Oscillating gradient improves the sensitivity of diffusion functional MRI
Dan Wu1, Hanbing Lu2, Yihong Yang2, and Jiangyang Zhang3

1Radiology, Johns Hopkins University School of Medicine, BALTIMORE, MD, United States, 2Neuroimaging Research Branch, National Institute on Drug Abuse, Intramural Research Program, National Institutes of Health, Baltimore, MD, United States, 3Radiology, New York University School of Medicine, NY, United States


Synopsis: Diffusion functional MRI (DfMRI) has been proposed to detect neuronal activations more directly than BOLD-fMRI, but its sensitivity to cell swelling associated with neuronal activities remains less known. Numerical simulations suggest that oscillating gradient spin echo (OGSE) diffusion MRI is more sensitive to changes in cell size than conventional pulsed gradient spin echo (PGSE) diffusion MRI. In adult rat brain DfMRI experiments with forepaw stimulation, ADC measured by OGSE showed significant reductions during stimulation, and the reductions were significantly larger than those measured by PGSE, suggesting OGSE may be more sensitive to cell swelling associated with neuronal activation than PGSE.


Diffusion function MRI (DfMRI) has been proposed as a more direct approach to detect neuronal activities than BOLD-based fMRI1, assuming that DfMRI signals reflect neuronal swelling during activation. However, the sensitivity of DfMRI to neuronal activation has not been well established. Some studies argued that the vascular components2,3 still have considerable effects on DfMRI signals. A recent in-vitro study reported that DfMRI can detect neuronal depolarization induced by pharmacological manipulations, but not by physiological stimulation4. Given this evidence, it is necessary to improve the sensitivity of DfMRI to cell swelling during normal neuronal activity.

In recent years, time-dependent diffusion MRI (dMRI) has been used to distinguish microstructural changes based on their spatial scales5. The development of oscillating gradient spin echo (OGSE) sequences allows us to achieve relatively short diffusion times to detect changes at small spatial scales6. In this study, we hypothesized that the OGSE-based DfMRI sequence can increase the sensitivity to detect neuronal activities than conventional pulsed gradient spin echo (PGSE)-based DfMRI.


Experiments were performed on adult Sprague-Dawley rats (n=5) using a 9.4 T MRI system by measuring the fMRI responses to electrical forepaw stimulation. Details on animal preparation and anesthesia can be found in (7). The stimulation paradigm consisted of seven alternating cycles of 40 s ON/40 s Off, starting with a 20-s pre-stimulation baseline. The acquisition included gradient-echo EPI (GE-EPI), PGSE-EPI (δ/Δ=10/13.8 ms, b=700 and 1500 s/mm2), and OGSE-EPI (f=100 Hz, one oscillating cycle, b=700 s/mm2). The imaging parameters were: FOV = 35x35 mm, matrix size = 64x64, TE = 13 ms for GE-EPI and 41.4 ms for PGSE/OGSE-EPI, TR = 1 s, 5 slices with a thickness of 1mm. After initial GE-EPI data showed positive BOLD responses in the sensory cortex, non-diffusion-weighted and diffusion-weighted images were acquired in an interleaved fashion, from which the temporal changes in apparent diffusion coefficient (ADC) were obtained. The stimulation session was repeated three times for each acquisition modality. The DfMRI data were analyzed using the AFNI software, and regions of interest were defined as the location of activation in the sensory cortex based on the non-diffusion-weighted images on individual rats. Numerical simulation of cell phantom was performed using the MIST Toolbox8, with an intracellular diffusivity of 2x10-3 mm2/s, sphere radius from 1-15 μm, and PGSE and OGSE waveforms that were matched to the rat brain experiments.


Numerical simulations (Fig. 1) of dMRI signals from a phantom of packed spheres showed that signals measured using OGSE-dMRI (50 Hz to 200 Hz, equivalent diffusion time = 5 ~ 1.25 ms) have higher sensitivity to cell swelling than PGSE-dMRI (diffusion time = 10 and 20 ms). The slopes of the curves reflect the sensitivity of dMRI signals to changes in sphere radius, as in the case of cell swelling. The OGSE-dMRI curves rise faster than PGSE-dMRI for sphere radius in the range of 1 to 10 μm, which is approximately the size of neurons.

In the in-vivo experiments, forepaw stimulation induced an increase in MRI signals in both non-diffusion-weighted and the diffusion-weighted images (either PGSE or OGSE) (Fig. 2). However, for the ADC measurements, only the time course of OGSE-ADC showed a slight decrease when stimulation was ON. Averaged signals during stimulation-on and -off periods showed that the non-diffusion-weighted and diffusion-weighted signals had approximately 2% increases (Fig. 3A-B). The average ADCs measured using OGSE during stimulation showed a 1% reduction compared to that without stimulation (Fig. 3C). In comparison, the ADC measured by PGSE at b=700 s/mm2 showed no significant difference between stimulation on and off; and ADC at b=1500 s/mm2 showed approximately 0.5% signal reduction during stimulation, which was slightly lower compared that from the OGSE experiments (p = 0.037, paired one-tailed t-test).


Neuronal activation is likely to cause only small changes in the cell size4. The simulation and in-vivo experiments suggest that OGSE dMRI has higher sensitivity to neuronal activation than conventional PGSE dMRI. The experiments were designed to minimize the contributions from the vascular components. By computing ADCs based on interleaved non-diffusion-weighted and diffusion-weighted signals, the effects of changes in T2 and vascular occupancy were mostly removed. In addition, the OGSE waveform is less sensitive to the effects of background gradients9. In comparison, the ADC measured by PGSE at b=1500 s/mm2 has higher sensitivity than that at b=700 s/mm2, which has been suggested to be more sensitive to background gradient effects.


Our results suggest that the OGSE sequence is more sensitive to PGSE sequence in detection of neuronal activities.


This work was made possible by the following funding supports: R01HD074593 (JZ) and R21 NS098018 (DW).


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Fig. 1: Numerical simulations suggest that OGSE-dMRI has higher sensitivity to cell swelling than PGSE-dMRI. The graph shows normalized dMRI signals measured using PGSE-dMRI (diffusion time Δ = 10 ms and 20 ms) and OGSE-dMRI (50 Hz to 200 Hz, equivalent diffusion time Δ = 5 -1.25 ms) in a phantom of packed spheres. The slopes of the curves reflect the sensitivity of OGSE-dMRI and PGSE-dMRI to changes in sphere radius. The OGSE-dMRI curves rise faster than PGSE-dMRI for sphere radius in the range of 1 to 10 mm, which is approximately the size of neurons and astrocytes.

Fig. 2: Time courses of the non-diffusion-weighted (b0) signal, diffusion-weighted (dwi) signal, and apparent diffusion coefficient (ADC) acquired using OGSE-dMRI (b = 700 s/mm2) and PGSE-dMRI (b = 700 and 1500 s/mm2). Data are presented as mean ± standard deviation at each repetition time in a stimulation cycle, which are averaged over 7 stimulation cycles per session, 3 stimulation sessions per mice, and a total of 5 rats. We plotted they data by the percentage of signal change with respected to the mean signal across a stimulation session.

Fig. 3: Non-diffusion-weighted signal, diffusion-weighted signal, and ADC averaged during stimulation ON and OFF, measured from OGSE-dMRI (b = 700 s/mm2) and PGSE-dMRI (b = 700 and 1500 s/mm2). The data were presented as the percentage of signal differences (ΔS between ON and OFF) with respected to baseline signal (S0) during stimulation OFF. The black asterisks indicates the significance of signal difference between stimulation ON and OFF, and the red asterisk indicates the significance of difference in ΔS/S0 obtained from different diffusion encodings. ** p<0.01 and * p<0.05 by paired Student’s t-test.

Proc. Intl. Soc. Mag. Reson. Med. 25 (2017)