Network Rerouting Under Ayahuasca: Temporally and Hemisphere-Resolved EEG Connectomics

Abstract

Abstract Ayahuasca profoundly alters conscious experience, yet robust, time-resolved EEG markers of its network-level effects remain limited. We combined machine learning with complex-network analysis to quantify how functional connectivity reorganizes across time and hemispheres in resting-state EEG from a randomized, double-blind, placebo-controlled trial including three 5-min sessions: pre-dose (T1), 2 h post-dose (T2), and 4 h post-dose (T3). The cohort consisted of naïve ayahuasca users, a population known to exhibit attenuated or more stable acute responses, making the detection of network-level changes particularly challenging. Connectivity was estimated using multiple metrics and sliding windows (10–120 s), and network features were computed and averaged to ensure statistical validity. A representation-selection step identified Spearman correlation and an intermediate temporal scale as optimal, with classification performance peaking at 60–70 s (independent-test AUC and accuracy = 0.93). Linear mixed models revealed a bilateral decrease in eigenvector centrality (weaker hub influence), increased degree heterogeneity in the right hemisphere, and reduced global efficiency in the left. Edge-level analyses localized these effects: Posterior-left connections weakened acutely (lowest at T2), whereas right temporal–central coupling transiently strengthened (highest at T2). Together, these convergent results support a mechanistic summary: as hub-centric short-cuts weaken, communication is increasingly routed through alternative, more distributed—and less efficient—pathways, with a right-lateralized expression at a later time. Methodologically, the window-optimized, hemisphere-resolved, and edge-validated pipeline extends prior EEG work and highlights temporal scale (approximately 60 s) as a biologically meaningful parameter for detecting psychedelic-induced network reorganization.