{"title":"首发精神分裂症的高阶脑网络特征提取和分类方法:一项脑电图研究。","authors":"Yanxia Kang, Jianghao Zhao, Yanli Zhao, Zilong Zhao, Yuan Dong, Manjie Zhang, Guimei Yin, Shuping Tan","doi":"10.3389/fnhum.2024.1452197","DOIUrl":null,"url":null,"abstract":"<p><strong>Introduction: </strong>A multimodal persistent topological feature extraction and classification method is proposed to enhance the recognition accuracy of first-episode schizophrenia patients. This approach addresses the limitations of traditional higher-order brain network analyses that rely on single persistent features (e.g., persistent images).</p><p><strong>Methods: </strong>The study utilized resting-state EEG data from 198 subjects recruited at Huilongguan Hospital in Beijing, comprising 102 males and 96 females, with a mean age of 30 years and mean education of 14 years. Persistent topological features were extracted using adaptive thresholding during persistent homology (PH) filtrations. The distribution of these features was visualized through heatmaps and persistence entropies, while the generation process was elucidated using Betti curves and persistence landscapes.</p><p><strong>Results: </strong>The classification performance of the multimodal persistent topological features was assessed using various machine learning classifiers. The classifier yielding the highest performance was selected for comparison with traditional brain network features derived from graph theory and single persistent topological features. The results revealed significant topological changes in first-episode schizophrenia patients throughout the persistent homology filtering compared to healthy subjects. The univariate feature selection algorithm achieved a classification accuracy of 94.6% with a combination of attributes meeting the criterion of AC ≥ 0.6.</p><p><strong>Discussion: </strong>The proposed method demonstrates clinical significance for the early identification and diagnosis of first-episode schizophrenia patients, offering a new research perspective for constructing higher-order functional connectivity networks and extracting topological structure features.</p>","PeriodicalId":12536,"journal":{"name":"Frontiers in Human Neuroscience","volume":"18 ","pages":"1452197"},"PeriodicalIF":2.4000,"publicationDate":"2024-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11537920/pdf/","citationCount":"0","resultStr":"{\"title\":\"High-order brain network feature extraction and classification method of first-episode schizophrenia: an EEG study.\",\"authors\":\"Yanxia Kang, Jianghao Zhao, Yanli Zhao, Zilong Zhao, Yuan Dong, Manjie Zhang, Guimei Yin, Shuping Tan\",\"doi\":\"10.3389/fnhum.2024.1452197\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Introduction: </strong>A multimodal persistent topological feature extraction and classification method is proposed to enhance the recognition accuracy of first-episode schizophrenia patients. This approach addresses the limitations of traditional higher-order brain network analyses that rely on single persistent features (e.g., persistent images).</p><p><strong>Methods: </strong>The study utilized resting-state EEG data from 198 subjects recruited at Huilongguan Hospital in Beijing, comprising 102 males and 96 females, with a mean age of 30 years and mean education of 14 years. Persistent topological features were extracted using adaptive thresholding during persistent homology (PH) filtrations. The distribution of these features was visualized through heatmaps and persistence entropies, while the generation process was elucidated using Betti curves and persistence landscapes.</p><p><strong>Results: </strong>The classification performance of the multimodal persistent topological features was assessed using various machine learning classifiers. The classifier yielding the highest performance was selected for comparison with traditional brain network features derived from graph theory and single persistent topological features. The results revealed significant topological changes in first-episode schizophrenia patients throughout the persistent homology filtering compared to healthy subjects. The univariate feature selection algorithm achieved a classification accuracy of 94.6% with a combination of attributes meeting the criterion of AC ≥ 0.6.</p><p><strong>Discussion: </strong>The proposed method demonstrates clinical significance for the early identification and diagnosis of first-episode schizophrenia patients, offering a new research perspective for constructing higher-order functional connectivity networks and extracting topological structure features.</p>\",\"PeriodicalId\":12536,\"journal\":{\"name\":\"Frontiers in Human Neuroscience\",\"volume\":\"18 \",\"pages\":\"1452197\"},\"PeriodicalIF\":2.4000,\"publicationDate\":\"2024-10-23\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11537920/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Frontiers in Human Neuroscience\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.3389/fnhum.2024.1452197\",\"RegionNum\":3,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2024/1/1 0:00:00\",\"PubModel\":\"eCollection\",\"JCR\":\"Q3\",\"JCRName\":\"NEUROSCIENCES\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Frontiers in Human Neuroscience","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.3389/fnhum.2024.1452197","RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2024/1/1 0:00:00","PubModel":"eCollection","JCR":"Q3","JCRName":"NEUROSCIENCES","Score":null,"Total":0}
High-order brain network feature extraction and classification method of first-episode schizophrenia: an EEG study.
Introduction: A multimodal persistent topological feature extraction and classification method is proposed to enhance the recognition accuracy of first-episode schizophrenia patients. This approach addresses the limitations of traditional higher-order brain network analyses that rely on single persistent features (e.g., persistent images).
Methods: The study utilized resting-state EEG data from 198 subjects recruited at Huilongguan Hospital in Beijing, comprising 102 males and 96 females, with a mean age of 30 years and mean education of 14 years. Persistent topological features were extracted using adaptive thresholding during persistent homology (PH) filtrations. The distribution of these features was visualized through heatmaps and persistence entropies, while the generation process was elucidated using Betti curves and persistence landscapes.
Results: The classification performance of the multimodal persistent topological features was assessed using various machine learning classifiers. The classifier yielding the highest performance was selected for comparison with traditional brain network features derived from graph theory and single persistent topological features. The results revealed significant topological changes in first-episode schizophrenia patients throughout the persistent homology filtering compared to healthy subjects. The univariate feature selection algorithm achieved a classification accuracy of 94.6% with a combination of attributes meeting the criterion of AC ≥ 0.6.
Discussion: The proposed method demonstrates clinical significance for the early identification and diagnosis of first-episode schizophrenia patients, offering a new research perspective for constructing higher-order functional connectivity networks and extracting topological structure features.
期刊介绍:
Frontiers in Human Neuroscience is a first-tier electronic journal devoted to understanding the brain mechanisms supporting cognitive and social behavior in humans, and how these mechanisms might be altered in disease states. The last 25 years have seen an explosive growth in both the methods and the theoretical constructs available to study the human brain. Advances in electrophysiological, neuroimaging, neuropsychological, psychophysical, neuropharmacological and computational approaches have provided key insights into the mechanisms of a broad range of human behaviors in both health and disease. Work in human neuroscience ranges from the cognitive domain, including areas such as memory, attention, language and perception to the social domain, with this last subject addressing topics, such as interpersonal interactions, social discourse and emotional regulation. How these processes unfold during development, mature in adulthood and often decline in aging, and how they are altered in a host of developmental, neurological and psychiatric disorders, has become increasingly amenable to human neuroscience research approaches. Work in human neuroscience has influenced many areas of inquiry ranging from social and cognitive psychology to economics, law and public policy. Accordingly, our journal will provide a forum for human research spanning all areas of human cognitive, social, developmental and translational neuroscience using any research approach.