4.6 Article

Abstinent Heroin Addicts Tend to Take Risks: ERP and Source Lolecalization

期刊

FRONTIERS IN NEUROSCIENCE
卷 11, 期 -, 页码 -

出版社

FRONTIERS MEDIA SA
DOI: 10.3389/fnins.2017.00681

关键词

gambling task; decision making; ERP; stimulus preceding negativity; feedback related negativity; P300; source localization; heroin addiction

资金

  1. National Basic Research Program of China (973 Program) [2014CB744600]
  2. Program of International Samp
  3. T Cooperation of MOST [2Q13DFA11140]
  4. National Natural Science Foundation of China [61210010, 61632014]
  5. National key foundation for developing scientific instruments [61627808]
  6. Program of Beijing Municipal Science amp
  7. Technology Commission [Z171100000117005]
  8. James Boswell Fellowship

向作者/读者索取更多资源

Abnormal decision making is a behavioral characteristic of drug addiction. Indeed, drug addicts prefer immediate rewards at the expense of future interests. Assessing the neurocognitive basis of decision-making related to drug dependence, combining event-related potential (ERP) analysis and source localization techniques, may provide new insights into understanding decision-making deficits in drug addicts and further guide withdrawal treatment. In this study, EEG was performed in 20 abstinent heroin addicts (AHAs) and 20 age-, education- and gender-matched healthy controls (HCs) while they participated in a simple two-choice gambling task (99 vs. 9). Our behavioral results showed that AHAs tend to select higher-risk choices compared with HCs (i.e., more 99 choices than 9). ERP results showed that right hemisphere preponderance of stimulus-preceding negativity was disrupted in AHAs, but not in HCs, Feedback-related negativity of difference wave was higher in AHAs than HCs, with the P300 amplitude associated with risk magnitude and valence. Using source localization that allows identification of abnormal brain activity in consequential cognitive stages, including the reward expectation and outcome evaluation stages, we found abnormalities in both behavioral and neural responses on gambling in AHAs. Taken together, our findings suggest AHAs have risk-prone tendency and dysfunction in adaptive decision making, since they continue to choose risky options even after accruing considerable negative scores, and fail to shift to a safer strategy to avoid risk. Such abnormal decision-making bias to risk and immediate reward seeking may be accompanied by abnormal reward expectation and evaluation in AHAs, which explains their high risk-seeking and impulsivity.

作者

我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。

评论

主要评分

4.6
评分不足

次要评分

新颖性
-
重要性
-
科学严谨性
-
评价这篇论文

推荐

Article Computer Science, Artificial Intelligence

Two flexible translation-based models for knowledge graph embedding

Zepeng Li, Rikui Huang, Yufeng Zhang, Jianghong Zhu, Bin Hu

Summary: Knowledge Graph Embedding (KGE) has been proven effective in completing and improving the quality of knowledge graphs. Existing models have achieved success, but there is still room for improvement in dealing with complex relations. This paper proposes single-directional-flexible (sdf) and multi-directional-flexible (mdf) models to increase the flexibility and expressiveness of entity embeddings, leading to significant improvement in triplet classification and link prediction tasks.

JOURNAL OF INTELLIGENT & FUZZY SYSTEMS (2023)

Editorial Material Radiology, Nuclear Medicine & Medical Imaging

Editorial for Age-Related Differences of Cortical Topology Across the Adult Lifespan: Evidence From a Multisite MRI Study With 1427 Individuals

Ariana M. Familiar, Anahita Fathi Kazerooni

JOURNAL OF MAGNETIC RESONANCE IMAGING (2023)

Article Engineering, Biomedical

Dual-Stream Multiple Instance Learning for Depression Detection With Facial Expression Videos

Zixuan Shangguan, Zhenyu Liu, Gang Li, Qiongqiong Chen, Zhijie Ding, Bin Hu

Summary: Depression is a common mental illness and automated detection using physiological signals is urgent and important. This study proposes a weakly supervised learning approach using multiple instance learning (MIL) on videos from depressed and healthy individuals. The method achieves high accuracy and recall, surpassing previous methods and showcasing the potential of MIL for depression classification.

IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING (2023)

Article Engineering, Biomedical

TAMFN: Time-Aware Attention Multimodal Fusion Network for Depression Detection

Li Zhou, Zhenyu Liu, Zixuan Shangguan, Xiaoyan Yuan, Yutong Li, Bin Hu

Summary: With the widespread popularity of the Internet, social media has become an essential tool for interaction and communication. Mental health research on social media has gained attention due to the convenience of data acquisition. Early detection of psychological disorders on social media can prevent further deterioration in at-risk individuals.

IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING (2023)

Article Engineering, Biomedical

Exploring the Intrinsic Features of EEG Signals via Empirical Mode Decomposition for Depression Recognition

Jian Shen, Yanan Zhang, Huajian Liang, Zeguang Zhao, Qunxi Dong, Kun Qian, Xiaowei Zhang, Bin Hu

Summary: Depression, a severe psychiatric illness, has a significant impact on patients' thoughts, behaviors, feelings, and well-being. However, current clinical practice lacks effective methods for recognizing and treating depression. Electroencephalogram (EEG) signals, which reflect the internal workings of the brain, show promise as an objective tool for depression recognition and diagnosis. In this study, we propose a regularization parameter-based improved intrinsic feature extraction method using empirical mode decomposition (EMD) to enhance depression recognition performance.

IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING (2023)

Article Psychiatry

Brain function changes reveal rapid antidepressant effects of nitrous oxide for treatment-resistant depression:Evidence from task-state EEG

Xuexiao Shao, Danfeng Yan, Wenwen Kong, Shuting Sun, Mei Liao, Wenwen Ou, Yan Zhang, Fang Zheng, Xiaowei Li, Lingjiang Li, Bin Hu

Summary: This study assessed the effect of nitrous oxide on brain function for treatment-resistant depression (TRD) using event-related potential (ERP) components and functional connectivity networks (FCNs) methods. The results showed that nitrous oxide improved depressive symptoms better than placebo, with significant differences found in ERP components and increased brain functional connectivity after nitrous oxide treatment. These findings suggest that nitrous oxide improves depression symptoms for TRD by modifying brain function.

PSYCHIATRY RESEARCH (2023)

Article Geriatrics & Gerontology

Evaluating the treatment outcomes of repetitive transcranial magnetic stimulation in patients with moderate-to-severe Alzheimer's disease

Shouzi Zhang, Lixin Liu, Li Zhang, Li Ma, Haiyan Wu, Xuelin He, Meng Cao, Rui Li

Summary: This study conducted a randomized, sham-controlled clinical trial of repetitive transcranial magnetic stimulation (rTMS) in 35 moderate-to-severe Alzheimer's disease patients. The results showed that rTMS significantly improved cognitive performance, reduced psychiatric symptoms, and improved the clinician's evaluation of change. Furthermore, resting-state functional connectivity in certain brain regions was proposed as a potential neuroimaging marker for predicting treatment outcomes.

FRONTIERS IN AGING NEUROSCIENCE (2023)

Article Clinical Neurology

Association of aberrant brain network dynamics with gut microbial composition uncovers disrupted brain-gut-microbiome interactions in irritable bowel syndrome: Preliminary findings

Lin Yang, Guangyao Liu, Shan Li, Chaofan Yao, Ziyang Zhao, Nan Chen, Pengfei Zhang, Yingying Shang, Yin Wang, Dekui Zhang, Xiaozhu Tian, Jing Zhang, Zhijun Yao, Bin Hu

Summary: This study aimed to explore abnormalities in brain-gut-microbiome (BGM) interactions in irritable bowel syndrome (IBS). The findings revealed four different dynamic functional states in IBS patients, with abnormal temporal properties observed in one of the states (State 4). Additionally, there was an association between IBS-related gut microbiota and functional connectivity variability. These findings provide new insights into the dysconnectivity hypothesis in IBS and establish a basis for future research on disrupted BGM interactions.

EUROPEAN JOURNAL OF NEUROLOGY (2023)

Article Neurosciences

Every individual makes a difference: A trinity derived from linking individual brain morphometry, connectivity and mentalising ability

Zhaoning Li, Qunxi Dong, Bin Hu, Haiyan Wu

Summary: Mentalising ability, which involves understanding others' beliefs, feelings, intentions, thoughts, and traits, is a crucial aspect of human social cognition. However, there has been limited research on individual differences in different components of mentalising ability and how these differences are related to the structural and functional patterns of the amygdala and hippocampus. In this study, the authors used inter-subject representational similarity analysis (IS-RSA) to examine the relationships between amygdala and hippocampal morphometry, connectivity, and mentalising ability. They found significant correlations between these three modalities, highlighting the unique patterns of brain morphometry, connectivity, and mentalising ability. Additionally, they discovered a region-specific association, with hippocampus being more related to self-self and self-other mentalisation, while the amygdala showed a closer link with other-self mentalisation. The findings suggest that IS-RSA can be a valuable tool for studying individual differences in mentalising abilities and enhancing our understanding of how individual brains contribute to these abilities.

HUMAN BRAIN MAPPING (2023)

Article Biochemical Research Methods

Aberrant Static and Dynamic Functional Brain Network in Depression Based on EEG Source Localization

Xiangbin Lin, Weizhuang Kong, Jianxiu Li, Xuexiao Shao, Changting Jiang, Ruilan Yu, Xiaowei Li, Bin Hu

Summary: This study analyzed the abnormal topology and changes in functional connectivity network (FCN) of depression using both static and dynamic methods. The results showed increased clustering coefficient and local efficiency, decreased characteristic path length and global efficiency in depression. Depressed patients had reduced connectivity in most resting state networks (RSNs) but increased connectivity in the default mode network, and there was a decoupling phenomenon between different RSNs. This research provides a deeper understanding of the neurophysiological mechanisms of depression and potential biomarkers for clinical diagnosis.

IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS (2023)

Article Computer Science, Cybernetics

ChatGPT for Computational Social Systems: From Conversational Applications to Human-Oriented Operating Systems

Fei-Yue Wang, Juanjuan Li, Rui Qin, Jing Zhu, Hong Mo, Bin Hu

Summary: The second issue of the IEEE Transactions on Computational Social Systems (TCSS) for 2023 has been released, with a record high CiteScore of 9.6, according to Elsevier Scopus's latest update on February 5, 2023. Many thanks to everyone for their hard work and support.

IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL SYSTEMS (2023)

Article Computer Science, Artificial Intelligence

Personal-Zscore: Eliminating Individual Difference for EEG-Based Cross-Subject Emotion Recognition

Huayu Chen, Shuting Sun, Jianxiu Li, Ruilan Yu, Nan Li, Xiaowei Li, Bin Hu

Summary: In this study, the individual differences in EEG were analyzed and a Personal-Zscore feature processing method was proposed to improve the accuracy and robustness of emotion recognition models. The findings are of great significance for the universal implementation of EEG-based applications.

IEEE TRANSACTIONS ON AFFECTIVE COMPUTING (2023)

Article Engineering, Electrical & Electronic

An FFT-Based DC Offset Compensation and I/Q Imbalance Correction Algorithm for Bioradar Sensors

Fuze Tian, Lixian Zhu, Qiuxia Shi, Xiaokun Jin, Ran Cai, Qunxi Dong, Qinglin Zhao, Bin Hu

Summary: This article introduces a challenge in noncontact presentation of human cardiopulmonary activity using a bioradar sensor, which is to linearly demodulate the Doppler cardiopulmonary diagram (DCD) signal from baseband signals. A fast Fourier transform (FFT)-based algorithm is proposed to compensate for time-varying DC offset and correct I/Q imbalance, improving the accuracy of arctangent demodulation.

IEEE TRANSACTIONS ON MICROWAVE THEORY AND TECHNIQUES (2023)

Article Computer Science, Artificial Intelligence

Brain Network Classification for Accurate Detection of Alzheimer's Disease via Manifold Harmonic Discriminant Analysis

Hongmin Cai, Xiaoqi Sheng, Guorong Wu, Bin Hu, Yiu-Ming Cheung, Jiazhou Chen

Summary: There is increasing evidence that Alzheimer's disease (AD) disrupts the brain network before clinical symptoms appear, allowing for early diagnosis. The current methods of analyzing brain networks treat the high-dimensional data as regular matrices or vectors, which leads to a loss of essential network topology and affects diagnosis accuracy. To address this issue, this article proposes a network manifold harmonic discriminant analysis (MHDA) method for accurately detecting AD. The effectiveness of the proposed method in stratifying cognitively normal controls, mild cognitive impairment, and AD is demonstrated through extensive experiments.

IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS (2023)

Article Optics

Tunable metalensing based on plasmonic resonators embedded on thermosresponsive hydrogel

Naeem Ullah, Ata Ur Rahman Khalid, Shehzad Ahmed, Shahid Iqbal, Muhammad Ismail Khan, Majeed Ur Rehman, Andleeb Mehmood, Bin Hu, Xiaoqing Tian

Summary: Active metasurfaces, specifically adjustable metalenses and intensity-tunable metalenses in the visible frequency regime, have been developed using hydrogel with hydrophilic and hydrophobic properties. The focal length of the metalenses can be continuously adjusted by controlling the phase transition of the hydrogel, and the devices exhibit diffraction-limited performance. Furthermore, the versatility of hydrogel-based metasurfaces is demonstrated by designing intensity-tunable metalenses capable of dynamically adjusting transmission intensity and confining it to the same focal spot under different states. The non-toxicity and biocompatibility of hydrogel-based active metasurfaces make them suitable for various applications in biomedical imaging, sensing, and encryption systems.

OPTICS EXPRESS (2023)

暂无数据