{"title":"微状态特征融合识别AD与MCI。","authors":"Yupan Shi, Qinying Ma, Chunyu Feng, Mingwei Wang, Hualong Wang, Bing Li, Jiyu Fang, Shaochen Ma, Xin Guo, Tongliang Li","doi":"10.1007/s13755-022-00186-8","DOIUrl":null,"url":null,"abstract":"<p><p>Electroencephalogram (EEG) microstates provide powerful tools for identifying EEG features due to their rich temporal information. In this study, we tested whether microstates can measure the severity of Alzheimer's disease (AD) and mild cognitive impairment (MCI) in patients and effectively distinguish AD from MCI. We defined two features using transition probabilities (<i>TPs</i>), and one was used to evaluate between-group differences in microstate parameters to assess the within-group consistency of <i>TPs</i> and MMSE scores. Another feature was used to distinguish AD from MCI in machine learning models. Tests showed that there were between-group differences in the temporal characteristics of microstates, and some kinds of <i>TPs</i> were significantly correlated with MMSE scores within groups. Based on our newly defined time-factor transition probabilities (<i>TTPs</i>) feature and partial accumulation strategy, we obtained promising scores for accuracy, sensitivity, and specificity of 0.938, 0.923, and 0.947, respectively. These results provide evidence for microstates as a neurobiological marker of AD.</p>","PeriodicalId":46312,"journal":{"name":"Health Information Science and Systems","volume":null,"pages":null},"PeriodicalIF":4.7000,"publicationDate":"2022-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9325930/pdf/","citationCount":"3","resultStr":"{\"title\":\"Microstate feature fusion for distinguishing AD from MCI.\",\"authors\":\"Yupan Shi, Qinying Ma, Chunyu Feng, Mingwei Wang, Hualong Wang, Bing Li, Jiyu Fang, Shaochen Ma, Xin Guo, Tongliang Li\",\"doi\":\"10.1007/s13755-022-00186-8\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>Electroencephalogram (EEG) microstates provide powerful tools for identifying EEG features due to their rich temporal information. In this study, we tested whether microstates can measure the severity of Alzheimer's disease (AD) and mild cognitive impairment (MCI) in patients and effectively distinguish AD from MCI. We defined two features using transition probabilities (<i>TPs</i>), and one was used to evaluate between-group differences in microstate parameters to assess the within-group consistency of <i>TPs</i> and MMSE scores. Another feature was used to distinguish AD from MCI in machine learning models. Tests showed that there were between-group differences in the temporal characteristics of microstates, and some kinds of <i>TPs</i> were significantly correlated with MMSE scores within groups. Based on our newly defined time-factor transition probabilities (<i>TTPs</i>) feature and partial accumulation strategy, we obtained promising scores for accuracy, sensitivity, and specificity of 0.938, 0.923, and 0.947, respectively. These results provide evidence for microstates as a neurobiological marker of AD.</p>\",\"PeriodicalId\":46312,\"journal\":{\"name\":\"Health Information Science and Systems\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":4.7000,\"publicationDate\":\"2022-07-26\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9325930/pdf/\",\"citationCount\":\"3\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Health Information Science and Systems\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.1007/s13755-022-00186-8\",\"RegionNum\":3,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2022/12/1 0:00:00\",\"PubModel\":\"eCollection\",\"JCR\":\"Q1\",\"JCRName\":\"MEDICAL INFORMATICS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Health Information Science and Systems","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1007/s13755-022-00186-8","RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2022/12/1 0:00:00","PubModel":"eCollection","JCR":"Q1","JCRName":"MEDICAL INFORMATICS","Score":null,"Total":0}
Microstate feature fusion for distinguishing AD from MCI.
Electroencephalogram (EEG) microstates provide powerful tools for identifying EEG features due to their rich temporal information. In this study, we tested whether microstates can measure the severity of Alzheimer's disease (AD) and mild cognitive impairment (MCI) in patients and effectively distinguish AD from MCI. We defined two features using transition probabilities (TPs), and one was used to evaluate between-group differences in microstate parameters to assess the within-group consistency of TPs and MMSE scores. Another feature was used to distinguish AD from MCI in machine learning models. Tests showed that there were between-group differences in the temporal characteristics of microstates, and some kinds of TPs were significantly correlated with MMSE scores within groups. Based on our newly defined time-factor transition probabilities (TTPs) feature and partial accumulation strategy, we obtained promising scores for accuracy, sensitivity, and specificity of 0.938, 0.923, and 0.947, respectively. These results provide evidence for microstates as a neurobiological marker of AD.
期刊介绍:
Health Information Science and Systems is a multidisciplinary journal that integrates artificial intelligence/computer science/information technology with health science and services, embracing information science research coupled with topics related to the modeling, design, development, integration and management of health information systems, smart health, artificial intelligence in medicine, and computer aided diagnosis, medical expert systems. The scope includes: i.) smart health, artificial Intelligence in medicine, computer aided diagnosis, medical image processing, medical expert systems ii.) medical big data, medical/health/biomedicine information resources such as patient medical records, devices and equipments, software and tools to capture, store, retrieve, process, analyze, optimize the use of information in the health domain, iii.) data management, data mining, and knowledge discovery, all of which play a key role in decision making, management of public health, examination of standards, privacy and security issues, iv.) development of new architectures and applications for health information systems.