autohrf - Automated Generation of Data-Informed GLM Models in Task-Based
fMRI Data Analysis
Analysis of task-related functional magnetic resonance
imaging (fMRI) activity at the level of individual participants
is commonly based on general linear modelling (GLM) that allows
us to estimate to what extent the blood oxygenation level
dependent (BOLD) signal can be explained by task response
predictors specified in the GLM model. The predictors are
constructed by convolving the hypothesised timecourse of neural
activity with an assumed hemodynamic response function (HRF).
To get valid and precise estimates of task response, it is
important to construct a model of neural activity that best
matches actual neuronal activity. The construction of models is
most often driven by predefined assumptions on the components
of brain activity and their duration based on the task design
and specific aims of the study. However, our assumptions about
the onset and duration of component processes might be wrong
and can also differ across brain regions. This can result in
inappropriate or suboptimal models, bad fitting of the model to
the actual data and invalid estimations of brain activity. Here
we present an approach in which theoretically driven models of
task response are used to define constraints based on which the
final model is derived computationally using the actual data.
Specifically, we developed 'autohrf' — a package for the 'R'
programming language that allows for data-driven estimation of
HRF models. The package uses genetic algorithms to efficiently
search for models that fit the underlying data well. The
package uses automated parameter search to find the onset and
duration of task predictors which result in the highest fitness
of the resulting GLM based on the fMRI signal under predefined
restrictions. We evaluate the usefulness of the 'autohrf'
package on publicly available datasets of task-related fMRI
activity. Our results suggest that by using 'autohrf' users can
find better task related brain activity models in a quick and
efficient manner.
