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Gschwind M, Hardmeier M, Van De Ville D, Tomescu MI, Penner IK, Naegelin Y, Fuhr P, Michel CM, Seeck M. Fluctuations of spontaneous EEG topographies predict disease state in relapsing-remitting multiple sclerosis. NEUROIMAGE-CLINICAL 2016; 12:466-77. [PMID: 27625987 PMCID: PMC5011177 DOI: 10.1016/j.nicl.2016.08.008] [Citation(s) in RCA: 66] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/12/2016] [Revised: 07/25/2016] [Accepted: 08/05/2016] [Indexed: 01/10/2023]
Abstract
Spontaneous fluctuations of neuronal activity in large-scale distributed networks are a hallmark of the resting brain. In relapsing-remitting multiple sclerosis (RRMS) several fMRI studies have suggested altered resting-state connectivity patterns. Topographical EEG analysis reveals much faster temporal fluctuations in the tens of milliseconds time range (termed “microstates”), which showed altered properties in a number of neuropsychiatric conditions. We investigated whether these microstates were altered in patients with RRMS, and if the microstates' temporal properties reflected a link to the patients' clinical features. We acquired 256-channel EEG in 53 patients (mean age 37.6 years, 45 females, mean disease duration 9.99 years, Expanded Disability Status Scale ≤ 4, mean 2.2) and 49 healthy controls (mean age 36.4 years, 33 females). We analyzed segments of a total of 5 min of EEG during resting wakefulness and determined for both groups the four predominant microstates using established clustering methods. We found significant differences in the temporal dynamics of two of the four microstates between healthy controls and patients with RRMS in terms of increased appearance and prolonged duration. Using stepwise multiple linear regression models with 8-fold cross-validation, we found evidence that these electrophysiological measures predicted a patient's total disease duration, annual relapse rate, disability score, as well as depression score, and cognitive fatigue measure. In RRMS patients, microstate analysis captured altered fluctuations of EEG topographies in the sub-second range. This measure of high temporal resolution provided potentially powerful markers of disease activity and neuropsychiatric co-morbidities in RRMS. EEG microstates analyses provide high resolution of temporal dynamics of brain networks. Temporal parameters of EEG microstates are altered in Multiple Sclerosis Altered microstate parameters predict several clinical characteristics in patients We propose an EEG microstate based marker to characterize disease evolution in patients
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Affiliation(s)
- Markus Gschwind
- Department of Neurology, University Hospital Geneva, Geneva, Switzerland; Functional Brain Mapping Laboratory, Department of Neuroscience, Biotech Campus, University of Geneva, Geneva, Switzerland
| | - Martin Hardmeier
- Neurologic Clinic and Policlinic and Clinical Neurophysiology, Departments of Medicine and Clinical Research, University Hospital Basel, University of Basel, Basel, Switzerland
| | - Dimitri Van De Ville
- Department of Radiology, Center for Biomedical Imaging, University Hospital Geneva, Geneva, Switzerland; Institute of Bioengineering, Ecole Polytechnique Fédérale de Lausanne, Lausanne, Switzerland; Center for Biomedical Imaging, Lausanne and Geneva, Switzerland
| | - Miralena I Tomescu
- Functional Brain Mapping Laboratory, Department of Neuroscience, Biotech Campus, University of Geneva, Geneva, Switzerland
| | - Iris-Katharina Penner
- Department of Cognitive Psychology and Methodology, University of Basel, Basel, Switzerland
| | - Yvonne Naegelin
- Neurologic Clinic and Policlinic and Clinical Neurophysiology, Departments of Medicine and Clinical Research, University Hospital Basel, University of Basel, Basel, Switzerland
| | - Peter Fuhr
- Neurologic Clinic and Policlinic and Clinical Neurophysiology, Departments of Medicine and Clinical Research, University Hospital Basel, University of Basel, Basel, Switzerland
| | - Christoph M Michel
- Functional Brain Mapping Laboratory, Department of Neuroscience, Biotech Campus, University of Geneva, Geneva, Switzerland; Center for Biomedical Imaging, Lausanne and Geneva, Switzerland
| | - Margitta Seeck
- Department of Neurology, University Hospital Geneva, Geneva, Switzerland; Functional Brain Mapping Laboratory, Department of Neuroscience, Biotech Campus, University of Geneva, Geneva, Switzerland
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52
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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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53
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Rieger K, Diaz Hernandez L, Baenninger A, Koenig T. 15 Years of Microstate Research in Schizophrenia - Where Are We? A Meta-Analysis. Front Psychiatry 2016; 7:22. [PMID: 26955358 PMCID: PMC4767900 DOI: 10.3389/fpsyt.2016.00022] [Citation(s) in RCA: 105] [Impact Index Per Article: 13.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/04/2015] [Accepted: 02/09/2016] [Indexed: 01/24/2023] Open
Abstract
Schizophrenia patients show abnormalities in a broad range of task demands. Therefore, an explanation common to all these abnormalities has to be sought independently of any particular task, ideally in the brain dynamics before a task takes place or during resting state. For the neurobiological investigation of such baseline states, EEG microstate analysis is particularly well suited, because it identifies subsecond global states of stable connectivity patterns directly related to the recruitment of different types of information processing modes (e.g., integration of top-down and bottom-up information). Meanwhile, there is an accumulation of evidence that particular microstate networks are selectively affected in schizophrenia. To obtain an overall estimate of the effect size of these microstate abnormalities, we present a systematic meta-analysis over all studies available to date relating EEG microstates to schizophrenia. Results showed medium size effects for two classes of microstates, namely, a class labeled C that was found to be more frequent in schizophrenia and a class labeled D that was found to be shortened. These abnormalities may correspond to core symptoms of schizophrenia, e.g., insufficient reality testing and self-monitoring as during auditory verbal hallucinations. As interventional studies have shown that these microstate features may be systematically affected using antipsychotic drugs or neurofeedback interventions, these findings may help introducing novel diagnostic and treatment options.
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Affiliation(s)
- Kathryn Rieger
- Translational Research Center, University Hospital of Psychiatry, University of Bern, Bern, Switzerland; Center for Cognition, Learning and Memory, University of Bern, Bern, Switzerland
| | - Laura Diaz Hernandez
- Translational Research Center, University Hospital of Psychiatry, University of Bern, Bern, Switzerland; Center for Cognition, Learning and Memory, University of Bern, Bern, Switzerland
| | - Anja Baenninger
- Translational Research Center, University Hospital of Psychiatry, University of Bern , Bern , Switzerland
| | - Thomas Koenig
- Translational Research Center, University Hospital of Psychiatry, University of Bern, Bern, Switzerland; Center for Cognition, Learning and Memory, University of Bern, Bern, Switzerland
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54
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Koenig T, Brandeis D. Inappropriate assumptions about EEG state changes and their impact on the quantification of EEG state dynamics. Neuroimage 2016; 125:1104-1106. [DOI: 10.1016/j.neuroimage.2015.06.035] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2015] [Revised: 06/02/2015] [Accepted: 06/09/2015] [Indexed: 10/23/2022] Open
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55
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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: 180] [Impact Index Per Article: 20.0] [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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56
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Khanna A, Pascual-Leone A, Michel CM, Farzan F. Microstates in resting-state EEG: current status and future directions. Neurosci Biobehav Rev 2014; 49:105-13. [PMID: 25526823 DOI: 10.1016/j.neubiorev.2014.12.010] [Citation(s) in RCA: 428] [Impact Index Per Article: 42.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2014] [Revised: 12/02/2014] [Accepted: 12/09/2014] [Indexed: 11/28/2022]
Abstract
Electroencephalography (EEG) is a powerful method of studying the electrophysiology of the brain with high temporal resolution. Several analytical approaches to extract information from the EEG signal have been proposed. One method, termed microstate analysis, considers the multichannel EEG recording as a series of quasi-stable "microstates" that are each characterized by a unique topography of electric potentials over the entire channel array. Because this technique simultaneously considers signals recorded from all areas of the cortex, it is capable of assessing the function of large-scale brain networks whose disruption is associated with several neuropsychiatric disorders. In this review, we first introduce the method of EEG microstate analysis. We then review studies that have discovered significant changes in the resting-state microstate series in a variety of neuropsychiatric disorders and behavioral states. We discuss the potential utility of this method in detecting neurophysiological impairments in disease and monitoring neurophysiological changes in response to an intervention. Finally, we discuss how the resting-state microstate series may reflect rapid switching among neural networks while the brain is at rest, which could represent activity of resting-state networks described by other neuroimaging modalities. We conclude by commenting on the current and future status of microstate analysis, and suggest that EEG microstates represent a promising neurophysiological tool for understanding and assessing brain network dynamics on a millisecond timescale in health and disease.
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Affiliation(s)
- Arjun Khanna
- Berenson-Allen Center for Non-invasive Brain Stimulation, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA
| | - Alvaro Pascual-Leone
- Berenson-Allen Center for Non-invasive Brain Stimulation, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA
| | - Christoph M Michel
- EEG Brain Mapping Core, Center for Biomedical Imaging of Lausanne and Geneva, Switzerland; The Functional Brain Mapping Laboratory, Departments of Fundamental and Clinical Neurosciences, University of Geneva and University Hospital Geneva, Switzerland
| | - Faranak Farzan
- Berenson-Allen Center for Non-invasive Brain Stimulation, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA; Temerty Centre for Therapeutic Brain Intervention, Centre for Addiction and Mental Health, University of Toronto, Toronto, Ontario, Canada.
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57
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van der Kruijs SJM, Bodde NMG, Carrette E, Lazeron RHC, Vonck KEJ, Boon PAJM, Langereis GR, Cluitmans PJM, Feijs LMG, Hofman PAM, Backes WH, Jansen JFA, Aldenkamp AP. Neurophysiological correlates of dissociative symptoms. J Neurol Neurosurg Psychiatry 2014; 85:174-9. [PMID: 23175855 DOI: 10.1136/jnnp-2012-302905] [Citation(s) in RCA: 38] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
Abstract
OBJECTIVE Dissociation is a mental process with psychological and somatoform manifestations, which is closely related to hypnotic suggestibility and essentially shows the ability to obtain distance from reality. An increased tendency to dissociate is a frequently reported characteristic of patients with functional neurological symptoms and syndromes (FNSS), which account for a substantial part of all neurological admissions. This review aims to investigate what heart rate variability (HRV), EEG and neuroimaging data (MRI) reveal about the nature of dissociation and related conditions. METHODS Studies reporting HRV, EEG and neuroimaging data related to hypnosis, dissociation and FNSS were identified by searching the electronic databases Pubmed and ScienceDirect. RESULTS The majority of the identified studies concerned the physiological characteristics of hypnosis; relatively few investigations on dissociation related FNSS were identified. General findings were increased parasympathetic functioning during hypnosis (as measured by HRV), and lower HRV in patients with FNSS. The large variety of EEG and functional MRI investigations with diverse results challenges definite conclusions, but evidence suggests that subcortical as well as (pre)frontal regions serve emotion regulation in dissociative conditions. Functional connectivity analyses suggest the presence of altered brain networks in patients with FNSS, in which limbic areas have an increased influence on motor preparatory regions. CONCLUSIONS HRV, EEG and (functional) MRI are sensitive methods to detect physiological changes related to dissociation and dissociative disorders such as FNSS, and can possibly provide more information about their aetiology. The use of such measures could eventually provide biomarkers for earlier identification of patients at risk and appropriate treatment of dissociative conditions.
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58
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A right hemisphere safety backup at work: Hypotheses for deep hypnosis, post-traumatic stress disorder, and dissociation identity disorder. Med Hypotheses 2013; 81:383-8. [DOI: 10.1016/j.mehy.2013.05.026] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2013] [Revised: 05/07/2013] [Accepted: 05/21/2013] [Indexed: 11/18/2022]
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59
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Michel CM, Murray MM. Towards the utilization of EEG as a brain imaging tool. Neuroimage 2012; 61:371-85. [DOI: 10.1016/j.neuroimage.2011.12.039] [Citation(s) in RCA: 333] [Impact Index Per Article: 27.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2011] [Accepted: 12/15/2011] [Indexed: 10/14/2022] Open
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60
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Lipari S, Baglio F, Griffanti L, Mendozzi L, Garegnani M, Motta A, Cecconi P, Pugnetti L. Altered and asymmetric default mode network activity in a “hypnotic virtuoso”: An fMRI and EEG study. Conscious Cogn 2012; 21:393-400. [DOI: 10.1016/j.concog.2011.11.006] [Citation(s) in RCA: 24] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2011] [Revised: 11/08/2011] [Accepted: 11/20/2011] [Indexed: 11/29/2022]
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61
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Lehmann D, Michel CM. EEG-defined functional microstates as basic building blocks of mental processes. Clin Neurophysiol 2011; 122:1073-4. [DOI: 10.1016/j.clinph.2010.11.003] [Citation(s) in RCA: 32] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2010] [Accepted: 11/06/2010] [Indexed: 11/15/2022]
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62
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Hinterberger T, Schoner J, Halsband U. Analysis of electrophysiological state patterns and changes during hypnosis induction. Int J Clin Exp Hypn 2011; 59:165-79. [PMID: 21390977 DOI: 10.1080/00207144.2011.546188] [Citation(s) in RCA: 20] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
Abstract
Hypnosis can be seen as a guided induction of various states of consciousness. This article details a time-series analysis that visualized the electrophysiological state changes during a session as a correlate to the instructions. Sixty-four channels of EEG and peripheral physiological measures were recorded in 1 highly susceptible subject. Significant state changes occurred synchronously with specific induction instructions. Some patterns could be physiologically explained, such as sensorimotor desynchronization over the right hemispheric hand area during left arm levitation. There was a highly significant increase in broadband activity during the stepwise trance induction that may point to a deep hypnotic state. This study provides illustrated proof for the detectability of physiological state changes as correlates to different states of awareness, consciousness, or cognition during hypnosis.
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Affiliation(s)
- Thilo Hinterberger
- Institut für Umweltmedizin und Krankenhaushygiene, Universitätsklinikum Freiburg, Breisacher Strasse 115 b, Freiburg, Germany.
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63
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EEG microstate sequences in healthy humans at rest reveal scale-free dynamics. Proc Natl Acad Sci U S A 2010; 107:18179-84. [PMID: 20921381 DOI: 10.1073/pnas.1007841107] [Citation(s) in RCA: 361] [Impact Index Per Article: 25.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
Abstract
Recent findings identified electroencephalography (EEG) microstates as the electrophysiological correlates of fMRI resting-state networks. Microstates are defined as short periods (100 ms) during which the EEG scalp topography remains quasi-stable; that is, the global topography is fixed but strength might vary and polarity invert. Microstates represent the subsecond coherent activation within global functional brain networks. Surprisingly, these rapidly changing EEG microstates correlate significantly with activity in fMRI resting-state networks after convolution with the hemodynamic response function that constitutes a strong temporal smoothing filter. We postulate here that microstate sequences should reveal scale-free, self-similar dynamics to explain this remarkable effect and thus that microstate time series show dependencies over long time ranges. To that aim, we deploy wavelet-based fractal analysis that allows determining scale-free behavior. We find strong statistical evidence that microstate sequences are scale free over six dyadic scales covering the 256-ms to 16-s range. The degree of long-range dependency is maintained when shuffling the local microstate labels but becomes indistinguishable from white noise when equalizing microstate durations, which indicates that temporal dynamics are their key characteristic. These results advance the understanding of temporal dynamics of brain-scale neuronal network models such as the global workspace model. Whereas microstates can be considered the "atoms of thoughts," the shortest constituting elements of cognition, they carry a dynamic signature that is reminiscent at characteristic timescales up to multiple seconds. The scale-free dynamics of the microstates might be the basis for the rapid reorganization and adaptation of the functional networks of the brain.
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Britz J, Van De Ville D, Michel CM. BOLD correlates of EEG topography reveal rapid resting-state network dynamics. Neuroimage 2010; 52:1162-70. [PMID: 20188188 DOI: 10.1016/j.neuroimage.2010.02.052] [Citation(s) in RCA: 567] [Impact Index Per Article: 40.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2009] [Revised: 02/02/2010] [Accepted: 02/17/2010] [Indexed: 01/19/2023] Open
Abstract
Resting-state functional connectivity studies with fMRI showed that the brain is intrinsically organized into large-scale functional networks for which the hemodynamic signature is stable for about 10s. Spatial analyses of the topography of the spontaneous EEG also show discrete epochs of stable global brain states (so-called microstates), but they remain quasi-stationary for only about 100 ms. In order to test the relationship between the rapidly fluctuating EEG-defined microstates and the slowly oscillating fMRI-defined resting states, we recorded 64-channel EEG in the scanner while subjects were at rest with their eyes closed. Conventional EEG-microstate analysis determined the typical four EEG topographies that dominated across all subjects. The convolution of the time course of these maps with the hemodynamic response function allowed to fit a linear model to the fMRI BOLD responses and revealed four distinct distributed networks. These networks were spatially correlated with four of the resting-state networks (RSNs) that were found by the conventional fMRI group-level independent component analysis (ICA). These RSNs have previously been attributed to phonological processing, visual imagery, attention reorientation, and subjective interoceptive-autonomic processing. We found no EEG-correlate of the default mode network. Thus, the four typical microstates of the spontaneous EEG seem to represent the neurophysiological correlate of four of the RSNs and show that they are fluctuating much more rapidly than fMRI alone suggests.
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Affiliation(s)
- Juliane Britz
- Department of Fundamental Neuroscience, University of Geneva, Geneva, Switzerland.
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Halsband U, Mueller S, Hinterberger T, Strickner S. Plasticity changes in the brain in hypnosis and meditation. ACTA ACUST UNITED AC 2009. [DOI: 10.1002/ch.386] [Citation(s) in RCA: 71] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
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66
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Mercier M, Schwartz S, Michel CM, Blanke O. Motion direction tuning in human visual cortex. Eur J Neurosci 2009; 29:424-34. [DOI: 10.1111/j.1460-9568.2008.06583.x] [Citation(s) in RCA: 19] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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Hennings K, Lelic D, Petrini L. An automated method for micro-state segmentation of evoked potentials. J Neurosci Methods 2008; 177:225-31. [PMID: 18977245 DOI: 10.1016/j.jneumeth.2008.09.029] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2008] [Revised: 09/08/2008] [Accepted: 09/25/2008] [Indexed: 11/28/2022]
Abstract
We present a method for segmenting evoked potentials into functional micro-states. The method is based on measuring the similarity between all the topographic maps in the evoked potential and grouping them into functional micro-states based on minimizing an error function. The similarity is measured as the normalized cross-correlation coefficient. The method was validated on simulated data and tested on its ability to segment a visual evoked potential. On simulated data the method missed from 1% to 8.5% of the micro-state boundaries for evoked potentials with a signal-to-noise ratio of 20-1dB, respectively. The proposed segmentation method was compared with segmentation based on K-mean clustering. It was found that the proposed method was better at detecting the correct number of micro-states and was computationally more efficient. The automatic segmentation of the visual evoked potential was compared to the manual segmentation performed by eleven EEG specialists. No significant difference in the deviation of micro-state boundaries was observed between two random EEG specialists and between a random EEG specialist and the automatic method. Thus it was found that the method could reliably segment evoked potentials into their functional micro-states.
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Affiliation(s)
- Kristian Hennings
- Mech-Sense, Department of Gastroenterology, Aalborg Hospital, Medicinerhuset, 4. sal, Mølleparkvej 4, DK-9000 Aalborg, Denmark.
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Britz J, Landis T, Michel CM. Right Parietal Brain Activity Precedes Perceptual Alternation of Bistable Stimuli. Cereb Cortex 2008; 19:55-65. [DOI: 10.1093/cercor/bhn056] [Citation(s) in RCA: 139] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
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69
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Murray MM, Brunet D, Michel CM. Topographic ERP analyses: a step-by-step tutorial review. Brain Topogr 2008; 20:249-64. [PMID: 18347966 DOI: 10.1007/s10548-008-0054-5] [Citation(s) in RCA: 756] [Impact Index Per Article: 47.3] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2008] [Accepted: 02/13/2008] [Indexed: 11/29/2022]
Abstract
In this tutorial review, we detail both the rationale for as well as the implementation of a set of analyses of surface-recorded event-related potentials (ERPs) that uses the reference-free spatial (i.e. topographic) information available from high-density electrode montages to render statistical information concerning modulations in response strength, latency, and topography both between and within experimental conditions. In these and other ways these topographic analysis methods allow the experimenter to glean additional information and neurophysiologic interpretability beyond what is available from canonical waveform analyses. In this tutorial we present the example of somatosensory evoked potentials (SEPs) in response to stimulation of each hand to illustrate these points. For each step of these analyses, we provide the reader with both a conceptual and mathematical description of how the analysis is carried out, what it yields, and how to interpret its statistical outcome. We show that these topographic analysis methods are intuitive and easy-to-use approaches that can remove much of the guesswork often confronting ERP researchers and also assist in identifying the information contained within high-density ERP datasets.
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Affiliation(s)
- Micah M Murray
- Electroencephalography Brain Mapping Core, Center for Biomedical Imaging of Lausanne and Geneva, Radiologie CHUV BH08.078, Bugnon 46 Lausanne, Switzerland.
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Beyond Conventional Event-related Brain Potential (ERP): Exploring the Time-course of Visual Emotion Processing Using Topographic and Principal Component Analyses. Brain Topogr 2008; 20:265-77. [DOI: 10.1007/s10548-008-0053-6] [Citation(s) in RCA: 88] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/24/2007] [Accepted: 02/04/2008] [Indexed: 10/22/2022]
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