AI Insight
Researchers developed a new mathematical method to analyze neural avalanches—bursts of coordinated brain activity—by combining optimal transport theory and dynamic time warping to compare events of different durations and spatial patterns. Applying this to 64-channel EEG data from 63 participants, they identified 12 recurring brain activity propagation patterns and discovered that psilocybin reduced oscillating sequences (patterns that alternate in polarity) relative to stable sequences (consistent polarity patterns). This shift was primarily driven by changes in one specific cluster, suggesting psilocybin affects concrete temporal neural dynamics.
Why it matters
This work provides a novel computational framework for tracking how specific brain activity patterns recur and change over time, which could help researchers better understand how psychedelics and other substances alter brain dynamics. The publicly available Python package enables other neuroscientists to apply these techniques to their own data, potentially advancing our understanding of consciousness, brain organization, and psychopharmacology.
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⚠️ Preprint – Noch nicht peer-reviewed
Dieser Artikel wurde noch nicht von unabhängigen Experten begutachtet. Die Ergebnisse sind vorläufig und sollten mit Vorsicht interpretiert werden.
Neural avalanches – or threshold-defined bursts of coordinated activity – are traditionally characterised by scale-free statistics. However the study of their detailed spatiotemporal structure and their recurrence within spontaneous activity has been hindered by the variability of avalanches in duration and spatial extent. To tackle this challenge, we propose the use of flexible alignment: we employ a distance metric combining unbalanced optimal transport with subsequence dynamic time warping, enabling comparison across events of different lengths and spatial configurations. Applied to 64-channel EEG from 63 participants in the PsiConnect psilocybin study, hierarchical clustering revealed 12 recurring propagation patterns. These cluster templates were then traced in the original continuous recordings, identifying sequences where the same pattern recurred consecutively and immediately at least twice. Sequences with alternating polarity were classified as oscillating; those with consistent polarity as stable. Oscillating sequences predominantly corresponded to clusters exhibiting visually confirmed spatial propagation, while stable sequences corresponded to spatially fixed patterns. Under psilocybin, oscillating sequences were reduced relative to stable sequences, shifting the polarity balance toward stable; this overall shift was confirmed by a subject-level permutation test, while the apparent task- and training-specific effects did not survive it. This effect was also mostly driven by a specific cluster, suggesting that there is concrete neural dynamics that are temporally affected by the consumption of psilocybin. The developed methodology has been implemented in a publicly available stppy Python package.