Abstract
Background
Cannabis is one of the most widely used substances in the world, with usage trending
upward in recent years. However, although the psychiatric burden associated with maladaptive
cannabis use has been well established, reliable and interpretable biomarkers associated
with chronic use remain elusive. In this study, we combine large-scale functional
magnetic resonance imaging with machine learning and network analysis and develop
an interpretable decoding model that offers both accurate prediction and novel insights
into chronic cannabis use.
Methods
Chronic cannabis users (n = 166) and nonusing healthy control subjects (n = 124) completed a cue-elicited craving task during functional magnetic resonance
imaging. Linear machine learning methods were used to classify individuals into chronic
users and nonusers based on whole-brain functional connectivity. Network analysis
was used to identify the most predictive regions and communities.
Results
We obtained high (∼80% out-of-sample) accuracy across 4 different classification models,
demonstrating that task-evoked connectivity can successfully differentiate chronic
cannabis users from nonusers. We also identified key predictive regions implicating
motor, sensory, attention, and craving-related areas, as well as a core set of brain
networks that contributed to successful classification. The most predictive networks
also strongly correlated with cannabis craving within the chronic user group.
CONCLUSIONS
This novel approach produced a neural signature of chronic cannabis use that is both
accurate in terms of out-of-sample prediction and interpretable in terms of predictive
networks and their relation to cannabis craving.
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Article Info
Publication History
Published online: May 31, 2022
Accepted:
April 27,
2022
Received in revised form:
April 10,
2022
Received:
December 8,
2021
Publication stage
In Press Journal Pre-ProofFootnotes
KRK and MS contributed equally to this work.
Identification
Copyright
© 2022 Society of Biological Psychiatry. Published by Elsevier Inc. All rights reserved.
