Population Inference for Node Level Differences in Multi-subject Functional Connectivity

Using Gaussian graphical models as the basis for functional connectivity, we propose new models and test statistics to detect whether subject covariates predict differences in network metrics in a population of subjects. Our approach emphasizes the need to account for errors in estimating subject le...

Full description

Saved in:
Bibliographic Details
Published in2015 International Workshop on Pattern Recognition in NeuroImaging pp. 53 - 56
Main Authors Narayan, Manjari, Allen, Genevera I.
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.06.2015
Subjects
covariates
fixed effects
fMRI
functional connectivity
gaussian graphical models
Graphical models
inear mixed effects
markov networks
Markov random fields
Measurement
Neuroimaging
resampling
Sociology
Statistics
Online AccessGet full text
DOI10.1109/PRNI.2015.34

Cover

More Information
Summary:Using Gaussian graphical models as the basis for functional connectivity, we propose new models and test statistics to detect whether subject covariates predict differences in network metrics in a population of subjects. Our approach emphasizes the need to account for errors in estimating subject level networks when conducting inference at the population level. Using simulations, we show that failure to do so reduces statistical power in detecting covariate effects for realistic graph structures. We illustrate the benefits of our procedure for clinical neuroimaging using a resting-state fMRI study of neurofibromatosis-I.
DOI:10.1109/PRNI.2015.34