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Review
. 2012 Aug 15;62(2):1017-23.
doi: 10.1016/j.neuroimage.2012.02.015. Epub 2012 Feb 14.

The continuing challenge of understanding and modeling hemodynamic variation in fMRI

Daniel A Handwerker  1 , Javier Gonzalez-Castillo, Mark D'Esposito, Peter A Bandettini
Affiliations
Review

The continuing challenge of understanding and modeling hemodynamic variation in fMRI

Daniel A Handwerker et al. Neuroimage. .

Abstract

Interpretation of fMRI data depends on our ability to understand or model the shape of the hemodynamic response (HR) to a neural event. Although the HR has been studied almost since the beginning of fMRI, we are still far from having robust methods to account for the full range of known HR variation in typical fMRI analyses. This paper reviews how the authors and others contributed to our understanding of HR variation. We present an overview of studies that describe HR variation across voxels, healthy volunteers, populations, and dietary or pharmaceutical modulations. We also describe efforts to minimize the effects of HR variation in intrasubject, group, population, and connectivity analyses and the limits of these methods.

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Figures

Figure 1
Figure 1
Hemodynamic responses from 20 subjects averaged across a region of interest in primary sensorimotor cortex in response to a single button press. Reprinted, with permission, from (Handwerker, Ollinger et al. 2004).
Figure 2
Figure 2
The left column shows the inputs to node 1 of the dynamic casual modeling (DCM) simulation and the right column shows the inputs to node 2. (A) Node 1 always includes a single HR shape. Node 2 includes the same HR shape or HR shapes that have delayed onsets, peak times, or undershoot magnitudes. (B) The HRs are convolved with a event-relate design of neural event times (black dots). This shows a 150 sec window of the 300 sec time series (C) Each node has a noise time series from different subjects’ scans of spontaneous fluctuations. (D). The HR time series in B are scaled and added to the noise in C. This figure shows how the time series look for the lower TSNR condition. (E) Schematics of the two models that were compared using DCM. (F) Comparisons of the two models in E. For each HR shape tested, if the blue or green lines are higher, that means node 1 is more likely to predict node 2. If the red or yellow bars are higher, node 2 is more likely to predict node 1. The dashed line at 0.9 is a typical significance threshold.
See this image and copyright information in PMC

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