Functional MRI Data Analysis

2018 ◽  
pp. 30-46
Author(s):  
Francisco Gómez ◽  
Gabriel Castellanos

Functional MRI (fMRI) data analysis aims to characterize neuronal dynamics by using observations of the hemodynamic phenomena associated with neuronal activity. These observations are indirect and highly “noisy” and commonly require different models for data interpretation. Many techniques have been developed in recent years for analyzing fMRI data. Despite advances in fMRI, there are some limitations that have to be considered in obtaining successful characterization of neuronal activity from fMRI data. In this chapter, the fundamentals of fMRI data analysis are described. Initially, the basis of the hemodynamic phenomena associated with neuronal activity is presented. This is followed by a description of the principal models used to perform fMRI data analysis. Finally, some of the most critical aspects of fMRI data analysis are covered.

2018 ◽  
Author(s):  
Anahid Ehtemami ◽  
Rollin Scott ◽  
Shonda Bernadin

2016 ◽  
Author(s):  
Joram Soch ◽  
Achim Pascal Meyer ◽  
John-Dylan Haynes ◽  
Carsten Allefeld

AbstractIn functional magnetic resonance imaging (fMRI), model quality of general linear models (GLMs) for first-level analysis is rarely assessed. In recent work (Soch et al., 2016: “How to avoid mismodelling in GLM-based fMRI data analysis: cross-validated Bayesian model selection”, NeuroImage, vol. 141, pp. 469-489; DOI: 10.1016/j. neuroimage.2016.07.047), we have introduced cross-validated Bayesian model selection (cvBMS) to infer the best model for a group of subjects and use it to guide second-level analysis. While this is the optimal approach given that the same GLM has to be used for all subjects, there is a much more efficient procedure when model selection only addresses nuisance variables and regressors of interest are included in all candidate models. In this work, we propose cross-validated Bayesian model averaging (cvBMA) to improve parameter estimates for these regressors of interest by combining information from all models using their posterior probabilities. This is particularly useful as different models can lead to different conclusions regarding experimental effects and the most complex model is not necessarily the best choice. We find that cvBMS can prevent not detecting established effects and that cvBMA can be more sensitive to experimental effects than just using even the best model in each subject or the model which is best in a group of subjects.


NeuroImage ◽  
2001 ◽  
Vol 13 (6) ◽  
pp. 89 ◽  
Author(s):  
A. Caprihan ◽  
Laura K. Anderson

Sign in / Sign up

Export Citation Format

Share Document