MARGM: A multi-subjects adaptive region growing method for group fMRI data analysis

2021 ◽  
Vol 69 ◽  
pp. 102882
Author(s):  
Yuhu Shi ◽  
Min Li ◽  
Weiming Zeng
Keyword(s):  
Data Analysis ◽  
Region Growing ◽  
Fmri Data ◽  
Fmri Data Analysis

A Survey of FMRI Data Analysis Methods

2018 ◽  
Author(s):  
Anahid Ehtemami ◽  
Rollin Scott ◽  
Shonda Bernadin
Keyword(s):  
Data Analysis ◽  
Fmri Data ◽  
Analysis Methods ◽  
Fmri Data Analysis ◽  
Data Analysis Methods

scholarly journals How to improve parameter estimates in GLM-based fMRI data analysis: cross-validated Bayesian model averaging

2016 ◽  
Author(s):  
Joram Soch ◽  
Achim Pascal Meyer ◽  
John-Dylan Haynes ◽  
Carsten Allefeld
Keyword(s):  
Data Analysis ◽  
Model Selection ◽  
Bayesian Model ◽  
Bayesian Model Averaging ◽  
Model Averaging ◽  
Bayesian Model Selection ◽  
Fmri Data ◽  
Parameter Estimates ◽  
Fmri Data Analysis ◽  
Level Analysis

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.

Evaluation of ICA methods for fMRI data analysis

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

scholarly journals A Projection CCA Method for Effective fMRI Data Analysis

2019 ◽  
Vol 66 (11) ◽  
pp. 3247-3256 ◽  
Author(s):  
Muhammad Ali Qadar ◽  
Abd-Krim Seghouane
Keyword(s):  
Data Analysis ◽  
Fmri Data ◽  
Fmri Data Analysis
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