Bayesian causal network modeling suggests adolescent cannabis use accelerates prefrontal cortical thinning

Translational psychiatry

Transl Psychiatry. 2022 May 6;12(1):188. doi: 10.1038/s41398-022-01956-4.


While there is substantial evidence that cannabis use is associated with differences in human brain development, most of this evidence is correlational in nature. Bayesian causal network (BCN) modeling attempts to identify probable causal relationships in correlational data using conditional probabilities to estimate directional associations between a set of interrelated variables. In this study, we employed BCN modeling in 637 adolescents from the IMAGEN study who were cannabis naïve at age 14 to provide evidence that the accelerated prefrontal cortical thinning found previously in adolescent cannabis users by Albaugh et al. [1] is a result of cannabis use causally affecting neurodevelopment. BCNs incorporated data on cannabis use, prefrontal cortical thickness, and other factors related to both brain development and cannabis use, including demographics, psychopathology, childhood adversity, and other substance use. All BCN algorithms strongly suggested a directional relationship from adolescent cannabis use to accelerated cortical thinning. While BCN modeling alone does not prove a causal relationship, these results are consistent with a body of animal and human research suggesting that adolescent cannabis use adversely affects brain development.

PMID:35523763 | PMC:PMC9076659 | DOI:10.1038/s41398-022-01956-4


Max M Owens
Matthew D Albaugh
Nicholas Allgaier
Dekang Yuan
Gabriel Robert
Renata B Cupertino
Philip A Spechler
Anthony Juliano
Sage Hahn
Tobias Banaschewski
Arun L W Bokde
Sylvane Desrivières
Herta Flor
Antoine Grigis
Penny Gowland
Andreas Heinz
Rüdiger Brühl
Jean-Luc Martinot
Marie-Laure Paillère Martinot
Eric Artiges
Frauke Nees
Dimitri Papadopoulos Orfanos
Herve Lemaitre
Tomáš Paus
Luise Poustka
Sabina Millenet
Juliane H Fröhner
Michael N Smolka
Henrik Walter
Robert Whelan
Scott Mackey
Gunter Schumann
Hugh Garavan
IMAGEN Consortium