Reconfiguration of Electroencephalography Microstate Networks after Breath-Focused, Digital Meditation Training

Bréchet, Lucie; Ziegler, David A.; Simon, Alexander J.; Brunet, Denis; Gazzaley, Adam; Michel, Christoph M.

doi:10.1089/brain.2020.0848

Cited by 25 publications

(40 citation statements)

References 70 publications

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“…These two distinct microstates topography are also in line with report of Bréchet et al (2021) showing that 6-week meditation training leads to change in the microstate topography identification at rest. In our case, however, k-means clustering identified also distinct topographies between groups from RS2/OM recording, that could reflect an effect of OM on microstate topography.…”

Section: Mindfulness Stabilize Microstate C Topographysupporting

confidence: 91%

“…Regarding state analysis, phenomenological reports were analyzed through Kendall's W and Mann-Whitney U tests for condition and group comparisons. Considering that temporal parameters can be compared only if microstates between conditions have the same topography (Bréchet et al, 2021;Grieder et al, 2016), the distinctive microstate « C » maps related to RS1 and RS2 in nonmeditators did not allow us to compare microstate between conditions within the non-meditator group, nor between groups. Nevertheless, we performed a repeated measure ANOVA to compare microstate temporal parameters between mental state (RS1, OM, RS2) within the meditator group.…”

Section: Discussionmentioning

confidence: 99%

“…To investigated the state effect on microstate topographies and temporal parameters, we applied the second k-means clustering across participants per conditions (RS1, RS2/OM, RS3) accordingly to Bréchet al (Bréchet et al, 2021). Giving the four microstates determined by previous trait analysis, we fixed the number of clusters to four for each condition and group clustering.…”

Section: Microstates Analysismentioning

confidence: 99%

“…As explained above, temporal parameters can be compared only if microstates between conditions have the same topography (Bréchet et al, 2021;Grieder et al, 2016). Accordingly, we compared microstate temporal parameters related to RS1, OM, and RS3 within the meditator group only.…”

Section: Microstate Analysismentioning

confidence: 99%

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Trait and state mindfulness modulate EEG microstates

Zarka

Cevallos

Ruiz

et al. 2021

Preprint

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Here, we aimed to characterized microstate dynamics induced by open-monitoring meditation (OM), which emphasizes a non-reactive stance toward lived experience, while participants were passively exposed to auditory stimuli. We recorded EEG signals from eighteen trained meditators before, during, and after an OM, that we compared to a matched control group at rest. To characterize brain state, we used a multidimensional-based analysis including source localization EEG microstates, phenomenological reports and personality trait questionnaires. We showed that microstate A was negatively correlated with mindfulness trait and decreased in frequency after OM compared to before in meditators. Microstate B was longer and was positively correlated to non-reactivity trait after OM in the meditator group. Microstate C was less frequent and shorter at rest before OM in meditators compared to non-meditators, and decreased in frequency after OM in meditators. Further, the occurrence of microstate C was negatively correlated to non-reactivity trait of meditators. Source localization analysis revealed that the mindfulness trait effect on microstate C at rest was explained by lower activity of the salience network (identified in the anterior cingulate cortex, thalamus and insula), while the mindfulness state effect relied on a strong contribution of (anterior and posterior) cerebellum during OM. While the decreased microstate A occurrence would be related to the mitigation of phonological aspect of thinking processes, the decrease of microstate C occurrence would represent an index of the cognitive defusion enabled by non-reactive monitoring underlying mindfulness meditation, for which the cerebellum appears to play a crucial role.SIGNIFICANCE STATEMENTWhile benefit of mindfulness meditations are extensively documented in wide range of scientific field, their neural mechanisms remain difficult to catch. Here, we characterize EEG microstate dynamics induced by open-monitoring meditation (OM) triangulated by multimodal approach including source localization, phenomenological reports and personality trait questionnaires. We found that temporal parameters of microstates related to phonological processes and mind wandering are negatively correlated to mindfulness trait and are modulated by OM. Source localization analysis revealed that the trait effect at rest was explained by lower activity of the salience network, while the state effect relied on a strong contribution of the cerebellum during OM. These findings suggested that EEG microstates could represent biomarkers of the cognitive effects of mindfulness.

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Section: Mindfulness Stabilize Microstate C Topographysupporting

confidence: 91%

Section: Discussionmentioning

confidence: 99%

Section: Microstates Analysismentioning

confidence: 99%

Section: Microstate Analysismentioning

confidence: 99%

See 2 more Smart Citations

Trait and state mindfulness modulate EEG microstates

Zarka

Cevallos

Ruiz

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…Past studies have associated these measures of microstate activity with information processing functions (e.g., Britz, Hernandez, Ro, & Michel, 2014), conscious cognitive acts (e.g., Brechet et al, 2019; Milz et al, 2016; Seitzman et al, 2017), states of alertness, arousal, perception, and meditation (e.g., Brodbeck et al, 2012; Comsa, Bekinschtein, & Chennu, 2019; Faber, Travis, Milz, & Parim, 2017; Zanesco, King, Skwara, & Saron, 2020), and spontaneous phenomenal experiences (e.g., Lehmann, Pascual‐Marqui, Strik, & Koenig, 2010; Lehmann, Strik, Henggeler, Koenig, & Koukkou, 1998; Pipinis et al, 2017). Importantly, one recent study found that 6 weeks of daily meditation practice led to clusters of optimal microstates with different topographic configurations in EEG collected at rest (Brechet et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

Meditation training modulates brain electric microstates and felt states of awareness

Zanesco

Skwara

King

et al. 2021

Human Brain Mapping

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Meditation practice is believed to foster states of mindful awareness and mental quiescence in everyday life. If so, then the cultivation of these qualities with training ought to leave its imprint on the activity of intrinsic functional brain networks. In an intensive longitudinal study, we investigated associations between meditation practitioners' experiences of felt mindful awareness and changes in the spontaneous electrophysiological dynamics of functional brain networks. Experienced meditators were randomly assigned to complete 3 months of full‐time training in focused‐attention meditation (during an initial intervention) or to serve as waiting‐list controls and receive training second (during a later intervention). We collected broadband electroencephalogram (EEG) during rest at the beginning, middle, and end of the two training periods. Using a data‐driven approach, we segmented the EEG into a time series of transient microstate intervals based on clustering of topographic voltage patterns. Participants also provided daily reports of felt mindful awareness and mental quiescence, and reported daily on four experiential qualities of their meditation practice during training. We found that meditation training led to increases in mindful qualities of awareness, which corroborate contemplative accounts of deepening mental calm and attentional focus. We also observed reductions in the strength and duration of EEG microstates across both interventions. Importantly, changes in the dynamic sequencing of microstates were associated with daily increases in felt attentiveness and serenity during training. Our results connect shifts in subjective qualities of meditative experience with the large‐scale dynamics of whole brain functional EEG networks at rest.

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