An international mega-analysis of psychedelic drug effects on brain circuit function | Manesh Girn | ResearchPod

Transcript: An international mega-analysis of psychedelic drug effects on brain circuit function

Sam: A mega-analysis of eleven independent fMRI datasets, published in Nature Medicine, points to one consistent signature of the psychedelic brain. It is increased functional connectivity between transmodal and unimodal networks, rather than a simple global disintegration.

Alex: That cuts against the prevailing narrative. The literature has been fragmented, with some groups reporting global connectivity increases and others the opposite. How does pooling the data resolve that?

Sam: Part of the ambiguity came from researcher degrees of freedom. Different preprocessing choices led to contradictory conclusions. Here a uniform pipeline was applied to all eleven datasets, and instead of null-hypothesis testing the authors used a Bayesian hierarchical model. Each study acts like a witness, and between-study variability is modeled as random effects. What survives is signal that holds up across studies and across drugs.

Alex: So it's a consensus filter. It weights evidence by consistency rather than just averaging. Which networks does it point to?

Sam: The most reliable signature is coupling between transmodal systems, like the default mode network, and unimodal systems, like visual cortex. Single-site reports suggested a broad breakdown. The pooled inference instead shows reductions that are weak and selective. So the earlier reports of global disintegration may partly reflect lab-specific processing.

Alex: That brings me to the preprocessing. The authors single out global signal regression as a major source of instability. Why that step in particular?

Sam: GSR is meant to remove global noise, like respiration and motion. But it also mathematically forces the mean correlation across the brain to zero. If psychedelics really do shift connectivity globally, GSR can induce negative correlations that aren't there. It scrubs part of the signal you're trying to measure.

Alex: Then you're stuck. Use it and you risk a false signal of disintegration. Skip it and you're left with physiological noise. How did they handle that?

Sam: They ran the entire Bayesian pipeline twice, with and without GSR, and compared the posterior distributions. That shows which effects depend on the denoising choice. GSR does shift the data systematically toward negative values. But the transmodal-unimodal coupling increase survives its inclusion.

Alex: I'd call that the load-bearing check. If the central effect had flipped with GSR, the whole claim would be a preprocessing artifact. What about the subcortical work? They highlight the striatum, the caudate and putamen.

Sam: Increased coupling between the striatum and unimodal cortex is described as a highly reliable feature. The authors read this in terms of the striatum's position on pathways linking cortex to the rest of the brain. On that reading, psychedelics don't just reorganize cortical networks. They also change how sensory areas connect to those pathways.

An international mega-analysis of psychedelic drug effects on brain circuit function

Manesh Girn

Apr 1, 2026·4:58·user_upload·Original Paper

Mega-analysisResting-state functional magnetic resonance imaging (rs-fMRI)Functional connectivityBayesian hierarchical modelingTransmodal and unimodal networksResting-state functional magnetic resonance imaging (rsfMRI)

Listen

5 min

Summary

Research Question and Approach

Psychedelic research has expanded rapidly, yet findings regarding the neural mechanisms of these drugs remain fragmented across independent, small-scale studies. To address this, the authors formed the BOLD Psychedelic Consortium, integrating 11 resting-state functional magnetic resonance imaging (rsfMRI) datasets from five countries. This mega-analysis included 267 participants and over 500 connectomes, covering four classic psychedelic classes: psilocybin, LSD, DMT (including ayahuasca), and mescaline. The researchers applied a uniform preprocessing pipeline and a Bayesian hierarchical modeling framework to quantify the consistency and magnitude of drug-induced changes in brain network connectivity, explicitly accounting for study- and drug-specific variability.

Main Findings

The study identifies a robust, cross-drug neural signature characterized by increased functional connectivity between transmodal association networks (such as the default mode and frontoparietal networks) and unimodal sensorimotor networks (visual and somatomotor). This finding suggests a flattening of the brain's intrinsic processing hierarchy under the influence of psychedelics. Additionally, the researchers observed selective increases in coupling between subcortical regions—specifically the caudate and putamen—and sensorimotor networks.

In contrast to previous reports, the Bayesian analysis revealed only weak-to-moderate and highly selective reductions in within-network functional connectivity. The authors argue that earlier claims of widespread network disintegration may have been overstated, as many within-network effects showed substantial variability and often failed to reach high-confidence levels in the Bayesian model.

Why It Matters

By synthesizing data across multiple research groups and drugs, this study provides a foundational, reproducible map of how psychedelics alter large-scale brain organization. It resolves inconsistencies in the existing literature by applying a rigorous Bayesian framework that prioritizes effect robustness over binary statistical thresholds. These results offer a clearer neurobiological basis for understanding the acute psychedelic experience and provide a benchmark for future clinical and neuroscientific investigations into psychedelic-assisted therapies.

Key Terms and Definitions

Mega-analysis — A statistical approach that integrates raw data from multiple independent studies into a single, unified analysis to increase sample size and statistical power.
Transmodal Networks — High-level brain networks, such as the default mode network, involved in abstract cognitive processing and integration rather than direct sensory input.
Unimodal Networks — Brain networks primarily responsible for processing specific sensory or motor information, such as the visual or somatomotor networks.
Bayesian Hierarchical Modeling — A statistical framework that estimates the full probability distribution of effects, allowing researchers to quantify both the magnitude of an effect and the uncertainty surrounding it across different groups.
Global Signal Regression (GSR) — A preprocessing technique used to remove global fluctuations from fMRI data, which can improve the specificity of connectivity estimates but may also remove meaningful neural signals.

Abstract

Psychedelic drugs are re-emerging as promising scientific and clinical tools. However, despite a rapidly expanding literature on their therapeutic value, the neural mechanisms underlying psychedelic effects remain unclear. Resting-state functional magnetic resonance imaging studies of acute psychedelic effects, conducted independently by several research groups, have so far yielded fragmented and sometimes inconsistent findings. Here, to help facilitate greater convergence, we conducted a 'mega-analysis' integrating 11 independent resting-state functional magnetic resonance imaging datasets across five psychedelic drugs (psilocybin, lysergic acid diethylamide, mescaline, N,N-dimethyltryptamine and ayahuasca) from research groups spanning three continents and five countries. By applying a uniform preprocessing pipeline and a Bayesian hierarchical modeling framework, we discovered several common features in the induced alterations to brain function across drugs and sites. Most prominently, we identified a core signature of increased functional connectivity between transmodal (default, frontoparietal and limbic) and unimodal networks (visual and somatomotor), with subnetwork specificity. Furthermore, key subcortical regions (thalamus, caudate and putamen) and the cerebellum exhibited altered coupling with sensorimotor networks. In contrast to several single-site reports, Bayesian modeling revealed weak-to-moderate and selective reductions in within-network functional connectivity, with substantial variability across drugs and networks. Together, these findings extend past work by demonstrating that psychedelics reconfigure large-scale cortical organization while selectively engaging subcortical circuitry. This study provides the most comprehensive synthesis of psychedelic brain action to date, helping resolve inconsistencies and offering a probabilistic map of how psychedelics alter large-scale brain organization. We hereby provide a cornerstone to benchmark and shepherd future psychedelic neuroimaging research.

An international mega-analysis of psychedelic drug effects on brain circuit function: Research Paper Podcast