Neurovascular factors in resting-state functional MRI

Abstract

There has been growing interest in the use of resting-state functional magnetic resonance imaging (rsfMRI) for the assessment of disease and treatment, and a number of studies have reported significant disease-related changes in resting-state blood oxygenation level dependent (BOLD) signal amplitude and functional connectivity. rsfMRI is particularly suitable for clinical applications because the approach does not require the patient to perform a task and scans can be obtained in a relatively short amount of time. However, the mechanisms underlying resting-state BOLD activity are not well understood and thus the interpretation of changes in resting state activity is not always straightforward. The BOLD signal represents the hemodynamic response to neural activity, and changes in resting-state activity can reflect a complex combination of neural, vascular, and metabolic factors. This paper examines the role of neurovascular factors in rsfMRI and reviews approaches for the interpretation and analysis of resting state measures in the presence of confounding factors.

Keywords: BOLD; Functional connectivity; Neurovascular coupling; fMRI.

Fig. 1

The correlation r between the measured BOLD time series (x₁ and x₂) from two different brain regions depends on the neurovascular coupling pathway. The measured BOLD time series in each region can be viewed as the sum of a BOLD component (s₁ and s₂ for regions 1 and 2, respectively) and a noise component (n₁ and n₂, respectively), where the BOLD component is obtained by convolving the neural power fluctuations (blue and red time courses on the left) with the hemodynamic response functions. The correlation between the underlying neural power fluctuations is designated as ρ. For the blue and red hemodynamic responses, the resultant BOLD components and measures are shown by the blue and red time series, respectively, with a correlation value of r = 0.54 between the measured BOLD time series. For a change in neurovascular coupling that reduces the amplitudes of the hemodynamic responses by one third (indicated by the black and green hemodynamic responses), there is a decrease (by one third) in both the amplitudes of the BOLD component time series and the SNR of the measured BOLD time series (black and green lines). With the SNR decrease, the correlation of the measured BOLD time series drops to r = 0.41.

Fig. 2

Plots of the percent BOLD versus percent CBF response amplitudes computed with the BOLD signal model presented in Eq. (1). (A) Percent BOLD versus percent CBF responses for different values of the parameter M and a fixed coupling parameter of n = 2.5. As the parameter M increases, the BOLD signal increases more rapidly for a given change in CBF. (B) Percent BOLD versus percent CBF responses for different values of the coupling parameter n and a fixed M parameter value of 8.0%. As the coupling parameter decreases, the CBF and CMRO₂ responses become more tightly coupled and the BOLD response decreases.

Fig. 3

(A) The measured correlation r = r_s(SNR + (r_n/r_s))/(SNR + 1) exhibits a dependence on SNR that depends on the relative relation between the source correlation r_s and the noise correlation r_n. As SNR increases the measured correlation approaches the source correlation value r_s, and as SNR decreases the measured correlation approaches the noise correlation value r_n. The dashed green line and solid blue line show the dependence when the noise correlation is lower (r_n = 0.3) or higher (r_n = 0.7), respectively, than the source correlation (r_s = 0.5). (B) Correlation as a function of SNR for the signals shown in Fig. 1. For the larger hemodynamic responses (blue and red responses in Fig. 1), the SNR is equal to 1 and the measured correlation of r = 0.54 is indicated by the red square. With a reduction in the amplitude of hemodynamic responses(black and green responses in Fig. 1), the SNR drops to 0.33 and the measured correlation of r = 0.41 is indicated by the black diamond.

Fig. 4

Effect of changes in the shape of the hemodynamic response on the correlation between the measured BOLD time series. The blue and red hemodynamic responses and the corresponding BOLD component and measured time series (also in blue and red) are identical to those depicted in Fig. 1. For this condition, the signal and noise correlations are r_s = 0.70 and r_n = 0.38 and the measured correlation is r = 0.54. With an overall slowing down of the hemodynamic responses (shown in black and green), the BOLD component time courses become delayed and smoother (black s₁ and green s₂) as compared to the original time courses (blue s₁ and red s₂), and the signal correlation drops to r_s = 0.63. The noise time courses remain unchanged, but the measured BOLD time series (black x₁ and green x₂) are altered, with a correlation value of r = 0.34. The decrease in correlation is even more pronounced if the hemodynamic response from one region changes while the other remains the same. For example, if the region 1 response is unchanged (blue hemo-dynamic response) while the region 2 response slows down (green hemodynamic response), the correlation value drops to r = 0.17 (reflecting the correlation between blue x₁ and green x₂).