RT Journal Article
SR Electronic
T1 Challenges for Bayesian Model Selection of Dynamic Causal Models
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 102293
DO 10.1101/102293
A1 Rebecca N. van den Honert
A1 Sarah Shultz
A1 Marcia K. Johnson
A1 Gregory McCarthy
YR 2017
UL http://biorxiv.org/content/early/2017/01/22/102293.abstract
AB Achieving a mechanistic explanation of brain function requires understanding causal relationships among regions. A relatively new technique to assess effective connectivity in fMRI data is Dynamic Causal Modeling (DCM). As DCM is more frequently used, it becomes increasingly important to further validate the technique and understand its limitations. With DCM, Bayesian Model Selection (BMS) is used to select the most likely causal model. We conducted simulations to test the degree to which BMS is robust to two types of challenges when applied to DCMs, those inherent to data (Category 1) and those inherent to model space (Category 2). Category 1 challenges tested properties of the data (low signal-to-noise, different response magnitudes and shapes across regions) that could either blur the distinction between models or potentially bias model selection. These challenges are impossible or difficult to measure and control in real data, so investigating their effect upon BMS through simulation is critical. Category 2 challenges tested properties of model space that create subsets of confusable models. Our results suggest that given data that conform to the prior assumptions of DCM, BMS is robust to challenges from Category 1. However, in the face of Category 2 challenges (when a more homogenous model space was tested) the false positive rate rose above an acceptable level. We show that such errors are neither trivial nor easily avoided with existing approaches. However, we argue that it is possible to detect Category 2 challenges, and avoid inappropriate interpretations by conducting simulations prior to applying DCM.DCMDynamic Causal ModelingBMSBayesian Model SelectionfMRIfunctional magnetic resonance imagingBOLDblood oxygen level dependentFMCFamily Model ComparisonHRFhemodynamic response functionROIregion of interestSNRsignal to noise ratioR1region 1R2region 2U1input 1