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Li Y, Xu L, Zhao Y, Meng M, Chen Y, Wang B, Cui B, Liu J, Han J, Wang N, Zhao T, Sun L, Ren Z, Han X. Topographic differences in EEG microstates: distinguishing juvenile myoclonic epilepsy from frontal lobe epilepsy. Cogn Neurodyn 2025; 19:72. [PMID: 40357334 PMCID: PMC12065695 DOI: 10.1007/s11571-025-10255-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/25/2024] [Revised: 03/09/2025] [Accepted: 04/12/2025] [Indexed: 05/15/2025] Open
Abstract
This study aims to develop an exploratory classification model for Juvenile Myoclonic Epilepsy (JME) based on electroencephalogram (EEG) microstate features to assist clinical diagnosis and reduce misdiagnosis rates. A total of 123 participants were included in this study, consisting of 74 patients diagnosed with JME and 49 patients with Frontal Lobe Epilepsy (FLE). Resting-state EEG data were retrospectively collected from all participants. After preprocessing, microstate analysis was performed, and 24 microstate features (including duration, occurrence rate, coverage, and transition probability) were extracted and analyzed. Finally, the extracted microstate parameters were used to train six machine learning classifiers to distinguish between the two types of epilepsy. The performance of these models was assessed by calculating accuracy, precision, recall, F1 score, and area under the curve (AUC). The study found that all parameters of microstate A showed high consistency between the two groups. However, the JME group exhibited lower occurrence and smaller coverage of microstate B compared to the FLE group, while showing longer durations for microstate C. Additionally, the transition probabilities from microstate B to C and D were lower in the JME group, while the transition probability from C to D was significantly higher. When EEG microstate features were integrated into the six machine learning classifiers, the linear discriminant analysis (LDA) algorithm achieved the best classification performance (accuracy of 76.4%, precision of 79.5%, and AUC of 0.817). This study found significant differences in EEG microstate characteristics between JME and FLE. Based on 24 microstate features, a classification model was successfully developed and validated. These findings underscore the potential of EEG microstates as neurophysiological biomarkers for distinguishing between these two epilepsy types. Supplementary Information The online version contains supplementary material available at 10.1007/s11571-025-10255-9.
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Affiliation(s)
- Ying Li
- Department of Neurology, Henan Provincial People’s Hospital, Zhengzhou, Henan Province China
| | - Lidao Xu
- South China Normal University, Guangzhou, Guangdong Province China
| | - Yibo Zhao
- Department of Neurology, Zhengzhou University People’s Hospital, Zhengzhou, Henan Province China
| | - Mingxian Meng
- The First Clinical Medical College of Henan University of Traditional Chinese Medicine, Zhengzhou, Henan Province China
| | - Yanan Chen
- Department of Neurology, Henan Provincial People’s Hospital, Zhengzhou, Henan Province China
| | - Bin Wang
- Department of Neurology, Henan Provincial People’s Hospital, Zhengzhou, Henan Province China
| | - Beijia Cui
- Department of Neurology, People’s Hospital of Henan University, Zhengzhou, Henan Province China
| | - Jin Liu
- Department of Neurology, Zhengzhou University People’s Hospital, Zhengzhou, Henan Province China
| | - Jiuyan Han
- Department of Neurology, Henan Provincial People’s Hospital, Zhengzhou, Henan Province China
| | - Na Wang
- Department of Neurology, Henan Provincial People’s Hospital, Zhengzhou, Henan Province China
| | - Ting Zhao
- Department of Neurology, Henan Provincial People’s Hospital, Zhengzhou, Henan Province China
| | - Lei Sun
- Department of Neurology, Henan Provincial People’s Hospital, Zhengzhou, Henan Province China
| | - Zhe Ren
- Department of Neurology, Henan Provincial People’s Hospital, Zhengzhou, Henan Province China
| | - Xiong Han
- Department of Neurology, Henan Provincial People’s Hospital, Zhengzhou, Henan Province China
- Department of Neurology, Zhengzhou University People’s Hospital, Zhengzhou, Henan Province China
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Wu Y, Zheng R, Xing H, Kou Y, Wang Y, Wu X, Zou F, Luo Y, Zhang M. Examining the role and neural electrophysiological mechanisms of adjective cues in size judgment. Neuropsychologia 2025; 213:109151. [PMID: 40254051 DOI: 10.1016/j.neuropsychologia.2025.109151] [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: 08/13/2024] [Revised: 02/15/2025] [Accepted: 04/17/2025] [Indexed: 04/22/2025]
Abstract
Numerous influential theories have attempted to elucidate the relationship between language and thought. The debate persists on whether language and thought are distinct entities or if language is deeply embedded in individual cognitive processes. This study employs adjective cues combined with a mental imagery size judgment task as an experimental paradigm, utilizing neurophysiological techniques to preliminarily explore the role of adjectives in size judgment tasks and their underlying neurophysiological mechanisms. Findings reveal that performance is best when adjectives are congruent with the size of the object, with EEG microstate results indicating strong activity in Class A, related to language networks under this condition. Additionally, when adjectives conflict with object size, the discovery of the Ni component suggests that individuals monitor and inhibit the conflict between adjectives and object size, leading to decreased task performance in this condition. Moreover, when object size is ambiguous, individuals' size judgments do not benefit significantly from clear adjective cues. Event-related potentials and EEG microstate results suggest that under this condition, top-down cognitive resources are recruited more extensively. In conclusion, language plays a more crucial role in simpler judgment tasks; as tasks become more complex, judgment processes engage a greater number of distributed brain regions to collaborate, while the language system remains active. This study provides initial cognitive neuroscience evidence for understanding the relationship between language and simple forms of thought, offering preliminary insights for future investigations into the connection between language and thought.
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Affiliation(s)
- Yihan Wu
- School of Nursing, Xinxiang Medical University, Xinxiang, 453003, Henan Province, China
| | - Ronglian Zheng
- School of Nursing, Xinxiang Medical University, Xinxiang, 453003, Henan Province, China
| | - Huili Xing
- Department of Psychology, Xinxiang Medical University, Xinxiang, 453003, Henan Province, China; Mental Illness and Cognitive Neuroscience Key Laboratory of Xinxiang (Xinxiang Medical University), Xinxiang, 453003, Henan Province, China
| | - Yining Kou
- Department of Psychology, Xinxiang Medical University, Xinxiang, 453003, Henan Province, China; Mental Illness and Cognitive Neuroscience Key Laboratory of Xinxiang (Xinxiang Medical University), Xinxiang, 453003, Henan Province, China
| | - Yufeng Wang
- Department of Psychology, Xinxiang Medical University, Xinxiang, 453003, Henan Province, China; Mental Illness and Cognitive Neuroscience Key Laboratory of Xinxiang (Xinxiang Medical University), Xinxiang, 453003, Henan Province, China
| | - Xin Wu
- Department of Psychology, Xinxiang Medical University, Xinxiang, 453003, Henan Province, China; Mental Illness and Cognitive Neuroscience Key Laboratory of Xinxiang (Xinxiang Medical University), Xinxiang, 453003, Henan Province, China
| | - Feng Zou
- Department of Psychology, Xinxiang Medical University, Xinxiang, 453003, Henan Province, China; Mental Illness and Cognitive Neuroscience Key Laboratory of Xinxiang (Xinxiang Medical University), Xinxiang, 453003, Henan Province, China
| | - Yanyan Luo
- School of Nursing, Xinxiang Medical University, Xinxiang, 453003, Henan Province, China.
| | - Meng Zhang
- School of Nursing, Xinxiang Medical University, Xinxiang, 453003, Henan Province, China; Department of Psychology, Xinxiang Medical University, Xinxiang, 453003, Henan Province, China; Mental Illness and Cognitive Neuroscience Key Laboratory of Xinxiang (Xinxiang Medical University), Xinxiang, 453003, Henan Province, China.
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Catania C, Mancuso M, Mazzeo A, Salamone EM, Orlando B, Morano A, Leodori G, Casciato S, Di Gennaro G, Giallonardo AT, Di Bonaventura C, Cerulli Irelli E. EEG microstates during resting-state and dissociative events in patients with psychogenic non-epileptic seizures. Clin Neurophysiol 2025; 173:124-131. [PMID: 40090237 DOI: 10.1016/j.clinph.2025.03.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2024] [Revised: 03/05/2025] [Accepted: 03/06/2025] [Indexed: 03/18/2025]
Abstract
OBJECTIVE Previous neuroimaging studies indicate complex network alterations in psychogenic non-epileptic seizures (PNES) patients, but brain activity modifications occurring during PNES remain unexplored. This study aimed to analyze EEG microstate metrics in PNES patients both during events and resting state to investigate the neurophysiological changes underlying these dissociative events. METHODS We recruited 22 PNES patients and 24 age- and sex-matched healthy controls. Inclusion criteria included a history of PNES and at least one recorded akinetic PNES during video-EEG. RESULTS A two-way repeated-measures ANOVA revealed significant class*condition interactions for microstate duration (p = 0.017), coverage (p = 0.012), and global field power (GFP) (p = 0.008). Post-hoc FDR-adjusted paired t-tests showed a significant decrease in microstate C duration (p = 0.036), coverage (p = 0.04), and GFP (p = 0.036) during PNES events compared to resting state. Mann-Whitney U tests showed significantly higher microstate C duration in PNES patients during resting state compared to controls (p = 0.009). CONCLUSIONS Microstate C, previously associated with the default mode network, showed increased duration during resting state and decreased representation during PNES, suggesting a prominent shift in neural activity dynamics within this network during dissociative events. SIGNIFICANCE These findings provide insights into neurophysiological changes occurring during PNES, suggestingan interplay between pathological and adaptive mechanisms in their pathophysiology.
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Affiliation(s)
- Cecilia Catania
- Department of Human Neurosciences, Sapienza University, Rome, Italy; EEG and Epilepsy Unit, Department of Neuroscience, University Hospital and Faculty of Medicine of Geneva, University of Geneva, Geneva, Switzerland
| | - Marco Mancuso
- Department of Human Neurosciences, Sapienza University, Rome, Italy
| | - Adolfo Mazzeo
- IRCCS Neuromed, Via Atinense 18 86077 Pozzilli, Italy
| | | | - Biagio Orlando
- Department of Human Neurosciences, Sapienza University, Rome, Italy
| | | | - Giorgio Leodori
- Department of Human Neurosciences, Sapienza University, Rome, Italy; IRCCS Neuromed, Via Atinense 18 86077 Pozzilli, Italy
| | - Sara Casciato
- Department of Neurosciences, S. Camillo-Forlanini Hospital, Rome, Italy
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Wei R, Wang Y, Fang H, Guan L, Gao J, Xu X, Ke X, Jin H. Distinct features of EEG microstates in autism spectrum disorder revealed by meta-analysis: the contribution of individual age to heterogeneity across studies. Front Psychiatry 2025; 16:1531694. [PMID: 40330653 PMCID: PMC12052564 DOI: 10.3389/fpsyt.2025.1531694] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/21/2024] [Accepted: 03/28/2025] [Indexed: 05/08/2025] Open
Abstract
Background and purpose Electroencephalographic (EEG) microstates, as quasi-stable scalp EEG spatial patterns, are characterized by their high temporal resolution, making them a potentially powerful approach for studying the function of large-scale brain networks. A substantial body of research has demonstrated that abnormalities in the function or structure of large-scale brain networks are closely related to many characteristics of autism spectrum disorder (ASD). Investigating the EEG microstate features of individuals with autism can help reveal the nature of autism. To date, numerous studies have observed unique resting-state microstate patterns in individuals with autism. However, the results of these studies have not been consistent. Therefore, the present study aims to assess the differences in microstate parameters between ASD and non-autistic groups through meta-analysis and to explore the sources of research heterogeneity. Method This meta-analysis was preregistered with PROSPERO (CRD42024599897) and followed PRISMA guidelines. Studies in English comparing EEG microstate patterns between ASD and Non-autistic groups were retrieved by database search to October 20, 2024. The meta-analysis was then conducted using RevMan5.2. Pooled results are expressed as standardized mean difference (SMD). Heterogeneity (I²) and publication bias were assessed using Stata15.0. Result Seven studies enrolling 194 ASD individuals were included, four deemed high quality and three moderate quality according to bias risk assessment. Microstate B duration and coverage were significantly greater in the pooled ASD group (duration SMD=0.83, 95%CI: 0.17-1.5; coverage SMD=0.54, 95%CI: 0.18-0.90), but heterogeneity could not be excluded. Microstate C occurrence frequency was also in the ASD group (SMD= -0.61, 95%CI: -1.08 to -0.15), and heterogeneity was significant. Sensitivity analysis revealed that only the group difference in microstate B coverage was robust. Subgroup analysis suggested that age was the main source of heterogeneity in microstate B and C coverage. Results were not affected by publication bias according to Egger's test. Conclusion Future studies on the EEG microstate characteristics of ASD must control for age as an important cofounding variable. Systematic Review Registration PROSPERO, identifier CRD42024599897.
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Affiliation(s)
- Ran Wei
- Children's Mental Health Research Center of the Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
- Child Healthcare Department of the Affiliated Suzhou Hospital of Nanjing Medical University, Suzhou, China
- Child Healthcare Department of the Suzhou Maternal and Child Health Hospital, Suzhou, China
| | - Yonglu Wang
- Children's Mental Health Research Center of the Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
| | - Hui Fang
- Children's Mental Health Research Center of the Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
| | - Luyang Guan
- Children's Mental Health Research Center of the Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
| | - Jianxing Gao
- Children's Mental Health Research Center of the Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
| | - Xinyue Xu
- Children's Mental Health Research Center of the Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
| | - Xiaoyan Ke
- Children's Mental Health Research Center of the Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
| | - Hua Jin
- Child Healthcare Department of the Affiliated Suzhou Hospital of Nanjing Medical University, Suzhou, China
- Child Healthcare Department of the Suzhou Maternal and Child Health Hospital, Suzhou, China
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Che Q, Xi C, Sun Y, Zhao X, Wang L, Wu K, Mao J, Huang X, Wang K, Tian Y, Ye R, Yu F. EEG microstate as a biomarker of personalized transcranial magnetic stimulation treatment on anhedonia in depression. Behav Brain Res 2025; 483:115463. [PMID: 39920912 DOI: 10.1016/j.bbr.2025.115463] [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: 10/13/2024] [Revised: 01/18/2025] [Accepted: 01/30/2025] [Indexed: 02/10/2025]
Abstract
Anhedonia, a core feature of major depressive disorder (MDD), presents significant treatment challenges with conventional methods. Circuit-targeted, personalized repetitive transcranial magnetic stimulation (rTMS) has shown potentiation by focusing on disruptions in specific networks related to anhedonia. However, how rTMS modulates brain network dynamics in anhedonia is not yet fully understood. This research sought to explore these effects using EEG microstate analysis. In this double-blind, randomized, sham-controlled study, resting-state functional MRI was employed to pinpoint the left dorsolateral prefrontal cortex (DLPFC) region that exhibited the strongest functional connectivity to the nucleus accumbens (NAcc), used as the target for rTMS stimulation. Rest-state EEG data from 49 depressive patients with anhedonia(active=26, sham=23) were analyzed both at baseline and after treatment. In addition, a group of 15 healthy participants was included to serve as baseline controls. Resting-state EEG data were collected at baseline and post-treatment. Using polarity-insensitive k-means clustering, EEG microstates were segmented into five categories (A-E). Circuit-targeted rTMS significantly alleviated symptoms of anhedonia and depression. Compared to healthy controls, patients with anhedonia showed reduced microstate B and C occurrence, along with increased microstate D duration. After rTMS targeting the DLPFC-NAcc pathway, the active treatment group exhibited normalization of microstate C occurrence and a reduction in microstate E duration. Notably, the increase in microstate C was significantly correlated with improvements in anticipatory anhedonia, and these changes were observed specifically in treatment responders. The findings suggest that microstate C is linked to anhedonia and could serve as a reliable biomarker for personalized rTMS treatment. These results provide insights into the neural mechanisms underlying rTMS for anhedonia and highlight the potential of EEG microstate analysis in guiding personalized treatment strategies for depression.
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Affiliation(s)
- QiangYan Che
- School of Mental Health and Psychological Sciences, Anhui Medical University, Hefei 230000, China.
| | - Chunhua Xi
- Department of Neurology, The Third Affiliated Hospital of Anhui Medical University, Hefei, China.
| | - Yunlin Sun
- School of Mental Health and Psychological Sciences, Anhui Medical University, Hefei 230000, China.
| | - Xingyu Zhao
- School of Mental Health and Psychological Sciences, Anhui Medical University, Hefei 230000, China.
| | - Lei Wang
- School of Mental Health and Psychological Sciences, Anhui Medical University, Hefei 230000, China.
| | - Ke Wu
- School of Mental Health and Psychological Sciences, Anhui Medical University, Hefei 230000, China.
| | - Junyu Mao
- School of Mental Health and Psychological Sciences, Anhui Medical University, Hefei 230000, China.
| | - Xinyu Huang
- School of Mental Health and Psychological Sciences, Anhui Medical University, Hefei 230000, China.
| | - Kai Wang
- School of Mental Health and Psychological Sciences, Anhui Medical University, Hefei 230000, China; Anhui Province Key Laboratory of Cognition and Neuropsychiatric Disorders, Hefei 230000, China; Collaborative Innovation Center of Neuropsychiatric Disorders and Mental Health, Anhui, China.
| | - Yanghua Tian
- Department of Psychology and Sleep Medicine, The Second Affiliated Hospital of Anhui Medical University, Anhui Medical University, Hefei, China.
| | - Rong Ye
- School of Mental Health and Psychological Sciences, Anhui Medical University, Hefei 230000, China; Anhui Province Key Laboratory of Cognition and Neuropsychiatric Disorders, Hefei 230000, China; Collaborative Innovation Center of Neuropsychiatric Disorders and Mental Health, Anhui, China.
| | - Fengqiong Yu
- Department of Psychology and Sleep Medicine, The Second Affiliated Hospital of Anhui Medical University, Anhui Medical University, Hefei, China; School of Mental Health and Psychological Sciences, Anhui Medical University, Hefei 230000, China; Anhui Province Key Laboratory of Cognition and Neuropsychiatric Disorders, Hefei 230000, China; Collaborative Innovation Center of Neuropsychiatric Disorders and Mental Health, Anhui, China.
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Haydock D, Kadir S, Leech R, Nehaniv CL, Antonova E. EEG microstate syntax analysis: A review of methodological challenges and advances. Neuroimage 2025; 309:121090. [PMID: 39961498 PMCID: PMC12043272 DOI: 10.1016/j.neuroimage.2025.121090] [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: 10/14/2024] [Revised: 02/11/2025] [Accepted: 02/13/2025] [Indexed: 02/23/2025] Open
Abstract
Electroencephalography (EEG) microstates are "quasi-stable" periods of electrical potential distribution in multichannel EEG derived from peaks in Global Field Power. Transitions between microstates form a temporal sequence that may reflect underlying neural dynamics. Mounting evidence indicates that EEG microstate sequences have long-range, non-Markovian dependencies, suggesting a complex underlying process that drives EEG microstate syntax (i.e., the transitional dynamics between microstates). Despite growing interest in EEG microstate syntax, the field remains fragmented, with inconsistent terminologies used between studies and a lack of defined methodological categories. To advance the understanding of functional significance of microstates and to facilitate methodological comparability and finding replicability across studies, we: i) derive categories of syntax analysis methods, reviewing how each may be utilised most readily; ii) define three "time-modes" for EEG microstate sequence construction; and iii) outline general issues concerning current microstate syntax analysis methods, suggesting that the microstate models derived using these methods are cross-referenced against models of continuous EEG. We advocate for these continuous approaches as they do not assume a winner-takes-all model inherent in the microstate derivation methods and contextualise the relationship between microstate models and EEG data. They may also allow for the development of more robust associative models between microstates and functional Magnetic Resonance Imaging data.
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Affiliation(s)
- David Haydock
- Biocomputation Research Group, School of Physics, Engineering and Computer Science, University of Hertfordshire, Hatfield, UK; Birkbeck-UCL Centre for Neuroimaging, Psychology and Language Sciences, University College London, UK.
| | - Shabnam Kadir
- Biocomputation Research Group, School of Physics, Engineering and Computer Science, University of Hertfordshire, Hatfield, UK
| | - Robert Leech
- Centre for Neuroimaging Science, King's College London, London, UK
| | - Chrystopher L Nehaniv
- Biocomputation Research Group, School of Physics, Engineering and Computer Science, University of Hertfordshire, Hatfield, UK; Centre for Theoretical Neuroscience, Departments of Systems Design Engineering and of Electrical & Computer Engineering, University of Waterloo, Waterloo, Ontario, Canada
| | - Elena Antonova
- Division of Psychology, Department of Life Sciences, College of Health, Medicine and Life Sciences, Centre for Cognitive and Clinical Neuroscience, Brunel University of London, Uxbridge, UK
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Ahn SH, Jang HN, Kim S, Kim MJ, Yum MS, Jeong DH. Identification of topological alterations using microstate dynamics in patients with infantile epileptic spasms syndrome. Sci Rep 2025; 15:9490. [PMID: 40108370 PMCID: PMC11923191 DOI: 10.1038/s41598-025-93385-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2024] [Accepted: 03/06/2025] [Indexed: 03/22/2025] Open
Abstract
Infantile epileptic spasm syndrome (IESS) is characterized by clustered epileptic spasms and hypsarrhythmia on electroencephalography (EEG). This study aimed to investigate the temporal dynamics and dynamic synchronization of neural networks in IESS using EEG microstate analysis of interictal recordings from 49 healthy controls (HC) and 42 patients with IESS. Five microstate maps were identified, and features including the global explained variance (GEV), mean correlation, occurrence, time coverage, mean time duration, and transition probabilities were extracted. Significant differences were observed in patients with IESS compared to HCs, with increased microstate features and transition probabilities in microstates A and B, and reduced values in microstates D and E. Furthermore, in patients with structural/metabolic etiologies, microstate A demonstrated heightened microstate features and transition probabilities compared to genetic/unknown etiologies. These microstate characteristics enabled accurate classification of IESS versus HCs and differentiation between structural/metabolic and genetic/unknown etiologies. The altered microstate topologies in IESS reflect disruptions in brain network dynamics, suggesting that specific microstate features and transition probabilities could serve as potential diagnostic biomarkers. This study underscores the potential of EEG microstate analysis in understanding neural dysfunction, particularly in structural/metabolic subtypes of IESS.
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Affiliation(s)
- Seong-Ho Ahn
- Department of Artificial Intelligence, The Catholic University of Korea, 43 Jibong-ro, Bucheon, 14662, Gyeonggi-do, Republic of Korea
| | - Han Na Jang
- Department of Pediatrics, Soonchunhyang University Cheonan Hospital, Cheonan, Republic of Korea
| | - Seeun Kim
- Weldon School of Biomedical Engineering, Purdue University, West Lafayette, USA
| | - Min-Jee Kim
- Department of Pediatrics, Asan Medical Center Children's Hospital, Ulsan University College of Medicine, 88 Olympic-ro 43-gil, Songpa-gu, Seoul, 05505, Republic of Korea
| | - Mi-Sun Yum
- Department of Pediatrics, Asan Medical Center Children's Hospital, Ulsan University College of Medicine, 88 Olympic-ro 43-gil, Songpa-gu, Seoul, 05505, Republic of Korea.
| | - Dong-Hwa Jeong
- Department of Artificial Intelligence, The Catholic University of Korea, 43 Jibong-ro, Bucheon, 14662, Gyeonggi-do, Republic of Korea.
- Department of Healthcare and Artificial Intelligence, The Catholic University of Korea, Bucheon, Republic of Korea.
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Hao X, Ma M, Meng F, Liang H, Liang C, Liu X, Zhang B, Ju Y, Liu S, Ming D. Diminished attention network activity and heightened salience-default mode transitions in generalized anxiety disorder: Evidence from resting-state EEG microstate analysis. J Affect Disord 2025; 373:227-236. [PMID: 39743145 DOI: 10.1016/j.jad.2024.12.095] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/11/2024] [Revised: 12/15/2024] [Accepted: 12/27/2024] [Indexed: 01/04/2025]
Abstract
Generalized anxiety disorder (GAD) is a common anxiety disorder characterized by excessive, uncontrollable worry and physical symptoms such as difficulty concentrating and sleep disturbances. Although functional magnetic resonance imaging (fMRI) studies have reported aberrant network-level activity related to cognition and emotion in GAD, its low temporal resolution restricts its ability to capture the rapid neural activity in mental processes. EEG microstate analysis offers millisecond-resolution for tracking the dynamic changes in brain electrical activity, thereby illuminating the neurophysiological mechanisms underlying the cognitive and emotional dysfunctions in GAD. This study collected 64-channel resting-state EEG data from 28 GAD patients and 28 healthy controls (HC), identifying five microstate classes (A-E) in both groups. Results showed that GAD patients exhibited significantly lower duration (p < 0.01), occurrence (p < 0.05), and coverage (p < 0.01) of microstate class D, potentially reflecting deficits in attention-related networks. Such alterations may contribute to the impairments in attention maintenance and cognitive control. Additionally, GAD patients displayed reduced transition probabilities in A → D, B → D, C → D, and E → D (all corrected p < 0.05), but increased in C → E (corrected p < 0.05) and E → C (corrected p < 0.01). These results highlight a significant reduction in the brain's ability to transition into microstate class D, alongside overactivity in switching between the default mode network and the salience network. Such neurophysiological changes may underlie cognitive control deficits, increased spontaneous rumination, and emotional regulation challenges observed in GAD. Together, these insights provide a new perspective for understanding the neurophysiological and pathological mechanisms underlying GAD.
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Affiliation(s)
- Xinyu Hao
- Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, People's Republic of China; Tianjin Key Laboratory of Brain Science and Neural Engineering, Tianjin University, Tianjin, People's Republic of China
| | - Mohan Ma
- Department of Psychiatry, National Clinical Research Center for Mental Disorders, and National Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha, People's Republic of China
| | - Fanyu Meng
- Department of Psychiatry, National Clinical Research Center for Mental Disorders, and National Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha, People's Republic of China
| | - Hui Liang
- Department of Psychiatry, National Clinical Research Center for Mental Disorders, and National Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha, People's Republic of China
| | - Chunyu Liang
- Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, People's Republic of China; Tianjin Key Laboratory of Brain Science and Neural Engineering, Tianjin University, Tianjin, People's Republic of China
| | - Xiaoya Liu
- Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, People's Republic of China; Tianjin Key Laboratory of Brain Science and Neural Engineering, Tianjin University, Tianjin, People's Republic of China; Haihe Laboratory of Brain-Computer Interaction and Human-Machine Integration, Tianjin, People's Republic of China
| | - Bo Zhang
- Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, People's Republic of China; Tianjin Key Laboratory of Brain Science and Neural Engineering, Tianjin University, Tianjin, People's Republic of China; Haihe Laboratory of Brain-Computer Interaction and Human-Machine Integration, Tianjin, People's Republic of China
| | - Yumeng Ju
- Department of Psychiatry, National Clinical Research Center for Mental Disorders, and National Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha, People's Republic of China
| | - Shuang Liu
- Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, People's Republic of China; Tianjin Key Laboratory of Brain Science and Neural Engineering, Tianjin University, Tianjin, People's Republic of China; Haihe Laboratory of Brain-Computer Interaction and Human-Machine Integration, Tianjin, People's Republic of China.
| | - Dong Ming
- Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, People's Republic of China; Tianjin Key Laboratory of Brain Science and Neural Engineering, Tianjin University, Tianjin, People's Republic of China; Haihe Laboratory of Brain-Computer Interaction and Human-Machine Integration, Tianjin, People's Republic of China.
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Li S, Hu R, Yan H, Chu L, Qiu Y, Gao Y, Li M, Li J. Neurophysiological Markers of Auditory Verbal Hallucinations in Patients with Schizophrenia: An EEG Microstates Study. Brain Topogr 2025; 38:29. [PMID: 39920494 DOI: 10.1007/s10548-025-01105-2] [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: 08/16/2024] [Accepted: 01/26/2025] [Indexed: 02/09/2025]
Abstract
Alterations in the temporal characteristics of EEG microstates in patients with schizophrenia (SCZ) have been repeatedly found in previous studies. Nevertheless, altered temporal characteristics of EEG microstates in auditory verbal hallucinations (AVHs) SCZ are still unknown. This study aimed to investigate whether SCZ patients with sAVHs exhibit abnormal EEG microstates. We analyzed high-density electroencephalography data that from 79 SCZ patients, including 38 severe AVHs patients (sAVH group), 17 moderate auditory verbal hallucinations patients (mid-AVH group), and 24 without auditory verbal hallucinations patients (non-AVH group). Microstates were compared between three groups. Microstate C exhibited significant differences in duration and coverage and microstate B exhibited significant differences in occurrence between patients with sAVHs and without AVHs. There was a significant negative correlation between the coverage in microstate C and the severity of sAVH. Microstate C in duration, microstate B in occurrence were efficient in detecting sAVH patients. The decreased class C microstates in duration and coverage and increased class B microstates in occurrence may contribute to the severity of symptoms in AVH patients. Furthermore, we have identified that microstates C could serve as potential neurophysiological markers for detecting AVHs in SCZ patients. These results can provide potential avenues for therapeutic intervention of AVHs.
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Affiliation(s)
- Shaobing Li
- Institute of Mental Health of Tianjin Anding Hospital, Mental Health Center of Tianjin Medical University, 13 Liulin Road, Hexi District, Tianjin, 300222, China
| | - Ruxin Hu
- Institute of Mental Health of Tianjin Anding Hospital, Mental Health Center of Tianjin Medical University, 13 Liulin Road, Hexi District, Tianjin, 300222, China
| | - Huiming Yan
- Institute of Mental Health of Tianjin Anding Hospital, Mental Health Center of Tianjin Medical University, 13 Liulin Road, Hexi District, Tianjin, 300222, China
| | - Lijun Chu
- Institute of Mental Health of Tianjin Anding Hospital, Mental Health Center of Tianjin Medical University, 13 Liulin Road, Hexi District, Tianjin, 300222, China
| | - Yuying Qiu
- Institute of Mental Health of Tianjin Anding Hospital, Mental Health Center of Tianjin Medical University, 13 Liulin Road, Hexi District, Tianjin, 300222, China
| | - Ying Gao
- Institute of Mental Health of Tianjin Anding Hospital, Mental Health Center of Tianjin Medical University, 13 Liulin Road, Hexi District, Tianjin, 300222, China
| | - Meijuan Li
- Institute of Mental Health of Tianjin Anding Hospital, Mental Health Center of Tianjin Medical University, 13 Liulin Road, Hexi District, Tianjin, 300222, China
| | - Jie Li
- Institute of Mental Health of Tianjin Anding Hospital, Mental Health Center of Tianjin Medical University, 13 Liulin Road, Hexi District, Tianjin, 300222, China.
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Gao T, Chen C, Liang G, Ran Y, Huang Q, Liao Z, He B, Liu T, Tang X, Chen H, Fan Y. Feature fusion analysis approach based on synchronous EEG-fNIRS signals: application in etomidate use disorder individuals. BIOMEDICAL OPTICS EXPRESS 2025; 16:382-397. [PMID: 39958843 PMCID: PMC11828439 DOI: 10.1364/boe.542078] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/11/2024] [Revised: 11/28/2024] [Accepted: 12/15/2024] [Indexed: 02/18/2025]
Abstract
Etomidate is commonly used for induction of anesthesia, but prolonged use can affect brain neurovascular mechanisms, potentially leading to use disorders. However, limited research exists on the impact of etomidate on brain function, and accurately and noninvasively extracting and analyzing neurovascular brain features remains a challenge. This study introduces a novel feature fusion approach based on whole-brain synchronous Electroencephalography (EEG)-functional near-infrared spectroscopy (fNIRS) signals aimed at addressing the difficulty of jointly analyzing neural and hemodynamic signals and features in specific locations, which is critical for understanding neurovascular mechanism changes in etomidate use disorder individuals. To address the challenge of optimizing the accuracy of neurovascular coupling analysis, we proposed a multi-band local neurovascular coupling (MBLNVC) method. This method enhances spatial precision in NVC analysis by integrating multi-modal brain signals. We then mapped the different brain features to the Yeo 7 brain networks and constructed feature vectors based on these networks. This multilayer feature fusion approach resolves the issue of analyzing complex neural and vascular signals together in specific brain locations. Our approach revealed significant neurovascular coupling enhancement in the sensorimotor and dorsal attention networks (p < 0.05, FDR corrected), corresponding with different frequency bands and brain networks from single-modal features. These features of the intersection of bands and networks showed high sensitivity to etomidate using machine learning classifiers compared to other features (accuracy: support vector machine (SVM) - 82.10%, random forest (RF) - 80.50%, extreme gradient boosting (XGBoost) - 78.40%). These results showed the potential of the proposed feature fusion analysis approach in exploring changes in brain mechanisms and provided new insights into the effects of etomidate on resting neurovascular brain mechanisms.
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Affiliation(s)
- Tianxin Gao
- School of Medical Technology, Beijing Institute of Technology, China
| | - Chao Chen
- School of Medical Technology, Beijing Institute of Technology, China
| | - Guangyao Liang
- School of Medical Technology, Beijing Institute of Technology, China
| | - Yuchen Ran
- School of Medical Technology, Beijing Institute of Technology, China
| | - Qiuping Huang
- Department of Psychology, School of Humanities and Management, Hunan University of Chinese Medicine, China
| | - Zhenjiang Liao
- Department of Psychiatry, The Second Xiangya Hospital, Central South University, Changsha, Hunan, China
| | - Bolin He
- Lituo Drug Rehabilitation Center of Hunan Province, Hunan, China
| | - Tefu Liu
- Lituo Drug Rehabilitation Center of Hunan Province, Hunan, China
| | - Xiaoying Tang
- School of Medical Technology, Beijing Institute of Technology, China
| | - Hongxian Chen
- Department of Psychiatry, The Second Xiangya Hospital, Central South University, Changsha, Hunan, China
| | - Yingwei Fan
- School of Medical Technology, Beijing Institute of Technology, China
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11
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Wu X, Liu Y, Che J, Cheng N, Wen D, Liu H, Dong X. Unveiling neural activity changes in mild cognitive impairment using microstate analysis and machine learning. J Alzheimers Dis 2025; 103:735-748. [PMID: 39772750 DOI: 10.1177/13872877241305961] [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] [Indexed: 01/11/2025]
Abstract
BACKGROUND Mild cognitive impairment (MCI) is recognized as a condition that may increase the risk of developing Alzheimer's disease (AD). Understanding the neural correlates of MCI is crucial for elucidating its pathophysiology and developing effective interventions. Electroencephalogram (EEG) microstates, reflecting brain activity changes, have shown promise in MCI research. However, current approaches often lack comprehensive characterization of the complex neural dynamics associated with MCI. OBJECTIVE This study aims to investigate neurophysiological changes associated with MCI using a comprehensive set of microstate features, including traditional temporal features and entropy measures. METHODS Resting-state EEG data were collected from 69 MCI patients and healthy controls (HC). Microstate analysis was performed to extract conventional features (duration, coverage) and entropy measures. Statistical analysis, principal component analysis (PCA), and machine learning (ML) techniques were employed to evaluate neurophysiological patterns associated with MCI. RESULTS MCI displayed altered microstate dynamics, with significantly longer coverage and duration in Microstate C but shorter in Microstates A, B, and D compared to HCs. PCA revealed two principal components, primarily composed of microstate dynamics and entropy measures, explaining over 75% of the variance. ML models achieved high accuracy in distinguishing MCI patterns. CONCLUSIONS Our comprehensive analysis of EEG microstate features provides new insights into neurophysiological changes associated with MCI, highlighting the potential of EEG microstates for investigating complex neural changes in cognitive decline.
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Affiliation(s)
- Xiaotian Wu
- Department of Biomedical Engineering, Chengde Medical University, Chengde City, Hebei Province, China
| | - Yanli Liu
- Department of Biomedical Engineering, Chengde Medical University, Chengde City, Hebei Province, China
| | - Jiajun Che
- Department of Psychology, Chengde Medical University, Chengde City, Hebei Province, China
- Department of Otolaryngology, Head and Neck Surgery, Sleep Medical Center, Beijing Tsinghua Changgung Hospital, Beijing, China
| | - Nan Cheng
- Department of Psychology, Chengde Medical University, Chengde City, Hebei Province, China
| | - Dong Wen
- Institute of Artificial Intelligence, University of Science and Technology Beijing, Beijing, China
| | - Haining Liu
- Department of Psychology, Chengde Medical University, Chengde City, Hebei Province, China
- Hebei Key Laboratory of Nerve Injury and Repair, Chengde Medical University, Chengde City, Hebei, China
| | - Xianling Dong
- Hebei Key Laboratory of Nerve Injury and Repair, Chengde Medical University, Chengde City, Hebei, China
- Hebei International Research Center of Medical Engineering, Chengde Medical University, Chengde City, Hebei, China
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12
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Ma M, Lu B, Gong Y, Xiao C, Yang Y, Ju Y, Xi Z, Gao Y, Ning X, Zhang Y. EEG microstate analysis and machine learning classification in patients with obsessive-compulsive disorder. J Psychiatr Res 2025; 182:186-194. [PMID: 39818106 DOI: 10.1016/j.jpsychires.2025.01.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/04/2024] [Revised: 12/24/2024] [Accepted: 01/06/2025] [Indexed: 01/18/2025]
Abstract
BACKGROUND Microstate characterization of electroencephalogram (EEG) is a data-driven approach to explore the functional changes and interrelationships of multiple brain networks on a millisecond scale. This study aimed to explore the pathological changes of whole-brain functional networks in patients with obsessive-compulsive disorders (OCD) through microstate analysis and further to explore its potential value as an auxiliary diagnostic index. METHODS Forty-eight OCD patients (33 with more than moderate anxiety symptoms, 15 with mild anxiety symptoms) and 52 healthy controls (HCs) were recruited. Brain activities during eyes-closed period were collected using 64-channel electroencephalography. The differences in microstate features between OCD patients and HCs were compared, and the relationship between the microstate features and clinical symptoms were explored. Key microstate features were selected for machine learning modeling to achieve targeted classifications. RESULTS The probability of transition from microstate B to C was significantly lower in OCD patients compared to HCs, and the obsessive thoughts factor scores were significantly correlated with the duration of microstate A, the occurrence of microstate B, and the transition probability from microstate C to B. The occurrence rate of microstate C was significantly negatively correlated with the Hamilton rating scale for anxiety (HAMA) scores. The AUC (Area Under the Receiver Operating Characteristic Curve) of the machine learning model in the test set classification between the above two groups and between OCD patients with more than moderate/mild anxiety symptoms could achieve 70.43% and 77.13%, respectively. CONCLUSION EEG microstate characteristics were altered in OCD patients, and these changes were closely associated with obsessive thoughts and anxiety symptoms. Besides, the machine learning classification model based on microstate features has limited ability to identify OCD, and further optimization on this classification approach is still needed in the future.
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Affiliation(s)
- Mohan Ma
- Department of Psychiatry, National Clinical Research Center for Mental Disorders, and National Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha, 410011, Hunan, China
| | - Bingxun Lu
- School of Instrumentation Science and Optoelectronics Engineering, Beihang University, Beijing, 100191, China
| | - Yumei Gong
- Hangzhou Extremely Weak Magnetic Field Major Science and Technology Infrastructure Research Institute, Hangzhou, 310000, China
| | - Chuman Xiao
- Department of Psychiatry, National Clinical Research Center for Mental Disorders, and National Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha, 410011, Hunan, China
| | - Yumeng Yang
- Department of Psychiatry, National Clinical Research Center for Mental Disorders, and National Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha, 410011, Hunan, China
| | - Yumeng Ju
- Department of Psychiatry, National Clinical Research Center for Mental Disorders, and National Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha, 410011, Hunan, China
| | - Zhenman Xi
- Department of Psychiatry, National Clinical Research Center for Mental Disorders, and National Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha, 410011, Hunan, China
| | - Yang Gao
- School of Instrumentation Science and Optoelectronics Engineering, Beihang University, Beijing, 100191, China.
| | - Xiaolin Ning
- School of Instrumentation Science and Optoelectronics Engineering, Beihang University, Beijing, 100191, China
| | - Yan Zhang
- Department of Psychiatry, National Clinical Research Center for Mental Disorders, and National Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha, 410011, Hunan, China.
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13
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Khan AF, Saleh N, Smith ZA. The Brain's Aging Resting State Functional Connectivity. J Integr Neurosci 2025; 24:25041. [PMID: 39862002 DOI: 10.31083/jin25041] [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: 05/30/2024] [Revised: 07/29/2024] [Accepted: 08/09/2024] [Indexed: 01/27/2025] Open
Abstract
Resting state networks (RSNs) of the brain are characterized as correlated spontaneous time-varying fluctuations in the absence of goal-directed tasks. These networks can be local or large-scale spanning the brain. The study of the spatiotemporal properties of such networks has helped understand the brain's fundamental functional organization under healthy and diseased states. As we age, these spatiotemporal properties change. Moreover, RSNs exhibit neural plasticity to compensate for the loss of cognitive functions. This narrative review aims to summarize current knowledge from functional magnetic resonance imaging (fMRI) studies on age-related alterations in RSNs. Underlying mechanisms influencing such changes are discussed. Methodological challenges and future directions are also addressed. By providing an overview of the current state of knowledge in this field, this review aims to guide future research endeavors aimed at promoting healthy brain aging and developing effective interventions for age-related cognitive impairment and neurodegenerative diseases.
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Affiliation(s)
- Ali F Khan
- Department of Neurosurgery, University of Oklahoma Health Sciences Center, Oklahoma City, OK 73104, USA
| | - Nada Saleh
- Graduate College, University of Oklahoma Health Sciences Center, Oklahoma City, OK 73104, USA
| | - Zachary A Smith
- Department of Neurosurgery, University of Oklahoma Health Sciences Center, Oklahoma City, OK 73104, USA
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Li Y, Zhao Y, Chen Y, Meng M, Ren Z, Zhao Z, Wang N, Zhao T, Cui B, Li M, Liu J, Wang Q, Han J, Wang B, Han X. Effects of anti-seizure medications on resting-state functional networks in juvenile myoclonic epilepsy: An EEG microstate analysis. Seizure 2025; 124:48-56. [PMID: 39647254 DOI: 10.1016/j.seizure.2024.12.004] [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: 09/08/2024] [Revised: 12/02/2024] [Accepted: 12/04/2024] [Indexed: 12/10/2024] Open
Abstract
OBJECTIVE Juvenile myoclonic epilepsy (JME) is associated with large-scale brain network dysfunction. This study aims to investigate how anti-seizure medication (ASM) treatment alters resting-state functional networks in JME patients through resting-state EEG microstate analysis. METHODS Ninety-six subjects participated in this study: 24 healthy controls (HC), 29 newly diagnosed JME patients who had not started ASMs therapy (JME-NM), and 43 JME patients on ASMs treatment with effective seizure control (JME-M). EEG data were collected for 10 min while participants were awake and resting with their eyes closed, using a standard 19-channel recording system. EEG topographies were categorized into four microstate classes (A, B, C, D), and parameters such as mean duration, occurrence rate, time coverage, and transition probabilities between microstates were computed and compared among the three groups. Advanced statistical methods were employed to ensure the robustness and validity of the findings. RESULTS Significant alterations in EEG microstate characteristics were observed in untreated JME patients (JME-NM) compared to both healthy controls and treated patients. Microstate B had a markedly reduced mean duration in the JME-NM group, while microstate A displayed an increased occurrence rate and greater time coverage. Transition probabilities between specific microstates, such as from A to C, A to D, and B to C, were also significantly different in the JME-NM group. The normalization of these parameters in the JME-M group suggests that ASMs effectively stabilize altered brain networks, potentially mitigating the pathophysiological disruptions associated with JME. CONCLUSION This study demonstrates that ASMs effectively normalize disruptions in sensory-motor and visual networks in JME patients. EEG microstate analysis provides a dynamic view of brain network alterations and offers potential as a biomarker for the diagnosis and monitoring of JME, as well as for evaluating treatment response. These findings advance our understanding of the neurophysiological mechanisms underlying JME.
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Affiliation(s)
- Ying Li
- Department of Neurology, Xinxiang Medical University, Henan Provincial People's Hospital, Zhengzhou, Henan Province, China
| | - Yibo Zhao
- Department of Neurology, Zhengzhou University People's Hospital, Zhengzhou, Henan Province, China
| | - Yanan Chen
- Department of Neurology, Henan Provincial People's Hospital, Zhengzhou, Henan Province, China
| | - Mingxian Meng
- The First Clinical Medical College of Henan University of Traditional Chinese Medicine, Zhengzhou, Henan Province, China
| | - Zhe Ren
- Department of Neurology, Henan Provincial People's Hospital, Zhengzhou, Henan Province, China
| | - Zongya Zhao
- School of Medical Engineering, Xinxiang Medical University, Xinxiang, Henan Province, China
| | - Na Wang
- Department of Neurology, Henan Provincial People's Hospital, Zhengzhou, Henan Province, China
| | - Ting Zhao
- Department of Neurology, Henan Provincial People's Hospital, Zhengzhou, Henan Province, China
| | - Beijia Cui
- Department of Neurology, People's Hospital of Henan University,Zhengzhou, Henan Province, China
| | - Mingmin Li
- Department of Neurology, Henan Provincial People's Hospital, Zhengzhou, Henan Province, China
| | - Jin Liu
- Department of Neurology, Zhengzhou University People's Hospital, Zhengzhou, Henan Province, China
| | - Qi Wang
- Department of Neurology, Zhengzhou University People's Hospital, Zhengzhou, Henan Province, China
| | - Jiuyan Han
- Department of Neurology, Henan Provincial People's Hospital, Zhengzhou, Henan Province, China
| | - Bin Wang
- Department of Neurology, Henan Provincial People's Hospital, Zhengzhou, Henan Province, China
| | - Xiong Han
- Department of Neurology, Henan Provincial People's Hospital, Zhengzhou, Henan Province, China; Department of Neurology, Zhengzhou University People's Hospital, Zhengzhou, Henan Province, China.
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15
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Han M, Wang Y, Liu X, Cheng X, Niu H, Liu T. Attention demands modulate brain electrical microstates and mental fatigue induced by simulated flight tasks. J Neural Eng 2024; 21:066024. [PMID: 39571279 DOI: 10.1088/1741-2552/ad95be] [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: 04/10/2024] [Accepted: 11/21/2024] [Indexed: 12/06/2024]
Abstract
Objective.Prolonged engagement in tasks with varying attention demands is thought to elicit distinct forms of mental fatigue, potentially indicating variations in neural activity. This study aimed to investigate the association between mental fatigue and changes in electroencephalogram microstate dynamics during tasks with varying attention demands.Approach.In the present study, we employed a 2 × 2 repeated measures ANOVA to analyze the temporal parameters of four distinct microstates (A, B, C, and D) across different levels of attention demands (high vs. low) and mental fatigue (high vs. low) within a controlled flight simulation task involving 17 college students.Main results.Significant variations in mean durations were observed, with microstates A and B exhibiting shorter durations under high fatigue during low attention demands, while their durations increased under high attention demands. Microstate C showed increased occurrences with high fatigue under low attention demands and decreased occurrences under high attention demands. The duration and occurrence of the microstates exhibited different trends throughout the course of mental fatigue, potentially reflecting distinct fatigue-related processes.Significance.These findings establish a link between different types of mental fatigue and microstate dynamics, contributing to a comprehensive understanding of the neural processing mechanisms underlying mental fatigue.
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Affiliation(s)
- Mingxiu Han
- Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical Engineering, Beihang University, Beijing, People's Republic of China
| | - Yuwen Wang
- Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical Engineering, Beihang University, Beijing, People's Republic of China
| | - Xinyi Liu
- Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical Engineering, Beihang University, Beijing, People's Republic of China
| | - Xiangxin Cheng
- Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical Engineering, Beihang University, Beijing, People's Republic of China
| | - Haijun Niu
- Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical Engineering, Beihang University, Beijing, People's Republic of China
| | - Tao Liu
- Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical Engineering, Beihang University, Beijing, People's Republic of China
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16
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Huang Q, Liu L, Huang L, Zheng W, Zhao Y, Zeng K, Xiao F, Luo J, Li F. Increased occurrence of microstate class B as the independent risk factor for persistent psychiatric symptoms related to omicron infection. Brain Res Bull 2024; 218:111107. [PMID: 39447766 DOI: 10.1016/j.brainresbull.2024.111107] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2024] [Revised: 10/19/2024] [Accepted: 10/21/2024] [Indexed: 10/26/2024]
Abstract
OBJECTIVE This study aimed to investigate the EEG microstate characteristics in patients with persistent Omicron-related Psychiatric Symptoms (ORPS) as well as their correlations with the severity of ORPS. METHODS This study included 31 patients with ORPS, and they were divided into remission group (n=19) and non-remission group (n=12) according to the decrease of Brief Psychiatric Rating Scale (BPRS) at discharge. Multivariate logistic models were applied to analyze the risk features associated with non-remission of ORPS at discharge, and the Spearman rank correlation was adopted to analyze the correlation between the occurrence of microstate class B and BPRS score at admission. RESULTS The analysis revealed that an increased occurrence of EEG microstate class B was significantly associated with a higher likelihood of non-remission of ORPS at discharge (p < 0.05). Furthermore, a moderate positive correlation was observed between the occurrence of microstate class B and BPRS scores at admission (r = 0.390, p = 0.030), indicating that patients with more frequent microstate class B tended to exhibit more severe psychiatric symptoms at onset. CONCLUSIONS The findings suggest that an increased occurrence of EEG microstate class B is an independent risk factor for non-remission of ORPS at discharge. Additionally, the positive correlation between microstate class B and BPRS scores underscores the potential of microstate class B as a biomarker for the severity of psychiatric symptoms in ORPS patients. SIGNIFICANCE Identifying the increased occurrence of microstate class B at admission could serve as a novel marker for early assessment of ORPS severity and prognostic evaluation.
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Affiliation(s)
- Qinlian Huang
- Department of Neurology, The First Affiliated Hospital of Chongqing Medical University, Chongqing 400016, PR China.
| | - Linqi Liu
- Department of Neurology, The First Affiliated Hospital of Chongqing Medical University, Chongqing 400016, PR China.
| | - Lihong Huang
- Institutes of Neuroscience, Chongqing Medical University, Chongqing 400016, PR China.
| | - Wei Zheng
- Department of Neurology, The First Affiliated Hospital of Chongqing Medical University, Chongqing 400016, PR China.
| | - Yuping Zhao
- Department of Neurology, The First Affiliated Hospital of Chongqing Medical University, Chongqing 400016, PR China.
| | - Kebin Zeng
- Department of Neurology, The First Affiliated Hospital of Chongqing Medical University, Chongqing 400016, PR China.
| | - Fei Xiao
- Department of Neurology, The First Affiliated Hospital of Chongqing Medical University, Chongqing 400016, PR China.
| | - Jing Luo
- Department of Neurology, The First Affiliated Hospital of Chongqing Medical University, Chongqing 400016, PR China.
| | - Feng Li
- Department of Neurology, The First Affiliated Hospital of Chongqing Medical University, Chongqing 400016, PR China.
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Marín–García A, Arzate-Mena JD, Corsi-Cabrera M, Muñoz-Torres Z, Olguín–Rodríguez PV, Ríos–Herrera WA, Rivera A, Müller MF. Stationary correlation pattern in highly non-stationary MEG recordings of healthy subjects and its relation to former EEG studies. PLoS One 2024; 19:e0307378. [PMID: 39436944 PMCID: PMC11495582 DOI: 10.1371/journal.pone.0307378] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2023] [Accepted: 07/04/2024] [Indexed: 10/25/2024] Open
Abstract
In this study, we analyze magnetoencephalographic (MEG) recordings from 48 clinically healthy subjects obtained from the Human Connectome Project (HCP) while they performed a working memory task and a motor task. Our results reveal a well-developed, stable interrelation pattern that spans the entire scalp and is nearly universal, being almost task- and subject-independent. Additionally, we demonstrate that this pattern closely resembles a stationary correlation pattern (SCP) observed in EEG signals under various physiological and pathological conditions (the distribution of Pearson correlations are centered at about 0.75). Furthermore, we identify the most effective EEG reference for studying the brain's functional network derived from lag-zero cross-correlations. We contextualize these findings within the theory of complex dynamical systems operating near a critical point of a phase transition.
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Affiliation(s)
- ArlexOscar Marín–García
- Centro de Ciencias de la Complejidad, Universidad Nacional Autónoma de México, Ciudad de México, México
| | - J. Daniel Arzate-Mena
- Centro de Investigación en Ciencias, Universidad Autónoma del Estado de Morelos, Cuernavaca, Morelos, México
| | - Mari Corsi-Cabrera
- Unidad de Neurodesarrollo, Instituto de Neurobiología, Universidad Nacional Autónoma de México, Juriquilla, México
- Facultad de Psicología, Universidad Nacional Autónoma de México, Ciudad de México, México
| | - Zeidy Muñoz-Torres
- Centro de Ciencias de la Complejidad, Universidad Nacional Autónoma de México, Ciudad de México, México
- Facultad de Psicología, Universidad Nacional Autónoma de México, Ciudad de México, México
| | - Paola Vanessa Olguín–Rodríguez
- Centro de Ciencias de la Complejidad, Universidad Nacional Autónoma de México, Ciudad de México, México
- Centro de Investigación en Ciencias, Universidad Autónoma del Estado de Morelos, Cuernavaca, Morelos, México
| | | | - AnaLeonor Rivera
- Centro de Ciencias de la Complejidad, Universidad Nacional Autónoma de México, Ciudad de México, México
- Instituto de Ciencias Nucleares, Universidad Nacional Autónoma 15 de México, Ciudad de México, México
| | - Markus F. Müller
- Centro de Ciencias de la Complejidad, Universidad Nacional Autónoma de México, Ciudad de México, México
- Centro de Investigación en Ciencias, Universidad Autónoma del Estado de Morelos, Cuernavaca, Morelos, México
- Centro Internacional de Ciencias A.C., Cuernavaca, Morelos, México
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18
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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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Zhang L, Shen X, Chu C, Liu S, Wang J, Wang Y, Zhang J, Cao T, Wang F, Zhu X, Liu C. Deep-learning-optimized microstate network analysis for early Parkinson's disease with mild cognitive impairment. Cogn Neurodyn 2024; 18:2589-2604. [PMID: 39555255 PMCID: PMC11564620 DOI: 10.1007/s11571-023-10016-6] [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: 02/12/2023] [Revised: 09/18/2023] [Accepted: 09/23/2023] [Indexed: 11/19/2024] Open
Abstract
Graph-theory-based topological impairment of the whole-brain network has been verified to be one of the characteristics of mild cognitive impairment (MCI). However, two major challenges impede the further understanding of topological features for the personalized functional connectivity network of early Parkinson's disease (ePD) with MCI. The uncertain of characteristic frequency band reflecting the abnormality of ePD-MCI and the setting of fixed length of sliding window at a second level in the construction of conventional brain network both limit a deeper exploration of network characteristics for ePD-MCI. Thus, a convolutional neural network is constructed first and the gradient-weighted class activation mapping method is used to determine the characteristic frequency band of the ePD-MCI. It is found that 1-4 Hz is a characteristic frequency band for recognizing MCI in ePD. Then, we propose a microstate window construction method based on electroencephalography microstate sequences to build brain functional network. By exploring the graph-theory-based topological features and their clinical correlations with cognitive impairment, it is shown that the clustering coefficient, global efficiency, and local efficiency of the occipital lobe significantly decrease in ePD-MCI, which reflects the low degree of nodes interconnection, low efficiency of parallel information transmission and low communication efficiency among the nodes in the brain network of the occipital lobe may be the neural marker of ePD-MCI. The finding of personalized topological impairments of the brain network may be a potential characteristic of early PD-MCI. Supplementary Information The online version contains supplementary material available at 10.1007/s11571-023-10016-6.
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Affiliation(s)
- Luxiao Zhang
- School of Electrical and Information Engineering, Tianjin University, Tianjin, 300072 China
| | - Xiao Shen
- Department of Neurology, Tianjin Neurological Institute, Tianjin Medical University General Hospital, Tianjin, 300052 China
| | - Chunguang Chu
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Chunguang Chu, Shanghai, China
| | - Shang Liu
- School of Electrical and Information Engineering, Tianjin University, Tianjin, 300072 China
| | - Jiang Wang
- School of Electrical and Information Engineering, Tianjin University, Tianjin, 300072 China
| | - Yanlin Wang
- Department of Neurology, Tianjin Neurological Institute, Tianjin Medical University General Hospital, Tianjin, 300052 China
| | - Jinghui Zhang
- Department of Neurology, Tianjin Neurological Institute, Tianjin Medical University General Hospital, Tianjin, 300052 China
| | - Tingyu Cao
- Department of Neurology, Tianjin Neurological Institute, Tianjin Medical University General Hospital, Tianjin, 300052 China
| | - Fei Wang
- Department of Neurology, Tianjin Neurological Institute, Tianjin Medical University General Hospital, Tianjin, 300052 China
| | - Xiaodong Zhu
- Department of Neurology, Tianjin Neurological Institute, Tianjin Medical University General Hospital, Tianjin, 300052 China
| | - Chen Liu
- School of Electrical and Information Engineering, Tianjin University, Tianjin, 300072 China
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Zhang J, Zhu C, Li J, Wu L, Zhang Y, Huang H, Lin W. A comprehensive prediction model of drug-refractory epilepsy based on combined clinical-EEG microstate features. Ther Adv Neurol Disord 2024; 17:17562864241276202. [PMID: 39371640 PMCID: PMC11456178 DOI: 10.1177/17562864241276202] [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: 05/14/2024] [Accepted: 07/31/2024] [Indexed: 10/08/2024] Open
Abstract
Background Epilepsy is a chronic neurological disorder characterized by recurrent seizures that significantly impact patients' quality of life. Identifying predictors is crucial for early intervention. Objective Electroencephalography (EEG) microstates effectively describe the resting state activity of the human brain using multichannel EEG. This study aims to develop a comprehensive prediction model that integrates clinical features with EEG microstates to predict drug-refractory epilepsy (DRE). Design Retrospective study. Methods This study encompassed 226 patients with epilepsy treated at the epilepsy center of a tertiary hospital between October 2020 and May 2023. Patients were categorized into DRE and non-DRE groups. All patients were randomly divided into training and testing sets. Lasso regression combined with Stepglm [both] algorithms was used to screen independent risk factors for DRE. These risk factors were used to construct models to predict the DRE. Three models were constructed: a clinical feature model, an EEG microstate model, and a comprehensive prediction model (combining clinical-EEG microstates). A series of evaluation methods was used to validate the accuracy and reliability of the prediction models. Finally, these models were visualized for display. Results In the training and testing sets, the comprehensive prediction model achieved the highest area under the curve values, registering 0.99 and 0.969, respectively. It was significantly superior to other models in terms of the C-index, with scores of 0.990 and 0.969, respectively. Additionally, the model recorded the lowest Brier scores of 0.034 and 0.071, respectively, and the calibration curve demonstrated good consistency between the predicted probabilities and observed outcomes. Decision curve analysis revealed that the model provided significant clinical net benefit across the threshold range, underscoring its strong clinical applicability. We visualized the comprehensive prediction model by developing a nomogram and established a user-friendly website to enable easy application of this model (https://fydxh.shinyapps.io/CE_model_of_DRE/). Conclusion A comprehensive prediction model for DRE was developed, showing excellent discrimination and calibration in both the training and testing sets. This model provided an intuitive approach for assessing the risk of developing DRE in patients with epilepsy.
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Affiliation(s)
- Jinying Zhang
- Department of Neurology, Fujian Medical University Union Hospital, Fuzhou, China
- Department of Neurology, The Second Affiliated Hospital of Fujian Medical University, Quanzhou, Fujian Province, China
| | - Chaofeng Zhu
- Department of Neurology, Fujian Medical University Union Hospital, Fuzhou, China
| | - Juan Li
- Department of Neurology, Fujian Medical University Union Hospital, Fuzhou, China
| | - Luyan Wu
- Department of Neurology, Fujian Medical University Union Hospital, Fuzhou, China
| | - Yuying Zhang
- Department of Neurology, Fujian Medical University Union Hospital, Fuzhou, China
| | - Huapin Huang
- Department of Neurology, Fujian Medical University Union Hospital, Xinquan Road 29#, Fuzhou, Fujian Province, China
- Fujian Key Laboratory of Molecular Neurology, Fuzhou, China
- Department of Geriatrics, Fujian Medical University Union Hospital, Fuzhou, China
| | - Wanhui Lin
- Department of Neurology, Fujian Medical University Union Hospital, Xinquan Road 29#, Fuzhou, Fujian Province, China
- Fujian Key Laboratory of Molecular Neurology, Fuzhou, China
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21
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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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22
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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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Liao C, Zhao S, Zhang J. Motor Imagery Recognition Based on GMM-JCSFE Model. IEEE Trans Neural Syst Rehabil Eng 2024; 32:3348-3357. [PMID: 39208037 DOI: 10.1109/tnsre.2024.3451716] [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: 09/04/2024]
Abstract
Features from EEG microstate models, such as time-domain statistical features and state transition probabilities, are typically manually selected based on experience. However, traditional microstate models assume abrupt transitions between states, and the classification features can vary among individuals due to personal differences. To date, both empirical and theoretical classification results of EEG microstate features have not been entirely satisfactory. Here, we introduce an enhanced feature extraction method that combines Joint label-Common and label-Specific Feature Exploration (JCSFE) with Gaussian Mixture Models (GMM) to explore microstate features. First, GMMs are employed to represent the smooth transitions of EEG spatiotemporal features within microstate models. Second, category-common and category-specific features are identified by applying regularization constraints to linear classifiers. Third, a graph regularizer is used to extract subject-invariant microstate features. Experimental results on publicly available datasets demonstrate that the proposed model effectively encodes microstate features and improves the accuracy of motor imagery recognition across subjects. The primary code is accessible for download from the website: https://github.com/liaoliao3450/GMM-JCSFE.
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24
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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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Zangeneh Soroush M, Zeng Y. EEG-based study of design creativity: a review on research design, experiments, and analysis. Front Behav Neurosci 2024; 18:1331396. [PMID: 39148896 PMCID: PMC11325867 DOI: 10.3389/fnbeh.2024.1331396] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2023] [Accepted: 05/07/2024] [Indexed: 08/17/2024] Open
Abstract
Brain dynamics associated with design creativity tasks are largely unexplored. Despite significant strides, there is a limited understanding of the brain-behavior during design creation tasks. The objective of this paper is to review the concepts of creativity and design creativity as well as their differences, and to explore the brain dynamics associated with design creativity tasks using electroencephalography (EEG) as a neuroimaging tool. The paper aims to provide essential insights for future researchers in the field of design creativity neurocognition. It seeks to examine fundamental studies, present key findings, and initiate a discussion on associated brain dynamics. The review employs thematic analysis and a forward and backward snowball search methodology with specific inclusion and exclusion criteria to select relevant studies. This search strategy ensured a comprehensive review focused on EEG-based creativity and design creativity experiments. Different components of those experiments such as participants, psychometrics, experiment design, and creativity tasks, are reviewed and then discussed. The review identifies that while some studies have converged on specific findings regarding EEG alpha band activity in creativity experiments, there remain inconsistencies in the literature. The paper underscores the need for further research to unravel the interplays between these cognitive processes. This comprehensive review serves as a valuable resource for readers seeking an understanding of current literature, principal discoveries, and areas where knowledge remains incomplete. It highlights both positive and foundational aspects, identifies gaps, and poses lingering questions to guide future research endeavors.
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Affiliation(s)
- Morteza Zangeneh Soroush
- Concordia Institute for Information Systems Engineering, Gina Cody School of Engineering and Computer Science, Concordia University, Montreal, QC, Canada
| | - Yong Zeng
- Concordia Institute for Information Systems Engineering, Gina Cody School of Engineering and Computer Science, Concordia University, Montreal, QC, Canada
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26
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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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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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Rong R, Zhang R, Xu Y, Wang X, Wang H, Wang X. The Role of EEG microstates in predicting oxcarbazepine treatment outcomes in patients with newly-diagnosed focal epilepsy. Seizure 2024; 119:63-70. [PMID: 38796953 DOI: 10.1016/j.seizure.2024.05.015] [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: 02/03/2024] [Revised: 05/10/2024] [Accepted: 05/22/2024] [Indexed: 05/29/2024] Open
Abstract
PURPOSE Microstates represent the global and topographical distribution of electrical brain activity from scalp-recorded EEG. This study aims to explore EEG microstates of patients with focal epilepsy prior to medication, and employ extracted microstate metrics for predicting treatment outcomes with Oxcarbazepine monotherapy. METHODS This study involved 25 newly-diagnosed focal epilepsy patients (13 females), aged 12 to 68, with various etiologies. Patients were categorized into Non-Seizure-Free (NSF) and Seizure-Free (SF) groups according to their first follow-up outcomes. From pre-medication EEGs, four representative microstates were identified by using clustering. The temporal parameters and transition probabilities of microstates were extracted and analyzed to discern group differences. With generating sample method, Support Vector Machine (SVM), Logistic Regression (LR), and Naïve Bayes (NB) classifiers were employed for predicting treatment outcomes. RESULTS In the NSF group, Microstate 1 (MS1) exhibited a significantly higher duration (mean±std. = 0.092±0.008 vs. 0.085±0.008, p = 0.047), occurrence (mean±std. = 2.587±0.334 vs. 2.260±0.278, p = 0.014), and coverage (mean±std. = 0.240±0.046 vs. 0.194±0.040, p = 0.014) compared to the SF group. Additionally, the transition probabilities from Microstate 2 (MS2) and Microstate 3 (MS3) to MS1 were increased. In MS2, the NSF group displayed a stronger correlation (mean±std. = 0.618±0.025 vs. 0.571±0.034, p < 0.001) and a higher global explained variance (mean±std. = 0.083±0.035 vs. 0.055±0.023, p = 0.027) than the SF group. Conversely, Microstate 4 (MS4) in the SF group demonstrated significantly greater coverage (mean±std. = 0.388±0.074 vs. 0.334±0.052, p = 0.046) and more frequent transitions from MS2 to MS4, indicating a distinct pattern. Temporal parameters contribute major predictive role in predicting treatment outcomes of Oxcarbazepine, with area under curves (AUCs) of 0.95, 0.70, and 0.86, achieved by LR, NB and SVM, respectively. CONCLUSION This study underscores the potential of EEG microstates as predictive biomarkers for Oxcarbazepine treatment responses in newly-diagnosed focal epilepsy patients.
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Affiliation(s)
- Rong Rong
- Department of Neurology, Nanjing Drum Tower Hospital Clinical College of Nanjing Medical University, Nanjing Medical University, Nanjing 210008, Jiangsu, PR China
| | - Runkai Zhang
- Key Laboratory of Child Development and Learning Science of Ministry of Education, School of Biological Science & Medical Engineering, Southeast University, Nanjing 210096, Jiangsu, PR China
| | - Yun Xu
- Department of Neurology, Nanjing Drum Tower Hospital Clinical College of Nanjing Medical University, Nanjing Medical University, Nanjing 210008, Jiangsu, PR China
| | - Xiaoyun Wang
- Department of Neurology, Nanjing Drum Tower Hospital Clinical College of Nanjing Medical University, Nanjing Medical University, Nanjing 210008, Jiangsu, PR China
| | - Haixian Wang
- Key Laboratory of Child Development and Learning Science of Ministry of Education, School of Biological Science & Medical Engineering, Southeast University, Nanjing 210096, Jiangsu, PR China.
| | - Xiaoshan Wang
- Department of Neurology, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing Medical University, Nanjing 210008, Jiangsu, PR China.
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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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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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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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32
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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: 5] [Impact Index Per Article: 5.0] [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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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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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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Kung YC, Li CW, Hsu AL, Liu CY, Wu CW, Chang WC, Lin CP. Neurovascular coupling in eye-open-eye-close task and resting state: Spectral correspondence between concurrent EEG and fMRI. Neuroimage 2024; 289:120535. [PMID: 38342188 DOI: 10.1016/j.neuroimage.2024.120535] [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: 09/09/2023] [Revised: 01/23/2024] [Accepted: 02/08/2024] [Indexed: 02/13/2024] Open
Abstract
Neurovascular coupling serves as an essential neurophysiological mechanism in functional neuroimaging, which is generally presumed to be robust and invariant across different physiological states, encompassing both task engagement and resting state. Nevertheless, emerging evidence suggests that neurovascular coupling may exhibit state dependency, even in normal human participants. To investigate this premise, we analyzed the cross-frequency spectral correspondence between concurrently recorded electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) data, utilizing them as proxies for neurovascular coupling during the two conditions: an eye-open-eye-close (EOEC) task and a resting state. We hypothesized that given the state dependency of neurovascular coupling, EEG-fMRI spectral correspondences would change between the two conditions in the visual system. During the EOEC task, we observed a negative phase-amplitude-coupling (PAC) between EEG alpha-band and fMRI visual activity. Conversely, in the resting state, a pronounced amplitude-amplitude-coupling (AAC) emerged between EEG and fMRI signals, as evidenced by the spectral correspondence between the EEG gamma-band of the midline occipital channel (Oz) and the high-frequency fMRI signals (0.15-0.25 Hz) in the visual network. This study reveals distinct scenarios of EEG-fMRI spectral correspondence in healthy participants, corroborating the state-dependent nature of neurovascular coupling.
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Affiliation(s)
- Yi-Chia Kung
- Department of Radiology, Tri-Service General Hospital, National Defense Medical Center, Taipei, Taiwan
| | - Chia-Wei Li
- Department of Radiology, Wan Fang Hospital, Taipei Medical University, Taipei, Taiwan
| | - Ai-Ling Hsu
- Bachelor Program in Artificial Intelligence, Chang Gung University, Taoyuan, Taiwan; Department of Psychiatry, Chang Gung Memorial Hospital at Linkou, Taoyuan, Taiwan
| | - Chi-Yun Liu
- Graduate Institute of Mind, Brain and Consciousness, Taipei Medical University, Taipei, Taiwan
| | - Changwei W Wu
- Graduate Institute of Mind, Brain and Consciousness, Taipei Medical University, Taipei, Taiwan; Research Center of Sleep Medicine, Taipei Medical University Hospital, Taipei, Taiwan.
| | - Wei-Chou Chang
- Department of Radiology, Tri-Service General Hospital, National Defense Medical Center, Taipei, Taiwan
| | - Ching-Po Lin
- Institute of Neuroscience, National Yang Ming Chiao Tung University, Hsinchu, Taiwan; Department of Education and Research, Taipei City Hospital, Taipei, Taiwan
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Zarka D, Cevallos C, Ruiz P, Petieau M, Cebolla AM, Bengoetxea A, Cheron G. Electroencephalography microstates highlight specific mindfulness traits. Eur J Neurosci 2024; 59:1753-1769. [PMID: 38221503 DOI: 10.1111/ejn.16247] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2023] [Revised: 11/29/2023] [Accepted: 12/18/2023] [Indexed: 01/16/2024]
Abstract
The present study aimed to investigate the spontaneous dynamics of large-scale brain networks underlying mindfulness as a dispositional trait, through resting-state electroencephalography (EEG) microstates analysis. Eighteen participants had attended a standardized mindfulness-based stress reduction training (MBSR), and 18 matched waitlist individuals (CTRL) were recorded at rest while they were passively exposed to auditory stimuli. Participants' mindfulness traits were assessed with the Five Facet Mindfulness Questionnaire (FFMQ). To further explore the relationship between microstate dynamics at rest and mindfulness traits, participants were also asked to rate their experience according to five phenomenal dimensions. After training, MBSR participants showed a highly significant increase in FFMQ score, as well as higher observing and non-reactivity FFMQ sub-scores than CTRL participants. Microstate analysis revealed four classes of microstates (A-D) in global clustering across all subjects. The MBSR group showed lower duration, occurrence and coverage of microstate C than the control group. Moreover, these microstate C parameters were negatively correlated to non-reactivity sub-scores of FFMQ across participants, whereas the microstate A occurrence was negatively correlated to FFMQ total score. Further analysis of participants' self-reports suggested that MBSR participants showed a better sensory-affective integration of auditory interferences. In line with previous studies, our results suggest that temporal dynamics of microstate C underlie specifically the non-reactivity trait of mindfulness. These findings encourage further research into microstates in the evaluation and monitoring of the impact of mindfulness-based interventions on the mental health and well-being of individuals.
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Affiliation(s)
- D Zarka
- Laboratory of Neurophysiology and Movement Biomechanics, Research Unit in Sciences of Osteopathy, Faculty of Human Motor Sciences, Université Libre de Bruxelles, Brussels, Belgium
- Research Unit in Sciences of Osteopathy, Faculty of Human Motor Sciences, Université Libre de Bruxelles, Brussels, Belgium
| | - C Cevallos
- Laboratory of Neurophysiology and Movement Biomechanics, Research Unit in Sciences of Osteopathy, Faculty of Human Motor Sciences, Université Libre de Bruxelles, Brussels, Belgium
- Departamento de Ingeniería Mecánica, Facultad de Ingeniería Mecánica, Escuela Politécnica Nacional, Quito, Ecuador
| | - P Ruiz
- Departamento de Ingeniería Mecánica, Facultad de Ingeniería Mecánica, Escuela Politécnica Nacional, Quito, Ecuador
| | - M Petieau
- Laboratory of Neurophysiology and Movement Biomechanics, Research Unit in Sciences of Osteopathy, Faculty of Human Motor Sciences, Université Libre de Bruxelles, Brussels, Belgium
| | - A M Cebolla
- Laboratory of Neurophysiology and Movement Biomechanics, Research Unit in Sciences of Osteopathy, Faculty of Human Motor Sciences, Université Libre de Bruxelles, Brussels, Belgium
| | - A Bengoetxea
- Research Unit in Sciences of Osteopathy, Faculty of Human Motor Sciences, Université Libre de Bruxelles, Brussels, Belgium
- Athenea Neuroclinics, San Sebastian, Spain
| | - G Cheron
- Laboratory of Neurophysiology and Movement Biomechanics, Research Unit in Sciences of Osteopathy, Faculty of Human Motor Sciences, Université Libre de Bruxelles, Brussels, Belgium
- Laboratory of Electrophysiology, Université de Mons, Mons, Belgium
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Islam S, Khanra P, Nakuci J, Muldoon SF, Watanabe T, Masuda N. State-transition dynamics of resting-state functional magnetic resonance imaging data: model comparison and test-to-retest analysis. BMC Neurosci 2024; 25:14. [PMID: 38438838 PMCID: PMC10913599 DOI: 10.1186/s12868-024-00854-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2023] [Accepted: 02/07/2024] [Indexed: 03/06/2024] Open
Abstract
Electroencephalogram (EEG) microstate analysis entails finding dynamics of quasi-stable and generally recurrent discrete states in multichannel EEG time series data and relating properties of the estimated state-transition dynamics to observables such as cognition and behavior. While microstate analysis has been widely employed to analyze EEG data, its use remains less prevalent in functional magnetic resonance imaging (fMRI) data, largely due to the slower timescale of such data. In the present study, we extend various data clustering methods used in EEG microstate analysis to resting-state fMRI data from healthy humans to extract their state-transition dynamics. We show that the quality of clustering is on par with that for various microstate analyses of EEG data. We then develop a method for examining test-retest reliability of the discrete-state transition dynamics between fMRI sessions and show that the within-participant test-retest reliability is higher than between-participant test-retest reliability for different indices of state-transition dynamics, different networks, and different data sets. This result suggests that state-transition dynamics analysis of fMRI data could discriminate between different individuals and is a promising tool for performing fingerprinting analysis of individuals.
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Affiliation(s)
- Saiful Islam
- Institute for Artificial Intelligence and Data Science, University at Buffalo, State University of New York at Buffalo, 215 Lockwood Hall, Buffalo, 14260, NY, USA
| | - Pitambar Khanra
- Department of Mathematics , University at Buffalo, State University of New York at Buffalo, 244 Mathematics Building , Buffalo, 14260, NY, USA
| | - Johan Nakuci
- School of Psychology, Georgia Institute of Technology, North Avenue, Atlanta, 30332, GA, USA
| | - Sarah F Muldoon
- Department of Mathematics , University at Buffalo, State University of New York at Buffalo, 244 Mathematics Building , Buffalo, 14260, NY, USA
- Institute for Artificial Intelligence and Data Science, University at Buffalo, State University of New York at Buffalo, 215 Lockwood Hall, Buffalo, 14260, NY, USA
- Neuroscience Program, University at Buffalo, State University of New York at Buffalo, 955 Main Street, Buffalo, 14203, NY, USA
| | - Takamitsu Watanabe
- International Research Centre for Neurointelligence, The University of Tokyo Institutes for Advanced Study, 731 Hongo Bunkyo-ku, Tokyo, 113-0033, Japan
| | - Naoki Masuda
- Department of Mathematics , University at Buffalo, State University of New York at Buffalo, 244 Mathematics Building , Buffalo, 14260, NY, USA.
- Institute for Artificial Intelligence and Data Science, University at Buffalo, State University of New York at Buffalo, 215 Lockwood Hall, Buffalo, 14260, NY, USA.
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38
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Mikutta CA, Knight RT, Sammler D, Müller TJ, Koenig T. Electrocorticographic Activation Patterns of Electroencephalographic Microstates. Brain Topogr 2024; 37:287-295. [PMID: 36939988 PMCID: PMC10884069 DOI: 10.1007/s10548-023-00952-1] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2022] [Accepted: 02/26/2023] [Indexed: 03/21/2023]
Abstract
Electroencephalography (EEG) microstates are short successive periods of stable scalp field potentials representing spontaneous activation of brain resting-state networks. EEG microstates are assumed to mediate local activity patterns. To test this hypothesis, we correlated momentary global EEG microstate dynamics with the local temporo-spectral evolution of electrocorticography (ECoG) and stereotactic EEG (SEEG) depth electrode recordings. We hypothesized that these correlations involve the gamma band. We also hypothesized that the anatomical locations of these correlations would converge with those of previous studies using either combined functional magnetic resonance imaging (fMRI)-EEG or EEG source localization. We analyzed resting-state data (5 min) of simultaneous noninvasive scalp EEG and invasive ECoG and SEEG recordings of two participants. Data were recorded during the presurgical evaluation of pharmacoresistant epilepsy using subdural and intracranial electrodes. After standard preprocessing, we fitted a set of normative microstate template maps to the scalp EEG data. Using covariance mapping with EEG microstate timelines and ECoG/SEEG temporo-spectral evolutions as inputs, we identified systematic changes in the activation of ECoG/SEEG local field potentials in different frequency bands (theta, alpha, beta, and high-gamma) based on the presence of particular microstate classes. We found significant covariation of ECoG/SEEG spectral amplitudes with microstate timelines in all four frequency bands (p = 0.001, permutation test). The covariance patterns of the ECoG/SEEG electrodes during the different microstates of both participants were similar. To our knowledge, this is the first study to demonstrate distinct activation/deactivation patterns of frequency-domain ECoG local field potentials associated with simultaneous EEG microstates.
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Affiliation(s)
- Christian A Mikutta
- Translational Research Center, University Hospital of Psychiatry and Psychotherapy, University of Bern, Bern, Switzerland
- Private Clinic Meiringen, Meiringen, Switzerland
- Interdisciplinary Biosciences Doctoral Training Partnership, Department of Physiology, Anatomy and Genetics, University of Oxford, Oxford, UK
| | - Robert T Knight
- Helen Wills Neuroscience Institute, University of California-Berkeley, 132 Barker Hall, 94720, Berkeley, CA, USA
| | - Daniela Sammler
- Research Group Neurocognition of Music and Language, Max Planck Institute for Empirical Aesthetics, Frankfurt am Main, Germany
| | - Thomas J Müller
- Translational Research Center, University Hospital of Psychiatry and Psychotherapy, University of Bern, Bern, Switzerland
- Private Clinic Meiringen, Meiringen, Switzerland
| | - Thomas Koenig
- Translational Research Center, University Hospital of Psychiatry and Psychotherapy, University of Bern, Bern, Switzerland.
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39
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Wiemers MC, Laufs H, von Wegner F. Frequency Analysis of EEG Microstate Sequences in Wakefulness and NREM Sleep. Brain Topogr 2024; 37:312-328. [PMID: 37253955 PMCID: PMC11374823 DOI: 10.1007/s10548-023-00971-y] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2022] [Accepted: 05/11/2023] [Indexed: 06/01/2023]
Abstract
The majority of EEG microstate analyses concern wakefulness, and the existing sleep studies have focused on changes in spatial microstate properties and on microstate transitions between adjacent time points, the shortest available time scale. We present a more extensive time series analysis of unsmoothed EEG microstate sequences in wakefulness and non-REM sleep stages across many time scales. Very short time scales are assessed with Markov tests, intermediate time scales by the entropy rate and long time scales by a spectral analysis which identifies characteristic microstate frequencies. During the descent from wakefulness to sleep stage N3, we find that the increasing mean microstate duration is a gradual phenomenon explained by a continuous slowing of microstate dynamics as described by the relaxation time of the transition probability matrix. The finite entropy rate, which considers longer microstate histories, shows that microstate sequences become more predictable (less random) with decreasing vigilance level. Accordingly, the Markov property is absent in wakefulness but in sleep stage N3, 10/19 subjects have microstate sequences compatible with a second-order Markov process. A spectral microstate analysis is performed by comparing the time-lagged mutual information coefficients of microstate sequences with the autocorrelation function of the underlying EEG. We find periodic microstate behavior in all vigilance states, linked to alpha frequencies in wakefulness, theta activity in N1, sleep spindle frequencies in N2, and in the delta frequency band in N3. In summary, we show that EEG microstates are a dynamic phenomenon with oscillatory properties that slow down in sleep and are coupled to specific EEG frequencies across several sleep stages.
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Affiliation(s)
- Milena C Wiemers
- Department of Neurology and Clinical Neurophysiology, Lüneburg Hospital, Bögelstrasse 1, 21339, Lüneburg, Germany
| | - Helmut Laufs
- Department of Neurology, Christian-Albrechts University Kiel, Arnold-Heller-Strasse 3, 24105, Kiel, Germany
| | - Frederic von Wegner
- School of Biomedical Sciences, University of New South Wales, Wallace Wurth Building, Kensington, NSW, 2052, Australia.
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40
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Baldini S, Duma GM, Danieli A, Antoniazzi L, Vettorel A, Baggio M, Da Rold M, Bonanni P. Electroencephalographic microstates as a potential neurophysiological marker differentiating bilateral from unilateral temporal lobe epilepsy. Epilepsia 2024; 65:664-674. [PMID: 38265624 DOI: 10.1111/epi.17893] [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: 08/04/2023] [Revised: 01/10/2024] [Accepted: 01/11/2024] [Indexed: 01/25/2024]
Abstract
OBJECTIVE Electroencephalographic (EEG) microstate abnormalities have been documented in different neurological disorders. We aimed to assess whether EEG microstates are altered also in patients with temporal epilepsy (TLE) and whether they show different activations in patients with unilateral TLE (UTLE) and bilateral TLE (BTLE). METHODS Nineteen patients with UTLE, 12 with BTLE, and 15 healthy controls were enrolled. Resting state high-density electroencephalography (128 channels) was recorded for 15 min with closed eyes. We obtained a set of stable scalp maps representing the EEG activity, named microstates, from which we acquired the following variables: global explained variance (GEV), mean duration (MD), time coverage (TC), and frequency of occurrence (FO). Two-way repeated measures analysis of variance was used to compare groups, and Spearman correlation was performed to study the maps in association with the clinical and neuropsychological data. RESULTS Patients with BTLE and UTLE showed differences in most of the parameters (GEV, MD, TC, FO) of the four microstate maps (A-D) compared to controls. Patients with BTLE showed a significant increase in all parameters for the microstates in Map-A and a decrease in Map-D compared to UTLE and controls. We observed a correlation between Map-A, disease duration, and spatial short-term memory, whereas microstate Map-D was correlated with the global intelligence score and short-term memory performance. SIGNIFICANCE A global alteration of the neural dynamics was observed in patients with TLE compared to controls. A different pattern of EEG microstate abnormalities was identified in BTLE compared to UTLE, which might represent a distinctive biomarker.
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Affiliation(s)
- Sara Baldini
- Clinical Unit of Neurology, Department of Medical Sciences, University Hospital and Health Services of Trieste, University of Trieste, Trieste, Italy
| | - Gian Marco Duma
- Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS) Eugenio Medea, Epilepsy Unit, Conegliano, TV, Italy
| | - Alberto Danieli
- Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS) Eugenio Medea, Epilepsy Unit, Conegliano, TV, Italy
| | - Lisa Antoniazzi
- Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS) Eugenio Medea, Epilepsy Unit, Conegliano, TV, Italy
| | | | - Martina Baggio
- Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS) Eugenio Medea, Epilepsy Unit, Conegliano, TV, Italy
| | | | - Paolo Bonanni
- Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS) Eugenio Medea, Epilepsy Unit, Conegliano, TV, Italy
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41
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Mazzeo A, Cerulli Irelli E, Leodori G, Mancuso M, Morano A, Giallonardo AT, Di Bonaventura C. Resting-state electroencephalography microstates as a marker of photosensitivity in juvenile myoclonic epilepsy. Brain Commun 2024; 6:fcae054. [PMID: 38444911 PMCID: PMC10914451 DOI: 10.1093/braincomms/fcae054] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2023] [Revised: 12/14/2023] [Accepted: 02/16/2024] [Indexed: 03/07/2024] Open
Abstract
Juvenile myoclonic epilepsy is an idiopathic generalized epilepsy syndrome associated with photosensitivity in approximately 30-40% of cases. Microstates consist of a brief period of time during which the topography of the whole resting-state electroencephalography signal is characterized by a specific configuration. Previous neurophysiological and neuroimaging studies have suggested that Microstate B may represent activity within the visual network. In this case-control study, we aimed to investigate whether anatomical and functional alterations in the visual network observed in individuals with photosensitivity could lead to changes in Microstate B dynamics in photosensitive patients with juvenile myoclonic epilepsy. Resting-state electroencephalography microstate analysis was performed on 28 patients with juvenile myoclonic epilepsy. Of these, 15 patients exhibited photosensitivity, while the remaining 13 served as non-photosensitive controls. The two groups were carefully matched in terms of age, sex, seizure control and anti-seizure medications. Multivariate analysis of variance and repeated-measures analysis of variance were performed to assess significant differences in microstate metrics and syntax between the photosensitive and the non-photosensitive group. Post hoc false discovery rate adjusted unpaired t-tests were used to determine differences in specific microstate classes between the two groups. The four classical microstates (Classes A, B, C and D) accounted for 72.8% of the total electroencephalography signal variance in the photosensitive group and 75.64% in the non-photosensitive group. Multivariate analysis of variance revealed a statistically significant class-group interaction on microstate temporal metrics (P = 0.021). False discovery rate adjusted univariate analyses of variance indicated a significant class-group interaction for both mean occurrence (P = 0.002) and coverage (P = 0.03), but not for mean duration (P = 0.14). Post hoc false discovery rate adjusted unpaired t-tests showed significantly higher coverage (P = 0.02) and occurrence (P = 0.04) of Microstate B in photosensitive patients compared with non-photosensitive participants, along with an increased probability of transitioning from Microstates C (P = 0.04) and D (P = 0.02) to Microstate B. No significant differences were found concerning the other microstate classes between the two groups. Our study provides novel insights on resting-state electroencephalography microstate dynamics underlying photosensitivity in patients with juvenile myoclonic epilepsy. The increased representation of Microstate B in these patients might reflect the resting-state overactivation of the visual system underlying photosensitivity. Further research is warranted to investigate microstate dynamics in other photosensitive epilepsy syndromes.
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Affiliation(s)
- Adolfo Mazzeo
- Department of Human Neurosciences, Sapienza University, Rome 00185, Italy
| | | | - Giorgio Leodori
- Department of Human Neurosciences, Sapienza University, Rome 00185, Italy
- IRCCS Neuromed, Pozzilli 86077, Italy
| | - Marco Mancuso
- Department of Human Neurosciences, Sapienza University, Rome 00185, Italy
| | - Alessandra Morano
- Department of Human Neurosciences, Sapienza University, Rome 00185, Italy
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42
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Ashwin P, Fadera M, Postlethwaite C. Network attractors and nonlinear dynamics of neural computation. Curr Opin Neurobiol 2024; 84:102818. [PMID: 38070404 DOI: 10.1016/j.conb.2023.102818] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2023] [Revised: 10/27/2023] [Accepted: 11/13/2023] [Indexed: 02/18/2024]
Abstract
The importance of understanding the nonlinear dynamics of neural systems, and the relation to cognitive systems more generally, has been recognised for a long time. Approaches that analyse neural systems in terms of attractors of autonomous networks can be successful in explaining system behaviours in the input-free case. Nonetheless, a computational system usually needs inputs from its environment to effectively solve problems, and this necessitates a non-autonomous framework where typically the effects of a changing environment can be studied. In this review, we highlight a variety of network attractors that can exist in autonomous systems and can be used to aid interpretation of the dynamics in the presence of inputs. Such network attractors (that consist of heteroclinic or excitable connections between invariant sets) lend themselves to modelling discrete-state computations with continuous inputs, and can sometimes be thought of as a hybrid model between classical discrete computation and continuous-time dynamical systems. Bibliographic info here.
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Affiliation(s)
- Peter Ashwin
- Department of Mathematics and Statistics, University of Exeter, Exeter EX4 4QF, United Kingdom.
| | - Muhammed Fadera
- Department of Mathematics and Statistics, University of Exeter, Exeter EX4 4QF, United Kingdom
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Li Y, Gao J, Yang Y, Zhuang Y, Kang Q, Li X, Tian M, Lv H, He J. Temporal and spatial variability of dynamic microstate brain network in disorders of consciousness. CNS Neurosci Ther 2024; 30:e14641. [PMID: 38385681 PMCID: PMC10883110 DOI: 10.1111/cns.14641] [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: 10/10/2023] [Revised: 01/17/2024] [Accepted: 02/01/2024] [Indexed: 02/23/2024] Open
Abstract
BACKGROUND Accurately diagnosing patients with the vegetative state (VS) and the minimally conscious state (MCS) reached a misdiagnosis of approximately 40%. METHODS A method combined microstate and dynamic functional connectivity (dFC) to study the spatiotemporal variability of the brain in disorders of consciousness (DOC) patients was proposed. Resting-state EEG data were obtained from 16 patients with MCS and 16 patients with VS. Mutual information (MI) was used to assess the EEG connectivity in each microstate. MI-based features with statistical differences were selected as the total feature subset (TFS), then the TFS was utilized to feature selection and fed into the classifier, obtaining the optimal feature subsets (OFS) in each microstate. Subsequently, an OFS-based MI functional connectivity network (MIFCN) was constructed in the cortex. RESULTS The group-average MI connectivity matrix focused on all channels revealed that all five microstates exhibited stronger information interaction in the MCS when comparing with the VS. While OFS-based MIFCN, which only focused on a few channels, revealed greater MI flow in VS patients than in MCS patients under microstates A, B, C, and E, except for microstate D. Additionally, the average classification accuracy of OFS in the five microstates was 96.2%. CONCLUSION Constructing features based on microstates to distinguish between two categories of DOC patients had effectiveness.
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Affiliation(s)
- Yaqian Li
- Key Laboratory of Cognitive Science of State Ethnic Affairs Commission, College of Biomedical EngineeringSouth‐Central Minzu UniversityWuhanChina
| | - Junfeng Gao
- Key Laboratory of Cognitive Science of State Ethnic Affairs Commission, College of Biomedical EngineeringSouth‐Central Minzu UniversityWuhanChina
| | - Ying Yang
- College of Foreign LanguagesWuhan University of TechnologyWuhanChina
| | - Yvtong Zhuang
- Department of Neurosurgery, Beijing Tiantan HospitalCapital Medical UniversityBeijingChina
| | - Qianruo Kang
- Key Laboratory of Cognitive Science of State Ethnic Affairs Commission, College of Biomedical EngineeringSouth‐Central Minzu UniversityWuhanChina
| | - Xiang Li
- Key Laboratory of Cognitive Science of State Ethnic Affairs Commission, College of Biomedical EngineeringSouth‐Central Minzu UniversityWuhanChina
| | - Min Tian
- Key Laboratory of Cognitive Science of State Ethnic Affairs Commission, College of Biomedical EngineeringSouth‐Central Minzu UniversityWuhanChina
| | - Haoan Lv
- Key Laboratory of Cognitive Science of State Ethnic Affairs Commission, College of Biomedical EngineeringSouth‐Central Minzu UniversityWuhanChina
| | - Jianghong He
- Department of Neurosurgery, Beijing Tiantan HospitalCapital Medical UniversityBeijingChina
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44
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Zhu C, Li J, Wei D, Wu L, Zhang Y, Huang H, Lin W. Intrinsic brain activity differences in perampanel-responsive and non-responsive drug-resistant epilepsy patients: an EEG microstate analysis. Ther Adv Neurol Disord 2024; 17:17562864241227293. [PMID: 38298737 PMCID: PMC10829497 DOI: 10.1177/17562864241227293] [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: 08/08/2023] [Accepted: 01/03/2024] [Indexed: 02/02/2024] Open
Abstract
Background Drug-resistant epilepsy (DRE) patients exhibit aberrant large-scale brain networks. Perampanel may be a therapeutic option for controlling seizures in these patients. Objective We aim to explore the differences of resting-state electroencephalogram (EEG) microstate in perampanel-responsive and non-responsive DRE patients. Design Retrospective study. Methods Clinical data were collected from DRE patients who received perampanel treatment at the Fujian Medical University Union Hospital from June 2020 to September 2021, with a minimum follow-up of 6 months. Patients were classified into three groups based on the extent of reduction in seizure frequency: non-responsive (seizure reduction <50%), responsive (seizure reduction >50% but not seizure-free), and seizure-free. Resting-state EEG data sets of all participants were subjected to EEG microstate analysis. The study comprehensively compared the mean duration, frequency per second, and temporal coverage of each microstate among the three groups. Results A total of 76 perampanel-treated DRE patients were categorized into three groups based on their response to treatment: non-responsive (n = 20), responsive (n = 36), and seizure-free (n = 20), according to the degree of seizure frequency reduction. The results of EEG microstate analysis revealed no statistically significant distinctions in frequency, duration, and coverage of microstate D in these DRE patients. However, the seizure-free group showed significantly increased duration and coverage of microstate A, frequency and coverage of microstate B, and significantly decreased duration, frequency, and coverage of microstate C when compared with the other groups. Conclusion Microstate A, B, and D is associated with the sensorimotor network, visual network, salience network, and attention network, respectively. This study demonstrates statistically significant differences in the sensorimotor, visual, and salience networks, but not in the attention network, between perampanel-responsive and non-responsive DRE patients.
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Affiliation(s)
- Chaofeng Zhu
- Department of Neurology, Fujian Medical University Union Hospital, Fuzhou, China
| | - Juan Li
- Department of Neurology, Fujian Medical University Union Hospital, Fuzhou, China
| | - Dazhu Wei
- Department of Neurology, Fujian Medical University Union Hospital, Fuzhou, China
| | - Luyan Wu
- Department of Neurology, Fujian Medical University Union Hospital, Fuzhou, China
| | - Yuying Zhang
- Department of Neurology, Fujian Medical University Union Hospital, Fuzhou, China
| | - Huapin Huang
- Department of Neurology, Fujian Medical University Union Hospital, Fuzhou, China
- Fujian Key Laboratory of Molecular Neurology, Fuzhou, China
- Department of Geriatrics, Fujian Medical University Union Hospital, Fuzhou, China
| | - Wanhui Lin
- Department of Neurology, Fujian Medical University Union Hospital, 29 Xinquan Road, Fuzhou 350001, Fujian, China
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Metzger M, Dukic S, McMackin R, Giglia E, Mitchell M, Bista S, Costello E, Peelo C, Tadjine Y, Sirenko V, Plaitano S, Coffey A, McManus L, Farnell Sharp A, Mehra P, Heverin M, Bede P, Muthuraman M, Pender N, Hardiman O, Nasseroleslami B. Functional network dynamics revealed by EEG microstates reflect cognitive decline in amyotrophic lateral sclerosis. Hum Brain Mapp 2024; 45:e26536. [PMID: 38087950 PMCID: PMC10789208 DOI: 10.1002/hbm.26536] [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: 05/25/2023] [Revised: 10/26/2023] [Accepted: 10/31/2023] [Indexed: 01/16/2024] Open
Abstract
Recent electroencephalography (EEG) studies have shown that patterns of brain activity can be used to differentiate amyotrophic lateral sclerosis (ALS) and control groups. These differences can be interrogated by examining EEG microstates, which are distinct, reoccurring topographies of the scalp's electrical potentials. Quantifying the temporal properties of the four canonical microstates can elucidate how the dynamics of functional brain networks are altered in neurological conditions. Here we have analysed the properties of microstates to detect and quantify signal-based abnormality in ALS. High-density resting-state EEG data from 129 people with ALS and 78 HC were recorded longitudinally over a 24-month period. EEG topographies were extracted at instances of peak global field power to identify four microstate classes (labelled A-D) using K-means clustering. Each EEG topography was retrospectively associated with a microstate class based on global map dissimilarity. Changes in microstate properties over the course of the disease were assessed in people with ALS and compared with changes in clinical scores. The topographies of microstate classes remained consistent across participants and conditions. Differences were observed in coverage, occurrence, duration, and transition probabilities between ALS and control groups. The duration of microstate class B and coverage of microstate class C correlated with lower limb functional decline. The transition probabilities A to D, C to B and C to B also correlated with cognitive decline (total ECAS) in those with cognitive and behavioural impairments. Microstate characteristics also significantly changed over the course of the disease. Examining the temporal dependencies in the sequences of microstates revealed that the symmetry and stationarity of transition matrices were increased in people with late-stage ALS. These alterations in the properties of EEG microstates in ALS may reflect abnormalities within the sensory network and higher-order networks. Microstate properties could also prospectively predict symptom progression in those with cognitive impairments.
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Affiliation(s)
- Marjorie Metzger
- Academic Unit of Neurology, School of Medicine, Trinity Biomedical Sciences Institute, Trinity College DublinUniversity of DublinDublinIreland
| | - Stefan Dukic
- Academic Unit of Neurology, School of Medicine, Trinity Biomedical Sciences Institute, Trinity College DublinUniversity of DublinDublinIreland
- Department of Neurology, University Medical Centre Utrecht Brain CentreUtrecht UniversityUtrechtThe Netherlands
| | - Roisin McMackin
- Academic Unit of Neurology, School of Medicine, Trinity Biomedical Sciences Institute, Trinity College DublinUniversity of DublinDublinIreland
- Discipline of Physiology, School of Medicine, Trinity Biomedical Sciences Institute, Trinity College DublinUniversity of DublinDublinIreland
| | - Eileen Giglia
- Academic Unit of Neurology, School of Medicine, Trinity Biomedical Sciences Institute, Trinity College DublinUniversity of DublinDublinIreland
| | - Matthew Mitchell
- Academic Unit of Neurology, School of Medicine, Trinity Biomedical Sciences Institute, Trinity College DublinUniversity of DublinDublinIreland
| | - Saroj Bista
- Academic Unit of Neurology, School of Medicine, Trinity Biomedical Sciences Institute, Trinity College DublinUniversity of DublinDublinIreland
| | - Emmet Costello
- Academic Unit of Neurology, School of Medicine, Trinity Biomedical Sciences Institute, Trinity College DublinUniversity of DublinDublinIreland
| | - Colm Peelo
- Academic Unit of Neurology, School of Medicine, Trinity Biomedical Sciences Institute, Trinity College DublinUniversity of DublinDublinIreland
| | - Yasmine Tadjine
- Academic Unit of Neurology, School of Medicine, Trinity Biomedical Sciences Institute, Trinity College DublinUniversity of DublinDublinIreland
| | - Vladyslav Sirenko
- Academic Unit of Neurology, School of Medicine, Trinity Biomedical Sciences Institute, Trinity College DublinUniversity of DublinDublinIreland
| | - Serena Plaitano
- Academic Unit of Neurology, School of Medicine, Trinity Biomedical Sciences Institute, Trinity College DublinUniversity of DublinDublinIreland
| | - Amina Coffey
- Academic Unit of Neurology, School of Medicine, Trinity Biomedical Sciences Institute, Trinity College DublinUniversity of DublinDublinIreland
| | - Lara McManus
- Academic Unit of Neurology, School of Medicine, Trinity Biomedical Sciences Institute, Trinity College DublinUniversity of DublinDublinIreland
| | - Adelais Farnell Sharp
- Academic Unit of Neurology, School of Medicine, Trinity Biomedical Sciences Institute, Trinity College DublinUniversity of DublinDublinIreland
| | - Prabhav Mehra
- Academic Unit of Neurology, School of Medicine, Trinity Biomedical Sciences Institute, Trinity College DublinUniversity of DublinDublinIreland
| | - Mark Heverin
- Academic Unit of Neurology, School of Medicine, Trinity Biomedical Sciences Institute, Trinity College DublinUniversity of DublinDublinIreland
| | - Peter Bede
- Academic Unit of Neurology, School of Medicine, Trinity Biomedical Sciences Institute, Trinity College DublinUniversity of DublinDublinIreland
| | - Muthuraman Muthuraman
- Neural Engineering with Signal Analytics and Artificial Intelligence, Department of NeurologyUniversity of WürzburgWürzburgGermany
| | - Niall Pender
- Academic Unit of Neurology, School of Medicine, Trinity Biomedical Sciences Institute, Trinity College DublinUniversity of DublinDublinIreland
- Department of PsychologyBeaumont HospitalDublinIreland
| | - Orla Hardiman
- Academic Unit of Neurology, School of Medicine, Trinity Biomedical Sciences Institute, Trinity College DublinUniversity of DublinDublinIreland
- Department of NeurologyBeaumont HospitalDublinIreland
| | - Bahman Nasseroleslami
- Academic Unit of Neurology, School of Medicine, Trinity Biomedical Sciences Institute, Trinity College DublinUniversity of DublinDublinIreland
- FutureNeuro ‐ SFI Research Centre for Chronic and Rare Neurological DiseasesRoyal College of SurgeonsDublinIreland
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Ye S, Bagić A, He B. Disentanglement of Resting State Brain Networks for Localizing Epileptogenic Zone in Focal Epilepsy. Brain Topogr 2024; 37:152-168. [PMID: 38112884 PMCID: PMC10771380 DOI: 10.1007/s10548-023-01025-z] [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: 05/15/2023] [Accepted: 11/20/2023] [Indexed: 12/21/2023]
Abstract
The objective of this study is to extract pathological brain networks from interictal period of E/MEG recordings to localize epileptic foci for presurgical evaluation. We proposed here a resting state E/MEG analysis framework, to disentangle brain functional networks represented by neural oscillations. By using an Embedded Hidden Markov Model, we constructed a state space for resting state recordings consisting of brain states with different spatiotemporal patterns. Functional connectivity analysis along with graph theory was applied on the extracted brain states to quantify the network features of the extracted brain states, based on which the source location of pathological states is determined. The method is evaluated by computer simulations and our simulation results revealed the proposed framework can extract brain states with high accuracy regarding both spatial and temporal profiles. We further evaluated the framework as compared with intracranial EEG defined seizure onset zone in 10 patients with drug-resistant focal epilepsy who underwent MEG recordings and were seizure free after surgical resection. The real patient data analysis showed very good localization results using the extracted pathological brain states in 6/10 patients, with localization error of about 15 mm as compared to the seizure onset zone. We show that the pathological brain networks can be disentangled from the resting-state electromagnetic recording and could be identified based on the connectivity features. The framework can serve as a useful tool in extracting brain functional networks from noninvasive resting state electromagnetic recordings, and promises to offer an alternative to aid presurgical evaluation guiding intracranial EEG electrodes implantation.
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Affiliation(s)
- Shuai Ye
- Department of Biomedical Engineering, Carnegie Mellon University, 5000 Forbes Avenue, Pittsburgh, PA, 15213, USA
| | - Anto Bagić
- Department of Neurology, University of Pittsburgh Comprehensive Epilepsy Center (UPCEC), University of Pittsburgh Medical School, Pittsburgh, PA, USA
| | - Bin He
- Department of Biomedical Engineering, Carnegie Mellon University, 5000 Forbes Avenue, Pittsburgh, PA, 15213, USA.
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Zhao Z, Ran X, Lv S, Wang J, Qiu M, Wang C, Xu Y, Guo X, Gao Z, Mu J, Yu Y. Causal link between prefrontal cortex and EEG microstates: evidence from patients with prefrontal lesion. Front Neurosci 2023; 17:1306120. [PMID: 38161794 PMCID: PMC10757643 DOI: 10.3389/fnins.2023.1306120] [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: 10/03/2023] [Accepted: 11/24/2023] [Indexed: 01/03/2024] Open
Abstract
Introduction At present, elucidating the cortical origin of EEG microstates is a research hotspot in the field of EEG. Previous studies have suggested that the prefrontal cortex is closely related to EEG microstate C and D, but whether there is a causal link between the prefrontal cortex and microstate C or D remains unclear. Methods In this study, pretrial EEG data were collected from ten patients with prefrontal lesions (mainly located in inferior and middle frontal gyrus) and fourteen matched healthy controls, and EEG microstate analysis was applied. Results Our results showed that four classical EEG microstate topographies were obtained in both groups, but microstate C topography in patient group was obviously abnormal. Compared to healthy controls, the average coverage and occurrence of microstate C significantly reduced. In addition, the transition probability from microstate A to C and from microstate B to C in patient group was significantly lower than those of healthy controls. Discussion The above results demonstrated that the damage of prefrontal cortex especially inferior and middle frontal gyrus could lead to abnormalities in the spatial distribution and temporal dynamics of microstate C not D, showing that there is a causal link between the inferior and middle frontal gyrus and the microstate C. The significance of our findings lies in providing new evidence for elucidating the cortical origin of microstate C.
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Affiliation(s)
- Zongya Zhao
- School of Medical Engineering, Xinxiang Medical University, Xinxiang, China
- Engineering Technology Research Center of Neurosense and Control of Henan Province, Xinxiang, China
- Henan International Joint Laboratory of Neural Information Analysis and Drug Intelligent Design, Xinxiang, China
- Henan Engineering Research Center of Medical VR Intelligent Sensing Feedback, Xinxiang, China
- The First Affiliated Hospital of Xinxiang Medical University, Weihui, China
| | - Xiangying Ran
- School of Medical Engineering, Xinxiang Medical University, Xinxiang, China
- Engineering Technology Research Center of Neurosense and Control of Henan Province, Xinxiang, China
- Henan International Joint Laboratory of Neural Information Analysis and Drug Intelligent Design, Xinxiang, China
- Henan Engineering Research Center of Medical VR Intelligent Sensing Feedback, Xinxiang, China
| | - Shiyang Lv
- School of Medical Engineering, Xinxiang Medical University, Xinxiang, China
- Engineering Technology Research Center of Neurosense and Control of Henan Province, Xinxiang, China
- Henan International Joint Laboratory of Neural Information Analysis and Drug Intelligent Design, Xinxiang, China
- Henan Engineering Research Center of Medical VR Intelligent Sensing Feedback, Xinxiang, China
| | - Junming Wang
- School of Medical Engineering, Xinxiang Medical University, Xinxiang, China
- Engineering Technology Research Center of Neurosense and Control of Henan Province, Xinxiang, China
- Henan International Joint Laboratory of Neural Information Analysis and Drug Intelligent Design, Xinxiang, China
- Henan Engineering Research Center of Medical VR Intelligent Sensing Feedback, Xinxiang, China
| | - Mengyue Qiu
- School of Medical Engineering, Xinxiang Medical University, Xinxiang, China
- Engineering Technology Research Center of Neurosense and Control of Henan Province, Xinxiang, China
- Henan International Joint Laboratory of Neural Information Analysis and Drug Intelligent Design, Xinxiang, China
- Henan Engineering Research Center of Medical VR Intelligent Sensing Feedback, Xinxiang, China
| | - Chang Wang
- School of Medical Engineering, Xinxiang Medical University, Xinxiang, China
- Engineering Technology Research Center of Neurosense and Control of Henan Province, Xinxiang, China
- Henan International Joint Laboratory of Neural Information Analysis and Drug Intelligent Design, Xinxiang, China
- Henan Engineering Research Center of Medical VR Intelligent Sensing Feedback, Xinxiang, China
| | - Yongtao Xu
- School of Medical Engineering, Xinxiang Medical University, Xinxiang, China
- Engineering Technology Research Center of Neurosense and Control of Henan Province, Xinxiang, China
- Henan International Joint Laboratory of Neural Information Analysis and Drug Intelligent Design, Xinxiang, China
- Henan Engineering Research Center of Medical VR Intelligent Sensing Feedback, Xinxiang, China
| | - Xiao Guo
- School of Medical Engineering, Xinxiang Medical University, Xinxiang, China
- Engineering Technology Research Center of Neurosense and Control of Henan Province, Xinxiang, China
- Henan International Joint Laboratory of Neural Information Analysis and Drug Intelligent Design, Xinxiang, China
- Henan Engineering Research Center of Medical VR Intelligent Sensing Feedback, Xinxiang, China
| | - Zhixian Gao
- School of Medical Engineering, Xinxiang Medical University, Xinxiang, China
- Engineering Technology Research Center of Neurosense and Control of Henan Province, Xinxiang, China
- Henan International Joint Laboratory of Neural Information Analysis and Drug Intelligent Design, Xinxiang, China
- Henan Engineering Research Center of Medical VR Intelligent Sensing Feedback, Xinxiang, China
| | - Junlin Mu
- The Second Affiliated Hospital of Xinxiang Medical University, Xinxiang, China
| | - Yi Yu
- School of Medical Engineering, Xinxiang Medical University, Xinxiang, China
- Engineering Technology Research Center of Neurosense and Control of Henan Province, Xinxiang, China
- Henan International Joint Laboratory of Neural Information Analysis and Drug Intelligent Design, Xinxiang, China
- Henan Engineering Research Center of Medical VR Intelligent Sensing Feedback, Xinxiang, China
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Zhu M, Gong Q. EEG spectral and microstate analysis originating residual inhibition of tinnitus induced by tailor-made notched music training. Front Neurosci 2023; 17:1254423. [PMID: 38148944 PMCID: PMC10750374 DOI: 10.3389/fnins.2023.1254423] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2023] [Accepted: 11/21/2023] [Indexed: 12/28/2023] Open
Abstract
Tailor-made notched music training (TMNMT) is a promising therapy for tinnitus. Residual inhibition (RI) is one of the few interventions that can temporarily inhibit tinnitus, which is a useful technique that can be applied to tinnitus research and explore tinnitus mechanisms. In this study, RI effect of TMNMT in tinnitus was investigated mainly using behavioral tests, EEG spectral and microstate analysis. To our knowledge, this study is the first to investigate RI effect of TMNMT. A total of 44 participants with tinnitus were divided into TMNMT group (22 participants; ECnm, NMnm, RInm represent that EEG recordings with eyes closed stimuli-pre, stimuli-ing, stimuli-post by TMNMT music, respectively) and Placebo control group (22 participants; ECpb, PBpb, RIpb represent that EEG recordings with eyes closed stimuli-pre, stimuli-ing, stimuli-post by Placebo music, respectively) in a single-blind manner. Behavioral tests, EEG spectral analysis (covering delta, theta, alpha, beta, gamma frequency bands) and microstate analysis (involving four microstate classes, A to D) were employed to evaluate RI effect of TMNMT. The results of the study showed that TMNMT had a stronger inhibition ability and longer inhibition time according to the behavioral tests compared to Placebo. Spectral analysis showed that RI effect of TMNMT increased significantly the power spectral density (PSD) of delta, theta bands and decreased significantly the PSD of alpha2 band, and microstate analysis showed that RI effect of TMNMT had shorter duration (microstate B, microstate C), higher Occurrence (microstate A, microstate C, microstate D), Coverage (microstate A) and transition probabilities (microstate A to microstate B, microstate A to microstate D and microstate D to microstate A). Meanwhile, RI effect of Placebo decreased significantly the PSD of alpha2 band, and microstate analysis showed that RI effect of Placebo had shorter duration (microstate C, microstate D), higher occurrence (microstate B, microstate C), lower coverage (microstate C, microstate D), higher transition probabilities (microstate A to microstate B, microstate B to microstate A). It was also found that the intensity of tinnitus symptoms was significant positively correlated with the duration of microstate B in five subgroups (ECnm, NMnm, RInm, ECpb, PBpb). Our study provided valuable experimental evidence and practical applications for the effectiveness of TMNMT as a novel music therapy for tinnitus. The observed stronger residual inhibition (RI) ability of TMNMT supported its potential applications in tinnitus treatment. Furthermore, the temporal dynamics of EEG microstates serve as novel functional and trait markers of synchronous brain activity that contribute to a deep understanding of the neural mechanism underlying TMNMT treatment for tinnitus.
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Affiliation(s)
- Min Zhu
- Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing, China
| | - Qin Gong
- Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing, China
- School of Medicine, Shanghai University, Shanghai, China
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49
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Wen Y, Li H, Huang Y, Qiao D, Ren T, Lei L, Li G, Yang C, Xu Y, Han M, Liu Z. Dynamic network characteristics of adolescents with major depressive disorder: Attention network mediates the association between anhedonia and attentional deficit. Hum Brain Mapp 2023; 44:5749-5769. [PMID: 37683097 PMCID: PMC10619388 DOI: 10.1002/hbm.26474] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2022] [Revised: 08/10/2023] [Accepted: 08/17/2023] [Indexed: 09/10/2023] Open
Abstract
Attention deficit is a critical symptom that impairs social functioning in adolescents with major depressive disorder (MDD). In this study, we aimed to explore the dynamic neural network activity associated with attention deficits and its relationship with clinical outcomes in adolescents with MDD. We included 188 adolescents with MDD and 94 healthy controls. By combining psychophysics, resting-state electroencephalography (EEG), and functional magnetic resonance imaging (fMRI) techniques, we aimed to identify dynamic network features through the investigation of EEG microstate characteristics and related temporal network features in adolescents with MDD. At baseline, microstate analysis revealed that the occurrence of Microstate C in the patient group was lower than that in healthy controls, whereas the duration and coverage of Microstate D increased in the MDD group. Mediation analysis revealed that the probability of transition from Microstate C to D mediated anhedonia and attention deficits in the MDD group. fMRI results showed that the temporal variability of the dorsal attention network (DAN) was significantly weaker in patients with MDD than in healthy controls. Importantly, the temporal variability of DAN mediated the relationship between anhedonia and attention deficits in the patient group. After acute-stage treatment, the response prediction group (RP) showed improvement in Microstates C and D compared to the nonresponse prediction group (NRP). For resting-state fMRI data, the temporal variability of DAN was significantly higher in the RP group than in the NRP group. Overall, this study enriches our understanding of the neural mechanisms underlying attention deficits in patients with MDD and provides novel clinical biomarkers.
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Affiliation(s)
- Yujiao Wen
- Department of PsychiatryThe First Hospital of Shanxi Medical UniversityTaiyuanChina
| | - Hong Li
- Department of PsychiatryThe First Hospital of Shanxi Medical UniversityTaiyuanChina
| | - Yangxi Huang
- Department of PsychiatryThe First Hospital of Shanxi Medical UniversityTaiyuanChina
| | - Dan Qiao
- Department of PsychiatryThe First Hospital of Shanxi Medical UniversityTaiyuanChina
| | - Tian Ren
- Department of PsychiatryThe First Hospital of Shanxi Medical UniversityTaiyuanChina
| | - Lei Lei
- Department of PsychiatryThe First Hospital of Shanxi Medical UniversityTaiyuanChina
| | - Gaizhi Li
- Department of PsychiatryThe First Hospital of Shanxi Medical UniversityTaiyuanChina
| | - Chunxia Yang
- Department of PsychiatryThe First Hospital of Shanxi Medical UniversityTaiyuanChina
| | - Yifan Xu
- Department of PsychiatryThe First Hospital of Shanxi Medical UniversityTaiyuanChina
| | - Min Han
- Department of PsychiatryThe First Hospital of Shanxi Medical UniversityTaiyuanChina
| | - Zhifen Liu
- Department of PsychiatryThe First Hospital of Shanxi Medical UniversityTaiyuanChina
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50
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Su K, Wang L, Wang Z, Ma J, Zhang C, Bi H, Wu J. The effect of acupuncture at the Taiyang acupoint on visual function and EEG microstates in myopia. Front Integr Neurosci 2023; 17:1234471. [PMID: 38035147 PMCID: PMC10684943 DOI: 10.3389/fnint.2023.1234471] [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: 06/04/2023] [Accepted: 09/06/2023] [Indexed: 12/02/2023] Open
Abstract
Objective Acupuncture has certain effects to improve myopia visual function, but its neural mechanism is unclear. In this study, we acupunctured at the right Taiyang acupoint of myopic patients to analyze the effects of acupuncture on visual function and electroencephalographic activity and to investigate the correlation between improvements in visual function and changes in the brain. Methods In this study, a total of 21 myopic patients were recruited. The contrast sensitivity (CS) of the subjects was examined before and after acupuncture, and electroencephalography (EEG) data of the entire acupuncture process were recorded. Results The study found that compared with before acupuncture, the CS of both eyes in myopic patients at each spatial frequency was increased after acupuncture; compared with the resting state, the contribution of microstate C was decreased during the post-acupuncture state, and the transition probability between microstate A and microstate C was reduced; in addition, the contribution of microstate C was negatively correlated with CS at both 12 and 18 cpd. Conclusion The contrast sensitivity of myopic patients was improved after acupuncture at the Taiyang acupoint (20 min), which may be related to microstate C.
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Affiliation(s)
- Kangna Su
- Medical College of Optometry and Ophthalmology, Shandong University of Traditional Chinese Medicine, Jinan, China
- Shandong Academy of Eye Disease Prevention and Therapy, Shandong Provincial Key Laboratory of Integrated Traditional Chinese and Western Medicine for Prevention and Therapy of Ocular Diseases, Shandong Provincial Clinical Research Center of Ophthalmology and Children Visual Impairment Prevention and Control, Shandong Engineering Technology Research Center of Visual Intelligence, Shandong Academy of Health and Myopia Prevention and Control of Children and Adolescents, Jinan, China
- Ophthalmology Department of Northwest University First Hospital, Xi’an, Shaanxi, China
| | - Lihan Wang
- Shandong Academy of Eye Disease Prevention and Therapy, Shandong Provincial Key Laboratory of Integrated Traditional Chinese and Western Medicine for Prevention and Therapy of Ocular Diseases, Shandong Provincial Clinical Research Center of Ophthalmology and Children Visual Impairment Prevention and Control, Shandong Engineering Technology Research Center of Visual Intelligence, Shandong Academy of Health and Myopia Prevention and Control of Children and Adolescents, Jinan, China
- Affiliated Eye Hospital of Shandong University of Traditional Chinese Medicine, Jinan, China
| | - Zhongqing Wang
- Medical College of Optometry and Ophthalmology, Shandong University of Traditional Chinese Medicine, Jinan, China
- Shandong Academy of Eye Disease Prevention and Therapy, Shandong Provincial Key Laboratory of Integrated Traditional Chinese and Western Medicine for Prevention and Therapy of Ocular Diseases, Shandong Provincial Clinical Research Center of Ophthalmology and Children Visual Impairment Prevention and Control, Shandong Engineering Technology Research Center of Visual Intelligence, Shandong Academy of Health and Myopia Prevention and Control of Children and Adolescents, Jinan, China
| | - Jiayao Ma
- Medical College of Optometry and Ophthalmology, Shandong University of Traditional Chinese Medicine, Jinan, China
- Shandong Academy of Eye Disease Prevention and Therapy, Shandong Provincial Key Laboratory of Integrated Traditional Chinese and Western Medicine for Prevention and Therapy of Ocular Diseases, Shandong Provincial Clinical Research Center of Ophthalmology and Children Visual Impairment Prevention and Control, Shandong Engineering Technology Research Center of Visual Intelligence, Shandong Academy of Health and Myopia Prevention and Control of Children and Adolescents, Jinan, China
| | - Chao Zhang
- Affiliated Eye Hospital of Shandong University of Traditional Chinese Medicine, Jinan, China
| | - Hongsheng Bi
- Medical College of Optometry and Ophthalmology, Shandong University of Traditional Chinese Medicine, Jinan, China
- Shandong Academy of Eye Disease Prevention and Therapy, Shandong Provincial Key Laboratory of Integrated Traditional Chinese and Western Medicine for Prevention and Therapy of Ocular Diseases, Shandong Provincial Clinical Research Center of Ophthalmology and Children Visual Impairment Prevention and Control, Shandong Engineering Technology Research Center of Visual Intelligence, Shandong Academy of Health and Myopia Prevention and Control of Children and Adolescents, Jinan, China
- Affiliated Eye Hospital of Shandong University of Traditional Chinese Medicine, Jinan, China
| | - Jianfeng Wu
- Medical College of Optometry and Ophthalmology, Shandong University of Traditional Chinese Medicine, Jinan, China
- Shandong Academy of Eye Disease Prevention and Therapy, Shandong Provincial Key Laboratory of Integrated Traditional Chinese and Western Medicine for Prevention and Therapy of Ocular Diseases, Shandong Provincial Clinical Research Center of Ophthalmology and Children Visual Impairment Prevention and Control, Shandong Engineering Technology Research Center of Visual Intelligence, Shandong Academy of Health and Myopia Prevention and Control of Children and Adolescents, Jinan, China
- Affiliated Eye Hospital of Shandong University of Traditional Chinese Medicine, Jinan, China
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