Resilience and Brain Changes in Long-Term Ayahuasca Users: Insights From Psychometric and fMRI Pattern Recognition
Long-term ayahuasca users exhibited higher psychological resilience and distinct patterns of emotional brain reactivity on fMRI, with a machine-learning classifier separating users from controls at 75% accuracy and a regression model predicting individual resilience (r = 0.69). These findings suggest long-term ayahuasca use is associated with neural adaptations in emotional processing detectable by multivariate pattern analysis.
Authors
- Rego Ramos, L.
- Fernandes Jr, O.
- Arruda Sanchez, T.
Published
Abstract
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. Evidence Level 4. Technical Efficacy Stage 1.
Research Summary of 'Resilience and Brain Changes in Long-Term Ayahuasca Users: Insights From Psychometric and fMRI Pattern Recognition'
Introduction
Ramos and colleagues situate their study within a growing literature on ayahuasca, an Amazonian psychedelic brew containing DMT and beta-carbolines, that has shown acute changes in perception, emotion and cognition and biochemical markers of neuroplasticity such as increased BDNF and sigma-1 receptor expression. Previous work has documented acute modulation of emotional circuitry (for example, default mode network and limbic regions including amygdala and insula), and recent neuroimaging studies of other psychedelics have used machine learning to reveal drug-related connectivity signatures and to predict individual clinical or behavioural traits. Against this background, the study aims to evaluate whether long-term, regular ayahuasca use is associated with lasting differences in emotional brain processing and psychological resilience when users are not acutely under the substance. Specifically, the investigators test whether experienced ayahuasca users differ from non-user controls on psychometric measures (notably the ER-89 resilience scale) and whether whole-brain fMRI patterns during an implicit emotional task can discriminate groups or predict individual resilience scores using multivariate pattern recognition methods.
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Study Details
- Study Typeindividual
- Journal
- Compounds
- Topic
- APA Citation
Ramos, L. R., Fernandes, O., & Sanchez, T. A. (2025). Resilience and Brain Changes in Long-Term Ayahuasca Users: Insights From Psychometric and fMRI Pattern Recognition. Journal of Magnetic Resonance Imaging, 62(6), 1782-1790. https://doi.org/10.1002/jmri.70063
References (12)
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