A Computationally Efficient, Exploratory Approach to Brain Connectivity Incorporating False Discovery Rate Control, A Priori Knowledge, and Group Inference
Graphical models appear well suited for inferring brain connectivity from fMRI data, as they can distinguish between direct and indirect brain connectivity. Nevertheless, biological interpretation requires not only that the multivariate time series are adequately modeled, but also that there is accu...
Main Authors: | Aiping Liu, Junning Li, Z. Jane Wang, Martin J. McKeown |
---|---|
Format: | Article |
Language: | English |
Published: |
Hindawi Limited
2012-01-01
|
Series: | Computational and Mathematical Methods in Medicine |
Online Access: | http://dx.doi.org/10.1155/2012/967380 |
Similar Items
-
Marginal false discovery rate approaches to inference on penalized regression models
by: Miller, Ryan
Published: (2018) -
Knowledge discovery from cDNA microarrays and a priori knowledge
by: Midelfart, Herman
Published: (2003) -
False discovery rates in spectral identification
by: Jeong Kyowon, et al.
Published: (2012-11-01) -
A Note on False Discovery Rate
by: Jian-Ping Lin, et al.
Published: (2009) -
Differentially private false discovery rate control
by: Cynthia Dwork, et al.
Published: (2021-09-01)