ArticleJournal of magnetic resonance imaging : JMRI2025
Resilience and Brain Changes in Long-Term Ayahuasca Users: Insights From Psychometric and fMRI Pattern Recognition.
Article in Journal of magnetic resonance imaging : JMRI, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
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The trial behind it
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Who cites it
2 citing papers in PubMed.
- Registered Clinical Trials of Ayahuasca and DMT: A Scoping Review.Clinical pharmacology and therapeutics · 2026Article
- Resilience and Brain Changes in Long-Term Ayahuasca Users: Insights From Psychometric and fMRI Pattern Recognition.Journal of magnetic resonance imaging : JMRI · 2025Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
backgroundAyahuasca 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).
resultsAyahuasca 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
conclusionLong-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. EVIDENCE LEVEL: 4. TECHNICAL EFFICACY: Stage 1.
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