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Webber HE, de Dios C, Kessler DA, Schmitz JM, Lane SD, Suchting R. Late positive potential as a candidate biomarker of motivational relevance in substance use: Evidence from a meta-analysis. Neurosci Biobehav Rev 2022; 141:104835. [PMID: 36031010 DOI: 10.1016/j.neubiorev.2022.104835] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2022] [Revised: 08/19/2022] [Accepted: 08/20/2022] [Indexed: 10/15/2022]
Abstract
The objective of the current meta-analysis was to assess the effect size of the Late Positive Potential (LPP) to drug and emotional cues in substance users compared to controls. The secondary objective was to test for moderation by: age, gender, years of use, use status, and substance type. Search was performed in August 2021 using PubMed. Inclusion criteria were: substance use disorder/dependence or validated self-report, LPP means, healthy control comparison, non-acute drug study, data available, peer-reviewed journal, English, and human participants. Selection bias was tested through modified Egger's regression and exploratory 3-parameter selection model tests. Results (k = 11) indicated LPP to drug cues was larger in substance use compared to control group, with a large effect size (Hedges' g=1.66, 95%CI [0.64,2.67], p = 0.005). There were no overall differences for emotional cues. Though threats of selection bias were not severe, inclusion of more studies with larger sample sizes in future meta-analyses will allow more robust tests of publication bias and more accurate measures of effect size.
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Affiliation(s)
- Heather E Webber
- Faillace Department of Psychiatry and Behavioral Sciences, University of Texas Health Science Center at Houston, 1941 East Road, Houston, TX 77054, USA.
| | - Constanza de Dios
- Faillace Department of Psychiatry and Behavioral Sciences, University of Texas Health Science Center at Houston, 1941 East Road, Houston, TX 77054, USA
| | - Danielle A Kessler
- College of Medicine at Tower Health, Drexel University, 50 Innovation Way, Wyomissing, PA 19610, USA
| | - Joy M Schmitz
- Faillace Department of Psychiatry and Behavioral Sciences, University of Texas Health Science Center at Houston, 1941 East Road, Houston, TX 77054, USA
| | - Scott D Lane
- Faillace Department of Psychiatry and Behavioral Sciences, University of Texas Health Science Center at Houston, 1941 East Road, Houston, TX 77054, USA
| | - Robert Suchting
- Faillace Department of Psychiatry and Behavioral Sciences, University of Texas Health Science Center at Houston, 1941 East Road, Houston, TX 77054, USA
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Zhang Y, Ou H, Yuan TF, Sun J. Electrophysiological indexes for impaired response inhibition and salience attribution in substance (stimulants and depressants) use disorders: A meta-analysis. Int J Psychophysiol 2021; 170:133-155. [PMID: 34687811 DOI: 10.1016/j.ijpsycho.2021.10.008] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2021] [Revised: 10/09/2021] [Accepted: 10/13/2021] [Indexed: 12/16/2022]
Abstract
The impairment of inhibitory control and reward system is the core feature underlying substance use disorder (SUD). Previous studies suggested that it can be regarded as impaired response inhibition and salience attribution syndrome (iRISA). The neural substrates of the two deficit functions were widely investigated in neuroimaging studies, and the impaired prefrontal cortex, limbic-orbitofrontal network, and fronto-insular-parietal network were observed. Previous Event-related potential (ERP) studies were also conducted to explore EEG indexes related to abnormal brain function. In the current meta-analysis, we aimed to explore the consistency of ERP indexes that can reflect the two aberrant processes: P300/slow potential (SP) for salience attribution and Error-related negativity (ERN)/Nogo-N200/Nogo-P300 for inhibitory control and conflict monitoring. Subgroup analyses for drug type and drug use conditions were also conducted. According to the 60 research studies, we found significantly enhanced drug-cue-induced P300 amplitude and attenuated Nogo-N200 amplitude in SUD individuals relative to Healthy control (HC), which supports the dual model. Moreover, the drug-cue-induced P300 displayed time-dependence recovery, suggesting a potential index for treatment evaluation. In conclusion, drug-cue-induced P300 and Nogo-N200 demonstrated high consistency, and the drug-cue-induced P300 can be used to track the changes of functional recovery for SUD. The integration of the two ERP components could be regarded as a potential biomarker for SUD, which may provide a new insight for clinical treatment and investigation.
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Affiliation(s)
- Yi Zhang
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China; School of Biomedical Engineering and Med-X Research Institute, Shanghai Jiao Tong University, Shanghai, China
| | - Hang Ou
- Research center of Brain and Cognitive Neuroscience, Liaoning Normal University, Dalian, China; Key Laboratory of Brain and Cognitive Neuroscience, Liaoning Province, China
| | - Ti-Fei Yuan
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China; Co-innovation Center of Neuroregeneration, Nantong University, Nantong, Jiangsu, China
| | - Junfeng Sun
- School of Biomedical Engineering and Med-X Research Institute, Shanghai Jiao Tong University, Shanghai, China.
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Dieterich R, Nickel S, Endrass T. Toward a valid electrocortical correlate of regulation of craving using single-trial regression. Int J Psychophysiol 2020; 155:152-161. [DOI: 10.1016/j.ijpsycho.2020.06.009] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2019] [Revised: 06/10/2020] [Accepted: 06/17/2020] [Indexed: 12/17/2022]
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Cui Y, Dong F, Li X, Xie D, Cheng Y, Tian S, Xue T, Li Y, Zhang M, Ren Y, Yuan K, Yu D. Electrophysiological Evidence of Event-Related Potential Changes Induced by 12 h Abstinence in Young Smokers Based on the Flanker Study. Front Psychiatry 2020; 11:424. [PMID: 32528322 PMCID: PMC7258559 DOI: 10.3389/fpsyt.2020.00424] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/11/2020] [Accepted: 04/27/2020] [Indexed: 11/23/2022] Open
Abstract
The cognitive control processes may be disrupted by abstinence in smokers, which may be helpful in the development and maintenance of addictive behavior. The purpose of this study was to measure the performance of cognitive task after 12 h of smoking abstinence by using event-related potentials (ERPs), including the error-related negativity (ERN) and the error positivity (Pe). In Eriksen flanker task, electroencephalography (EEG) signals of 24 smokers were recorded in two conditions: satiety and 12 h abstinence. In the behavioral data, both conditions exhibited more errors and more time on the incongruent trials than congruence. Meantime, the Minnesota Nicotine Withdrawal Scale (MNWS) score was increased during abstinence. Smokers showed reduced ERN and Pe after 12 h of abstinence, compared with satiety condition. The results indicate that the diminished error processing in young smokers after 12 h of abstinence. It may be related to increased withdrawal symptoms. In conclusion, the disrupted neurophysiological indexes in the general behavior monitoring system may be caused by abstinence. The results of this study may provide us with new ideas about the effects of short-term abstinence on brain cognitive neuroscience and be helpful for the solution of relapse.
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Affiliation(s)
- Yongting Cui
- Inner Mongolia Key Laboratory of Pattern Recognition and Intelligent Image Processing, School of Information Engineering, Inner Mongolia University of Science and Technology, Baotou, China
| | - Fang Dong
- Inner Mongolia Key Laboratory of Pattern Recognition and Intelligent Image Processing, School of Information Engineering, Inner Mongolia University of Science and Technology, Baotou, China
| | - Xiaojian Li
- Inner Mongolia Key Laboratory of Pattern Recognition and Intelligent Image Processing, School of Information Engineering, Inner Mongolia University of Science and Technology, Baotou, China
| | - Dongdong Xie
- Inner Mongolia Key Laboratory of Pattern Recognition and Intelligent Image Processing, School of Information Engineering, Inner Mongolia University of Science and Technology, Baotou, China
| | - Yongxin Cheng
- Inner Mongolia Key Laboratory of Pattern Recognition and Intelligent Image Processing, School of Information Engineering, Inner Mongolia University of Science and Technology, Baotou, China
| | - Shiyu Tian
- Inner Mongolia Key Laboratory of Pattern Recognition and Intelligent Image Processing, School of Information Engineering, Inner Mongolia University of Science and Technology, Baotou, China
| | - Ting Xue
- Inner Mongolia Key Laboratory of Pattern Recognition and Intelligent Image Processing, School of Information Engineering, Inner Mongolia University of Science and Technology, Baotou, China
| | - Yangding Li
- College of Information Science and Engineering, Hunan Normal University, Changsha, China
| | - Ming Zhang
- Inner Mongolia Key Laboratory of Pattern Recognition and Intelligent Image Processing, School of Information Engineering, Inner Mongolia University of Science and Technology, Baotou, China
| | - Yan Ren
- Inner Mongolia Key Laboratory of Pattern Recognition and Intelligent Image Processing, School of Information Engineering, Inner Mongolia University of Science and Technology, Baotou, China
| | - Kai Yuan
- Inner Mongolia Key Laboratory of Pattern Recognition and Intelligent Image Processing, School of Information Engineering, Inner Mongolia University of Science and Technology, Baotou, China.,Life Sciences Research Center, School of Life Science and Technology, Xidian University, Xi'an, China
| | - Dahua Yu
- Inner Mongolia Key Laboratory of Pattern Recognition and Intelligent Image Processing, School of Information Engineering, Inner Mongolia University of Science and Technology, Baotou, China
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Liu C, Dong F, Li Y, Ren Y, Xie D, Wang X, Xue T, Zhang M, Ren G, von Deneen KM, Yuan K, Yu D. 12 h Abstinence-Induced ERP Changes in Young Smokers: Electrophysiological Evidence From a Go/NoGo Study. Front Psychol 2019; 10:1814. [PMID: 31474901 PMCID: PMC6703154 DOI: 10.3389/fpsyg.2019.01814] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2019] [Accepted: 07/22/2019] [Indexed: 11/13/2022] Open
Abstract
Decreased inhibition control ability and increased craving may be the most important causes of relapsing in smoking. Although inhibition control defects in young smokers were investigated, the effects of short-term abstinence on inhibition control in young smokers were still unclear. Thirty young smokers participated in the present study. The EEG signals during the Go/NoGo task were recorded in both satiety and 12 h abstinence conditions. The task performances were observed and compared between the two conditions. Event-related potential (ERP) analysis was used to investigate changes in N200 and P300 amplitude and latency induced by 12 h of abstinence. After 12 h of abstinence, the latency of N200 was prolonged in young smokers. No significant changes were found in the number of NoGo errors and the response time of Go in young smokers after 12 h of abstinence. Correlation analysis showed that the N200 latency of abstinence condition was significantly correlated with the number of NoGo errors and the response time of Go in the abstinence condition. The present findings may improve the understanding of the effect of short-term abstinence in young smokers. We suggested that the latency of N200 may be associated with inefficient inhibitory control of the abstinence condition in young smokers. Our results may contribute new insights into the neural mechanism of nicotine abstinence in young smokers.
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Affiliation(s)
- Chang Liu
- Inner Mongolia Key Laboratory of Pattern Recognition and Intelligent Image Processing, School of Information Engineering, Inner Mongolia University of Science and Technology, Baotou, China
| | - Fang Dong
- Inner Mongolia Key Laboratory of Pattern Recognition and Intelligent Image Processing, School of Information Engineering, Inner Mongolia University of Science and Technology, Baotou, China
| | - Yangding Li
- Guangxi Key Laboratory of Multi-Source Information Mining and Security, Guangxi Normal University, Guilin, China
| | - Yan Ren
- Inner Mongolia Key Laboratory of Pattern Recognition and Intelligent Image Processing, School of Information Engineering, Inner Mongolia University of Science and Technology, Baotou, China
| | - Dongdong Xie
- Inner Mongolia Key Laboratory of Pattern Recognition and Intelligent Image Processing, School of Information Engineering, Inner Mongolia University of Science and Technology, Baotou, China
| | - Xianfu Wang
- Inner Mongolia Key Laboratory of Pattern Recognition and Intelligent Image Processing, School of Information Engineering, Inner Mongolia University of Science and Technology, Baotou, China
| | - Ting Xue
- Inner Mongolia Key Laboratory of Pattern Recognition and Intelligent Image Processing, School of Information Engineering, Inner Mongolia University of Science and Technology, Baotou, China
| | - Ming Zhang
- Inner Mongolia Key Laboratory of Pattern Recognition and Intelligent Image Processing, School of Information Engineering, Inner Mongolia University of Science and Technology, Baotou, China
| | - Guoyin Ren
- Inner Mongolia Key Laboratory of Pattern Recognition and Intelligent Image Processing, School of Information Engineering, Inner Mongolia University of Science and Technology, Baotou, China
| | | | - Kai Yuan
- Inner Mongolia Key Laboratory of Pattern Recognition and Intelligent Image Processing, School of Information Engineering, Inner Mongolia University of Science and Technology, Baotou, China
- Guangxi Key Laboratory of Multi-Source Information Mining and Security, Guangxi Normal University, Guilin, China
- School of Life Sciences and Technology, Xidian University, Xi’an, China
| | - Dahua Yu
- Inner Mongolia Key Laboratory of Pattern Recognition and Intelligent Image Processing, School of Information Engineering, Inner Mongolia University of Science and Technology, Baotou, China
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