Association Between Resting-State Microstates and Ratings on the Amsterdam Resting-State Questionnaire

Pipinis, Evaldas; Mėlynytė, Sigita; Koenig, Thomas; Jarutytė, Lina; Linkenkaer‐Hansen, Klaus; Rukšėnas, Osvaldas; Griškova-Bulanova, Inga

doi:10.1007/s10548-016-0522-2

Cited by 63 publications

(95 citation statements)

References 19 publications

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“…More interestingly, the individual variability in the visual imagery experienced during acquisition was revealed to be positively related to fractional occupancy of VIS (Figure 7). Similar correlations were reported by previous studies (Pipinis et al, 2017;Stoffers et al, 2015). In addition, the significant relationships assessed between the mental imagery with AUD, DAN and VIS may explain the cross-interactions observed between these networks forming one large module during time (datasets 1,2,3).…”

Section: Discussionsupporting

confidence: 88%

“…Here, betweensubjects variation in the temporal characteristics of specific modules, mainly VIS, AUD and DAN, was associated with self-report rating of mental visual imagery as measured by the resting-state questionnaire. The dependence of observed brain activity on the inner thoughts and feeling experienced during resting acquisition was emphasized by multiple studies (Diaz et al, 2016;Pipinis et al, 2017;Stoffers et al, 2015). The discrepancy of results obtained from different datasets may be due to some difference in the datasets such as the sample size and age of subjects.…”

Section: Discussionmentioning

confidence: 99%

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The dynamic modular fingerprints of the human brain at rest

Kabbara

Paban

Hassan

2020

Preprint

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The human brain is a dynamic modular network that can be decomposed into a set of modules and its activity changes permanently over time. At rest, several brain networks, known as Resting-State Networks (RSNs), emerge and cross-communicate even at subsecond temporal scale. Here, we seek to decipher the fast reshaping in spontaneous brain modularity and its relationship to RSNs. We use Electro/Magneto-Encephalography (EEG/MEG) to track dynamics of modular brain networks, in three independent datasets (N= 568) of healthy subjects at rest. We show the presence of striking spatiotemporal network pattern consistent over participants. We also show that some RSNs, such as default mode network and temporal network, are not necessary 'unified units' but rather can be divided into multiple sub-networks over time. Using the resting state questionnaire, our results revealed also that brain network dynamics are strongly correlated to mental imagery at rest. These findings add new perspectives to brain dynamic analysis and highlight the importance of tracking fast reconfiguration of electrophysiological networks at rest. (Desikan et al., 2006). Then, the regional time series of each subject were reconstructed using the weighted minimum norm estimate inverse solution (WMNE) for Datasets 1 and 2, and beamforming for Dataset 3. (C) Using a sliding window technique, the dynamic brain networks were computed. (D) The first step in the modularity-based algorithm is to parcellate each temporal network into communities. Then, the similarity between different temporal modular structures is assessed. (E) The similarity matrix is segmented into different communities where each one represents a modular state of specific spatial topology combining different time windows. (F) Following this, the dominant modules (common modules between different MSs) are extracted for each subject. The correspondence between these modules and the well-known RSNs is also detected. (G) The mean dwell time and fractional occupancies are calculated for the derived dominant modules. interest (ROIs) by the means of an anatomical atlas

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Section: Discussionsupporting

confidence: 88%

Section: Discussionmentioning

confidence: 99%

The dynamic modular fingerprints of the human brain at rest

Kabbara

Paban

Hassan

2020

Preprint

View full text Add to dashboard Cite

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“…References not in the text: (Corradini and Persinger, 2014;Pipinis et al, 2017;Schlegel et al, 2012). …”

Section: A C C E P T E D Accepted Manuscriptmentioning

confidence: 99%

EEG microstates as a tool for studying the temporal dynamics of whole-brain neuronal networks: A review

2018

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SummaryThe 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 largescale 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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“…Continuous EEG data may be analyzed based on a wide range of methods. The EEG microstate analysis has gained increasing popularity (Khanna et al, 2015;Koenig and Brandeis, 2016;Mégevand et al, 2008;Pedroni et al, 2016;Pipinis et al, 2016) in recent years. It is particularly attractive for the investigation of cognitive and emotional brain processes (Lehmann, 1990) since it can identify discontinuous, non-linear changes of global functional brain states at a very high temporal resolution (e.g.…”

Section: Introductionmentioning

confidence: 99%

The EEG microstate topography is predominantly determined by intracortical sources in the alpha band

et al. 2017

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Human brain electric activity can be measured at high temporal and fairly good spatial resolution via electroencephalography (EEG). The EEG microstate analysis is an increasingly popular method used to investigate this activity at a millisecond resolution by segmenting it into quasi-stable states of approximately 100 ms duration. These so-called EEG microstates were postulated to represent atoms of thoughts and emotions and can be classified into four classes of topographies A through D, which explain up to 90% of the variance of continuous EEG. The present study investigated whether these topographies are primarily driven by alpha activity originating from the posterior cingulate cortex (all topographies), left and right posterior cortices, and the anterior cingulate cortex (topographies A, B, and C, respectively). We analyzed two 64-channel resting state EEG datasets (N = 61 and N = 78) of healthy participants. Sources of head-surface signals were determined via exact low resolution electromagnetic tomography (eLORETA). The Hilbert transformation was applied to identify instantaneous source strength of four EEG frequency bands (delta through beta). These source strength values were averaged for each participant across time periods belonging to a particular microstate. For each dataset, these averages of the different microstate classes were compared for each voxel. Consistent differences across datasets were identified via a conjunction analysis. The intracortical strength and spatial distribution of alpha band activity mainly determined whether a head-surface topography of EEG microstate class A, B, C, or D was induced. EEG microstate class C was characterized by stronger alpha activity compared to all other classes in large portions of the cortex. Class A was associated with stronger left posterior alpha activity than classes B and D, and class B was associated with stronger right posterior alpha activity than A and D. Previous results indicated that EEG microstate dynamics reflect a fundamental mechanism of the human brain that is altered in different mental states in health and disease. They are characterized by systematic transitions between four head-surface topographies, the EEG microstate classes. Our results show that intra-cortical alpha oscillations, which likely reflect decreased cortical excitability, primarily account for the emergence of these classes. We suggest that microstate class dynamics reflect transitions between four global attractor states that are characterized by selective inhibition of specific intra-cortical regions.

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Association Between Resting-State Microstates and Ratings on the Amsterdam Resting-State Questionnaire

Cited by 63 publications

References 19 publications

The dynamic modular fingerprints of the human brain at rest

The dynamic modular fingerprints of the human brain at rest

EEG microstates as a tool for studying the temporal dynamics of whole-brain neuronal networks: A review

The EEG microstate topography is predominantly determined by intracortical sources in the alpha band

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