Abstract Huntington’s disease (HD) is a neurodegenerative disorder causing cognitive and motor impairments, evolving to death within 15–20 years after symptom onset. We previously established a mouse model with the entire human HD gene containing 128 CAG repeats (YAC128) which accurately recapitulates the natural history of the human disease. Defined time points in this natural history enable the understanding of longitudinal trajectories from the neurochemical and structural points of view using non-invasive high-resolution multi-modal imaging. Accordingly, we designed a longitudinal structural imaging (MRI and DTI) and spectroscopy (1H-MRS) study in YAC128, at 3, 6, 9 and 12 months of age, at 9.4 T. Structural analysis (MRI/DTI), confirmed that the striatum is the earliest affected brain region, but other regions were also identified through connectivity analysis (pre-frontal cortex, hippocampus, globus pallidus and thalamus), suggesting a striking homology with the human disease. Importantly, we found for the first time, a negative correlation between striatal and hippocampal changes only in YAC128. In fact, the striatum showed accelerated volumetric decay in HD, as opposed to the hippocampus. Neurochemical analysis of the HD striatum suggested early neurometabolic alterations in neurotransmission and metabolism, with a significant increase in striatal GABA levels, and specifically anticorrelated levels of N-acetyl aspartate and taurine, suggesting that the later is homeostatically adjusted for neuroprotection, as neural loss, indicated by the former, is progressing. These results provide novel insights into the natural history of HD and prove a valuable role for longitudinal multi-modal panels of structural and metabolite/neurotransmission in the YAC128 model. Introduction Huntington’s disease (HD, phenotype MIM # 143100) is an autosomal-dominant neurodegenerative disorder characterized by behavioural and motor dysfunction including chorea and dystonia (1). It is caused by a CAG triplet expansion in the huntingtin gene (HTT; MIM # 613004) on chromosome 4p16.3, which results in an excessively long polyglutamine stretch at the N-terminus of the huntingtin HTT protein (2). The CAG repeat number is known to be inversely correlated with the age of onset (3,4). Usually, in humans, the onset of overt clinical symptoms occurs in between 35 and 45 years of age, and progression until death occurs over a period of approximately 15–20 years (5). Currently, there are no known treatments to effectively slow down HD progression (6), and it is estimated that it affects approximately 6 in 100 000 people in the western societies (7). One of the authors of this study has previously defined a yeast artificial chromosome (YAC) mouse model with the complete human HD gene containing 128 CAG repeats (8). The full-length form of huntingtin is expressed under the control of the endogenous huntingtin promoter and regulatory elements, which probably explains why the YAC128 mice mimic so well the temporal trajectory of neuropathologic and neurobehavioural features of the human disease. Critical time points of the phenotype of this model have been defined, at 3, 6, 9 and 12 months of age. At 3 months no neuroanatomic alterations were identified and only hyperactivity was identified. Overt behavioural manifestations become apparent at 6 months, and striatal loss becomes detectable at 9 months. At 12 months, gross cortical atrophy becomes evident. This previous knowledge about the disease evolution in YAC128 mouse model (8) provides the opportunity to study the trajectory of early neurochemical, structural and connectivity changes, prior to clinical onset. It is well-established that the earliest and most prominent feature of HD is striatal atrophy, which leads to a significant increase of lateral ventricular volume (9). Caudate degeneration is known to be present in the prodromal phase and to progress along early HD stages (10–12). Atrophy of the striatum is therefore recognized as an important hallmark that may be used to assess the disease stage and progression (12,13). Nonetheless, other brain regions are also severely affected during the course of HD (14,15). In humans, Rosas et al. observed atrophy in several regions, including the nucleus accumbens, hippocampus, amygdala, thalamus and brainstem (15). Likewise, reports in diverse HD transgenic mouse models have documented that besides the striatum, cortical, hippocampal and globus pallidus (GP) volumes are significantly reduced (16–19). A magnetic resonance imaging (MRI) study was conducted in ex vivo YAC128 mice by Carroll et al. (20), at 1, 3, 8 and 12 months of age. This study used different animals at distinct time points and was not therefore not longitudinal, although it provided valuable information on natural history of HD. The authors observed increases in ventricular volumes, and reduction in striatal, thalamic, cortical, GP and white matter (WM) volumes, without detection of changes at the level of the hippocampus. Direct comparison with in vivo studies should be taken with care because of the volumetric changes ex vivo. Additionally, diffusion tensor imaging (DTI) studies have revealed that decreased fractional anisotropy (FA) is present in premanifest and early stage human HD subjects, suggesting abnormalities in WM pathways, including striatal projection fibres and the corpus callosum (21–23). WM integrity of two mouse models have been studied by Teo et al. (24) using DTI, including YAC128 and BACH mice, at 1.5, 3, 6, 9 and 12 months of age; they observed a FA reduction at pre-manifest stages (1.5 and 3 months of age) in anterior commissure, corpus callosum, internal and external capsule, cingulum and cerebral peduncle. Intriguingly, some studies have suggested the existence of potential compensatory changes between distinct brain structures and the striatum, both in humans and animals (25–27). Lerch et al. found an increase in the thickness of the sensorimotor cortex and correlations between this measure and decreased striatal volume in transgenic mice (25). In turn, Feigin et al. when studying presymptomatic HD patients using 15O positron emission tomography reported that enhanced activation in thalamocortical pathways may compensate for caudate degeneration during motor learning (26). In the same line, others noted that increased cortical and subcortical activation could also reflect compensatory mechanisms during task performance (27). Proton magnetic resonance spectroscopy (1H-MRS) plays an important role in addressing HD pathophysiological changes, given its known impact on energy metabolism (28). It allows for the quantification of pivotal brain metabolites such as creatine (Cr), γ-aminobutyric acid (GABA), glutamine (Gln), N-acetyl aspartate (NAA), taurine (Tau), among others (29,30). Investigation of the neurochemical profile in HD may reveal intrinsic metabolic and neurotransmitter disturbances in its progression and their relationship with clinical, behavioural and structural alterations (31–37). As an example, Sturrock et al. (32,37) found decreased levels of NAA in premanifest and early HD subjects in the left caudate putamen (CPu), and found a correlation with motor and disease burden scores, but not with CPu volumes (37). Others, using high-resolution MRS, suggested abnormalities in the neuronal energetic metabolism in the CPu, as demonstrated by lower concentrations of NAA and Cr in early HD (35). Accordingly, Heikkinen et al. also observed decreased levels of NAA and increased Cr, Gln and Tau levels in the striatum of 12 month-old HD transgenic mice (36). Tkac et al. (38) also found decreased NAA and increased glycerophosphocholine (GPC), Tau, Gln and GPC + phosphocholine (GPC + PCh) in R6/2 mice studied at early disease stages (few weeks). MRI is a promising tool to characterize brain phenotypes and disease progression in HD since it allows for detailed in vivo longitudinal assessment and the quantification of brain morphology, neurochemistry and function. It is therefore becoming an important and reliable tool to monitor HD-related changes in the same animal over time, which is not possible with standard histology (39,40). On the other hand, the YAC128 model of HD, where precise time points are identified in the natural history of the disease, is very useful in preclinical research, because they are amenable to a swift evaluation of therapeutic targets and follow up of disease evolution and interventional approaches over time. We therefore designed a longitudinal multi-modal in vivo MRI, DTI and MRS study to investigate structural and neurochemical alterations associated with the disorder at different stages and brain structures (i.e. PFC, striatum, GP, hippocampus and thalamus), to understand the stage-dependent (premanifest, early and late HD) nature of neural changes in transgenic YAC128 mice, expressing the human full-length mutant HTT. Results Volumetric analysis The analysis of temporal evolution of structure volumes [as in reflected in the slope estimates measured through β1 coefficients (see explanation in Eq. (1)), see Materials and Methods section], revealed significant differences between wild-type (WT) and YAC128 mice for the striatum (P < 0.001) and hippocampus (P = 0.009), as detailed in Figure 1, while no differences in β1 were observed for the volumes of the remaining structures of interest (SOIs, see Supplementary Material, Fig. S1). These observations seem to reflect a prominent neurodegenerative effect of HD in the striatum (more pronounced volumetric reduction). Conversely, the age-dependent reduction of hippocampal volume was attenuated in YAC128 mice, since the mean regression β1WT was lower than β1HD for this structure (irrespective of the age-related volume decreases that were observed on both mice groups, β1 <0). Figure 1. View largeDownload slide Longitudinal analysis (least square regression) of brain structure volumes for WT and YAC128 mice. Temporal evolution of (A) striatal volumes and (B) hippocampal volumes. (C) Results from statistical analysis including β1 coefficients [see Eq. (1)], P-values (from β1WT versus β1HD comparisons), mean and SD at each time point. HD: YAC128 mice; M: months of age. Figure 1. View largeDownload slide Longitudinal analysis (least square regression) of brain structure volumes for WT and YAC128 mice. Temporal evolution of (A) striatal volumes and (B) hippocampal volumes. (C) Results from statistical analysis including β1 coefficients [see Eq. (1)], P-values (from β1WT versus β1HD comparisons), mean and SD at each time point. HD: YAC128 mice; M: months of age. However, the analysis of the β0 coefficients (intercepts, that represent mean changes irrespective of trajectory), showed that atrophy in YAC128 mice is also present at the level of the hippocampus (P = 0.002), as well as at the prefrontal cortex (PFC, P = 0.002), striatum (P = 0.032) and GP (P = 0.001), as shown in Table 1. Table 1. β0 coefficients from volumetric analysis [see Eq. (1)] SOI β0 [mm3] p WT HD PFC 0.002 65.99 64.05 Striatum 0.032 25.4 24.82 GP 0.001 1.754 1.535 Hippocampus 0.002 24.72 23.97 SOI β0 [mm3] p WT HD PFC 0.002 65.99 64.05 Striatum 0.032 25.4 24.82 GP 0.001 1.754 1.535 Hippocampus 0.002 24.72 23.97 Lower coefficients for YAC128 mice at PFC, striatum, GP and hippocampus may evidence atrophy on these structures. Table 1. β0 coefficients from volumetric analysis [see Eq. (1)] SOI β0 [mm3] p WT HD PFC 0.002 65.99 64.05 Striatum 0.032 25.4 24.82 GP 0.001 1.754 1.535 Hippocampus 0.002 24.72 23.97 SOI β0 [mm3] p WT HD PFC 0.002 65.99 64.05 Striatum 0.032 25.4 24.82 GP 0.001 1.754 1.535 Hippocampus 0.002 24.72 23.97 Lower coefficients for YAC128 mice at PFC, striatum, GP and hippocampus may evidence atrophy on these structures. Concerning gender analysis, since we did not find any interaction between group and sex, we have pooled for gender on the remaining statistical analysis. DTI analysis Analysis of average mean diffusivity (MD) in the SOI, for WT and YAC128 mice is summarized in Figure 2 and Table 2. The YAC128 mice experienced a progressive reduction of MD over time in the striatum [negative slopes represented by β1 coefficients, Eq. (1)], while it remained almost constant in the WT group (P = 0.0219, Fig. 2A and C). On the other hand, in the thalamus, WT mice experienced faster MD reduction over time than YAC128 mice (P = 0.0335, Fig. 2B and C). No significant differences for MD slopes were found for the remaining brain structures (Supplementary Material, Fig. S2). Moreover, differences on the intercepts [β0 coefficients from Eq. (1)] were observed for the PFC and striatum (Table 2). Table 2. β0 coefficients from average MD and FA analysis [see Eq. (1)] SOI MD FA β0 (*e−4) β0 P WT HD P WT HD PFC 0.019 7.4 7.6 <0.001 0.124 0.165 Striatum 0.002 6.8 7.1 0.002 0.128 0.174 GP – – – 0.001 0.139 0.184 Hippocampus – – – <0.001 0.121 0.171 Thalamus – – – <0.001 0.128 0.162 SOI MD FA β0 (*e−4) β0 P WT HD P WT HD PFC 0.019 7.4 7.6 <0.001 0.124 0.165 Striatum 0.002 6.8 7.1 0.002 0.128 0.174 GP – – – 0.001 0.139 0.184 Hippocampus – – – <0.001 0.121 0.171 Thalamus – – – <0.001 0.128 0.162 Only significant statistical results are explicitly depicted; the symbol ‘–’ means ns. Table 2. β0 coefficients from average MD and FA analysis [see Eq. (1)] SOI MD FA β0 (*e−4) β0 P WT HD P WT HD PFC 0.019 7.4 7.6 <0.001 0.124 0.165 Striatum 0.002 6.8 7.1 0.002 0.128 0.174 GP – – – 0.001 0.139 0.184 Hippocampus – – – <0.001 0.121 0.171 Thalamus – – – <0.001 0.128 0.162 SOI MD FA β0 (*e−4) β0 P WT HD P WT HD PFC 0.019 7.4 7.6 <0.001 0.124 0.165 Striatum 0.002 6.8 7.1 0.002 0.128 0.174 GP – – – 0.001 0.139 0.184 Hippocampus – – – <0.001 0.121 0.171 Thalamus – – – <0.001 0.128 0.162 Only significant statistical results are explicitly depicted; the symbol ‘–’ means ns. Figure 2. View largeDownload slide Longitudinal analysis (least square regression) of average MD for WT and YAC128 mice. Temporal evolution of MD at (A) striatum and (B) thalamus. (C) Results from statistical analysis including β1 coefficients, P-values (from β1WT versus β1HD comparisons), mean and SD at each time point. HD: YAC128 mice; M: months of age. Figure 2. View largeDownload slide Longitudinal analysis (least square regression) of average MD for WT and YAC128 mice. Temporal evolution of MD at (A) striatum and (B) thalamus. (C) Results from statistical analysis including β1 coefficients, P-values (from β1WT versus β1HD comparisons), mean and SD at each time point. HD: YAC128 mice; M: months of age. On the other hand, from FA analysis we observed for all the SOIs a similar pattern, as shown in Figure 3: while FA decreased along time in YAC128 mice (negative β1 coefficients), an increment is observed in WT mice (positive β1 coefficients), being significantly different. While the increases observed in WT mice are in agreement with the progression of microstructural organization, the reduction of FA in YAC128 mice could be attributed to the characteristic neurodegenerative effects of the disease. Significant differences in β0 coefficients (representing mean offset values, irrespective of trajectory) were also found for all SOI, as presented in Table 2. No sex effect was found in DTI measurements. Figure 3. View largeDownload slide Longitudinal analysis (least square regression) of average FA for WT and YAC128 mice. Temporal evolution of FA at (A) PFC and (B) striatum; (C) GP; (D) hippocampus; (E) thalamus. (F) Results from statistical analysis including β1 coefficients, P-values (from β1WT versus β1HD comparisons), mean and SD at each time point. HD: YAC128 mice; M: months of age. Figure 3. View largeDownload slide Longitudinal analysis (least square regression) of average FA for WT and YAC128 mice. Temporal evolution of FA at (A) PFC and (B) striatum; (C) GP; (D) hippocampus; (E) thalamus. (F) Results from statistical analysis including β1 coefficients, P-values (from β1WT versus β1HD comparisons), mean and SD at each time point. HD: YAC128 mice; M: months of age. MRS results The spectrum enabled a reliable signal assignment and quantification of several brain metabolites in the striatum, as exemplified in Figure 4. Statistically significant age effects were found for both groups in a series of metabolites. These include NAA (F = 14.33, P = 6.38e-5), Cr + phosphocreatine (Cr + PCr, F = 8.27, P = 0.0062) and GPC + PCh (F = 11.57, P = 0.00025). A post-hoc analysis suggests that YAC128 mice have increased concentration for GABA levels at 6 and 9 months of age (t = −1.73, P < 0.05). For the other metabolites, there was a clear time dependence: regarding alanine (Ala, t = 2.51, P = 0.02), NAA (t = 2, 72; P = 0.013), GPC + PCh (t = 2.35; P = 0.028), the levels were higher for YAC128 than for WT mice at 6 months of age, but then decreased to lower levels at late illness stages (P < 0.043). Regarding Cr + PCr, YAC128 mice showed increased concentration compared to WT at all time points (t = −2.87, P = 0.006). Figure 5 presents the scatter plots of these results, including the individual animal data. Additionally, we tested (Mann–Whitney test) the gender differences for both groups and no significant sex differences were found for WT animals. On the other hand, we found a significant increase of PCh (U = 4, P = 0.008), Tau (U = 5, P = 0.013) and Cr + PCr (U = 4, P = 0.008) for the YAC128 mouse males at 9 months of age, but no other sex effects for the remaining time points. Figure 4. View largeDownload slide Group average spectrum of a voxel acquired in vivo in striatum with a 9.4 T MRS. WT and HD spectrum at 12 months of age are shown. Figure 4. View largeDownload slide Group average spectrum of a voxel acquired in vivo in striatum with a 9.4 T MRS. WT and HD spectrum at 12 months of age are shown. Figure 5. View largeDownload slide Scatter plots represent the individual MRS quantification data per metabolite at 6, 9 and 12 months of age. WT: black dots; HD: grey dots. *Metabolites presenting significant age effects: NAA (P < 6.38e-5), GPC + PCh (P < 0.00025) and Cr + PCr (P < 0.0062). Figure 5. View largeDownload slide Scatter plots represent the individual MRS quantification data per metabolite at 6, 9 and 12 months of age. WT: black dots; HD: grey dots. *Metabolites presenting significant age effects: NAA (P < 6.38e-5), GPC + PCh (P < 0.00025) and Cr + PCr (P < 0.0062). Correlations between parameters Correlations between brain structures volumes When correlating the p1l coefficients [see Eq. (2)] obtained from measurements of striatum volumes with that from the volumes of the remaining brain structures, we found a significant negative correlation with the hippocampus, specific of the YAC128 mice (P = 0.036, r = −0.79), while no such correlation was found for the WT group (P = 0.76, r = 0.19). Also, PFC volumes presented significant positive correlations with striatal volumes for both WT (P = 0.022, r = 0.93) and YAC128 (P = 0.021, r = 0.83) mice, as expected from the known strong connectivity between the striatum and PFC. No significant correlations were found with the volumes of the remaining brain structures. Correlations across metabolites From the Spearman correlation analysis (and considering only metabolites presenting significant differences between groups), we found significant correlations for the NAA versus Tau (r = −0.857, P = 0.01 for WT, r = 0.952, P = 0.0002 for YAC128) for both mice groups at 9 months of age, as shown in Figure 6. Notably, this correlation is negative for WT and positive for HD. Figure 6. View largeDownload slide Correlation plot showing the distribution of data for NAA versus Tau at 9 months of age (r = −0.857, P = 0.01 for WT, r = 0.952, P = 0.0002 for YAC128). Figure 6. View largeDownload slide Correlation plot showing the distribution of data for NAA versus Tau at 9 months of age (r = −0.857, P = 0.01 for WT, r = 0.952, P = 0.0002 for YAC128). Correlation between striatum volumes and metabolites Regarding evolution of striatal volumes and their metabolite levels, we found significant correlations for Cr (P = 0.019, r = 0.84) and GPC + PCh (P = 0.004, r = 0.92) only in the YAC128 mice (Fig. 7). No significant correlations were observed for the remaining metabolites. Figure 7. View largeDownload slide Correlations of p1l coefficients between striatum volumes and their metabolite levels: (A) Cr (left side, P = 0.019, r = 0.84) and (B) GPC + PCh (right side, P = 0.004, r = 0.92). Figure 7. View largeDownload slide Correlations of p1l coefficients between striatum volumes and their metabolite levels: (A) Cr (left side, P = 0.019, r = 0.84) and (B) GPC + PCh (right side, P = 0.004, r = 0.92). Discussion The main novelty of our study was to provide a detailed temporal insight into the natural history of HD, reflected by the slow progression of the YAC128 transgenic mouse model. This model, with the entire human HD gene containing 128 CAG repeats, was previously established by one of the co-authors (8) and accurately recapitulates the human disease across defined time points, thereby providing relevant setting for our longitudinal study. We analysed along four critical time points (3, 6, 9 and 12 months of age) neurochemical profiles, local connectivity and morphometric phenotypes. We found critical striatal-hippocampal dissociations in the longitudinal trajectories of WT and YAC128 mice, which were also true in which concerns DTI analysis, as well as in the neurometabolic domain in the striatum (Tau versus aspartate; Cr pool versus striatal volume). Importantly, one cannot exclude the fact that the nature of some of the early changes might be neurodevelopmental in nature. Our volumetric analysis did go beyond the notion that the striatum is the most sensitive brain structure to early damage, by showing that it suffers from accelerated atrophy, which is anticorrelated with changes in hippocampal volume. Volume decays were not observed in the striatum of WT mice, while in YAC128 mice we observed a pronounced effect. Concerning hippocampal volumes, WT actually decayed faster than HD mouse model, suggesting that this structure might be more demanded in HD due to the striatal loss, since they are functionally connected (41). This is further corroborated by the observed negative correlation between striatal and hippocampal volumes in HD. The higher the reduction of striatal volumes, the lower the reduction of hippocampal volumes along ageing. To the best of our knowledge, this observation has never been reported before in HD studies, and we suggest that it may reflect an adaptive mechanism, although alternative explanations are possible. Interestingly, in a functional neuroimaging study in HD patients, this compensatory mechanism between hippocampus and striatum have been suggested (42) highlighting the adaptability of the YAC128 mouse model to represent the natural history of the disease. These results also suggests that longitudinal measurements of morphometric changes of brain structures may be more useful as potential biomarkers for assessing disease evolution and therapeutic effects, since previous ex vivo studies in YAC128 mice were unable to evidence hippocampal involvement (20), as well as simple absolute measures that have been also used in human studies (10–13). Indeed, additional longitudinal studies in different mouse models have also evidenced hippocampal compromise (18,19). Concerning the striatum and PFC volumes, we found a positive correlation between volumetric trajectories of these two physiological connected structures, as expected, in both WT and YAC128 mice. Such a correlation could only be detected with the longitudinal analysis (correlation of p1l coefficients) implemented here, which is not possible in cross-sectional studies, which are less appropriate to assess how structural volumes co-vary. In the Supplementary Material, Table S1 is presented with previous data in humans and animals. DTI analysis revealed a generalized reduction over time in FA for YAC128 mice, while increases were observed in WT mice. These findings seem to reflect the degenerative effect of the disease. It is also in agreement with previous observations in YAC128 mice, where WM tracts also presented FA reduction at pre-manifest stages. Moreover, from cross-sectional human studies, reductions on FA were related to abnormalities in WM pathways (21–23). From our MD analysis a higher group mean (β0) for YAC128 mice was observed at the striatum and PFC, which are also in agreement with the notion of axonal degenerative effect. Despite significant temporal reduction of MD was observed over time for YAC128 mice at the striatal level, these values still remained higher for YAC128 mice along time. These diffusivity differences observed only at the striatal level are also consistent with the specifically increased susceptibility of this structure in HD. Importantly, volumetric and diffusivity analyses of β0 reflect that divergences between WT and YAC128 mice are present even at pre-manifest stages. It is consistent with recent studies in pre-symptomatic HD patients where abnormalities have been observed (43,44), as well as in mutant HD mice (45). In these human studies, striatum and cortex were mainly affected at presymptomatic stages, and these are the two brain structures that presented β0 divergences in our MD analysis. Concerning MRS, we have observed important group and ageing-related differences. These include NAA that seemed to fluctuate, but the finally reduced levels in YAC128 mice at 12 months of age seem to reflect ultimate neural loss. Nevertheless, atrophy/shrinkage of cells may also occur. As previously reported, GABA, through the indirect striatal-cortical pathway underlies the inhibition of unwanted movements and impairments in this pathway have been associated to the chorea symptoms in HD (46). Furthermore, this theory of early motor changes in HD may also be related to a dysfunction of the basal ganglia circuits. In our study, we found high GABA levels between 6 and 9 months of age, although differences with WT mice were not found at 1 months of age. Gln or glutamate (Glu) level group differences were absent. Given the differences in GABA levels, this suggests a time-dependent increased inhibition/excitation ratios (at the early stages but not latter on). It is important to note that one should not interpret tissue GABA and GLU levels in strictly synaptic terms, since the metabolites have diverse roles across cell types. Tkac et al. (38) had observed increased Gln levels between 2 and 3 months of age in R6/2 mice and also increased Glu levels at 3 months of age, and related these findings with compromised glutamatergic neurotransmission and astrocyte proliferation. In this line, mechanisms underlying motor alterations are possibly more complex than simply reflecting a change in GABA levels, thus further work should clarify the nature of these metabolite changes and their correlation to those symptoms. Increases of Tau levels have been observed in diverse mice models (33,36). Because the synthesis of Tau in astrocytes may also be related to regulation of osmosis (47), the distinct pattern of correlation between Tau and aspartate levels in the HD mouse model suggests a changed neuroprotective mechanism (48,49) although alternative explanations should be considered. The relation of this mechanism to the higher levels of Cr + PCr observed early on remains intriguing. Tkac et al (38) also observed increases of Cr + PCr between 2 and 3 months of life as well as an increase in PCr/Cr ratio, relating it to decreases in striatal energy demand. Here we replicated those findings and extended it by showing that Cr + PCr is increased through the ageing brain up to 12 months of age. Increases of GPC + PCh were also observed at 6 months of age, suggesting imbalance in phospholipid metabolism. Increases in GPC have also been reported in mice studied at early illness stages (4 weeks of age) (38). Overall, we can conclude that metabolic changes occur early on in YAC128 transgenic mice (Ala, NAA, Tau and GPC + PCh), reflecting an initial acute pathophysiological response that may dampen with illness progression as a result of progressive tissue atrophy. Our longitudinal data acquired in the striatum may be critical to clarify the sometimes disparate patterns of metabolite changes reported previously with less available time points (33,35,36). Previous studies focused mainly in the CPu at particular time points (33,35) and here we studied the striatum longitudinally. Accordingly, we suggest that the metabolite changes are time and region dependent and further studies should also address other brain areas. Importantly, we identified an orthogonal correlation pattern between Tau and NAA at 9 months of age, when comparing YAC128 mice WT, suggesting a distinct neuroprotective response in response to changes in NAA. However, NAA is not a simple mirror of neural loss. Accordingly, and concerning the relation between striatal volumes and metabolite levels, we found a positive relation with Cr specifically in YAC128 mice. The choline compounds (GPC + PCh) analysis (precursors of cell membrane components and acetylcholine), also showed a positive correlation with striatum volumes only in the HD mouse mode, suggesting high metabolic turnover when volumetric integrity is still present. In sum, the present four-time point study, in an animal model incorporating the human huntingtin gene and which strongly mimics the human natural history of the disease, sheds new light on longitudinal changes that occur in HD from the volumetric, structural connectivity and neurochemical point of view, within and beyond the striatum. An intriguing disease-specific negative correlation between striatum and hippocampal changes over time was found. Moreover, a significant increase in striatal GABA levels, and changes in NAA and Tau and the sign of their correlation in HD were found. The structural and metabolic characteristics significantly vary along the natural history of the disease, and our longitudinal approach goes well beyond previous cross-sectional studies and that may potentially shed light on the role of distinct biomarkers that can be used for follow and assessment of interventional approaches over time. Materials and Methods Mouse model The YAC128 transgenic hemizygous mouse model of HD (line 53), described in (8), and non-transgenic WT littermates at 3, 6, 9 and 12 months of age were used in this study. The animals were housed in IVC racks, subjected to 12 h dark/light cycles, at 22°C, fed with laboratory mouse chow and tap water available ad libitum. All animals were generated and genotyped from the CNC.IBILI (University of Coimbra) local colony, with breeding couples provided by Professor Michael Hayden (University of British Columbia). YAC128 mice were maintained on FVB/N background and compared with WT littermate mice (FVB/N strain). The structural (MRI/DTI) and neurochemical (MRS) brain characterizations were implemented at pre/early manifest (3 months of life), early (6 months of life) and late (9 and 12 months of life) illness stages. Seven WT (three males and four females) and eight YAC128 transgenic (four males and four females) mice were analysed at each stage. Animal experiments were approved by the University Ethics Board and conducted according to the European Council Directives on Animal Care and to the National Authorities. MR acquisition In vivo image acquisitions were conducted with a 9.4 T magnetic resonance small animal scanner (BioSpec 94/20, Bruker Corporation, Germany) at the Institute for Nuclear Sciences Applied to Health (ICNAS), University of Coimbra. Animals were anesthetized with isoflurane (delivered through the system E-Z SA800, Euthanex, Palmer, USA), with constant temperature monitoring (Haake SC 100, Thermo Scientific, USA) and assessment of cardiorespiratory function (1030, SA Instruments Inc., NY, USA). For volumetric analyses, T2-weighted images were acquired in coronal planes using a RARE sequence: TR = 3800 ms; TE = 33 ms; 10 averages; pixel size of 0.0781 mm × 0.0781 mm and slice thickness of 0.5 mm without spacing between slices (total head volume: 256 pixels × 256 pixels × 34 slices). Because of technical reasons, two WT and one YAC128 mice had to be excluded from this analysis. For diffusivity analyses, DTI images were acquired using an EPI sequence with 85 gradient directions; b-value = 1000 s/mm2; TR = 3000 ms; TE = 18.5121 ms; 8 averages; pixel size = 0.1563 mm × 0.1563 mm and slice thickness = 0.5 mm, without spacing between slices (total head volume 128 pixels × 128 pixels × 18 slices). Because of technical reasons, two WT and three YAC128 mice should be excluded from this analysis. 1H-MRS data were collected on a volume of interest placed on the striatum (voxel size: 1.8 mm × 1.8 mm × 1.6 mm), using multi-slice RARE images, in three orthogonal directions (horizontal, coronal and sagittal) with the following parameters: TR = 2500 ms, TE = 33 ms, matrix size = 256 pixels × 256 pixels, field of view = 20 mm × 20 mm, slice thickness = 0.5 mm, 22 slices, RARE factor = 8, average = 1 and scanning time = 1 min 20 s. The voxel was placed accordingly with coronal, sagittal and axial T2-weighted images and adjusted to fit the anatomical structure of striatum minimizing partial volume effects. b0 maps were acquired and MAPSHIM was employed to automatically adjust first- and second-order shim coils with iterative correction. Spectral line widths of water around 13–18 Hz were obtained. A PRESS sequence was used in combination with outer volume suppression and VAPOR water suppression. The following parameters were used: TR = 2500 ms, TE = 16.225 ms, number of averages = 720, three flip angles = 90°, 142° and 142°, bandwidth = 5000 Hz, number of acquired points = 2048 yielding a spectral resolution of 1.22 Hz/point. For each animal, an unsuppressed water signal (TE = 16.225 ms, TR = 2500 ms, 16 averages and none water suppression) was acquired immediately before acquiring the water-suppressed spectrum. Volumetric analysis For the analyses of brain structure volumes, the images were segmented in Matlab R2012b, following the procedure described below. (a) Correction of magnetic field inhomogeneity generated from the surface coil: the corrections were implemented using intensity curves from T2-weighted images obtained for a homogeneous phantom, and acquired with the same coil and system configuration. (b) Brain intensities were normalized between 0 and 7000 grey levels after exclusion of outliers (0.5%). (c) Images were segmented using a semi-automatic custom-made algorithm based on border detection. This procedure involves automatic skull stripping, automatic borders detection followed by manual tracing of undetected contours. (d) Subsequently, the regions corresponding to each SOI were identified, including: PFC, striatum, GP, hippocampus and thalamus; an example of a segmented T2 sequence is presented in Figure 8. (e) The volume values were finally obtained by multiplying the number of voxels selected as belonging to each SOI by the voxel size. Figure 8. View largeDownload slide T2 sequence acquired for volumetric analysis and the corresponding segmented SOIs: PFC (blue); striatum (green); thalamus (red); GP (cyan) and hippocampus (magenta). Figure 8. View largeDownload slide T2 sequence acquired for volumetric analysis and the corresponding segmented SOIs: PFC (blue); striatum (green); thalamus (red); GP (cyan) and hippocampus (magenta). DTI analysis The parameters analysed from DTI were MD and FA, involving the following steps. (a) Eddy current correction was applied using the FDT diffusion toolbox of the FMRIB’s Software Library (FSL). (b) Using the same software and toolbox, parametric maps were constructed, including images with inactive diffusion gradient (b0), MD and FA maps. (c) Using the b0 maps, images from each mouse and each acquisition time were co-registered to the same space (by means of an average b0 map) and the resultant transformation matrix was applied to the remaining parametric maps. In this step, an affine transformation with nine parameters was applied, using Matlab R2012b. (d) Parametric maps were smoothed using the Statistical Parametric Mapping 8 (SPM8) software, with an isotropic Gaussian kernel, FWHM [mm] = [0.5 0.5 0.5]. (e) Statistical analysis for comparison between WT and YAC128 structural diffusivity was implemented by computing the average MD and FA into the SOIs, using binary masks manually traced from the average b0 map, as show in Figure 9, for each animal and acquisition time. Figure 9. View largeDownload slide Diffusion tensor images. (A) Average b0 maps including the masks for the analysed SOIs: PFC (cyan); striatum (magenta); GP (blue); thalamus (red) and hippocampus (green). (B) MD maps and (C) FA maps obtained from a WT mice. Figure 9. View largeDownload slide Diffusion tensor images. (A) Average b0 maps including the masks for the analysed SOIs: PFC (cyan); striatum (magenta); GP (blue); thalamus (red) and hippocampus (green). (B) MD maps and (C) FA maps obtained from a WT mice. Neurospectroscopy data analysis Data were saved as FIDs, corrected for the frequency drift and for residual eddy current effects using the reference water signal. Then 1H-MRS peak concentrations for major metabolites (e.g. Ala, Cr, NAA, GABA, Tau and Glu + Gln-Glx-) were analysed using the LCModel software package (Stephen Provencher Inc., Oakville, Canada; Provencher 1993) and results are given relative to water content in tissue. Briefly, the LCModel analysis calculates the best fit to the acquired spectrum as a linear combination of the model basis set of brain metabolites. The Cramer–Rao lower bound (CRLB) provided by LCModel was used as a measure of the reliability, and metabolite concentrations with CRLB higher than 24% were not included in the analysis. Spectral quality was evaluated by visual inspection of the signal-to-noise ratio that were provided by LCModel (50). CRLB values per group and age are reported for each metabolite as Supplementary Material (Supplementary Material, Table S2). Statistical analysis For statistical analysis of structural measurements (volumes, MD and FA), least squares regression has been applied for fitting data to the linear model present in Eq. (1), in order to obtain a better representation of longitudinal data for continuous predictor variables. On this model, intersect (β0) represents the group mean irrespective of trajectory while the slope (β1) represents the changes in the dependent variable (Y) from the group mean as a function of time. X represents time and Y represents the parameter to be measured: volumes, MD or FA. Y=β0+β1·X (1) Welch’s t-test was subsequently used to depict if the coefficients (β0 and β1) significantly differs between mice groups. In addition, means and standard deviations for each time point and mice group have been provided. Finally, ANOVA and Tukey’s range tests were applied to evaluate sex dependency in measured parameters, through p1l coefficients obtained from linear fit for each individual [see Eq. (2)]. Statistical analysis of the 1H-MRS data was performed using SPSS 22.0 (SPSS Inc., Chicago, IL, USA) to identify changes in metabolite levels. An ANOVA repeated measures test (considered only at 6, 9 and 12 months of age because technical reasons) was used to evaluate whether the averaged metabolite concentration differed significantly between WT and YAC128 mouse groups. Studied metabolites included Ala, Cr, PCr, GABA, Gln, Glu, PCh, glutathione (GSH), myo-inositol (MI), lactate (Lac), NAA, N-acetylaspartylglutamate (NAAG), Tau, Cr + PCr, Glx, GPC + PCh and NAA + NAAG. A Mann–Whitney test was used to test for gender differences. False discovery rate (FDR) correction for multiple comparisons was applied with alpha set to 0.05. Correlations between parameters We first assessed the Pearson correlation between striatal volumes and the volumes of the remaining SOIs. Also, the metabolite levels found at the striatum were analysed in terms of their correlation with volume. In all these cases, we applied linear fit to the data obtained from each individual, as shown in Eq. (2): Y=p1l * X+p2l, (2) where X represents time and Y the measured parameters (volumes and metabolite levels at striatum). Finally, the correlation between p1l values was evaluated considering a significance level of 5%. Additionally, a Spearman correlation analysis (P < 0.05) was performed between the metabolites that reached significant differences between groups (previous to FDR correction) at each time point. Supplementary Material Supplementary Material is available at HMG online. Conflict of Interest statement. The authors certify that they have no affiliations with or involvement in any organization or entity with any financial interest or non-financial interest in the subject matter or materials discussed in this manuscript. Funding This work was supported by Santa Casa da Misericórdia de Lisboa (SCML), grant number MB-29–2013 to A.C.R. European Regional Development Fund (ERDF), through the Centro 2020 Regional Operational Programme under project BIGDATIMAGE, CENTRO-01–0145-FEDER-000016 also financed by Centro 2020 through the COMPETE 2020. Operational Programme for Competitiveness and Internationalisation and Portuguese national funds via FCT—Fundação para a Ciência e a Tecnologia, I.P., under projects COMPETE, POCI-01–0145-FEDER-007440, FCT, UID/NEU/04539/2013–2020, PAC—MEDPERSYST, POCI-01–0145-FEDER-016428, FLAD Life Sciences to M.C.B.; FCT-SFRH/BPD/112863/2015 to L.I.P. References 1 Roos R.A., Hermans J., Vegter-van der Vlis M., van Ommen G.J., Bruyn G.W. ( 1993) Duration of illness in Huntington’s disease is not related to age at onset. J. Neurol. Neurosurg. Psychiatry , 56, 98– 100. Google Scholar CrossRef Search ADS PubMed 2 Mac Donald M. ( 1993) A novel gene containing a trinucleotide repeat that is expanded and unstable on Huntington’s disease chromosomes. Cell , 72, 971– 983. Google Scholar CrossRef Search ADS PubMed 3 Stine O.C., Pleasant N., Franz M.L., Abbott M.H., Folstein S.E., Ross C.A. ( 1993) Correlation between the onset age of Huntington’s disease and length of the trinucleotide repeat in IT-15. Hum. Mol. Genet ., 2, 1547– 1549. Google Scholar CrossRef Search ADS PubMed 4 Langbehn D.R., Brinkman R.R., Falush D., Paulsen J.S., Hayden M.R. ( 2004) A new model for prediction of the age of onset and penetrance for Huntington’s disease based on CAG length. Clin. Genet ., 65, 267– 277. Google Scholar CrossRef Search ADS PubMed 5 Liebert M.A. ( 2006) Forum review. Stress Int. J. Biol. Stress , 8, 152– 162. 6 Handley O.J., Naji J.J., Dunnett S.B., Rosser A.E. ( 2006) Pharmaceutical, cellular and genetic therapies for Huntington’s disease. Clin. Sci ., 110, 73– 88. Google Scholar CrossRef Search ADS PubMed 7 Pringsheim T., Wiltshire K., Day L., Dykeman J., Steeves T., Jette N. ( 2012) The incidence and prevalence of Huntington’s disease: a systematic review and meta-analysis. Mov. Disord ., 27, 1083– 1091. Google Scholar CrossRef Search ADS PubMed 8 Slow E.J., van Raamsdonk J., Rogers D., Coleman S.H., Graham R.K., Deng Y., Oh R., Bissada N., Hossain S.M., Yang Y.Z. et al. ( 2003) Selective striatal neuronal loss in a YAC128 mouse model of Huntington disease. Hum. Mol. Genet ., 12, 1555– 1567. Google Scholar CrossRef Search ADS PubMed 9 Halliday G.M., McRitchie D.A., Macdonald V., Double K.L., Trent R.J., McCusker E. ( 1998) Regional specificity of brain atrophy in Huntington’s disease. Exp. Neurol ., 154, 663– 672. Google Scholar CrossRef Search ADS PubMed 10 Paulsen J.S., Hayden M., Stout J.C., Langbehn D.R., Aylward E., Ross C.A., Guttman M., Nance M., Kieburtz K., Oakes D. et al. ( 2006) Preparing for preventive clinical trials: the Predict-HD study. Arch. Neurol ., 63, 883– 890. Google Scholar CrossRef Search ADS PubMed 11 Aylward E.H., Sparks B.F., Field K.M., Yallapragada V., Shpritz B.D., Rosenblatt A., Brandt J., Gourley L.M., Liang K., Zhou H. et al. ( 2004) Onset and rate of striatal atrophy in preclinical Huntington disease. Neurology , 63, 66– 72. Google Scholar CrossRef Search ADS PubMed 12 Hobbs N.Z., Barnes J., Frost C., Henley S.M.D., Wild E.J., Macdonald K., Barker R.A., Scahill R.I., Fox N.C., Tabrizi S.J. ( 2010) Onset and progression of pathologic atrophy in Huntington disease: a longitudinal MR imaging study. Am. J. Neuroradiol ., 31, 1036– 1041. Google Scholar CrossRef Search ADS 13 Aylward E.H. ( 2014) MRI striatal volumes - a biomarker for clinical trials in HD. Mov. Disord ., 29, 1429– 1433. Google Scholar CrossRef Search ADS PubMed 14 Paulsen J.S., Magnotta V.A., Mikos A.E., Paulson H.L., Penziner E., Andreasen N.C., Nopoulos P.C. ( 2006) Brain structure in preclinical Huntington’s disease. Biol. Psychiatry , 59, 57– 63. Google Scholar CrossRef Search ADS PubMed 15 Rosas H.D., Koroshetz W.J., Chen Y.I., Skeuse C., Vangel M., Cudkowicz M.E., Caplan K., Marek K., Seidman L.J., Makris N. et al. ( 2003) Evidence for more widespread cerebral pathology in early HD: an MRI-based morphometric analysis. Neurology , 60, 1615– 1620. Google Scholar CrossRef Search ADS PubMed 16 Sawiak S.J., Wood N.I., Williams G.B., Morton A.J., Carpenter T.A. ( 2009) Use of magnetic resonance imaging for anatomical phenotyping of the R6/2 mouse model of Huntington’s disease. Neurobiol. Dis ., 33, 12– 19. Google Scholar CrossRef Search ADS PubMed 17 Cheng Y., Peng Q., Hou Z., Aggarwal M., Zhang J., Mori S., Ross C.A., Duan W. ( 2011) Structural MRI detects progressive regional brain atrophy and neuroprotective effects in N171-82Q Huntington’s disease mouse model. Neuroimage , 56, 1027– 1034. Google Scholar CrossRef Search ADS PubMed 18 Steventon J.J., Trueman R.C., Ma D., Yhnell E., Bayram-Weston Z., Modat M., Cardoso J., Ourselin S., Lythgoe M., Stewart A. et al. ( 2016) Longitudinal in vivo MRI in a Huntington’s disease mouse model: global atrophy in the absence of white matter microstructural damage. Sci. Rep ., 6, 32423. Google Scholar CrossRef Search ADS PubMed 19 Rattray I., Smith E.J., Crum W.R., Walker T.A., Gale R., Bates G.P., Modo M. ( 2017) Correlations of behavioral deficits with brain pathology assessed through longitudinal MRI and histopathology in the Hdh Q150/Q150 mouse model of Huntington’s disease. PLoS One , 12, e0168556. Google Scholar CrossRef Search ADS PubMed 20 Carroll J.B., Lerch J.P., Franciosi S., Spreeuw A., Bissada N., Henkelman R.M., Hayden M.R. ( 2011) Natural history of disease in the YAC128 mouse reveals a discrete signature of pathology in Huntington disease. Neurobiol. Dis ., 43, 257– 265. Google Scholar CrossRef Search ADS PubMed 21 Rosas H.D., Tuch D.S., Hevelone N.D., Zaleta A.K., Vangel M., Hersch S.M., Salat D.H. ( 2006) Diffusion tensor imaging in presymptomatic and early Huntington’s disease: selective white matter pathology and its relationship to clinical measures. Mov. Disord ., 21, 1317– 1325. Google Scholar CrossRef Search ADS PubMed 22 Klöppel S., Draganski B., Golding C.V., Chu C., Nagy Z., Cook P.A., Hicks S.L., Kennard C., Alexander D.C., Parker G.J.M. et al. ( 2008) White matter connections reflect changes in voluntary-guided saccades in pre-symptomatic Huntington’s disease. Brain , 131, 196– 204. Google Scholar CrossRef Search ADS PubMed 23 Poudel G.R., Stout J.C., Domínguez J.F., Salmon L., Churchyard A., Chua P., Georgiou-Karistianis N., Egan G.F. ( 2014) White matter connectivity reflects clinical and cognitive status in Huntington’s disease. Neurobiol. Dis ., 65, 180– 187. Google Scholar CrossRef Search ADS PubMed 24 Yi Teo R.T., Hong X., Yu-Taeger L., Huang Y., Tan L.J., Xie Y., To X.V., Guo L., Rajendran R., Novati A. et al. ( 2016) Structural and molecular myelination deficits occur prior to neuronal loss in the YAC128 and BACHD models of Huntington disease. Hum. Mol. Genet ., 25, 2621– 2632. Google Scholar PubMed 25 Lerch J.P., Carroll J.B., Dorr A., Spring S., Evans A.C., Hayden M.R., Sled J.G., Henkelman R.M. ( 2008) Cortical thickness measured from MRI in the YAC128 mouse model of Huntington’s disease. Neuroimage , 41, 243– 251. Google Scholar CrossRef Search ADS PubMed 26 Feigin A., Ghilardi M.-F., Huang C. ( 2008) Preclinical Huntington’s disease: compensatory brain responses during learning. Ann. Neurol ., 15, 1203– 1214. 27 Georgiou-Karistianis N., Poudel G.R., Domínguez J.F., Langmaid R., Gray M.A., Churchyard A., Chua P., Borowsky B., Egan G.F., Stout J.C. ( 2013) Functional and connectivity changes during working memory in Huntington’s disease: 18month longitudinal data from the IMAGE-HD study. Brain Cogn ., 83, 80– 91. Google Scholar CrossRef Search ADS PubMed 28 Liot G., Valette J., Pépin J., Flament J., Brouillet E. ( 2017) Energy defects in Huntington’s disease: why ‘in vivo’ evidence matters. Biochem. Biophys. Res. Commun ., 483, 1084– 1095. Google Scholar CrossRef Search ADS PubMed 29 Yager J.R., Gasparovic C., Magnotta V.A., Adams W., Fiedorowicz J., Paulsen J., Jorge R., Beglinger L.J. ( 2014) Preliminary study of the association of white-matter metabolite concentrations with disease severity in patients with Huntington’s disease. J. Neuropsychiatry Clin. Neurosci ., 26, 101– 104. Google Scholar CrossRef Search ADS PubMed 30 Rae C.D. ( 2014) A guide to the metabolic pathways and function of metabolites observed in human brain 1H magnetic resonance spectra. Neurochem. Res ., 39, 1– 36. Google Scholar CrossRef Search ADS PubMed 31 Reynolds N.C., Prost R.W., Mark L.P. ( 2005) Heterogeneity in 1H-MRS profiles of presymptomatic and early manifest Huntington’s disease. Brain Res ., 1031, 82– 89. Google Scholar CrossRef Search ADS PubMed 32 Sturrock A., Laule C., Decolongon J., Dar Santos R., Coleman A.J., Creighton S., Bechtel N., Reilmann R., Hayden M.R., Tabrizi S.J. et al. ( 2010) Magnetic resonance spectroscopy biomarkers in premanifest and early Huntington disease. Neurology , 75, 1702– 1710. Google Scholar CrossRef Search ADS PubMed 33 Zacharoff L., Tkac I., Song Q., Tang C., Bolan P.J., Mangia S., Henry P.-G., Li T., Dubinsky J.M. ( 2012) Cortical metabolites as biomarkers in the R6/2 model of Huntington’s disease. J. Cereb. Blood Flow Metab ., 32, 502– 514. Google Scholar CrossRef Search ADS PubMed 34 Unschuld P.G., Edden R.A.E., Carass A., Liu X., Shanahan M., Wang X., Oishi K., Brandt J., Bassett S.S., Redgrave G.W. et al. ( 2012) Brain metabolite alterations and cognitive dysfunction in early Huntington’s disease. Mov. Disord ., 27, 895– 902. Google Scholar CrossRef Search ADS PubMed 35 den Bogaard S.J.A., Dumas E.M., Teeuwisse W.M., Kan H.E., Webb A., Roos R.A.C., Grond J. ( 2011) Exploratory 7-Tesla magnetic resonance spectroscopy in Huntington’s disease provides in vivo evidence for impaired energy metabolism. J. Neurol ., 258, 2230– 2239. Google Scholar CrossRef Search ADS PubMed 36 Heikkinen T., Lehtimäki K., Vartiainen N., Puoliväli J., Hendricks S.J., Glaser J.R., Bradaia A., Wadel K., Touller C., Kontkanen O. et al. ( 2012) Characterization of neurophysiological and behavioral changes, MRI brain volumetry and 1H MRS in zQ175 knock-in mouse model of Huntington’s disease. PLoS One , 7, e50717. Google Scholar CrossRef Search ADS PubMed 37 Sturrock A., Laule C., Wyper K., Milner R.A., Decolongon J., Santos R.D., Coleman A.J., Carter K., Creighton S., Bechtel N. et al. ( 2015) A longitudinal study of magnetic resonance spectroscopy Huntington’s disease biomarkers. Mov. Disord ., 30, 393– 401. Google Scholar CrossRef Search ADS PubMed 38 Tkac I., Dubinsky J.M., Keene C.D., Gruetter R., Low W.C. ( 2007) Neurochemical changes in Huntington R6/2 mouse striatum detected by in vivo 1 H NMR spectroscopy. J. Neurochem ., 100, 1397– 1406. Google Scholar CrossRef Search ADS PubMed 39 Badea A., Ali-Sharief A., Johnson G.A. ( 2007) Morphometric analysis of the C57BL/6J mouse brain. Neuroimage , 37, 683– 693. Google Scholar CrossRef Search ADS PubMed 40 Zhang J., Peng Q., Li Q., Jahanshad N., Hou Z., Jiang M., Masuda N., Langbehn D.R., Miller M.I., Mori S. et al. ( 2010) Longitudinal characterization of brain atrophy of a Huntington’s disease mouse model by automated morphological analyses of magnetic resonance images. Neuroimage , 49, 2340– 2351. Google Scholar CrossRef Search ADS PubMed 41 Ross R.S., Sherrill K.R., Stern C.E. ( 2011) The hippocampus is functionally connected to the striatum and orbitofrontal cortex during context dependent decision making. Brain Res ., 1423, 53– 66. Google Scholar CrossRef Search ADS PubMed 42 Voermans N.C., Petersson K.M., Daudey L., Weber B., Van Spaendonck K.P., Kremer H.P.H., Fernández G. ( 2004) Interaction between the human hippocampus and the caudate nucleus during route recognition. Neuron , 43, 427– 435. Google Scholar CrossRef Search ADS PubMed 43 Nopoulos P.C., Aylward E.H., Ross C.A., Mills J.A., Langbehn D.R., Johnson H.J., Magnotta V.A., Pierson R.K., Beglinger L.J., Nance M.A. et al. ( 2011) Smaller intracranial volume in prodromal Huntington’s disease: evidence for abnormal neurodevelopment. Brain , 134, 137– 142. Google Scholar CrossRef Search ADS PubMed 44 Kerschbamer E., Biagioli M. ( 2016) Huntington’s disease as neurodevelopmental disorder: altered chromatin regulation, coding, and non-coding RNA transcription. Front. Neurosci ., 9, 1– 5. Google Scholar CrossRef Search ADS 45 Auerbach W., Hurlbert M.S., Hilditch-Maguire P., Wadghiri Y.Z., Wheeler V.C., Cohen S.I., Joyner A.L., MacDonald M.E., Turnbull D.H. ( 2001) The HD mutation causes progressive lethal neurological disease in mice expressing reduced levels of huntingtin. Hum. Mol. Genet ., 10, 2515– 2523. Google Scholar CrossRef Search ADS PubMed 46 Crossman A.R. ( 2000) Functional anatomy of movement disorders. J. Anat ., 196, 519– 525. Google Scholar CrossRef Search ADS PubMed 47 Vitvitsky V., Garg S.K., Banerjee R. ( 2011) Taurine biosynthesis by neurons and astrocytes. J. Biol. Chem ., 286, 32002– 32010. Google Scholar CrossRef Search ADS PubMed 48 El Idrissi A., Trenkner E. ( 1999) Growth factors and taurine protect against excitotoxicity by stabilizing calcium homeostasis and energy metabolism. J. Neurosci. Off. J. Soc. Neurosci ., 19, 9459– 9468. Google Scholar CrossRef Search ADS 49 Foos T.M., Wu J.Y. ( 2002) The role of taurine in the central nervous system and the modulation of intracellular calcium homeostasis. Neurochem. Res ., 27, 21– 26. Google Scholar CrossRef Search ADS PubMed 50 Provencher S.W. ( 1993) Estimation of metabolite concentrations from localized in vivo proton NMR spectra. Magn. Reson. Med ., 30, 672– 679. Google Scholar CrossRef Search ADS PubMed © The Author(s) 2018. Published by Oxford University Press. All rights reserved. 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Human Molecular Genetics – Oxford University Press
Published: Apr 16, 2018
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