Miniature pig model of human adolescent brain white matter development

Abstract

Background: Neuroscience research in brain development and disorders can benefit from an in vivo animal model that portrays normal white matter (WM) development trajectories and has a sufficiently large cerebrum for imaging with human MRI scanners and protocols.

New method: Twelve three-month-old Sinclair™ miniature pigs (Sus scrofa domestica) were longitudinally evaluated during adolescent development using advanced diffusion weighted imaging (DWI) focused on cerebral WM. Animals had three MRI scans every 23.95 ± 3.73 days using a 3-T scanner. The DWI imaging protocol closely modeled advanced human structural protocols and consisted of fifteen b-shells (b = 0-3500 s/mm²) with 32-directions/shell. DWI data were analyzed using diffusion kurtosis and bi-exponential modeling that provided measurements that included fractional anisotropy (FA), radial kurtosis, kurtosis anisotropy (KA), axial kurtosis, tortuosity, and permeability-diffusivity index (PDI).

Results: Significant longitudinal effects of brain development were observed for whole-brain average FA, KA, and PDI (all p < 0.001). There were expected regional differences in trends, with corpus callosum fibers showing the highest rate of change.

Comparison with existing method(s): Pigs have a large, gyrencephalic brain that can be studied using clinical MRI scanners/protocols. Pigs are less complex than non-human primates thus satisfying the "replacement" principle of animal research.

Conclusions: Longitudinal effects were observed for whole-brain and regional diffusion measurements. The changes in diffusion measurements were interepreted as evidence for ongoing myelination and maturation of cerebral WM. Corpus callosum and superficial cortical WM showed the expected higher rates of change, mirroring results in humans.

Keywords: Adolescent brain development; Diffusion weighted imaging; Miniature swine; White matter.

Fig. 1

T1-weighted and DWI-weighted (average across all b-value) images collected in a live pig. T1-weighted image demonstrates fully gyrified cortex with excellent gray matter/white matter contrast. DWI-weighted image demonstrates excellent resolution and lack of shape distortion artifacts.

Fig. 2

Analysis of DWI data. DWI data collected with reversal of phase-encoding gradients was denoised using MP-PCA filtering and corrected for shape distortions using TopUp and eddy current corrections (Step 1). In step 2, DWI model parameters were estimated including Fractional Anisotropy (FA) and Axial and Radial Diffusivity (AD and RD), Kurtosis Anisotropy (KA), Axial and Radial Kurtosis (AK and RK) and Permeability Diffusivity Index (PDI). Tract-based spatial statistics (TBSS) approach was used to warp individual FA images to a population-optimized FA atlas and project the center of the major WM tracts on the population-wide skeleton (Step 3). The other DWI parameters were projected on the skeleton based on FA projection maps for each subject (Step3). A piglet-based brain atlas was used to register the DWI brain template (Step 4). The overlap between regions of interests provided by the atlas and the skeleton of the cerebral white matter was used to calculate the regional average DWI parameters (Step 5).

Fig. 3

Average whole-brain Fractional Anisotropy (FA), Kurtosis Anisotropy (KA), and Permeability-diffusivity index (PDI) showed significant longitudinal effects of age. The fitted regresson line is shown in black. The average whole-brain diffusion trajectory for each individual pig is shown in color.

Fig. 4

Average whole-brain AD, RD, AK, and RK diffusion measures versus age (days). AK and RK showed suggestive significance with age (p = 0.02). The fitted regression line is indicated in black.