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Data analysis is challenging in arterial spin labeling (ASL) perfusion fMRI due to the intrinsic low SNR of ASL data. To boost up the detection sensitivity, this paper presented a multivariate method based group analysis approach to analyze ASL perfusion fMRI data. A spatial discriminance map (SDM) was first extracted for each subject by support vector machine learning (SVM) algorithm; a population...
Data analysis is challenging in arterial spin labeling (ASL) perfusion fMRI due to the intrinsic low SNR of ASL data. To boost up the detection sensitivity, this paper presented a multivariate method based group analysis approach to analyze ASL perfusion fMRI data. A spatial discriminance map (SDM) was first extracted for each subject by support vector machine learning (SVM) algorithm; a population...
Data reduction through conventional principal component analysis is impractical for temporal independent component analysis (tICA) on fMRI data, since the data covariance matrix is too huge to be manipulated. It is also computationally intensive for spatial ICA (sICA) on long time fMRI scans. To solve this problem, a cascade recursive least squared networks based PCA (CRLS-PCA) was used to reduce...
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