Microstate Dynamics of Focused Attention Meditation
Chuong Ngo, Erkin Bek, Monika Stasytyte, Lionel A Newman, Rodrigo Elizalde, Amit Kanthi, Nk Manjunath, Christoph M. Michel
bioRxiv Preprint Server January 19, 2026 preprint DOI: 10.64898/2026.01.19.700274 (opens in new tab)
Study at a glance
AI-extracted from the abstract| Characteristics | Within-subjects experimental study |
|---|---|
| Sample size | 22 |
| Population | Experienced meditation practitioners |
| Intervention | Focused-attention meditation on the breath (Ānāpānasati) |
| Topics | Meditation |
| Key findings | Focused-attention meditation robustly reduced Microstate C and increased Microstates D and E, indicating a reorganization of large-scale brain dynamics away from self-referential and memory-based processing toward attentional stability and internal monitoring. |
Abstract
Focused-attention meditation provides a tractable model for examining how large-scale brain dynamics support attention and self-regulation. Using high-density EEG microstate analysis, we investigated how focused-attention meditation on the breath (Ānāpānasati) modulates intrinsic brain activity in 22 experienced practitioners, compared with baseline rest and deliberate mental imagery. Five canonical microstate classes (A-E) were identified. Meditation produced a robust reduction of Microstate C across coverage, duration, and occurrence, accompanied by increased presence of Microstates D and E (all Microstate x Condition interactions p < 0.0001). Source localization revealed that Microstate C was generated primarily in medial and lateral temporal regions including the hippocampus and parahippocampal cortex, whereas Microstate D involved posterior midline regions including the posterior cingulate cortex and precuneus, and Microstate E engaged frontoparietal and orbitolimbic networks. Together, these results indicate that focused-attention meditation reorganizes the temporal architecture of large-scale brain dynamics by downregulating microstate patterns associated with self-referential and memory-based processing while enhancing neural states supporting attentional stability and internal monitoring.