Enhancing task fMRI preprocessing via individualized model‐based filtering of intrinsic activity dynamics

NeuroImage

Anxu Wang¹  Todd S. Braver²  Michael Cole³  ShiNung Ching⁴  Matthew F. Singh⁵ 

[1] Brain Sciences, Campus Box 1125, One Brookings Drive, Saint Louis, MO, 63130, USA.;Center for Molecular and Behavioral Neuroscience, Rutgers University, Newark, NJ 07102, 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;;Corresponding author at: Department of Psychological &

关键词: Task fMRI;    Resting state fMRI;    Brain dynamics;    Causal modeling;    Individual differences;    Cognitive control;   

DOI  :  

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown