Yuguang Meng1, Anthony W.S. Chan2,3, and Xiaodong Zhang1,3
1Yerkes Imaging Center, Yerkes National Primate Research Center, Emory University, Atlanta, GA, United States, 2Department of Human Genetics, School of Medicine, Emory University, Atlanta, GA, United States, 3Division of Neuropharmacology and Neurologic Diseases, Yerkes National Primate Research Center, Emory University, Atlanta, GA, United States
Synopsis
This study examined the
developmental changes of white matter integrity in rhesus monkey brains with
the HD gene mutation using diffusion tensor imaging (DTI). Widespread developmental alterations are seen in
striatum, and the frontal, motor, sensory and visual brain areas. The findings reveal the temporal-spatial evolution
of abnormal white matter maturation during the development of the brain with the
Huntington’s Disease, and suggest altered neural substrates associated with motor
and cognitive dysfunctions in HD patients.PURPOSE
To access whole brain white matter alterations in
developmental macaque brains with transgenic Huntington’s disease (HD).
METHODS
Three HD rhesus
monkeys (male) were generated by lentiviral-mediated transgenesis as previously
described
1. Four age-matched wild-type non-transgenic rhesus
macaques (2 males and 2 females) were used as control. Animals
were scanned every 6 months from 6 months to 4 years old. During MRI scanning,
they were anesthetized with ~1.0% isoflurane mixed with 100% O
2.
Diffusion weighted images
were acquired with a Siemens 8-channel phased-array volume coil and a dual
spin-echo, echo planar imaging (EPI) sequence and the following imaging
parameters: TE = 89 ms, TR = 5700 ms,
voxel size = 1.3 mm × 1.3 mm × 1.3 mm, a single b-value of 1000 s/mm
2
with 30 diffusion encoding directions. T1-weighted images acquired with
a 3D MPRAGE with inversion time = 950 ms were used for structural
identification and constructing an anatomical macaque template for the DTI
image registration. FA maps were nonlinearly
registered to a population-specific FA template, and then skeletonised with TBSS
toolbox (FMRIB, Oxford). The ages for the
maximum FA were calculated by fitting with a Poisson regression model: FA =
A*age*e
-B*age + C, where A, B and C are the parameters of the model
2.
After confirming normal distribution of the calculated ages by a one-sample
Kolmogorov-Smirnov test, a two
sample independent t-test was used to test the group difference within the skeletonized FA
maps with FDR correction (q=0.05). The
age for the maximum FA was averaged within the ROIs with group difference, and compared with independent
t-tests with FDR correction (q= 0.05).
RESULTS
Widespread
differences in brain maturation are illustrated in Fig. 1. These areas include medial
prefrontal cortex (mPFC), ventral lateral prefrontal cortex (vlPFC), dorsal
premoter (dPMC) and primary motor cortex (PMC), somatosensory cortex (SSC),
temporal cortex (TC), parietal cortex (PC) and visual cortex (VC), and also
included caudate (CA), putamen (PU), thalamus (TH), corpus callosum (CC), brain
stem (BS) and cerebrum (CB). Maximum FA in HD animals is much lower than that
in control group (Fig. 2). In particular, white matter in HD animals degenerate
much earlier than that in control animals (22.8±1.1 vs 50.9±1.5 months).
DISCUSSIONS
In this work,
spatial-temporal changes of white matter in HD monkey brains were examined
using DTI. Abnormal white matter maturation and degeneration in the motor,
sensory and cognitive brain areas are consistent with those seen in prodromal
and symptomatic HD patients
3, 4. Furthermore, progressive changes
in affected brain areas were also aligned with symptom onset and associated
with specific cognitive and motor behavioral changes as disease progress in the
same HD monkeys reported previously
5. HD monkeys can potentially
be used as preclinical large animal model to facilitate the development of
novel biomarkers through noninvasive imaging.
Acknowledgements
Grants ORIP/NIH (OD010930), the
National Center for Research Resources P51RR000165 and the Office of Research
Infrastructure Programs / OD P51OD011132.References
[1] Yang, SH, et al.
Nature. 2008; 453:921-924. [2] Lebel C, et al. Neuroimage. 2010;52:20-31. [3]
Kloppel S, et al. Brain. 2008;131:196-204. [4] Rosas, HD, et al. Neurology
2002;58:695-701. [5] Chan, AW, et al. PLOS One 2015;10: e0122335.