R Casanova, C T Whitlow, B Wagner, M A Espeland, J A Maldjian
{"title":"结合图形和机器学习方法分析性别之间功能连接的差异。","authors":"R Casanova, C T Whitlow, B Wagner, M A Espeland, J A Maldjian","doi":"10.2174/1874440001206010001","DOIUrl":null,"url":null,"abstract":"<p><p>In this work we combine machine learning methods and graph theoretical analysis to investigate gender associated differences in resting state brain network connectivity. The set of all correlations computed from the fMRI resting state data is used as input features for classification. Two ensemble learning methods are used to perform the detection of the set of discriminative edges between groups (males vs. females) of brain networks: 1) Random Forest and 2) an ensemble method based on least angle shrinkage and selection operator (lasso) regressors. Permutation testing is used not only to assess significance of classification accuracy but also to evaluate significance of feature selection. Finally, these methods are applied to data downloaded from the Connectome Project website. Our results suggest that gender differences in brain function may be related to sexually dimorphic regional connectivity between specific critical nodes via gender-discriminative edges.</p>","PeriodicalId":37431,"journal":{"name":"Open Neuroimaging Journal","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2012-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/49/51/TONIJ-6-1.PMC3271304.pdf","citationCount":"33","resultStr":"{\"title\":\"Combining graph and machine learning methods to analyze differences in functional connectivity across sex.\",\"authors\":\"R Casanova, C T Whitlow, B Wagner, M A Espeland, J A Maldjian\",\"doi\":\"10.2174/1874440001206010001\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>In this work we combine machine learning methods and graph theoretical analysis to investigate gender associated differences in resting state brain network connectivity. The set of all correlations computed from the fMRI resting state data is used as input features for classification. Two ensemble learning methods are used to perform the detection of the set of discriminative edges between groups (males vs. females) of brain networks: 1) Random Forest and 2) an ensemble method based on least angle shrinkage and selection operator (lasso) regressors. Permutation testing is used not only to assess significance of classification accuracy but also to evaluate significance of feature selection. Finally, these methods are applied to data downloaded from the Connectome Project website. Our results suggest that gender differences in brain function may be related to sexually dimorphic regional connectivity between specific critical nodes via gender-discriminative edges.</p>\",\"PeriodicalId\":37431,\"journal\":{\"name\":\"Open Neuroimaging Journal\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2012-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/49/51/TONIJ-6-1.PMC3271304.pdf\",\"citationCount\":\"33\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Open Neuroimaging Journal\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.2174/1874440001206010001\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2012/1/26 0:00:00\",\"PubModel\":\"Epub\",\"JCR\":\"Q4\",\"JCRName\":\"Medicine\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Open Neuroimaging Journal","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.2174/1874440001206010001","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2012/1/26 0:00:00","PubModel":"Epub","JCR":"Q4","JCRName":"Medicine","Score":null,"Total":0}
Combining graph and machine learning methods to analyze differences in functional connectivity across sex.
In this work we combine machine learning methods and graph theoretical analysis to investigate gender associated differences in resting state brain network connectivity. The set of all correlations computed from the fMRI resting state data is used as input features for classification. Two ensemble learning methods are used to perform the detection of the set of discriminative edges between groups (males vs. females) of brain networks: 1) Random Forest and 2) an ensemble method based on least angle shrinkage and selection operator (lasso) regressors. Permutation testing is used not only to assess significance of classification accuracy but also to evaluate significance of feature selection. Finally, these methods are applied to data downloaded from the Connectome Project website. Our results suggest that gender differences in brain function may be related to sexually dimorphic regional connectivity between specific critical nodes via gender-discriminative edges.
期刊介绍:
The Open Neuroimaging Journal is an Open Access online journal, which publishes research articles, reviews/mini-reviews, and letters in all important areas of brain function, structure and organization including neuroimaging, neuroradiology, analysis methods, functional MRI acquisition and physics, brain mapping, macroscopic level of brain organization, computational modeling and analysis, structure-function and brain-behavior relationships, anatomy and physiology, psychiatric diseases and disorders of the nervous system, use of imaging to the understanding of brain pathology and brain abnormalities, cognition and aging, social neuroscience, sensorimotor processing, communication and learning.