PLoS ONE
December 16, 2022
Caroline L. Alves, Rubens Gisbert Cury, Kirstin Roster et al.
13 citations
Ayahuasca, an Amazonian plant blend used in traditional medicine for centuries, is a promising therapy for neurological and mental diseases. Using an EEG dataset, machine learning and complex network analysis automatically detected changes in brain activity at three data abstraction levels. Connectivity changes between brain regions (correlation of EEG time series) yielded the highest accuracy (92%), followed by raw EEG (88%) and complex network measures (83%). The frontal and temporal lobes were most activated, consistent with prior work. A novel finding identified F3 and PO4 as the most important brain connections, possibly linked to face-recognition-like cognitive processes during visual hallucinations.
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.
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.
Figshare
January 1, 2022
Caroline L. Alves
The text describes a Pearson's connection matrix derived from EEG experiments on subjects who ingested ayahuasca, measured at a time point before the psychedelic activation period. This suggests the matrix captures baseline neural connectivity patterns prior to the onset of the substance's psychoactive effects, providing a reference for comparing changes during the psychedelic state.
Figshare
January 1, 2022
Caroline L. Alves
The abstract describes a study that used Pearson's connection matrix to analyze EEG experiments from subjects who had ingested ayahuasca, focusing on the period after psychedelic activation. The work examines brain connectivity patterns during the psychedelic state induced by ayahuasca, likely investigating how neural networks reorganize under the influence of the substance.