An exploratory persistent-homology analysis of resting-state fMRI functional connectivity under Ayahuasca
Tales Ramos Monteiro Dos Santos, Dráulio B. Araújo, Helcio Felippe, José Garcia Vivas Miranda, Fernanda Palhano-Fontes, Raphael Silva Do Rosário, Heloisa Onias, Aline Viol, Gandhimohan M. Viswanathan, Fernando A. N. Santos
Chaos Solitons & Fractals May 30, 2026 DOI: 10.1016/j.chaos.2026.118554 via OpenAlex
Summary
AI-generated from the abstractPsychedelic states can help researchers understand how the brain reorganizes at a large scale. In nine people scanned before and after taking ayahuasca, topological data analysis of resting-state fMRI connectivity showed a nominal decrease in persistent entropy of H2 features—a measure of higher-dimensional topological structure—that did not survive correction for multiple comparisons and was not reproduced with signed correlations. Exploratory analyses of signal complexity found descriptive but non-significant increases. These preliminary, hypothesis-generating results highlight persistent homology as a potential framework for studying psychedelic-related brain changes, but replication in larger placebo-controlled studies is needed.
Study at a glance
| Characteristics | Observational cohort Placebo-controlled Peer reviewed |
|---|---|
| Sample size | 9 |
| Population | Adults before and after ayahuasca ingestion |
| Intervention | Ayahuasca |
| Topics | Ayahuasca |
| Keywords | Functional connectivity Exploratory analysis Resting State FMRI Functional analysis |
| Key finding | Persistent entropy of H2 features showed a nominal pre/post decrease that did not survive correction for multiple comparisons and was not reproduced with signed correlations. |
Abstract
Psychedelic states offer a useful setting for studying changes in large-scale brain organization. Here, we applied Topological Data Analysis (TDA) to resting-state fMRI functional connectivity from nine participants scanned before and after Ayahuasca ingestion. Vietoris–Rips filtrations were constructed from correlation-derived dissimilarity matrices, and persistent entropy was used to quantify the distribution of persistence lifetimes across homology dimensions S 0 – S 3 . In the primary absolute-correlation analysis, persistent entropy of H 2 features showed a nominal pre/post decrease ( W = 4 . 0 , p = 0 . 027 , rank-biserial correlation = 0 . 822 ). This effect did not survive correction across the four tested homology dimensions ( q FDR = 0 . 109 ) and was not reproduced when signed correlations were preserved using d ( i , j ) = 1 − r ( i , j ) . Exploratory signal-complexity analyses using Lempel–Ziv complexity and sample entropy showed descriptive but statistically non-significant increases in temporal complexity. These results should therefore be interpreted as preliminary and hypothesis-generating, particularly given the small sample size, lack of placebo control, availability of only GSR-preprocessed connectivity data, and sensitivity to the distance definition. The study suggests that persistent homology may provide a useful framework for studying psychedelic-associated changes in the higher-dimensional topology induced by functional connectivity, but replication in larger placebo-controlled datasets is required.