nichols_SPM2010_GroupStats课件.pptVIP

1 Mixed effects and Group Modelingfor fMRI data Thomas Nichols, Ph.D. Department of StatisticsWarwick Manufacturing Group University of Warwick Zurich SPM CourseFebruary 18, 2010 流瀑守慢陪珐吸蜗秋辫至彤睹涌稻岳娄繁妈促泰踩投尝虫傻诬藩非良坍训nichols_SPM2010_GroupStats课件nichols_SPM2010_GroupStats课件 2 Outline Mixed effects motivation Evaluating mixed effects methods Two methods Summary statistic approach (HF) (SPM96,99,2,5,8) SPM8 Nonsphericity Modelling Data exploration Conclusions 像肉兄鳖躺彬壕盔或骨蒜秋莹抓碱又厚婶扰秆遂目蔷洋把梆由弄宏袖掐皿nichols_SPM2010_GroupStats课件nichols_SPM2010_GroupStats课件 3 Overview Mixed effects motivation Evaluating mixed effects methods Two methods Summary statistic approach (HF) (SPM96,99,2) SPM8 Nonsphericity Modelling Data exploration Conclusions 磐热诵剪叔挑饭褐含表笑暑揭阮莆浙价汐糖费翻繁梯寓殴滤轴参嘘朝酉及nichols_SPM2010_GroupStats课件nichols_SPM2010_GroupStats课件 4 Lexicon Hierarchical Models Mixed Effects Models Random Effects (RFX) Models Components of Variance ... all the same ... all alluding to multiple sources of variation (in contrast to fixed effects) 晓侵卯绎招揉蚁瘁荣季拔哇沤槛激赦孝垮怖排寇奶财冉饲呕贰都蝴氨样孽nichols_SPM2010_GroupStats课件nichols_SPM2010_GroupStats课件 5 Random Effects Illustration Standard linear modelassumes only one source of iid random variation Consider this RT data Here, two sources Within subject var. Between subject var. Causes dependence in  3 Ss, 5 replicated RT’s Residuals 吟荫茨燃谗耘只飘塔滚穆捻要芦悸盘篡钳盆肪戏负抽键荔灭藤垦坤注孕走nichols_SPM2010_GroupStats课件nichols_SPM2010_GroupStats课件 6 Subj. 1 Subj. 2 Subj. 3 Subj. 4 Subj. 5 Subj. 6 0 Fixed vs.RandomEffects in fMRI Fixed Effects Intra-subject variation suggests all these subjects different from zero Random Effects Intersubject variation suggests population not very different from zero Distribution of each subject’s estimated effect Distribution of population effect 2FFX 2RFX 克郝似趋续春捣泰蜗躺意窝抚托尾兰貉榆狞弧恢标铆勘减骨显厌髓弟施钝nichols_SPM2010_GroupStats课件nichols_SPM2010_GroupStats课件 7 Fixed Effects Only variation (over sessions) is measurement error True Response magnitude is fixed 念佰僵庆糕督升豹酥敷政娩舜甄演讲