Synopsis
Our study
of long-term use of high fat diet inducing mild ketonemia in Wistar rats showed
improvements in learning and memory, as well as larger hippocampi and higher
concentrations of tNAA (marker of neuronal viability), tCho (involved in
metabolite turnover), and tCr (involved in cell energetics). Here, we applied
voxel based morphometry (VBM) for structural images and used TARQUIN for 1H MRS
data obtained at 7T. Results of VBM and TARQUIN provided results consistent
with previous analyses. However, the use of a literature-based template lead to
tissue contractions not detectable with study specific template.Purpose
Our study
of long-term use of high fat diet that lead to mild ketonemia in Wistar rats showed
improvements in learning and memory, as well as larger hippocampi and higher
concentrations of tNAA (marker of neuronal viability), tCho (involved in
metabolite turnover), and tCr (involved in cell energetics). Here, we apply
independent post-processing methodology to ascertain that our findings were
genuine.
Introduction
We have
recently observed that one-year use of high fat diet (HFD) that induced mild
ketonemia lead to better learning and memory, larger hippocampi volumes without
any changes to cortical volumes, as well as higher concentrations of total NAA
(tNAA: N-acetylaspartate and N-acetylaspartateglutame; marker of neuronal
viability), total Cho (tCho: Glycerophosphocholine +Phosphocholine, which are
believed to be primarily involved in cell membrane breakdown and synthesis) and
total Cr (tCr: creatine + phospo-creatine – involved in cell bioenergetics)
1. We performed ROI analyses and used LC
Model for spectral processing.
Here, we
applied voxel-wise analysis to determine focal changes in brain tissue
structure. Furthermore, we compared the effects of template selection
(Valdés-Hernández et. al. template
2 vs. study specific template) on the
results. Moreover, for spectral processing we used TARQUIN
3, an open source alternative that
was demonstrated to work comparably well to LCModel with wide range of 1.5T and
3.0T proton spectra. However, it has not been used to fit proton, animal
spectra acquired at 7.0T.
Methods
Twenty five
male Wistar rats were put on HFD (~60% energy from fat, ~28% from carbohydrates)
on their 55th day of life, while 22 control male rats (CON) remained on chow.
Structural T2-weighted TurboRARE (TR/TE=4700/30ms, RARE factor=4,
resolution=125x125x500μm, no gap, NEX=7, TA=27 min) acquired on Bruker BioSpin
working at 7T, with a transmit cylindrical radiofrequency coil (15 cm inner
diameter) and a receive-only coil array (2x2 elements) positioned over the
animal’s head. Localized proton
spectroscopy at short echo was performed using PRESS sequence (TR/TE = 3500/20
ms, 256 averages, 8,192 points, TA=15min) with VAPOR water suppression, the
outer volume suppression, and frequency drift correction (flip angle 7 deg.) was performed to obtain metabolite concentrations. Each
measurement was carried out in a single volume of interest (8 x 2 x 2 mm3)
encompassing hippocampus.
For volume
based morphology, 18 images acquired for CON, 18 datasets for HFD were selected. Images were resampled to isotropic resolution of
125μm/vox and processed with N4 algorithm to correct for intensity
inhomogenity. Image of each specimen was registered into the Valdés-Hernández
et. al. template2 or study-specific template
using SyN algorithm4 resulting in a series of
deformation field. Jacobian determinant of each deformation field was then
computed and modulated with a gray matter probability, blurred with Gaussian
filter of 250μm. Significance of differences between CON and HFD was determined
with two-sample unpaired t-test. Threshold-Free Cluster Enhancement permutation
method5 was used to threshold t-maps (FSL-randomise software). 10,000
permutations were used in tests and p=0.05 was chosen as a significance
threshold.
Spectroscopic data was reanalyzed in TARQUIN3 which differs from LC Model in fitting domain and algorithm. We had adapted TARQUIN's 7T basis set to be consistent with the LCModel's one.
Results
Hippocampal
volume are larger in HFD-fed rats than in controls, especially in hippocampal
CA1 field, but also in surrounding cortical areas, regardless of used template.
Results obtained using Valdés-Hernández et. al. template
2 show areas of tissue expansion and
areas of tissue contraction (Figure 1). Study specific template-based results
do not show regions of smaller volumes in HFD fed group compared to control (Figure
2). Moreover, the concentration of tNAA, Glx and tCr were higher in the HFD-fed
group than in controls (6.8%, p=0.01, 6.5%, p=0.03, and 4.5%, p=0.03,
respectively), consistent with the results obtained by LC Model. Method specific differences between processing with TARQUIN and LC Model were analyzed with Bland Altman and the coefficients of variation for Glx, tNAA, and tCr are < 7%. Concentrations of selected metabolites are compared between groups in Figure 3.
Discussion
The results
confirm our ROI findings of larger hippocampal volumes in HFD fed rats, but also
point to focal volume increases in temporal association cortex and ectorhinal
cortex. Moreover, use of study specific template yielded similar regions of
tissue expansion due to HFD, but regions of tissue contraction were absent. Spectral
analysis with TARQUIN yielded similar results as those obtained with LC Model,
showing that they are genuine, not a product of a certain post-processing
methodology. In summary, our results support the use of study specific
templates in animal studies. Furthermore, they do not
support the thesis that HFD per se leads
to degeneration of the nervous system.
Acknowledgements
Polish National Science Centre, grants no: 2011/03/B/NZ4/03771 and 2013/09/B/NZ7/03763.References
1. Setkowicz, Z.,
Gazdzinska, A., Osoba, J. J., Karwowska, K., Majka, P., Orzel, J., …
Gazdzinski, S. P. (2015). Does Long-Term High Fat Diet Always Lead to Smaller
Hippocampi Volumes, Metabolite Concentrations, and Worse Learning and Memory? A
Magnetic Resonance and Behavioral Study in Wistar Rats. Plos One, 10(10),
e0139987. http://doi.org/10.1371/journal.pone.0139987
2. Valdes-Hernandez
PA, Sumiyoshi A, Nonaka H, et al. An in vivo MRI Template Set for Morphometry,
Tissue Segmentation, and fMRI Localization in Rats. Frontiers in
neuroinformatics 2011;5:26-26.
3. Wilson M,
Reynolds G, Kauppinen RA, Arvanitis TN, Peet AC. A Constrained Least-Squares
Approach to the Automated Quantitation of In Vivo H-1 Magnetic Resonance
Spectroscopy Data. Magnetic Resonance in Medicine 2011;65:1-12.
4. Avants BB,
Epstein CL, Grossman M, Gee JC. Symmetric diffeomorphic image registration with
cross-correlation: Evaluating automated labeling of elderly and
neurodegenerative brain. Medical Image Analysis 2008;12:26-41.
5. Smith, S. M., & Nichols, T. E. (2009). Threshold-free
cluster enhancement: addressing problems of smoothing, threshold dependence and
localisation in cluster inference. NeuroImage, 44(1), 83–98. doi:10.1016/j.neuroimage.2008.03.061