Enhancing task fMRI preprocessing via individualized model‐based filtering of intrinsic activity dynamics
Matthew F. Singh,
Anxu Wang,
Michael Cole,
ShiNung Ching,
Todd S. Braver
Affiliations
Matthew F. Singh
Corresponding author at: Department of Psychological & Brain Sciences, Campus Box 1125, One Brookings Drive, Saint Louis, MO, 63130, USA.; Department of Electrical and Systems Engineering, Washington University in St. Louis, St. Louis, MO, USA; Department of Psychological and Brain Sciences, Washington University in St. Louis, St. Louis, MO, USA; Center for Molecular and Behavioral Neuroscience, Rutgers University, Newark, NJ 07102, USA
Anxu Wang
Department of Electrical and Systems Engineering, Washington University in St. Louis, St. Louis, MO, USA; Department of Psychological and Brain Sciences, Washington University in St. Louis, St. Louis, MO, USA
Michael Cole
Center for Molecular and Behavioral Neuroscience, Rutgers University, Newark, NJ 07102, USA
ShiNung Ching
Department of Electrical and Systems Engineering, Washington University in St. Louis, St. Louis, MO, USA; Department of Biomedical Engineering, Washington University in St. Louis, St. Louis, MO, USA
Todd S. Braver
Department of Psychological and Brain Sciences, Washington University in St. Louis, St. Louis, MO, USA; Department of Radiology, Washington University in St. Louis, St. Louis, MO, USA
Brain responses recorded during fMRI are thought to reflect both rapid, stimulus-evoked activity and the propagation of spontaneous activity through brain networks. In the current work, we describe a method to improve the estimation of task-evoked brain activity by first “filtering-out the intrinsic propagation of pre-event activity from the BOLD signal. We do so using Mesoscale Individualized NeuroDynamic (MINDy; Singh et al. 2020b) models built from individualized resting-state data to subtract the propagation of spontaneous activity from the task-fMRI signal (MINDy-based Filtering). After filtering, time-series are analyzed using conventional techniques. Results demonstrate that this simple operation significantly improves the statistical power and temporal precision of estimated group-level effects. Moreover, use of MINDy-based filtering increased the similarity of neural activation profiles and prediction accuracy of individual differences in behavior across tasks measuring the same construct (cognitive control). Thus, by subtracting the propagation of previous activity, we obtain better estimates of task-related neural effects.
