Network Neuroscience
December 23, 2022
Gustavo Deco, Yonatan Sanz Perl, Laura de la Fuente et al.
43 citations
The default mode network (DMN) may coordinate the recruitment and scheduling of brain networks for solving cognitive tasks, supported by evidence that DMN regions are physically and functionally distant from sensorimotor areas. Using a thermodynamics-inspired, deep learning-based Temporal Evolution NETwork (TENET) framework to measure the 'arrow of time'—a marker of nonreversibility and hierarchy in brain signals—analysis of Human Connectome Project data from nearly a thousand participants suggests the DMN orchestrates hierarchy levels that shift between rest and seven cognitive tasks. This hierarchy differs significantly in health versus neuropsychiatric disorders, offering insights into brain dynamics for cognition.
Frontiers in Psychiatry
November 5, 2021
Enzo Tagliazucchi, Federico Zamberlán, Federico Cavanna et al.
23 citations
Inhaled DMT, a classic psychedelic, produces short but profound shifts in consciousness. In 35 healthy volunteers, electroencephalography recorded before and during the drug's acute effects in a natural setting showed marked reductions in alpha and beta brain oscillations and increases in delta, theta, and gamma power, particularly in posterior regions. The power of fronto-temporal theta oscillations inversely correlated with feelings of unity and transcendence—core features of mystical-type experiences. These findings suggest that baseline brain activity prior to psychedelic use may help predict the likelihood of such experiences, which are linked to lasting well-being and improved therapeutic outcomes.
bioRxiv (Cold Spring Harbor Laboratory)
February 22, 2022
Camila Sanz, Federico Cavanna, Stephanie Müller et al.
1 citation
preprint
Low doses of psilocybin (microdoses) can be detected in natural speech. In a double-blind, placebo-controlled experiment, participants given 0.5 g of psilocybin mushrooms showed significant differences in verbosity and sentiment scores compared to placebo, though semantic variability did not differ. Machine learning classifiers using these speech metrics distinguished between the psilocybin and placebo conditions with high accuracy (AUC≈0.8). These findings suggest that unconstrained natural language may serve as a practical, low-cost tool for monitoring microdosing effects, addressing limitations of existing questionnaires designed for larger psychedelic doses.