%61%64%64%69%63%74%69%6f%6e
-
Secret to Drug Addiction Relapse Found: Brain's Addiction Circuit Identified
<(From Left) Dr. Minju Jeong,(UCSD), Prof. Byung Kook Lim (UCSD), Prof. Se-Bum Paik (KAIST)>
Drug addiction carries an extremely high risk of relapse, as cravings can be reignited by minor stimuli even long after one has stopped using. Previously, this phenomenon was attributed to a decline in the function of the prefrontal cortex (PFC), which regulates impulses. However, a joint international research team has recently revealed that the cause of addiction relapse is not a simple decline in brain function, but rather an imbalance in specific neural circuits.
KAIST announced on March 9th that a research team led by Prof. Se-Bum Paik from the Department of Brain and Cognitive Sciences and Prof. Byung Kook Lim from the University of California, San Diego (UCSD) has identified the core principle by which specific inhibitory neurons in the prefrontal cortex regulate cocaine-seeking behavior.
In particular, the research team focused on parvalbumin-positive (PV) inhibitory neurons, which regulate the balance of neural signals by suppressing the activity of other neurons in the brain. They confirmed that these cells act as a "brake gate" that controls excitatory signals in the brain and serve as a crucial factor in determining drug-seeking behavior that emerges after withdrawal.
The prefrontal cortex (PFC) of our brain can properly perform its "braking" function to suppress impulses when excitatory and inhibitory signals are in balance. To investigate how chronic drug exposure disrupts this balance, the research team conducted cocaine administration experiments on mice. During this process, they tracked when inhibitory neurons in the PFC were activated and how they sent signals to downstream brain regions.
The experimental results showed that parvalbumin (PV) cells, which account for about 60-70% of the inhibitory neurons in the PFC, were highly active when the mice attempted to seek cocaine. However, when "extinction training"—training to stop seeking the drug—was conducted, the activity of these cells significantly decreased. This demonstrates that the activity patterns of PV cells are not permanently fixed by addiction but can be readjusted through the extinction process.
<Figure 1. Experimental design illustrating cocaine self-administration and longitudinal tracking of prefrontal cortical neural activity during cocaine-seeking behavior>
The research team confirmed that artificially suppressing PV cell activity significantly reduced cocaine-seeking behavior in mice. Conversely, activating these cells caused the drug-seeking behavior to persist even after the extinction process. This effect was specifically observed in drug-addiction behavior and did not appear with general rewards like sugar water. Furthermore, this phenomenon was not observed in somatostatin (SOM) cells—another type of inhibitory neuron—indicating that PV cells selectively regulate drug addiction behavior.
<Figure 2. Comparison of single-neuron activity, population activity patterns, and behavioral modulation of prefrontal inhibitory neurons across different stages of cocaine-seeking behavior>
The team also identified the specific brain circuit through which these PV cells operate. Signals originating from the prefrontal cortex are transmitted to the reward circuit of the Ventral Tegmental Area (VTA), a key brain region related to reward. This pathway emerged as the central channel for regulating addiction behavior, determining whether or not to seek the drug again. In this process, PV neurons act as a "regulatory switch," controlling the flow of signals to influence dopamine signaling and deciding whether to maintain or suppress addictive behavior.
In short, the study revealed that addiction relapse is not due to an overall functional decline of the prefrontal cortex, but is determined by whether PV neurons regulate the neural pathway connecting the PFC to the reward circuit.
<Figure 3. Schematic illustrating the prefrontal–reward circuit mechanism that determines drug-seeking behavior>
Prof. Se-Bum Paik stated, "This research shows that drug addiction is a circuit-level problem arising from a collapse in the regulatory balance of specific neurons and downstream neural circuits. The discovery that parvalbumin (PV) cells act as a 'gate' for addictive behavior will provide a crucial lead for developing precision-targeted treatment strategies in the future."
This study was led by Dr. Minju Jeong (UCSD) as the first author, with Prof. Byung Kook Lim (UCSD) and Prof. Se-Bum Paik (KAIST) serving as co-corresponding authors. The findings were published online on February 26 in Neuron, a premier journal in the field of neuroscience.
Paper Title: Distinct Interneuronal Dynamics Selectively Gate Target-Specific Cortical Projections in Drug Seeking
DOI: 10.1016/j.neuron.2026.01.002
Full Author List: Minju Jeong, Seungdae Baek, Qingdi Wang, Li Yao, Eun Ji Lee, Arturo Marroquin Rivera, Joann Jocelynn Lee, Hyeonseok Jang, Dhananjay Bambah-Mukku, Christine Hyun-Seung Mun, Tyler Boesen, Sumit Nanda, Cheol Ryong Ku, Hong-wei Dong, Benoit Labonté, Se-Bum Paik, and Byung Kook Lim.
This research was conducted with the support of the Basic Research Program in Science and Engineering of the National Research Foundation of Korea.
2026.03.10 View 1104 -
What Guides Habitual Seeking Behavior Explained
A new role of the ventral striatum explains habitual seeking behavior
Researchers have been investigating how the brain controls habitual seeking behaviors such as addiction. A recent study by Professor Sue-Hyun Lee from the Department of Bio and Brain Engineering revealed that a long-term value memory maintained in the ventral striatum in the brain is a neural basis of our habitual seeking behavior. This research was conducted in collaboration with the research team lead by Professor Hyoung F. Kim from Seoul National University. Given that addictive behavior is deemed a habitual one, this research provides new insights for developing therapeutic interventions for addiction.
Habitual seeking behavior involves strong stimulus responses, mostly rapid and automatic ones. The ventral striatum in the brain has been thought to be important for value learning and addictive behaviors. However, it was unclear if the ventral striatum processes and retains long-term memories that guide habitual seeking.
Professor Lee’s team reported a new role of the human ventral striatum where long-term memory of high-valued objects are retained as a single representation and may be used to evaluate visual stimuli automatically to guide habitual behavior.
“Our findings propose a role of the ventral striatum as a director that guides habitual behavior with the script of value information written in the past,” said Professor Lee.
The research team investigated whether learned values were retained in the ventral striatum while the subjects passively viewed previously learned objects in the absence of any immediate outcome. Neural responses in the ventral striatum during the incidental perception of learned objects were examined using fMRI and single-unit recording.
The study found significant value discrimination responses in the ventral striatum after learning and a retention period of several days. Moreover, the similarity of neural representations for good objects increased after learning, an outcome positively correlated with the habitual seeking response for good objects.
“These findings suggest that the ventral striatum plays a role in automatic evaluations of objects based on the neural representation of positive values retained since learning, to guide habitual seeking behaviors,” explained Professor Lee.
“We will fully investigate the function of different parts of the entire basal ganglia including the ventral striatum. We also expect that this understanding may lead to the development of better treatment for mental illnesses related to habitual behaviors or addiction problems.”
This study, supported by the National Research Foundation of Korea, was reported at Nature Communications (https://doi.org/10.1038/s41467-021-22335-5.)
-ProfileProfessor Sue-Hyun LeeDepartment of Bio and Brain EngineeringMemory and Cognition Laboratoryhttp://memory.kaist.ac.kr/lecture
KAIST
2021.06.03 View 16631 -
An Exploratory Study on Smartphone Abuse among College Students
Professor Uichin Lee
Professor Uichin Lee of the Department of Knowledge Service Engineering, KAIST, and his research team developed a system that automatically diagnoses the levels of smartphone addiction based on an analysis of smartphone use records.
Professor Lee investigated the usage patterns of 95 smartphone users (college students) by conducting surveys and interviews and collecting logged data. The research team divided participants into “risk” and “non-risk” groups based on a self-reported rating scale to evaluate their abuse of smartphones. As a result, 36 students were categorized as “high risk” and 59 were categorized as “low risk.”
The researchers collected over 50,000 hours of smartphone use encompassing power levels, screen, battery status, application use, internet use, calling, and texting.
The results showed that the “high risk” group used only 1~2 applications, focusing on mobile messengers (Kakotalk, etc.) and SNS (Facebook, etc.).
In addition, a relationship was found between alarm function and addiction levels. Users who set alarms for Kakaotalk messages and SNS comments used smartphones for an additional 38 minutes per day on average.
Results also showed that “high risk” students were on their smartphones for 4 hours and 13 minutes per day, 46 minutes longer than “low risk” students who used smartphones for 3 hours and 27 minutes. The difference was prevalent during 6 am and noon, and 6pm and midnight. In addition, “high risk” students accessed their smartphones 11.4 times more than “low risk” students.
Based on the collected data, Professor Lee developed an automatic system that distinguished users into “high risk” or “low risk” categories with 80% accuracy. The new system is expected to give an early diagnosis of addiction to smartphone users, thereby allowing for early treatment and intervention before the user becomes addicted.
Professor Lee commented that, "the conventional addiction analysis based on self-analysis surveys did not provide real-time data and were largely inaccurate. The new system overcomes these limitations through data science and personal big data analysis" and that he is "developing an application that monitors smartphone abuse."
Figure 1. Usage amount: overall and application-specific results
Figure 2. Usage frequency: overall and application-specific results
Figure 3. Overall diurnal usage time and frequency
2014.06.05 View 10188