Prediction of Persistent Drug-seeking Behavior by Brain-Wide Network
Poster No:
436
Submission Type:
Abstract Submission
Authors:
wenlei zhang1, Yuhan Cao2, Xiaocheng zhang3, Yansen Wang4, Rongwei Zhai3, Kan Ren2, Haifeng Jiang1, Zhi-Qi Xiong5, Min Zhao1
Institutions:
1Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China, 2ShanghaiTech University, Shanghai, China, 3Lingang Laboratory, Shanghai, China, 4Microsoft Research Asia, Shanghai, shanghai, 5CAS Center for Excellence in Brain Science and Intelligence Technology, Shanghai, China
First Author:
wenlei zhang
Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine
Shanghai, China
Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine
Shanghai, China
Co-Author(s):
Haifeng Jiang
Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine
Shanghai, China
Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine
Shanghai, China
Min Zhao
Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine
Shanghai, China
Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine
Shanghai, China
Introduction:
Substance use disorders (SUDs) are chronic conditions involving abnormal activity across multiple brain regions(Volkow & Blanco, 2023), characterized by persistent drug-seeking behavior (Lüscher et al., 2020). It remains unclear whether persistent drug-seeking behavior is encoded by single brain regions or by neural networks.
In this work, we aimed to compare the predictive performance of single brain regions versus networks for persistent drug-seeking behavior. Specifically, we simultaneously monitored 12 brain regions associated with drug-seeking behavior during methamphetamine self-administration over several months in rhesus monkeys. We employed a spatial relation decomposition (SRD) framework (Fang et al., 2023) to predict persistent drug-seeking.
In this work, we aimed to compare the predictive performance of single brain regions versus networks for persistent drug-seeking behavior. Specifically, we simultaneously monitored 12 brain regions associated with drug-seeking behavior during methamphetamine self-administration over several months in rhesus monkeys. We employed a spatial relation decomposition (SRD) framework (Fang et al., 2023) to predict persistent drug-seeking.
Methods:
6~7 depth electrodes (48~56 contacts) were surgically implanted in cortical/subcortical targets in each of the 4 rhesus monkeys with methamphetamine self-administration histories. These targets included orbitofrontal cortex (OFC), dorsolateral prefrontal cortex (DLPFC), ventromedial prefrontal cortex (VMPFC), anterior cingulate cortex (ACC), middle cingulate cortex (MCC), putamen (including three subregions), caudate, ventral striatum (VS), anterior hippocampus (AH), amygdala, posterior insula, and motor cortex.
We overall recorded 150 sessions during methamphetamine self-administration in 4 monkeys. Lever pressing during inter-trial intervals (ITIs) was employed to quantify drug-seeking behavior (Pascoli et al., 2015), with a primary focus on analyzing the first ten ITIs. The sliding window method (window length: 1s, step size: 0.5s) was applied to identify time windows containing pressing and non-pressing events.
A spatial relation decomposition (SRD) framework was employed to construct a model using data from all recording brain regions to differentiate the brain states associated with pressing and non-pressing events. Subsequently, ablation experiments were performed to infer the predictive performance of single brain regions by isolating single-region data. The predictive performance of single-region models was then compared with the multi-region model. All predictions were generated using 20 random seeds to evaluate the consistency and stability, and the Mann-Whitney U test was used to statistically compare the predictive performance between single-region and multi-region.
We overall recorded 150 sessions during methamphetamine self-administration in 4 monkeys. Lever pressing during inter-trial intervals (ITIs) was employed to quantify drug-seeking behavior (Pascoli et al., 2015), with a primary focus on analyzing the first ten ITIs. The sliding window method (window length: 1s, step size: 0.5s) was applied to identify time windows containing pressing and non-pressing events.
A spatial relation decomposition (SRD) framework was employed to construct a model using data from all recording brain regions to differentiate the brain states associated with pressing and non-pressing events. Subsequently, ablation experiments were performed to infer the predictive performance of single brain regions by isolating single-region data. The predictive performance of single-region models was then compared with the multi-region model. All predictions were generated using 20 random seeds to evaluate the consistency and stability, and the Mann-Whitney U test was used to statistically compare the predictive performance between single-region and multi-region.
Results:
Modeling results showed that the multi-region model effectively predicted persistent drug-seeking behavior, with a high and consistent area under the curve (AUC = 0.87) (Fig.1). In contrast, the predictive performance of single brain regions was generally poor and unstable, with most AUC values falling below 0.7 (Fig.1). Additionally, AUC values for individual regions were significantly lower than those achieved by the multi-region model (all p < 0.0000).
Conclusions:
Our preliminary results indicate that while many previously identified regions contained information regarding drug-seeking behavior, individual behavior across longitudinal timescales was only successfully decoded at the network level. Our findings suggest that for brain–computer interfaces in SUDs, identifying a long-term neural biomarker requires consideration of multiple distributed brain regions
Disorders of the Nervous System:
Psychiatric (eg. Depression, Anxiety, Schizophrenia) 1
Modeling and Analysis Methods:
Classification and Predictive Modeling 2
Connectivity (eg. functional, effective, structural)
Novel Imaging Acquisition Methods:
EEG
Keywords:
Addictions
Electroencephaolography (EEG)
ELECTROPHYSIOLOGY
Machine Learning
Modeling
Psychiatric Disorders
1|2Indicates the priority used for review
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Lüscher, C., Robbins, T. W., & Everitt, B. J. (2020). The transition to compulsion in addiction. Nature Reviews Neuroscience, 21(5), 247–263.
Pascoli, V., Terrier, J., Hiver, A., & Lüscher, C. (2015). Sufficiency of Mesolimbic Dopamine Neuron Stimulation for the Progression to Addiction. Neuron, 88(5), 1054–1066.
Volkow, N. D., & Blanco, C. (2023). Substance use disorders: A comprehensive update of classification, epidemiology, neurobiology, clinical aspects, treatment and prevention. World Psychiatry, 22(2), 203–229.