The spatial resolution of functional magnetic resonance imaging (fMRI) is fundamentally limited by effects from large draining veins. Here we describe an analysis method that provides data-driven estimates of these effects in task-based fMRI. The method involves fitting a one-dimensional manifold that characterizes variation in response timecourses observed in a given dataset, and then using identified early and late timecourses as basis functions for decomposing responses into components related to the microvasculature (capillaries and small venules) and the macrovasculature (large veins), respectively. We show the removal of late components substantially reduces the superficial cortical depth bias of fMRI responses and helps eliminate artifacts in cortical activity maps. This method provides insight into the origins of the fMRI signal and can be used to improve the spatial accuracy of fMRI.
Bibliographical noteFunding Information:
We thank E. Margalit and N. Petridou for helpful discussions and L. Dowdle for assistance with preparing data in BIDS format. This work was supported by National Institutes of Health grant nos. P41 EB015894 (K.U.), P41 EB027061 (K.U.), P30 NS076408 (K.U.), S10 RR026783 (K.U.), S10 OD017974-01 (K.U.) and U01 EB025144 (K.U.), and the W. M. Keck Foundation (K.U.).
© 2020, The Author(s), under exclusive licence to Springer Nature America, Inc.
PubMed: MeSH publication types
- Journal Article
- Research Support, N.I.H., Extramural
- Research Support, Non-U.S. Gov't