Dynamic causal modeling of low-density resting-state EEG in long-term meditation practitioners

Abstract

Meditation is a complex cognitive practice associated with significant neurophysiological changes, particularly in long-term practitioners. These individuals represent an ideal human model for investigating neural changes associated to their consistent, frequent, and sustained cognitive engagement. However, in Western societies, long-term practitioners are relatively rare compared to Eastern monastic communities. In this study, we leverage a collaboration with a unique monastic population, the Monks and Geshes of the Tibetan University of Sera Jey in India, to examine the long-term effects of meditation on resting-state effective brain connectivity. Specifically, we hypothesize that different levels of meditation experience modulate intrinsic connections in two resting-state brain networks: the default mode network (DMN) and the salience network (SN). To test this, we apply dynamic causal modeling for EEG to analyze effective connectivity and validate our hypothesis. Our results reveal that long-term meditation practice can alter connectivity within the DMN and SN, with distinct patterns of modulation based on meditator experience. Experienced meditators appear to exhibit enhanced self-referential processing in the DMN and reduced reactivity in the SN, supporting the notion that meditation refines attentional control and internal awareness. These findings provide new insights into the neurophysiological mechanisms underlying long-term meditation and highlight the role of monastic practitioners as an invaluable model for studying experience-related modifications in the human brain.