Resilience and Brain Changes in Long-Term Ayahuasca Users: Insights From Psychometric and fMRI Pattern Recognition.

Long-term ayahuasca use may be associated with altered emotional brain reactivity and increased psychological resilience. In a study of 38 healthy male participants (19 long-term ayahuasca users and 19 non-user controls), users showed significantly higher resilience scores (mean = 43.89) compared to controls (mean = 39.05). A machine learning classifier distinguished users from controls with 75% accuracy, and a regression model significantly predicted individual resilience scores. These findings support neural patterns consistent with long-term adaptations from ayahuasca detectable via fMRI and machine learning.

Study at a glance

Abstract

Ayahuasca is an Amazonian psychedelic brew that contains dimethyltryptamine (DMT) and beta carbolines. Prolonged use has shown changes in cognitive-behavioral tasks, and in humans, there is evidence of changes in cortical thickness and an increase in neuroplasticity factors that could lead to modifications in functional neural circuits. To investigate the long-term effects of Ayahuasca usage through psychometric scales and fMRI data related to emotional processing using artificial intelligence tools. Retrospective Cross-sectional, case-control study. 38 healthy male participants (19 long-term Ayahuasca users and 19 non-user controls). 1.5 Tesla; gradient-echo T2*-weighted echo-planar imaging sequence during an implicit emotion processing task. Participants completed standardized psychometric scales including the Ego Resilience Scale (ER89). During fMRI, participants performed a gender judgment task using faces with neutral or aversive (disgust/fear) expressions. Whole-brain fMRI data were analyzed using multivariate pattern recognition. Group comparisons of psychometric scores were performed using Student's t-tests or Mann-Whitney U tests based on normality. Multivariate pattern classification and regression were performed using machine learning algorithms: Multiple Kernel Learning (MKL), Support Vector Machine (SVM), and Gaussian Process Classification/Regression (GPC/GPR), with k-fold cross-validation and permutation testing (n = 100-1000) to assess model significance (α = 0.05). Ayahuasca users (mean = 43.89; SD = 5.64) showed significantly higher resilience scores compared to controls (mean = 39.05; SD = 5.34). The MKL classifier distinguished users from controls with 75% accuracy (p = 0.005). The GPR model significantly predicted individual resilience scores (r = 0.69). Long-term Ayahuasca use may be associated with altered emotional brain reactivity and increased psychological resilience. These findings support a neural patterns consistent with long-term adaptations of Ayahuasca detectable via fMRI and machine learning-based pattern analysis. 4. Stage 1.