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CannChange: a protocol for a feasibility study using fMRI-based neurofeedback to change the neurobiology of craving
Ethan Murphy1, Amir Hossein Dakhili1, Saampras Ganesan2,3,4,5
1Neuroscience of Addiction and Mental Health Program, Healthy Brain and Mind Research Centre, School of Behavioral and Health Sciences, Faculty of Health, Australian Catholic University, Fitzroy, Victoria, Australia.
Introduction:
Cannabis use disorder (CUD) affects ∼33 million people globally and can be underscored by intense cravings to use cannabis, which can trigger compulsive use and relapse. Functional MRI (fMRI) evidence demonstrates hyperactivity of addiction brain pathways during cannabis cue-reactivity, consistent with prominent neuroscientific theories of addiction, particularly within the anterior cingulate cortex (ACC). The ACC also emerges as a key region of real-time fMRI-based neurofeedback (fMRI-neurofeedback) studies demonstrating voluntary changes during cravings in persons who use substances. However, this notion is untested in CUD.
Methods And Analysis:
We aim to develop a protocol that tests the feasibility of fMRI-neurofeedback to enable persons with a moderate-to-severe CUD to increase the activity of the ACC during cannabis-induced craving to provide mechanistic insights on treatment targets; and decrease ACC activity during cue-induced craving to pave the way for reducing brain reactivity. The primary outcome measure is the change in ACC activity during fMRI-neurofeedback compared with a non-regulation condition.
Ethics And Dissemination:
This feasibility study has been approved by the Human Research Ethics Committee of Australian Catholic University. On completion, the findings from this study will be published in academic journals, presented at conferences and disseminated to clinicians and to individuals who use cannabis. The results from this feasibility study have the potential to inform the conduct of powered trials to examine how fMRI-neurofeedback can identify and reduce craving-related brain dysfunction in CUD.
