200. THE BIDIRECTIONAL CLAUSTRUM-ANTERIOR CINGULATE CORTEX CIRCUIT DIFFERENTIALLY REGULATES METHAMPHETAMINE TAKING AND SEEKING IN A RODENT SELF-ADMINISTRATION MODEL

Abstract Background Methamphetamine (METH) is a highly addictive psychostimulant with high relapse rates, imposing a substantial public health burden. METH addiction typically progresses from recreational use to compulsive drug-seeking behavior, persisting even after extended periods of abstinence. The anterior cingulate cortex (ACC) has been implicated in reward processing, motivation, and cue-reactivity. However, its precise role in drug-taking and drug-seeking behaviors remains unclear. Aims & Objectives The study aimed to elucidate the role of ACC circuitry in METH taking and cue-induced drug-seeking using a rodent self-administration model. Method Rats were trained to self-administer METH (0.05mg/kg/infusion) paired with light and tone cues, followed by tests assessing either drug-taking or cue-induced reinstatement. Saline-trained rats were served as controls. Brain-wide c-Fos mapping and machine learning was applied to unbiasedly investigate key brain regions involved in regulating both drug-taking and drug-seeking processes. Activity-dependent labeling was used to trace ACC ensembles activated during METH taking, enabling examination of their reactivation during cue-induced seeking. AAV-based designer receptor exclusively activated by designer drugs (DREADDs) was injected into the ACC and/or the claustrum (CLA) to manipulate specific bottom-up or top-down circuits. Results The combination of brain activation mapping and machine learning revealed that activation of the ACC and CLA serves as reliable indicators of METH drug-taking and cue-induced drug-seeking. Circuit-tracing experiments revealed the bidirectional projections between the ACC and CLA. Activity-dependent labeling showed that ACC ensembles activated during METH taking were partially reactivated during cue-induced seeking, with additional ACC neurons were recruited exclusively during reinstatement. Chemogenetic inhibition of the CLA-ACC glutamatergic circuit selectively attenuated METH taking, whereas inhibition of the ACC-CLA glutamatergic circuit selectively reduced cue-induced drug-seeking behavior. Discussion & Conclusions Our findings demonstrate that METH drug-taking and drug-seeking behaviors are differentially regulated by the bidirectional CLA-ACC circuit. This study provides novel insights into the neural circuitry mechanisms underlying reward processing during transition from binge drug-taking to compulsive drug-seeking, offering potential targets for therapeutic intervention.

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Journal
The International Journal of Neuropsychopharmacology
Published
2025-08-01
DOI
https://doi.org/10.1093/ijnp/pyaf052.178
Primary Topic
Memory and Neural Mechanisms
Type
article
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article

200. THE BIDIRECTIONAL CLAUSTRUM-ANTERIOR CINGULATE CORTEX CIRCUIT DIFFERENTIALLY REGULATES METHAMPHETAMINE TAKING AND SEEKING IN A RODENT SELF-ADMINISTRATION MODEL

Ming‐Shiang Wu, Hongbo Liu, Meng-Lung Lai, Wenhua Zhou et al.
The International Journal of Neuropsychopharmacology
Memory and Neural Mechanisms
article

200. THE BIDIRECTIONAL CLAUSTRUM-ANTERIOR CINGULATE CORTEX CIRCUIT DIFFERENTIALLY REGULATES METHAMPHETAMINE TAKING AND SEEKING IN A RODENT SELF-ADMINISTRATION MODEL

Ming‐Shiang Wu, Hongbo Liu, Meng-Lung Lai, Wenhua Zhou, Yingsong Zhou, Fudi Wang, S. Shi, Min Zhao, Yingjie Cheng
article en

Abstract

Abstract Background Methamphetamine (METH) is a highly addictive psychostimulant with high relapse rates, imposing a substantial public health burden. METH addiction typically progresses from recreational use to compulsive drug-seeking behavior, persisting even after extended periods of abstinence. The anterior cingulate cortex (ACC) has been implicated in reward processing, motivation, and cue-reactivity. However, its precise role in drug-taking and drug-seeking behaviors remains unclear. Aims & Objectives The study aimed to elucidate the role of ACC circuitry in METH taking and cue-induced drug-seeking using a rodent self-administration model. Method Rats were trained to self-administer METH (0.05mg/kg/infusion) paired with light and tone cues, followed by tests assessing either drug-taking or cue-induced reinstatement. Saline-trained rats were served as controls. Brain-wide c-Fos mapping and machine learning was applied to unbiasedly investigate key brain regions involved in regulating both drug-taking and drug-seeking processes. Activity-dependent labeling was used to trace ACC ensembles activated during METH taking, enabling examination of their reactivation during cue-induced seeking. AAV-based designer receptor exclusively activated by designer drugs (DREADDs) was injected into the ACC and/or the claustrum (CLA) to manipulate specific bottom-up or top-down circuits. Results The combination of brain activation mapping and machine learning revealed that activation of the ACC and CLA serves as reliable indicators of METH drug-taking and cue-induced drug-seeking. Circuit-tracing experiments revealed the bidirectional projections between the ACC and CLA. Activity-dependent labeling showed that ACC ensembles activated during METH taking were partially reactivated during cue-induced seeking, with additional ACC neurons were recruited exclusively during reinstatement. Chemogenetic inhibition of the CLA-ACC glutamatergic circuit selectively attenuated METH taking, whereas inhibition of the ACC-CLA glutamatergic circuit selectively reduced cue-induced drug-seeking behavior. Discussion & Conclusions Our findings demonstrate that METH drug-taking and drug-seeking behaviors are differentially regulated by the bidirectional CLA-ACC circuit. This study provides novel insights into the neural circuitry mechanisms underlying reward processing during transition from binge drug-taking to compulsive drug-seeking, offering potential targets for therapeutic intervention.

The International Journal of NeuropsychopharmacologyVol. 28(Supplement_2)
Shanghai Mental Health Center (CN), Ningbo Kangning Hospital (CN)
Openalex Percentile: Top 79%
Memory and Neural Mechanisms
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