Background: 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.
Purpose: To investigate the long-term effects of Ayahuasca usage through psychometric scales and fMRI data related to emotional processing using artificial intelligence tools.
Study Type: Retrospective Cross-sectional, case–control study.
Subjects: 38 healthy male participants (19 long-term Ayahuasca users and 19 non-user controls).
Field Strength/Sequence: 1.5 Tesla; gradient-echo T2*-weighted echo-planar imaging sequence during an implicit emotion processing task.
Assessment: 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.
Statistical Tests: 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).
Results: 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).
Data Conclusion: 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.