ABSTRACT
Background
Cannabis is one of the most widely used substances in the world, with usage trending
upward in recent years. However, though 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 (fMRI) 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 non-using healthy controls (n=124) completed a
cue-elicited craving task during fMRI. Linear machine learning methods were used to
classify individuals into chronic users and non-users 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 four different classification
models, demonstrating that task-evoked connectivity can successfully differentiate
chronic cannabis users from non-users. We also identified key predictive regions implicating
motor, sensory, attention and craving-related areas, as well as a core set of brain
networks that contribute to successful classification. Importantly, the most predictive
networks also strongly correlated with cannabis craving within the chronic user group.
Conclusion
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.
To read this article in full you will need to make a payment
Purchase one-time access:
Academic & Personal: 24 hour online accessCorporate R&D Professionals: 24 hour online accessOne-time access price info
- For academic or personal research use, select 'Academic and Personal'
- For corporate R&D use, select 'Corporate R&D Professionals'
Subscribe:
Subscribe to Biological Psychiatry: Cognitive Neuroscience and NeuroimagingAlready a print subscriber? Claim online access
Already an online subscriber? Sign in
Register: Create an account
Institutional Access: Sign in to ScienceDirect
REFERENCES
- Cannabis use and cannabis use disorder.Nat Rev Dis Primer. 2021; 7: 1-24
- Prevalence and Correlates of DSM-5 Cannabis Use Disorder, 2012-2013: Findings from the National Epidemiologic Survey on Alcohol and Related Conditions–III.Am J Psychiatry. 2016; 173: 588-599
- Early evidence of the impact of cannabis legalization on cannabis use, cannabis use disorder, and the use of other substances: Findings from state policy evaluations.Am J Drug Alcohol Abuse. 2019; 45: 644-663
- Cannabis withdrawal in the United States: a general population study.J Clin Psychiatry. 2008; 69: 1354-1363
- Neurobiology of addiction: a neurocircuitry analysis.Lancet Psychiatry. 2016; 3: 760-773
- The effects of adolescent cannabis use on educational attainment: a review.Addiction. 2000; 95: 1621-1630
- Unemployment and Substance Outcomes in the United States 2002-2010.Drug Alcohol Depend. 2014; 0: 350-353
- Persistent cannabis users show neuropsychological decline from childhood to midlife.Proc Natl Acad Sci U S A. 2012; 109: E2657-E2664
- Applications of machine learning in addiction studies: A systematic review.Psychiatry Res. 2019; 275: 53-60
- Resting-state connectivity biomarkers define neurophysiological subtypes of depression.Nat Med. 2017; 23: 28-38
- An fMRI-Based Neurologic Signature of Physical Pain.N Engl J Med. 2013; 368: 1388-1397
- Towards a brain-based predictome of mental illness.Hum Brain Mapp. 2020; 41: 3468-3535
- Neuroimaging-based Individualized Prediction of Cognition and Behavior for Mental Disorders and Health: Methods and Promises.Biol Psychiatry. 2020; 88: 818-828
- Building better biomarkers: brain models in translational neuroimaging [no. 3].Nat Neurosci. 2017; 20: 365-377
- Toward a unified framework for interpreting machine-learning models in neuroimaging [no. 4].Nat Protoc. 2020; 15: 1399-1435
- Classification and Prediction of Brain Disorders Using Functional Connectivity: Promising but Challenging.Front Neurosci. 2018; 12https://doi.org/10.3389/fnins.2018.00525
- Marijuana craving in the brain.Proc Natl Acad Sci. 2009; 106: 13016-13021
- fMRI study of neural sensitization to hedonic stimuli in long-term, daily cannabis users.Hum Brain Mapp. 2016; 37: 3431-3443
- The clinical significance of drug craving.Ann N Y Acad Sci. 2012; 1248: 1-17
- Craving cannabis: a meta-analysis of self-report and psychophysiological cue—reactivity studies.Addiction. 2016; 111 (1923–1934)
- Marijuana Craving Questionnaire: development and initial validation of a self-report instrument.Addiction. 2001; 96: 1023-1034
- Marijuana withdrawal among adults seeking treatment for marijuana dependence.Addiction. 1999; 94: 1311-1322
- Comparison of extended versus brief treatments for marijuana use.J Consult Clin Psychol. 2000; 68: 898-908
First MB, Williams JBW, Benjamin LS, Spitzer RL (2016): SCID-5-SPQ: Structured Clinical Interview for DSM-5 Screening Personality Questionnaire: Designed to Be Used as a Screener for the Structured Clinical Interview for DSM-5 Personality Disorders (SCID-5-PD). Arlington, VA: American Psychiatric Assoication Publishing.
- A methodological checklist for fMRI drug cue reactivity studies: development and expert consensus [no. 3].Nat Protoc. 2022; 17: 567-595
- fMRIPrep: a robust preprocessing pipeline for functional MRI [no. 1].Nat Methods. 2019; 16: 111-116
- Methods and software for fMRI analysis of clinical subjects.Neuroimage. 2009; 47: S58
- Decoding Subject-Driven Cognitive States with Whole-Brain Connectivity Patterns.Cereb Cortex. 2012; 22: 158-165
Alpaydin E (2021): Machine Learning, Revised And Updated Edition. The MIT Press. Retrieved May 16, 2021, from https://mitpress.mit.edu/books/machine-learning-revised-and-updated-edition
- Machine learning for neuroimaging with scikit-learn.Front Neuroinformatics. 2014; 8https://doi.org/10.3389/fninf.2014.00014
Pedregosa F, Varoquaux G (2011): Scikit-learn: Machine Learning in Python. J Mach … 12: 2825–2830.
- Complex brain networks: graph theoretical analysis of structural and functional systems [no. 3].Nat Rev Neurosci. 2009; 10: 186-198
- Brain Graphs: Graphical Models of the Human Brain Connectome.Annu Rev Clin Psychol. 2011; 7: 113-140
- Complex network measures of brain connectivity: Uses and interpretations.NeuroImage. 2010; 52: 1059-1069
- The Problem of Thresholding in Small-World Network Analysis.PLOS ONE. 2013; 8e53199
- Combining Multiple Resting-State fMRI Features during Classification: Optimized Frameworks and Their Application to Nicotine Addiction.Front Hum Neurosci. 2017; 11https://doi.org/10.3389/fnhum.2017.00362
- Classifying smoking urges via machine learning.Comput Methods Programs Biomed. 2016; 137: 203-213
- Machine learning classification of resting state functional connectivity predicts smoking status.Front Hum Neurosci. 2014; 8https://doi.org/10.3389/fnhum.2014.00425
- Classification of cocaine-dependent participants with dynamic functional connectivity from functional magnetic resonance imaging data.J Neurosci Res. 2019; 97: 790-803
- Successful classification of cocaine dependence using brain imaging: a generalizable machine learning approach.BMC Bioinformatics. 2016; 17: 357
- Evaluating effects of methylphenidate on brain activity in cocaine addiction: a machine-learning approach.Medical Imaging 2016: Biomedical Applications in Molecular, Structural, and Functional Imaging. 2016; (vol. 9788 9788: 97880O)
- Resting state functional magnetic resonance imaging reveals distinct brain activity in heavy cannabis users – a multi-voxel pattern analysis.J Psychopharmacol (Oxf). 2014; 28: 1030-1040
- How do substance use disorders compare to other psychiatric conditions on structural brain abnormalities? A cross-disorder meta-analytic comparison using the ENIGMA consortium findings.Hum Brain Mapp. 2022; 43: 399-413
- Associations Between Prenatal Cannabis Exposure and Childhood Outcomes: Results From the ABCD Study.JAMA Psychiatry. 2021; 78: 64-76
- Risk-taking in humans and the medial orbitofrontal cortex reward system.NeuroImage. 2022; 249118893
- Sensory and motor aspects of addiction.Behav Brain Res. 2010; 207: 215-222
- A role of right middle frontal gyrus in reorienting of attention: a case study.Front Syst Neurosci. 2015; 9https://doi.org/10.3389/fnsys.2015.00023
- Cannabinoid receptor CNR1 expression and DNA methylation in human prefrontal cortex, hippocampus and caudate in brain development and schizophrenia [no. 1].Transl Psychiatry. 2020; 10: 1-13
- Anterior cingulate cortex hypoactivations to an emotionally salient task in cocaine addiction.Proc Natl Acad Sci. 2009; 106: 9453-9458
- Prefrontal–striatal pathway underlies cognitive regulation of craving.Proc Natl Acad Sci. 2010; 107: 14811-14816
- Altered affective response in marijuana smokers: An FMRI study.Drug Alcohol Depend. 2009; 105: 139-153
- Abstinent adolescent marijuana users show altered fMRI response during spatial working memory.Psychiatry Res Neuroimaging. 2008; 163: 40-51
- The hyper-sentient addict: an exteroception model of addiction.Am J Drug Alcohol Abuse. 2015; 41: 374-381
- Differential roles of medial prefrontal subregions in the regulation of drug seeking.Brain Res. 2015; 1628: 130-146
Article Info
Publication History
Accepted:
April 27,
2022
Received in revised form:
April 10,
2022
Received:
December 8,
2021
Publication stage
In Press Journal Pre-ProofFootnotes
DATA AND CODE AVAILABILITY
All the code related to analyses in this study is publicly available at https://github.com/kulkarnik/mj_classifier. The deidentified, parcellated data used for classification are available in the same repository.
AUTHOR CONTRIBUTIONS
K.K., M.S., and X.G. conceptualized the study. K.K. and M.S. designed the predictive-explanatory modeling framework, carried out the implementation, and analyzed the data. G.P. provided feedback on the modeling framework. V.C., F.F., K.H., G.P., D.S., and X.G. contributed to the interpretation of the results. K.K. and M.S. wrote the manuscript with critical feedback from all authors. F.F., K.H. and V.C. collected and organized the data. X.G. supervised the project.
Identification
Copyright
© 2022 Society of Biological Psychiatry. Published by Elsevier Inc. All rights reserved.
