A setup for real-time fMRI Neurofeedback for Cannabis Craving: A Pilot Study
Poster No:
1897
Submission Type:
Abstract Submission
Authors:
Amir Hossein Dakhili1, Ethan Murphy2, Saampras Ganesan3, Govinda Poudel2, Anastasia Paloubis4, Sylvia Lin2, Bradford Moffat5, Andrew Zalesky6, Rebecca Glarin7, Chao Suo8, Valentina Lorenzetti9
Institutions:
1Australian Catholic University, Fitzroy, Victoria, 2Australian Catholic University, Melbourne, Victoria, 3University of Melbourne, Victoria, Australia, 4Australian Catholic University, Melbourne, vic, 5University of Melbourne, Melbourne, VIC, 6The University of Melbourne and Melbourne Health, Melbourne, VIC, 7University of Melbourne, Melbourne, Victoria, 8Monash University, Melbourne, vic, 9Australian Catholic University, Fitzroy, Australia
First Author:
Co-Author(s):
Introduction:
Real-time fMRI neurofeedback (NFB) is a novel technique that allows participants to receive immediate feedback from their brain function and consciously regulate it (Fede et al., 2020). This approach holds potential for treating psychopathologies such as substance use disorders (SUDs), which are underpinned by altered brain reward function, particularly during craving states (Martz et al., 2020). fMRI NFB focused on the anterior cingulate cortex (ACC) as a region of interest for feedback, given its involvement in the addiction neurocircuitry (Koob & Volkow, 2010), craving (Sehl et al., 2021) and craving regulation (Murphy et al., 2024). Most addiction studies use continuous fMRI NFB, where feedback is delivered immediately after brain activity is recorded (Martz et al., 2020). A key factor in continuous fMRI NFB is minimizing the delay between brain activity and feedback presentation. Shorter delays are essential for effective learning, as they strengthen the link between neural activity and behavioral outcomes, such as craving in SUDs (Dewiputri & Auer, 2013; Stoeckel et al., 2014).
This pilot study aimed to test the feasibility and optimization of a novel real-time fMRI NFB setup, utilizing an ultra-high field MRI scanner. Specifically, the study aims to minimize the feedback delay and ensure the precision of real-time feedback delivery and to enable personalized regulation of brain activity during craving in SUDs.
This pilot study aimed to test the feasibility and optimization of a novel real-time fMRI NFB setup, utilizing an ultra-high field MRI scanner. Specifically, the study aims to minimize the feedback delay and ensure the precision of real-time feedback delivery and to enable personalized regulation of brain activity during craving in SUDs.
Methods:
Seven healthy participants completed a total of nine runs of a real-time fMRI NFB protocol on a Siemens 7 Tesla scanner at the Melbourne Brain Centre Imaging Unit, The University of Melbourne. Each run includes 471 volumes, whereby each volume represented one repetition time (TR). Multi-band fMRI data (TE= 22ms, TR= 1000ms, Voxel size = 1.6 isotropic) was acquired (Moeller et al., 2010) and automatically transferred to the 'back-end' – which is workstation for real-time fMRI data analysis using Turbo-BrainVoyager software version 4.2. The target voxel of interest (VOI), defined as the 33% most activated voxels located within the ACC, was personalized based on a cue-induced craving fMRI task (i.e., functional localizer) to enable mapping of the craving neurocircuitry at the individual level. Confound VOI was used to measure and account for nuisance physiological effects. The 'front-end' was a NFB task script, developed using Psychtoolbox 3.0.19 in MATLAB 2023a. This front-end concurrently presents: (i) cannabis-related cues demonstrated to induce cannabis craving (Sehl et al., 2021), (ii) 1-to-10 visual analogue scale for participants to rate their subjective craving in real-time, (iii) feedback calculation in real-time using a GLM model with signal from the target and confound VOIs. The feedback values were presented as a thermometer-like brain-computer interface (BCI) with 20 levels, displayed to the participant on a screen inside the scanner. Higher scores reflected more efficient real-time regulation of brain activation.
Results:
Feedback details, timings, and delays for each TR were recorded and monitored in real-time. The current NFB processing setup allows for the feedback calculation within 218-2630 ms across all participants (except three outliers of 4600, 3610 and 3007 ms), with a mean delay of 985.21 ms (SD = 173.80 ms) for each TR in real-time. Also, there was no significant correlation between participants' real-time regulation score and feedback processing timing (ρ = -0.05).
Conclusions:
This technical setup enabled continuous feedback processing and delivery with a short delay. This delay also did not show a significant correlation with real-time regulation score, which indicates that there is no effect of the technical delay on real-time regulation of brain activation. This innovative setup may advance NFB as a tool for regulating craving-related brain activity in CUD and support the development of personalized interventions.
Emotion, Motivation and Social Neuroscience:
Reward and Punishment
Modeling and Analysis Methods:
Methods Development 2
Novel Imaging Acquisition Methods:
BOLD fMRI 1
Keywords:
Addictions
FUNCTIONAL MRI
Other - neurofeedback
1|2Indicates the priority used for review
·The components of a fMRI-based NFB experiment.
·Illustration of the delay of the NFB data processing setup.
Abstract Information
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Healthy subjects only or patients (note that patient studies may also involve healthy subjects):
Was this research conducted in the United States?
Were any human subjects research approved by the relevant Institutional Review Board or ethics panel? NOTE: Any human subjects studies without IRB approval will be automatically rejected.
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3. Koob, G. F., & Volkow, N. D. (2010). Neurocircuitry of addiction. Neuropsychopharmacology, 35(1), 217-238. https://doi.org/10.1038/npp.2009.110
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