Computing Brain Activity Maps from fMRI Time-Series Images
fMRI is a very popular method for researchers and clinicians to image human brain activity in response to given mental tasks. This book presents a comprehensive review of the methods for computing activity maps, while providing an intuitive and mathematical outline of how each method works. The approaches include statistical parametric maps (SPM), hemodynamic response modeling and deconvolution, Bayesian, Fourier and nonparametric methods. The newest activity maps provide information on regional connectivity and include principal and independent component analysis, crisp and fuzzy clustering, structural equation modeling, and dynamic causal modeling. Preprocessing and experimental design issues are discussed with references made to the software available for implementing the various methods. Aimed at graduate students and researchers, it will appeal to anyone with an interest in fMRI and who is looking to expand their perspectives of this technique.
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ˆβ active voxels analysis approach autocorrelation average basis functions Bayesian blocked design blood BOLD response BOLD signal brain canonical correlation analysis columns components continuous wavelet transform correlation covariance deﬁned denoising design matrix distribution efﬁciency eigenvectors estimate event-related design F statistic ﬁeld map ﬁlter ﬁnd ﬁrst ﬁxed ﬂow fMRI data set fMRI time-series Fourier transform frequency frequentist Friston functional connectivity fuzzy clustering Gaussian geometric distortion given by Equation global signal gradient hyperparameter identiﬁed k-space linear magnetic ﬁeld measured methods model HRF model of Equation motion multivariate neural noise nonlinear number of clusters parameters permutation physiologic pixel preprocessing pulse sequence regions replicator dynamics represents Section signiﬁcance slice smoothing space spatial speciﬁc statistic stimulus task temporal threshold time-series values variables variance variation Volterra kernels voxel voxel time courses wavelet coefﬁcients wavelet transform