The role of auxilliary parameters in evaluating voxel-wise encoding models for 3T and 7T BOLD fMRI data
Boos, M.; Guntupalli, J. S.; Rieger, J. W.; Hanke, M.
Show abstract
In neuroimaging, voxel-wise encoding models are a popular tool to predict brain activity elicited by a stimulus. To evaluate the accuracy of these predictions across multiple voxels, one can choose between multiple quality metrics. However, each quality metric requires specifying auxiliary parameters such as the number and selection criteria of voxels, whose influence on model validation is unknown. In this study, we systematically vary these parameters and observe their effects on three common quality metrics of voxel-wise encoding models in two open datasets of 3- and 7-Tesla BOLD fMRI activity elicited by musical stimuli. We show that such auxiliary parameters not only exert substantial influence on model validation, but also differ in how they affect each quality metric. Finally, we give several recommendations for validating voxel-wise encoding models that may limit variability due to different numbers of voxels, voxel selection criteria, and magnetic field strengths.
Matching journals
The top 2 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Prediction of individual melodic contour processing in sensory association cortices from resting state functional connectivity 96%
- Simultaneous Modeling of Reaction Times and Brain Dynamics in a Spatial Cuing Task 96%
- Isolating the Sources of Pipeline-Variability in Group-Level Task-fMRI results 95%
Similar papers in this journal
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.