Synopsis

Neurite Orientation Dispersion and Density Imaging (NODDI) is a rapidly emerging diffusion MRI (dMRI) technique used to characterize microstructural complexity through the compartmental modelling of neural water fractions into Intra-neurite, Extra-neurite and CSF volume fractions. This project aimed to further the ability of preclinical diffusion imaging through the application of NODDI to a rodent model with the objective to determine its utility, precision and reliability at 9.4 Tesla.

Purpose

Methods

Imaging: MRI was performed on a 9.4-T/31-cm small animal MRI scanner (Agilent, Santa Clara, CA, USA). A 7-channel phased array receive coil with a 2-channel transmit coil was designed for this study. 8 Male Sprague Dawley Rats (age 102 ± 13 days, weight 323 ± 37g) were scanned twice with 7 days ± 1 day between scans.

Sequence: The NODDI diffusion encoding scheme utilized a multi-shot spin-echo echo planar imaging (SE-EPI) acquisition pulse sequence (slice thickness = 500 mm, 250 x 250 mm in plane resolution, 31 total slices, TE = 36 ms, TR=5.0s, 4 shot EPI acquisition, 2 averages). A two-shell diffusion protocol was used resulting in a total imaging time of 96 minutes (Shell one: 36 directions, b-value = 1000 s/mm2, G = 149 mT/m, Δ = 17 ms, δ = 4.5 ms, TR = 5.0s, 4 b=0 volumes; Shell two: 72 directions, b-value = 2000 s/mm2, G = 298 mT/m, Δ = 17 ms, δ = 4.5 ms, TR = 5.0s, 8 b=0 volumes).

Analyses: Images were pre-processed using fMRI Software Library (FSL, v.5.0.10, Oxford, UK). EDDY was used to correct for eddy current and susceptibility-induced distortions.⁷ The NODDI Matlab toolbox (UCL Microstructure Imaging Group) was then used to produce maps of ODI, NDI, and IsoVF. Both mean region of interest (ROI) analyses, and whole brain voxel-wise analyses were performed. ROI analyses focused on four relevant regions of interest (thalamus, corpus callosum, dentate gyrus, and hippocampus) as well as whole brain white and grey matter. In both the ROI and voxel-wise analyses the scan-rescan reproducibility and reliability were characterized using the coefficient of variation (CV).

Results

When analyzing the resulting NODDI maps (Figure 1) using mean values for a given ROI, both ODI and NDI showed low CVs within all regions, as well as low dispersion between values. Mean CVs for ODI ranged from 4-9% within all ROI’s while values of NDI ranged from 2-11% within all ROI’s. In contrast, mean IsoVF CVs were very high and widely dispersed, ranging from 9-49% (Figure 2).

In the voxel-wise analysis, in the between subject histogram (Figure 3) over 90% of voxels fell below a CV of 20% for ODI and 15% for NDI. The within subject’s histogram showed even lower voxel-wise CV with over 90% of voxels below a CV of 17% for ODI and 12% for NDI (Figure 4). Again, high CVs were observed for IsoVF, ranging well above 100% for many voxels.

Using the between subject whole brain voxel-wise CV’s, the minimum number of subjects was determined on a voxel-wise basis that would allow detection of a statistically significant change in each metric of 5%-20% between subjects (Figure 5). NDI was the most sensitive metric, with detection of even subtle changes (on the order of 5%) in all voxels with feasible sample sizes (n < 6). ODI changes on the order 10% were detectable in all voxels for small sample sizes, but required large sample sizes (n > 10) for whole brain voxel-wise detection of very small changes. Very large sample sizes were found necessary to detect changes in IsoVF of any magnitude on a voxel-wise basis. Using the within subject whole brain voxel-wise CV’s, the minimum statistically significant change that could be detected in each metric on a voxel-wise basis was determined based on a scan re-scan protocol for sample sizes of 6, 8 and 10 within each group. Once again NDI was the most sensitive metric, detecting very small changes (<5%) with standard sample sizes. ODI was slightly less sensitive but again was able to detect small changes (<10%) on a scan-rescan basis with standard sample sizes. Finally, IsoVF again was unable to detect significant changes at feasible samples sizes.

Conclusions

Acknowledgements

References

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