Selective effects of the loss of NMDA or mGluR5 receptors in the reward system on adaptive decision-making

Selecting the most advantageous actions in a changing environment is a central feature of adaptive behavior. The midbrain dopamine (DA) neurons along with the major targets of their projections, including dopaminoceptive neurons in the frontal cortex and basal ganglia, play a key role in this process. Here, we investigate the consequences of a selective genetic disruption of NMDA receptor and metabotropic glutamate receptor 5 (mGluR5) in the DA system on adaptive choice behavior in mice. We tested the effects of the mutation on performance in the probabilistic reinforcement learning and probability-discounting tasks. In case of the probabilistic choice, both the loss of NMDA receptors in dopaminergic neurons or the loss mGluR5 receptors in D1 receptor-expressing dopaminoceptive neurons reduced the probability of selecting the more rewarded alternative and lowered the likelihood of returning to the previously rewarded alternative (win-stay). When observed behavior was fitted to reinforcement learning models, we found that these two mutations were associated with a reduced effect of the expected outcome on choice (i.e., more random choices). None of the mutations affected probability discounting, which indicates that all animals had a normal ability to assess probability. However, in both behavioral tasks animals with targeted loss of NMDA receptors in dopaminergic neurons or mGluR5 receptors in D1 neurons were significantly slower to perform choices. In conclusion, these results show that glutamate receptor-dependent signaling in the DA system is essential for the speed and accuracy of choices, but at the same time probably is not critical for correct estimation of probable outcomes.OAIID:RECH_ACHV_DSTSH_NO:T201832771RECH_ACHV_FG:RR00200001ADJUST_YN:EMP_ID:A080561CITE_RATE:0DEPT_NM:심리학과EMAIL:[email protected]_YN:YY

Information processing differences in persons with autogenousreactive obsessions

학위논문(석사) --서울대학교 대학원 :심리학과 임상·상담심리 전공,2006.Maste

미세회로용 스퍼터링FCCL의 신뢰성 연구

학위논문(박사)--아주대학교 일반대학원 :산업공학과,2014. 8첨단 모바일 기기의 출현에 가장 혁신적인 공헌을 한 부품을 꼽는 다면 단연 터치스크린패널(TSP; Touch Screen Panel)과 연성인쇄회로 기판 (FPCB, Flexible Printed Circuit Board)일 것이다. 연성인쇄회로 기판은 터치스크린의 신호를 회로기판까지 전달해 주는 역할과 배터리 단자 연결 등의 단순한 기능뿐만 아니라 최근에는 본 회로기판(Main Board) 으로써의 활용 또한 늘어나고 있다. FPCB 의 주요 재료는 FCCL(Flexible Copper Clad Lami nate)이다. FCCL은 절연 층인 폴리이미드(Polyimide) 필름 의 양쪽 면에 구리 박 (Copper Foil)이 입혀져 있는 구조이다. 폴리이미드 필름과 구리 박 사이 에 접착 층이 있는 3층 라미네이팅 FCCL, 구리 박에 폴리이미드 레진 (Resin)을 녹여 붙이는 2층 캐스팅FCCL 그리고 폴리이미드 필름에 스퍼터링 (Sputtering)으로 구리를 증착 하는 2층 스퍼터링 FCCL 이 있다. 이 중에서 3층FCCL과 2층 캐스팅 FCCL은 전주도금 또는 압연방식으로 제조된 구리 박 (12~18um)을 사용한다. 그러나 2층 스퍼터링FCCL은 구리를 플라즈마로 증착 한 후 전해 동도금으로 두께를 올리므로 PI필름 위에 구리 층을1~12um두께로 자유롭게 성장시켜 얇은 동박을 만들 수 있다. 얇은 동박은 SAP(Semi Additive Process) 공법 적용이 가능하여 회로 폭/스페이스 30um/30um 이하의 미세회로 제조가 가능하다. 그러나 플라즈마 증착 방법의 FCCL은 구리 층과 폴리이미드 필름과의 부착력이 2층 캐스팅 FCCL 보다 약하다. 본 연구에서는 폴리이미드 필름의 전처리 조건과 시드층(Seed Layer)의 증착 메탈인 니켈크롬(NiCr)합금의 두께, 비율 등에 따른 부착력의 변화를 살펴 보았다. 이온빔을 이용하여 폴리이미드 필름의 표면적을 넓혀 증착 된 니켈크롬 원자와 필름을 구성하는 탄소원자와의 접촉 량을 늘려 반데르발스 힘을 극대화 시켜 부착력을 올리는 방법을 제안하였다. 또한 이온빔 전처리 가스를 산소와 아르곤의 혼합가스를 사용하여 활성 가스인 산소가 플라즈마 상태로 필름 표면에 입사되어 라디칼을 형성하여 화학적 연결 고리를 만들어 부착력을 올리는 방법을 실험 하였다. 두 방법 모두 부착력 향상에 도움이 되었다. 따라서 두 방법을 동시에 적용하여 실험 하였다. 위의 방법으로 제작된 2층 스퍼터링 FCCL의 부착신뢰성, 회로형성 신뢰성 그리고 전기 화학적 이온마이그레이션 등을 실험을 통하여 검증 하였다.제 I장 서론 1 1.1절 연구 배경 및 목적 1 1.2절 FCCL의 정의와 종류 8 1.2.1 라미네이팅 법 12 1.2.2 캐스팅 법 14 1.2.3 스퍼터링 법 16 1.3절 연구 범위 및 내용 18 제 II장 관련 연구 22 2.1절 Polyimide Film의 물리적 표면 개질 23 2.2절 Polyimide Film의 화학적 표면 개질 26 2.3절 Seed Layer 28 2.4절 Electrochemical Migration 30 제III장 FCCL의 부착력 향상 33 3.1절 FCCL 의 부착력 33 3.2절 FCCL의 부착력 향상 37 3.2.1 이온빔의 전압변화와 반응가스 배합 39 3.2.2 이온빔 전처리 시간의 변화 46 3.2.3 이온건과 필름간의 거리 48 3.2.4 이온빔 반응 가스 량 51 3.2.5 이온빔 시간, 가스량 및 기재와의 거리 53 3.2.6 스퍼터링 시드층의 합금비율 56 제IV장 FCCL의 부착 신뢰성 60 4.1절 서론 60 4.1.1 상온 방치 시간에 따른 부착력의 변화 62 4.1.2 열 공정 후의 부착력의 변화 66 4.2절 미세회로의 부착력 측정 68 제V장 FCCL의 회로 형성 71 5.1절 FCCL의 회로 신뢰성 71 5.2절 시드층의 두께와 이온마이그레이션 72 5.3절 시드층과 회로 엣칭 77 5.3.1 시드층의 두께와 엣칭 면 잔사 80 5.3.2 이온빔 반응가스와 동도금 면의 조직 83 제VI장 결 론 85 참 고 문 헌 89 Abstract 96Doctora

Resting-state EEG, impulsiveness, and personality in daily and nondaily smokers

Objectives: Resting EEG is sensitive to transient, acute effects of nicotine administration and abstinence, but the chronic effects of smoking on EEG are poorly characterized. This study measures the resting EEG profile of chronic smokers in a non-deprived, non-peak state to test whether differences in smoking behavior and personality traits affect pharmaco-EEG response. Methods: Resting EEG, impulsiveness, and personality measures were collected from daily smokers (n = 22), nondaily smokers (n = 31), and non-smokers (n = 30). Results: Daily smokers had reduced resting delta and alpha EEG power and higher impulsiveness (Barratt Impulsiveness Scale) compared to nondaily smokers and non-smokers. Both daily and nondaily smokers discounted delayed rewards more steeply, reported lower conscientiousness (NEO-FFI), and reported greater disinhibition and experience seeking (Sensation Seeking Scale) than non-smokers. Nondaily smokers reported greater sensory hedonia than nonsmokers. Conclusions: Altered resting EEG power in daily smokers demonstrates differences in neural signaling that correlated with greater smoking behavior and dependence. Although nondaily smokers share some characteristics with daily smokers that may predict smoking initiation and maintenance, they differ on measures of impulsiveness and resting EEG power. Significance: Resting EEG in non-deprived chronic smokers provides a standard for comparison to peak and trough nicotine states and may serve as a biomarker for nicotine dependence, relapse risk, and recovery. (C) 2015 International Federation of Clinical Neurophysiology. Published by Elsevier Ireland Ltd. All rights reserved.OAIID:RECH_ACHV_DSTSH_NO:T201635354RECH_ACHV_FG:RR00200001ADJUST_YN:EMP_ID:A080561CITE_RATE:3.866DEPT_NM:심리학과EMAIL:[email protected]_YN:YY

Machine-learning identifies substance-specific behavioral markers for opiate and stimulant dependence

Background: Recent animal and human studies reveal distinct cognitive and neurobiological differences between opiate and stimulant addictions; however, our understanding of the common and specific effects of these two classes of drugs remains limited due to the high rates of polysubstance-dependence among drug users. Methods: The goal of the current study was to identify multivariate substance-specific markers classifying heroin dependence (HD) and amphetamine dependence (AD), by using machine-learning approaches. Participants included 39 amphetamine mono-dependent, 44 heroin mono-dependent, 58 polysubstance dependent, and 81 non-substance dependent individuals. The majority of substance dependent participants were in protracted abstinence. We used demographic, personality (trait impulsivity, trait psychopathy, aggression, sensation seeking), psychiatric (attention deficit hyperactivity disorder, conduct disorder, antisocial personality disorder, psychopathy, anxiety, depression), and neurocognitive impulsivity measures (Delay Discounting, Go/No-Go, Stop Signal, Immediate Memory, Balloon Analogue Risk, Cambridge Gambling, and Iowa Gambling tasks) as predictors in a machine-learning algorithm. Results: The machine-learning approach revealed substance-specific multivariate profiles that classified HD and AD in new samples with high degree of accuracy. Out of 54 predictors, psychopathy was the only classifier common to both types of addiction. Important dissociations emerged between factors classifying HD and AD, which often showed opposite patterns among individuals with HD and AD. Conclusions: These result's suggest that different mechanisms may underlie HD and AD, challenging the unitary account of drug addiction. This line of work may shed light on the development of standardized and cost-efficient clinical diagnostic tests and facilitate the development of individualized prevention and intervention programs for HD and AD. (c) 2016 Elsevier Ireland Ltd. All rights reserved.OAIID:RECH_ACHV_DSTSH_NO:T201736004RECH_ACHV_FG:RR00200001ADJUST_YN:EMP_ID:A080561CITE_RATE:3.222DEPT_NM:심리학과EMAIL:[email protected]_YN:YY

Challenges and promises for translating computational tools into clinical practice

© 2016 Elsevier Ltd.Computational modeling and associated methods have greatly advanced our understanding of cognition and neurobiology underlying complex behaviors and psychiatric conditions. Yet, no computational methods have been successfully translated into clinical settings. This review discusses three major methodological and practical challenges (A. precise characterization of latent neurocognitive processes, B. developing optimal assays, C. developing large-scale longitudinal studies and generating predictions from multi-modal data) and potential promises and tools that have been developed in various fields including mathematical psychology, computational neuroscience, computer science, and statistics. We conclude by highlighting a strong need to communicate and collaborate across multiple disciplines.OAIID:RECH_ACHV_DSTSH_NO:T201736003RECH_ACHV_FG:RR00200001ADJUST_YN:EMP_ID:A080561CITE_RATE:0DEPT_NM:심리학과EMAIL:[email protected]_YN:YY

The outcome‐representation learning model: a novel reinforcement learning model of the iowa gambling task

The Iowa Gambling Task (IGT) is widely used to study decision-making within healthy and psychiatric populations. However, the complexity of the IGT makes it difficult to attribute variation in performance to specific cognitive processes. Several cognitive models have been proposed for the IGT in an effort to address this problem, but currently no single model shows optimal performance for both short- and long-term prediction accuracy and parameter recovery. Here, we propose the Outcome-Representation Learning (ORL) model, a novel model that provides the best compromise between competing models. We test the performance of the ORL model on 393 subjects' data collected across multiple research sites, and we show that the ORL reveals distinct patterns of decision-making in substance-using populations. Our work highlights the importance of using multiple model comparison metrics to make valid inference with cognitive models and sheds light on learning mechanisms that play a role in underweighting of rare events.OAIID:RECH_ACHV_DSTSH_NO:T201832772RECH_ACHV_FG:RR00200001ADJUST_YN:EMP_ID:A080561CITE_RATE:2.617FILENAME:Haines_et_al-2018-Cognitive_Science (1).pdfDEPT_NM:심리학과EMAIL:[email protected]_YN:YFILEURL:https://srnd.snu.ac.kr/eXrepEIR/fws/file/f842958f-ad20-4c4a-9737-58c7fc2edc61/linkY