RT Journal Article
SR Electronic
T1 Enhancing Task fMRI Preprocessing via Individualized Model-Based Filtering of Intrinsic Activity Dynamics
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 2020.12.10.420273
DO 10.1101/2020.12.10.420273
A1 Matthew F. Singh
A1 Anxu Wang
A1 Michael Cole
A1 ShiNung Ching
A1 Todd S. Braver
YR 2021
UL http://biorxiv.org/content/early/2021/07/16/2020.12.10.420273.abstract
AB 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; [1]) models built from individualized resting-state data (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 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 activity.Competing Interest StatementThe authors have declared no competing interest.