بهبود عملكرد الگوريتم آناليز مولفه هاي مستقل در تحليل داده هاي تصويربرداري عملكردي مغناطيسي مغز به كمك فيلترهاي زماني و مكاني

Improving the Performance oflCA Algorithm for fMRI Simulated Data Analysis Using Temporal and Spatial Filters in the Preprocessing Phase

The accuracy of analyzing Functional MRI (fMRI) data is usually decreases in the presence of noise and artifact sources. A common solution in for analyzing fMRl data having high noise is to use suitable preprocessing methods with the aim of data denoising. Some effects of preprocessing methods on the parametric methods such as general linear model (GLM) have previously been evaluated methods such as. In this study, besides the comparison of simple and noisy Independent Component Analysis (lCA) algorithms, the quantity effects of some spatial and temporal filtering have been evaluated on the functionality of ICA algorithms. Noisy ICA algorithms perform with a higher accuracy (up to 16%) in comparison to simple ICA for noisy fMRI data, although it is more time consuming than simple ICA. The accuracy of the results is improved by 8-10% using spatial and temporal filtering prior to simple lCA. Materials and Methods: Simple ICA and noisy ICA methods have been compared for analyzing simulated fMRI data sets. The impact of some temporal and spatial filters on the functionality of simple ICA algorithms has been evaluated. Implemented filters have been proposed in low and high pass group. Results: The sensitivity, specificity and temporal accuracy of simple ICA algorithms has been improved by using high pass filters. Although low pass filtering has some positive effects on the performance of simple ICA algorithms in the low SNR levels, in the high signal-noise Ratio (SNR) levels these low pass filters may cause a decrease in the sensitivity, specificity and temporal accuracy of simple KʹA methods. Discussion and Conclusion: The results obtained from simple and noisy leA algorithms for analyzing fMRI data having high SNR levels are approximately similar. Infomax algorithm uses Gradient based methods for estimating unmixing matrix has better sensitivity, specificity and temporal accuracy than Fast ICA for analyzing noisy ICA data. An alternative to the complicated and time consuming noisy KʹA algorithms is to preprocess and denoise fMRI data prior to analyzing it by simple ICA algorithms.