Poster No:
19
Submission Type:
Abstract Submission
Authors:
Shiyan Qu1, Haifeng Jiang2
Institutions:
1Shanghai Jiaotong University, Shanghai, Shanghai, 2Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, Shanghai
First Author:
Shiyan Qu
Shanghai Jiaotong University
Shanghai, Shanghai
Co-Author:
Haifeng Jiang
Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine
Shanghai, Shanghai
Introduction:
Methamphetamine (MA) addiction is a significant public health issue in China and globally, resulting in severe cognitive impairments and high relapse rates. This disorder is characterized by dysfunction in the orbitofrontal cortex-nucleus accumbens pathway, reflecting altered decision-making and reinforcement learning mechanisms.
Methods:
This study employed a case-control design to investigate the impact of methamphetamine on the reward feedback phase and its underlying neural mechanisms, utilizing a monetary reinforcement learning task and EEG data from 81 subjects (27 mild addicts, 27 moderately severe addicts, and 27 healthy controls). A computational model analyzed behavioral data, and feedback-related negativity (FRN) in event-related potentials (ERPs) was isolated for analysis. In the second phase, a randomized controlled trial was conducted, applying theta-gamma peak-coupled transcranial alternating current stimulation (tACS) to the left frontal lobe for 30 minutes per day, lasting for five days in 32 moderate to severe MA addicts (16 real stimulation, 16 sham), and the effect was evaluated by the task.
Results:
In the behavior model, the Beta value which indicate the sensitivity to expected reward values during reward trials was significantly higher in the moderate to severe group compared to healthy controls (F=6.151, p=0.0443), and positively correlated with addiction symptoms (r=0.289, p=0.0344). FRN showed lower occipital peak values (cluster t=329.340, p=0.00280) and higher left frontal peak values (cluster t=238.775, p=0.0196) for moderate to severe addicts. Source analysis indicated involvement of the frontoparietal cortex (cluster t=149.195, p<0.001). Time-frequency analysis revealed significantly higher theta band energy in the moderate to severe group (frontal cluster t=403.166, p<0.001; occipital cluster t=68.595, p=0.0236). And after tACS, the real stimulation group showed significantly higher post-stimulus Beta values during punishment trials (Estimate=2.548, P=0.039) compared to the sham group.
Conclusions:
This study examined the neural characteristics of individuals with moderate to severe MA addiction and implemented a tACS intervention system. The results indicate that the impairment in reward learning associated with MA dependency is reflected in the compulsive behaviors exhibited by severely addicted individuals under positive conditions. Enhanced theta band activity in the prefrontal cortex serves as the neural basis for these behaviors, and theta-gamma coupled tACS has the potential to ameliorate these characteristic impairments.
Brain Stimulation:
Non-invasive Electrical/tDCS/tACS/tRNS 1
Emotion, Motivation and Social Neuroscience:
Reward and Punishment 2
Higher Cognitive Functions:
Executive Function, Cognitive Control and Decision Making
Modeling and Analysis Methods:
EEG/MEG Modeling and Analysis
Novel Imaging Acquisition Methods:
EEG
Keywords:
Addictions
Electroencephaolography (EEG)
ELECTROPHYSIOLOGY
1|2Indicates the priority used for review
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Please indicate below if your study was a "resting state" or "task-activation” study.
Task-activation
Healthy subjects only or patients (note that patient studies may also involve healthy subjects):
Patients
Was this research conducted in the United States?
No
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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Were any animal research approved by the relevant IACUC or other animal research panel?
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Not applicable
Please indicate which methods were used in your research:
EEG/ERP
Neurophysiology
Behavior
Neuropsychological testing
Computational modeling
Provide references using APA citation style.
Everitt, B. J. (2005). Neural systems of reinforcement for drug addiction: From actions to habits to compulsion. Nature Neuroscience, 8(11), 1481-1489.
Hyman, S. E. (2006). Neural mechanisms of addiction: The role of reward-related learning and memory. Annual Review of Neuroscience, 29, 565-598.
Shukla, M. (2021). Methamphetamine abuse disturbs the dopaminergic system to impair hippocampal-based learning and memory: An overview of animal and human investigations. Neuroscience & Biobehavioral Reviews, 131, 541-559.
Rumpf, J.-J. (2017). Structural abnormality of substantia nigra induced by methamphetamine abuse. Mov Disord, 32(12), 1784-1788.
Homer, B. D., et al. (2008). Methamphetamine abuse and impairment of social functioning: A review of the underlying neurophysiological causes and behavioral implications. Psychological Bulletin, 134(3), 301-310.
Lüscher, C. (2020). The transition to compulsion in addiction. Nature Reviews Neuroscience, 21, 247-263.
Groman, S. M. (2022). Reinforcement learning detuned in addiction: Integrative and translational approaches. Trends in Neuroscience, 45, 96-105.
Sutton, R. S., & Barto, A. G. (2018). Reinforcement Learning, Second Edition: An Introduction. MIT Press.
Delgado, M. R., Phelps, E. A., & Robbins, T. W. (2011). Decision Making, Affect, and Learning: Attention and Performance XXIII. OUP Oxford.
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