Abstract
This paper presents a data-driven approach for modeling the temporal profile of pharmacological magnetic resonance imaging (phMRI) data, in which the blood oxygen level-dependent (BOLD) response to an acute drug challenge is measured. To date, this type of data have typically been analysed using general linear models applied to each voxel individually, an approach that requires a pre-defined model of the expected response to the pharmacological stimulus. Previous approaches have defined this model using pharmacokinetic profiles, phMRI data from pilot studies, cognitive or physiological variables that have been acquired during the experiment or a simple pre-post boxcar profile. In contrast, the approach presented here is data-driven; a basis function is fitted to the data in a Bayesian framework using Gaussian processes. This method outperforms two previous multivariate approaches to fMRI analysis while also providing information about the shape of the BOLD response and hence, increasing the model interpretability.
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Doyle, O.M., Mehta, M.A., Brammer, M.J., Schwarz, A.J., De Simoni, S., Marquand, A.F. (2012). Data-Driven Modeling of BOLD Drug Response Curves Using Gaussian Process Learning. In: Langs, G., Rish, I., Grosse-Wentrup, M., Murphy, B. (eds) Machine Learning and Interpretation in Neuroimaging. Lecture Notes in Computer Science(), vol 7263. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-34713-9_27
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DOI: https://doi.org/10.1007/978-3-642-34713-9_27
Publisher Name: Springer, Berlin, Heidelberg
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