Learning effective connectivity from fMRI using autoregressive hidden Markov model with missing data.

Abstract

BACKGROUND Effective connectivity (EC) analysis of neuronal groups using fMRI delivers insights about functional-integration. However, fMRI signal has low-temporal resolution due to down-sampling and indirectly measures underlying neuronal activity. NEW METHOD The aim is to address above issues for more reliable EC estimates. This paper proposes use of… (More)
DOI: 10.1016/j.jneumeth.2016.12.019

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Cite this paper

@article{Dang2017LearningEC, title={Learning effective connectivity from fMRI using autoregressive hidden Markov model with missing data.}, author={Shilpa Dang and Santanu Chaudhury and Brejesh Lall and Prasun Kumar Roy}, journal={Journal of neuroscience methods}, year={2017}, volume={278}, pages={87-100} }