Developmental White Matter Alterations in Monkey Brains with Huntington’s Disease
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


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.


To access whole brain white matter alterations in developmental macaque brains with transgenic Huntington’s disease (HD).


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% O2. 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/mm2 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).


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).


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.


Grants ORIP/NIH (OD010930), the National Center for Research Resources P51RR000165 and the Office of Research Infrastructure Programs / OD P51OD011132.


[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.


Figure 1 TBSS results for the whole brain through the horizontal direction (z). Areas with red-yellow color (thickened) indicate significant group difference of ages for the maximum FA.

Figure 2 An illustration of the fitting curves of FA changing with ages in caudate of HD and control animals, fitted with the Poisson model 2.

Table 1 ROI analysis results with significant group difference in age for the maximum FA.

Proc. Intl. Soc. Mag. Reson. Med. 24 (2016)