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Kim DY, Lisinski J, Caton M, Casas B, LaConte S, Chiu PH. Regulation of craving for real-time fMRI neurofeedback based on individual classification. Philos Trans R Soc Lond B Biol Sci 2024; 379:20230094. [PMID: 39428878 PMCID: PMC11491846 DOI: 10.1098/rstb.2023.0094] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2023] [Revised: 02/13/2024] [Accepted: 02/14/2024] [Indexed: 10/22/2024] Open
Abstract
In previous real-time functional magnetic resonance imaging neurofeedback (rtfMRI-NF) studies on smoking craving, the focus has been on within-region activity or between-region connectivity, neglecting the potential predictive utility of broader network activity. Moreover, there is debate over the use and relative predictive power of individual-specific and group-level classifiers. This study aims to further advance rtfMRI-NF for substance use disorders by using whole-brain rtfMRI-NF to assess smoking craving-related brain patterns, evaluate the performance of group-level or individual-level classification (n = 31) and evaluate the performance of an optimized classifier across repeated NF runs. Using real-time individual-level classifiers derived from whole-brain support vector machines, we found that classification accuracy between crave and no-crave conditions and between repeated NF runs increased across repeated runs at both individual and group levels. In addition, individual-level accuracy was significantly greater than group-level accuracy, highlighting the potential increased utility of an individually trained whole-brain classifier for volitional control over brain patterns to regulate smoking craving. This study provides evidence supporting the feasibility of using whole-brain rtfMRI-NF to modulate smoking craving-related brain responses and the potential for learning individual strategies through optimization across repeated feedback runs. This article is part of the theme issue 'Neurofeedback: new territories and neurocognitive mechanisms of endogenous neuromodulation'.
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Affiliation(s)
- Dong-Youl Kim
- Fralin Biomedical Research Institute at VTC, Virginia Tech, Roanoke, VA, USA
| | - Jonathan Lisinski
- Fralin Biomedical Research Institute at VTC, Virginia Tech, Roanoke, VA, USA
| | - Matthew Caton
- Fralin Biomedical Research Institute at VTC, Virginia Tech, Roanoke, VA, USA
| | - Brooks Casas
- Fralin Biomedical Research Institute at VTC, Virginia Tech, Roanoke, VA, USA
- Department of Psychology, Virginia Tech, Blacksburg, VA, USA
| | - Stephen LaConte
- Fralin Biomedical Research Institute at VTC, Virginia Tech, Roanoke, VA, USA
- Department of Biomedical Engineering and Mechanics, Virginia Tech, Blacksburg, VA, USA
| | - Pearl H. Chiu
- Fralin Biomedical Research Institute at VTC, Virginia Tech, Roanoke, VA, USA
- Department of Psychology, Virginia Tech, Blacksburg, VA, USA
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2
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van de Weijer MP, Vermeulen J, Schrantee A, Munafò MR, Verweij KJH, Treur JL. The potential role of gray matter volume differences in the association between smoking and depression: A narrative review. Neurosci Biobehav Rev 2024; 156:105497. [PMID: 38100958 DOI: 10.1016/j.neubiorev.2023.105497] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2023] [Revised: 11/14/2023] [Accepted: 11/28/2023] [Indexed: 12/17/2023]
Abstract
Tobacco use and major depression are both leading contributors to the global burden of disease and are also highly comorbid. Previous research indicates bi-directional causality between tobacco use and depression, but the mechanisms that underlie this causality are unclear, especially for the causality from tobacco use to depression. Here we narratively review the available evidence for a potential causal role of gray matter volume in the association. We summarize the findings of large existing neuroimaging meta-analyses, studies in UK Biobank, and the Enhancing NeuroImaging Genetics through MetaAnalysis (ENIGMA) consortium and assess the overlap in implicated brain areas. In addition, we review two types of methods that allow us more insight into the causal nature of associations between brain volume and depression/smoking: longitudinal studies and Mendelian Randomization studies. While the available evidence suggests overlap in the alterations in brain volumes implicated in tobacco use and depression, there is a lack of research examining the underlying pathophysiology. We conclude with recommendations on (genetically-informed) causal inference methods useful for studying these associations.
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Affiliation(s)
- Margot P van de Weijer
- Department of Psychiatry, Amsterdam University Medical Centers, University of Amsterdam, Amsterdam, the Netherlands.
| | - Jentien Vermeulen
- Department of Psychiatry, Amsterdam University Medical Centers, University of Amsterdam, Amsterdam, the Netherlands
| | - Anouk Schrantee
- Department of Radiology and Nuclear Medicine, Amsterdam University Medical Centers, University of Amsterdam, Amsterdam, the Netherlands
| | - Marcus R Munafò
- School of Psychological Science, University of Bristol, Bristol, the United Kingdom
| | - Karin J H Verweij
- Department of Psychiatry, Amsterdam University Medical Centers, University of Amsterdam, Amsterdam, the Netherlands
| | - Jorien L Treur
- Department of Psychiatry, Amsterdam University Medical Centers, University of Amsterdam, Amsterdam, the Netherlands
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3
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Zhang S, Yang W, Li M, Wang S, Zhang J, Liu J, Yuan K. Partial recovery of the left DLPFC-right insula circuit with reduced craving in abstinent heroin users: a longitudinal study. Brain Imaging Behav 2022; 16:2647-2656. [PMID: 36136203 DOI: 10.1007/s11682-022-00721-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 08/26/2022] [Indexed: 11/28/2022]
Abstract
The phenomenon of brain recovery after long-term abstinence has been reported in substance use disorders. However, few longitudinal studies have been conducted to observe the potential recovery in heroin users, and little is known about the neural mechanism underlying the decreased craving after prolonged abstinence. The 8-month longitudinal study was carried out in 29 heroin users and 30 healthy controls. By choosing the L_DLPFC, which was activated by the heroin cue as the seeding region, different brain connection patterns were compared between healthy controls and heroin users by using Granger causality analysis (GCA) at baseline. Then, a paired t test was employed to detect the potential recovery of L_DLPFC circuits after prolonged abstinence. The visual analog scale (VAS) and trail-making test-A (TMT-A) were adopted to investigate craving and cognitive control impairment, respectively. The neuroimaging changes were then correlated with behavioral improvements. Similar analyses were applied for the mirrored right DLPFC to verify the lateralization hypothesis of the DLPFC in addiction. In the longitudinal study, enhanced GCA coefficients were observed in the L_DLPFC-R_insula circuit of heroin users after long-term abstinence and were associated with craving score changes. At baseline, decreased GCA coefficients from the left DLPFC to the bilateral SMA and right putamen, together with the reduced GCA strength from the bilateral OFC to the left DLPFC, were found between HUs and HCs. Our findings extended the brain recovery phenomenon into the field of heroin and suggested that the increased regulation of the L_DLPFC over the insula after prolonged abstinence was important for craving inhibition.
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Affiliation(s)
- Shan Zhang
- Center for Brain Imaging, School of Life Science and Technology, Xidian University, Xi'an, 710126, Shaanxi, China.,Engineering Research Center of Molecular and Neuro Imaging Ministry of Education, Xi'an, 710071, Shaanxi, China
| | - Wenhan Yang
- Department of Radiology, Second Xiangya Hospital, Central South University, Changsha, 410011, People's Republic of China
| | - Minpeng Li
- Center for Brain Imaging, School of Life Science and Technology, Xidian University, Xi'an, 710126, Shaanxi, China.,Engineering Research Center of Molecular and Neuro Imaging Ministry of Education, Xi'an, 710071, Shaanxi, China
| | - Shicong Wang
- Center for Brain Imaging, School of Life Science and Technology, Xidian University, Xi'an, 710126, Shaanxi, China.,Engineering Research Center of Molecular and Neuro Imaging Ministry of Education, Xi'an, 710071, Shaanxi, China
| | - Jun Zhang
- Hunan Judicial Police Academy, Changsha, China
| | - Jun Liu
- Department of Radiology, Second Xiangya Hospital, Central South University, Changsha, 410011, People's Republic of China.
| | - Kai Yuan
- Center for Brain Imaging, School of Life Science and Technology, Xidian University, Xi'an, 710126, Shaanxi, China. .,Engineering Research Center of Molecular and Neuro Imaging Ministry of Education, Xi'an, 710071, Shaanxi, China. .,Information Processing Laboratory, School of Information Engineering, Inner Mongolia University of Science and Technology, Baotou, 014010, Inner Mongolia, China. .,International Joint Research Center for Advanced Medical Imaging and Intelligent Diagnosis and Treatment & Xi'an Key Laboratory of Intelligent Sensing and Regulation of Trans-Scale Life Information, School of Life Science and Technology, Xidian University, Xi'an, 710126, Shaanxi, China.
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4
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Tolomeo S, Yu R. Brain network dysfunctions in addiction: a meta-analysis of resting-state functional connectivity. Transl Psychiatry 2022; 12:41. [PMID: 35091540 PMCID: PMC8799706 DOI: 10.1038/s41398-022-01792-6] [Citation(s) in RCA: 26] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/16/2021] [Revised: 01/05/2022] [Accepted: 01/12/2022] [Indexed: 11/16/2022] Open
Abstract
Resting-state functional connectivity (rsFC) provides novel insights into variabilities in neural networks associated with the use of addictive drugs or with addictive behavioral repertoire. However, given the broad mix of inconsistent findings across studies, identifying specific consistent patterns of network abnormalities is warranted. Here we aimed at integrating rsFC abnormalities and systematically searching for large-scale functional brain networks in substance use disorder (SUD) and behavioral addictions (BA), through a coordinate-based meta-analysis of seed-based rsFC studies. A total of fifty-two studies are eligible in the meta-analysis, including 1911 SUD and BA patients and 1580 healthy controls. In addition, we performed multilevel kernel density analysis (MKDA) for the brain regions reliably involved in hyperconnectivity and hypoconnectivity in SUD and BA. Data from fifty-two studies showed that SUD was associated with putamen, caudate and middle frontal gyrus hyperconnectivity relative to healthy controls. Eight BA studies showed hyperconnectivity clusters within the putamen and medio-temporal lobe relative to healthy controls. Altered connectivity in salience or emotion-processing areas may be related to dysregulated affective and cognitive control-related networks, such as deficits in regulating elevated sensitivity to drug-related stimuli. These findings confirm that SUD and BA might be characterized by dysfunctions in specific brain networks, particularly those implicated in the core cognitive and affective functions. These findings might provide insight into the development of neural mechanistic biomarkers for SUD and BA.
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Affiliation(s)
- Serenella Tolomeo
- Institute of High Performance Computing, Agency for Science, Technology and Research (A*STAR), Singapore, Singapore.
| | - Rongjun Yu
- Department of Management, Hong Kong Baptist University, Hong Kong, China.
- Department of Sport, Physical Education and Health, Hong Kong Baptist University, Hong Kong, China.
- Department of Physics, Hong Kong Baptist University, Hong Kong, China.
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Li X, Dong F, Zhang Y, Wang J, Wang Z, Sun Y, Zhang M, Xue T, Ren Y, Lv X, Yuan K, Yu D. Altered resting-state electroencephalography microstate characteristics in young male smokers. Front Psychiatry 2022; 13:1008007. [PMID: 36267852 PMCID: PMC9577082 DOI: 10.3389/fpsyt.2022.1008007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/31/2022] [Accepted: 09/06/2022] [Indexed: 11/24/2022] Open
Abstract
The development of nicotine addiction was associated with the abnormalities of intrinsic functional networks during the resting state in young adult smokers. As a whole-brain imaging approach, EEG microstate analysis treated multichannel EEG recordings as a series of quasi-steady microscopic states which were related to the resting-state networks (RSNs) found by fMRI. The aim of this study was to examine whether the resting-state EEG microstate analysis may provide novel insights into the abnormal temporal properties of intrinsic brain activities in young smokers. We used 64-channel resting-state EEG datasets to investigate alterations in microstate characteristics between twenty-five young smokers and 25 age- and gender-matched non-smoking controls. Four classic EEG microstates (microstate A, B, C, and D) were obtained, and the four temporal parameters of each microstate were extracted, i.e., duration, occurrence, coverage, and transition probabilities. Compared with non-smoking controls, young smokers showed decreased occurrence of microstate C and increased duration of microstate D. Furthermore, both the duration and coverage of microstate D were significantly negatively correlated with Fagerstrom Test of Nicotine Dependence (FTND) in young smoker group. The complex changes in the microstate time-domain parameters might correspond to the abnormalities of RSNs in analyses of FC measured with fMRI in the previous studies and indicate the altered specific brain functions in young smokers. Microstate D could be potentially represented as a selective biomarker for predicting the dependence degree of adolescent smokers on cigarettes. These results suggested that EEG microstate analysis might detect the deviant functions of large-scale cortical activities in young smokers and provide a new perspective for the study of brain networks of adolescent smokers.
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Affiliation(s)
- 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
| | - 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
| | - Yunmiao 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
| | - Juan 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
| | - Zhengxi 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
| | - Yaning Sun
- 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
| | - 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
| | - 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
| | - Xiaoqi Lv
- College of Information Engineering, Inner Mongolia University of Technology, Hohhot, 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.,School of Life Sciences and Technology, Xidian University, Xi'an, Shaanxi, 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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