Adrián Ponce‐Alvarez

The paper investigates how excitation-inhibition balance in zebrafish larvae's optic tectum influences neuronal avalanche dynamics, finding critical behavior at balanced or slightly excitatory ratios.

The paper introduces FDTest, a method to detect memory effects in brain dynamics by violating the Fluctuation-Dissipation Theorem, showing stronger memory in wakefulness than sleep.

This paper investigates the long-term effects of psilocybin on fronto-striatal-thalamic circuit dynamics and connectivity using longitudinal fMRI data and computational modeling.

This study uses Ising models and algorithmic complexity to analyze fMRI data and finds LSD increases brain complexity and moves it further from criticality.

This study examines how brain dynamics change during low-level states of consciousness, finding reduced stability in brain hubs and decreased heterogeneity in synchronization patterns.

The paper identifies circuit mechanisms by which catecholamines and acetylcholine modulate cortex-wide network interactions and exploration behavior in humans.

This study uses fMRI to analyze brain activity in monkeys during wakefulness and anesthesia, identifying macroscopic properties and network changes associated with loss of consciousness.

This paper uses a whole-brain computational model to simulate perturbations under LSD and finds increased dynamical complexity and recovery time, with strongest effects in resting state networks.

The paper shows cortical state transitions are governed by stiff parameters and stimulus responses by sloppy parameters in anesthetized rats.

This study identifies a distinct functional connectivity mode during movie-watching that reflects individual differences and is stable over time.

This study examines how fluctuations in functional connectivity and BOLD variance relate to dynamic changes in resting state networks, finding that these fluctuations are not random but reflect non-stationary processes.

This study uses tensor decomposition and a mean field model to show stationary dynamics can explain resting state networks in both empirical and simulated data.