Abstract
A hierarchical model based on the Multivariate Autoreges- sive (MAR) process is proposed to jointly model neurological time-series collected from multiple subjects, and to characterize the distribution of MAR coefficients across the population from which those subjects were drawn. Thus, inference about effective connectivity between brain re- gions may be generalized beyond those subjects studied. The posterior on population- and subject-level connectivity parameters are estimated in a Variational Bayesian (VB) framework, and structural model param- eters are chosen by the corresponding evidence criteria. The significance of resulting connectivity statistics are evaluated by permutation-based approximations to the null distribution. The method is demonstrated on simulated data and on actual multi-subject neurological time-series.
This research was supported by grants NIH 5 P41 RR13218 and FIRST BIRN.
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Cosman, E.R., Wells-III, W.M. (2005). Bayesian Population Modeling of Effective Connectivity.. In: Christensen, G.E., Sonka, M. (eds) Information Processing in Medical Imaging. IPMI 2005. Lecture Notes in Computer Science, vol 3565. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11505730_4
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DOI: https://doi.org/10.1007/11505730_4
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