Analysis of fMRI Signal Using Independent Component Analysis.
- Author:
Chan Hong MOON
1
;
Dong Gyu NA
;
Hyun Wook PARK
;
Jae Wook RYOO
;
Eun Jung RHEE
;
Hong Sik BYUN
Author Information
1. Department of Radiology, Samsung medical Center, Sungkyunkwan University School of Medicine.
- Publication Type:Original Article
- MeSH:
Freedom;
Magnetic Resonance Imaging*;
Noise;
Nose;
Principal Component Analysis
- From:Journal of the Korean Society of Magnetic Resonance in Medicine
1999;3(2):188-196
- CountryRepublic of Korea
- Language:Korean
-
Abstract:
The fMRI signals are composed of many various signals. It is very difficult to find the accurate parameter for the model of fMRI signal containing only neural activity, though we may estimating the signal patterns by the modeling of several signal components. Besides the nose by the physiologic motion, the motion of object and noise of MR instruments make it more difficult to analyze signals of fMRI. Therefore, it is not easy to select an accurate reference data that can accurately reflect neural activity, and the method of an analysis of various signal patterns containing the information of neural activity is an issue of the post-processing methods for fMRI. In the present study, fMRI data was analyzed with the Independent Component Analysis(ICA) method that doesn't need a priori-knowledge or reference data. ICA can be more effective over the analytic method using cross-correlation analysis and can separate the signal patterns of the signals with delayed response or motion related components. The Principal Component Analysis (PCA) threshold, wavelet spatial filtering and analysis of a part of whole images can be used for the reduction of the freedom of data before ICA analysis, and these preceding analyses may be useful for a more effective analysis. As a result, ICA method will be effective for the analysis of signal patterns in fMRI and the pre-filtering may be necessary for the reduction of the degree of freedom of the data.
