The combination of univariate and multivariate method for fMRI data analysis

被引：0
|
作者：
Xia, WW [1 ]
Yan, LR [1 ]
Zhou, ZT [1 ]
Liu, YD [1 ]
Hu, DW [1 ]
机构：
[1] Natl Univ Def Technol, Coll Mechatron & Automat, Dept Automat Control, Changsha 410073, Hunan, Peoples R China
来源：
PROCEEDINGS OF THE 2005 INTERNATIONAL CONFERENCE ON NEURAL NETWORKS AND BRAIN, VOLS 1-3 | 2005年
关键词：
single-frame; Z-score; region of interest(ROI); independent component analysis(ICA); sICA; tICA;
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
A combined method of univariate and multivariate analysis is presented in this paper to give a new way for fMRI data analysis. The univaritate single-frame approach, which detects activations evoked by specific task and describes the temporal characteristics of activations without prior assumption of hemodynamic response function (HRF), can be applied as the first processing step. While the multivariate methods, i.e., spatial and temporal independent component analyses (sICA and tICA), are then brought in for analyzing the derived spatiotemporal activations in the regions of interest (ROIs). The ICAs can, in our combined approach, reveal the subtle spatial patterns of the regional activation areas.
引用
收藏
页码：1568 / 1573
页数：6
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