Information parity increases on functional brain networks under influence of a psychedelic substance

The dataset comprised seven right-handed adult volunteers (age range 24–47 years, mean ≈31.3 years) who were scanned twice: once before and once about 40 minutes after ingesting ayahuasca. Participants had no current psychiatric or neurological disorders and were abstinent from alcohol, caffeine, nicotine and medications for at least three months. Each volunteer consumed 120–200 ml (≈2.2 ml kg^-1 body weight) of ayahuasca containing 0.8 mg ml^-1 DMT and 0.21 mg ml^-1 harmine. During both scans subjects were in awake resting state with eyes closed. Functional MRI preprocessing included slice-timing and head-motion correction, spatial smoothing (Gaussian kernel, FWHM = 5 mm) and spatial normalisation to MNI152. Nine regressors were included in a general linear model: six motion regressors, white-matter signal, cerebrospinal fluid signal and global signal. Time series were extracted from brain regions defined by the Harvard–Oxford atlas, yielding 110 regions initially; six regions were excluded for technical reasons, producing 104 nodes for analysis. The regional time series were band-pass filtered (≈0.03–0.07 Hz) via a maximum overlap wavelet transform. Pairwise Pearson correlations between wavelet coefficients produced a 104 × 104 correlation matrix for each scan. Adjacency matrices for unweighted, undirected networks were constructed by thresholding absolute correlation values. Because network measures depend on density, the authors evaluated a range of thresholds that produced connected, small-world networks whose local and global efficiencies satisfied specified criteria; networks analysed had average degrees in the range 24 ⩽ ⟨k⟩ ⩽ 39. Comparisons before versus after ayahuasca were made at matched network densities. Information parity is defined using the distribution p_i(r) of nodes at distance r from node i (where distance is shortest-path, or geodesic, length) and the joint distribution p_ij(r) of nodes at distance r from both nodes i and j. The measure quantifies the shared information in the pairwise topological profiles across neighbourhood radii up to r_max. The paper refers readers to supplementary material for simple network examples and further formal detail.

Across the evaluated threshold range and for every subject, average information parity on functional networks increased after ayahuasca intake. The authors compared networks before and after ingestion at matched average degree values (24 ⩽ ⟨k⟩ ⩽ 39) and found positive differences (ΔIP = IP_after − IP_before) independent of network density. Although the mean increase was consistent across subjects, the specific pattern of which region-to-region pairs changed varied substantially between individuals, and the authors advise caution against over-interpreting single-link changes. Focusing on anatomically guided region clusters, the investigators examined information parity between frontal cortex and limbic regions (including hippocampus and amygdala), between limbic and posterior cortical regions, and between frontal and posterior regions as a control. The only change that was consistent across all participants was an increase in information parity between frontal and limbic regions. By contrast, limbic–posterior changes were heterogeneous across subjects. The frontal–limbic increase persisted across the range of network densities examined and was reported as statistically significant by a two-sided t-test. Visualisations provided in the paper illustrate denser patterns of pairwise information parity after ayahuasca for example networks (for instance at ⟨k⟩ = 28), and a plot of information parity averaged across individuals shows the effect across thresholds. The supplementary material is cited as containing complementary regional details and examples.

Viol and colleagues interpret the observed increase in average information parity as an increase in pairwise topological redundancy: although earlier studies indicate that psychedelics increase entropy and flexibility of brain activity, the present results suggest that, at the level of pairs of nodes, the topological profiles become more similar under ayahuasca. The authors relate this to prior reports of increased network segregation after ayahuasca—manifest as higher local efficiency, clustering and geodesic distances, and lower global efficiency—and to increased geodesic entropy, which indicates more diverse individual node influences. Within that context, a rise in information parity could represent a coordinated increase in redundancy that helps preserve functional resilience even as constraints on activity are loosened. A key interpretive point is the consistent frontal–limbic increase in information parity. The frontal cortex is commonly associated with top-down modulation, while limbic structures subserve memory and emotional processes. The authors suggest that a flattening of functional hierarchy under psychedelics—previously reported as reduced transfer entropy from frontal regions—would make frontal and limbic regions exert more comparable statistical influences on the rest of the brain, which aligns with their findings. The study’s limitations are acknowledged. The sample size is small (seven participants), there was no placebo control, region clusters were chosen a priori rather than by systematic modular analysis, and no detailed per-region statistical mapping was performed to avoid over-interpretation given the limited data. The authors therefore recommend replication in larger samples and extension to other altered states of consciousness. They conclude that the information-parity framework complements existing network analyses and can provide insights into the neural organisation of non-ordinary conscious states.