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Thaise G. L. de O. Toutain

Universidade de São Paulo

2 papers in the library · publishing 2022-2025

Papers

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

bioRxiv (Cold Spring Harbor Laboratory) December 11, 2025 Caroline L. Alves, Fernanda Palhano-Fontes, Thaise G. L. de O. Toutain et al.

Ayahuasca alters conscious experience, and this study identifies EEG markers of its network-level effects using machine learning and complex-network analysis. In a randomized, double-blind, placebo-controlled trial with naïve ayahuasca users, resting-state EEG was recorded before dosing, 2 hours after, and 4 hours after. Connectivity was estimated with sliding windows; optimal classification performance occurred at 60–70 seconds (AUC and accuracy = 0.93). Network analysis revealed a bilateral decrease in eigenvector centrality (weaker hub influence), increased degree heterogeneity in the right hemisphere, and reduced global efficiency in the left. Posterior-left connections weakened acutely, while right temporal–central coupling transiently strengthened. The findings suggest that hub-centric shortcuts weaken, routing communication through more distributed, less efficient pathways with right-lateralized expression.

Application of machine learning and complex network measures to an EEG dataset from DMT experiments

medRxiv June 16, 2022 Caroline L. Alves, Thaise G. L. de O. Toutain, Joel Augusto Moura Porto et al. preprint

A machine-learning method using support vector machines classified EEG data from volunteers before and after inhaling the psychedelic DMT. Complex network measures derived from brain connectivity achieved 89% AUC, outperforming raw connectivity matrices. Key distinguishing features included connections between temporal and central cortex regions (TP8-C3) linked to finger movements, and between precentral gyrus and lateral occipital cortex (FC5-P8) potentially related to emotional and mystical experiences. Closeness centrality was the most important network measure. DMT increased community size and average path length, disrupting the balance between functional segregation and integration, supporting the idea that cortical activity becomes more entropic under psychedelics.