Alteration of Dynamic Brain States Associated with Abstaining from Alcohol
Poster No:
577
Submission Type:
Late-Breaking Abstract Submission
Authors:
Mohammadreza Khodaei1, Hope Peterson-Sockwell2, Paul Laurienti1, Sean Simpson1, Heather Shappell1
Institutions:
1Wake Forest University School of Medicine, Winston Salem, NC, 2University of North Carolina at Chapel Hill, Chapel Hill, NC
First Author:
Co-Author(s):
Introduction:
It is reported that approximately 70% of US adults are alcohol consumers, and more than 11% of adults are considered to have Alcohol Use Disorder (AUD). Although studies have shown that people with AUD usually do not tolerate abstinence, fewer studies have investigated the moderate-to-heavy drinkers' response to abstinence. In this resting state functional magnetic resonance imaging (fMRI) study, we examine changes in brain functional connectivity states among moderate-to-heavy drinkers during typical drinking and after a 3-day period of abstinence.
Methods:
The sample consisted of 38 moderate-to-heavy drinkers with an average age of 41 years and no history of AUD. Each participant completed two sessions of a resting state fMRI scan. Prior to one scanning session, participants drank according to their typical drinking routine, and prior to the other, they abstained from any alcohol consumption for three consecutive days. The fMRI data were preprocessed, and time-series signals for 51 regions, including regions in the default mode, salience, and executive control networks were extracted for each subject. The signals were fed to a recently developed MIND-Map toolbox, which utilizes a novel Hidden Semi Markov Model (HSMM) approach for studying dynamic brain functional connectivity (Khodaei, 2024; Shappell, 2019). The HSMM framework uses the individuals' time series data (across all brain regions of interest during an fMRI scan) to infer a series of repeating brain states, where each state is characterized by a unique connectivity structure that represents the overall interaction of brain regions. Not only does the modeling framework estimate the network states, but it also estimates the dynamics of switching between the states. For example, total time spent in each state (occupancy time) and transition probabilities between the states were calculated and compared for each of the two sessions.
Results:
The model identified 6 unique states that were dynamically occupied across the study sessions. In the abstinence condition, participants spent significantly less time in state 4 (P=0.023) while spending more time in others, including states 2 and 5. In addition, they had fewer transitions to state 3 (P=0.005) while having more transitions to other states, including states 1 and 5. State 3 demonstrated the highest activation of the salience network when compared to other networks. State 4 illustrated high activation in DMN and the division of some parts of posterior medial DMN and CEN into a single module. In addition, state 5 showed the highest posterior DMN activity and lowest anterior DMN activity compared to the other states. Figure 1 illustrates each network's average of normalized BOLD signal and segregated modules for states 3 and 4.
Conclusions:
Our study suggests that during abstinence, participants transition less to state 3, where activation in regions within the salience network is very high. This network is commonly believed to direct attention to internal or external experiences depending on the ascribed importance, or salience, of the stimuli. Thus, state 3 may serve as a mediatory state that appears to be diminished in abstinent individuals. Our findings also indicate that during typical drinking conditions, participants spend more time in state 4, where the DMN exhibits high activity, and the brain network is highly modular with strong connections. However, during abstinence, they tend to spend more time in other states, specifically state 5, which is characterized by higher activity of the posterior part and lower activity of the anterior part. This shift can enhance self-referential thinking under abstinence and enhance alcohol cravings.
There is growing interest in neuromodulatory techniques, such as transcranial magnetic stimulation, for treating AUD. However, optimal targets for these techniques are unknown. Our results suggest future work could target the SN and DMN to modulate the dynamics of these circuits to suppress alcohol cravings.
There is growing interest in neuromodulatory techniques, such as transcranial magnetic stimulation, for treating AUD. However, optimal targets for these techniques are unknown. Our results suggest future work could target the SN and DMN to modulate the dynamics of these circuits to suppress alcohol cravings.
Disorders of the Nervous System:
Psychiatric (eg. Depression, Anxiety, Schizophrenia) 1
Modeling and Analysis Methods:
fMRI Connectivity and Network Modeling 2
Keywords:
Addictions
Data analysis
Psychiatric
Other - Brain Networks, Alcohol Use Disorder
1|2Indicates the priority used for review
Abstract Information
By submitting your proposal, you grant permission for the Organization for Human Brain Mapping (OHBM) to distribute your work in any format, including video, audio print and electronic text through OHBM OnDemand, social media channels, the OHBM website, or other electronic publications and media.
The Open Science Special Interest Group (OSSIG) is introducing a reproducibility challenge for OHBM 2025. This new initiative aims to enhance the reproducibility of scientific results and foster collaborations between labs. Teams will consist of a “source” party and a “reproducing” party, and will be evaluated on the success of their replication, the openness of the source work, and additional deliverables. Click here for more information. Propose your OHBM abstract(s) as source work for future OHBM meetings by selecting one of the following options:
Please indicate below if your study was a "resting state" or "task-activation” study.
Healthy subjects only or patients (note that patient studies may also involve healthy subjects):
Was this research conducted in the United States?
Are you Internal Review Board (IRB) certified? Please note: Failure to have IRB, if applicable will lead to automatic rejection of abstract.
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.
Were any animal research approved by the relevant IACUC or other animal research panel? NOTE: Any animal studies without IACUC approval will be automatically rejected.
Please indicate which methods were used in your research:
For human MRI, what field strength scanner do you use?
Which processing packages did you use for your study?
Provide references using APA citation style.
Shappell, H., et al. (2019). Improved state change estimation in dynamic functional connectivity using hidden semi-Markov models. NeuroImage, 191, 243-257.