological condition, e.g., after stroke. While extensive practice plays an essential role in the acquisition of good motor performance, it is still unknown whether certain person-specific traits may predetermine the rate of motor learning. In particular, learners' functional brain organisation might play an important role in appropriately performing motor tasks. In this paper, we aimed to study how two critical cognitive brain networks-the Attention Network (AN) and the Default Mode Network (DMN)-affect the posterior motor performance in a complex visuomotor task: virtual surfing. We hypothesised that the preactivation of the AN would affect how participants divert their attention towards external stimuli, resulting in robust motor performance. Conversely, the excessive involvement of the DMN-linked to internally diverted attention and mind-wandering-would be detrimental for posterior motor performance. We extracted seven widely accepted microstates-representing participants mind states at rest-out of the Electroencephalography (EEG) resting-state recordings of 36 healthy volunteers, prior to execution of the virtual surfing task. By correlating neural biomarkers (microstates) and motor behavioural metrics, we confirmed that the preactivation of the posterior DMN was correlated with poor posterior performance in the motor task. However, we only found a non-significant association between AN preactivation and the posterior motor performance. In this EEG study, we propose the preactivation of the posterior DMN-imaged using EEG microstates-as a neural trait related to poor posterior motor performance. Our findings suggest that the role of the executive control system is to preserve an homeostasis between the AN and the DMN. Therefore, neurofeedback-based downregulation of DMN preactivation could help optimise motor training.
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Affiliation(s)
- Joaquin A Penalver-Andres
- Motor Learning and Neurorehabilitation Laboratory, ARTORG Center for Biomedical Engineering Research, University of Bern, Bern, Switzerland.
- Psychosomatic Medicine, Department of Neurology, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland.
| | - Karin A Buetler
- Motor Learning and Neurorehabilitation Laboratory, ARTORG Center for Biomedical Engineering Research, University of Bern, Bern, Switzerland
| | - Thomas Koenig
- Translational Research Center, University Hospital of Psychiatry, University of Bern, Bern, Switzerland
| | - René M Müri
- Perception and Eye Movement Laboratory, Department of Biomedical Research (DBMR) and Department of Neurology, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland
- Gerontechnology and Rehabilitation Group, ARTORG Center for Biomedical Engineering Research, University of Bern, Bern, Switzerland
| | - Laura Marchal-Crespo
- Motor Learning and Neurorehabilitation Laboratory, ARTORG Center for Biomedical Engineering Research, University of Bern, Bern, Switzerland
- Department of Cognitive Robotics, Delft University of Technology, Delft, The Netherlands
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23
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Bagdasarov A, Roberts K, Brunet D, Michel CM, Gaffrey MS. Exploring the Association Between EEG Microstates During Resting-State and Error-Related Activity in Young Children. Brain Topogr 2024; 37:552-570. [PMID: 38141125 PMCID: PMC11199242 DOI: 10.1007/s10548-023-01030-2] [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: 04/26/2023] [Accepted: 12/11/2023] [Indexed: 12/24/2023]
Abstract
The error-related negativity (ERN) is a negative deflection in the electroencephalography (EEG) waveform at frontal-central scalp sites that occurs after error commission. The relationship between the ERN and broader patterns of brain activity measured across the entire scalp that support error processing during early childhood is unclear. We examined the relationship between the ERN and EEG microstates - whole-brain patterns of dynamically evolving scalp potential topographies that reflect periods of synchronized neural activity - during both a go/no-go task and resting-state in 90, 4-8-year-old children. The mean amplitude of the ERN was quantified during the -64 to 108 millisecond (ms) period of time relative to error commission, which was determined by data-driven microstate segmentation of error-related activity. We found that greater magnitude of the ERN associated with greater global explained variance (GEV; i.e., the percentage of total variance in the data explained by a given microstate) of an error-related microstate observed during the same -64 to 108 ms period (i.e., error-related microstate 3), and to greater anxiety risk as measured by parent-reported behavioral inhibition. During resting-state, six data-driven microstates were identified. Both greater magnitude of the ERN and greater GEV values of error-related microstate 3 associated with greater GEV values of resting-state microstate 4, which showed a frontal-central scalp topography. Source localization results revealed overlap between the underlying neural generators of error-related microstate 3 and resting-state microstate 4 and canonical brain networks (e.g., ventral attention) known to support the higher-order cognitive processes involved in error processing. Taken together, our results clarify how individual differences in error-related and intrinsic brain activity are related and enhance our understanding of developing brain network function and organization supporting error processing during early childhood.
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Affiliation(s)
- Armen Bagdasarov
- Department of Psychology & Neuroscience, Duke University, Reuben-Cooke Building, 417 Chapel Drive, Durham, NC, 27708, USA.
| | - Kenneth Roberts
- Duke Institute for Brain Sciences, Duke University, 308 Research Drive, Durham, NC, 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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24
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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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25
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Liu W, Guo Y, Xie J, Wu Y, Zhao D, Xing Z, Fu X, Zhou S, Zhang H, Wang X. Establishment and validation of a bad outcomes prediction model based on EEG and clinical parameters in prolonged disorder of consciousness. Front Hum Neurosci 2024; 18:1387471. [PMID: 38952644 PMCID: PMC11215084 DOI: 10.3389/fnhum.2024.1387471] [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: 02/17/2024] [Accepted: 06/04/2024] [Indexed: 07/03/2024] Open
Abstract
Objective This study aimed to explore the electroencephalogram (EEG) indicators and clinical factors that may lead to poor prognosis in patients with prolonged disorder of consciousness (pDOC), and establish and verify a clinical predictive model based on these factors. Methods This study included 134 patients suffering from prolonged disorder of consciousness enrolled in our department of neurosurgery. We collected the data of sex, age, etiology, coma recovery scales (CRS-R) score, complications, blood routine, liver function, coagulation and other laboratory tests, resting EEG data and follow-up after discharge. These patients were divided into two groups: training set (n = 107) and verification set (n = 27). These patients were divided into a training set of 107 and a validation set of 27 for this study. Univariate and multivariate regression analysis were used to determine the factors affecting the poor prognosis of pDOC and to establish nomogram model. We use the receiver operating characteristic (ROC) and calibration curves to quantitatively test the effectiveness of the training set and the verification set. In order to further verify the clinical practical value of the model, we use decision curve analysis (DCA) to evaluate the model. Result The results from univariate and multivariate logistic regression analyses suggested that an increased frequency of occurrence microstate A, reduced CRS-R scores at the time of admission, the presence of episodes associated with paroxysmal sympathetic hyperactivity (PSH), and decreased fibrinogen levels all function as independent prognostic factors. These factors were used to construct the nomogram. The training and verification sets had areas under the curve of 0.854 and 0.920, respectively. Calibration curves and DCA demonstrated good model performance and significant clinical benefits in both sets. Conclusion This study is based on the use of clinically available and low-cost clinical indicators combined with EEG to construct a highly applicable and accurate model for predicting the adverse prognosis of patients with prolonged disorder of consciousness. It provides an objective and reliable tool for clinicians to evaluate the prognosis of prolonged disorder of consciousness, and helps clinicians to provide personalized clinical care and decision-making for patients with prolonged disorder of consciousness and their families.
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Affiliation(s)
- Wanqing Liu
- Department of Neurosurgery, The Fifth Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Yongkun Guo
- Department of Neurosurgery, The Fifth Affiliated Hospital of Zhengzhou University, Zhengzhou, China
- Henan Engineering Research Center for Prevention and Treatment of Brain Injuries, Zhengzhou, China
- Henan Key Laboratory of Brain Science and Brain Computer Interface Technology, Zhengzhou, China
| | - Jingwei Xie
- Department of Neurosurgery, The Fifth Affiliated Hospital of Zhengzhou University, Zhengzhou, China
- Henan Engineering Research Center for Prevention and Treatment of Brain Injuries, Zhengzhou, China
- Henan Key Laboratory of Brain Science and Brain Computer Interface Technology, Zhengzhou, China
| | - Yanzhi Wu
- Department of Neurosurgery, The Fifth Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Dexiao Zhao
- Department of Neurosurgery, The Fifth Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Zhe Xing
- Department of Neurosurgery, The Fifth Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Xudong Fu
- Department of Neurosurgery, The Fifth Affiliated Hospital of Zhengzhou University, Zhengzhou, China
- Henan Engineering Research Center for Prevention and Treatment of Brain Injuries, Zhengzhou, China
- Henan Key Laboratory of Brain Science and Brain Computer Interface Technology, Zhengzhou, China
| | - Shaolong Zhou
- Department of Neurosurgery, The Fifth Affiliated Hospital of Zhengzhou University, Zhengzhou, China
- Henan Engineering Research Center for Prevention and Treatment of Brain Injuries, Zhengzhou, China
- Henan Key Laboratory of Brain Science and Brain Computer Interface Technology, Zhengzhou, China
| | - Hengwei Zhang
- Department of Neurosurgery, The Fifth Affiliated Hospital of Zhengzhou University, Zhengzhou, China
- Henan Engineering Research Center for Prevention and Treatment of Brain Injuries, Zhengzhou, China
- Henan Key Laboratory of Brain Science and Brain Computer Interface Technology, Zhengzhou, China
| | - Xinjun Wang
- Department of Neurosurgery, The Fifth Affiliated Hospital of Zhengzhou University, Zhengzhou, China
- Henan Engineering Research Center for Prevention and Treatment of Brain Injuries, Zhengzhou, China
- Henan Key Laboratory of Brain Science and Brain Computer Interface Technology, Zhengzhou, China
- Department of Neurosurgery, The Third Affiliated Hospital of Zhengzhou University, Zhengzhou, China
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26
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Keihani A, Mayeli A, Donati F, Janssen SA, Huston CA, Colacot RM, Al Zoubi O, Murphy M, Ferrarelli F. Changes in electroencephalographic microstates between evening and morning are associated with overnight sleep slow waves in healthy individuals. Sleep 2024; 47:zsae053. [PMID: 38416814 PMCID: PMC11168754 DOI: 10.1093/sleep/zsae053] [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: 10/26/2023] [Revised: 02/05/2024] [Indexed: 03/01/2024] Open
Abstract
STUDY OBJECTIVES Microstates are semi-stable voltage topographies that account for most of electroencephalogram (EEG) variance. However, the impact of time of the day and sleep on microstates has not been examined. To address this gap, we assessed whether microstates differed between the evening and morning and whether sleep slow waves correlated with microstate changes in healthy participants. METHODS Forty-five healthy participants were recruited. Each participant underwent 6 minutes of resting state EEG recordings in the evening and morning, interleaved by sleep EEGs. Evening-to-morning changes in microstate duration, coverage, and occurrence were assessed. Furthermore, correlation between microstate changes and sleep slow-wave activity (SWA) and slow-wave density (SWD) were performed. RESULTS Two-way ANOVAs with microstate class (A, B, C, and D) and time (evening and morning) revealed significant microstate class × time interaction for duration (F(44) = 5.571, p = 0.002), coverage (F(44) = 6.833, p = 0.001), and occurrence (F(44) = 5.715, p = 0.002). Post hoc comparisons showed significant effects for microstate C duration (padj = 0.048, Cohen's d = -0.389), coverage (padj = 0.002, Cohen's d = -0.580), and occurrence (padj = 0.002, Cohen's d = -0.606). Topographic analyses revealed inverse correlations between SWD, but not SWA, and evening-to-morning changes in microstate C duration (r = -0.51, padj = 0.002), coverage (r = -0.45, padj = 0.006), and occurrence (r = -0.38, padj = 0.033). CONCLUSIONS Microstate characteristics showed significant evening-to-morning changes associated with, and possibly regulated by, sleep slow waves. These findings suggest that future microstate studies should control for time of day and sleep effects.
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Affiliation(s)
- Ahmadreza Keihani
- Department of Psychiatry, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
| | - Ahmad Mayeli
- Department of Psychiatry, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
| | - Francesco Donati
- Department of Psychiatry, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
| | - Sabine A Janssen
- Department of Psychiatry, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
| | - Chloe A Huston
- Department of Psychiatry, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
| | - Rebekah M Colacot
- Department of Psychiatry, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
| | - Obada Al Zoubi
- McLean Hospital, Harvard Medical School, Boston, MA, USA
| | - Michael Murphy
- Department of Psychiatry, McLean Hospital, Harvard Medical School, Boston, MA, USA
| | - Fabio Ferrarelli
- Department of Psychiatry, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
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27
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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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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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Guo Q, Liu S, Wang L, Feng K, Yang S. Analysis of microstate features for Parkinson's disease based on reliability validation. J Neurosci Methods 2024; 406:110115. [PMID: 38531478 DOI: 10.1016/j.jneumeth.2024.110115] [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: 11/29/2023] [Revised: 03/13/2024] [Accepted: 03/20/2024] [Indexed: 03/28/2024]
Abstract
BACKGROUND Parkinson's disease (PD) is a disorder with abnormal changes in brain activity. The lack of objective indicators makes the assessment of PD progression difficult. Assessment of brain activity changes in PD may offer a potential solution. NEW METHOD Electroencephalogram (EEG) microstates reflect global dynamic changes in the brain. Therefore, we utilized microstates to assess changes in PD brain activity. However, the effect of epoch duration on the reliability of microstate analyses in PD is unclear. Thus, we first assessed the effect of data duration on the reliability of microstate topography and temporal features in PD and older healthy individuals. According to the reliability assessment, EEG epochs with high reliability were selected for microstate analysis in PD. Finally, we investigated the correlation between microstate features and clinical scales to determine whether these features could serve as objective indicators to evaluate PD progression. RESULTS Microstate analysis features that show high reliability for 3 min and above epoch durations. The topology of microstate D was significantly changed in PD compared to healthy controls, as well as the temporal features of microstates C and D. Additionally, the occurrence of C was negatively correlated with MoCA, and the duration of D was positively correlated with UPDRS. COMPARISON WITH EXISTING METHOD(S) High reliability of PD microstate features obtained by our approach. CONCLUSION EEG for PD microstate analysis should be at least 3 min. Microstate analysis is expected to provide new ideas and objective indicators for assessing Parkinson's disease progression in the clinical setting.
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Affiliation(s)
- Qingfang Guo
- Hebei Key Laboratory of Bioelectromagnetics and Neural Engineering, School of Health Sciences and Biomedical Engineering, Hebei University of Technology, Tianjin 300131, China; State Key Laboratory of Reliable and Intelligence of Electrical Equipment, Hebei University of Technology, Tianjin 300130, China
| | - Shuo Liu
- Hebei Key Laboratory of Bioelectromagnetics and Neural Engineering, School of Health Sciences and Biomedical Engineering, Hebei University of Technology, Tianjin 300131, China; State Key Laboratory of Reliable and Intelligence of Electrical Equipment, Hebei University of Technology, Tianjin 300130, China
| | - Lei Wang
- Hebei Key Laboratory of Bioelectromagnetics and Neural Engineering, School of Health Sciences and Biomedical Engineering, Hebei University of Technology, Tianjin 300131, China; State Key Laboratory of Reliable and Intelligence of Electrical Equipment, Hebei University of Technology, Tianjin 300130, China
| | - Keke Feng
- Department of Neurosurgery, Tianjin Huanhu Hospital, Tianjin, China.
| | - Shuo Yang
- Hebei Key Laboratory of Bioelectromagnetics and Neural Engineering, School of Health Sciences and Biomedical Engineering, Hebei University of Technology, Tianjin 300131, China; State Key Laboratory of Reliable and Intelligence of Electrical Equipment, Hebei University of Technology, Tianjin 300130, China.
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Sahoo A, Tayade P, Muthukrishnan SP, Kaur S, Sharma R, Nayyar M. Effect of short-term exposure to Raag Bilawal of North Indian classical music on young Indian adults: a high-density electroencephalogram microstate study. Pan Afr Med J 2024; 48:24. [PMID: 39220561 PMCID: PMC11364891 DOI: 10.11604/pamj.2024.48.24.40977] [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: 07/05/2023] [Accepted: 05/09/2024] [Indexed: 09/04/2024] Open
Abstract
Introduction the objective of the study was to find out the microstate map topographies and their parameters generated during the resting state and during listening to North Indian classical Music Raag 'the Raag Bilawal'. It was hypothesized that in the resting state and during listening to music conditions, there would be a difference in microstate parameters i.e. mean duration, global explained variance (GEV), and time coverage. Methods a 128-channel electroencephalogram (EEG) was recorded for 12 Indian subjects (average age 26.1+1.4 years) while resting and listening to music using the EEG microstate investigation. Investigation and comparison of the microstate parameters were the mean duration, global explained variance (GEV), and time coverage between both conditions were performed. Results seven microstate maps were found to represent the resting state and listening to music condition, four canonical and three novel maps. No statistically significant difference was found between the two conditions for time coverage and mean duration. The statistical significance levels of the map-1, map-2, map-3, map-4, map-5, map-6, and map-7 for the mean duration were 0.4, 0.6, 0.97, 0.34, 0.32, 0.69, and 0.29 respectively; and for time coverage were 0.92, 0.92, 0.96, 0.64, 0.78, 0.38, and 0.76 respectively. Map-1, map-4, and map-7 were the three novel maps we found in our study. Conclusion similarities regarding stability and predominance of maps with small vulnerability exist in both conditions indicating that phonological, visual, and dorsal attention networks may be activated in both resting state and listening to music condition.
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Affiliation(s)
- Abhisek Sahoo
- Department of Physiology, All India Institute of Medical Sciences, New Delhi, India
| | - Prashant Tayade
- Department of Physiology, All India Institute of Medical Sciences, New Delhi, India
| | | | - Simran Kaur
- Department of Physiology, All India Institute of Medical Sciences, New Delhi, India
| | - Ratna Sharma
- Department of Physiology, All India Institute of Medical Sciences, New Delhi, India
| | - Madhavi Nayyar
- Department of Physiology, All India Institute of Medical Sciences, New Delhi, India
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Nazare K, Tomescu MI. Valence-specific EEG microstate modulations during self-generated affective states. Front Psychol 2024; 15:1300416. [PMID: 38855303 PMCID: PMC11160840 DOI: 10.3389/fpsyg.2024.1300416] [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: 09/23/2023] [Accepted: 04/26/2024] [Indexed: 06/11/2024] Open
Abstract
Introduction This study aims to explore the temporal dynamics of brain networks involved in self-generated affective states, specifically focusing on modulating these states in both positive and negative valences. The overarching goal is to contribute to a deeper understanding of the neurodynamic patterns associated with affective regulation, potentially informing the development of biomarkers for therapeutic interventions in mood and anxiety disorders. Methods Utilizing EEG microstate analysis during self-generated affective states, we investigated the temporal dynamics of five distinct microstates across different conditions, including baseline resting state and self-generated states of positive valence (e.g., awe, contentment) and negative valence (e.g., anger, fear). Results The study revealed noteworthy modulations in microstate dynamics during affective states. Additionally, valence-specific mechanisms of spontaneous affective regulation were identified. Negative valence affective states were characterized by the heightened presence of attention-associated microstates and reduced occurrence of salience-related microstates during negative valence states. In contrast, positive valence affective states manifested a prevalence of microstates related to visual/autobiographical memory and a reduced presence of auditory/language-associated microstates compared to both baseline and negative valence states. Discussion This study contributes to the field by employing EEG microstate analysis to discern the temporal dynamics of brain networks involved in self-generated affective states. Insights from this research carry significant implications for understanding neurodynamic patterns in affective regulation. The identification of valence-specific modulations and mechanisms has potential applications in developing biomarkers for mood and anxiety disorders, offering novel avenues for therapeutic interventions.
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Affiliation(s)
- Karina Nazare
- CINETic Center, Department of Research and Development, National University of Theatre and Film “I.L. Caragiale”, Bucharest, Romania
- Faculty of Automatic Control and Computers, POLITEHNICA University of Bucharest, Bucharest, Romania
| | - Miralena I. Tomescu
- CINETic Center, Department of Research and Development, National University of Theatre and Film “I.L. Caragiale”, Bucharest, Romania
- Department of Psychology, Faculty of Educational Sciences, University “Stefan cel Mare” of Suceava, Suceava, Romania
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Xue R, Li X, Deng W, Liang C, Chen M, Chen J, Liang S, Wei W, Zhang Y, Yu H, Xu Y, Guo W, Li T. Shared and distinct electroencephalogram microstate abnormalities across schizophrenia, bipolar disorder, and depression. Psychol Med 2024:1-8. [PMID: 38738283 DOI: 10.1017/s0033291724001132] [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] [Indexed: 05/14/2024]
Abstract
BACKGROUND Microstates of an electroencephalogram (EEG) are canonical voltage topographies that remain quasi-stable for 90 ms, serving as the foundational elements of brain dynamics. Different changes in EEG microstates can be observed in psychiatric disorders like schizophrenia (SCZ), major depressive disorder (MDD), and bipolar disorder (BD). However, the similarities and disparatenesses in whole-brain dynamics on a subsecond timescale among individuals diagnosed with SCZ, BD, and MDD are unclear. METHODS This study included 1112 participants (380 individuals diagnosed with SCZ, 330 with BD, 212 with MDD, and 190 demographically matched healthy controls [HCs]). We assembled resting-state EEG data and completed a microstate analysis of all participants using a cross-sectional design. RESULTS Our research indicates that SCZ, BD, and MDD exhibit distinct patterns of transition among the four EEG microstate states (A, B, C, and D). The analysis of transition probabilities showed a higher frequency of switching from microstates A to B and from B to A in each patient group compared to the HC group, and less frequent transitions from microstates A to C and from C to A in the SCZ and MDD groups compared to the HC group. And the probability of the microstate switching from C to D and D to C in the SCZ group significantly increased compared to those in the patient and HC groups. CONCLUSIONS Our findings provide crucial insights into the abnormalities involved in distributing neural assets and enabling proper transitions between different microstates in patients with major psychiatric disorders.
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Affiliation(s)
- Rui Xue
- Affiliated Mental Health Center & Hangzhou Seventh People's Hospital and School of Brain Science and Brain Medicine, Zhejiang University School of Medicine, Hangzhou, 310058, China
- Psychiatric Laboratory, State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu 610041, Sichuan, China
- Liangzhu Laboratory, MOE Frontier Science Center for Brain Science and Brain-machine Integration, State Key Laboratory of Brain-machine Intelligence, Zhejiang University, 1369 West Wenyi Road, Hangzhou 311121, China
- NHC and CAMS Key Laboratory of Medical Neurobiology, Zhejiang University, Hangzhou 310058, China
| | - Xiaojing Li
- Affiliated Mental Health Center & Hangzhou Seventh People's Hospital and School of Brain Science and Brain Medicine, Zhejiang University School of Medicine, Hangzhou, 310058, China
- Liangzhu Laboratory, MOE Frontier Science Center for Brain Science and Brain-machine Integration, State Key Laboratory of Brain-machine Intelligence, Zhejiang University, 1369 West Wenyi Road, Hangzhou 311121, China
- NHC and CAMS Key Laboratory of Medical Neurobiology, Zhejiang University, Hangzhou 310058, China
| | - Wei Deng
- Affiliated Mental Health Center & Hangzhou Seventh People's Hospital and School of Brain Science and Brain Medicine, Zhejiang University School of Medicine, Hangzhou, 310058, China
- Liangzhu Laboratory, MOE Frontier Science Center for Brain Science and Brain-machine Integration, State Key Laboratory of Brain-machine Intelligence, Zhejiang University, 1369 West Wenyi Road, Hangzhou 311121, China
- NHC and CAMS Key Laboratory of Medical Neurobiology, Zhejiang University, Hangzhou 310058, China
| | - Chengqian Liang
- School of Mental Health, Wenzhou Medical University, Wenzhou, Zhejiang, China
| | - Mingxia Chen
- School of Mental Health, Wenzhou Medical University, Wenzhou, Zhejiang, China
| | - Jianning Chen
- Affiliated Mental Health Center & Hangzhou Seventh People's Hospital and School of Brain Science and Brain Medicine, Zhejiang University School of Medicine, Hangzhou, 310058, China
- Liangzhu Laboratory, MOE Frontier Science Center for Brain Science and Brain-machine Integration, State Key Laboratory of Brain-machine Intelligence, Zhejiang University, 1369 West Wenyi Road, Hangzhou 311121, China
- NHC and CAMS Key Laboratory of Medical Neurobiology, Zhejiang University, Hangzhou 310058, China
| | - Sugai Liang
- Affiliated Mental Health Center & Hangzhou Seventh People's Hospital and School of Brain Science and Brain Medicine, Zhejiang University School of Medicine, Hangzhou, 310058, China
- Liangzhu Laboratory, MOE Frontier Science Center for Brain Science and Brain-machine Integration, State Key Laboratory of Brain-machine Intelligence, Zhejiang University, 1369 West Wenyi Road, Hangzhou 311121, China
- NHC and CAMS Key Laboratory of Medical Neurobiology, Zhejiang University, Hangzhou 310058, China
| | - Wei Wei
- Affiliated Mental Health Center & Hangzhou Seventh People's Hospital and School of Brain Science and Brain Medicine, Zhejiang University School of Medicine, Hangzhou, 310058, China
- Liangzhu Laboratory, MOE Frontier Science Center for Brain Science and Brain-machine Integration, State Key Laboratory of Brain-machine Intelligence, Zhejiang University, 1369 West Wenyi Road, Hangzhou 311121, China
- NHC and CAMS Key Laboratory of Medical Neurobiology, Zhejiang University, Hangzhou 310058, China
| | - Yamin Zhang
- Affiliated Mental Health Center & Hangzhou Seventh People's Hospital and School of Brain Science and Brain Medicine, Zhejiang University School of Medicine, Hangzhou, 310058, China
- Liangzhu Laboratory, MOE Frontier Science Center for Brain Science and Brain-machine Integration, State Key Laboratory of Brain-machine Intelligence, Zhejiang University, 1369 West Wenyi Road, Hangzhou 311121, China
- NHC and CAMS Key Laboratory of Medical Neurobiology, Zhejiang University, Hangzhou 310058, China
| | - Hua Yu
- Affiliated Mental Health Center & Hangzhou Seventh People's Hospital and School of Brain Science and Brain Medicine, Zhejiang University School of Medicine, Hangzhou, 310058, China
- Liangzhu Laboratory, MOE Frontier Science Center for Brain Science and Brain-machine Integration, State Key Laboratory of Brain-machine Intelligence, Zhejiang University, 1369 West Wenyi Road, Hangzhou 311121, China
- NHC and CAMS Key Laboratory of Medical Neurobiology, Zhejiang University, Hangzhou 310058, China
| | - Yan Xu
- Affiliated Mental Health Center & Hangzhou Seventh People's Hospital and School of Brain Science and Brain Medicine, Zhejiang University School of Medicine, Hangzhou, 310058, China
- Liangzhu Laboratory, MOE Frontier Science Center for Brain Science and Brain-machine Integration, State Key Laboratory of Brain-machine Intelligence, Zhejiang University, 1369 West Wenyi Road, Hangzhou 311121, China
- NHC and CAMS Key Laboratory of Medical Neurobiology, Zhejiang University, Hangzhou 310058, China
| | - Wanjun Guo
- Affiliated Mental Health Center & Hangzhou Seventh People's Hospital and School of Brain Science and Brain Medicine, Zhejiang University School of Medicine, Hangzhou, 310058, China
- Liangzhu Laboratory, MOE Frontier Science Center for Brain Science and Brain-machine Integration, State Key Laboratory of Brain-machine Intelligence, Zhejiang University, 1369 West Wenyi Road, Hangzhou 311121, China
- NHC and CAMS Key Laboratory of Medical Neurobiology, Zhejiang University, Hangzhou 310058, China
| | - Tao Li
- Affiliated Mental Health Center & Hangzhou Seventh People's Hospital and School of Brain Science and Brain Medicine, Zhejiang University School of Medicine, Hangzhou, 310058, China
- Liangzhu Laboratory, MOE Frontier Science Center for Brain Science and Brain-machine Integration, State Key Laboratory of Brain-machine Intelligence, Zhejiang University, 1369 West Wenyi Road, Hangzhou 311121, China
- NHC and CAMS Key Laboratory of Medical Neurobiology, Zhejiang University, Hangzhou 310058, China
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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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Rubega M, Passarotto E, Paramento M, Formaggio E, Masiero S. EEG Microstate as a Marker of Adolescent Idiopathic Scoliosis. IEEE OPEN JOURNAL OF ENGINEERING IN MEDICINE AND BIOLOGY 2024; 5:339-344. [PMID: 38899012 PMCID: PMC11186641 DOI: 10.1109/ojemb.2024.3399469] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2023] [Revised: 03/22/2024] [Accepted: 05/07/2024] [Indexed: 06/21/2024] Open
Abstract
The pathophysiology of Adolescent Idiopathic Scoliosis (AIS) is not yet fully understood, but multifactorial hypotheses have been proposed that include defective central nervous system (CNS) control of posture, biomechanics, and body schema alterations. To deepen CNS control of posture in AIS, electroencephalographic (EEG) activity during a simple balance task in adolescents with and without AIS was parsed into EEG microstates. Microstates are quasi-stable spatial distributions of the electric potential of the brain that last tens of milliseconds. The spatial distribution of the EEG characterised by the orientation from left-frontal to right-posterior remains stable for a greater amount of time in AIS compared to controls. This spatial distribution of EEG, commonly named in the literature as class B, has been found to be correlated with the visual resting state network. Both vision and proprioception networks provide critical information in mapping the extrapersonal environment. This neurophysiological marker probably unveils an alteration in the postural control mechanism in AIS, suggesting a higher information processing load due to the increased postural demands caused by scoliosis.
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Affiliation(s)
- M. Rubega
- Department of NeuroscienceUniversity of Padova, Section of Rehabilitation35128PadovaItaly
| | - E. Passarotto
- Department of NeuroscienceUniversity of Padova, Section of Rehabilitation35128PadovaItaly
| | - M. Paramento
- Department of NeuroscienceUniversity of Padova, Section of Rehabilitation35128PadovaItaly
- Department of Information EngineeringUniversity of Padova35131PadovaItaly
| | - E. Formaggio
- Department of NeuroscienceUniversity of Padova, Section of Rehabilitation35128PadovaItaly
| | - S. Masiero
- Department of NeuroscienceUniversity of Padova, Section of Rehabilitation35128PadovaItaly
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Li Y, Yang B, Ma J, Gao S, Zeng H, Wang W. Assessment of rTMS treatment effects for methamphetamine use disorder based on EEG microstates. Behav Brain Res 2024; 465:114959. [PMID: 38494128 DOI: 10.1016/j.bbr.2024.114959] [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: 12/10/2023] [Revised: 03/10/2024] [Accepted: 03/14/2024] [Indexed: 03/19/2024]
Abstract
Microstates have been proposed as topographical maps representing large-scale resting-state networks and have recently been suggested as markers for methamphetamine use disorder (MUD). However, it is unknown whether and how they change after repetitive transcranial magnetic stimulation (rTMS) intervention. This study included a comprehensive subject population to investigate the effect of rTMS on MUD microstates. 34 patients with MUD underwent a 4-week randomized, double-blind rTMS intervention (active=17, sham=17). Two resting-state EEG recordings and VAS evaluations were conducted before and after the intervention period. Additionally, 17 healthy individuals were included as baseline controls. The modified k-means clustering method was used to calculate four microstates (MS-A∼MS-D) of EEG, and the FC network was also analyzed. The differences in microstate indicators between groups and within groups were compared. The durations of MS-A and MS-B microstates in patients with MUD were significantly lower than that in HC but showed significant improvements after rTMS intervention. Changes in microstate indicators were found to be significantly correlated with changes in craving level. Furthermore, selective modulation of the resting-state network by rTMS was observed in the FC network. The findings indicate that changes in microstates in patients with MUD are associated with craving level improvement following rTMS, suggesting they may serve as valuable evaluation markers.
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Affiliation(s)
- Yongcong Li
- School of Medicine, School of Mechatronic Engineering and Automation, Shanghai University, Shanghai 200444, China.
| | - Banghua Yang
- School of Medicine, School of Mechatronic Engineering and Automation, Shanghai University, Shanghai 200444, China.
| | - Jun Ma
- School of Medicine, School of Mechatronic Engineering and Automation, Shanghai University, Shanghai 200444, China
| | - Shouwei Gao
- School of Medicine, School of Mechatronic Engineering and Automation, Shanghai University, Shanghai 200444, China
| | - Hui Zeng
- School of Medicine, School of Mechatronic Engineering and Automation, Shanghai University, Shanghai 200444, China
| | - Wen Wang
- Department of Radiology & Functional and Molecular Imaging Key Lab of Shaanxi Province, Tangdu Hospital, Air Force Medical University, Shaanxi 710038, China.
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Cao Q, Wang Y, Ji Y, He Z, Lei X. Resting-State EEG Reveals Abnormal Microstate Characteristics of Depression with Insomnia. Brain Topogr 2024; 37:388-396. [PMID: 36892651 DOI: 10.1007/s10548-023-00949-w] [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: 09/22/2022] [Accepted: 02/16/2023] [Indexed: 03/10/2023]
Abstract
Previous research revealed various aspects of resting-state EEG for depression and insomnia. However, the EEG characteristics of depressed subjects with insomnia are rarely studied, especially EEG microstates that capture the dynamic activities of the large-scale brain network. To fill these research gaps, the present study collected resting-state EEG data from 32 subclinical depression subjects with insomnia (SDI), 31 subclinical depression subjects without insomnia (SD), and 32 healthy controls (HCs). Four topographic maps were generated from clean EEG data after clustering and rearrangement. Temporal characteristics were obtained for statistical analysis, including cross-group variance analysis (ANOVA) and intra-group correlation analysis. In our study, the global clustering of all individuals in the EEG microstate analysis revealed the four previously discovered categories of microstates (A, B, C, and D). The occurrence of microstate B was lower in SDI than in SD and HC subjects. The correlation analysis showed that the total Pittsburgh Sleep Quality Index (PSQI) score negatively correlated with the occurrence of microstate C in SDI (r = - 0.415, p < 0.05). Conversely, there was a positive correlation between Self-rating Depression Scale (SDS) scores and the duration of microstate C in SD (r = 0.359, p < 0.05). These results indicate that microstates reflect altered large-scale brain network dynamics in subclinical populations. Abnormalities in the visual network corresponding to microstate B are an electrophysiological characteristic of subclinical individuals with symptoms of depressive insomnia. Further investigation is needed for microstate changes related to high arousal and emotional problems in people suffering from depression and insomnia.
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Affiliation(s)
- Qike Cao
- Sleep and NeuroImaging Center, Faculty of Psychology, Southwest University, Chongqing, 400715, China
- Key Laboratory of Cognition and Personality (Southwest University), Ministry of Education, Chongqing, 400715, China
| | - Yulin Wang
- Sleep and NeuroImaging Center, Faculty of Psychology, Southwest University, Chongqing, 400715, China
- Key Laboratory of Cognition and Personality (Southwest University), Ministry of Education, Chongqing, 400715, China
| | - Yufang Ji
- Sleep and NeuroImaging Center, Faculty of Psychology, Southwest University, Chongqing, 400715, China
- Key Laboratory of Cognition and Personality (Southwest University), Ministry of Education, Chongqing, 400715, China
| | - Zhihui He
- The Ninth People's Hospital of Chongqing, Chongqing, 400700, China
| | - Xu Lei
- Sleep and NeuroImaging Center, Faculty of Psychology, Southwest University, Chongqing, 400715, China.
- Key Laboratory of Cognition and Personality (Southwest University), Ministry of Education, Chongqing, 400715, China.
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Fang S, Zhu C, Zhang J, Wu L, Zhang Y, Huang H, Lin W. EEG microstates in epilepsy with and without cognitive dysfunction: Alteration in intrinsic brain activity. Epilepsy Behav 2024; 154:109729. [PMID: 38513568 DOI: 10.1016/j.yebeh.2024.109729] [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: 10/24/2023] [Revised: 02/27/2024] [Accepted: 02/27/2024] [Indexed: 03/23/2024]
Abstract
OBJECTIVE This study aims to investigate the difference between epilepsy comorbid with and without cognitive dysfunction. METHOD Participants were classified into patients with epilepsy comorbid cognitive dysfunction (PCCD) and patients with epilepsy without comorbid cognitive dysfunction (nPCCD). Microstate analysis was applied based on 20-channel electroencephalography (EEG) to detect the dynamic changes in the whole brain. The coverage, occurrence per second, duration, and transition probability were calculated. RESULT The occurrence per second and the coverage of microstate B in the PCCD group were higher than that of the nPCCD group. Coverage in microstate D was lower in the PCCD group than in the nPCCD group. In addition, the PCCD group has a higher probability of A to B and B to A transitions and a lower probability of A to D and D to A transitions. CONCLUSION Our research scrutinizes the disparities observed within EEG microstates among epilepsy patients both with and without comorbid cognitive dysfunction. SIGNIFICANCE EEG microstate analysis offers a novel metric for assessing neuropsychiatric disorders and supplies evidence for investigating the mechanisms and the dynamic change of epilepsy comorbid cognitive dysfunction.
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Affiliation(s)
- Shenzhi Fang
- Department of Neurology, Fujian Medical University Union Hospital, Fuzhou, PR China
| | - Chaofeng Zhu
- Department of Neurology, Fujian Medical University Union Hospital, Fuzhou, PR China
| | - Jinying Zhang
- Department of Neurology, Fujian Medical University Union Hospital, Fuzhou, PR China
| | - Luyan Wu
- Department of Neurology, Fujian Medical University Union Hospital, Fuzhou, PR China
| | - Yuying Zhang
- Department of Neurology, Fujian Medical University Union Hospital, Fuzhou, PR China
| | - Huapin Huang
- Department of Neurology, Fujian Medical University Union Hospital, Fuzhou, PR China; Fujian Key Laboratory of Molecular Neurology, Fuzhou, PR China; Department of Geriatrics, Fujian Medical University Union Hospital, Fuzhou, PR China.
| | - Wanhui Lin
- Department of Neurology, Fujian Medical University Union Hospital, Fuzhou, PR China; Fujian Key Laboratory of Molecular Neurology, Fuzhou, PR China.
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Jia H, Wu X, Zhang X, Guo M, Yang C, Wang E. Resting-state EEG Microstate Features Can Quantitatively Predict Autistic Traits in Typically Developing Individuals. Brain Topogr 2024; 37:410-419. [PMID: 37833486 DOI: 10.1007/s10548-023-01010-6] [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: 04/22/2023] [Accepted: 09/20/2023] [Indexed: 10/15/2023]
Abstract
Autism spectrum disorder (ASD) is not a discrete disorder and that symptoms of ASD (i.e., so-called "autistic traits") are found to varying degrees in the general population. Typically developing individuals with sub-clinical yet high-level autistic traits have similar abnormities both in behavioral performances and cortical activation patterns to individuals diagnosed with ASD. Thus it's crucial to develop objective and efficient tools that could be used in the assessment of autistic traits. Here, we proposed a novel machine learning-based assessment of the autistic traits using EEG microstate features derived from a brief resting-state EEG recording. The results showed that: (1) through the Least Absolute Shrinkage and Selection Operator (LASSO) algorithm and correlation analysis, the mean duration of microstate class D, the occurrence rate of microstate class A, the time coverage of microstate class D and the transition rate from microstate class B to D were selected to be crucial microstate features which could be used in autistic traits prediction; (2) in the support vector regression (SVR) model, which was constructed to predict the participants' autistic trait scores using these four microstate features, the out-of-sample predicted autistic trait scores showed a significant and good match with the self-reported scores. These results suggest that the resting-state EEG microstate analysis technique can be used to predict autistic trait to some extent.
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Affiliation(s)
- Huibin Jia
- Institute of Psychology and Behavior, Henan University, Kaifeng, 475004, China
- School of Psychology, Henan University, Kaifeng, 475004, China
| | - Xiangci Wu
- Institute of Psychology and Behavior, Henan University, Kaifeng, 475004, China
- School of Psychology, Henan University, Kaifeng, 475004, China
| | - Xiaolin Zhang
- Institute of Psychology and Behavior, Henan University, Kaifeng, 475004, China
- School of Psychology, Henan University, Kaifeng, 475004, China
| | - Meiling Guo
- Institute of Psychology and Behavior, Henan University, Kaifeng, 475004, China
- School of Psychology, Henan University, Kaifeng, 475004, China
| | - Chunying Yang
- School of Special Education, Zhengzhou Normal University, Zhengzhou, 450000, China.
| | - Enguo Wang
- Institute of Psychology and Behavior, Henan University, Kaifeng, 475004, China.
- School of Psychology, Henan University, Kaifeng, 475004, China.
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Ding Z, Wang Y, Niu Z, Ouyang G, Li X. The effect of EEG microstate on the characteristics of TMS-EEG. Comput Biol Med 2024; 173:108332. [PMID: 38555703 DOI: 10.1016/j.compbiomed.2024.108332] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2024] [Revised: 02/26/2024] [Accepted: 03/17/2024] [Indexed: 04/02/2024]
Abstract
OBJECTIVE Differences in neural states at the time of transcranial magnetic stimulation (TMS) can lead to variations in the effectiveness of TMS stimulation. Strategies that aim to lock neural activity states and improve the precision of stimulation timing in TMS optimization should gradually receive attention. One feasible approach is to utilize microstate locking for TMS stimulation, and understanding the impact of microstates at the time of stimulation on TMS response forms the foundation of this approach. APPROACH TMS-EEG data were extracted from 21 healthy subjects through experiments. Based on the different microstates at the time of stimulation, the trials were classified into four datasets. TMS-evoked potential (TEP), topographical distribution, and natural frequency, were computed for each dataset to explore the differences in TMS-EEG characteristics across different microstates. MAIN RESULTS The N100 component of microstate C group (-2.376 μV) was significantly higher (p = 0.003) than of microstate D group (-1.739 μV), and the P180 component of microstate D group (2.482 μV) was significantly higher (p = 0.024) than of microstate B group (1.766 μV) and slightly higher (p = 0.058) than of microstate C group (1.863 μV) by calculating the ROI. The topographical distribution of TEP components during microstate C and microstate D still retained the template characteristics of the microstate at the time of stimulation, and the natural frequencies did not differ among the four classical microstates. SIGNIFICANCE This study showed the potential for future closed-loop TMS based on microstates and would guiding the development of microstate-based closed-loop TMS techniques.
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Affiliation(s)
- Zhaohuan Ding
- Shien-Ming Wu School of Intelligent Engineering, South China University of Technology, Guangzhou, 510640, China
| | - Yong Wang
- Zhuhai UM Science & Technology Research Institute, Zhuhai, 519031, China
| | - Zikang Niu
- Aviation Psychology Research Office, Air Force Medical Center, Beijing, 100142, China
| | - Gaoxiang Ouyang
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, 100875, China.
| | - Xiaoli Li
- Guangdong Artificial Intelligence and Digital Economy Laboratory (Guangzhou), Guangzhou, 510335, China; School of Automation Science and Engineering, South China University of Technology, Guangzhou, 510641, China.
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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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Chivu A, Pascal SA, Damborská A, Tomescu MI. EEG Microstates in Mood and Anxiety Disorders: A Meta-analysis. Brain Topogr 2024; 37:357-368. [PMID: 37615799 PMCID: PMC11026263 DOI: 10.1007/s10548-023-00999-0] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2023] [Accepted: 08/06/2023] [Indexed: 08/25/2023]
Abstract
To reduce the psycho-social burden increasing attention has focused on brain abnormalities in the most prevalent and highly co-occurring neuropsychiatric disorders, such as mood and anxiety. However, high inter-study variability in these patients results in inconsistent and contradictory alterations in the fast temporal dynamics of large-scale networks as measured by EEG microstates. Thus, in this meta-analysis, we aim to investigate the consistency of these changes to better understand possible common neuro-dynamical mechanisms of these disorders.In the systematic search, twelve studies investigating EEG microstate changes in participants with mood and anxiety disorders and individuals with subclinical depression were included in this meta-analysis, adding up to 787 participants.The results suggest that EEG microstates consistently discriminate mood and anxiety impairments from the general population in patients and subclinical states. Specifically, we found a small significant effect size for B microstates in patients compared to healthy controls, with larger effect sizes for increased B presence in unmedicated patients with comorbidity. In a subgroup meta-analysis of ten mood disorder studies, microstate D showed a significant effect size for decreased presence. When investigating only the two anxiety disorder studies, we found a significantly small effect size for the increased microstate A and a medium effect size for decreased microstate E (one study). However, more studies are needed to elucidate whether these findings are diagnostic-specific markers.Results are discussed in relation to the functional meaning of microstates and possible contribution to an explanatory mechanism of overlapping symptomatology of mood and anxiety disorders.
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Affiliation(s)
- Alina Chivu
- CINETic Center, National University of Theatre and Film "I.L. Caragiale" Bucharest, Bucharest, Romania
- Faculty of Psychology and Educational Sciences, Department of Applied Psychology and Psychotherapy, University of Bucharest, Bucharest, Romania
| | - Simona A Pascal
- Faculty of Psychology and Educational Sciences, Department of Applied Psychology and Psychotherapy, University of Bucharest, Bucharest, Romania
| | - Alena Damborská
- Department of Psychiatry, Faculty of Medicine, Masaryk University and University Hospital Brno, Brno, Czech Republic
- Multimodal and Functional Neuroimaging Research Group, CEITEC - Central European Institute of Technology, Masaryk University, Brno, Czech Republic
| | - Miralena I Tomescu
- CINETic Center, National University of Theatre and Film "I.L. Caragiale" Bucharest, Bucharest, Romania.
- Faculty of Educational Sciences, Department of Psychology, University "Stefan cel Mare" of Suceava, Suceava, Romania.
- Faculty of Psychology and Educational Sciences, Department of Cognitive Sciences, University of Bucharest, Bucharest, Romania.
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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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Toplutaş E, Aydın F, Hanoğlu L. EEG Microstate Analysis in Patients with Disorders of Consciousness and Its Clinical Significance. Brain Topogr 2024; 37:377-387. [PMID: 36735192 DOI: 10.1007/s10548-023-00939-y] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2022] [Accepted: 01/19/2023] [Indexed: 02/04/2023]
Abstract
Disorders of Consciousness are divided into two major categories such as vegetative and minimally conscious states. Objective measures that allow correct identification of patients with vegetative and minimally conscious state are needed. EEG microstate analysis is a promising approach that we believe has the potential to be effective in examining the resting state activities of the brain in different stages of consciousness by allowing the proper identification of vegetative and minimally conscious patients. As a result, we try to identify clinical evaluation scales and microstate characteristics with resting state EEGs from individuals with disorders of consciousness. Our prospective observational study included 28 individuals with a disorder of consciousness. Control group included 18 healthy subjects with proper EEG data. We made clinical evaluations using patient behavior scales. We also analyzed the EEGs using microstate analysis. In our study, microstate D coverage differed substantially between vegetative and minimally conscious state patients. Also, there was a strong connection between microstate D characteristics and clinical scale scores. Consequently, we have demonstrated that the most accurate parameter for representing consciousness level is microstate D. Microstate analysis appears to be a strong option for future use in the diagnosis, follow-up, and treatment response of patients with Disorders of Consciousness.
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Affiliation(s)
- Eren Toplutaş
- Department of Neurology, Istanbul Eyupsultan Public Hospital, Istanbul, Turkey.
- Program of Neuroscience Ph.D., Graduate School of Health Sciences,, Istanbul Medipol University, Istanbul, Turkey.
| | - Fatma Aydın
- Program of Neuroscience Ph.D., Graduate School of Health Sciences,, Istanbul Medipol University, Istanbul, Turkey
| | - Lütfü Hanoğlu
- Department of Neurology, School of Medicine, Istanbul Medipol University, Istanbul, Turkey
- Neuroimaging and Neuromodulation Lab, Clinical Electrophysiology, REMER, Istanbul Medipol University, Istanbul, Turkey
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44
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Takarae Y, Zanesco A, Erickson CA, Pedapati EV. EEG Microstates as Markers for Cognitive Impairments in Fragile X Syndrome. Brain Topogr 2024; 37:432-446. [PMID: 37751055 DOI: 10.1007/s10548-023-01009-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: 04/23/2023] [Accepted: 09/12/2023] [Indexed: 09/27/2023]
Abstract
Fragile X syndrome (FXS) is one of the most common inherited causes of intellectual disabilities. While there is currently no cure for FXS, EEG is considered an important method to investigate the pathophysiology and evaluate behavioral and cognitive treatments. We conducted EEG microstate analysis to investigate resting brain dynamics in FXS participants. Resting-state recordings from 70 FXS participants and 71 chronological age-matched typically developing control (TDC) participants were used to derive microstates via modified k-means clustering. The occurrence, mean global field power (GFP), and global explained variance (GEV) of microstate C were significantly higher in the FXS group compared to the TDC group. The mean GFP was significantly negatively correlated with non-verbal IQ (NVIQ) in the FXS group, where lower NVIQ scores were associated with greater GFP. In addition, the occurrence, mean duration, mean GFP, and GEV of microstate D were significantly greater in the FXS group than the TDC group. The mean GFP and occurrence of microstate D were also correlated with individual alpha frequencies in the FXS group, where lower IAF frequencies accompanied greater microstate GFP and occurrence. Alterations in microstates C and D may be related to the two well-established cognitive characteristics of FXS, intellectual disabilities and attention impairments, suggesting that microstate parameters could serve as markers to study cognitive impairments and evaluate treatment outcomes in this population. Slowing of the alpha peak frequency and its correlation to microstate D parameters may suggest changes in thalamocortical dynamics in FXS, which could be specifically related to attention control. (250 words).
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Affiliation(s)
- Yukari Takarae
- Department of Psychiatry and Behavioral Sciences, University of California, Davis, Sacramento, CA, USA.
- M.I.N.D. Institute, University of California, Davis, Sacramento, CA, USA.
| | - Anthony Zanesco
- Department of Psychology, University of Miami, Coral Gables, FL, USA
| | - Craig A Erickson
- Division of Child and Adolescent Psychiatry, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA
- Department of Psychiatry, University of Cincinnati College of Medicine, Cincinnati, OH, USA
| | - Ernest V Pedapati
- Division of Child and Adolescent Psychiatry, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA
- Department of Psychiatry, University of Cincinnati College of Medicine, Cincinnati, OH, USA
- Division of Neurology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA
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45
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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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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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Huang Y, Cao C, Dai S, Deng H, Su L, Zheng JS. Magnetoencephalography-derived oscillatory microstate patterns across lifespan: the Cambridge centre for ageing and neuroscience cohort. Brain Commun 2024; 6:fcae150. [PMID: 38745970 PMCID: PMC11091929 DOI: 10.1093/braincomms/fcae150] [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: 08/16/2023] [Revised: 03/01/2024] [Accepted: 04/26/2024] [Indexed: 05/16/2024] Open
Abstract
The aging brain represents the primary risk factor for many neurodegenerative disorders. Whole-brain oscillations may contribute novel early biomarkers of aging. Here, we investigated the dynamic oscillatory neural activities across lifespan (from 18 to 88 years) using resting Magnetoencephalography (MEG) in a large cohort of 624 individuals. Our aim was to examine the patterns of oscillation microstates during the aging process. By using a machine-learning algorithm, we identify four typical clusters of microstate patterns across different age groups and different frequency bands: left-to-right topographic MS1, right-to-left topographic MS2, anterior-posterior MS3 and fronto-central MS4. We observed a decreased alpha duration and an increased alpha occurrence for sensory-related microstate patterns (MS1 & MS2). Accordingly, theta and beta changes from MS1 & MS2 may be related to motor decline that increased with age. Furthermore, voluntary 'top-down' saliency/attention networks may be reflected by the increased MS3 & MS4 alpha occurrence and complementary beta activities. The findings of this study advance our knowledge of how the aging brain shows dysfunctions in neural state transitions. By leveraging the identified microstate patterns, this study provides new insights into predicting healthy aging and the potential neuropsychiatric cognitive decline.
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Affiliation(s)
- Yujing Huang
- Zhejiang Key Laboratory of Multi-Omics in Infection and Immunity, Center for Infectious Disease Research, School of Medicine, Westlake University, Hangzhou 310024, Zhejiang Province, China
- Research Center for Industries of the Future, School of Life Sciences, Westlake University, Hangzhou 310024, Zhejiang Province, China
- Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou 310024, Zhejiang Province, China
- Institute of Biology, Westlake Institute for Advanced Study, Hangzhou 310024, Zhejiang Province, China
| | - Chenglong Cao
- Department of Neurosurgery, The First Affiliated Hospital of University of Science and Technology of China, Hefei 230001, Anhui, China
| | - Shenyi Dai
- Department of Economics and Management, China Jiliang University, Hangzhou 310024, Zhejiang Province, China
- Hangzhou iNeuro Technology Co., LTD, Hangzhou 310024, Zhejiang Province, China
| | - Hu Deng
- Peking University Huilongguan Clinical Medical School, Beijing Huilongguan Hospital, Beijing 100096, China
| | - Li Su
- Department of Psychiatry, University of Cambridge, Cambridge CB20SZ, United Kingdom
- Neuroscience Institute, University of Sheffield, Sheffield, South Yorkshire S102HQ, United Kingdom
| | - Ju-Sheng Zheng
- Zhejiang Key Laboratory of Multi-Omics in Infection and Immunity, Center for Infectious Disease Research, School of Medicine, Westlake University, Hangzhou 310024, Zhejiang Province, China
- Research Center for Industries of the Future, School of Life Sciences, Westlake University, Hangzhou 310024, Zhejiang Province, China
- Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou 310024, Zhejiang Province, China
- Institute of Biology, Westlake Institute for Advanced Study, Hangzhou 310024, Zhejiang Province, China
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Li H, Wang C, Ma L, Xu C, Li H. EEG analysis in patients with schizophrenia based on microstate semantic modeling method. Front Hum Neurosci 2024; 18:1372985. [PMID: 38638803 PMCID: PMC11024310 DOI: 10.3389/fnhum.2024.1372985] [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: 01/19/2024] [Accepted: 03/19/2024] [Indexed: 04/20/2024] Open
Abstract
Introduction Microstate analysis enables the characterization of quasi-stable scalp potential fields on a sub-second timescale, preserving the temporal dynamics of EEG and spatial information of scalp potential distributions. Owing to its capacity to provide comprehensive pathological insights, it has been widely applied in the investigation of schizophrenia (SCZ). Nevertheless, previous research has primarily concentrated on differences in individual microstate temporal characteristics, neglecting potential distinctions in microstate semantic sequences and not fully considering the issue of the universality of microstate templates between SCZ patients and healthy individuals. Methods This study introduced a microstate semantic modeling analysis method aimed at schizophrenia recognition. Firstly, microstate templates corresponding to both SCZ patients and healthy individuals were extracted from resting-state EEG data. The introduction of a dual-template strategy makes a difference in the quality of microstate sequences. Quality features of microstate sequences were then extracted from four dimensions: Correlation, Explanation, Residual, and Dispersion. Subsequently, the concept of microstate semantic features was proposed, decomposing the microstate sequence into continuous sub-sequences. Specific semantic sub-sequences were identified by comparing the time parameters of sub-sequences. Results The SCZ recognition test was performed on the public dataset for both the quality features and semantic features of microstate sequences, yielding an impressive accuracy of 97.2%. Furthermore, cross-subject experimental validation was conducted, demonstrating that the method proposed in this paper achieves a recognition rate of 96.4% between different subjects. Discussion This research offers valuable insights for the clinical diagnosis of schizophrenia. In the future, further studies will seek to augment the sample size to enhance the effectiveness and reliability of this method.
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Affiliation(s)
- Hongwei Li
- Faculty of Computing, Harbin Institute of Technology, Harbin, China
| | - Changming Wang
- Department of Neurosurgery, XuanWu Hospital, Capital Medical University, Beijing, China
| | - Lin Ma
- Faculty of Computing, Harbin Institute of Technology, Harbin, China
| | - Cong Xu
- Faculty of Computing, Harbin Institute of Technology, Harbin, China
| | - Haifeng Li
- Faculty of Computing, Harbin Institute of Technology, Harbin, China
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Kučikienė D, Rajkumar R, Timpte K, Heckelmann J, Neuner I, Weber Y, Wolking S. EEG microstates show different features in focal epilepsy and psychogenic nonepileptic seizures. Epilepsia 2024; 65:974-983. [PMID: 38289522 DOI: 10.1111/epi.17897] [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: 08/17/2023] [Revised: 01/12/2024] [Accepted: 01/12/2024] [Indexed: 04/16/2024]
Abstract
OBJECTIVE Electroencephalography (EEG) microstate analysis seeks to cluster the scalp's electric field into semistable topographical EEG activity maps at different time points. Our study aimed to investigate the features of EEG microstates in subjects with focal epilepsy and psychogenic nonepileptic seizures (PNES). METHODS We included 62 adult subjects with focal epilepsy or PNES who received video-EEG monitoring at the epilepsy monitoring unit. The subjects (mean age = 42.8 ± 21.2 years) were distributed equally between epilepsy and PNES groups. We extracted microstates from a 4.4 ± 1.0-min, 21-channel resting-state EEG. We excluded subjects with interictal epileptiform discharges during resting-state EEGs. After preprocessing, we derived five main EEG microstates-MS1 to MS5-for the full frequency band (1-30 Hz) and frequency subbands (delta, 1-4 Hz; theta, 4-8 Hz; alpha, 8-12 Hz; beta, 12-30 Hz), using the MATLAB-based EEGLAB toolkit. Statistical features of microstates (duration, occurrence, contribution, global field power [GFP]) were compared between the groups, using logistic regression corrected for age and sex. RESULTS We detected no differences in microstate parameters in the full frequency band. We found a longer duration (delta: B = -7.680, p = .046; theta: B = -16.200, p = .043) and a higher contribution (delta: B = -7.414, p = .035; theta: B = -7.509, p = .031) of MS4 in lower frequency bands in the epilepsy group. The PNES group showed a higher occurrence of MS5 in the delta subband (B = 3.283, p = .032). In the theta subband, a higher GFP of MS1 was associated with the PNES group (B = 5.674, p = .025), whereas a higher GFP of MS2 was associated with the epilepsy group (B = -6.579, p = .026). SIGNIFICANCE Microstate features show differences between patients with focal epilepsy and PNES. EEG microstates could be a promising parameter, helping to understand changes in brain dynamics in subjects with epilepsy, and should be explored as a potential biomarker.
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Affiliation(s)
- Domantė Kučikienė
- Department of Epileptology and Neurology, Rheinisch-Westfälische Technische Hochschule (RWTH) University Hospital Aachen, Aachen, Germany
| | - Ravichandran Rajkumar
- Department of Psychiatry, Psychotherapy, and Psychosomatics, RWTH Aachen University, Aachen, Germany
- JARA-BRAIN-Translational Medicine, Jülich, Germany
- Institute of Neuroscience and Medicine, INM-4, Forschungszentrum Jülich, Jülich, Germany
| | - Katharina Timpte
- Department of Epileptology and Neurology, Rheinisch-Westfälische Technische Hochschule (RWTH) University Hospital Aachen, Aachen, Germany
| | - Jan Heckelmann
- Department of Epileptology and Neurology, Rheinisch-Westfälische Technische Hochschule (RWTH) University Hospital Aachen, Aachen, Germany
| | - Irene Neuner
- Department of Psychiatry, Psychotherapy, and Psychosomatics, RWTH Aachen University, Aachen, Germany
- JARA-BRAIN-Translational Medicine, Jülich, Germany
- Institute of Neuroscience and Medicine, INM-4, Forschungszentrum Jülich, Jülich, Germany
| | - Yvonne Weber
- Department of Epileptology and Neurology, Rheinisch-Westfälische Technische Hochschule (RWTH) University Hospital Aachen, Aachen, Germany
| | - Stefan Wolking
- Department of Epileptology and Neurology, Rheinisch-Westfälische Technische Hochschule (RWTH) University Hospital Aachen, Aachen, Germany
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Wu X, Lei Z, Wu Y, Jiang M, Luo H, Chen X, Ruan J. Dynamics of Cerebral Function in Patients with Acute Cerebellar Infarction. CEREBELLUM (LONDON, ENGLAND) 2024; 23:374-382. [PMID: 36810748 DOI: 10.1007/s12311-023-01534-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 02/13/2023] [Indexed: 02/24/2023]
Abstract
Few studies were devoted to investigating cerebral functional changes after acute cerebellar infarction (CI). The purpose of this study was to examine the brain functional dynamics of CI using electroencephalographic (EEG) microstate analysis. And the possible heterogenicity in neural dynamics between CI with vertigo and CI with dizziness was explored. Thirty-four CI patients and 37 age- and gender-matched healthy controls(HC) were included in the study. Each included subject underwent a 19-channel video EEG examination. Five 10-s resting-state EEG epochs were extracted after data preprocessing. Then, microstate analysis and source localization were performed using the LORETA-KEY tool. Microstate parameters such as duration, coverage, occurrence, and transition probability are all extracted. The current study showed that the duration, coverage, and occurrence of microstate(Ms) B significantly increased in CI patients, but the duration and coverage of MsA and MsD decreased. Compared CI with vertigo to dizziness, finding a decreased trend in the coverage of MsD and the transition from MsA and MsB to MsD. Taken together, our study sheds new light on the dynamics of cerebral function after CI, mainly reflecting increased activity in functional networks involved in MsB and decreased activity in functional networks involved in MsA and MsD. Vertigo and dizziness post-CI may be suggested by cerebral functional dynamics. Further longitudinal studies are needed to validate and explore the alterations in brain dynamics to what extent depict the clinical traits and their potential applications in the recovery of CI.
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Affiliation(s)
- Xin Wu
- Department of Neurology, The Affiliated Hospital of Southwest Medical University, Luzhou, 646000, China
- Laboratory of Neurological Diseases and Brain Function, Luzhou, 646000, China
| | - Ziye Lei
- Department of Neurology, The Affiliated Hospital of Southwest Medical University, Luzhou, 646000, China
- Laboratory of Neurological Diseases and Brain Function, Luzhou, 646000, China
| | - Yusi Wu
- Department of Neurology, The Affiliated Hospital of Southwest Medical University, Luzhou, 646000, China
- Laboratory of Neurological Diseases and Brain Function, Luzhou, 646000, China
| | - Mingqing Jiang
- 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
| | - 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
| | - 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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