The combination of univariate and multivariate method for fMRI data analysis

A combined method of univariate and multivariate analysis is presented in this paper to give a new way for fMRI data analysis. The univariate single-frame approach, which detects activations evoked by a specific task and describes the temporal characteristics of activations without prior assumptions...

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Bibliographic Details
Main Authors: Weiwei Xia, Lirong Yan, Zongtan Zhou, Yadong Liu, Dewen Hu
Format: Conference Proceeding
Language:English
Subjects:
Analysis of variance
Data analysis
Image analysis
Independent component analysis
independent component analysis(ICA)
Magnetic analysis
Magnetic resonance imaging
Positron emission tomography
Principal component analysis
region of interest(ROI)
sICA
Single-frame
Spatiotemporal phenomena
Statistical analysis
tICA
Z-score
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Summary:A combined method of univariate and multivariate analysis is presented in this paper to give a new way for fMRI data analysis. The univariate single-frame approach, which detects activations evoked by a specific task and describes the temporal characteristics of activations without prior assumptions 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 to analyze the derived spatiotemporal activations in the regions of interest (ROIs). The ICAs can, in the combined approach, reveal the subtle spatial patterns of the regional activation areas
DOI:10.1109/ICNNB.2005.1614931