fMRI数据分析系统SM原理与应用.pptVIP

SPM 中的多重比较校正的原理 根据数据的空间相关程度计算独立观测个 数（独立比较的次数Nindepentent） 根据整体虚警概率poverall和Nindepentent得到单 个体元的pvoxel值 pvoxel = poverall/ Nindepentent SPM个体激活区检测基本过程 个体水平effect 计算的SPM实现 （个体激活区检测） 模型定义 Design Matrix Specification 数据定义 参数估计 Data Specification Parameter Estimation 统计结果 Result 参数估计 常用工具与 参数设置 预处理部分 First-level 模型构建与 Second-level Lecture Outline SPM I: Intro, Preprocessing SPM II: Single-subject analyses SPM III: Group analyses How do we compare across subjects? 建立不同人之间的可比性 Normalization ROI 多个被试的统计分析 Fixed-effects Model Random-effects Model Fixed-effects Model Assume that the experimental manipulation has same effect in each subject Uses data from all subjects to construct statistical test Averaging/connecting across subjects before a t-test Sensitive to extreme results from individual subject strong effect in one subject can lead to significance even when others show weak or no effects Allows inference to subject sample you can say that effect was significant in your group of subjects but cannot generalize to other subjects that you didn’t test How about the population? Random effect analysis Assumes that effect varies across the population Accounts for inter-subject variance in analyses Allows inferences to population from which subjects are drawn Especially important for group comparisons Required by many reviewers/journals SPM双层统计 First-level：个体水平effect 计算 Second-level：群体水平effect 计算 SPM个体激活区检测基本过程 Fixed- Random- effects Model 小结 Fixed-effects Model Assumes that effect is constant (“fixed”) in the population Uses data from all subjects to construct statistical test Averaging/connecting across subjects before a t-test Allows inference to subject sample Sensitive to extreme results from individual subject Random-effects Model Assumes that effect varies across the population Accounts for inter-subject variance in analyses Allows inferences to population from which subjects are drawn Especially important for group comparisons Required by many r