Stationary EEG pattern relates to large-scale resting state networks – An EEG-fMRI study connecting brain networks across time-scales

Arzate-Mena, J. Daniel; Abela, Eugenio; Olguín-Rodríguez, Paola V.; Ríos-Herrera, Wady A.; Alcauter, Sarael; Schindler, Kaspar; Wiest, Roland; Müller, Markus; Rummel, Christian

doi:10.1016/j.neuroimage.2021.118763

Cited by 10 publications

(14 citation statements)

References 144 publications

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“…In other words, the same region in microstate space is re‐visited faster at first and gradually the recurrence speed decreases. Our findings are supported by the evidence presented in Arzate‐Mena et al (2021), Olguín‐Rodríguez et al (2018), and Singer (2013), in which the nonstationary dynamical features and a kind of shadow of the evolution of an attractor in phase space are emphasized.…”

Section: Discussionsupporting

confidence: 90%

Time course of EEG complexity reflects attentional engagement during listening to speech in noise

Eqlimi,

Bockstael,

Schönwiesner

et al. 2023

Eur J of Neuroscience

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Auditory distractions are recognized to considerably challenge the quality of information encoding during speech comprehension. This study explores electroencephalography (EEG) microstate dynamics in ecologically valid, noisy settings, aiming to uncover how these auditory distractions influence the process of information encoding during speech comprehension. We examined three listening scenarios: (1) speech perception with background noise (LA), (2) focused attention on the background noise (BA), and (3) intentional disregard of the background noise (BUA). Our findings showed that microstate complexity and unpredictability increased when attention was directed towards speech compared with tasks without speech (LA > BA & BUA). Notably, the time elapsed between the recurrence of microstates increased significantly in LA compared with both BA and BUA. This suggests that coping with background noise during speech comprehension demands more sustained cognitive effort. Additionally, a two‐stage time course for both microstate complexity and alpha‐to‐theta power ratio was observed. Specifically, in the early epochs, a lower level was observed, which gradually increased and eventually reached a steady level in the later epochs. The findings suggest that the initial stage is primarily driven by sensory processes and information gathering, while the second stage involves higher level cognitive engagement, including mnemonic binding and memory encoding.

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Section: Discussionsupporting

confidence: 90%

Time course of EEG complexity reflects attentional engagement during listening to speech in noise

Eqlimi,

Bockstael,

Schönwiesner

et al. 2023

Eur J of Neuroscience

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“…Children displayed a similar SCP to that observed previously in healthy adults in different physiological states -wakefulness and sleep stages-as well as with epilepsy using the same analytical approach. In addition, pronounced correlations between distant electrode pairs has been found using the median EEG-reference, but then with negative sign (Arzate-Mena et al, 2022;Müeller et al, 2014;Olguín-Rodríguez et al, 2018). The finding of an SCP indicates that children´s brains exhibit a stable functional organization pattern from an early age, while also highlighting the importance of spontaneous brain activity in the resting state where, though not related to a known synchronized event, diverse cognitive activity is occurring.…”

Section: Discussionmentioning

confidence: 99%

“…between each pair of electrodes, adapting the procedure followed in previous studies (Arzate-Mena et al, 2022;Müller et al, 2014;Olguín-Rodríguez et al, 2018). Here C ij denotes the cross-correlation matrix of two time series X i (t k ) X j (t k ), T denotes the number of samples of the data segment (i,j = 1, .…”

Section: Eeg Analysismentioning

confidence: 99%

“…To obtain the similarity metric, we calculated the correlation values (Pearson´s analysis) between the SCP and the correlation matrix of each EEG segment based on the procedure used by Olguín-Rodríguez et al (2018) and Arzate-Mena et al (2022). Thereby, we ordered the triangle of each matrix in a vector and normalized its elements to zero mean unit variance to ensure that Pearson coefficients take values between one and minus one.…”

Section: Eeg Analysismentioning

confidence: 99%

“…Nevertheless, some related work has also been performed using brain electrical activity (EEG) (Chu et al, 2015). Moreover, Arzate-Mena et al (2022) encountered a straight relationship between the EEG stable correlation pattern (SCP) and the fMRI resting-state network that reflects different time expressions of the same brain activity.…”

Section: Introductionmentioning

confidence: 99%

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EEG functional brain connectivity strengthens with age during attentional processing to faces in children

Ramos-Loyo

Olguín-Rodríguez

Espinosa-Denenea

et al. 2022

Front. Netw. Physiol.

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Studying functional connectivity may generate clues to the maturational changes that occur in children, expressed by the dynamical organization of the functional network assessed by electroencephalographic recordings (EEG). In the present study, we compared the EEG functional connectivity pattern estimated by linear cross-correlations of the electrical brain activity of three groups of children (6, 8, and 10 years of age) while performing odd-ball tasks containing facial stimuli that are chosen considering their importance in socioemotional contexts in everyday life. On the first task, the children were asked to identify the sex of faces, on the second, the instruction was to identify the happy expressions of the faces. We estimated the stable correlation pattern (SCP) by the average cross-correlation matrix obtained separately for the resting state and the task conditions and quantified the similarity of these average matrices comparing the different conditions. The accuracy improved with higher age. Although the topology of the SCPs showed high similarity across all ages, the two older groups showed a higher correlation between regions associated with the attentional and face processing networks compared to the youngest group. Only in the youngest group, the similarity metric decreased during the sex condition. In general, correlation values strengthened with age and during task performance compared to rest. Our findings indicate that there is a spatially extended stable brain network organization in children like that reported in adults. Lower similarity scores between several regions in the youngest children might indicate a lesser ability to cope with tasks. The brain regions associated with the attention and face networks presented higher synchronization across regions with increasing age, modulated by task demands.

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Static and dynamic functional connectomes represent largely similar information

Matkovič¹,

Antičević

Murray

et al. 2023

Preprint

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Functional connectivity (FC) in blood oxygen level-dependent (BOLD) fMRI time series can be estimated using methods that differ in sensitivity to the temporal order of time points (static vs. dynamic) and the number of regions considered in estimating a single edge (bivariate vs. multivariate). Previous research suggests that dynamic FC explains variability in FC fluctuations and behavior beyond static FC. Our aim was to systematically compare methods on both dimensions. We compared five FC methods: Pearson's/full correlation (static, bivariate), lagged correlation (dynamic, bivariate), partial correlation (static, multivariate) and multivariate AR model with and without self-connections (dynamic, multivariate). We compared these methods (i) directly, by assessing the similarities between the FC matrices, and (ii) indirectly, by comparing the patterns of brain-behavior associations. Although FC estimates did not differ as a function of sensitivity to temporal order, we observed differences between the multivariate and bivariate FC methods. The dynamic FC estimates were highly correlated with the static FC estimates, especially when comparing group-level FC matrices. Similarly, there were high correlations between the patterns of brain-behavior associations obtained using the dynamic and static FC methods. We conclude that the dynamic FC estimates represent information largely similar to that of the static FC.

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Stationary EEG pattern relates to large-scale resting state networks – An EEG-fMRI study connecting brain networks across time-scales

Cited by 10 publications

References 144 publications

Time course of EEG complexity reflects attentional engagement during listening to speech in noise

Time course of EEG complexity reflects attentional engagement during listening to speech in noise

EEG functional brain connectivity strengthens with age during attentional processing to faces in children

Static and dynamic functional connectomes represent largely similar information

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