Cortical layer-dependent arterial blood volume changes: improved spatial specificity relative to BOLD fMRI

Abstract

The spatial specificity of functional hemodynamic responses was examined by simultaneous mapping of BOLD changes and quantitative changes in cerebral arterial blood volume (DeltaCBV(a)) across the cortical depth in cats (n=7) during 40-s visual stimulation. Studies were performed at 9.4 T using the recently developed, non-invasive magnetization transfer (MT)-varied gradient-echo (GE) fMRI technique to separate signals from MT-independent arterial blood and MT-dependent tissue. The highest conventional BOLD signal changes occurred at the cortical surface, where large pial veins exist, whereas the highest CBV(a) changes occurred in the middle of the cortex, where T(1)-weighted images show a hyperintense layer. In the middle cortical region, the average BOLD change (echo time=20 ms) was 1.16+/-0.45% during stimulation and -0.59+/-0.31% during the post-stimulus period, while the average DeltaCBV(a) was 0.33+/-0.02 ml/100 g during stimulation and -0.08+/-0.12 ml/100 g post-stimulus (post-stimulus DeltaCBV(a) is not statistically significant). Time-dependencies of the DeltaCBV(a) cortical profiles are similar to total CBV responses previously measured during visual stimulation in cats with a susceptibility contrast agent indicating, that blood volume changes mostly originate from arterial vessels. Our findings demonstrate the value of non-invasive and quantitative DeltaCBV(a) measurement in high-resolution MT-varied GE fMRI studies, where spatial specificity is better localized to sites of neural activity as compared with conventional GE BOLD changes.

Fig. 1

Schematic diagrams of MT-varied GE fMRI signals. To visualize the effect of flip angle to functional BOLD fMRI, signal changes were simulated with Eq. [1] – Eq. [6] with two flip angles (θ) = 10° (closed symbols) and 90° (open symbols). See texts for other parameters. The total signal is the sum of the signal from the tissue and arterial blood components. (a) The normalized signal change of tissue has a linear dependence on MT level (blue line), while that from the artery is independent of MT (red line). When ΔS_ss,MT / S₀ is linearly fitted against the normalized baseline signal, S_ss,MT / S₀ (black line), an intercept yields Δν_a. The smaller flip angle, θ = 10° provides a greater dynamic range for linear fitting than data obtained with the larger flip angle, θ = 90°. (b) Percentage signal changes (S_ss,MT / S_ss,MT) (black lines with closed circle symbols for θ = 10° and open circle symbols for θ = 90°) are non-linearly increased with MT levels because percentage signals of MT-independent Δν_a (red line for θ= 10 and purple line for θ= 90°) increased with MT, while the percentage change of tissue signals (blue line) is independent of MT. The larger flip angle induces greater saturation of the steady-state tissue signals, increasing the relative Δν_a contribution to the fMRI signal; the contribution of arterial blood signals with θ = 90° (purple line) is higher than with θ = 10° (red line).

Fig. 2

Selection of regions of interest. (a) The middle cortical ROI is delineated by yellow, which was determined along the white band within the cortical area in T₁-weighted images, likely the stripe of myelin-rich Gennari. These bands were manually traced for comparison with arterial blood volume change maps (blue dashed lines pointed by arrow marks in Fig. 2B). Green contours: gray matter. (b) Two quadrangular ROIs within visual area 18 were defined for the cortical depth analysis from the surface of cortex to the white matter. D: dorsal, L: lateral

Fig. 3

MT-varied baseline images (a–c) and GE fMRI maps (d–f). (a–c) Baseline signals (S_ss,MT) decreased with an increase of MT level, as expected. (d–f) Percentage signal changes (ΔS_ss,MT/S_ss,MT) corresponding to visual stimulation increased with MTR_ss. The highest percentage signal changes occurred at the surface, but did not increased with MT. Percentage signal changes at the middle of the cortex, indicated by arrows, increased with MT.

Fig. 4

ΔCBV_a maps of three animals. Data from the animal shown in Fig. 3 is shown in panel A (Cat #1). Red/yellow pixels represent increases in ΔCBV_a, and blue/purple pixels indicate negative changes. The peaks of ΔCBV_a responses matched well with the white bands within the cortex in T₁-weighted images (blue dashed contours), likely to be layer IV. The negative response was not detected in most studies.

Fig. 5

MT-varied GE fMRI (a) and ΔCBV_a time courses (b) obtained from the middle cortical ROI. The red bar indicates the stimulation period. Error bars: SD. Error bars of MTR_ss = 0.36 and 0.6 are show every 2 s for better visualization.

Fig. 6

Average cortical depth profiles of GE BOLD fMRI (without MT) and ΔCBV_a generated from the quadrangular ROIs in each animal. The surface of the cortex is zero. Approximate cortical layer locations were determined by relative distances of those layers in area 18 (Payne and Peters, 2002) and the profile of T₁-weighted image (gray line in panel A; a.u. = arbitrary units); layer IV should be located in the region between ~0.65 mm and ~1.08 mm from the surface of the cortex (blue color band). Supra-(layer II–III) and infra-granular layers (layer V–VI) are indicated by pink and purple color-bands, respectively. It is known that layer IV has highest capillary density, and during stimulation it has highest increases in neural activity, metabolism and blood flow. (a and b) Cortical depth profiles were calculated at every 10-s time period from stimulation onset to examine time-dependencies of the cortical profile. (c) BOLD and ΔCBV_a profiles were obtained from the entire 40-s stimulation period. The largest BOLD change is located at the surface of the cortex, while the highest ΔCBV_a change is observed at the middle of the cortex. Error bars: SEM.

Fig. 7

Effect of CSF contributions to ΔCBV_a. (a) Intercept maps were calculated for pixels in which MTR_ss is below 2 standard deviation of the mean MTR_ss in the middle cortical ROI. (b) A plot of the intercept vs. relative MTR_ss, which was calculated by dividing the pixel MTR_ss value by the averaged MTR_ss value of the middle cortical ROI (= 0.60 ± 0.04, n = 7). Each point indicates each negative intercept pixel, and different symbols indicate the different animals. Pixels with larger negative intercepts have the lower relative MTR_ss values (i.e. a smaller MT effects), indicating the larger CSF partial volume fraction.

Fig. 8

ΔCBV_a-weighted fMRI signals determined by a simplified method. (a) ΔCBV_a-weighted maps were calculated by the difference between GE fMRI percentage maps with MTR_ss = 0 and MTR_ss = 0.6 using Eq. [8]. The maps are from the same three animals (Cats #1 – #3) as seen in Fig. 4. The largest responses were observed at the middle of the cortex. (b) Pixel-wise ΔCBV_a–weighted signal intensity, determined by the subtraction method, is plotted against quantitative ΔCBV_a determined by the linear fit method. Comparisons from three cats shown in panel A are presented. The average pixel-wise correlation coefficient between the two values was 0.95 ± 0.01 (n = 7). This indicates that ΔCBV_a-weighted fMRI with two MT levels provides a good alternative to quantitative ΔCBV_a measurements. (c) Cortical profiles of fMRI percentage signal changes of MTR_ss = 0 and MTR_ss = 0.6, and ΔCBV_a–weighted responses (n = 7). The fMRI percentage signal changes at the middle of the cortex increased with MT effect, while those at the surface of the cortex did not changed. As a result, the profiles of ΔCBV_a-weighted signals exhibit the highest response at the middle of the cortex. The blue color band presents approximate layer IV.