Fast Optimization Methods for L1 Regularization: A Comparative Study and Two New Approaches
ECML '07 Proceedings of the 18th European conference on Machine Learning
Shrinkage algorithms for MMSE covariance estimation
IEEE Transactions on Signal Processing
Detecting brain activation in fMRI using group random walker
MICCAI'10 Proceedings of the 13th international conference on Medical image computing and computer-assisted intervention: Part II
Deconfounding the effects of resting state activity on task activation detection in fMRI
MBIA'12 Proceedings of the Second international conference on Multimodal Brain Image Analysis
A novel sparse graphical approach for multimodal brain connectivity inference
MICCAI'12 Proceedings of the 15th international conference on Medical Image Computing and Computer-Assisted Intervention - Volume Part I
Fiber connectivity integrated brain activation detection
IPMI'13 Proceedings of the 23rd international conference on Information Processing in Medical Imaging
A novel sparse group Gaussian graphical model for functional connectivity estimation
IPMI'13 Proceedings of the 23rd international conference on Information Processing in Medical Imaging
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A growing interest has emerged in studying the correlation structure of spontaneous and task-induced brain activity to elucidate the functional architecture of the brain. In particular, functional networks estimated from resting state (RS) data were shown to exhibit high resemblance to those evoked by stimuli. Motivated by these findings, we propose a novel generative model that integrates RS-connectivity and stimulus-evoked responses under a unified analytical framework. Our model permits exact closed-form solutions for both the posterior activation effect estimates and the model evidence. To learn RS networks, graphical LASSO and the oracle approximating shrinkage technique are deployed. On a cohort of 65 subjects, we demonstrate increased sensitivity in fMRI activation detection using our connectivity-informed model over the standard univariate approach. Our results thus provide further evidence for the presence of an intrinsic relationship between brain activity during rest and task, the exploitation of which enables higher detection power in task-driven studies.