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Bu J, Young KD, Hong W, Ma R, Song H, Wang Y, Zhang W, Hampson M, Hendler T, Zhang X. Effect of deactivation of activity patterns related to smoking cue reactivity on nicotine addiction. Brain 2020; 142:1827-1841. [PMID: 31135053 DOI: 10.1093/brain/awz114] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2018] [Revised: 01/24/2019] [Accepted: 02/24/2019] [Indexed: 02/04/2023] Open
Abstract
With approximately 75% of smokers resuming cigarette smoking after using the Gold Standard Programme for smoking cessation, investigation into novel therapeutic approaches is warranted. Typically, smoking cue reactivity is crucial for smoking behaviour. Here we developed a novel closed-loop, smoking cue reactivity patterns EEG-based neurofeedback protocol and evaluated its therapeutic efficacy on nicotine addiction. During an evoked smoking cue reactivity task participants' brain activity patterns corresponding to smoking cues were obtained with multivariate pattern analysis of all EEG channels data, then during neurofeedback the EEG activity patterns of smoking cue reactivity were continuously deactivated with adaptive closed-loop training. In a double-blind, placebo-controlled, randomized clinical trial, 60 nicotine-dependent participants were assigned to receive two neurofeedback training sessions (∼1 h/session) either from their own brain (n = 30, real-feedback group) or from the brain activity pattern of a matched participant (n = 30, yoked-feedback group). Cigarette craving and craving-related P300 were assessed at pre-neurofeedback and post-neurofeedback. The number of cigarettes smoked per day was assessed at baseline, 1 week, 1 month, and 4 months following the final neurofeedback visit. In the real-feedback group, participants successfully deactivated EEG activity patterns of smoking cue reactivity. The real-feedback group showed significant decrease in cigarette craving and craving-related P300 amplitudes compared with the yoked-feedback group. The rates of cigarettes smoked per day at 1 week, 1 month and 4 months follow-up decreased 30.6%, 38.2%, and 27.4% relative to baseline in the real-feedback group, compared to decreases of 14.0%, 13.7%, and 5.9% in the yoked-feedback group. The neurofeedback effects on craving change and smoking amount at the 4-month follow-up were further predicted by neural markers at pre-neurofeedback. This novel neurofeedback training approach produced significant short-term and long-term effects on cigarette craving and smoking behaviour, suggesting the neurofeedback protocol described herein is a promising brain-based tool for treating addiction.
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Affiliation(s)
- Junjie Bu
- Hefei National Laboratory for Physical Sciences at the Microscale and School of Life Sciences, University of Science and Technology of China, Hefei, China
| | - Kymberly D Young
- Department of Psychiatry, University of Pittsburgh School of Medicine, Pittsburgh, USA
| | - Wei Hong
- Hefei National Laboratory for Physical Sciences at the Microscale and School of Life Sciences, University of Science and Technology of China, Hefei, China
| | - Ru Ma
- Hefei National Laboratory for Physical Sciences at the Microscale and School of Life Sciences, University of Science and Technology of China, Hefei, China
| | - Hongwen Song
- School of Humanities and Social Science, University of Science and Technology of China, Hefei, China
| | - Ying Wang
- Hefei National Laboratory for Physical Sciences at the Microscale and School of Life Sciences, University of Science and Technology of China, Hefei, China
| | - Wei Zhang
- Hefei National Laboratory for Physical Sciences at the Microscale and School of Life Sciences, University of Science and Technology of China, Hefei, China
| | - Michelle Hampson
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA
| | - Talma Hendler
- Functional Brain Center, Tel-Aviv University, Tel-Aviv, Israel
| | - Xiaochu Zhang
- Hefei National Laboratory for Physical Sciences at the Microscale and School of Life Sciences, University of Science and Technology of China, Hefei, China.,School of Humanities and Social Science, University of Science and Technology of China, Hefei, China.,Hefei Medical Research Center on Alcohol Addiction, Anhui Mental Health Center, Hefei, China.,Academy of Psychology and Behaviour, Tianjin Normal University, Tianjin, China
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Abstract
PURPOSE OF REVIEW Adopting an addiction perspective on eating disorders and obesity may have practical implications for diagnostic classification, prevention, and treatment of these disorders. The present article critically examines these implications derived from the food addiction concept. RECENT FINDINGS Introducing food addiction as a new disorder in diagnostic classification system seems redundant as most individuals with an addiction-like eating behavior are already covered by established eating disorder diagnoses. Food addiction may be a useful metaphor in the treatment of binge eating, but would be inappropriate for the majority of obese individuals. Implying an addiction to certain foods is not necessary when applying certain approaches inspired by the addiction field for prevention and treatment of obesity. The usefulness of abstinence models in the treatment of eating disorders and obesity needs to be rigorously tested in future studies. Some practical implications derived from the food addiction concept provide promising avenues for future research (e.g., using an addiction framework in the treatment of binge eating or applying abstinence models). For others, however, the necessity of implying an addiction to some foods needs to be scrutinized.
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Affiliation(s)
- Adrian Meule
- Department of Psychology, University of Salzburg, Hellbrunner Straße 34, 5020, Salzburg, Austria.
- Center for Cognitive Neuroscience, University of Salzburg, Salzburg, Austria.
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