3
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Metzger M, Dukic S, McMackin R, Giglia E, Mitchell M, Bista S, Costello E, Peelo C, Tadjine Y, Sirenko V, Plaitano S, Coffey A, McManus L, Farnell Sharp A, Mehra P, Heverin M, Bede P, Muthuraman M, Pender N, Hardiman O, Nasseroleslami B. Functional network dynamics revealed by EEG microstates reflect cognitive decline in amyotrophic lateral sclerosis. Hum Brain Mapp 2024; 45:e26536. [PMID: 38087950 PMCID: PMC10789208 DOI: 10.1002/hbm.26536] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2023] [Revised: 10/26/2023] [Accepted: 10/31/2023] [Indexed: 01/16/2024] Open
Abstract
Recent electroencephalography (EEG) studies have shown that patterns of brain activity can be used to differentiate amyotrophic lateral sclerosis (ALS) and control groups. These differences can be interrogated by examining EEG microstates, which are distinct, reoccurring topographies of the scalp's electrical potentials. Quantifying the temporal properties of the four canonical microstates can elucidate how the dynamics of functional brain networks are altered in neurological conditions. Here we have analysed the properties of microstates to detect and quantify signal-based abnormality in ALS. High-density resting-state EEG data from 129 people with ALS and 78 HC were recorded longitudinally over a 24-month period. EEG topographies were extracted at instances of peak global field power to identify four microstate classes (labelled A-D) using K-means clustering. Each EEG topography was retrospectively associated with a microstate class based on global map dissimilarity. Changes in microstate properties over the course of the disease were assessed in people with ALS and compared with changes in clinical scores. The topographies of microstate classes remained consistent across participants and conditions. Differences were observed in coverage, occurrence, duration, and transition probabilities between ALS and control groups. The duration of microstate class B and coverage of microstate class C correlated with lower limb functional decline. The transition probabilities A to D, C to B and C to B also correlated with cognitive decline (total ECAS) in those with cognitive and behavioural impairments. Microstate characteristics also significantly changed over the course of the disease. Examining the temporal dependencies in the sequences of microstates revealed that the symmetry and stationarity of transition matrices were increased in people with late-stage ALS. These alterations in the properties of EEG microstates in ALS may reflect abnormalities within the sensory network and higher-order networks. Microstate properties could also prospectively predict symptom progression in those with cognitive impairments.
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Affiliation(s)
- Marjorie Metzger
- Academic Unit of Neurology, School of Medicine, Trinity Biomedical Sciences Institute, Trinity College DublinUniversity of DublinDublinIreland
| | - Stefan Dukic
- Academic Unit of Neurology, School of Medicine, Trinity Biomedical Sciences Institute, Trinity College DublinUniversity of DublinDublinIreland
- Department of Neurology, University Medical Centre Utrecht Brain CentreUtrecht UniversityUtrechtThe Netherlands
| | - Roisin McMackin
- Academic Unit of Neurology, School of Medicine, Trinity Biomedical Sciences Institute, Trinity College DublinUniversity of DublinDublinIreland
- Discipline of Physiology, School of Medicine, Trinity Biomedical Sciences Institute, Trinity College DublinUniversity of DublinDublinIreland
| | - Eileen Giglia
- Academic Unit of Neurology, School of Medicine, Trinity Biomedical Sciences Institute, Trinity College DublinUniversity of DublinDublinIreland
| | - Matthew Mitchell
- Academic Unit of Neurology, School of Medicine, Trinity Biomedical Sciences Institute, Trinity College DublinUniversity of DublinDublinIreland
| | - Saroj Bista
- Academic Unit of Neurology, School of Medicine, Trinity Biomedical Sciences Institute, Trinity College DublinUniversity of DublinDublinIreland
| | - Emmet Costello
- Academic Unit of Neurology, School of Medicine, Trinity Biomedical Sciences Institute, Trinity College DublinUniversity of DublinDublinIreland
| | - Colm Peelo
- Academic Unit of Neurology, School of Medicine, Trinity Biomedical Sciences Institute, Trinity College DublinUniversity of DublinDublinIreland
| | - Yasmine Tadjine
- Academic Unit of Neurology, School of Medicine, Trinity Biomedical Sciences Institute, Trinity College DublinUniversity of DublinDublinIreland
| | - Vladyslav Sirenko
- Academic Unit of Neurology, School of Medicine, Trinity Biomedical Sciences Institute, Trinity College DublinUniversity of DublinDublinIreland
| | - Serena Plaitano
- Academic Unit of Neurology, School of Medicine, Trinity Biomedical Sciences Institute, Trinity College DublinUniversity of DublinDublinIreland
| | - Amina Coffey
- Academic Unit of Neurology, School of Medicine, Trinity Biomedical Sciences Institute, Trinity College DublinUniversity of DublinDublinIreland
| | - Lara McManus
- Academic Unit of Neurology, School of Medicine, Trinity Biomedical Sciences Institute, Trinity College DublinUniversity of DublinDublinIreland
| | - Adelais Farnell Sharp
- Academic Unit of Neurology, School of Medicine, Trinity Biomedical Sciences Institute, Trinity College DublinUniversity of DublinDublinIreland
| | - Prabhav Mehra
- Academic Unit of Neurology, School of Medicine, Trinity Biomedical Sciences Institute, Trinity College DublinUniversity of DublinDublinIreland
| | - Mark Heverin
- Academic Unit of Neurology, School of Medicine, Trinity Biomedical Sciences Institute, Trinity College DublinUniversity of DublinDublinIreland
| | - Peter Bede
- Academic Unit of Neurology, School of Medicine, Trinity Biomedical Sciences Institute, Trinity College DublinUniversity of DublinDublinIreland
| | - Muthuraman Muthuraman
- Neural Engineering with Signal Analytics and Artificial Intelligence, Department of NeurologyUniversity of WürzburgWürzburgGermany
| | - Niall Pender
- Academic Unit of Neurology, School of Medicine, Trinity Biomedical Sciences Institute, Trinity College DublinUniversity of DublinDublinIreland
- Department of PsychologyBeaumont HospitalDublinIreland
| | - Orla Hardiman
- Academic Unit of Neurology, School of Medicine, Trinity Biomedical Sciences Institute, Trinity College DublinUniversity of DublinDublinIreland
- Department of NeurologyBeaumont HospitalDublinIreland
| | - Bahman Nasseroleslami
- Academic Unit of Neurology, School of Medicine, Trinity Biomedical Sciences Institute, Trinity College DublinUniversity of DublinDublinIreland
- FutureNeuro ‐ SFI Research Centre for Chronic and Rare Neurological DiseasesRoyal College of SurgeonsDublinIreland
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5
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Zhou Y, Gong L, Yang Y, Tan L, Ruan L, Chen X, Luo H, Ruan J. Spatio-temporal dynamics of resting-state brain networks are associated with migraine disability. J Headache Pain 2023; 24:13. [PMID: 36800935 PMCID: PMC9940435 DOI: 10.1186/s10194-023-01551-y] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2022] [Accepted: 02/13/2023] [Indexed: 02/21/2023] Open
Abstract
OBJECTIVE The changes in resting-state functional networks and their correlations with clinical traits remain to be clarified in migraine. Here we aim to investigate the brain spatio-temporal dynamics of resting-state networks and their possible correlations with the clinical traits in migraine. METHODS Twenty Four migraine patients without aura and 26 healthy controls (HC) were enrolled. Each included subject underwent a resting-state EEG and echo planar imaging examination. The disability of migraine patients was evaluated by Migraine Disability Assessment (MIDAS). After data acquisition, EEG microstates (Ms) combining functional connectivity (FC) analysis based on Schafer 400-seven network atlas were performed. Then, the correlation between obtained parameters and clinical traits was investigated. RESULTS Compared with HC group, the brain temporal dynamics depicted by microstates showed significantly increased activity in functional networks involving MsB and decreased activity in functional networks involving MsD; The spatial dynamics were featured by decreased intra-network FC within the executive control network( ECN) and inter-network FC between dorsal attention network (DAN) and ECN (P < 0.05); Moreover, correlation analysis showed that the MIDAS score was positively correlated with the coverage and duration of MsC, and negatively correlated with the occurrence of MsA; The FC within default mode network (DMN), and the inter-FC of ECN- visual network (VN), ECN- limbic network, VN-limbic network was negatively correlated with MIDAS. However, the FC of DMN-ECN was positively correlated with MIDAS; Furthermore, significant interactions between the temporal and spatial dynamics were also obtained. CONCLUSIONS Our study confirmed the notion that altered spatio-temporal dynamics exist in migraine patients during resting-state. And the temporal dynamics, the spatial changes and the clinical traits such as migraine disability interact with each other. The spatio-temporal dynamics obtained from EEG microstate and fMRI FC analyses may be potential biomarkers for migraine and with a huge potential to change future clinical practice in migraine.
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Affiliation(s)
- Yan Zhou
- Department of Neurology, Jianyang People's Hospital, Jianyang, 641400, China
- Department of Neurology, The Affiliated Hospital of Southwest Medical University, Luzhou, 646000, China
- Laboratory of Neurological Diseases and Brain Function, Luzhou, 646000, China
| | - Liusheng Gong
- Department of Neurology, Jianyang People's Hospital, Jianyang, 641400, China
- Department of Neurology, The Affiliated Hospital of Southwest Medical University, Luzhou, 646000, China
| | - Yushu Yang
- Department of Neurology, The Affiliated Hospital of Southwest Medical University, Luzhou, 646000, China
- Laboratory of Neurological Diseases and Brain Function, Luzhou, 646000, China
| | - Linjie Tan
- Department of Neurology, The Affiliated Hospital of Southwest Medical University, Luzhou, 646000, China
- Laboratory of Neurological Diseases and Brain Function, Luzhou, 646000, China
| | - Lili Ruan
- Department of Neurology, The Affiliated Hospital of Southwest Medical University, Luzhou, 646000, China
- Laboratory of Neurological Diseases and Brain Function, Luzhou, 646000, China
| | - Xiu Chen
- Department of Neurology, The Affiliated Hospital of Southwest Medical University, Luzhou, 646000, China
- Laboratory of Neurological Diseases and Brain Function, Luzhou, 646000, China
| | - Hua Luo
- Department of Neurology, The Affiliated Hospital of Southwest Medical University, Luzhou, 646000, China
- Laboratory of Neurological Diseases and Brain Function, Luzhou, 646000, China
| | - Jianghai Ruan
- Department of Neurology, The Affiliated Hospital of Southwest Medical University, Luzhou, 646000, China.
- Laboratory of Neurological Diseases and Brain Function, Luzhou, 646000, China.
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9
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Nami M, Thatcher R, Kashou N, Lopes D, Lobo M, Bolanos JF, Morris K, Sadri M, Bustos T, Sanchez GE, Mohd-Yusof A, Fiallos J, Dye J, Guo X, Peatfield N, Asiryan M, Mayuku-Dore A, Krakauskaite S, Soler EP, Cramer SC, Besio WG, Berenyi A, Tripathi M, Hagedorn D, Ingemanson M, Gombosev M, Liker M, Salimpour Y, Mortazavi M, Braverman E, Prichep LS, Chopra D, Eliashiv DS, Hariri R, Tiwari A, Green K, Cormier J, Hussain N, Tarhan N, Sipple D, Roy M, Yu JS, Filler A, Chen M, Wheeler C, Ashford JW, Blum K, Zelinsky D, Yamamoto V, Kateb B. A Proposed Brain-, Spine-, and Mental- Health Screening Methodology (NEUROSCREEN) for Healthcare Systems: Position of the Society for Brain Mapping and Therapeutics. J Alzheimers Dis 2022; 86:21-42. [PMID: 35034899 DOI: 10.3233/jad-215240] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022]
Abstract
The COVID-19 pandemic has accelerated neurological, mental health disorders, and neurocognitive issues. However, there is a lack of inexpensive and efficient brain evaluation and screening systems. As a result, a considerable fraction of patients with neurocognitive or psychobehavioral predicaments either do not get timely diagnosed or fail to receive personalized treatment plans. This is especially true in the elderly populations, wherein only 16% of seniors say they receive regular cognitive evaluations. Therefore, there is a great need for development of an optimized clinical brain screening workflow methodology like what is already in existence for prostate and breast exams. Such a methodology should be designed to facilitate objective early detection and cost-effective treatment of such disorders. In this paper we have reviewed the existing clinical protocols, recent technological advances and suggested reliable clinical workflows for brain screening. Such protocols range from questionnaires and smartphone apps to multi-modality brain mapping and advanced imaging where applicable. To that end, the Society for Brain Mapping and Therapeutics (SBMT) proposes the Brain, Spine and Mental Health Screening (NEUROSCREEN) as a multi-faceted approach. Beside other assessment tools, NEUROSCREEN employs smartphone guided cognitive assessments and quantitative electroencephalography (qEEG) as well as potential genetic testing for cognitive decline risk as inexpensive and effective screening tools to facilitate objective diagnosis, monitor disease progression, and guide personalized treatment interventions. Operationalizing NEUROSCREEN is expected to result in reduced healthcare costs and improving quality of life at national and later, global scales.
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Affiliation(s)
- Mohammad Nami
- Society for Brain Mapping and Therapeutics (SBMT), Los Angeles, CA, USA.,Brain Mapping Foundation (BMF), Los Angeles, CA, USA.,Neuroscience Center, Instituto de Investigaciones Científicas y Servicios de Alta Tecnología (INDICASAT AIP), City of Knowledge, Panama.,Department of Neuroscience, School of Advanced Medical Sciences and Technologies, and Dana Brain Health Institute, Shiraz University of Medical Sciences, Shiraz, Iran.,Inclusive Brain Health and BrainLabs International, Swiss Alternative Medicine, Geneva, Switzerland
| | - Robert Thatcher
- Society for Brain Mapping and Therapeutics (SBMT), Los Angeles, CA, USA.,Applied Neuroscience, Inc., St Petersburg, FL, USA
| | - Nasser Kashou
- Society for Brain Mapping and Therapeutics (SBMT), Los Angeles, CA, USA.,Brain Mapping Foundation (BMF), Los Angeles, CA, USA
| | - Dahabada Lopes
- Society for Brain Mapping and Therapeutics (SBMT), Los Angeles, CA, USA.,Brain Mapping Foundation (BMF), Los Angeles, CA, USA
| | - Maria Lobo
- Society for Brain Mapping and Therapeutics (SBMT), Los Angeles, CA, USA.,Brain Mapping Foundation (BMF), Los Angeles, CA, USA
| | - Joe F Bolanos
- Society for Brain Mapping and Therapeutics (SBMT), Los Angeles, CA, USA.,Brain Mapping Foundation (BMF), Los Angeles, CA, USA
| | - Kevin Morris
- Society for Brain Mapping and Therapeutics (SBMT), Los Angeles, CA, USA.,Brain Mapping Foundation (BMF), Los Angeles, CA, USA
| | - Melody Sadri
- Society for Brain Mapping and Therapeutics (SBMT), Los Angeles, CA, USA.,Brain Mapping Foundation (BMF), Los Angeles, CA, USA
| | - Teshia Bustos
- Society for Brain Mapping and Therapeutics (SBMT), Los Angeles, CA, USA.,Brain Mapping Foundation (BMF), Los Angeles, CA, USA
| | - Gilberto E Sanchez
- Society for Brain Mapping and Therapeutics (SBMT), Los Angeles, CA, USA.,Brain Mapping Foundation (BMF), Los Angeles, CA, USA
| | - Alena Mohd-Yusof
- Society for Brain Mapping and Therapeutics (SBMT), Los Angeles, CA, USA.,Brain Mapping Foundation (BMF), Los Angeles, CA, USA
| | - John Fiallos
- Society for Brain Mapping and Therapeutics (SBMT), Los Angeles, CA, USA.,Brain Mapping Foundation (BMF), Los Angeles, CA, USA
| | - Justin Dye
- Department of Neurosurgery, Loma Linda University, Loma Linda, CA, USA
| | - Xiaofan Guo
- Department of Neurology, Loma Linda University, CA, USA
| | | | - Milena Asiryan
- Society for Brain Mapping and Therapeutics (SBMT), Los Angeles, CA, USA.,Brain Mapping Foundation (BMF), Los Angeles, CA, USA
| | - Alero Mayuku-Dore
- Society for Brain Mapping and Therapeutics (SBMT), Los Angeles, CA, USA.,Brain Mapping Foundation (BMF), Los Angeles, CA, USA
| | - Solventa Krakauskaite
- Society for Brain Mapping and Therapeutics (SBMT), Los Angeles, CA, USA.,Brain Mapping Foundation (BMF), Los Angeles, CA, USA
| | - Ernesto Palmero Soler
- Society for Brain Mapping and Therapeutics (SBMT), Los Angeles, CA, USA.,Brain Mapping Foundation (BMF), Los Angeles, CA, USA
| | - Steven C Cramer
- Department of Neurology, UCLA, and California Rehabilitation Institute, Los Angeles, CA, USA
| | - Walter G Besio
- Electrical Computer and Biomedical Engineering Department and Interdisciplinary Neuroscience Program, University of Rhode Island, RI, USA
| | - Antal Berenyi
- The Neuroscience Institute, New York University, New York, NY, USA
| | - Manjari Tripathi
- Department of Neurology, All India Institute of Medical Sciences, New Delhi, India
| | | | | | | | - Mark Liker
- Department of Neurosurgery, Children's Hospital Los Angeles, Los Angeles, CA, USA
| | - Yousef Salimpour
- Department of Neurosurgery, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | | | | | | | | | - Dawn S Eliashiv
- Society for Brain Mapping and Therapeutics (SBMT), Los Angeles, CA, USA.,UCLA David Geffen, School of Medicine, Department of Neurology, Los Angeles, CA, USA
| | - Robert Hariri
- Society for Brain Mapping and Therapeutics (SBMT), Los Angeles, CA, USA.,Brain Mapping Foundation (BMF), Los Angeles, CA, USA.,Celularity Corporation, Warren, NJ, USA.,Weill Cornell School of Medicine, Department of Neurosurgery, New York, NY, USA.,Brain Technology and Innovation Park, Los Angeles, CA, USA
| | - Ambooj Tiwari
- Departments of Neurology, Radiology & Neurosurgery - NYU Grossman School of Medicine, New York, NY, USA
| | - Ken Green
- Society for Brain Mapping and Therapeutics (SBMT), Los Angeles, CA, USA.,Brain Mapping Foundation (BMF), Los Angeles, CA, USA
| | - Jason Cormier
- Society for Brain Mapping and Therapeutics (SBMT), Los Angeles, CA, USA.,Brain Mapping Foundation (BMF), Los Angeles, CA, USA.,Lafayette Surgical Specialty Hospital, Lafayette, LA, USA
| | - Namath Hussain
- Society for Brain Mapping and Therapeutics (SBMT), Los Angeles, CA, USA.,Department of Psychiatry, Faculty of Medicine, Uskudar University, Turkey
| | - Nevzat Tarhan
- Department of Psychiatry, Faculty of Medicine, Uskudar University, Turkey
| | - Daniel Sipple
- Society for Brain Mapping and Therapeutics (SBMT), Los Angeles, CA, USA.,Brain Mapping Foundation (BMF), Los Angeles, CA, USA.,Midwest Spine and Brain Institute, Roseville, MN, USA
| | - Michael Roy
- Society for Brain Mapping and Therapeutics (SBMT), Los Angeles, CA, USA.,Brain Mapping Foundation (BMF), Los Angeles, CA, USA.,Uniformed Services University Health Science (USUHS), Baltimore, MD, USA
| | - John S Yu
- Department of Neurosurgery, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Aaron Filler
- Society for Brain Mapping and Therapeutics (SBMT), Los Angeles, CA, USA.,Institute for Nerve Medicine, Santa Monica, CA, USA.,Department of Neurosurgery, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Mike Chen
- Society for Brain Mapping and Therapeutics (SBMT), Los Angeles, CA, USA.,Department of Neurosurgery, City of Hope Comprehensive Cancer Center, Duarte, CA, USA
| | - Chris Wheeler
- Society for Brain Mapping and Therapeutics (SBMT), Los Angeles, CA, USA.,Brain Mapping Foundation (BMF), Los Angeles, CA, USA
| | | | - Kenneth Blum
- Division of Addiction Research, Center for Psychiatry, Medicine, and Primary Care, Western Health Sciences, Pomona, CA, USA
| | | | - Vicky Yamamoto
- Society for Brain Mapping and Therapeutics (SBMT), Los Angeles, CA, USA.,Brain Mapping Foundation (BMF), Los Angeles, CA, USA.,USC Keck School of Medicine, The USC Caruso Department of Otolaryngology-Head and Neck Surgery, Los Angeles, CA, USA.,USC-Norris Comprehensive Cancer Center, Los Angeles, CA, USA
| | - Babak Kateb
- Society for Brain Mapping and Therapeutics (SBMT), Los Angeles, CA, USA.,Brain Mapping Foundation (BMF), Los Angeles, CA, USA.,Loma Linda University, Department of Neurosurgery, Loma Linda, CA, USA.,National Center for NanoBioElectronic (NCNBE), Los Angeles, CA, USA.,Brain Technology and Innovation Park, Los Angeles, CA, USA
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10
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Li X, Dong F, Zhang Y, Wang J, Wang Z, Sun Y, Zhang M, Xue T, Ren Y, Lv X, Yuan K, Yu D. Altered resting-state electroencephalography microstate characteristics in young male smokers. Front Psychiatry 2022; 13:1008007. [PMID: 36267852 PMCID: PMC9577082 DOI: 10.3389/fpsyt.2022.1008007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/31/2022] [Accepted: 09/06/2022] [Indexed: 11/24/2022] Open
Abstract
The development of nicotine addiction was associated with the abnormalities of intrinsic functional networks during the resting state in young adult smokers. As a whole-brain imaging approach, EEG microstate analysis treated multichannel EEG recordings as a series of quasi-steady microscopic states which were related to the resting-state networks (RSNs) found by fMRI. The aim of this study was to examine whether the resting-state EEG microstate analysis may provide novel insights into the abnormal temporal properties of intrinsic brain activities in young smokers. We used 64-channel resting-state EEG datasets to investigate alterations in microstate characteristics between twenty-five young smokers and 25 age- and gender-matched non-smoking controls. Four classic EEG microstates (microstate A, B, C, and D) were obtained, and the four temporal parameters of each microstate were extracted, i.e., duration, occurrence, coverage, and transition probabilities. Compared with non-smoking controls, young smokers showed decreased occurrence of microstate C and increased duration of microstate D. Furthermore, both the duration and coverage of microstate D were significantly negatively correlated with Fagerstrom Test of Nicotine Dependence (FTND) in young smoker group. The complex changes in the microstate time-domain parameters might correspond to the abnormalities of RSNs in analyses of FC measured with fMRI in the previous studies and indicate the altered specific brain functions in young smokers. Microstate D could be potentially represented as a selective biomarker for predicting the dependence degree of adolescent smokers on cigarettes. These results suggested that EEG microstate analysis might detect the deviant functions of large-scale cortical activities in young smokers and provide a new perspective for the study of brain networks of adolescent smokers.
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Affiliation(s)
- Xiaojian Li
- Inner Mongolia Key Laboratory of Pattern Recognition and Intelligent Image Processing, School of Information Engineering, Inner Mongolia University of Science and Technology, Baotou, China
| | - Fang Dong
- Inner Mongolia Key Laboratory of Pattern Recognition and Intelligent Image Processing, School of Information Engineering, Inner Mongolia University of Science and Technology, Baotou, China
| | - Yunmiao Zhang
- Inner Mongolia Key Laboratory of Pattern Recognition and Intelligent Image Processing, School of Information Engineering, Inner Mongolia University of Science and Technology, Baotou, China
| | - Juan Wang
- Inner Mongolia Key Laboratory of Pattern Recognition and Intelligent Image Processing, School of Information Engineering, Inner Mongolia University of Science and Technology, Baotou, China
| | - Zhengxi Wang
- Inner Mongolia Key Laboratory of Pattern Recognition and Intelligent Image Processing, School of Information Engineering, Inner Mongolia University of Science and Technology, Baotou, China
| | - Yaning Sun
- Inner Mongolia Key Laboratory of Pattern Recognition and Intelligent Image Processing, School of Information Engineering, Inner Mongolia University of Science and Technology, Baotou, China
| | - Ming Zhang
- Inner Mongolia Key Laboratory of Pattern Recognition and Intelligent Image Processing, School of Information Engineering, Inner Mongolia University of Science and Technology, Baotou, China
| | - Ting Xue
- Inner Mongolia Key Laboratory of Pattern Recognition and Intelligent Image Processing, School of Information Engineering, Inner Mongolia University of Science and Technology, Baotou, China
| | - Yan Ren
- Inner Mongolia Key Laboratory of Pattern Recognition and Intelligent Image Processing, School of Information Engineering, Inner Mongolia University of Science and Technology, Baotou, China
| | - Xiaoqi Lv
- College of Information Engineering, Inner Mongolia University of Technology, Hohhot, China
| | - Kai Yuan
- Inner Mongolia Key Laboratory of Pattern Recognition and Intelligent Image Processing, School of Information Engineering, Inner Mongolia University of Science and Technology, Baotou, China.,School of Life Sciences and Technology, Xidian University, Xi'an, Shaanxi, China
| | - Dahua Yu
- Inner Mongolia Key Laboratory of Pattern Recognition and Intelligent Image Processing, School of Information Engineering, Inner Mongolia University of Science and Technology, Baotou, China
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