This study investigated the application of oscillating gradient dMRI to study tissue microstructure and white matter pathology in the mouse brain. We found new frequency-dependent tissue contrasts in the mouse brain and also demonstrated the sensitivity of this method to microstructural changes associated with white matter pathology in the cuprizone mouse model. The results indicate that this technique can potentially be used to explore multi-scale restrictive effects of various tissue components and reveal additional structural information in the CNS.
There have been several reports on using the oscillating gradient dMRI technique to study systems of restricted diffusion (16
). Frequency or diffusion time dependence of ADC measurements have been demonstrated in phantoms (21
). ADC measurements in the rat cortex and sub-cortical grey matter have been shown to increase with frequency (24
). With the vast heterogeneity in tissue microstructural properties and organization within the brain, sampling the diffusion spectrum over different frequency domains can provide complementary tissue contrasts to distinguish microscopic structures in regions that otherwise appear homogeneous in conventional PGSE-based dMRI results. In the present study, high-resolution 3D diffusion tensor images acquired with relatively high frequency oscillating gradients revealed novel tissue contrasts in the mouse brain. The ADCs measured in several grey and white matter structures increased with the frequency of the oscillating gradients. We discovered that certain structures in the cerebellum (the CBGr layer) and the hippocampal formation (the GrDG and Py layers) were highlighted by significant frequency-dependent ADC increase. From maps representing the rate of change in ADC versus frequency, regions within the cerebral cortex, olfactory bulb, and the piriform cortex also showed enhancements. These findings suggest that oscillating gradient dMRI can generate unique tissue contrasts not available from conventional PGSE based experiments.
Our knowledge on the potential mechanisms of the tissue contrasts reported here remains limited. Using phantoms consisting of impermeable microspheres ranging from 1 μm to 400 μm in diameter, Parsons et al.
) reported that, as the oscillation frequency increased from 0 to 200 Hz, ADCs measured in the phantoms with large microspheres increased faster with frequency than the phantoms with small microspheres. Another report by Xu et al.
) used simulations to study the effects of the ratio of nuclear volume to cellular volume, or nuclear volume fraction, on ADC measurements. They demonstrated that while the PGSE results showed no change in ADC measurements with this ratio, ADCs measured using the OGSE sequence increased with this ratio. The simulation results also showed that the rate of change in ADC with frequency increased with the nuclear volume fraction. These reports and the correlations between the highlighted regions in the fitted ADC versus frequency maps and staining intensity in the soma-specific Nissl /nuclei-specific DAPI stained sections suggest that the tissue contrasts observed in this study may be related to certain cytoarchitectural features in these regions. These highlighted regions all contain densely packed neurons, which resemble the model of close-packed spherical cells constructed by Xu et al.
for their simulations (28
). Two of the highlighted regions with high rates of increase in ADC with frequency, the CBGr and GrDG layers, contain a large population of densely packed granule cells, which are among the smallest neurons in the brain and have a thin cytoplasm (35
). Another highlighted region, the Py region in the hippocampus, consists of densely packed pyramidal cells, which have relatively larger soma and more cytoplasm than the granule cells. Quantitative data on the cellular and nuclear morphology of these cells are rather limited. The diameters of the granule and pyramidal cells (5-6 μm for the granule cells in the cerebellum, 10-18 μm for the granule cells in the dentate gyrus, and approximately 20 μm for the pyramidal cells in the hippocampus) (35
) are comparable to the one-dimensional root-mean-square displacements of water molecules within the range of diffusion times used in this study (3-10 μm within 1.67-15 ms at 37°C). The granule cells in the CBGr have the thinnest cytoplasm, with only 0.03 - 0.5 μm distance between the nuclear surface and cell membrane (38
). The nuclei of the granule cells in the GrDG have an average diameter of 5.66 μm (39
), and the diameters of the nuclei of the pyramidal cells in the hippocampus are approximately 6.5 - 7.4 μm (40
). Assuming spherical shaped cells and nuclei, the nuclear volume fractions of these cells are approximately 0.3 - 0.7 for the granule cells in the CBGr, 0.03 - 0.18 for the granule cells in the GrDG, and 0.03 - 0.05 for the pyramidal cells in the Py region. In comparison, the granule cells in the CBGr had the highest rate of frequency-dependent increase in ADCs, followed by the granule cells in the GrDG, and then the pyramidal cells in the Py (). These results suggest that the measured rate of ADC changes with frequency as well as the associated tissue contrasts may be correlated with the nuclear volume fractions in these densely packed neuronal regions, as described in (28
). Further investigations with simulations and more detailed cytoarchitectural analyses are necessary to identify other factors that may contribute to the tissue contrast and clarify the mechanisms that govern contrast generation in oscillating gradient dMRI.
Using the tensor model of diffusion, the frequency dependent behaviors of λ
, and FA in the mouse brain white matter were studied. ADC and diffusivities measured in several white matter structures increased with frequency. Because λ┴
increased more rapidly than λ
in the white matter structures studied here, the corresponding FA values decreased with frequency. We did not observe any apparent frequency-dependent change in the primary orientation of anisotropy in these tracts or any grey matter regions that have a predominant diffusion orientation in the PGSE results, although such changes may be possible at higher frequencies than achieved in this study. We further examined the sensitivity of oscillating gradient dMRI to white matter pathology in the well characterized mouse cuprizone model. In this model, profound demyelination (90%) can be consistently observed in the caudal corpus callosum at 4-5 weeks after the start of a cuprizone diet (41
). The application of oscillating gradient DTI in the cuprizone model showed significantly increased λ┴
in the caudal corpus callosum at the 4 and 6 week time points. The increases in λ┴
at the 4 and 6 week time points measured using the PGSE sequence have been attributed to demyelination in this model (42
). These results suggest that λ┴
measured using the OGSE sequence at the frequencies used in this study was also sensitive to demyelination. The significant increases in OGSE based λ┴
at 100 and 150 Hz and frequency-dependence of λ┴
in the caudal corpus callosum at the 4 week time point compared to the 6 week time point is interesting, because no significant difference was found between the PGSE-based λ┴
between the two time points in this study. Swelling of the caudal corpus callosum due to infiltration of immune cells in the cuprizone model has been reported before (44
), and can also be appreciated in our immuno-stained sections in . These pathological changes might be the underlying causes of the increase in frequency-dependence of λ┴
since the size of the microglia, as barriers to water diffusion, is much larger than the diameter of axons (< 1 μm) and can result in an increase in the rate of frequency-dependent changes in ADC. These findings demonstrate the potential of oscillating gradient DTI in characterizing white matter pathology, and suggest that changes in OGSE based diffusion measurements may be indicative of microscopic changes induced by white matter pathology, occurring over spatial scales that conventional PGSE-based DTI may not be sensitive to. It is, however, necessary to perform additional studies with detailed histopathological analyses to evaluate the sensitivity of OGSE based dMRI to white matter pathology with respect to conventional PGSE based dMRI.
Based on our current understanding of the origins of diffusion MR signals, more detailed analysis of the frequency-dependent contrast changes, for instance estimating the relative contributions of intra- and extra-cellular compartments, is difficult. Advanced mathematical models have been proposed to study the time-dependence of diffusion in restricted systems with known geometries (11
). For the range of b
-values used in our study, it has been shown that the signal decay in the PGSE-based diffusion MR experiments is dominated by the fast diffusing component, which has been suggested to originate from the extracellular space (46
). However, further studies are necessary to investigate the exact correlates of the restriction effects responsible for frequency-dependent dMRI contrast changes observed with the OGSE technique. Further optimization of the imaging protocol requires detailed understanding of the underlying contrast generation mechanism, in order to determine the range of diffusion times and diffusion-weighting (b
-values) to obtain sensitivity to specific structures. It is necessary to note that the images presented here were acquired from postmortem brain specimens perfusion fixed with 4% PFA. Several studies have shown that PFA fixation can result in significant changes in tissue microstructural properties, such as increased apparent restriction size and membrane water exchange (47
). As a result, the frequency dependent changes in ADC measured in live animal brains may differ from the results shown here.
The oscillating gradient dMRI technique also has certain limitations. Limitations of current gradient hardware place a constraint on the minimum achievable diffusion time for a given b
-value (). Decreasing the diffusion time further entails increasing the gradient oscillation frequency, which, in turn, requires even stronger gradient amplitudes to maintain a constant b
-value. The demand for strong gradient amplitudes also makes this technique technically difficult to implement on current clinical scanners. The long diffusion gradient durations necessary for oscillating gradient waveforms result in lengthening the echo time for image acquisitions, thereby worsening the signal-to-noise ratio (SNR) in the already low SNR technique. Additionally, the need to acquire diffusion data at multiple frequencies with this technique leads to long total acquisition times. With further understanding of the contrast properties and their frequency-dependent characteristics, it may be possible to acquire data at fewer frequencies, thereby reducing the total imaging time and improving feasibility for application to in vivo
studies. As for oscillating gradient DTI, it inherits the limitations of DTI as a simplified model for complex tissue structures, such as crossing or branching fibers. In many cases, it is not necessary to acquire the complete tensor dataset if ADC measurement is sufficient, which can reduce the total time required. The ADC measurements obtained at different frequencies in our study were fitted to a linear model, since within the relatively narrow frequency domain used in our study the frequency-dependence of ADC can be approximated by a linear curve. The diffusion spectrum over a broader frequency range, however, is nonlinear (21
), and the linear model will have to be replaced by appropriate nonlinear models over wider frequency ranges.
To summarize, this study demonstrated unique tissue contrasts in the mouse brain using oscillating gradient dMRI. The distinctive delineation of densely packed neuronal regions with this technique has interesting implications for studies investigating neuronal defects in the hippocampus or cerebellum in mouse models of related disorders. The finding that frequency-dependent DTI contrasts are sensitive to microstructural white matter changes in the cuprizone model also renders this technique useful for applications in mouse models of white matter disorders.