Commonalities in whole-brain functional connectivity associated with the psychedelic state determined using machine learning techniques applied to fMRI experiments.

CARLA PALLAVICINI; FEDERICO ZAMBERLAN; MIRTA VILLARREAL; ROBIN CARHART-HARRIS; DAVID NUTT; ENZO TAGLIAZUCCHI

Córdoba

Congreso; XXXIII Reunión Anual de la la Sociedad Argentina de Investigación en Neurociencias; 2018

Classicpsychedelics (5-HT2A agonists)elicit profound transientmodifications in the consciousness of the self and the environment.Other substances that modulate the level and quality of consciouscontent andare of potential therapeutic value are entactogen psychedelics suchas MDMA andthe dissociative ketamine (NMDAantagonist).Despite variability in the elicited subjective effects, these drugsshare as a common motif the induction of a non-ordinary or alteredstate of consciousness. Wesearch for the commonalities and divergences between the changes inwhole-brain activity elicited by twoclassicpsychedelics:LSD &psilocybin; MDMA, ketamineand a control non-psychedelic drug: Modafinil (dopaminergicstimulant). fMRIdata acquired from different scanners was processed according to aunified standard. Thenwe computed the functional connectivity between all pairs of 90neuroanatomical regions and trained a random forest classifier toidentifythem from their associated placebo condition. We investigated whethera classifier trained using data from one drug could generalize todetect the changes in brain activity associated with all other drugs,and mapped in anatomical space the network of functional connectionsassociated with the successful generalization. Our results suggestthat the shared effects of psychedelics can be quantitativelymeasured using fMRI, bringing us closer to dissect the varieties ofthe psychedelic state and their associated neural correlates.p { margin-bottom: 0.1in; line-height: 120%; }