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Zhou H, Zhang J, Gao J, Zeng X, Min X, Zhan H, Zheng H, Hu H, Yang Y, Wei S. Identification of Methamphetamine Abusers Can Be Supported by EEG-Based Wavelet Transform and BiLSTM Networks. Brain Topogr 2024:10.1007/s10548-024-01062-2. [PMID: 38955901 DOI: 10.1007/s10548-024-01062-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2023] [Accepted: 06/04/2024] [Indexed: 07/04/2024]
Abstract
Methamphetamine (MA) is a neurological drug, which is harmful to the overall brain cognitive function when abused. Based on this property of MA, people can be divided into those with MA abuse and healthy people. However, few studies to date have investigated automatic detection of MA abusers based on the neural activity. For this reason, the purpose of this research was to investigate the difference in the neural activity between MA abusers and healthy persons and accordingly discriminate MA abusers. First, we performed event-related potential (ERP) analysis to determine the time range of P300. Then, the wavelet coefficients of the P300 component were extracted as the main features, along with the time and frequency domain features within the selected P300 range to classify. To optimize the feature set, F_score was used to remove features below the average score. Finally, a Bidirectional Long Short-term Memory (BiLSTM) network was performed for classification. The experimental result showed that the detection accuracy of BiLSTM could reach 83.85%. In conclusion, the P300 component of EEG signals of MA abusers is different from that in normal persons. Based on this difference, this study proposes a novel way for the prevention and diagnosis of MA abuse.
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Affiliation(s)
- Hui Zhou
- Key Laboratory of Cognitive Science of State Ethnic Affairs Commission, College of Biomedical Engineering, South-Central Minzu University, Minzu Road, Wuhan, 430070, China
- Hubei Key Laboratory of Medical Information Analysis & Tumor Diagnosis and Treatment, Minzu Road, Wuhan, 430070, China
| | - Jiaqi Zhang
- Hubei Key Laboratory of Medical Information Analysis & Tumor Diagnosis and Treatment, Minzu Road, Wuhan, 430070, China
| | - Junfeng Gao
- Key Laboratory of Cognitive Science of State Ethnic Affairs Commission, College of Biomedical Engineering, South-Central Minzu University, Minzu Road, Wuhan, 430070, China.
- Hubei Key Laboratory of Medical Information Analysis & Tumor Diagnosis and Treatment, Minzu Road, Wuhan, 430070, China.
| | - Xuanwei Zeng
- Key Laboratory of Cognitive Science of State Ethnic Affairs Commission, College of Biomedical Engineering, South-Central Minzu University, Minzu Road, Wuhan, 430070, China
| | - Xiangde Min
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030, China
| | - Huimiao Zhan
- Key Laboratory of Cognitive Science of State Ethnic Affairs Commission, College of Biomedical Engineering, South-Central Minzu University, Minzu Road, Wuhan, 430070, China
| | - Hua Zheng
- Department of anesthesiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030, China
| | - Huaifei Hu
- Key Laboratory of Cognitive Science of State Ethnic Affairs Commission, College of Biomedical Engineering, South-Central Minzu University, Minzu Road, Wuhan, 430070, China
- Hubei Key Laboratory of Medical Information Analysis & Tumor Diagnosis and Treatment, Minzu Road, Wuhan, 430070, China
| | - Yong Yang
- School of Computer Science and Technology, Tiangong University, Tianjin, 300387, China
| | - Shuguang Wei
- Department of Psychology, College of Education, Hebei Normal University, Shijiazhuang, 050054, China.
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Li Y, Yang B, Ma J, Gao S, Zeng H, Wang W. Assessment of rTMS treatment effects for methamphetamine use disorder based on EEG microstates. Behav Brain Res 2024; 465:114959. [PMID: 38494128 DOI: 10.1016/j.bbr.2024.114959] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2023] [Revised: 03/10/2024] [Accepted: 03/14/2024] [Indexed: 03/19/2024]
Abstract
Microstates have been proposed as topographical maps representing large-scale resting-state networks and have recently been suggested as markers for methamphetamine use disorder (MUD). However, it is unknown whether and how they change after repetitive transcranial magnetic stimulation (rTMS) intervention. This study included a comprehensive subject population to investigate the effect of rTMS on MUD microstates. 34 patients with MUD underwent a 4-week randomized, double-blind rTMS intervention (active=17, sham=17). Two resting-state EEG recordings and VAS evaluations were conducted before and after the intervention period. Additionally, 17 healthy individuals were included as baseline controls. The modified k-means clustering method was used to calculate four microstates (MS-A∼MS-D) of EEG, and the FC network was also analyzed. The differences in microstate indicators between groups and within groups were compared. The durations of MS-A and MS-B microstates in patients with MUD were significantly lower than that in HC but showed significant improvements after rTMS intervention. Changes in microstate indicators were found to be significantly correlated with changes in craving level. Furthermore, selective modulation of the resting-state network by rTMS was observed in the FC network. The findings indicate that changes in microstates in patients with MUD are associated with craving level improvement following rTMS, suggesting they may serve as valuable evaluation markers.
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Affiliation(s)
- Yongcong Li
- School of Medicine, School of Mechatronic Engineering and Automation, Shanghai University, Shanghai 200444, China.
| | - Banghua Yang
- School of Medicine, School of Mechatronic Engineering and Automation, Shanghai University, Shanghai 200444, China.
| | - Jun Ma
- School of Medicine, School of Mechatronic Engineering and Automation, Shanghai University, Shanghai 200444, China
| | - Shouwei Gao
- School of Medicine, School of Mechatronic Engineering and Automation, Shanghai University, Shanghai 200444, China
| | - Hui Zeng
- School of Medicine, School of Mechatronic Engineering and Automation, Shanghai University, Shanghai 200444, China
| | - Wen Wang
- Department of Radiology & Functional and Molecular Imaging Key Lab of Shaanxi Province, Tangdu Hospital, Air Force Medical University, Shaanxi 710038, China.
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3
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Mo X, Jiang P, Sun J, Lu L, Li L, Huang X, Xu J, Li J, Zhang J, Gong Q. Mapping structural covariance networks of emotional withdrawal symptoms in males with methamphetamine use disorder during abstinence. Addict Biol 2024; 29:e13394. [PMID: 38627958 PMCID: PMC11021798 DOI: 10.1111/adb.13394] [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: 05/18/2023] [Revised: 01/15/2024] [Accepted: 03/18/2024] [Indexed: 04/19/2024]
Abstract
Individuals with methamphetamine use disorder (MUD) often experience anxiety and depressive symptoms during abstinence, which can worsen the likelihood of relapse. Thus, it is essential to understand the neuro-mechanism behind methamphetamine use and its associated emotional withdrawal symptoms in order to develop effective clinical strategies. This study aimed to evaluate associations between emotional withdrawal symptoms and structural covariance networks (SCNs) based on cortical thickness (CTh) across the brain. The CTh measures were obtained from Tl-weighted MRI data from a sample of 48 males with MUD during abstinence and 48 male healthy controls. The severity of anxiety and depressive symptoms was assessed by the Hamilton Anxiety Scale (HAMA) and depression (HAMD) scales. Two important nodes belonging to the brain reward system, the right rostral anterior cingulate cortex (rACC) and medial prefrontal cortex (medPFC), were selected as seeds to conduct SCNs and modulation analysis by emotional symptoms. MUDs showed higher structural covariance between the right rACC and regions in the dorsal attention, right frontoparietal, auditory, visual and limbic networks. They also displayed higher structural covariance between the right medPFC and regions in the limbic network. Moreover, the modulation analysis showed that higher scores on HAMA were associated with increased covariance between the right rACC and the left parahippocampal and isthmus cingulate cortex in the default mode network. These outcomes shed light on the complex neurobiological mechanisms underlying methamphetamine use and its associated emotional withdrawal symptoms and may provide new insights into the development of effective treatments for MUD.
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Affiliation(s)
- Xian Mo
- West China Biomedical Big Data Center, West China HospitalSichuan UniversityChengduChina
- College of Electrical EngineeringSichuan UniversityChengduChina
| | - Ping Jiang
- Department of Radiology and Huaxi MR Research Center (HMRRC), Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China HospitalSichuan UniversityChengduChina
- West China Medical Publishers, West China HospitalSichuan UniversityChengduChina
- Research Unit of PsychoradiologyChinese Academy of Medical SciencesChengduChina
| | - Jiayu Sun
- Department of RadiologyWest China Hospital of Sichuan UniversityChengduChina
| | - Lu Lu
- Department of Radiology and Huaxi MR Research Center (HMRRC), Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China HospitalSichuan UniversityChengduChina
- Research Unit of PsychoradiologyChinese Academy of Medical SciencesChengduChina
| | - Lei Li
- Department of Radiology and Huaxi MR Research Center (HMRRC), Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China HospitalSichuan UniversityChengduChina
- Research Unit of PsychoradiologyChinese Academy of Medical SciencesChengduChina
| | - Xiaoqi Huang
- Department of Radiology and Huaxi MR Research Center (HMRRC), Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China HospitalSichuan UniversityChengduChina
- Research Unit of PsychoradiologyChinese Academy of Medical SciencesChengduChina
| | - Jiajun Xu
- Mental Health CenterWest China Hospital of Sichuan UniversityChengduChina
| | - Jing Li
- Mental Health CenterWest China Hospital of Sichuan UniversityChengduChina
| | - Junran Zhang
- College of Electrical EngineeringSichuan UniversityChengduChina
| | - Qiyong Gong
- Department of Radiology and Huaxi MR Research Center (HMRRC), Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China HospitalSichuan UniversityChengduChina
- Research Unit of PsychoradiologyChinese Academy of Medical SciencesChengduChina
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Zolfaghari S, Sarbaz Y, Shafiee‐Kandjani AR. Analysing the behaviour change of brain regions of methamphetamine abusers using electroencephalogram signals: Hope to design a decision support system. Addict Biol 2024; 29:e13362. [PMID: 38380772 PMCID: PMC10898830 DOI: 10.1111/adb.13362] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2023] [Revised: 10/28/2023] [Accepted: 11/17/2023] [Indexed: 02/22/2024]
Abstract
Long-term use of methamphetamine (meth) causes cognitive and neuropsychological impairments. Analysing the impact of this substance on the human brain can aid prevention and treatment efforts. In this study, the electroencephalogram (EEG) signals of meth abusers in the abstinence period and healthy subjects were recorded during eyes-closed and eyes-opened states to distinguish the brain regions that meth can significantly influence. In addition, a decision support system (DSS) was introduced as a complementary method to recognize substance users accompanied by biochemical tests. According to these goals, the recorded EEG signals were pre-processed and decomposed into frequency bands using the discrete wavelet transform (DWT) method. For each frequency band, energy, KS entropy, Higuchi and Katz fractal dimensions of signals were calculated. Then, statistical analysis was applied to select features whose channels contain a p-value less than 0.05. These features between two groups were compared, and the location of channels containing more features was specified as discriminative brain areas. Due to evaluating the performance of features and distinguishing the two groups in each frequency band, features were fed into a k-nearest neighbour (KNN), support vector machine (SVM), multilayer perceptron neural networks (MLP) and linear discriminant analysis (LDA) classifiers. The results indicated that prolonged consumption of meth has a considerable impact on the brain areas responsible for working memory, motor function, attention, visual interpretation, and speech processing. Furthermore, the best classification accuracy, almost 95.8%, was attained in the gamma band during the eyes-closed state.
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Affiliation(s)
- Sepideh Zolfaghari
- Biological System Modeling Laboratory, Department of Biomedical Engineering, Faculty of Electrical and Computer EngineeringUniversity of TabrizTabrizIran
| | - Yashar Sarbaz
- Biological System Modeling Laboratory, Department of Biomedical Engineering, Faculty of Electrical and Computer EngineeringUniversity of TabrizTabrizIran
| | - Ali Reza Shafiee‐Kandjani
- Research Center of Psychiatry and Behavioral SciencesTabriz University of Medical SciencesTabrizIran
- Department of Psychiatry, Faculty of MedicineTabriz University of Medical SciencesTabrizIran
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5
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Luo D, He W, Shen D, Tang B, Tao H, Tang Q, Lai M, Liu J, Liu Y, Xu J, Meng J, Li J. Alterations in the brain functional network of abstinent male individuals with methamphetamine use disorder. Cereb Cortex 2024; 34:bhad523. [PMID: 38300175 DOI: 10.1093/cercor/bhad523] [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: 09/06/2023] [Revised: 12/12/2023] [Accepted: 12/13/2023] [Indexed: 02/02/2024] Open
Abstract
Methamphetamine is a highly addictive psychostimulant drug that is abused globally and is a serious threat to health worldwide. Unfortunately, the specific mechanism underlying addiction remains unclear. Thus, this study aimed to investigate the characteristics of functional connectivity in the brain network and the factors influencing methamphetamine use disorder in patients using magnetic resonance imaging. We included 96 abstinent male participants with methamphetamine use disorder and 46 age- and sex-matched healthy controls for magnetic resonance imaging. Compared with healthy controls, participants with methamphetamine use disorder had greater impulsivity, fewer small-world attributes of the resting-state network, more nodal topological attributes in the cerebellum, greater functional connectivity strength within the cerebellum and between the cerebellum and brain, and decreased frontoparietal functional connectivity strength. In addition, after controlling for covariates, the partial correlation analysis showed that small-world properties were significantly associated with methamphetamine use frequency, psychological craving, and impulsivity. Furthermore, we revealed that the small-word attribute significantly mediated the effect of methamphetamine use frequency on motor impulsivity in the methamphetamine use disorder group. These findings may further improve our understanding of the neural mechanism of impulse control dysfunction underlying methamphetamine addiction and assist in exploring the neuropathological mechanism underlying methamphetamine use disorder-related dysfunction and rehabilitation.
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Affiliation(s)
- Dan Luo
- Mental Health Center, West China Hospital of Sichuan University, No. 28 Dian Xin Nan Jie, Wuhou District, Chengdu, China
| | - Wanlin He
- Radiology Department, Hospital of Chengdu Office of People's Government of Tibetan Autonomous Region (Hospital. C.T.), No. 20, Xi Mian Qiao Heng Jie, Wuhou District, Chengdu, China
| | - Danlin Shen
- Mental Health Center, West China Hospital of Sichuan University, No. 28 Dian Xin Nan Jie, Wuhou District, Chengdu, China
| | - Bin Tang
- Chengdu Compulsory Detoxification Center, No. 9 Xue Fu Lu, Shuangliu District, Chengdu, China
| | - Hongge Tao
- Chengdu Compulsory Detoxification Center, No. 9 Xue Fu Lu, Shuangliu District, Chengdu, China
| | - Qiao Tang
- Mental Health Center, West China Hospital of Sichuan University, No. 28 Dian Xin Nan Jie, Wuhou District, Chengdu, China
| | - Mingfeng Lai
- Mental Health Center, West China Hospital of Sichuan University, No. 28 Dian Xin Nan Jie, Wuhou District, Chengdu, China
| | - Jun Liu
- Sichuan Drug Rehabilitation Administration, No. 90 Shu Tong Jie, Jinniu District, Chengdu, China
| | - Yishan Liu
- Sichuan Drug Rehabilitation Administration, No. 90 Shu Tong Jie, Jinniu District, Chengdu, China
| | - Jiajun Xu
- Mental Health Center, West China Hospital of Sichuan University, No. 28 Dian Xin Nan Jie, Wuhou District, Chengdu, China
| | - Jinli Meng
- Radiology Department, Hospital of Chengdu Office of People's Government of Tibetan Autonomous Region (Hospital. C.T.), No. 20, Xi Mian Qiao Heng Jie, Wuhou District, Chengdu, China
| | - Jing Li
- Mental Health Center, West China Hospital of Sichuan University, No. 28 Dian Xin Nan Jie, Wuhou District, Chengdu, China
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6
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Li X, Cong J, Liu K, Wang P, Sun M, Wei B. Aberrant intrinsic functional brain topology in methamphetamine-dependent individuals after six-months of abstinence. MATHEMATICAL BIOSCIENCES AND ENGINEERING : MBE 2023; 20:19565-19583. [PMID: 38052615 DOI: 10.3934/mbe.2023867] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/07/2023]
Abstract
Our aim was to explore the aberrant intrinsic functional topology in methamphetamine-dependent individuals after six months of abstinence using resting-state functional magnetic imaging (rs-fMRI). Eleven methamphetamines (MA) abstainers who have abstained for six months and eleven healthy controls (HC) were recruited for rs-fMRI examination. The graph theory and functional connectivity (FC) analysis were employed to investigate the aberrant intrinsic functional brain topology between the two groups at multiple levels. Compared with the HC group, the characteristic shortest path length ($ {L}_{p} $) showed a significant decrease at the global level, while the global efficiency ($ {E}_{glob} $) and local efficiency ($ {E}_{loc} $) showed an increase considerably. After FDR correction, we found significant group differences in nodal degree and nodal efficiency at the regional level in the ventral attentional network (VAN), dorsal attentional network (DAN), somatosensory network (SMN), visual network (VN) and default mode network (DMN). In addition, the NBS method presented the aberrations in edge-based FC, including frontoparietal network (FPN), subcortical network (SCN), VAN, DAN, SMN, VN and DMN. Moreover, the FC of large-scale functional brain networks revealed a decrease within the VN and SCN and between the networks. These findings suggest that some functions, e.g., visual processing skills, object recognition and memory, may not fully recover after six months of withdrawal. This leads to the possibility of relapse behavior when confronted with MA-related cues, which may contribute to explaining the relapse mechanism. We also provide an imaging basis for revealing the neural mechanism of MA-dependency after six months of abstinence.
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Affiliation(s)
- Xiang Li
- First Clinical Medical College, Shandong University of Traditional Chinese Medicine, Jinan 250355, China
- Center for Medical Artificial Intelligence, Shandong University of Traditional Chinese Medicine, Qingdao 266112, China
- Qingdao Academy of Chinese Medical Sciences, Shandong University of Traditional Chinese Medicine, Qingdao 266112, China
| | - Jinyu Cong
- Center for Medical Artificial Intelligence, Shandong University of Traditional Chinese Medicine, Qingdao 266112, China
- Qingdao Academy of Chinese Medical Sciences, Shandong University of Traditional Chinese Medicine, Qingdao 266112, China
| | - Kunmeng Liu
- Center for Medical Artificial Intelligence, Shandong University of Traditional Chinese Medicine, Qingdao 266112, China
- Qingdao Academy of Chinese Medical Sciences, Shandong University of Traditional Chinese Medicine, Qingdao 266112, China
| | - Pingping Wang
- Center for Medical Artificial Intelligence, Shandong University of Traditional Chinese Medicine, Qingdao 266112, China
- Qingdao Academy of Chinese Medical Sciences, Shandong University of Traditional Chinese Medicine, Qingdao 266112, China
| | - Min Sun
- Shandong Detoxification Monitoring and Treatment Institute, Zibo 255311, China
| | - Benzheng Wei
- Center for Medical Artificial Intelligence, Shandong University of Traditional Chinese Medicine, Qingdao 266112, China
- Qingdao Academy of Chinese Medical Sciences, Shandong University of Traditional Chinese Medicine, Qingdao 266112, China
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7
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Parsa M, Rad HY, Vaezi H, Hossein-Zadeh GA, Setarehdan SK, Rostami R, Rostami H, Vahabie AH. EEG-based classification of individuals with neuropsychiatric disorders using deep neural networks: A systematic review of current status and future directions. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2023; 240:107683. [PMID: 37406421 DOI: 10.1016/j.cmpb.2023.107683] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/05/2022] [Revised: 05/23/2023] [Accepted: 06/18/2023] [Indexed: 07/07/2023]
Abstract
The use of deep neural networks for electroencephalogram (EEG) classification has rapidly progressed and gained popularity in recent years, but automatic feature extraction from EEG signals remains a challenging task. The classification of neuropsychiatric disorders demands the extraction of neuro-markers for use in automated EEG classification. Numerous advanced deep learning algorithms can be used for this purpose. In this article, we present a comprehensive review of the main factors and parameters that affect the performance of deep neural networks in classifying different neuropsychiatric disorders using EEG signals. We also analyze the EEG features used for improving classification performance. Our analysis includes 82 scientific journal papers that applied deep neural networks for subject-wise classification based on EEG signals. We extracted information on the EEG dataset and types of disorders, deep neural network structures, performance, and hyperparameters. The results show that most studies have focused on clinical classification, achieving an average accuracy of 91.83 ± 7.34, with convolutional neural networks (CNNs) being the most frequently used network architecture and resting-state EEG signals being the most commonly used data type. Additionally, the review reveals that depression (N = 18), Alzheimer's (N = 11), and schizophrenia (N = 11) were studied more frequently than other types of neuropsychiatric disorders. Our review provides insight into the performance of deep neural networks in EEG classification and highlights the importance of EEG feature extraction in improving classification accuracy. By identifying the main factors and parameters that affect deep neural network performance in EEG classification, our review can guide future research in this area. We hope that our findings will encourage further exploration of deep learning methods for EEG classification and contribute to the development of more accurate and effective methods for diagnosing and monitoring neuropsychiatric disorders using EEG signals.
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Affiliation(s)
- Mohsen Parsa
- School of Electrical and Computer Engineering, College of Engineering, University of Tehran, North Kargar St., P.O. Box 14395/515, Tehran, Iran; School of Cognitive Sciences, Institute for Research in Fundamental Sciences, Artesh Highway, P.O. Box 19568-36484, Tehran, Iran
| | - Habib Yousefi Rad
- School of Electrical and Computer Engineering, College of Engineering, University of Tehran, North Kargar St., P.O. Box 14395/515, Tehran, Iran; School of Cognitive Sciences, Institute for Research in Fundamental Sciences, Artesh Highway, P.O. Box 19568-36484, Tehran, Iran
| | - Hadi Vaezi
- School of Electrical and Computer Engineering, College of Engineering, University of Tehran, North Kargar St., P.O. Box 14395/515, Tehran, Iran; School of Cognitive Sciences, Institute for Research in Fundamental Sciences, Artesh Highway, P.O. Box 19568-36484, Tehran, Iran
| | - Gholam-Ali Hossein-Zadeh
- Control and Intelligent Processing Center of Excellence, Faculty of Electrical and Computer Engineering, University of Tehran, North Kargar St., P.O. Box 14395/515, Tehran, Iran
| | - Seyed Kamaledin Setarehdan
- Control and Intelligent Processing Center of Excellence, Faculty of Electrical and Computer Engineering, University of Tehran, North Kargar St., P.O. Box 14395/515, Tehran, Iran
| | - Reza Rostami
- Faculty of Psychology and Education, University of Tehran, Jalal-Al-e-Ahmed, P.O. Box 14155-6456, Tehran, Iran
| | - Hana Rostami
- ACNC, Atieh Clinical Neuroscience Center, Valiasr St., P.O. Box 19697-13663, Tehran, Iran
| | - Abdol-Hossein Vahabie
- Faculty of Psychology and Education, University of Tehran, Jalal-Al-e-Ahmed, P.O. Box 14155-6456, Tehran, Iran; Cognitive Systems Laboratory, Control and Intelligent Processing Center of Excellence (CIPCE), School of Electrical and Computer Engineering, College of Engineering, University of Tehran, P.O. Box 14395/515, Tehran, Iran; Pasargad Institute for Advanced Innovative Solutions (PIAIS), Tehran, Iran.
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Marvi N, Haddadnia J, Fayyazi Bordbar MR. An automated drug dependence detection system based on EEG. Comput Biol Med 2023; 158:106853. [PMID: 37030264 DOI: 10.1016/j.compbiomed.2023.106853] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2022] [Revised: 02/13/2023] [Accepted: 03/30/2023] [Indexed: 04/05/2023]
Abstract
OBJECTIVE Substance abuse causes damage to the brain structure and function. This research aim is to design an automated drug dependence detection system based on EEG signals in a Multidrug (MD) abuser. METHODS EEG signals were recorded from participants categorized into MD-dependents (n = 10) and Healthy Control (HC) (n = 12). The Recurrence Plot investigates the dynamic characteristics of the EEG signal. The entropy index (ENTR) measured from the Recurrence Quantification Analysis was considered the complexity index of the delta, theta, alpha, beta, gamma, and all-band EEG signals. Statistical analysis was performed by t-test. The support vector machine technique was used for the data classification. RESULTS The results show decreased ENTR indices in the delta, alpha, beta, gamma, and all-band EEG signal and increased theta band in MD abusers compared to the HC group. That indicated the reduction of complexity in the delta, alpha, beta, gamma, and all-band EEG signals in the MD group. Additionally, the SVM classifier distinguished the MD group from the HC group with 90% accuracy, 89.36% sensitivity, 90.7% specificity, and 89.8% F1 score. CONCLUSIONS AND SIGNIFICANCE The nonlinear analysis of brain data was used to build an automatic diagnostic aid system that could identify HC people apart from those who abuse MD.
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Yang L, Du Y, Yang W, Liu J. Machine learning with neuroimaging biomarkers: Application in the diagnosis and prediction of drug addiction. Addict Biol 2023; 28:e13267. [PMID: 36692873 DOI: 10.1111/adb.13267] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2022] [Revised: 10/19/2022] [Accepted: 12/14/2022] [Indexed: 01/18/2023]
Abstract
Drug abuse is a serious problem worldwide. Owing to intermittent intake of certain substances and the early inconspicuous clinical symptoms, this brings huge challenges for timely diagnosing addiction status and preventing substance use disorders (SUDs). As a non-invasive technique, neuroimaging can capture neurobiological signatures of abnormality in multiple brain regions caused by drug consumption in each clinical stage, like parenchymal morphology alteration as well as aberrant functional activity and connectivity of cerebral areas, making it realizable to diagnosis, prediction and even preemptive therapy of addiction. Machine learning (ML) algorithms primarily used for classification have been extensively applied in analysing medical imaging datasets. Significant neurobiological characteristics employed and revealed by classifiers were used to diagnose addictive states and predict initiation and vulnerability to drug usage, treatment abstinence, relapse and resilience of addicts and the risk of SUD. In this review, we summarize application of ML methods in neuroimaging focusing on addicts' diagnosis of clinical status and risk prediction and elucidate the discriminative neurobiological features from brain electrophysiological, morphological and functional perspectives that contribute most to the classifier, finally highlighting the auxiliary role of ML in addiction treatment.
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Affiliation(s)
- Longtao Yang
- Department of Radiology, The Second Xiangya Hospital of Central South University, Changsha, China
| | - Yanyao Du
- Department of Radiology, The Second Xiangya Hospital of Central South University, Changsha, China
| | - Wenhan Yang
- Department of Radiology, The Second Xiangya Hospital of Central South University, Changsha, China
| | - Jun Liu
- Department of Radiology, The Second Xiangya Hospital of Central South University, Changsha, China.,Clinical Research Center for Medical Imaging in Hunan Province, Changsha, China.,Department of Radiology Quality Control Center in Hunan Province, Changsha, China
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10
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Chen YH, Yang J, Wu H, Beier KT, Sawan M. Challenges and future trends in wearable closed-loop neuromodulation to efficiently treat methamphetamine addiction. Front Psychiatry 2023; 14:1085036. [PMID: 36911117 PMCID: PMC9995819 DOI: 10.3389/fpsyt.2023.1085036] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/31/2022] [Accepted: 02/03/2023] [Indexed: 02/25/2023] Open
Abstract
Achieving abstinence from drugs is a long journey and can be particularly challenging in the case of methamphetamine, which has a higher relapse rate than other drugs. Therefore, real-time monitoring of patients' physiological conditions before and when cravings arise to reduce the chance of relapse might help to improve clinical outcomes. Conventional treatments, such as behavior therapy and peer support, often cannot provide timely intervention, reducing the efficiency of these therapies. To more effectively treat methamphetamine addiction in real-time, we propose an intelligent closed-loop transcranial magnetic stimulation (TMS) neuromodulation system based on multimodal electroencephalogram-functional near-infrared spectroscopy (EEG-fNIRS) measurements. This review summarizes the essential modules required for a wearable system to treat addiction efficiently. First, the advantages of neuroimaging over conventional techniques such as analysis of sweat, saliva, or urine for addiction detection are discussed. The knowledge to implement wearable, compact, and user-friendly closed-loop systems with EEG and fNIRS are reviewed. The features of EEG and fNIRS signals in patients with methamphetamine use disorder are summarized. EEG biomarkers are categorized into frequency and time domain and topography-related parameters, whereas for fNIRS, hemoglobin concentration variation and functional connectivity of cortices are described. Following this, the applications of two commonly used neuromodulation technologies, transcranial direct current stimulation and TMS, in patients with methamphetamine use disorder are introduced. The challenges of implementing intelligent closed-loop TMS modulation based on multimodal EEG-fNIRS are summarized, followed by a discussion of potential research directions and the promising future of this approach, including potential applications to other substance use disorders.
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Affiliation(s)
- Yun-Hsuan Chen
- CenBRAIN Neurotech Center of Excellence, School of Engineering, Westlake University, Hangzhou, China.,Institute of Advanced Technology, Westlake Institute for Advanced Study, Hangzhou, China
| | - Jie Yang
- CenBRAIN Neurotech Center of Excellence, School of Engineering, Westlake University, Hangzhou, China.,Institute of Advanced Technology, Westlake Institute for Advanced Study, Hangzhou, China
| | - Hemmings Wu
- Department of Neurosurgery, Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, China
| | - Kevin T Beier
- Department of Physiology and Biophysics, University of California, Irvine, Irvine, CA, United States.,Department of Neurobiology and Behavior, University of California, Irvine, Irvine, CA, United States.,Department of Biomedical Engineering, University of California, Irvine, Irvine, CA, United States.,Department of Pharmaceutical Sciences, University of California, Irvine, Irvine, CA, United States.,Center for the Neurobiology of Learning and Memory, University of California, Irvine, Irvine, CA, United States
| | - Mohamad Sawan
- CenBRAIN Neurotech Center of Excellence, School of Engineering, Westlake University, Hangzhou, China.,Institute of Advanced Technology, Westlake Institute for Advanced Study, Hangzhou, China
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11
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Differences in small-world networks between methamphetamine and heroin use disorder patients and their relationship with psychiatric symptoms. Brain Imaging Behav 2022; 16:1973-1982. [PMID: 36018531 DOI: 10.1007/s11682-022-00667-0] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 03/22/2022] [Indexed: 11/02/2022]
Abstract
Both methamphetamine use disorder (MAUD) and heroin use disorder (HUD) implicated in substance-induced psychosis, but the psychiatirc symptoms induced by MAUD and HUD are significantly different. The functional network organizations that may underlie these differences remains unknown. Image data was acquired by resting-state fMRI from 19 MAUD patients, 21 HUD patients, and 20 healthy controls. The small-world properties, node attributes, and functional connectivity of brain regions were analyzed among the three groups. Psychiatric status was evaluated by the Symptom Checklist 90 in all participants. The MAUD patients had significantly higher psychiatric scores than the controls and HUD patients. Both MAUD and HUD patients still had economical small-world properties. The MAUD patients showed increased nodal efficiency and betweenness centrality in the right inferior occipital gyrus, left insular lobe, bilateral Heschl gyrus, and bilateral superior temporal gyrus, while the node attributes decreased in the right parahippocampal gyrus and right hippocampus compared to the HUD patients. The MAUD patients also showed reduced edge connectivity between left dorsolateral prefrontal cortex (dlPFC) and left middle occipital gyrus (MOG), as well as between bilateral orbitofrontal cortex (OFC) and bilateral superior occipital gyrus (SOG), left MOG, or right cuneus. In the MAUD group, the functional connection between left dlPFC and left MOG was negatively correlated with depression, while the connection between right cuneus lobe and right OFC was negatively correlated with depression and interpersonal sensitivity. These brain regions related to cognitive, emotional, and auditory/visual regulation may play an important role in the psychiatric symptoms of MAUD.
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12
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Enhanced brain network flexibility by physical exercise in female methamphetamine users. Cogn Neurodyn 2022. [DOI: 10.1007/s11571-022-09848-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/03/2022] Open
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13
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Mansoory MS, Allahverdy A, Behboudi M, Khodamoradi M. Local efficiency analysis of restingstate functional brain network in methamphetamine users. Behav Brain Res 2022; 434:114022. [PMID: 35870617 DOI: 10.1016/j.bbr.2022.114022] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2022] [Revised: 07/11/2022] [Accepted: 07/19/2022] [Indexed: 11/12/2022]
Abstract
This study set out to assess restingstate functional connectivity (rs-FN) and graph theorybased local efficiency within the left and right hemispheres of methamphetamine (MA) abusers. Functional brain networks of 19 MA abusers and 21 control participants were analyzed using restingstate fMRI. Graph edges in functional networks of the brain were defined and recurrence plot was used. We found that MA abuse may be accompanied by alterations of rs-FN within the defaultmode network (DMN), executive control network (ECN), and the salience network (SN) in both hemispheres of the brain. We also observed that such effects of MA may be correlated with duration of MA abuse and abstinence in many components of the DMN and SN. The results would seem to suggest that MAinduced alterations of local efficiency may, in part, account for maladaptive decision making, deficits in executive function and control over drug seeking/taking, and relapse.
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Affiliation(s)
- Meysam Siyah Mansoory
- Department of Biomedical Engineering, School of Medicine, Kermanshah University of Medical Sciences, Kermanshah, Iran
| | - Armin Allahverdy
- Department of Radiology, School of Allied Medical Sciences, Mazandaran University of Medical Sciences, Sari, Iran
| | - Maryam Behboudi
- Department of Statistics, Science and Research Branch, Islamic Azad University, Tehran, Iran
| | - Mehdi Khodamoradi
- Substance Abuse Prevention Research Center, Health Institute, Kermanshah University of Medical Sciences, Kermanshah, Iran.
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14
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Liu Y, Chen Y, Fraga-González G, Szpak V, Laverman J, Wiers RW, Richard Ridderinkhof K. Resting-state EEG, Substance use and Abstinence After Chronic use: A Systematic Review. Clin EEG Neurosci 2022; 53:344-366. [PMID: 35142589 DOI: 10.1177/15500594221076347] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
Resting-state EEG reflects intrinsic brain activity and its alteration represents changes in cognition that are related to neuropathology. Thereby, it provides a way of revealing the neurocognitive mechanisms underpinning chronic substance use. In addition, it is documented that some neurocognitive functions can recover following sustained abstinence. We present a systematic review to synthesize how chronic substance use is associated with resting-state EEG alterations and whether these spontaneously recover from abstinence. A literature search in Medline, PsycINFO, Embase, CINAHL, Web of Science, and Scopus resulted in 4088 articles, of which 57 were included for evaluation. It covered the substance of alcohol (18), tobacco (14), cannabis (8), cocaine (6), opioids (4), methamphetamine (4), and ecstasy (4). EEG analysis methods included spectral power, functional connectivity, and network analyses. It was found that long-term substance use with or without substance use disorder diagnosis was associated with broad intrinsic neural activity alterations, which were usually expressed as neural hyperactivation and decreased neural communication between brain regions. Some studies found the use of alcohol, tobacco, cocaine, cannabis, and methamphetamine was positively correlated with these changes. These alterations can partly recover from abstinence, which differed between drugs and may reflect their neurotoxic degree. Moderating factors that may explain results inconsistency are discussed. In sum, resting-state EEG may act as a potential biomarker of neurotoxic effects of chronic substance use. Recovery effects awaits replication in larger samples with prolonged abstinence. Balanced sex ratio, enlarged sample size, advanced EEG analysis methods, and transparent reporting are recommended for future studies.
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Affiliation(s)
- Yang Liu
- 12544Department of Psychology, School of Education, Shanghai Normal University, Shanghai, China
| | - Yujie Chen
- 12544Department of Psychology, School of Education, Shanghai Normal University, Shanghai, China
| | - Gorka Fraga-González
- 27217Department of Child and Adolescent Psychiatry and Psychotherapy, Psychiatric Hospital, University of Zurich, Zurich, Switzerland
| | - Veronica Szpak
- 1234Department of Psychology, University of Amsterdam, Amsterdam, Netherlands
| | - Judith Laverman
- 1234Department of Psychology, University of Amsterdam, Amsterdam, Netherlands
| | - Reinout W Wiers
- 1234Addiction Development and Psychopathology (ADAPT)-Lab, Department of Psychology and Centre for Urban Mental Health, University of Amsterdam, Amsterdam, Netherlands
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15
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Characterization of the Brain Functional Architecture of Psychostimulant Withdrawal Using Single-Cell Whole-Brain Imaging. eNeuro 2021; 8:ENEURO.0208-19.2021. [PMID: 34580158 PMCID: PMC8570684 DOI: 10.1523/eneuro.0208-19.2021] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2019] [Revised: 08/08/2021] [Accepted: 08/09/2021] [Indexed: 02/03/2023] Open
Abstract
Numerous brain regions have been identified as contributing to withdrawal behaviors, but it is unclear the way in which these brain regions as a whole lead to withdrawal. The search for a final common brain pathway that is involved in withdrawal remains elusive. To address this question, we implanted osmotic minipumps containing either saline, nicotine (24 mg/kg/d), cocaine (60 mg/kg/d), or methamphetamine (4 mg/kg/d) for one week in male C57BL/6J mice. After one week, the minipumps were removed and brains collected 8 h (saline, nicotine, and cocaine) or 12 h (methamphetamine) after removal. We then performed single-cell whole-brain imaging of neural activity during the withdrawal period when brains were collected. We used hierarchical clustering and graph theory to identify similarities and differences in brain functional architecture. Although methamphetamine and cocaine shared some network similarities, the main common neuroadaptation between these psychostimulant drugs was a dramatic decrease in modularity, with a shift from a cortical-driven to subcortical-driven network, including a decrease in total hub brain regions. These results demonstrate that psychostimulant withdrawal produces the drug-dependent remodeling of functional architecture of the brain and suggest that the decreased modularity of brain functional networks and not a specific set of brain regions may represent the final common pathway associated with withdrawal.
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16
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Jiang P, Sun J, Zhou X, Lu L, Li L, Huang X, Li J, Kendrick K, Gong Q. Functional connectivity abnormalities underlying mood disturbances in male abstinent methamphetamine abusers. Hum Brain Mapp 2021; 42:3366-3378. [PMID: 33939234 PMCID: PMC8249885 DOI: 10.1002/hbm.25439] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2020] [Revised: 03/09/2021] [Accepted: 03/26/2021] [Indexed: 02/05/2023] Open
Abstract
Anxiety and depression are the most common withdrawal symptoms of methamphetamine (METH) abuse, which further exacerbate relapse of METH abuse. To date, no effective pharmacotherapy exists for METH abuse and its withdrawal symptoms. Therefore, understanding the neuromechanism underlying METH abuse and its withdrawal symptoms is essential for developing clinical strategies and improving patient care. The aims of this study were to investigate brain network abnormalities in METH abusers (MAs) and their associations with affective symptoms. Forty-eight male abstinent MAs and 48 age-gender matched healthy controls were recruited and underwent resting state functional magnetic resonance imaging (fMRI). The severity of patient anxiety and depressive symptoms were measured by Hamilton anxiety and depression rating scales, which decreased across the duration of abstinence. Independent component analysis was used to investigate the brain network functional connectivity (FC) properties. Compared with healthy controls, MAs demonstrated hypo-intra-network FC in the cerebellar network and hyper-intra-network FC in the posterior salience network. A whole-brain regression analysis revealed that FC strength of clusters located in the right rostral anterior cingulate cortex (rACC) within the ventromedial network (VMN) was associated with affective symptoms in the patients. Importantly, the intra-network FC strength of the rACC in VMN mediated the association between abstinence duration and the severity level of affective symptoms. Our results demonstrate alterations in brain functional networks underlying METH abuse, and that the FC of rACC within VMN serve as a neural substrate in the association between abstinence length and affective symptom severity in the MAs.
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Affiliation(s)
- Ping Jiang
- Huaxi MR Research Center (HMRRC), Department of RadiologyWest China Hospital of Sichuan UniversityChengduChina
- Research Unit of PsychoradiologyChinese Academy of Medical SciencesChengduChina
- Functional and Molecular Imaging Key Laboratory of Sichuan ProvinceChengduChina
| | - Jiayu Sun
- Department of RadiologyWest China Hospital of Sichuan UniversityChengduChina
| | - Xiaobo Zhou
- Department of PsychosomaticsAcademy of Medical Sciences & Sichuan Provincial People's HospitalChengduSichuanChina
| | - Lu Lu
- Huaxi MR Research Center (HMRRC), Department of RadiologyWest China Hospital of Sichuan UniversityChengduChina
- Research Unit of PsychoradiologyChinese Academy of Medical SciencesChengduChina
- Functional and Molecular Imaging Key Laboratory of Sichuan ProvinceChengduChina
| | - Lei Li
- Huaxi MR Research Center (HMRRC), Department of RadiologyWest China Hospital of Sichuan UniversityChengduChina
- Research Unit of PsychoradiologyChinese Academy of Medical SciencesChengduChina
- Functional and Molecular Imaging Key Laboratory of Sichuan ProvinceChengduChina
| | - Xiaoqi Huang
- Huaxi MR Research Center (HMRRC), Department of RadiologyWest China Hospital of Sichuan UniversityChengduChina
- Research Unit of PsychoradiologyChinese Academy of Medical SciencesChengduChina
- Functional and Molecular Imaging Key Laboratory of Sichuan ProvinceChengduChina
| | - Jing Li
- Mental Health CenterWest China Hospital of Sichuan UniversityChengduChina
| | - Keith Kendrick
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for NeuroInformationUniversity of Electronic Science and Technology of ChinaChengduChina
| | - Qiyong Gong
- Huaxi MR Research Center (HMRRC), Department of RadiologyWest China Hospital of Sichuan UniversityChengduChina
- Research Unit of PsychoradiologyChinese Academy of Medical SciencesChengduChina
- Functional and Molecular Imaging Key Laboratory of Sichuan ProvinceChengduChina
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17
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Chen Z, Liu P, Zhang C, Yu Z, Feng T. Neural markers of procrastination in white matter microstructures and networks. Psychophysiology 2021; 58:e13782. [PMID: 33586198 DOI: 10.1111/psyp.13782] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2020] [Revised: 01/13/2021] [Accepted: 01/13/2021] [Indexed: 01/20/2023]
Abstract
More than 15% of adults suffer from pathological procrastination, which leads to substantial harm to their mental and psychiatric health. Our previous work demonstrated the role of three neuroanatomical networks as neural substrates of procrastination, but their potential interaction remains unknown. Three large-scale independent samples (total n = 901) were recruited. In sample A, tract-based spatial statistics (TBSS) and connectome-based graph-theoretical analysis was conducted to probe association between topological properties of white matter (WM) network and procrastination. In sample B, the above analysis was reproduced to demonstrate replicability. In sample C, machine learning models were built to predict individual procrastination. TBSS results showed a negative association between procrastination and WM integrity of limbic-prefrontal connection, and a positive relationship between intra-connection within the limbic system and procrastination. Also, both the efficiency and integrity of limbic WM network were found to be linked to procrastination. The above findings were all confirmed to replicate in an independent sample; prediction models demonstrated that these WM features can predict procrastination accurately in sample C. In conclusion, this study moves forward our understanding of procrastination by clarifying the role of interplay of self-control and emotional regulation with it.
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Affiliation(s)
- Zhiyi Chen
- Faculty of Psychology, Southwest University, Chongqing, China.,Key Laboratory of Cognition and Personality, Ministry of Education, Chongqing, China
| | - Peiwei Liu
- Department of Psychology, University of Florida, Gainesville, FL, USA
| | - Chenyan Zhang
- Cognitive Psychology Unit, Faculty of Social and Behavioural Sciences, The Institute of Psychology, Leiden University, Leiden, Netherlands
| | - Zeyuan Yu
- Teacher College, Southwest University, Chongqing, China
| | - Tingyong Feng
- Faculty of Psychology, Southwest University, Chongqing, China.,Key Laboratory of Cognition and Personality, Ministry of Education, Chongqing, China
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18
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Zink N, Lenartowicz A, Markett S. A new era for executive function research: On the transition from centralized to distributed executive functioning. Neurosci Biobehav Rev 2021; 124:235-244. [PMID: 33582233 DOI: 10.1016/j.neubiorev.2021.02.011] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2020] [Accepted: 02/04/2021] [Indexed: 02/07/2023]
Abstract
"Executive functions" (EFs) is an umbrella term for higher cognitive control functions such as working memory, inhibition, and cognitive flexibility. One of the most challenging problems in this field of research has been to explain how the wide range of cognitive processes subsumed as EFs are controlled without an all-powerful but ill-defined central executive in the brain. Efforts to localize control mechanisms in circumscribed brain regions have not led to a breakthrough in understanding how the brain controls and regulates itself. We propose to re-conceptualize EFs as emergent consequences of highly distributed brain processes that communicate with a pool of highly connected hub regions, thus precluding the need for a central executive. We further discuss how graph-theory driven analysis of brain networks offers a unique lens on this problem by providing a reference frame to study brain connectivity in EFs in a holistic way and helps to refine our understanding of the mechanisms underlying EFs by providing new, testable hypotheses and resolves empirical and theoretical inconsistencies in the EF literature.
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Affiliation(s)
- Nicolas Zink
- Department of Psychiatry and Biobehavioral Sciences, University of California, Los Angeles, Los Angeles, United States.
| | - Agatha Lenartowicz
- Department of Psychiatry and Biobehavioral Sciences, University of California, Los Angeles, Los Angeles, United States
| | - Sebastian Markett
- Department of Psychology, Humboldt University Berlin, Berlin, Germany
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19
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Ding X, Li Y, Li D, Li L, Liu X. Using machine-learning approach to distinguish patients with methamphetamine dependence from healthy subjects in a virtual reality environment. Brain Behav 2020; 10:e01814. [PMID: 32862513 PMCID: PMC7667292 DOI: 10.1002/brb3.1814] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/20/2019] [Revised: 08/08/2020] [Accepted: 08/09/2020] [Indexed: 11/25/2022] Open
Abstract
BACKGROUND The aim of this study was to evaluate whether machine learning (ML) can be used to distinguish patients with methamphetamine dependence from healthy controls by using their surface electroencephalography (EEG) and galvanic skin response (GSR) in a drug-simulated virtual reality (VR) environment. METHODS A total of 333 participants with methamphetamine (METH) dependence and 332 healthy control subjects were recruited between January 2018 and January 2019. EEG (five electrodes) and GSR signals were collected under four VR environments: one neutral scenario and three METH-simulated scenarios. Three ML classification techniques were evaluated: random forest (RF), support vector machine (SVM), and logistic regression (LR). RESULTS The MANOVA showed no interaction effects among the two subject groups and the 4 VR scenarios. Taking patient groups as the main effect, the METH user group had significantly lower GSR, lower EEG power in delta (p < .001), and alpha bands (p < .001) than healthy subjects. The EEG power of beta band (p < .001) and gamma band (p < .001) was significantly higher in METH group than the control group. Taking the VR scenarios (Neutral versus METH-VR) as the main effects, the GSR, EEG power in delta, theta, and alpha bands in neutral scenario were significantly higher than in the METH-VR scenario (p < .001). The LR algorithm showed the highest specificity and sensitivity in distinguishing methamphetamine-dependent patients from healthy controls. CONCLUSION The study shows the potential of using machine learning to distinguish methamphetamine-dependent patients from healthy subjects by using EEG and GSR data. The LR algorithm shows the best performance comparing with SVM and RF algorithm.
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Affiliation(s)
- Xinfang Ding
- School of Medical Humanities, Capital Medical University, Beijing, China
| | - Yuanhui Li
- Adai Technology (Beijing) Ltd., Co, Beijing, China
| | - Dai Li
- Adai Technology (Beijing) Ltd., Co, Beijing, China
| | - Ling Li
- School of Computing, University of Kent, Kent, UK
| | - Xiuyun Liu
- Department of Anesthesiology and Critical Care Medicine, School of Medicine, Johns Hopkins University, Baltimore, MD, USA.,School of Precision Instrument and Optoelectronics Engineering, Tianjin University, Tianjin, China
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20
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Zink N, Kang K, Li SC, Beste C. Anodal transcranial direct current stimulation enhances the efficiency of functional brain network communication during auditory attentional control. J Neurophysiol 2020; 124:207-217. [PMID: 32233902 DOI: 10.1152/jn.00074.2020] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022] Open
Abstract
Attentional control is crucial for selectively attending to relevant information when our brain is confronted with a multitude of sensory signals. Graph-theoretical measures provide a powerful tool for investigating the efficiency of brain network communication in separating and integrating information. Albeit, it has been demonstrated that anodal transcranial direct current stimulation (atDCS) can boost auditory attention in situations with high control demands, its effect on neurophysiological mechanisms of functional brain network communication in situations when attentional focus conflicts with perceptual saliency remain unclear. This study investigated the effects of atDCS on network connectivity and θ-oscillatory power under different levels of attentional-perceptual conflict. We hypothesized that the benefit of atDCS on network communication efficiency would be particularly apparent in conditions requiring high attentional control. Thirty young adults participated in a dichotic listening task with intensity manipulation, while EEG activity was recorded. In a cross-over design, participants underwent right frontal atDCS and sham stimulations in two separate sessions. Time-frequency decomposition and graph-theoretical analyses of network efficiency (using "small-world" properties) were used to quantify θ-oscillatory power and brain network efficiency, respectively. The atDCS-induced effect on task efficiency in the most demanding condition was mirrored only by an increase in network efficiency during atDCS compared with the sham stimulation. These findings are corroborated by Bayesian analyses. AtDCS-induced performance enhancement under high levels of attentional-perceptual conflicts is accompanied by an increase in network efficiency. Graph-theoretical measures can serve as a metric to quantify the effects of noninvasive brain stimulation on the separation and integration of information in the brain.NEW & NOTEWORTHY As compared with sham stimulation, application of atDCS enhances θ-oscillation-based network efficiency, but it has no impact on θ-oscillation power. Individual differences in θ-oscillation-based network efficiency correlated with performance efficiency under the sham stimulation.
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Affiliation(s)
- Nicolas Zink
- Cognitive Neurophysiology, Department of Child and Adolescent Psychiatry, Faculty of Medicine of the Technische Universität Dresden, Germany
| | - Kathleen Kang
- Lifespan Developmental Neuroscience, Faculty of Psychology, Technische Universität Dresden, Dresden, Germany
| | - Shu-Chen Li
- Lifespan Developmental Neuroscience, Faculty of Psychology, Technische Universität Dresden, Dresden, Germany.,Centre for Tactile Internet with Human-in-the-Loop, Technische Universität Dresden, Dresden, Germany
| | - Christian Beste
- Cognitive Neurophysiology, Department of Child and Adolescent Psychiatry, Faculty of Medicine of the Technische Universität Dresden, Germany
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21
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Chen T, Su H, Zhong N, Tan H, Li X, Meng Y, Duan C, Zhang C, Bao J, Xu D, Song W, Zou J, Liu T, Zhan Q, Jiang H, Zhao M. Disrupted brain network dynamics and cognitive functions in methamphetamine use disorder: insights from EEG microstates. BMC Psychiatry 2020; 20:334. [PMID: 32580716 PMCID: PMC7315471 DOI: 10.1186/s12888-020-02743-5] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/11/2020] [Accepted: 06/18/2020] [Indexed: 01/07/2023] Open
Abstract
BACKGROUND Dysfunction in brain network dynamics has been found to correlate with many psychiatric disorders. However, there is limited research regarding resting electroencephalogram (EEG) brain network and its association with cognitive process for patients with methamphetamine use disorder (MUD). This study aimed at using EEG microstate analysis to determine whether brain network dynamics in patients with MUD differ from those of healthy controls (HC). METHODS A total of 55 MUD patients and 27 matched healthy controls were included for analysis. The resting brain activity was recorded by 64-channel electroencephalography. EEG microstate parameters and intracerebral current sources of each EEG microstate were compared between the two groups. Generalized linear regression model was used to explore the correlation between significant microstates with drug history and cognitive functions. RESULTS MUD patients showed lower mean durations of the microstate classes A and B, and a higher global explained variance of the microstate class C. Besides, MUD patients presented with different current density power in microstates A, B, and C relative to the HC. The generalized linear model showed that MA use frequency is negatively correlated with the MMD of class A. Further, the generalized linear model showed that MA use frequency, scores of Two-back task, and the error rate of MA word are correlated with the MMD and GEV of class B, respectively. CONCLUSIONS Intracranial current source densities of resting EEG microstates are disrupted in MUD patients, hence causing temporal changes in microstate topographies, which are correlated with attention bias and history of drug use.
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Affiliation(s)
- Tianzhen Chen
- grid.16821.3c0000 0004 0368 8293Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, 600 Wan Ping Nan Road, Shanghai, 200030 China
| | - Hang Su
- grid.16821.3c0000 0004 0368 8293Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, 600 Wan Ping Nan Road, Shanghai, 200030 China
| | - Na Zhong
- grid.16821.3c0000 0004 0368 8293Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, 600 Wan Ping Nan Road, Shanghai, 200030 China
| | - Haoye Tan
- grid.16821.3c0000 0004 0368 8293Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, 600 Wan Ping Nan Road, Shanghai, 200030 China
| | - Xiaotong Li
- grid.16821.3c0000 0004 0368 8293Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, 600 Wan Ping Nan Road, Shanghai, 200030 China
| | - Yiran Meng
- Yunnan Institute on Drug Dependence, Kunming, Yunnan China
| | - Chunmei Duan
- Yunnan Institute on Drug Dependence, Kunming, Yunnan China
| | - Congbin Zhang
- Yunnan Institute on Drug Dependence, Kunming, Yunnan China
| | - Juwang Bao
- grid.28703.3e0000 0000 9040 3743Institute of Higher Education, Beijing University of Technology, Beijing, China
| | - Ding Xu
- Shanghai Bureau of Drug Rehabilitation Administration, Shanghai, China
| | - Weidong Song
- Shanghai Bureau of Drug Rehabilitation Administration, Shanghai, China
| | - Jixue Zou
- Department of Health, Yunnan Bureau of Drug Rehabilitation Administration, Kunming, Yunnan China
| | - Tao Liu
- Yunnan Third Compulsory Drug Dependence Rehablitation Center Hospital, Kunming, Yunnan China
| | - Qingqing Zhan
- Yunnan Third Compulsory Drug Dependence Rehablitation Center Hospital, Kunming, Yunnan China
| | - Haifeng Jiang
- Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, 600 Wan Ping Nan Road, Shanghai, 200030, China. .,Shanghai Key Laboratory of Psychotic Disorders, Shanghai, China.
| | - Min Zhao
- grid.16821.3c0000 0004 0368 8293Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, 600 Wan Ping Nan Road, Shanghai, 200030 China ,grid.415630.50000 0004 1782 6212Shanghai Key Laboratory of Psychotic Disorders, Shanghai, China ,grid.16821.3c0000 0004 0368 8293Institute of Psychological and Behavioral Science, Shanghai Jiao Tong University, Shanghai, China ,grid.9227.e0000000119573309CAS Center for Excellence in Brain Science and Intelligence Technology (CEBSIT), Chinese Academy of Sciences, Shanghai, China
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22
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The synthetic cathinone 3,4-methylenedioxypyrovalerone increases impulsive action in rats. Behav Pharmacol 2020; 31:309-321. [DOI: 10.1097/fbp.0000000000000548] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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23
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Jurado-Barba R, Sion A, Martínez-Maldonado A, Domínguez-Centeno I, Prieto-Montalvo J, Navarrete F, García-Gutierrez MS, Manzanares J, Rubio G. Neuropsychophysiological Measures of Alcohol Dependence: Can We Use EEG in the Clinical Assessment? Front Psychiatry 2020; 11:676. [PMID: 32765317 PMCID: PMC7379886 DOI: 10.3389/fpsyt.2020.00676] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/27/2019] [Accepted: 06/29/2020] [Indexed: 01/03/2023] Open
Abstract
Addiction management is complex, and it requires a bio-psycho-social perspective, that ought to consider the multiple etiological and developmental factors. Because of this, a large amount of resources has been allocated to assess the vulnerability to dependence, i.e., to identify the processes underlying the transition from substance use to dependence, as well as its course, in order to determine the key points in its prevention, treatment, and recovery. Consequently, knowledge \from neuroscience must be taken into account, which is why different initiatives have emerged with this objective, such as the "Research Domain Criteria" (RDoC), and the "Addiction Neuroclinical Assessment" (ANA). Particularly, neuropsychophysiological measures could be used as markers of cognitive and behavioral attributes or traits in alcohol dependence, and even trace clinical change. In this way, the aim of this narrative review is to provide an overview following ANA clinical framework, to the most robust findings in neuropsychophysiological changes in alcohol dependence, that underlie the main cognitive domains implicated in addiction: incentive salience, negative emotionality, and executive functioning. The most consistent results have been found in event-related potential (ERP) analysis, especially in the P3 component, that could show a wide clinical utility, mainly for the executive functions. The review also shows the usefulness of other components, implicated in affective and substance-related processing (P1, N1, or the late positive potential LPP), as well as event-related oscillations, such as theta power, with a possible use as vulnerability or clinical marker in alcohol dependence. Finally, new tools emerging from psychophysiology research, based on functional connectivity or brain graph analysis could help toward a better understanding of altered circuits in alcohol dependence, as well as communication efficiency and effort during mental operations. This review concludes with an examination of these tools as possible markers in the clinical field and discusses methodological differences, the need for more replicability studies and incipient lines of work. It also uses consistent findings in psychophysiology to draw possible treatment targets and cognitive profiles in alcohol dependence.
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Affiliation(s)
- Rosa Jurado-Barba
- Biomedical Research Institute, Hospital 12 de Octubre, Madrid, Spain.,Department of Psychology, Education and Health Science Faculty, Camilo José Cela University, Madrid, Spain
| | - Ana Sion
- Biomedical Research Institute, Hospital 12 de Octubre, Madrid, Spain.,Addictive Disorders Network, Carlos III Institute, Madrid, Spain
| | | | - Isabel Domínguez-Centeno
- Department of Psychology, Education and Health Science Faculty, Camilo José Cela University, Madrid, Spain
| | | | - Francisco Navarrete
- Addictive Disorders Network, Carlos III Institute, Madrid, Spain.,Neuroscience Institute, Miguel Hernández University-CSIC, Alicante, Spain
| | - María Salud García-Gutierrez
- Addictive Disorders Network, Carlos III Institute, Madrid, Spain.,Neuroscience Institute, Miguel Hernández University-CSIC, Alicante, Spain
| | - Jorge Manzanares
- Addictive Disorders Network, Carlos III Institute, Madrid, Spain.,Neuroscience Institute, Miguel Hernández University-CSIC, Alicante, Spain
| | - Gabriel Rubio
- Biomedical Research Institute, Hospital 12 de Octubre, Madrid, Spain.,Addictive Disorders Network, Carlos III Institute, Madrid, Spain.,Medicine Faculty, Complutense de Madrid University, Madrid, Spain
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24
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Khajehpour H, Makkiabadi B, Ekhtiari H, Bakht S, Noroozi A, Mohagheghian F. Disrupted resting-state brain functional network in methamphetamine abusers: A brain source space study by EEG. PLoS One 2019; 14:e0226249. [PMID: 31825996 PMCID: PMC6906079 DOI: 10.1371/journal.pone.0226249] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2019] [Accepted: 11/15/2019] [Indexed: 01/03/2023] Open
Abstract
This study aimed to examine the effects of chronic methamphetamine use on the topological organization of whole-brain functional connectivity network (FCN) by reconstruction of neural-activity time series at resting-state. The EEG of 36 individuals with methamphetamine use disorder (IWMUD) and 24 normal controls (NCs) were recorded, pre-processed and source-reconstructed using standardized low-resolution tomography (sLORETA). The brain FCNs of participants were constructed and between-group differences in network topological properties were investigated using graph theoretical analysis. IWMUD showed decreased characteristic path length, increased clustering coefficient and small-world index at delta and gamma frequency bands compared to NCs. Moreover, abnormal changes in inter-regional connectivity and network hubs were observed in all the frequency bands. The results suggest that the IWMUD and NCs have distinct FCNs at all the frequency bands, particularly at the delta and gamma bands, in which deviated small-world brain topology was found in IWMUD.
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Affiliation(s)
- Hassan Khajehpour
- Department of Medical Physics and Biomedical Engineering, School of Medicine, Tehran University of Medical Sciences (TUMS), Tehran, Iran
- Research Center for Biomedical Technology and Robotics (RCBTR), Institute of Advanced Medical Technologies (IAMT), Tehran University of Medical Sciences (TUMS), Tehran, Iran
| | - Bahador Makkiabadi
- Department of Medical Physics and Biomedical Engineering, School of Medicine, Tehran University of Medical Sciences (TUMS), Tehran, Iran
- Research Center for Biomedical Technology and Robotics (RCBTR), Institute of Advanced Medical Technologies (IAMT), Tehran University of Medical Sciences (TUMS), Tehran, Iran
| | - Hamed Ekhtiari
- Laureate Institute for Brain Research (LIBR), Tulsa, OK, United States of America
- Iranian National Center for Addiction Studies (INCAS), Tehran University of Medical Sciences (TUMS), Tehran, Iran
| | - Sepideh Bakht
- Department of Cognitive Psychology, Institute for Cognitive Sciences Studies (ICSS), Tehran, Iran
| | - Alireza Noroozi
- Iranian National Center for Addiction Studies (INCAS), Tehran University of Medical Sciences (TUMS), Tehran, Iran
- Neuroscience and Addiction Studies Department, School of Advanced Technologies in Medicine (SATiM), Tehran University of Medical Sciences (TUMS), Tehran, Iran
| | - Fahimeh Mohagheghian
- Department of Biomedical Engineering, University of Connecticut, Storrs, CT, United States of America
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25
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Khajehpour H, Mohagheghian F, Ekhtiari H, Makkiabadi B, Jafari AH, Eqlimi E, Harirchian MH. Computer-aided classifying and characterizing of methamphetamine use disorder using resting-state EEG. Cogn Neurodyn 2019; 13:519-530. [PMID: 31741689 PMCID: PMC6825232 DOI: 10.1007/s11571-019-09550-z] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2019] [Revised: 07/18/2019] [Accepted: 08/01/2019] [Indexed: 12/17/2022] Open
Abstract
Methamphetamine (meth) is potently addictive and is closely linked to high crime rates in the world. Since meth withdrawal is very painful and difficult, most abusers relapse to abuse in traditional treatments. Therefore, developing accurate data-driven methods based on brain functional connectivity could be helpful in classifying and characterizing the neural features of meth dependence to optimize the treatments. Accordingly, in this study, computation of functional connectivity using resting-state EEG was used to classify meth dependence. Firstly, brain functional connectivity networks (FCNs) of 36 meth dependent individuals and 24 normal controls were constructed by weighted phase lag index, in six frequency bands: delta (1-4 Hz), theta (4-8 Hz), alpha (8-15 Hz), beta (15-30 Hz), gamma (30-45 Hz) and wideband (1-45 Hz).Then, significant differences in graph metrics and connectivity values of the FCNs were used to distinguish the two groups. Support vector machine classifier had the best performance with 93% accuracy, 100% sensitivity, 83% specificity and 0.94 F-score for differentiating between MDIs and NCs. The best performance yielded when selected features were the combination of connectivity values and graph metrics in the beta frequency band.
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Affiliation(s)
- Hassan Khajehpour
- Department of Medical Physics and Biomedical Engineering, School of Medicine, Tehran University of Medical Sciences (TUMS), Tehran, Iran
- Research Center for Biomedical Technology and Robotics (RCBTR), Institute of Advanced Medical Technologies (IAMT), Tehran University of Medical Sciences (TUMS), Tehran, Iran
| | - Fahimeh Mohagheghian
- Department of Medical Physics and Biomedical Engineering, School of Medicine, Shahid Beheshti University of Medical Sciences (SBMU), Tehran, Iran
| | - Hamed Ekhtiari
- Laureate Institute for Brain Research (LIBR), Tulsa, OK USA
- Iranian National Center for Addiction Studies (INCAS), Tehran University of Medical Sciences (TUMS), Tehran, Iran
| | - Bahador Makkiabadi
- Department of Medical Physics and Biomedical Engineering, School of Medicine, Tehran University of Medical Sciences (TUMS), Tehran, Iran
- Research Center for Biomedical Technology and Robotics (RCBTR), Institute of Advanced Medical Technologies (IAMT), Tehran University of Medical Sciences (TUMS), Tehran, Iran
| | - Amir Homayoun Jafari
- Department of Medical Physics and Biomedical Engineering, School of Medicine, Tehran University of Medical Sciences (TUMS), Tehran, Iran
- Research Center for Biomedical Technology and Robotics (RCBTR), Institute of Advanced Medical Technologies (IAMT), Tehran University of Medical Sciences (TUMS), Tehran, Iran
| | - Ehsan Eqlimi
- Department of Medical Physics and Biomedical Engineering, School of Medicine, Tehran University of Medical Sciences (TUMS), Tehran, Iran
- Research Center for Biomedical Technology and Robotics (RCBTR), Institute of Advanced Medical Technologies (IAMT), Tehran University of Medical Sciences (TUMS), Tehran, Iran
| | - Mohammad Hossein Harirchian
- Iranian Center of Neurological Research, Neuroscience Institute, Tehran University of Medical Sciences (TUMS), Tehran, Iran
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26
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Repeated administration of synthetic cathinone 3,4-methylenedioxypyrovalerone persistently increases impulsive choice in rats. Behav Pharmacol 2019; 30:555-565. [DOI: 10.1097/fbp.0000000000000492] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/04/2023]
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27
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Prashad S, Dedrick ES, Filbey FM. Cannabis users exhibit increased cortical activation during resting state compared to non-users. Neuroimage 2018; 179:176-186. [PMID: 29894828 PMCID: PMC6693493 DOI: 10.1016/j.neuroimage.2018.06.031] [Citation(s) in RCA: 20] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2018] [Revised: 05/18/2018] [Accepted: 06/08/2018] [Indexed: 12/16/2022] Open
Abstract
Studies have shown altered task-based brain functioning as a result of cannabis use. To date, however, whether similar alterations in baseline resting state and functional organization of neural activity are observable in cannabis users remains unknown. We characterized global resting state cortical activations and functional connectivity via electroencephalography (EEG) in cannabis users and related these activations to measures of cannabis use. Resting state EEG in the eyes closed condition was collected from age- and sex-matched cannabis users (N = 17; 6 females; mean age = 30.9 ± 7.4 years) and non-using controls (N = 21; 9 females; mean age = 33.1 ± 11.6 years). Power spectral density and spectral coherence were computed to determine differences in cortical activations and connectivity between the two groups in the delta (1-4Hz), theta (4-7 Hz), alpha (8-12 Hz), beta (13-30 Hz), and gamma (31-50 Hz) frequency bands. Cannabis users exhibited decreased delta and increased theta, beta, and gamma power compared to controls, suggesting increased cortical activation in resting state and a disinhibition of inhibitory functions that may interrupt cognitive processes. Cannabis users also exhibited increased interhemispheric and intrahemispheric coherence relative to controls, reduced mean network degree, and increased clustering coefficient in specific regions and frequencies. This increased cortical activity may indicate a loss of neural refinement and efficiency that may indicate a "noisy" brain. Lastly, measures related to cannabis use were correlated with spectral power and functional connectivity measures, indicating that specific electrophysiological signals are associated with cannabis use. These results suggest that there are differences in cortical activity and connectivity between cannabis users and non-using controls in the resting state that may be related to putative cognitive impairments and can inform effectiveness of intervention programs.
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Affiliation(s)
- Shikha Prashad
- Center for BrainHealth, School of Behavioral and Brain Sciences, University of Texas at Dallas, Dallas, TX, USA
| | - Elizabeth S Dedrick
- Center for BrainHealth, School of Behavioral and Brain Sciences, University of Texas at Dallas, Dallas, TX, USA
| | - Francesca M Filbey
- Center for BrainHealth, School of Behavioral and Brain Sciences, University of Texas at Dallas, Dallas, TX, USA.
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28
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Functional Connectivity of Chronic Cocaine Use Reveals Progressive Neuroadaptations in Neocortical, Striatal, and Limbic Networks. eNeuro 2018; 5:eN-NWR-0081-18. [PMID: 30073194 PMCID: PMC6071197 DOI: 10.1523/eneuro.0081-18.2018] [Citation(s) in RCA: 29] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2018] [Revised: 06/25/2018] [Accepted: 07/10/2018] [Indexed: 12/21/2022] Open
Abstract
Brain imaging studies indicate that chronic cocaine users display altered functional connectivity between prefrontal cortical, thalamic, striatal, and limbic regions; however, the use of cross-sectional designs in these studies precludes measuring baseline brain activity prior to cocaine use. Animal studies can circumvent this limitation by comparing functional connectivity between baseline and various time points after chronic cocaine use. In the present study, adult male Long–Evans rats were trained to self-administer cocaine intravenously for 6 h sessions daily over 14 consecutive days. Two additional groups serving as controls underwent sucrose self-administration or exposure to the test chambers alone. Functional magnetic resonance imaging was conducted before self-administration and after 1 and 14 d of abstinence (1d and 14d Abs). After 1d Abs from cocaine, there were increased clustering coefficients in brain areas involved in reward seeking, learning, memory, and autonomic and affective processing, including amygdala, hypothalamus, striatum, hippocampus, and thalamus. Similar changes in clustering coefficient after 1d Abs from sucrose were evident in predominantly thalamic brain regions. Notably, there were no changes in strength of functional connectivity at 1 or 14 d after either cocaine or sucrose self-administration. The results suggest that cocaine and sucrose can change the arrangement of functional connectivity of brain regions involved in cognition and emotion, but that these changes dissipate across the early stages of abstinence. The study also emphasizes the importance of including baseline measures in longitudinal functional neuroimaging designs seeking to assess functional connectivity in the context of substance use.
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29
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Wang WW, Lu YC, Tang WJ, Zhang JH, Sun HP, Feng XY, Liu HQ. Small-worldness of brain networks after brachial plexus injury: A resting-state functional magnetic resonance imaging study. Neural Regen Res 2018; 13:1061-1065. [PMID: 29926834 PMCID: PMC6022461 DOI: 10.4103/1673-5374.233450] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 05/02/2018] [Indexed: 12/18/2022] Open
Abstract
Research on brain function after brachial plexus injury focuses on local cortical functional reorganization, and few studies have focused on brain networks after brachial plexus injury. Changes in brain networks may help understanding of brain plasticity at the global level. We hypothesized that topology of the global cerebral resting-state functional network changes after unilateral brachial plexus injury. Thus, in this cross-sectional study, we recruited eight male patients with unilateral brachial plexus injury (right handedness, mean age of 27.9 ± 5.4 years old) and eight male healthy controls (right handedness, mean age of 28.6 ± 3.2). After acquiring and preprocessing resting-state magnetic resonance imaging data, the cerebrum was divided into 90 regions and Pearson's correlation coefficient calculated between regions. These correlation matrices were then converted into a binary matrix with affixed sparsity values of 0.1-0.46. Under sparsity conditions, both groups satisfied this small-world property. The clustering coefficient was markedly lower, while average shortest path remarkably higher in patients compared with healthy controls. These findings confirm that cerebral functional networks in patients still show small-world characteristics, which are highly effective in information transmission in the brain, as well as normal controls. Alternatively, varied small-worldness suggests that capacity of information transmission and integration in different brain regions in brachial plexus injury patients is damaged.
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Affiliation(s)
- Wei-Wei Wang
- Department of Radiology, Huashan Hospital, Fudan University, Shanghai, China
| | - Ye-Chen Lu
- Department of Hand Surgery, Huashan Hospital, Fudan University, Shanghai, China
| | - Wei-Jun Tang
- Department of Radiology, Huashan Hospital, Fudan University, Shanghai, China
| | - Jun-Hai Zhang
- Department of Radiology, Huashan Hospital, Fudan University, Shanghai, China
| | - Hua-Ping Sun
- Department of Radiology, Huashan Hospital, Fudan University, Shanghai, China
| | - Xiao-Yuan Feng
- Department of Radiology, Huashan Hospital, Fudan University, Shanghai, China
| | - Han-Qiu Liu
- Department of Radiology, Huashan Hospital, Fudan University, Shanghai, China
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30
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Colon-Perez LM, Pino JA, Saha K, Pompilus M, Kaplitz S, Choudhury N, Jagnarine DA, Geste JR, Levin BA, Wilks I, Setlow B, Bruijnzeel AW, Khoshbouei H, Torres GE, Febo M. Functional connectivity, behavioral and dopaminergic alterations 24 hours following acute exposure to synthetic bath salt drug methylenedioxypyrovalerone. Neuropharmacology 2018; 137:178-193. [PMID: 29729891 DOI: 10.1016/j.neuropharm.2018.04.031] [Citation(s) in RCA: 25] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2017] [Revised: 04/26/2018] [Accepted: 04/29/2018] [Indexed: 12/22/2022]
Abstract
Among cathinone drugs known as bath salts, methylenedioxypyrovalerone (MDPV) exerts its potent actions via the dopamine (DA) system, and at intoxicating doses may produce adverse behavioral effects. Previous work by our group suggests that prolonged alterations in correlated neural activity between cortical and striatal areas could underlie, at least in part, the adverse reactions to this bath salt drug. In the present study, we assessed the effect of acute MDPV administration on brain functional connectivity at 1 and 24 h in rats. Using graph theory metrics to assess in vivo brain functional network organization we observed that 24 h after MDPV administration there was an increased clustering coefficient, rich club index, and average path length. Increases in these metrics suggests that MDPV produces a prolonged pattern of correlated activity characterized by greater interactions between subsets of high degree nodes but a reduced interaction with regions outside this core subset. Further analysis revealed that the core set of nodes include prefrontal cortical, amygdala, hypothalamic, somatosensory and striatal areas. At the molecular level, MDPV downregulated the dopamine transporter (DAT) in striatum and produced a shift in its subcellular distribution, an effect likely to involve rapid internalization at the membrane. These new findings suggest that potent binding of MDPV to DAT may trigger internalization and a prolonged alteration in homeostatic regulation of DA and functional brain network reorganization. We propose that the observed MDPV-induced network reorganization and DAergic changes may contribute to previously reported adverse behavioral responses to MDPV.
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Affiliation(s)
- Luis M Colon-Perez
- Department of Psychiatry, College of Medicine, University of Florida, Gainesville, FL 32610, USA; Center for Addiction Research and Education (CARE), College of Medicine, University of Florida, Gainesville, FL 32610, USA; Advanced Magnetic Resonance Imaging and Spectroscopy (AMRIS) Facility, College of Medicine, University of Florida, Gainesville, FL 32610, USA
| | - Jose A Pino
- Department of Pharmacology and Therapeutics, College of Medicine, University of Florida, Gainesville, FL 32610, USA
| | - Kaustuv Saha
- Department of Neuroscience, College of Medicine, University of Florida, Gainesville, FL 32610, USA
| | - Marjory Pompilus
- Department of Psychiatry, College of Medicine, University of Florida, Gainesville, FL 32610, USA
| | - Sherman Kaplitz
- Department of Psychiatry, College of Medicine, University of Florida, Gainesville, FL 32610, USA
| | - Nafisa Choudhury
- Department of Psychiatry, College of Medicine, University of Florida, Gainesville, FL 32610, USA
| | - Darin A Jagnarine
- Department of Psychiatry, College of Medicine, University of Florida, Gainesville, FL 32610, USA
| | - Jean R Geste
- Department of Psychiatry, College of Medicine, University of Florida, Gainesville, FL 32610, USA
| | - Brandon A Levin
- Department of Psychiatry, College of Medicine, University of Florida, Gainesville, FL 32610, USA
| | - Isaac Wilks
- Department of Psychiatry, College of Medicine, University of Florida, Gainesville, FL 32610, USA
| | - Barry Setlow
- Department of Psychiatry, College of Medicine, University of Florida, Gainesville, FL 32610, USA; Department of Neuroscience, College of Medicine, University of Florida, Gainesville, FL 32610, USA; The McKnight Brain Institute, College of Medicine, University of Florida, Gainesville, FL 32610, USA; Center for Addiction Research and Education (CARE), College of Medicine, University of Florida, Gainesville, FL 32610, USA
| | - Adriaan W Bruijnzeel
- Department of Psychiatry, College of Medicine, University of Florida, Gainesville, FL 32610, USA; Department of Neuroscience, College of Medicine, University of Florida, Gainesville, FL 32610, USA; The McKnight Brain Institute, College of Medicine, University of Florida, Gainesville, FL 32610, USA; Center for Addiction Research and Education (CARE), College of Medicine, University of Florida, Gainesville, FL 32610, USA
| | - Habibeh Khoshbouei
- Department of Neuroscience, College of Medicine, University of Florida, Gainesville, FL 32610, USA; The McKnight Brain Institute, College of Medicine, University of Florida, Gainesville, FL 32610, USA; Center for Addiction Research and Education (CARE), College of Medicine, University of Florida, Gainesville, FL 32610, USA
| | - Gonzalo E Torres
- Department of Pharmacology and Therapeutics, College of Medicine, University of Florida, Gainesville, FL 32610, USA; Center for Addiction Research and Education (CARE), College of Medicine, University of Florida, Gainesville, FL 32610, USA
| | - Marcelo Febo
- Department of Psychiatry, College of Medicine, University of Florida, Gainesville, FL 32610, USA; Department of Neuroscience, College of Medicine, University of Florida, Gainesville, FL 32610, USA; The McKnight Brain Institute, College of Medicine, University of Florida, Gainesville, FL 32610, USA; Center for Addiction Research and Education (CARE), College of Medicine, University of Florida, Gainesville, FL 32610, USA; Advanced Magnetic Resonance Imaging and Spectroscopy (AMRIS) Facility, College of Medicine, University of Florida, Gainesville, FL 32610, USA.
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31
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Ipser JC, Uhlmann A, Taylor P, Harvey BH, Wilson D, Stein DJ. Distinct intrinsic functional brain network abnormalities in methamphetamine-dependent patients with and without a history of psychosis. Addict Biol 2018; 23:347-358. [PMID: 27917569 DOI: 10.1111/adb.12478] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2016] [Revised: 10/05/2016] [Accepted: 11/02/2016] [Indexed: 01/13/2023]
Abstract
Chronic methamphetamine use is associated with executive functioning deficits that suggest dysfunctional cognitive control networks (CCNs) in the brain. Likewise, abnormal connectivity between intrinsic CCNs and default mode networks (DMNs) has also been associated with poor cognitive function in clinical populations. Accordingly, we tested the extent to which methamphetamine use predicts abnormal connectivity between these networks, and whether, as predicted, these abnormalities are compounded in patients with a history of methamphetamine-associated psychosis (MAP). Resting-state fMRI data were acquired from 46 methamphetamine-dependent patients [19 with MAP, 27 without (MD)], as well as 26 healthy controls (CTRL). Multivariate network modelling and whole-brain voxel-wise connectivity analyses were conducted to identify group differences in intrinsic connectivity across four cognitive control and three DMN networks identified using an independent components analysis approach (meta-ICA). The relationship of network connectivity and psychotic symptom severity, as well as antipsychotic treatment and methamphetamine use variables, was also investigated. Robust evidence of hyper-connectivity was observed between the right frontoparietal and anterior DMN networks in MAP patients, and 'normalized' with increased duration of treatment with antipsychotics. Attenuation of anticorrelated anterior DMN-dorsal attention network activity was also restricted to this group. Elevated coupling detected in MD participants between anterior and posterior DMN networks became less apparent with increasing duration of abstinence from methamphetamine. In summary, we observed both alterations of RSN connectivity between DMN networks with chronic methamphetamine exposure, as well as DMN-CCN coupling abnormalities consistent with possible MAP-specific frontoparietal deficits in the biasing of task-appropriate network activity.
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Affiliation(s)
- Jonathan C. Ipser
- Department of Psychiatry Mental Health; University of Cape Town; South Africa
| | - Anne Uhlmann
- Department of Psychiatry Mental Health; University of Cape Town; South Africa
- Anxiety and Stress Disorders Research Unit, Medical Research Council; South Africa
| | - Paul Taylor
- MRC/UCT Medical Imaging Research Unit, Department of Human Biology; University of Cape Town; South Africa
- African Institute for Mathematical Sciences; South Africa
- Scientific and Statistical Computing Core; National Institute of Mental Health; Bethesda MD USA
| | - Brian H. Harvey
- Center of Excellence for Pharmaceutical Sciences; North-West University; South Africa
| | - Don Wilson
- Department of Psychiatry Mental Health; University of Cape Town; South Africa
| | - Dan J. Stein
- Department of Psychiatry Mental Health; University of Cape Town; South Africa
- Anxiety and Stress Disorders Research Unit, Medical Research Council; South Africa
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32
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Sannino S, Stramaglia S, Lacasa L, Marinazzo D. Visibility graphs for fMRI data: Multiplex temporal graphs and their modulations across resting-state networks. Netw Neurosci 2017; 1:208-221. [PMID: 29911672 PMCID: PMC5988401 DOI: 10.1162/netn_a_00012] [Citation(s) in RCA: 24] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2017] [Accepted: 04/06/2017] [Indexed: 01/02/2023] Open
Abstract
Visibility algorithms are a family of methods that map time series into graphs, such that the tools of graph theory and network science can be used for the characterization of time series. This approach has proved a convenient tool, and visibility graphs have found applications across several disciplines. Recently, an approach has been proposed to extend this framework to multivariate time series, allowing a novel way to describe collective dynamics. Here we test their application to fMRI time series, following two main motivations, namely that (a) this approach allows vs to simultaneously capture and process relevant aspects of both local and global dynamics in an easy and intuitive way, and (b) this provides a suggestive bridge between time series and network theory that nicely fits the consolidating field of network neuroscience. Our application to a large open dataset reveals differences in the similarities of temporal networks (and thus in correlated dynamics) across resting-state networks, and gives indications that some differences in brain activity connected to psychiatric disorders could be picked up by this approach.
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Affiliation(s)
- Speranza Sannino
- Department of Data Analysis, Faculty of Psychology and Educational Sciences, University of Ghent, Belgium
- Department of Electric and Electronic Engineering, University of Cagliari, Italy
| | | | - Lucas Lacasa
- School of Mathematical Sciences, Queen Mary University of London, United Kingdom
| | - Daniele Marinazzo
- Department of Data Analysis, Faculty of Psychology and Educational Sciences, University of Ghent, Belgium
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33
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Siyah Mansoory M, Oghabian MA, Jafari AH, Shahbabaie A. Analysis of Resting-State fMRI Topological Graph Theory Properties in Methamphetamine Drug Users Applying Box-Counting Fractal Dimension. Basic Clin Neurosci 2017; 8:371-385. [PMID: 29167724 PMCID: PMC5691169 DOI: 10.18869/nirp.bcn.8.5.371] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/03/2023] Open
Abstract
Introduction: Graph theoretical analysis of functional Magnetic Resonance Imaging (fMRI) data has provided new measures of mapping human brain in vivo. Of all methods to measure the functional connectivity between regions, Linear Correlation (LC) calculation of activity time series of the brain regions as a linear measure is considered the most ubiquitous one. The strength of the dependence obligatory for graph construction and analysis is consistently underestimated by LC, because not all the bivariate distributions, but only the marginals are Gaussian. In a number of studies, Mutual Information (MI) has been employed, as a similarity measure between each two time series of the brain regions, a pure nonlinear measure. Owing to the complex fractal organization of the brain indicating self-similarity, more information on the brain can be revealed by fMRI Fractal Dimension (FD) analysis. Methods: In the present paper, Box-Counting Fractal Dimension (BCFD) is introduced for graph theoretical analysis of fMRI data in 17 methamphetamine drug users and 18 normal controls. Then, BCFD performance was evaluated compared to those of LC and MI methods. Moreover, the global topological graph properties of the brain networks inclusive of global efficiency, clustering coefficient and characteristic path length in addict subjects were investigated too. Results: Compared to normal subjects by using statistical tests (P<0.05), topological graph properties were postulated to be disrupted significantly during the resting-state fMRI. Conclusion: Based on the results, analyzing the graph topological properties (representing the brain networks) based on BCFD is a more reliable method than LC and MI.
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Affiliation(s)
- Meysam Siyah Mansoory
- Department of Medical Physics & Biomedical Engineering, School of Medicine, Tehran University of Medical Sciences, Tehran, Iran.,Department of Neuro-Imaging and Analysis, Research Center for Molecular and Cellular Imaging, Tehran University of Medical Sciences, Tehran, Iran
| | - Mohammad Ali Oghabian
- Department of Medical Physics & Biomedical Engineering, School of Medicine, Tehran University of Medical Sciences, Tehran, Iran.,Department of Neuro-Imaging and Analysis, Research Center for Molecular and Cellular Imaging, Tehran University of Medical Sciences, Tehran, Iran
| | - Amir Homayoun Jafari
- Department of Medical Physics & Biomedical Engineering, School of Medicine, Tehran University of Medical Sciences, Tehran, Iran.,Research Center for Biomedical Technologies and Robotics, Tehran University of Medical Sciences, Tehran, Iran
| | - Alireza Shahbabaie
- Department of Neuro-Imaging and Analysis, Research Center for Molecular and Cellular Imaging, Tehran University of Medical Sciences, Tehran, Iran.,Iranian National Center for Addiction Studies, Tehran University of Medical Sciences, Tehran, Iran.,Substance Abuse and Dependence Research Center, University of Social Welfare and Rehabilitation Sciences, Tehran, Iran
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Regner MF, Saenz N, Maharajh K, Yamamoto DJ, Mohl B, Wylie K, Tregellas J, Tanabe J. Top-Down Network Effective Connectivity in Abstinent Substance Dependent Individuals. PLoS One 2016; 11:e0164818. [PMID: 27776135 PMCID: PMC5077096 DOI: 10.1371/journal.pone.0164818] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2016] [Accepted: 09/30/2016] [Indexed: 01/25/2023] Open
Abstract
Objective We hypothesized that compared to healthy controls, long-term abstinent substance dependent individuals (SDI) will differ in their effective connectivity between large-scale brain networks and demonstrate increased directional information from executive control to interoception-, reward-, and habit-related networks. In addition, using graph theory to compare network efficiencies we predicted decreased small-worldness in SDI compared to controls. Methods 50 SDI and 50 controls of similar sex and age completed psychological surveys and resting state fMRI. fMRI results were analyzed using group independent component analysis; 14 networks-of-interest (NOI) were selected using template matching to a canonical set of resting state networks. The number, direction, and strength of connections between NOI were analyzed with Granger Causality. Within-group thresholds were p<0.005 using a bootstrap permutation. Between group thresholds were p<0.05, FDR-corrected for multiple comparisons. NOI were correlated with behavioral measures, and group-level graph theory measures were compared. Results Compared to controls, SDI showed significantly greater Granger causal connectivity from right executive control network (RECN) to dorsal default mode network (dDMN) and from dDMN to basal ganglia network (BGN). RECN was negatively correlated with impulsivity, behavioral approach, and negative affect; dDMN was positively correlated with impulsivity. Among the 14 NOI, SDI showed greater bidirectional connectivity; controls showed more unidirectional connectivity. SDI demonstrated greater global efficiency and lower local efficiency. Conclusions Increased effective connectivity in long-term abstinent drug users may reflect improved cognitive control over habit and reward processes. Higher global and lower local efficiency across all networks in SDI compared to controls may reflect connectivity changes associated with drug dependence or remission and requires future, longitudinal studies to confirm.
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Affiliation(s)
- Michael F. Regner
- Department of Radiology, University of Colorado School of Medicine, Aurora, CO, 80045, United States of America
- Department of Bioengineering, University of Colorado College of Engineering and Applied Sciences, Aurora, CO, 80045, United States of America
- * E-mail:
| | - Naomi Saenz
- Department of Radiology, University of Colorado School of Medicine, Aurora, CO, 80045, United States of America
| | - Keeran Maharajh
- Department of Psychiatry, University of Colorado School of Medicine, Aurora, CO, 80045, United States of America
| | - Dorothy J. Yamamoto
- Department of Radiology, University of Colorado School of Medicine, Aurora, CO, 80045, United States of America
| | - Brianne Mohl
- Department of Radiology, University of Colorado School of Medicine, Aurora, CO, 80045, United States of America
| | - Korey Wylie
- Department of Psychiatry, University of Colorado School of Medicine, Aurora, CO, 80045, United States of America
| | - Jason Tregellas
- Department of Psychiatry, University of Colorado School of Medicine, Aurora, CO, 80045, United States of America
| | - Jody Tanabe
- Department of Radiology, University of Colorado School of Medicine, Aurora, CO, 80045, United States of America
- Department of Psychiatry, University of Colorado School of Medicine, Aurora, CO, 80045, United States of America
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Lacasa L, Nicosia V, Latora V. Network structure of multivariate time series. Sci Rep 2015; 5:15508. [PMID: 26487040 PMCID: PMC4614448 DOI: 10.1038/srep15508] [Citation(s) in RCA: 50] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2015] [Accepted: 09/28/2015] [Indexed: 11/09/2022] Open
Abstract
Our understanding of a variety of phenomena in physics, biology and economics crucially depends on the analysis of multivariate time series. While a wide range tools and techniques for time series analysis already exist, the increasing availability of massive data structures calls for new approaches for multidimensional signal processing. We present here a non-parametric method to analyse multivariate time series, based on the mapping of a multidimensional time series into a multilayer network, which allows to extract information on a high dimensional dynamical system through the analysis of the structure of the associated multiplex network. The method is simple to implement, general, scalable, does not require ad hoc phase space partitioning, and is thus suitable for the analysis of large, heterogeneous and non-stationary time series. We show that simple structural descriptors of the associated multiplex networks allow to extract and quantify nontrivial properties of coupled chaotic maps, including the transition between different dynamical phases and the onset of various types of synchronization. As a concrete example we then study financial time series, showing that a multiplex network analysis can efficiently discriminate crises from periods of financial stability, where standard methods based on time-series symbolization often fail.
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Affiliation(s)
- Lucas Lacasa
- School of Mathematical Sciences, Queen Mary University of London, Mile End Road, E14NS London, UK
| | - Vincenzo Nicosia
- School of Mathematical Sciences, Queen Mary University of London, Mile End Road, E14NS London, UK
| | - Vito Latora
- School of Mathematical Sciences, Queen Mary University of London, Mile End Road, E14NS London, UK
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Wang Z, Suh J, Li Z, Li Y, Franklin T, O'Brien C, Childress AR. A hyper-connected but less efficient small-world network in the substance-dependent brain. Drug Alcohol Depend 2015; 152:102-8. [PMID: 25957794 PMCID: PMC4458212 DOI: 10.1016/j.drugalcdep.2015.04.015] [Citation(s) in RCA: 41] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/07/2015] [Revised: 04/17/2015] [Accepted: 04/17/2015] [Indexed: 11/19/2022]
Abstract
BACKGROUND The functional interconnections of the addicted brain may differ from the non-addicted population in important ways, but prioranalytic approaches were usually limited to the study of connections between a few number of selected brain regions. Recent approaches enable examination of the vast functional interactions within the entire brain, the functional connectome (FCM). The purpose of this study was to characterize FCM alterations in addiction using resting state functional Magnetic Resonance Imaging (rsfMRI) and to assess their relations to addiction-related symptoms. METHODS rsfMRI data were acquired from 20 chronic polydrug users whose primary diagnosis was cocaine dependence (DRUG) and 19 age-matched non-drug using healthy controls (CTL). FCM was assessed using graph theoretical analysis. RESULTS Among the assessed 90 brain subdivisions, DRUG showed stronger functional connectivity. After controlling functional connectivity difference and the resultant network density, DRUG showed reduced communication efficiency and reduced small-worldness. CONCLUSIONS The increased connection strength in drug users' brain suggests an elevated dynamic resting state that may enable a rapid, semi-automatic, execution of behaviors directed toward drug-related goals.The reduced FCM communication efficiency and reduced small-worldness suggest a loss of normal inter-regional communications and topology features that makes it difficult to inhibit the drug seeking behavior.
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Affiliation(s)
- Ze Wang
- Zhejiang Key Laboratory for Research in Assessment of Cognitive Impairments, China; Center for Cognition and Brain Disorders, Hangzhou Normal University, China; Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, USA.
| | - Jesse Suh
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, USA; VISN-4 Mental Illness Research, Education and Clinical Center, VA Medical Center, Philadelphia, PA 19104, USA
| | - Zhengjun Li
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, USA
| | - Yin Li
- Zhejiang Key Laboratory for Research in Assessment of Cognitive Impairments, China
| | - Teresa Franklin
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, USA
| | - Charles O'Brien
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, USA
| | - Anna Rose Childress
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, USA
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Rezazade M, Lashani Z, Ahmadi K. Mental health status among the staff of harm reduction centers. INTERNATIONAL JOURNAL OF HIGH RISK BEHAVIORS & ADDICTION 2014; 3:e12244. [PMID: 24971296 PMCID: PMC4070187 DOI: 10.5812/ijhrba.12244] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Received: 05/19/2013] [Revised: 12/14/2013] [Accepted: 01/27/2014] [Indexed: 11/16/2022]
Abstract
BACKGROUND Creating a supportive environment encourages charity services to help risk groups and individuals which has magnificent impacts on reducing their harm. OBJECTIVES According to this plan, the purpose of this study was to investigate the mental health status in the staff of harm reduction centers. MATERIALS AND METHODS The clustered sample of this comparative study consisted of 49 staff of harm reduction centers. The study was supported by the United Nations Development Program in Tehran, Iran. The participants completed GHQ-28 and DASS-21 questionnaires along with sociologic forms and the results were evaluated by descriptive statistics indexes and independent sample t-test. RESULTS One-hundred percent of the participants in this study showed the symptoms of psychological disorders, and approximately 16 percent suffered from moderate to high degree of anxiety, depression and stress. The level of anxiety (P ˂ 0.04) and stress (P ˂ 0.01) in the younger staff (less than 40 years) was significantly higher than older staff (more than 40 years old). In addition, somatic symptoms (P ˂ 0.05) and social withdrawal (P ˂ 0.01) were significantly higher in women than men. CONCLUSIONS Accordingly, major mental disorders in the staff of harm reduction centers, especially women and younger people need to be considered more than before.
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Affiliation(s)
- Majid Rezazade
- AIDS Prevention and Control Committee, Welfare Organization State, Tehran, IR Iran
- Corresponding author: Majid Rezazade, AIDS Prevention and Control Committee, Welfare Organization State, P.O.Box: 19395‑5487, Tehran, IR Iran. Tel: +98-2155951581, Fax: +98-2151212404, E-mail:
| | - Zeynab Lashani
- Department of Human Sciences, Shahed University, Tehran, IR Iran
| | - Khodabakhsh Ahmadi
- Behavioral Sciences Research Center, Baqiyatallah University of Medical Sciences, Tehran, IR Iran
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Kwiatkowski MA, Roos A, Stein DJ, Thomas KGF, Donald K. Effects of prenatal methamphetamine exposure: a review of cognitive and neuroimaging studies. Metab Brain Dis 2014; 29:245-54. [PMID: 24370774 DOI: 10.1007/s11011-013-9470-7] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/15/2013] [Accepted: 12/06/2013] [Indexed: 10/25/2022]
Abstract
Prenatal methamphetamine exposure (PME) is a significant problem in several parts of the world and poses important health risks for the developing fetus. Research on the short- and long-term outcomes of PME is scarce, however. Here, we summarize present knowledge on the cognitive and behavioral outcomes of PME, based on a review of the neuroimaging, neuropsychology, and neuroscience literature published in the past 15 years. Several studies have reported that the behavioral and cognitive sequelae of PME include broad deficits in the domains of attention, memory, and visual-motor integration. Knowledge regarding brain-behavior relationships is poor, however, in large part because imaging studies are rare. Hence, the effects of PME on developing neurocircuitry and brain architecture remain speculative, and are largely deductive. Some studies have implicated the dopamine-rich fronto-striatal pathways; however, cognitive deficits (e.g., impaired visual-motor integration) that should be associated with damage to those pathways are not manifested consistently across studies. We conclude by discussing challenges endemic to research on prenatal drug exposure, and argue that they may account for some of the inconsistencies in the extant research on PME. Studies confirming predicted brain-behavior relationships in PME, and exploring possible mechanisms underlying those relationships, are needed if neuroscience is to address the urgency of this growing public health problem.
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Ahmadlou M, Adeli A, Bajo R, Adeli H. Complexity of functional connectivity networks in mild cognitive impairment subjects during a working memory task. Clin Neurophysiol 2014; 125:694-702. [DOI: 10.1016/j.clinph.2013.08.033] [Citation(s) in RCA: 104] [Impact Index Per Article: 10.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2013] [Revised: 07/30/2013] [Accepted: 08/06/2013] [Indexed: 01/25/2023]
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Hemmati S, Ahmadlou M, Gharib M, Vameghi R, Sajedi F. Down syndrome's brain dynamics: analysis of fractality in resting state. Cogn Neurodyn 2013; 7:333-40. [PMID: 24427209 PMCID: PMC3713204 DOI: 10.1007/s11571-013-9248-y] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2012] [Revised: 12/16/2012] [Accepted: 03/14/2013] [Indexed: 11/26/2022] Open
Abstract
To the best knowledge of the authors there is no study on nonlinear brain dynamics of down syndrome (DS) patients, whereas brain is a highly complex and nonlinear system. In this study, fractal dimension of EEG, as a key characteristic of brain dynamics, showing irregularity and complexity of brain dynamics, was used for evaluation of the dynamical changes in the DS brain. The results showed higher fractality of the DS brain in almost all regions compared to the normal brain, which indicates less centrality and higher irregular or random functioning of the DS brain regions. Also, laterality analysis of the frontal lobe showed that the normal brain had a right frontal laterality of complexity whereas the DS brain had an inverse pattern (left frontal laterality). Furthermore, the high accuracy of 95.8 % obtained by enhanced probabilistic neural network classifier showed the potential of nonlinear dynamic analysis of the brain for diagnosis of DS patients. Moreover, the results showed that the higher EEG fractality in DS is associated with the higher fractality in the low frequencies (delta and theta), in broad regions of the brain, and the high frequencies (beta and gamma), majorly in the frontal regions.
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Affiliation(s)
- Sahel Hemmati
- />Pediatric Neurorehabilitation Research Center, University of Social Welfare and Rehabilitation Sciences, Tehran, Iran
| | - Mehran Ahmadlou
- />Pediatric Neurorehabilitation Research Center, University of Social Welfare and Rehabilitation Sciences, Tehran, Iran
- />Dynamic Brain Research Group, Tehran, Iran
- />Netherlands Institute for Neuroscience, Amsterdam, The Netherlands
| | - Masoud Gharib
- />Pediatric Neurorehabilitation Research Center, University of Social Welfare and Rehabilitation Sciences, Tehran, Iran
| | - Roshanak Vameghi
- />Pediatric Neurorehabilitation Research Center, University of Social Welfare and Rehabilitation Sciences, Tehran, Iran
| | - Firoozeh Sajedi
- />Pediatric Neurorehabilitation Research Center, University of Social Welfare and Rehabilitation Sciences, Tehran, Iran
- />Dynamic Brain Research Group, Tehran, Iran
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Sajedi F, Ahmadlou M, Vameghi R, Gharib M, Hemmati S. Linear and nonlinear analysis of brain dynamics in children with cerebral palsy. RESEARCH IN DEVELOPMENTAL DISABILITIES 2013; 34:1388-1396. [PMID: 23474991 DOI: 10.1016/j.ridd.2013.01.016] [Citation(s) in RCA: 10] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/01/2012] [Revised: 01/20/2013] [Accepted: 01/21/2013] [Indexed: 06/01/2023]
Abstract
This study was carried out to determine linear and nonlinear changes of brain dynamics and their relationships with the motor dysfunctions in CP children. For this purpose power of EEG frequency bands (as a linear analysis) and EEG fractality (as a nonlinear analysis) were computed in eyes-closed resting state and statistically compared between 26 CP and 26 normal children. Based on these characteristics accuracy of the classification between the two groups was obtained by enhanced probabilistic neural network (EPNN). Severity of gross motor and manual disabilities was determined by standard systems and the relation between the deficient brain dynamics and severity of the motor dysfunctions was obtained by Pearson's correlation coefficient. A definitely higher delta and lower theta and alpha powers, and higher EEG complexity in CP patients. As such a high accuracy of 94.8% in distinguishing the two groups was obtained. Moreover significant positive correlations were found between beta power and severity of manual disabilities and gross motor dysfunctions in the boys with CP. It is concluded that the obtained brain dynamics' characteristics are useful in diagnosis of CP. Furthermore severity of the motor dysfunctions in boys with CP could be evaluated by the beta activity.
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Affiliation(s)
- Firoozeh Sajedi
- Pediatric Neurorehabilitation Research Center, University of Social Welfare and Rehabilitation Sciences, Tehran, Iran
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Ahmadlou M, Gharib M, Hemmati S, Vameghi R, Sajedi F. Disrupted small-world brain network in children with Down Syndrome. Clin Neurophysiol 2013; 124:1755-64. [PMID: 23583023 DOI: 10.1016/j.clinph.2013.03.004] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2012] [Revised: 03/08/2013] [Accepted: 03/12/2013] [Indexed: 12/14/2022]
Abstract
OBJECTIVE To explore how the global organization or topology of the functional brain connectivity (FBC) is affected in Down Syndrome (DS). METHODS As the brain is a highly complex network including numerous nonlinearly interacted neuronal areas, the FBCs of typically developing (TD) children and DS patients were computed using a nonlinear synchronization method. Then the differences in global organization of the obtained FBCs of the two groups were analyzed, in all electroencephalogram (EEG) frequency bands, in the framework of Small-Worldness Network (a network with optimum balance between segregation and integration of information). RESULTS The topology of the functional connectivity of DS patients is disrupted in the whole brain in alpha and theta bands, and especially in the left intra-hemispheric brain networks in upper alpha band. CONCLUSIONS The global organization of the DS brain does not resemble a Small-World network, but it works as a random network. SIGNIFICANCE It is the first study on global organization of the FBC in DS.
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Affiliation(s)
- Mehran Ahmadlou
- Netherlands Institute for Neuroscience, Amsterdam, Netherlands
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