F. Biessmann, Michael Gaebler, Jan-Peter Lamke, Uijong Ju, S. Hetzer, C. Wallraven, K. Müller
{"title":"Data-driven multisubject neuroimaging analyses for naturalistic stimuli","authors":"F. Biessmann, Michael Gaebler, Jan-Peter Lamke, Uijong Ju, S. Hetzer, C. Wallraven, K. Müller","doi":"10.1109/PRNI.2014.6858511","DOIUrl":null,"url":null,"abstract":"A central question in neuroscience is how the brain reacts to real world sensory stimuli. Naturalistic and complex (e.g. movie) stimuli are increasingly used in empirical research but their analysis often relies on considerable human efforts to label or extract stimulus features. Here we present data-driven analysis strategies that help to obtain interpretable results from multisubject neuroimaging data when complex movie stimuli are used. These analyses a) enable localization and visualization of brain activity using standard statistical parametric maps in the subspace of brain activity shared between subjects and b) facilitate interpretation of intersubject correlations. We show experimental results obtained from 50 subjects.","PeriodicalId":133286,"journal":{"name":"2014 International Workshop on Pattern Recognition in Neuroimaging","volume":"7 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2014-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2014 International Workshop on Pattern Recognition in Neuroimaging","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/PRNI.2014.6858511","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 3
Abstract
A central question in neuroscience is how the brain reacts to real world sensory stimuli. Naturalistic and complex (e.g. movie) stimuli are increasingly used in empirical research but their analysis often relies on considerable human efforts to label or extract stimulus features. Here we present data-driven analysis strategies that help to obtain interpretable results from multisubject neuroimaging data when complex movie stimuli are used. These analyses a) enable localization and visualization of brain activity using standard statistical parametric maps in the subspace of brain activity shared between subjects and b) facilitate interpretation of intersubject correlations. We show experimental results obtained from 50 subjects.