AFNI: software for analysis and visualization of functional magnetic resonance neuroimages
Computers and Biomedical Research
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Pharmacological FMRI in humans involves BOLD signal acquisition before, during and after the administration of a drug, and often results in a heterogeneous pattern of drug-induced hemodynamic responses in the brain. Exploratory techniques, including blind source separation, can be useful for BOLD data that contains patterns of cross-dependencies. Bayesian source separation (BSS) is a multivariate technique used to calculate the presence of unobserved signal sources in measured FMRI data, as well as the covariance between data voxels and between reference waveforms. Unlike conventional univariate regression analysis, BSS does not assume independence between voxel time series or source components. In this study, BOLD measurement of the acute effect of an intravenous dose of cocaine, a substance shown previously to engage multiple sites within the orbitofrontal cortex, was processed with BSS. The utility of BSS in pharmacological FMRI applications was demonstrated in multiple examples featuring single-ROI, multiple-ROI and whole-slice data. The flexibility of the BSS technique was shown by choosing different modeling strategies to form the prior reference functions, including approximating the pharmacokinetics of cocaine, interpolating simultaneously measured behavioral data and using observed BOLD responses from known subcortical afferents to the cortex of interest.