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Liu W, Guo Y, Xie J, Wu Y, Zhao D, Xing Z, Fu X, Zhou S, Zhang H, Wang X. Establishment and validation of a bad outcomes prediction model based on EEG and clinical parameters in prolonged disorder of consciousness. Front Hum Neurosci 2024; 18:1387471. [PMID: 38952644 PMCID: PMC11215084 DOI: 10.3389/fnhum.2024.1387471] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2024] [Accepted: 06/04/2024] [Indexed: 07/03/2024] Open
Abstract
Objective This study aimed to explore the electroencephalogram (EEG) indicators and clinical factors that may lead to poor prognosis in patients with prolonged disorder of consciousness (pDOC), and establish and verify a clinical predictive model based on these factors. Methods This study included 134 patients suffering from prolonged disorder of consciousness enrolled in our department of neurosurgery. We collected the data of sex, age, etiology, coma recovery scales (CRS-R) score, complications, blood routine, liver function, coagulation and other laboratory tests, resting EEG data and follow-up after discharge. These patients were divided into two groups: training set (n = 107) and verification set (n = 27). These patients were divided into a training set of 107 and a validation set of 27 for this study. Univariate and multivariate regression analysis were used to determine the factors affecting the poor prognosis of pDOC and to establish nomogram model. We use the receiver operating characteristic (ROC) and calibration curves to quantitatively test the effectiveness of the training set and the verification set. In order to further verify the clinical practical value of the model, we use decision curve analysis (DCA) to evaluate the model. Result The results from univariate and multivariate logistic regression analyses suggested that an increased frequency of occurrence microstate A, reduced CRS-R scores at the time of admission, the presence of episodes associated with paroxysmal sympathetic hyperactivity (PSH), and decreased fibrinogen levels all function as independent prognostic factors. These factors were used to construct the nomogram. The training and verification sets had areas under the curve of 0.854 and 0.920, respectively. Calibration curves and DCA demonstrated good model performance and significant clinical benefits in both sets. Conclusion This study is based on the use of clinically available and low-cost clinical indicators combined with EEG to construct a highly applicable and accurate model for predicting the adverse prognosis of patients with prolonged disorder of consciousness. It provides an objective and reliable tool for clinicians to evaluate the prognosis of prolonged disorder of consciousness, and helps clinicians to provide personalized clinical care and decision-making for patients with prolonged disorder of consciousness and their families.
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Affiliation(s)
- Wanqing Liu
- Department of Neurosurgery, The Fifth Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Yongkun Guo
- Department of Neurosurgery, The Fifth Affiliated Hospital of Zhengzhou University, Zhengzhou, China
- Henan Engineering Research Center for Prevention and Treatment of Brain Injuries, Zhengzhou, China
- Henan Key Laboratory of Brain Science and Brain Computer Interface Technology, Zhengzhou, China
| | - Jingwei Xie
- Department of Neurosurgery, The Fifth Affiliated Hospital of Zhengzhou University, Zhengzhou, China
- Henan Engineering Research Center for Prevention and Treatment of Brain Injuries, Zhengzhou, China
- Henan Key Laboratory of Brain Science and Brain Computer Interface Technology, Zhengzhou, China
| | - Yanzhi Wu
- Department of Neurosurgery, The Fifth Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Dexiao Zhao
- Department of Neurosurgery, The Fifth Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Zhe Xing
- Department of Neurosurgery, The Fifth Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Xudong Fu
- Department of Neurosurgery, The Fifth Affiliated Hospital of Zhengzhou University, Zhengzhou, China
- Henan Engineering Research Center for Prevention and Treatment of Brain Injuries, Zhengzhou, China
- Henan Key Laboratory of Brain Science and Brain Computer Interface Technology, Zhengzhou, China
| | - Shaolong Zhou
- Department of Neurosurgery, The Fifth Affiliated Hospital of Zhengzhou University, Zhengzhou, China
- Henan Engineering Research Center for Prevention and Treatment of Brain Injuries, Zhengzhou, China
- Henan Key Laboratory of Brain Science and Brain Computer Interface Technology, Zhengzhou, China
| | - Hengwei Zhang
- Department of Neurosurgery, The Fifth Affiliated Hospital of Zhengzhou University, Zhengzhou, China
- Henan Engineering Research Center for Prevention and Treatment of Brain Injuries, Zhengzhou, China
- Henan Key Laboratory of Brain Science and Brain Computer Interface Technology, Zhengzhou, China
| | - Xinjun Wang
- Department of Neurosurgery, The Fifth Affiliated Hospital of Zhengzhou University, Zhengzhou, China
- Henan Engineering Research Center for Prevention and Treatment of Brain Injuries, Zhengzhou, China
- Henan Key Laboratory of Brain Science and Brain Computer Interface Technology, Zhengzhou, China
- Department of Neurosurgery, The Third Affiliated Hospital of Zhengzhou University, Zhengzhou, China
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2
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Denaro CM, Reed CL, Joshi J, Petropoulos A, Thapar A, Hartley AA. Age-related similarities and differences in cognitive and neural processing revealed by task-related microstate analysis. Neurobiol Aging 2024; 136:9-22. [PMID: 38286071 DOI: 10.1016/j.neurobiolaging.2024.01.007] [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: 05/16/2023] [Revised: 01/12/2024] [Accepted: 01/16/2024] [Indexed: 01/31/2024]
Abstract
We explored neural processing differences associated with aging across four cognitive functions. In addition to ERP analysis, we included task-related microstate analyses, which identified stable states of neural activity across the scalp over time, to explore whole-head neural activation differences. Younger and older adults (YA, OA) completed face perception (N170), word-pair judgment (N400), visual oddball (P3), and flanker (ERN) tasks. Age-related effects differed across tasks. Despite age-related delayed latencies, N170 ERP and microstate analyses indicated no age-related differences in amplitudes or microstates. However, age-related condition differences were found for P3 and N00 amplitudes and scalp topographies: smaller condition differences were found for in OAs as well as broader centroparietal scalp distributions. Age group comparisons for the ERN revealed similar focal frontocentral activation loci, but differential activation patterns. Our findings of differential age effects across tasks are most consistent with the STAC-r framework which proposes that age-related effects differ depending on the resources available and the kinds of processing and cognitive load required of various tasks.
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3
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Guo Y, Li R, Zhang R, Liu C, Zhang L, Zhao D, Shan Q, Wang X, Hu Y. Dynamic Changes of Brain Activity in Patients With Disorders of Consciousness During Recovery of Consciousness. Front Neurosci 2022; 16:878203. [PMID: 35720697 PMCID: PMC9201077 DOI: 10.3389/fnins.2022.878203] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2022] [Accepted: 05/10/2022] [Indexed: 11/13/2022] Open
Abstract
The disorder of brain activity dynamics is one of the main characteristics leading to disorders of consciousness (DOC). However, few studies have explored whether the dynamics of brain activity can be modulated, and whether the dynamics of brain activity can help to evaluate the state of consciousness and the recovery progress of consciousness. In current study, 20 patients with minimally conscious state (MCS) and 13 patients with vegetative state (VS) were enrolled, and resting state electroencephalogram (EEG) data and the coma recovery scale-revised (CRS-R) scores were collected three times before and after high-definition transcranial direct current stimulation (HD-tDCS) treatment. The patients were divided into the improved group and the unimproved group according to whether the CRS-R scores were improved after the treatment, and the dynamic changes of resting state EEG microstate parameters during treatment were analyzed. The results showed the occurrence per second (OPS) of microstate D was significantly different between the MCS group and VS group, and it was positively correlated with the CRS-R before the treatment. After 2 weeks of the treatment, the OPS of microstate D improved significantly in the improved group. Meanwhile, the mean microstate duration (MMD), ratio of time coverage (Cov) of microstate C and the Cov of microstate D were significantly changed after the treatment. Compared with the microstates parameters before the treatment, the dynamic changes of parameters with significant difference in the improved group showed a consistent trend after the treatment. In contrast, the microstates parameters did not change significantly after the treatment in the unimproved group. The results suggest that the dynamics of EEG brain activity can be modulated by HD-tDCS, and the improvement in brain activity dynamics is closely related to the recovery of DOC, which is helpful to evaluate the level of DOC and the progress of recovery of consciousness.
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Affiliation(s)
- Yongkun Guo
- The Fifth Affiliated Hospital of Zhengzhou University, Zhengzhou, China
- Henan Engineering Research Center for Prevention and Treatment of Brain Injury, Zhengzhou, China
| | - Ruiqi Li
- School of Electrical Engineering, Zhengzhou University, Zhengzhou, China
| | - Rui Zhang
- School of Electrical Engineering, Zhengzhou University, Zhengzhou, China
| | - Chunying Liu
- Henan Engineering Research Center for Prevention and Treatment of Brain Injury, Zhengzhou, China
| | - Lipeng Zhang
- School of Electrical Engineering, Zhengzhou University, Zhengzhou, China
| | - Dexiao Zhao
- The Fifth Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Qiao Shan
- The Fifth Affiliated Hospital of Zhengzhou University, Zhengzhou, China
- Henan Engineering Research Center for Prevention and Treatment of Brain Injury, Zhengzhou, China
| | - Xinjun Wang
- The Fifth Affiliated Hospital of Zhengzhou University, Zhengzhou, China
- Henan Engineering Research Center for Prevention and Treatment of Brain Injury, Zhengzhou, China
- *Correspondence: Xinjun Wang,
| | - Yuxia Hu
- School of Electrical Engineering, Zhengzhou University, Zhengzhou, China
- Yuxia Hu,
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4
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Billings J, Tivadar R, Murray MM, Franceschiello B, Petri G. Topological Features of Electroencephalography are Robust to Re-referencing and Preprocessing. Brain Topogr 2022; 35:79-95. [PMID: 35001322 DOI: 10.1007/s10548-021-00882-w] [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: 10/16/2020] [Accepted: 11/05/2021] [Indexed: 11/30/2022]
Abstract
Electroencephalography (EEG) is among the most widely diffused, inexpensive, and adopted neuroimaging techniques. Nonetheless, EEG requires measurements against a reference site(s), which is typically chosen by the experimenter, and specific pre-processing steps precede analyses. It is therefore valuable to obtain quantities that are minimally affected by reference and pre-processing choices. Here, we show that the topological structure of embedding spaces, constructed either from multi-channel EEG timeseries or from their temporal structure, are subject-specific and robust to re-referencing and pre-processing pipelines. By contrast, the shape of correlation spaces, that is, discrete spaces where each point represents an electrode and the distance between them that is in turn related to the correlation between the respective timeseries, was neither significantly subject-specific nor robust to changes of reference. Our results suggest that the shape of spaces describing the observed configurations of EEG signals holds information about the individual specificity of the underlying individual's brain dynamics, and that temporal correlations constrain to a large degree the set of possible dynamics. In turn, these encode the differences between subjects' space of resting state EEG signals. Finally, our results and proposed methodology provide tools to explore the individual topographical landscapes and how they are explored dynamically. We propose therefore to augment conventional topographic analyses with an additional-topological-level of analysis, and to consider them jointly. More generally, these results provide a roadmap for the incorporation of topological analyses within EEG pipelines.
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Affiliation(s)
- Jacob Billings
- ISI Foundation, Turin, Italy
- Department of Complex Systems, Institute for Computer Science, Czech Academy of Science, Prague, Czechia
| | - Ruxandra Tivadar
- Laboratory for Investigative Neurophysiology, Department of Radiology, Lausanne University Hospital and University of Lausanne (CHUV-UNIL), Lausanne, Switzerland
- Department of Ophthalmology, Fondation Asile des aveugles and University of Lausanne, Lausanne, Switzerland
- Cognitive Computational Neuroscience Group, Institute for Computer Science, University of Bern, Bern, Switzerland
| | - Micah M Murray
- Laboratory for Investigative Neurophysiology, Department of Radiology, Lausanne University Hospital and University of Lausanne (CHUV-UNIL), Lausanne, Switzerland
- Department of Ophthalmology, Fondation Asile des aveugles and University of Lausanne, Lausanne, Switzerland
- EEG CHUV-UNIL Section, CIBM Center for Biomedical Imaging, Lausanne, Switzerland
- Department of Hearing and Speech Sciences, Vanderbilt University, Nashville, TN, USA
| | - Benedetta Franceschiello
- Laboratory for Investigative Neurophysiology, Department of Radiology, Lausanne University Hospital and University of Lausanne (CHUV-UNIL), Lausanne, Switzerland
- Department of Ophthalmology, Fondation Asile des aveugles and University of Lausanne, Lausanne, Switzerland
- EEG CHUV-UNIL Section, CIBM Center for Biomedical Imaging, Lausanne, Switzerland
| | - Giovanni Petri
- ISI Foundation, Turin, Italy.
- ISI Global Science Foundation, New York, NY, USA.
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5
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Teipel SJ, Brüggen K, Temp AGM, Jakobi K, Weber MA, Berger C. Simultaneous Assessment of Electroencephalography Microstates and Resting State Intrinsic Networks in Alzheimer's Disease and Healthy Aging. Front Neurol 2021; 12:637542. [PMID: 34220668 PMCID: PMC8249002 DOI: 10.3389/fneur.2021.637542] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2020] [Accepted: 05/24/2021] [Indexed: 12/02/2022] Open
Abstract
Electroencephalography (EEG) microstate topologies may serve as building blocks of functional brain activity in humans. Here, we studied the spatial and temporal correspondences between simultaneously acquired EEG microstate topologies and resting state functional MRI (rs-fMRI) intrinsic networks in 14 patients with Alzheimer's disease (AD) and 14 healthy age and sex matched controls. We found an anteriorisation of EEG microstates' topologies in AD patients compared with controls; this corresponded with reduced spatial expression of default mode and increased expression of frontal lobe networks in rs-fMRI. In a hierarchical cluster analysis the time courses of the EEG microstates were associated with the time courses of spatially corresponding rs-fMRI networks. We found prevalent negative correlations of time courses between anterior microstate topologies and posterior rs-fMRI components as well as between posterior microstate topology and anterior rs-fMRI components. These negative correlations were significantly more expressed in controls than in AD patients. In conclusion, our data support the notion that the time courses of EEG microstates underlie the temporal expression of rs-fMRI networks. Furthermore, our findings indicate that the anterior-to-posterior connectivity of microstates and rs-fMRI components may be reduced in AD, indicative of a break-down of long-reaching intrahemispheric connections.
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Affiliation(s)
- Stefan J Teipel
- German Center for Neurodegenerative Diseases (DZNE), Rostock, Germany.,Department of Psychosomatic Medicine, Rostock University Medical Center, Rostock, Germany
| | - Katharina Brüggen
- German Center for Neurodegenerative Diseases (DZNE), Rostock, Germany.,Department of Psychosomatic Medicine, Rostock University Medical Center, Rostock, Germany
| | | | - Kristina Jakobi
- Institute of Diagnostic and Interventional Radiology, Pediatric Radiology and Neuroradiology, Rostock University Medical Center, Rostock, Germany
| | - Marc-André Weber
- Institute of Diagnostic and Interventional Radiology, Pediatric Radiology and Neuroradiology, Rostock University Medical Center, Rostock, Germany
| | - Christoph Berger
- Department of Psychiatry, Neurology, Psychosomatics, and Psychotherapy in Childhood and Adolescence, Rostock University Medical Center, Rostock, Germany
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6
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Resting-State EEG Microstates Parallel Age-Related Differences in Allocentric Spatial Working Memory Performance. Brain Topogr 2021; 34:442-460. [PMID: 33871737 PMCID: PMC8195770 DOI: 10.1007/s10548-021-00835-3] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2020] [Accepted: 03/30/2021] [Indexed: 11/08/2022]
Abstract
Alterations of resting-state EEG microstates have been associated with various neurological disorders and behavioral states. Interestingly, age-related differences in EEG microstate organization have also been reported, and it has been suggested that resting-state EEG activity may predict cognitive capacities in healthy individuals across the lifespan. In this exploratory study, we performed a microstate analysis of resting-state brain activity and tested allocentric spatial working memory performance in healthy adult individuals: twenty 25–30-year-olds and twenty-five 64–75-year-olds. We found a lower spatial working memory performance in older adults, as well as age-related differences in the five EEG microstate maps A, B, C, C′ and D, but especially in microstate maps C and C′. These two maps have been linked to neuronal activity in the frontal and parietal brain regions which are associated with working memory and attention, cognitive functions that have been shown to be sensitive to aging. Older adults exhibited lower global explained variance and occurrence of maps C and C′. Moreover, although there was a higher probability to transition from any map towards maps C, C′ and D in young and older adults, this probability was lower in older adults. Finally, although age-related differences in resting-state EEG microstates paralleled differences in allocentric spatial working memory performance, we found no evidence that any individual or combination of resting-state EEG microstate parameter(s) could reliably predict individual spatial working memory performance. Whether the temporal dynamics of EEG microstates may be used to assess healthy cognitive aging from resting-state brain activity requires further investigation.
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7
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Yan D, Liu J, Liao M, Liu B, Wu S, Li X, Li H, Ou W, Zhang L, Li Z, Zhang Y, Li L. Prediction of Clinical Outcomes With EEG Microstate in Patients With Major Depressive Disorder. Front Psychiatry 2021; 12:695272. [PMID: 34483990 PMCID: PMC8415359 DOI: 10.3389/fpsyt.2021.695272] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/14/2021] [Accepted: 07/19/2021] [Indexed: 02/05/2023] Open
Abstract
Background: The difficulty in timely evaluating patient response to antidepressants has brought great challenge to the treatment of major depressive disorder (MDD). Some studies found that the electroencephalogram (EEG) microstates might be a reliable marker to evaluate patient response to treatment. The present study aims to evaluate the relationship between EEG microstate parameters and MDD symptoms before and after treatment to identify predictive biological markers for patient response. Methods: Thirty drug-naïve MDD patients (20 females and 10 males) were enrolled in this study. All the patients received effective dosages of selective serotonin reuptake inhibitors, and EEG recordings were collected at baseline and 2 weeks of treatment. Brain activities during the eyes-closed state were recorded using 64-channel electroencephalography, and the patients' microstates were clustered into four maps according to their topography (labeled A, B, C, and D). The differences of EEG microstates before and after treatment were compared using paired t-test. Spearman correlation coefficients were calculated to identify the relationships between the improvement of depression and anxiety symptoms and microstate parameters. Results: The mean duration (69.67 ± 10.33 vs. 64.00 ± 7.70, p < 0.001) and occurrence (4.06 ± 0.69, vs. 3.69 ± 0.70, p = 0.002) of microstate B decreased significantly after treatment. The proportion of microstate B also decreased (27.53 ± 5.81, vs. 23.23 ± 4.61, p < 0.001), while the occurrence of microstate A increased after treatment. A significant negative correlation was found between the change of score of Hamilton Rating Scale for Anxiety and the increase of the occurrence of microstate A (r = -0.431, p < 0.05) after 2 weeks of treatment. The reduction of the duration of microstate B was found to be predictive of patient response to antidepressants after 3 months. Conclusion: This study explored the relationship between changes of EEG microstates and patient response to antidepressants. Depression symptoms might be associated with the duration of microstate B and anxiety symptoms related to the occurrence of microstate A. Therefore, the duration of microstate B and the occurrence of microstate A are potential biological markers for MDD patients' early response and further clinical outcomes.
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Affiliation(s)
- Danfeng Yan
- National Clinical Research Center for Mental Disorders, Department of Psychiatry, The Second Xiangya Hospital of Central South University, Changsha, China.,Hunan Key Laboratory of Psychiatry and Mental Health, Institute of Mental Health, Hunan Medical Center of Mental Health, China National Technology Institute on Mental Disorders, Changsha, China.,Shanxi Mental Health Center, Taiyuan, China
| | - Jin Liu
- National Clinical Research Center for Mental Disorders, Department of Psychiatry, The Second Xiangya Hospital of Central South University, Changsha, China.,Hunan Key Laboratory of Psychiatry and Mental Health, Institute of Mental Health, Hunan Medical Center of Mental Health, China National Technology Institute on Mental Disorders, Changsha, China
| | - Mei Liao
- National Clinical Research Center for Mental Disorders, Department of Psychiatry, The Second Xiangya Hospital of Central South University, Changsha, China.,Hunan Key Laboratory of Psychiatry and Mental Health, Institute of Mental Health, Hunan Medical Center of Mental Health, China National Technology Institute on Mental Disorders, Changsha, China
| | - Bangshan Liu
- National Clinical Research Center for Mental Disorders, Department of Psychiatry, The Second Xiangya Hospital of Central South University, Changsha, China.,Hunan Key Laboratory of Psychiatry and Mental Health, Institute of Mental Health, Hunan Medical Center of Mental Health, China National Technology Institute on Mental Disorders, Changsha, China
| | - Shibin Wu
- Department of Psychiatry, Nanning Fifth People's Hospital, Nanning, China
| | - Xueqin Li
- National Clinical Research Center for Mental Disorders, Department of Psychiatry, The Second Xiangya Hospital of Central South University, Changsha, China.,Hunan Key Laboratory of Psychiatry and Mental Health, Institute of Mental Health, Hunan Medical Center of Mental Health, China National Technology Institute on Mental Disorders, Changsha, China
| | - Haolun Li
- National Clinical Research Center for Mental Disorders, Department of Psychiatry, The Second Xiangya Hospital of Central South University, Changsha, China.,Hunan Key Laboratory of Psychiatry and Mental Health, Institute of Mental Health, Hunan Medical Center of Mental Health, China National Technology Institute on Mental Disorders, Changsha, China
| | - Wenwen Ou
- National Clinical Research Center for Mental Disorders, Department of Psychiatry, The Second Xiangya Hospital of Central South University, Changsha, China.,Hunan Key Laboratory of Psychiatry and Mental Health, Institute of Mental Health, Hunan Medical Center of Mental Health, China National Technology Institute on Mental Disorders, Changsha, China
| | - Li Zhang
- National Clinical Research Center for Mental Disorders, Department of Psychiatry, The Second Xiangya Hospital of Central South University, Changsha, China.,Hunan Key Laboratory of Psychiatry and Mental Health, Institute of Mental Health, Hunan Medical Center of Mental Health, China National Technology Institute on Mental Disorders, Changsha, China
| | - Zexuan Li
- National Clinical Research Center for Mental Disorders, Department of Psychiatry, The Second Xiangya Hospital of Central South University, Changsha, China.,Hunan Key Laboratory of Psychiatry and Mental Health, Institute of Mental Health, Hunan Medical Center of Mental Health, China National Technology Institute on Mental Disorders, Changsha, China
| | - Yan Zhang
- National Clinical Research Center for Mental Disorders, Department of Psychiatry, The Second Xiangya Hospital of Central South University, Changsha, China.,Hunan Key Laboratory of Psychiatry and Mental Health, Institute of Mental Health, Hunan Medical Center of Mental Health, China National Technology Institute on Mental Disorders, Changsha, China
| | - Lingjiang Li
- National Clinical Research Center for Mental Disorders, Department of Psychiatry, The Second Xiangya Hospital of Central South University, Changsha, China.,Hunan Key Laboratory of Psychiatry and Mental Health, Institute of Mental Health, Hunan Medical Center of Mental Health, China National Technology Institute on Mental Disorders, Changsha, China
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8
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Murphy M, Whitton AE, Deccy S, Ironside ML, Rutherford A, Beltzer M, Sacchet M, Pizzagalli DA. Abnormalities in electroencephalographic microstates are state and trait markers of major depressive disorder. Neuropsychopharmacology 2020; 45:2030-2037. [PMID: 32590838 PMCID: PMC7547108 DOI: 10.1038/s41386-020-0749-1] [Citation(s) in RCA: 68] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/11/2020] [Revised: 06/15/2020] [Accepted: 06/17/2020] [Indexed: 01/20/2023]
Abstract
Neuroimaging studies have shown that major depressive disorder (MDD) is characterized by abnormal neural activity and connectivity. However, hemodynamic imaging techniques lack the temporal resolution needed to resolve the dynamics of brain mechanisms underlying MDD. Moreover, it is unclear whether putative abnormalities persist after remission. To address these gaps, we used microstate analysis to study resting-state brain activity in major depressive disorder (MDD). Electroencephalographic (EEG) "microstates" are canonical voltage topographies that reflect brief activations of components of resting-state brain networks. We used polarity-insensitive k-means clustering to segment resting-state high-density (128-channel) EEG data into microstates. Data from 79 healthy controls (HC), 63 individuals with MDD, and 30 individuals with remitted MDD (rMDD) were included. The groups produced similar sets of five microstates, including four widely-reported canonical microstates (A-D). The proportion of microstate D was decreased in MDD and rMDD compared to the HC group (Cohen's d = 0.63 and 0.72, respectively) and the duration and occurrence of microstate D was reduced in the MDD group compared to the HC group (Cohen's d = 0.43 and 0.58, respectively). Among the MDD group, proportion and duration of microstate D were negatively correlated with symptom severity (Spearman's rho = -0.34 and -0.46, respectively). Finally, microstate transition probabilities were nonrandom and the MDD group, relative to the HC and the rMDD groups, exhibited multiple distinct transition probabilities, primarily involving microstates A and C. Our findings highlight both state and trait abnormalities in resting-state brain activity in MDD.
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Affiliation(s)
- Michael Murphy
- Department of Psychiatry, Harvard Medical School, Boston, MA, USA
- McLean Hospital, Belmont, MA, USA
| | - Alexis E Whitton
- Department of Psychiatry, Harvard Medical School, Boston, MA, USA
- McLean Hospital, Belmont, MA, USA
- University of Sydney, Sydney, Australia
| | - Stephanie Deccy
- McLean Hospital, Belmont, MA, USA
- Albert Einstein College of Medicine, Bronx, NY, USA
| | - Manon L Ironside
- McLean Hospital, Belmont, MA, USA
- University of California-Berkeley, Berkeley, CA, USA
| | | | - Miranda Beltzer
- McLean Hospital, Belmont, MA, USA
- University of Virginia, Charlottesville, VA, USA
| | - Matthew Sacchet
- Department of Psychiatry, Harvard Medical School, Boston, MA, USA
- McLean Hospital, Belmont, MA, USA
| | - Diego A Pizzagalli
- Department of Psychiatry, Harvard Medical School, Boston, MA, USA.
- Center for Depression, Anxiety, and Stress Research, McLean Hospital, Belmont, MA, USA.
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9
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EEG microstates are correlated with brain functional networks during slow-wave sleep. Neuroimage 2020; 215:116786. [PMID: 32276057 DOI: 10.1016/j.neuroimage.2020.116786] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2019] [Revised: 04/02/2020] [Accepted: 04/03/2020] [Indexed: 11/20/2022] Open
Abstract
Electroencephalography (EEG) microstates have been extensively studied in wakefulness and have been described as the "atoms of thought". Previous studies of EEG have found four microstates, i.e., microstates A, B, C and D, that are consistent among participants across the lifespan during the resting state. Studies using simultaneous EEG and functional magnetic resonance imaging (fMRI) have provided evidence for correlations between EEG microstates and fMRI networks during the resting state. Microstates have also been found during non-rapid eye movement (NREM) sleep. Slow-wave sleep (SWS) is considered the most restorative sleep stage and has been associated with the maintenance of sleep. However, the relationship between EEG microstates and brain functional networks during SWS has not yet been investigated. In this study, simultaneous EEG-fMRI data were collected during SWS to test the correspondence between EEG microstates and fMRI networks. EEG microstate-informed fMRI analysis revealed that three out of the four microstates showed significant correlations with fMRI data: 1) fMRI fluctuations in the insula and posterior temporal gyrus positively correlated with microstate B, 2) fMRI signals in the middle temporal gyrus and fusiform gyrus negatively correlated with microstate C, and 3) fMRI fluctuations in the occipital lobe negatively correlated with microstate D, while fMRI signals in the anterior cingulate and cingulate gyrus positively correlated with this microstate. Functional brain networks were then assessed using group independent component analysis based on the fMRI data. The group-level spatial correlation analysis showed that the fMRI auditory network overlapped the fMRI activation map of microstate B, the executive control network overlapped the fMRI deactivation of microstate C, and the visual and salience networks overlapped the fMRI deactivation and activation maps of microstate D. In addition, the subject-level spatial correlations between the general linear model (GLM) beta map of each microstate and the individual maps of each component yielded by dual regression also showed that EEG microstates were closely associated with brain functional networks measured using fMRI during SWS. Overall, the results showed that EEG microstates were closely related to brain functional networks during SWS, which suggested that EEG microstates provide an important electrophysiological basis underlying brain functional networks.
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10
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EEG microstates as a continuous phenomenon. Neuroimage 2020; 208:116454. [DOI: 10.1016/j.neuroimage.2019.116454] [Citation(s) in RCA: 29] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2019] [Revised: 10/05/2019] [Accepted: 12/06/2019] [Indexed: 11/18/2022] Open
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11
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Sikka A, Jamalabadi H, Krylova M, Alizadeh S, van der Meer JN, Danyeli L, Deliano M, Vicheva P, Hahn T, Koenig T, Bathula DR, Walter M. Investigating the temporal dynamics of electroencephalogram (EEG) microstates using recurrent neural networks. Hum Brain Mapp 2020; 41:2334-2346. [PMID: 32090423 PMCID: PMC7267981 DOI: 10.1002/hbm.24949] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2019] [Revised: 11/28/2019] [Accepted: 01/09/2020] [Indexed: 12/29/2022] Open
Abstract
Electroencephalogram (EEG) microstates that represent quasi‐stable, global neuronal activity are considered as the building blocks of brain dynamics. Therefore, the analysis of microstate sequences is a promising approach to understand fast brain dynamics that underlie various mental processes. Recent studies suggest that EEG microstate sequences are non‐Markovian and nonstationary, highlighting the importance of the sequential flow of information between different brain states. These findings inspired us to model these sequences using Recurrent Neural Networks (RNNs) consisting of long‐short‐term‐memory (LSTM) units to capture the complex temporal dependencies. Using an LSTM‐based auto encoder framework and different encoding schemes, we modeled the microstate sequences at multiple time scales (200–2,000 ms) aiming to capture stably recurring microstate patterns within and across subjects. We show that RNNs can learn underlying microstate patterns with high accuracy and that the microstate trajectories are subject invariant at shorter time scales (≤400 ms) and reproducible across sessions. Significant drop in the reconstruction accuracy was observed for longer sequence lengths of 2,000 ms. These findings indirectly corroborate earlier studies which indicated that EEG microstate sequences exhibit long‐range dependencies with finite memory content. Furthermore, we find that the latent representations learned by the RNNs are sensitive to external stimulation such as stress while the conventional univariate microstate measures (e.g., occurrence, mean duration, etc.) fail to capture such changes in brain dynamics. While RNNs cannot be configured to identify the specific discriminating patterns, they have the potential for learning the underlying temporal dynamics and are sensitive to sequence aberrations characterized by changes in metal processes. Empowered with the macroscopic understanding of the temporal dynamics that extends beyond short‐term interactions, RNNs offer a reliable alternative for exploring system level brain dynamics using EEG microstate sequences.
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Affiliation(s)
- Apoorva Sikka
- Department of Computer Science and Engineering, Indian Institute of Technology Ropar, Rupnagar, Punjab, India
| | - Hamidreza Jamalabadi
- Department of Psychiatry and Psychotherapy, Division for Translational Psychiatry, University of Tübingen, Tübingen, Germany
| | - Marina Krylova
- Department of Psychiatry and Psychotherapy, Division for Translational Psychiatry, University of Tübingen, Tübingen, Germany
| | - Sarah Alizadeh
- Department of Psychiatry and Psychotherapy, Division for Translational Psychiatry, University of Tübingen, Tübingen, Germany
| | | | - Lena Danyeli
- Clinical Affective Neuroimaging Laboratory, Magdeburg, Germany.,Leibniz Institute for Neurobiology, Magdeburg, Germany
| | | | - Petya Vicheva
- Clinical Affective Neuroimaging Laboratory, Magdeburg, Germany.,Department of Psychiatry, Otto von Guericke University of Magdeburg, Magdeburg, Germany
| | - Tim Hahn
- Institute of Translational Psychiatry, University of Muenster, Muenster, Germany
| | - Thomas Koenig
- Translational Research Center, University Hospital of Psychiatry, University of Bern, Bern, Switzerland
| | - Deepti R Bathula
- Department of Computer Science and Engineering, Indian Institute of Technology Ropar, Rupnagar, Punjab, India
| | - Martin Walter
- Department of Psychiatry and Psychotherapy, Division for Translational Psychiatry, University of Tübingen, Tübingen, Germany.,Clinical Affective Neuroimaging Laboratory, Magdeburg, Germany.,Leibniz Institute for Neurobiology, Magdeburg, Germany.,Max Planck Institute for biological cybernetics, Tübingen, Germany.,Department of Psychiatry and Psychotherapy, Jena University Hospital, Jena, Germany
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12
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Frehlick Z, Lakhani B, Fickling SD, Livingstone AC, Danilov Y, Sackier JM, D'Arcy RCN. Human translingual neurostimulation alters resting brain activity in high-density EEG. J Neuroeng Rehabil 2019; 16:60. [PMID: 31133021 PMCID: PMC6537158 DOI: 10.1186/s12984-019-0538-4] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2018] [Accepted: 05/16/2019] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Despite growing evidence of a critical link between neuromodulation technologies and neuroplastic recovery, the underlying mechanisms of these technologies remain elusive. OBJECTIVE To investigate physiological evidence of central nervous system (CNS) changes in humans during translingual neurostimulation (TLNS). METHODS We used high-density electroencephalography (EEG) to measure changes in resting brain activity before, during, and after high frequency (HF) and low frequency (LF) TLNS. RESULTS Wavelet power analysis around Cz and microstate analysis revealed significant changes after 20 min of stimulation compared to baseline. A secondary effect of exposure order was also identified, indicating a differential neuromodulatory influence of HF TLNS relative to LF TLNS on alpha and theta signal power. CONCLUSIONS These results further our understanding of the effects of TLNS on underlying resting brain activity, which in the long-term may contribute to the critical link between clinical effect and changes in brain activity.
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Affiliation(s)
| | | | | | | | - Yuri Danilov
- Pavlov Institute of Physiology, Russian Academy of Science, Sankt Petersburg, Russia
| | - Jonathan M Sackier
- Helius Medical Technologies, Newtown, PA, USA.,Oxford University, Oxford, UK
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13
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Zappasodi F, Perrucci MG, Saggino A, Croce P, Mercuri P, Romanelli R, Colom R, Ebisch SJH. EEG microstates distinguish between cognitive components of fluid reasoning. Neuroimage 2019; 189:560-573. [PMID: 30710677 DOI: 10.1016/j.neuroimage.2019.01.067] [Citation(s) in RCA: 30] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2018] [Revised: 01/14/2019] [Accepted: 01/26/2019] [Indexed: 01/31/2023] Open
Abstract
Fluid reasoning is considered central to general intelligence. How its psychometric structure relates to brain function remains poorly understood. For instance, what is the dynamic composition of ability-specific processes underlying fluid reasoning? We investigated whether distinct fluid reasoning abilities could be differentiated by electroencephalography (EEG) microstate profiles. EEG microstates specifically capture rapidly altering activity of distributed cortical networks with a high temporal resolution as scalp potential topographies that dynamically vary over time in an organized manner. EEG was recorded simultaneously with functional magnetic resonance imaging (fMRI) in twenty healthy adult participants during cognitively distinct fluid reasoning tasks: induction, spatial relationships and visualization. Microstate parameters successfully discriminated between fluid reasoning and visuomotor control tasks as well as between the fluid reasoning tasks. Mainly, microstate B coverage was significantly higher during spatial relationships and visualization, compared to induction, while microstate C coverage was significantly decreased during spatial relationships and visualization, compared to induction. Additionally, microstate D coverage was highest during spatial relationships and microstate A coverage was most strongly reduced during the same condition. Consistently, multivariate analysis with a leave-one-out cross-validation procedure accurately classified the fluid reasoning tasks based on the coverage parameter. These EEG data and their correlation with fMRI data suggest that especially the tasks most strongly relying on visuospatial processing modulated visual and default mode network activity. We propose that EEG microstates can provide valuable information about neural activity patterns with a dynamic and complex temporal structure during fluid reasoning, suggesting cognitive ability-specific interplays between multiple brain networks.
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Affiliation(s)
- Filippo Zappasodi
- Department of Neuroscience, Imaging and Clinical Sciences, G. d'Annunzio University of Chieti-Pescara, Chieti, Italy; Institute of Advanced Biomedical Technologies (ITAB), G. d'Annunzio University of Chieti-Pescara, Chieti, Italy
| | - Mauro Gianni Perrucci
- Department of Neuroscience, Imaging and Clinical Sciences, G. d'Annunzio University of Chieti-Pescara, Chieti, Italy; Institute of Advanced Biomedical Technologies (ITAB), G. d'Annunzio University of Chieti-Pescara, Chieti, Italy
| | - Aristide Saggino
- School of Medicine and Health Sciences, G. d'Annunzio University of Chieti-Pescara, Chieti, Italy
| | - Pierpaolo Croce
- Department of Neuroscience, Imaging and Clinical Sciences, G. d'Annunzio University of Chieti-Pescara, Chieti, Italy
| | - Pasqua Mercuri
- School of Medicine and Health Sciences, G. d'Annunzio University of Chieti-Pescara, Chieti, Italy
| | - Roberta Romanelli
- School of Medicine and Health Sciences, G. d'Annunzio University of Chieti-Pescara, Chieti, Italy
| | | | - Sjoerd J H Ebisch
- Department of Neuroscience, Imaging and Clinical Sciences, G. d'Annunzio University of Chieti-Pescara, Chieti, Italy; Institute of Advanced Biomedical Technologies (ITAB), G. d'Annunzio University of Chieti-Pescara, Chieti, Italy.
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14
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Vanneste S, To WT, De Ridder D. Tinnitus and neuropathic pain share a common neural substrate in the form of specific brain connectivity and microstate profiles. Prog Neuropsychopharmacol Biol Psychiatry 2019; 88:388-400. [PMID: 30142355 DOI: 10.1016/j.pnpbp.2018.08.015] [Citation(s) in RCA: 34] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/13/2018] [Revised: 07/06/2018] [Accepted: 08/19/2018] [Indexed: 12/12/2022]
Abstract
Tinnitus and neuropathic pain share similar pathophysiological, clinical, and treatment characteristics. In this EEG study, a group of tinnitus (n = 100) and neuropathic pain (n = 100) patients are compared to each other and to a healthy control group (n = 100). Spectral analysis demonstrates gamma band activity within the primary auditory and somatosensory cortices in patients with tinnitus and neuropathic pain, respectively. A conjunction analysis further demonstrates an overlap of tinnitus and pain related activity in the anterior and posterior cingulate cortex as well as in the dorsolateral prefrontal cortex in comparison to healthy controls. Further analysis reveals that similar states characterize tinnitus and neuropathic pain patients, two of which differ from the healthy group and two of which are shared. Both pain and tinnitus patients spend half of the time in one specific microstate. Seed-based functional connectivity with the source within the predominant microstate shows delta, alpha1, and gamma lagged phase synchronization overlap with multiple brain areas between pain and tinnitus. These data suggest that auditory and somatosensory phantom perceptions share an overlapping brain network with common activation and connectivity patterns and are differentiated by specific sensory cortex gamma activation.
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Affiliation(s)
- Sven Vanneste
- School of Behavioral and Brain Sciences, The University of Texas at Dallas, USA.
| | - Wing Ting To
- School of Behavioral and Brain Sciences, The University of Texas at Dallas, USA
| | - Dirk De Ridder
- Department of Surgical Sciences, Dunedin School of Medicine, University of Otago, New Zealand
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15
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16
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Gao F, Jia H, Feng Y. Microstate and Omega Complexity Analyses of the Resting-state Electroencephalography. J Vis Exp 2018. [PMID: 29985306 DOI: 10.3791/56452] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/31/2022] Open
Abstract
Microstate and omega complexity are two reference-free electroencephalography (EEG) measures that can represent the temporal and spatial complexities of EEG data and have been widely used to investigate the neural mechanisms in some brain disorders. The goal of this article is to describe the protocol underlying EEG microstate and omega complexity analyses step by step. The main advantage of these two measures is that they could eliminate the reference-dependent problem inherent to traditional spectrum analysis. In addition, microstate analysis makes good use of high time resolution of resting-state EEG, and the four obtained microstate classes could match the corresponding resting-state networks respectively. The omega complexity characterizes the spatial complexity of the whole brain or specific brain regions, which has obvious advantage compared with traditional complexity measures focusing on the signal complexity in a single channel. These two EEG measures could complement each other to investigate the brain complexity from the temporal and spatial domain respectively.
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Affiliation(s)
- Fei Gao
- Department of Pain Medicine, Peking University People's Hospital
| | - Huibin Jia
- Key Laboratory of Child Development and Learning Science of Ministry of Education, Research Center for Learning Science, School of Biological Sciences & Medical Engineering, Southeast University;
| | - Yi Feng
- Department of Pain Medicine, Peking University People's Hospital;
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17
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Abstract
Brain activity and connectivity are distributed in the three-dimensional space and evolve in time. It is important to image brain dynamics with high spatial and temporal resolution. Electroencephalography (EEG) and magnetoencephalography (MEG) are noninvasive measurements associated with complex neural activations and interactions that encode brain functions. Electrophysiological source imaging estimates the underlying brain electrical sources from EEG and MEG measurements. It offers increasingly improved spatial resolution and intrinsically high temporal resolution for imaging large-scale brain activity and connectivity on a wide range of timescales. Integration of electrophysiological source imaging and functional magnetic resonance imaging could further enhance spatiotemporal resolution and specificity to an extent that is not attainable with either technique alone. We review methodological developments in electrophysiological source imaging over the past three decades and envision its future advancement into a powerful functional neuroimaging technology for basic and clinical neuroscience applications.
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Affiliation(s)
- Bin He
- Department of Biomedical Engineering, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213, USA;
- Department of Biomedical Engineering, University of Minnesota, Minneapolis, Minnesota 55455, USA
| | - Abbas Sohrabpour
- Department of Biomedical Engineering, University of Minnesota, Minneapolis, Minnesota 55455, USA
| | - Emery Brown
- Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, USA
| | - Zhongming Liu
- Weldon School of Biomedical Engineering, School of Electrical and Computer Engineering, and Purdue Institute of Integrative Neuroscience, Purdue University, West Lafayette, Indiana 47906, USA
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18
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Tomescu MI, Rihs TA, Rochas V, Hardmeier M, Britz J, Allali G, Fuhr P, Eliez S, Michel CM. From swing to cane: Sex differences of EEG resting-state temporal patterns during maturation and aging. Dev Cogn Neurosci 2018; 31:58-66. [PMID: 29742488 PMCID: PMC6969216 DOI: 10.1016/j.dcn.2018.04.011] [Citation(s) in RCA: 78] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2017] [Revised: 04/19/2018] [Accepted: 04/27/2018] [Indexed: 12/18/2022] Open
Abstract
While many insights on brain development and aging have been gained by studying resting-state networks with fMRI, relating these changes to cognitive functions is limited by the temporal resolution of fMRI. In order to better grasp short-lasting and dynamically changing mental activities, an increasing number of studies utilize EEG to define resting-state networks, thereby often using the concept of EEG microstates. These are brief (around 100 ms) periods of stable scalp potential fields that are influenced by cognitive states and are sensitive to neuropsychiatric diseases. Despite the rising popularity of the EEG microstate approach, information about age changes is sparse and nothing is known about sex differences. Here we investigated age and sex related changes of the temporal dynamics of EEG microstates in 179 healthy individuals (6-87 years old, 90 females, 204-channel EEG). We show strong sex-specific changes in microstate dynamics during adolescence as well as at older age. In addition, males and females differ in the duration and occurrence of specific microstates. These results are of relevance for the comparison of studies in populations of different age and sex and for the understanding of the changes in neuropsychiatric diseases.
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Affiliation(s)
- M I Tomescu
- Functional Brain Mapping Laboratory, Department of fundamental neuroscience, University of Geneva, Geneva, Switzerland.
| | - T A Rihs
- Functional Brain Mapping Laboratory, Department of fundamental neuroscience, University of Geneva, Geneva, Switzerland
| | - V Rochas
- Functional Brain Mapping Laboratory, Department of fundamental neuroscience, University of Geneva, Geneva, Switzerland
| | - M Hardmeier
- Department of Neurology, University Hospital of Basel, Basel, Switzerland
| | - J Britz
- Functional Brain Mapping Laboratory, Department of fundamental neuroscience, University of Geneva, Geneva, Switzerland
| | - G Allali
- Functional Brain Mapping Laboratory, Department of fundamental neuroscience, University of Geneva, Geneva, Switzerland
| | - P Fuhr
- Department of Neurology, University Hospital of Basel, Basel, Switzerland
| | - S Eliez
- Developmental Imaging and Psychopathology Laboratory, Department of Psychiatry, University of Geneva, Geneva, Switzerland
| | - C M Michel
- Functional Brain Mapping Laboratory, Department of fundamental neuroscience, University of Geneva, Geneva, Switzerland; Biomedical Imaging Center (CIBM), Lausanne, Geneva, Switzerland
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19
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Michel CM, Koenig T. EEG microstates as a tool for studying the temporal dynamics of whole-brain neuronal networks: A review. Neuroimage 2017; 180:577-593. [PMID: 29196270 DOI: 10.1016/j.neuroimage.2017.11.062] [Citation(s) in RCA: 540] [Impact Index Per Article: 77.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2017] [Revised: 11/07/2017] [Accepted: 11/27/2017] [Indexed: 12/27/2022] Open
Abstract
The present review discusses a well-established method for characterizing resting-state activity of the human brain using multichannel electroencephalography (EEG). This method involves the examination of electrical microstates in the brain, which are defined as successive short time periods during which the configuration of the scalp potential field remains semi-stable, suggesting quasi-simultaneity of activity among the nodes of large-scale networks. A few prototypic microstates, which occur in a repetitive sequence across time, can be reliably identified across participants. Researchers have proposed that these microstates represent the basic building blocks of the chain of spontaneous conscious mental processes, and that their occurrence and temporal dynamics determine the quality of mentation. Several studies have further demonstrated that disturbances of mental processes associated with neurological and psychiatric conditions manifest as changes in the temporal dynamics of specific microstates. Combined EEG-fMRI studies and EEG source imaging studies have indicated that EEG microstates are closely associated with resting-state networks as identified using fMRI. The scale-free properties of the time series of EEG microstates explain why similar networks can be observed at such different time scales. The present review will provide an overview of these EEG microstates, available methods for analysis, the functional interpretations of findings regarding these microstates, and their behavioral and clinical correlates.
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Affiliation(s)
- Christoph M Michel
- Department of Basic Neurosciences, University of Geneva, Campus Biotech, Switzerland; Lemanic Biomedical Imaging Centre (CIBM), Lausanne and Geneva, Switzerland.
| | - Thomas Koenig
- Translational Research Center, University Hospital of Psychiatry, University of Bern, Switzerland
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20
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A single-bout of Endurance Exercise Modulates EEG Microstates Temporal Features. Brain Topogr 2017; 30:461-472. [PMID: 28528447 DOI: 10.1007/s10548-017-0570-2] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2016] [Accepted: 05/11/2017] [Indexed: 01/20/2023]
Abstract
Electrical neuroimaging is a promising method to explore the spontaneous brain function after physical exercise. The present study aims to investigate the effect of acute physical exercise on the temporal dynamic of the resting brain activity captured by the four conventional map topographies (microstates) described in the literature, and to associate these brain changes with the post-exercise neuromuscular function. Twenty endurance-trained subjects performed a 30-min biking task at 60% of their maximal aerobic power followed by a 10 km all-out time trial. Before and after each exercise, knee-extensor neuromuscular function and resting EEG were collected. Both exercises resulted in a similar increase in microstate class C stability and duration, as well as an increase in transition probability of moving toward microstate class C. After the first exercise, the increase in class C global explained variance was correlated with the indice of muscle alterations (100 Hz paired stimuli). After the second exercise, the increase in class C mean duration was correlated with the 100 Hz paired stimuli, but also with the reduction in maximal voluntary force. Interestingly, microstate class C has been associated with the salience resting-state network, which participates in integrating multisensory modalities. We speculate that temporal reorganization of the brain state after exercise could be partially modulated by the muscle afferents that project into the salience resting-state network, and indirectly participates in modulating the motor behavior.
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21
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Sperdin HF, Schaer M. Aberrant Development of Speech Processing in Young Children with Autism: New Insights from Neuroimaging Biomarkers. Front Neurosci 2016; 10:393. [PMID: 27610073 PMCID: PMC4997090 DOI: 10.3389/fnins.2016.00393] [Citation(s) in RCA: 29] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2016] [Accepted: 08/10/2016] [Indexed: 12/13/2022] Open
Abstract
From the time of birth, a newborn is continuously exposed and naturally attracted to human voices, and as he grows, he becomes increasingly responsive to these speech stimuli, which are strong drivers for his language development and knowledge acquisition about the world. In contrast, young children with autism spectrum disorder (ASD) are often insensitive to human voices, failing to orient and respond to them. Failure to attend to speech in turn results in altered development of language and social-communication skills. Here, we review the critical role of orienting to speech in ASD, as well as the neural substrates of human voice processing. Recent functional neuroimaging and electroencephalography studies demonstrate that aberrant voice processing could be a promising marker to identify ASD very early on. With the advent of refined brain imaging methods, coupled with the possibility of screening infants and toddlers, predictive brain function biomarkers are actively being examined and are starting to emerge. Their timely identification might not only help to differentiate between phenotypes, but also guide the clinicians in setting up appropriate therapies, and better predicting or quantifying long-term outcome.
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Affiliation(s)
- Holger F. Sperdin
- Office Médico-Pédagogique, Department of Psychiatry, University of Geneva School of MedicineGeneva, Switzerland
| | - Marie Schaer
- Office Médico-Pédagogique, Department of Psychiatry, University of Geneva School of MedicineGeneva, Switzerland
- Stanford Cognitive & Systems Neuroscience Laboratory, Stanford University School of MedicinePalo Alto, CA, USA
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22
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Drissi NM, Szakács A, Witt ST, Wretman A, Ulander M, Ståhlbrandt H, Darin N, Hallböök T, Landtblom AM, Engström M. Altered Brain Microstate Dynamics in Adolescents with Narcolepsy. Front Hum Neurosci 2016; 10:369. [PMID: 27536225 PMCID: PMC4971065 DOI: 10.3389/fnhum.2016.00369] [Citation(s) in RCA: 51] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2016] [Accepted: 07/11/2016] [Indexed: 11/13/2022] Open
Abstract
Narcolepsy is a chronic sleep disorder caused by a loss of hypocretin-1 producing neurons in the hypothalamus. Previous neuroimaging studies have investigated brain function in narcolepsy during rest using positron emission tomography (PET) and single photon emission computed tomography (SPECT). In addition to hypothalamic and thalamic dysfunction they showed aberrant prefrontal perfusion and glucose metabolism in narcolepsy. Given these findings in brain structure and metabolism in narcolepsy, we anticipated that changes in functional magnetic resonance imaging (fMRI) resting state network (RSN) dynamics might also be apparent in patients with narcolepsy. The objective of this study was to investigate and describe brain microstate activity in adolescents with narcolepsy and correlate these to RSNs using simultaneous fMRI and electroencephalography (EEG). Sixteen adolescents (ages 13-20) with a confirmed diagnosis of narcolepsy were recruited and compared to age-matched healthy controls. Simultaneous EEG and fMRI data were collected during 10 min of wakeful rest. EEG data were analyzed for microstates, which are discrete epochs of stable global brain states obtained from topographical EEG analysis. Functional MRI data were analyzed for RSNs. Data showed that narcolepsy patients were less likely than controls to spend time in a microstate which we found to be related to the default mode network and may suggest a disruption of this network that is disease specific. We concluded that adolescents with narcolepsy have altered resting state brain dynamics.
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Affiliation(s)
- Natasha M Drissi
- Department of Medical and Health Sciences (IMH), Linköping UniversityLinköping, Sweden; Center for Medical Image Science and Visualization (CMIV), Linköping UniversityLinköping, Sweden
| | - Attila Szakács
- Department of Paediatrics, Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg Gothenburg, Sweden
| | - Suzanne T Witt
- Center for Medical Image Science and Visualization (CMIV), Linköping University Linköping, Sweden
| | - Anna Wretman
- Department of Behavioral Science and Learning, Linköping University Linköping, Sweden
| | - Martin Ulander
- Department of Clinical and Experimental Medicine, Linköping University Linköping, Sweden
| | | | - Niklas Darin
- Department of Paediatrics, Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg Gothenburg, Sweden
| | - Tove Hallböök
- Department of Paediatrics, Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg Gothenburg, Sweden
| | - Anne-Marie Landtblom
- Department of Clinical and Experimental Medicine, Linköping UniversityLinköping, Sweden; Department of Neurology, Uppsala UniversityUppsala, Sweden
| | - Maria Engström
- Department of Medical and Health Sciences (IMH), Linköping UniversityLinköping, Sweden; Center for Medical Image Science and Visualization (CMIV), Linköping UniversityLinköping, Sweden
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23
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Cacioppo S, Cacioppo JT. Dynamic spatiotemporal brain analyses using high-performance electrical neuroimaging, Part II: A step-by-step tutorial. J Neurosci Methods 2015; 256:184-97. [PMID: 26363189 DOI: 10.1016/j.jneumeth.2015.09.004] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2015] [Revised: 08/31/2015] [Accepted: 09/03/2015] [Indexed: 11/19/2022]
Abstract
Our recently published analytic toolbox (Cacioppo et al., 2014), running under MATLAB environment and Brainstorm, offered a theoretical framework and set of validation studies for the automatic detection of event-related changes in the global pattern and global field power of electrical brain activity. Here, we provide a step-by-step tutorial of this toolbox along with a detailed description of analytical plans (aka the Chicago Electrical Neuroimaging Analytics, CENA) for the statistical analysis of brain microstate configuration and global field power in within and between-subject designs. Available CENA functions include: (1) a difference wave function; (2) a high-performance microsegmentation suite (HPMS), which consists of three specific analytic tools: (i) a root mean square error (RMSE) metric for identifying stable states and transition states across discrete event-related brain microstates; (ii) a similarity metric based on cosine distance in n dimensional sensor space to determine whether template maps for successive brain microstates differ in configuration of brain activity, and (iii) global field power (GFP) metrics for identifying changes in the overall level of activation of the brain; (3) a bootstrapping function for assessing the extent to which the solutions identified in the HPMS are robust (reliable, generalizable) and for empirically deriving additional experimental hypotheses; and (4) step-by-step procedures for performing a priori contrasts for data analysis. CENA is freely available for brain data spatiotemporal analyses at https://hpenlaboratory.uchicago.edu/page/cena, with sample data, user tutorial videos, and documentation.
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Affiliation(s)
- Stephanie Cacioppo
- High-Performance Electrical Neuroimaging Laboratory, Biological Science Division, University of Chicago Pritzker School of Medicine, Chicago, IL 60637, USA.
| | - John T Cacioppo
- Center for Cognitive and Social Neuroscience, University of Chicago, Chicago, IL 60637, USA.
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24
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Kirov R, Kolev V, Verleger R, Yordanova J. Labile sleep promotes awareness of abstract knowledge in a serial reaction time task. Front Psychol 2015; 6:1354. [PMID: 26441730 PMCID: PMC4561346 DOI: 10.3389/fpsyg.2015.01354] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2015] [Accepted: 08/24/2015] [Indexed: 11/13/2022] Open
Abstract
Sleep has been identified as a critical brain state enhancing the probability of gaining insight into covert task regularities. Both non-rapid eye movement (NREM) and rapid eye movement (REM) sleep have been implicated with offline re-activation and reorganization of memories supporting explicit knowledge generation. According to two-stage models of sleep function, offline processing of information during sleep is sequential requiring multiple cycles of NREM and REM sleep stages. However, the role of overnight dynamic sleep macrostructure for insightfulness has not been studied so far. In the present study, we test the hypothesis that the frequency of interactions between NREM and REM sleep stages might be critical for awareness after sleep. For that aim, the rate of sleep stage transitions was evaluated in 53 participants who learned implicitly a serial reaction time task (SRTT) in which a determined sequence was inserted. The amount of explicit knowledge about the sequence was established by verbal recall after a night of sleep following SRTT learning. Polysomnography was recorded in this night and in a control night before and was analyzed to compare the rate of sleep-stage transitions between participants who did or did not gain awareness of task regularity after sleep. Indeed, individual ability of explicit knowledge generation was strongly associated with increased rate of transitions between NREM and REM sleep stages and between light sleep stages and slow wave sleep. However, the rate of NREM-REM transitions specifically predicted the amount of explicit knowledge after sleep in a trait-dependent way. These results demonstrate that enhanced lability of sleep goes along with individual ability of knowledge awareness. Observations suggest that facilitated dynamic interactions between sleep stages, particularly between NREM and REM sleep stages play a role for offline processing which promotes rule extraction and awareness.
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Affiliation(s)
- Roumen Kirov
- Cognitive Psychophysiology, Institute of Neurobiology, Bulgarian Academy of SciencesSofia, Bulgaria
| | - Vasil Kolev
- Cognitive Psychophysiology, Institute of Neurobiology, Bulgarian Academy of SciencesSofia, Bulgaria
- Department of Neurology, University of LübeckLübeck, Germany
| | - Rolf Verleger
- Department of Neurology, University of LübeckLübeck, Germany
- Institute of Psychology II, University of LübeckLübeck, Germany
| | - Juliana Yordanova
- Cognitive Psychophysiology, Institute of Neurobiology, Bulgarian Academy of SciencesSofia, Bulgaria
- Department of Neurology, University of LübeckLübeck, Germany
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Milz P, Faber PL, Lehmann D, Koenig T, Kochi K, Pascual-Marqui RD. The functional significance of EEG microstates--Associations with modalities of thinking. Neuroimage 2015; 125:643-656. [PMID: 26285079 DOI: 10.1016/j.neuroimage.2015.08.023] [Citation(s) in RCA: 182] [Impact Index Per Article: 20.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2015] [Revised: 07/07/2015] [Accepted: 08/10/2015] [Indexed: 10/23/2022] Open
Abstract
The momentary, global functional state of the brain is reflected by its electric field configuration. Cluster analytical approaches consistently extracted four head-surface brain electric field configurations that optimally explain the variance of their changes across time in spontaneous EEG recordings. These four configurations are referred to as EEG microstate classes A, B, C, and D and have been associated with verbal/phonological, visual, subjective interoceptive-autonomic processing, and attention reorientation, respectively. The present study tested these associations via an intra-individual and inter-individual analysis approach. The intra-individual approach tested the effect of task-induced increased modality-specific processing on EEG microstate parameters. The inter-individual approach tested the effect of personal modality-specific parameters on EEG microstate parameters. We obtained multichannel EEG from 61 healthy, right-handed, male students during four eyes-closed conditions: object-visualization, spatial-visualization, verbalization (6 runs each), and resting (7 runs). After each run, we assessed participants' degrees of object-visual, spatial-visual, and verbal thinking using subjective reports. Before and after the recording, we assessed modality-specific cognitive abilities and styles using nine cognitive tests and two questionnaires. The EEG of all participants, conditions, and runs was clustered into four classes of EEG microstates (A, B, C, and D). RMANOVAs, ANOVAs and post-hoc paired t-tests compared microstate parameters between conditions. TANOVAs compared microstate class topographies between conditions. Differences were localized using eLORETA. Pearson correlations assessed interrelationships between personal modality-specific parameters and EEG microstate parameters during no-task resting. As hypothesized, verbal as opposed to visual conditions consistently affected the duration, occurrence, and coverage of microstate classes A and B. Contrary to associations suggested by previous reports, parameters were increased for class A during visualization, and class B during verbalization. In line with previous reports, microstate D parameters were increased during no-task resting compared to the three internal, goal-directed tasks. Topographic differences between conditions included particular sub-regions of components of the metabolic default mode network. Modality-specific personal parameters did not consistently correlate with microstate parameters except verbal cognitive style which correlated negatively with microstate class A duration and positively with class C occurrence. This is the first study that aimed to induce EEG microstate class parameter changes based on their hypothesized functional significance. Beyond the associations of microstate classes A and B with visual and verbal processing, respectively, our results suggest that a finely-tuned interplay between all four EEG microstate classes is necessary for the continuous formation of visual and verbal thoughts. Our results point to the possibility that the EEG microstate classes may represent the head-surface measured activity of intra-cortical sources primarily exhibiting inhibitory functions. However, additional studies are needed to verify and elaborate on this hypothesis.
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Affiliation(s)
- P Milz
- The KEY Institute for Brain-Mind Research, Department of Psychiatry, Psychotherapy and Psychosomatics, University Hospital of Psychiatry, P.O. Box 1931, CH-8032 Zurich, Switzerland.
| | - P L Faber
- The KEY Institute for Brain-Mind Research, Department of Psychiatry, Psychotherapy and Psychosomatics, University Hospital of Psychiatry, P.O. Box 1931, CH-8032 Zurich, Switzerland.
| | - D Lehmann
- The KEY Institute for Brain-Mind Research, Department of Psychiatry, Psychotherapy and Psychosomatics, University Hospital of Psychiatry, P.O. Box 1931, CH-8032 Zurich, Switzerland.
| | - T Koenig
- Department of Psychiatric Neurophysiology, University Hospital of Psychiatry, University of Bern, Bern, Switzerland.
| | - K Kochi
- The KEY Institute for Brain-Mind Research, Department of Psychiatry, Psychotherapy and Psychosomatics, University Hospital of Psychiatry, P.O. Box 1931, CH-8032 Zurich, Switzerland.
| | - R D Pascual-Marqui
- The KEY Institute for Brain-Mind Research, Department of Psychiatry, Psychotherapy and Psychosomatics, University Hospital of Psychiatry, P.O. Box 1931, CH-8032 Zurich, Switzerland.
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Gärtner M, Brodbeck V, Laufs H, Schneider G. A stochastic model for EEG microstate sequence analysis. Neuroimage 2015; 104:199-208. [DOI: 10.1016/j.neuroimage.2014.10.014] [Citation(s) in RCA: 26] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2014] [Revised: 09/09/2014] [Accepted: 10/06/2014] [Indexed: 10/24/2022] Open
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Meehan TP, Bressler SL. Neurocognitive networks: Findings, models, and theory. Neurosci Biobehav Rev 2012; 36:2232-47. [DOI: 10.1016/j.neubiorev.2012.08.002] [Citation(s) in RCA: 53] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2012] [Revised: 07/27/2012] [Accepted: 08/08/2012] [Indexed: 11/26/2022]
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Kühnis J, Elmer S, Meyer M, Jäncke L. Musicianship Boosts Perceptual Learning of Pseudoword-Chimeras: An Electrophysiological Approach. Brain Topogr 2012; 26:110-25. [DOI: 10.1007/s10548-012-0237-y] [Citation(s) in RCA: 26] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2011] [Accepted: 06/07/2012] [Indexed: 11/28/2022]
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Brodbeck V, Kuhn A, von Wegner F, Morzelewski A, Tagliazucchi E, Borisov S, Michel CM, Laufs H. EEG microstates of wakefulness and NREM sleep. Neuroimage 2012; 62:2129-39. [PMID: 22658975 DOI: 10.1016/j.neuroimage.2012.05.060] [Citation(s) in RCA: 161] [Impact Index Per Article: 13.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2011] [Revised: 05/11/2012] [Accepted: 05/15/2012] [Indexed: 11/16/2022] Open
Abstract
EEG-microstates exploit spatio-temporal EEG features to characterize the spontaneous EEG as a sequence of a finite number of quasi-stable scalp potential field maps. So far, EEG-microstates have been studied mainly in wakeful rest and are thought to correspond to functionally relevant brain-states. Four typical microstate maps have been identified and labeled arbitrarily with the letters A, B, C and D. We addressed the question whether EEG-microstate features are altered in different stages of NREM sleep compared to wakefulness. 32-channel EEG of 32 subjects in relaxed wakefulness and NREM sleep was analyzed using a clustering algorithm, identifying the most dominant amplitude topography maps typical of each vigilance state. Fitting back these maps into the sleep-scored EEG resulted in a temporal sequence of maps for each sleep stage. All 32 subjects reached sleep stage N2, 19 also N3, for at least 1 min and 45 s. As in wakeful rest we found four microstate maps to be optimal in all NREM sleep stages. The wake maps were highly similar to those described in the literature for wakefulness. The sleep stage specific map topographies of N1 and N3 sleep showed a variable but overall relatively high degree of spatial correlation to the wake maps (Mean: N1 92%; N3 87%). The N2 maps were the least similar to wake (mean: 83%). Mean duration, total time covered, global explained variance and transition probabilities per subject, map and sleep stage were very similar in wake and N1. In wake, N1 and N3, microstate map C was most dominant w.r.t. global explained variance and temporal presence (ratio total time), whereas in N2 microstate map B was most prominent. In N3, the mean duration of all microstate maps increased significantly, expressed also as an increase in transition probabilities of all maps to themselves in N3. This duration increase was partly--but not entirely--explained by the occurrence of slow waves in the EEG. The persistence of exactly four main microstate classes in all NREM sleep stages might speak in favor of an in principle maintained large scale spatial brain organization from wakeful rest to NREM sleep. In N1 and N3 sleep, despite spectral EEG differences, the microstate maps and characteristics were surprisingly close to wakefulness. This supports the notion that EEG microstates might reflect a large scale resting state network architecture similar to preserved fMRI resting state connectivity. We speculate that the incisive functional alterations which can be observed during the transition to deep sleep might be driven by changes in the level and timing of activity within this architecture.
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Affiliation(s)
- Verena Brodbeck
- Brain Imaging Center, Department of Neurology, University of Frankfurt, a.M., Germany.
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