Beyond BOLD: optimizing functional imaging in stroke populations

Abstract

Blood oxygenation level-dependent (BOLD) signal changes are often assumed to directly reflect neural activity changes. Yet the real relationship is indirect, reliant on numerous assumptions, and subject to several sources of noise. Deviations from the core assumptions of BOLD contrast functional magnetic resonance imaging (fMRI), and their implications, have been well characterized in healthy populations, but are frequently neglected in stroke populations. In addition to conspicuous local structural and vascular changes after stroke, there are many less obvious challenges in the imaging of stroke populations. Perilesional ischemic changes, remodeling in regions distant to lesion sites, and diffuse perfusion changes all complicate interpretation of BOLD signal changes in standard fMRI protocols. Most stroke patients are also older than the young populations on which assumptions of neurovascular coupling and the typical analysis pipelines are based. We present a review of the evidence to show that the basic assumption of neurovascular coupling on which BOLD-fMRI relies does not capture the complex changes arising from stroke, both pathological and recovery related. As a result, estimating neural activity using the canonical hemodynamic response function is inappropriate in a number of contexts. We review methods designed to better estimate neural activity in stroke populations. One promising alternative to event-related fMRI is a resting-state-derived functional connectivity approach. Resting-state fMRI is well suited to stroke populations because it makes no performance demands on patients and is capable of revealing network-based pathology beyond the lesion site.

Keywords: MRI; aging; brain; cerebrovascular accident; connectivity; functional; multimodal imaging; neurovascular coupling; perfusion; vascular injury.

Figure 1

The canonical hemodynamic response (HR) function captures the slow rise, peak, and undershoot of the BOLD signal. Labels illustrate typical parameters of interest in estimation of the HR and sources of variability in the response across populations. [Color figure can be viewed in the online issue, which is available at http://wileyonlinelibrary.com.]

Figure 2

Average blood oxygen level‐dependent (BOLD), cerebral blood flow‐weighted (CBF), and CBV‐weighted (CBV), signal time course in M1 associated with cued hand movements (gray bars). Error bars represent standard error in each group. Reproduced with permission from Blicher et al., J Cereb Blood Flow Metab, 2012, Vol. 32, 2044–2054, © Macmillan Publishers.

Figure 3

Four representative brains, all with left hemisphere lesions, shown in native space compared to after normalization with and without cost function masking. Reproduced with permission from Andersen et al., Neuroimage, 2010, Vol. 53, 78–84, © Elsevier.