Is Model Fitting Necessary for Model-Based fMRI?

ABSTRACT: Model-based analysis of fMRI data is an important tool for investigating the computational role of different brain regions. With this method, theoretical models of behavior can be leveraged to find the brain structures underlying variables from specific algorithms, such as prediction errors in reinforcement learning. One potential weakness with this approach is that models often have free parameters and thus the results of the analysis may depend on how these free parameters are set. In this work we asked whether this hypothetical weakness is a problem in practice. We first developed general closed-form expressions for the relationship between results of fMRI analyses using different regressors, e.g., one corresponding to the true process underlying the measured data and one a model-derive

SUBMITTER: Wilson RC 

PROVIDER: S-EPMC4472514 | biostudies-literature | 2015 Jun

REPOSITORIES: biostudies-literature