NeuroImage 61 (2012) 558–564 Contents lists available at SciVerse ScienceDirect NeuroImage journal homepage: www.elsevier.com/locate/ynimg Elevated brain iron is independent from atrophy in Huntington's Disease☆ Eve M. Dumas a,⁎, 1, Maarten J. Versluis b, 1, Simon J.A. van den Bogaard a, 1, Matthias J.P. van Osch b, c, 1, Ellen P. Hart a, 1, Willeke M.C. van Roon-Mom d, 1, Mark A. van Buchem b, c, 1, Andrew G. Webb b, c, 1, Jeroen van der Grond c, 1, Raymund A.C. Roos a, 1 a Department of Neurology, Leiden University Medical Center, The Netherlands CJ Gorter Center for High Field MRI, Department of Radiology, Leiden University Medical Center, The Netherlands Department of Radiology, Leiden University Medical Center, The Netherlands d Department of Human Genetics, Leiden University Medical Center, The Netherlands b c a r t i c l e i n f o Article history: Accepted 19 March 2012 Available online 28 March 2012 Keywords: Huntington's Disease Iron Magnetic field correlation MRI Disease state a b s t r a c t Increased iron in subcortical structures in patients with Huntington's Disease (HD) has been suggested as a causal factor of neuronal degeneration. The present study examines iron accumulation, measured using magnetic resonance imaging (MRI), in premanifest gene carriers and in early HD patients as compared to healthy controls. In total 27 early HD patients, 22 premanifest gene carriers and 25 healthy controls, from the Leiden site of the TRACK-HD study, underwent 3 T MRI including high resolution 3D T1- and T2-weighted and asymmetric spin echo (ASE) sequences. Magnetic Field Correlation (MFC) maps of iron levels were constructed to assess magnetic field inhomogeneities and compared between groups in the caudate nucleus, putamen, globus pallidus, hippocampus, amygdala, accumbens nucleus, and thalamus. Subsequently the relationship of MFC value to volumetric data and disease state was examined. Higher MFC values were found in the caudate nucleus (p b 0.05) and putamen (p b 0.005) of early HD compared to controls and premanifest gene carriers. No differences in MFC were found between premanifest gene carriers and controls. MFC in the caudate nucleus and putamen is a predictor of disease state in HD. No correlation was found between the MFC value and volume of these subcortical structures. We conclude that Huntington's disease patients in the early stages of the disease, but not premanifest gene carriers, have higher iron concentrations in the caudate nucleus and putamen. We have demonstrated that the iron content of these structures relates to disease state in gene carriers, independently of the measured volume of these structures. © 2012 Elsevier Inc. All rights reserved. Introduction Huntington's Disease (HD) is an autosomal dominant neurodegenerative disorder characterised by brain atrophy and clinical deterioration in the domains of motor function, cognition and behaviour. HD is caused by an expanded CAG repeat in the HTT gene on the short arm of chromosome 4. Genetic testing can be performed in those at risk, prior to disease onset, to ascertain that they carry the gene and will develop the disease in the future. Histological reports describe profound cellular structure deterioration of the putamen and caudate nucleus (Roos and Bots, 1983; Roos et al., 1985; Vonsattel and DiFiglia, 1998), as well as iron accumulation (Simmons et al., 2007). Autopsy brain tissue extractions confirmed these findings in end-stage patients by ☆ Funding: This work was supported by Cure for Huntington's Disease Initiative (CHDI)/High Q Foundation, a not-for-profit organisation dedicated to finding treatments for HD. ⁎ Corresponding author at: Department of Neurology (J3-R-40), Leiden University Medical Centre, P.O. Box 9600, 2300 RC Leiden, The Netherlands. Fax: +31 71 526 4466. E-mail address: e.m.dumas@lumc.nl (E.M. Dumas). 1 On behalf of the TRACK-HD investigators. 1053-8119/$ – see front matter © 2012 Elsevier Inc. All rights reserved. doi:10.1016/j.neuroimage.2012.03.056 demonstrating increased absolute iron levels in these structures (Dexter et al., 1991). MRI has been shown to be a valuable tool for estimating iron levels in vivo (Aquino et al., 2009; Haacke et al., 2005). Several MRI techniques have been used to assess the distribution of iron in the brain in normal ageing (Gelman et al., 1999; Haacke et al., 2010; Xu et al., 2008) and in neurodegenerative diseases (Bartzokis et al., 1999; Brass et al., 2006; Graham et al., 2000; Schenck and Zimmerman, 2004). These include T2 and T2* mapping, as well as susceptibility weighted imaging (SWI). Each of these techniques is simple to perform, but has significant potential limitations. A confounding factor of both T2- and T2⁎-based imagings is that measurements are also affected by changes in the water content – associated for example with destructive brain processes (Bartzokis et al., 2004a) – and can mask the changes in iron content (Gelman et al., 1999; Haacke et al., 2005; Haller et al., 2010; Schenck and Zimmerman, 2004; Vymazal et al., 2007). Ex vivo histological staining of putamen samples from HD patients demonstrated elevated putamen iron levels (Chen et al., 1993) but there was little correlation to T2-weighted imaging of these samples. To overcome these limitations T2 can be measured at two different field strengths to remove the E.M. Dumas et al. / NeuroImage 61 (2012) 558–564 field independent contribution to T2 changes. Because the T2 value of water is relatively field strength independent (Bartzokis et al., 2007), this method is a more sensitive measurement of iron content. However, scanning subjects on two different MRI systems is clinically impractical. MRI approaches based on changes in T2* relaxation time have also been widely applied (Vymazal et al., 2007). In addition to the sensitivity of the measurement to water content, a specific limitation of T2⁎ imaging techniques is that measurements are affected by local background sources of magnetic field inhomogeneities that cause signal loss unrelated to the internal iron content of the tissue (Haacke et al., 2005). SWI is a technique that provides an additional measure for detecting iron related changes by combining magnitude and phase data into a single image (Haacke et al., 2004). Because changes in the magnetic field also lead to changes in the MRI signal phase, it has been suggested as a more sensitive method for detecting neurodegenerative disease related iron changes (Haller et al., 2010). However SWI is not a quantitative method and the limitations mentioned above for T2* techniques are also true for SWI. Hence, the limitations posed by the above described methods demonstrate the need for further development and application of existing and new techniques (Brass et al., 2006). The recently developed quantitative technique of magnetic field correlation (MFC) imaging has the potential to solve the limitations of previously applied MRI techniques in HD. MFC is sensitive to spatially inhomogeneous magnetic fields, such as those generated by iron-rich regions. An advantage is that this technique is insensitive to changes in water concentration. For this reason MFC has previously been used to study increased iron concentrations in the basal ganglia in patients with aceruloplasminemia (Jensen et al., 2009), patients with traumatic brain injury (Raz et al., 2011) and in patients with multiple sclerosis (Ge et al., 2007). The relationship between the measured MFC values and magnetic field inhomogeneities is more direct compared to other relaxometry measurements, allowing for a clearer physical interpretation of MFC measurements (Jensen et al., 2006, 2009). Furthermore, MFC imaging can be implemented practically in a single short scan at one field strength. Previous MRI studies have suggested that increased iron in the striatum could be a causal factor of the symptoms of HD (Bartzokis et al., 1999, 2007). Given that both prior to, and after, disease onset, brain changes in the form of volumetric reductions have been systematically reported (Aylward et al., 1997; Paulsen et al., 2006; Tabrizi et al., 2009; van den Bogaard et al., 2010), it is possible that iron levels change both in the premanifest and in the manifest stages of the disease. However, this hypothesis has only been examined previously in one group of premanifest gene carriers of HD (Jurgens et al., 2010). It has been suggested that increases in iron could promote neurotoxicity through the induction of oxidative reactions (Brass et al., 2006; Stankiewicz and Brass, 2009). A question still debated is whether these iron changes are causal or secondary factors of disease processes. With this current study we aim to determine at which disease stage iron accumulations increase in HD. It remains unclear to what extent iron changes are independent of volume decreases in both the premanifest and manifest phases of the disease, or whether iron levels alter as a direct result of volumetric change (Aylward et al., 2000; Paulsen et al., 2006; Tabrizi et al., 2009). Volume decreases may concentrate the iron that is already present, or alternatively more iron may accumulate as the disease progresses. It is important for our understanding of HD, as well as for future research, to differentiate between these pathophysiological mechanisms. For this reason, the second aim of this study is to examine and relate the potential iron changes to the amount of atrophy present in the related subcortical grey matter structures. The current reports of iron in HD examined only a selection of the subcortical grey matter structures. However, recently atrophy has been demonstrated in seven major subcortical structures in the progressive stages of HD (van den Bogaard et al., 2010). In this current 559 study we investigate in both premanifest and early HD the extent to which elevated iron may be present. We hypothesise that iron concentrations will be higher in these subcortical grey matter structures in HD patients. Furthermore, we expect that iron may play a role in HD that is not explained by volumetric differences. Material and methods Participants Participants were recruited from the Leiden University Medical Centre (LUMC) study site of the longitudinal TRACK-HD study (Tabrizi et al., 2009), 27 manifest gene carriers in the early disease stages one or two (early HD) (Shoulson and Fahn, 1979), 22 premanifest gene carriers (prior to disease onset) and 25 healthy controls underwent 3 T MRI scanning including an asymmetric spin echo sequence and functional assessment. Inclusion criteria for the early HD group were a positive genetic test for the HTT gene with 40 or more CAG repeats, the presence of motor disturbances quantified by more than five points on the Unified Huntington's Disease Rating Scale-motor score (UHDRS-TMS), and a minimum Total Functional Capacity (TFC) score of seven points (Shoulson and Fahn, 1979). Inclusion criteria for premanifest gene carriers consisted of 40 or more CAG repeats, and the absence of motor disturbances with five or less points on the UHDRS-TMS. A burden of pathology score greater than 250 (Penney et al., 1997) was required. Age- and gender-matched gene-negative relatives of HD gene carriers were included as healthy controls. Level of education in accordance to the International Standard Classification of Education (ISCED) was recorded. The study was approved by the Medical Ethical Committee and all participants gave informed consent. MRI protocol An MRI protocol including high-resolution 3D T1-weighted and asymmetric spin echo (ASE) sequences was applied using a 3 T whole body scanner (Achieva, Philips Healthcare, Best, The Netherlands) with an eight channel receive array head coil. Participants were positioned carefully with the application of strapping or cushioning where needed, to reduce the potential occurrence of involuntary head movement. A 9.5 minute isotropic 1 mm3 3D T1-weighted scan was acquired with the following parameters: repetition time (TR)/echo time (TE)=7.7 ms/ 3.5 ms, field-of-view (FOV)=24×24×16.4 cm3. A T2-weighted image (turbo spin echo) was acquired with the same volumetric spatial resolutions as the T1-weighted images, with TE=250 ms and TR=2500 ms, also with a duration of 9.5 min. The T2-weighted image for this study was only used to exclude any possible comorbidity. An 8 minute ASE sequence was implemented with the following scan parameters: TR/TE/flip angle=1005 ms/38 ms/90°. The FOV was 22×19×7 cm3 with a voxel size of 1.9×1.7×2 mm3 for 18 slices with an interslice gap of 2 mm, positioned through the basal ganglia and an acquisition bandwidth of 290 Hz. The position of the refocusing radiofrequency pulse was shifted from its original position towards the excitation radiofrequency pulse by several time shifts=0, 2.3, 6.9, 11.5 and 13.8 ms, thus varying the sensitivity of the sequence to magnetic field variations. Post-processing of MRI images Magnetic field correlation Prior to post-processing all images were screened for artefacts and clinical abnormalities by a clinical neuroradiologist from the radiology department of the LUMC. Furthermore structural images were subjected to external quality control by a contract research organisation (IXICO Ltd, London, UK). ASE images were fitted to a theoretical model relating the signal decay to the homogeneity of the magnetic 560 E.M. Dumas et al. / NeuroImage 61 (2012) 558–564 field (Jensen et al., 2006). The resulting MFC maps display the amount of magnetic field inhomogeneities present for each voxel. Higher MFC values correspond to a more inhomogeneous magnetic field. A correction was applied to the MFC maps to account for the contributions of macroscopic magnetic field inhomogeneities (Jensen et al., 2009) using the non shifted image and the first asymmetric echo image of 2.3 ms. These macroscopic field inhomogeneities arising from, for example, the cavernous sinus and the skull are unrelated to iron content. The contributions from such areas were corrected to retain the underlying microscopic variations, such as those caused by iron, in the magnetic field in the remaining brain tissue. Image segmentation Segmentation of the accumbens nucleus, amygdala, caudate nucleus, hippocampus, globus pallidus, putamen and thalamus was performed on the T1-weighted scans using the FIRST tool (Patenaude et al., 2011) from FMRIB's Software Library (FSL, Oxford). Based on these segmentations, the absolute volume of each structure was calculated with FSLstats (FSL, Oxford), where the resulting value represents the total bi-lateral volume of a single structure. The volumetric analysis procedure, including a correction for intracranial volume, was identical to that described previously (van den Bogaard et al., 2010) making use of the FSL tools (FSL, Oxford). The segmentation masks were registered to the MFC maps using the T1-weighted scans and the quality of the registration was visually inspected. Average MFC values were calculated for each segmented bilateral structure. Statistics To examine differences in MFC and volume between the groups, one-way Multivariate Analyses of Covariance (ANCOVA) was performed with the MFC value, or volumetric value (corrected for intracranial volume), of each subcortical structure as the outcome variable, while controlling for age and gender. The p value after post-hoc Bonferroni correction was considered significant at p ≤ 0.05. To determine the independent potential for MFC value as a marker of disease state in HD, again only gene carriers were examined (premanifest + early HD, n = 49). Logistic regression analysis was performed with disease state (premanifest vs. early HD) as the variable to be predicted. Age, gender, CAG repeat length, MFC value and volume of the relevant subcortical structures were included as predictor variables (Bartzokis et al., 2007; Langbehn et al., 2004). These were entered in one block (ENTER method) during analysis. All statistical analyses were performed with the SPSS 17.0 package (SPSS Inc., Chicago, USA). Subsequently, two analyses were performed to investigate the potential correlation between MFC values in any affected regions and volume changes in all gene carriers (premanifest + early HD). First, to investigate the direct relationship between MFC value and volume of the individual subcortical structures, Pearson's partial correlation analysis was performed, whilst controlling for the same covariates as in the logistic regression, namely age, gender and CAG repeat length. Secondly, to test for the potential influence of the interaction term (MFC value ⁎ volume) between MFC value and volume on disease state (premanifest or early HD), a general linear model univariate analysis of variance was performed. To understand the potential relationship between iron levels and clinical measures, Pearson's partial correlation analysis was performed between iron in regions showing aberrant levels and clinical measures while controlling for age, gender and CAG repeat length. This was performed in the gene carrier group for the Unified Huntington's Disease Rating Scale-Total Motor Score (UHDRS), the Unified Huntington's Disease Rating Scale-Total functional Capacity measure (TFC), the number of correct answers on the Symbol Digit Modalities Test (SDMT), the number of correct answer on the Stroop word reading test (SWR) and the Beck's Depression Inventory 2nd Edition (BDIII). For the premanifest gene carriers only an additional analysis was performed to understand any relationship between iron levels and the number of years to predicted disease onset, calculated according to the method by Langbehn et al. (2004). Pearson correlation was applied without covariates, as it is not appropriate to include covariates that are used in the prediction of expected disease onset. The p value was Bonferroni corrected for multiple comparisons. Results MFC value No significant group differences were found for age, gender or education levels between controls, premanifest gene carriers, and early HD (Table 1). The early HD group demonstrated lower scores on the TFC and higher total scores on the UHDRS (p b 0.001) compared to the healthy control group and premanifest gene carriers. Typical examples of the brain structure segmentations (Fig. 1a) and MFC maps in a healthy control (Fig. 1b), a premanifest gene carrier (Fig. 1c) and an early manifest patient (Fig. 1d) are shown in Fig. 1. This figure demonstrates the spatial distribution of the magnetic field inhomogeneities, with the highest values visible in the caudate nucleus, putamen, and globus pallidus. Higher MFC values were observed in the early HD group: the MFC group averages can be found in Table 2 and are displayed in Fig. 2. The MFC values in the caudate nucleus and putamen of HD patients were found to be significantly higher than in healthy controls (p = 0.03 and p = 0.003, respectively). No significant differences were found between premanifest gene carriers and controls. No other nuclei showed differences either between early HD patients and controls, or between premanifest gene carriers and controls. Volume The volumes of the caudate nucleus and putamen were smaller in early HD patients compared to premanifest gene carriers and controls, and also smaller in premanifest gene carriers as compared to controls (all p b 0.0001). Smaller volumes of the accumbens nucleus Table 1 Demographic information of controls, premanifest gene carriers, and early Huntington's disease. N Gender Age Education level UHDRS TFC Female/male Mean yrs ± SD Range (min–max) Mean ISCED level ± SD Mean total score ± SD Mean ± SD; range: 0–13 Healthy controls Premanifest gene carriers Early manifest patients 25 13/12 50.3 ± 8.3 36–66 3.4 ± 1.1 2.1 ± 1.7 12.9 ± 0.2 22 13/9 45.6 ± 8.5 27–62 3.8 ± 1.1 2.6 ± 1.4 12.5 ± 0.8 27 19/8 50.0 ± 9.9 30–64 3.2 ± 1.3 25.2 ± 15.4⁎ 9.8 ± 2.8⁎ N = number of participants, SD = standard deviation, ISCED = International Standard Classification of Education (range 0–6), UHDRS = Unified Huntington's Disease Rating ScaleTotal Motor Score, TFC = total functional capacity. ⁎ Significantly different from both healthy controls and premanifest HD at p b 0.001. E.M. Dumas et al. / NeuroImage 61 (2012) 558–564 561 Fig. 1. A) Magnitude image showing segmentation of caudate nucleus (yellow), putamen (brown) and globus pallidus (orange). The corresponding MFC maps for a healthy control (B), premanifest gene carrier (C) and early Huntington's disease patient (D). High MFC values are found in the subcortical grey matter structures, known to correspond with high iron concentrations. Highest values are found in the early HD patient. The high MFC values found laterally near the tissue-skull interface are caused by macroscopic magnetic field inhomogeneities from the skull tissue interface that could not be properly corrected. The distance to the areas of interest is so large that no interference with measurements in the deep grey matter structures is expected. (p b 0.0001), globus pallidus (p b 0.0001) and thalamus (p = 0.001) were also found in early HD as compared to healthy controls only (Table 2). Predicting disease state Logistic regression was performed to assess the predictive quality of subcortical MFC values of the caudate nucleus and putamen to disease state in all gene carriers (premanifest or early HD) as shown in Table 2 Average MFC values and subcortical grey matter volumes per subcortical nucleus per study group. Accumbens nucleus Amygdala Caudate nucleus Hippocampus Globus pallidus Putamen Thalamus Control group Premanifest Early HD Control group Premanifest Early HD Control group Premanifest Early HD Control group Premanifest Early HD Control group Premanifest Early HD Control group Premanifest Early HD Control group Premanifest Early HD Mean MFC (s− 2) SD 385.6 343.2 450.3 446.6 419.0 412.2 398.6 372.4 478.1⁎,§§ 452.0 400.0 409.6 643.7 715.3 807.4 481.8 491.8 632.1⁎⁎,§ 388.4 377.8 373.6 101.2 93.7 219.1 122.0 108.6 111.9 55.0 76.8 153.1 102.8 59.8 102.2 120.8 215.2 368.9 120.6 154.0 190.6 60.3 92.9 82.1 Volume (ml) 1.10 .98⁎ .76⁎⁎,§ 2.54 2.46 2.29 6.96 5.86⁎⁎ 4.72⁎⁎,§§ 7.91 7.80 7.21 3.54 3.27 2.67⁎⁎,§§ 4.83 4.14⁎⁎ 3.28⁎⁎,§§ 15.23 14.80 13.67⁎⁎ SD .25 .19 .17 .44 .45 .42 .91 .72 .76 .82 .80 .86 .42 .47 .44 .71 .50 .50 1.45 1.39 1.39 MFC values represent average value in (s− 2). Volumetric analysis performed with correction for intercranial volume, volumes reported represent absolute total volume of each left and right nucleus (mm3 = ml). Bold represents significant finding. ⁎ Significantly different from healthy controls (p b 0.05). ⁎⁎ Significantly different from healthy controls (p b 0.005). § Significantly different from premanifest gene carriers (p b 0.05). §§ Significantly different from premanifest gene carriers (p b 0.005). Table 3. The MFC values and volumes of the caudate nucleus, and putamen were significant predictors of disease state. In contrast, age, gender and CAG repeat length were not significant contributors to the prediction of disease state. Fig. 3 shows the relationship between volume and MFC value for the caudate nucleus and putamen. In assessing further the independence of these two significant predictors the Pearson's partial correlation showed that the MFC value and volume were not related to one another in the caudate nucleus (r = −0.105, p = 0.486) or putamen (r = − 0.132, p = 0.382) in gene carriers (premanifest + early HD). The univariate analysis of variance assessing any potential interaction effect of MFC and volume on disease state also showed no interaction (caudate nucleus: p = 0.916; putamen: p = 0.992). Relationship to clinical measures The relationship between clinical measures and iron levels in the caudate nucleus and putamen was examined for both gene carrier groups. No relationship was found between UHDRS total motor score, TFC, SDMT, SWR or BDI-II and iron levels in the putamen or caudate nucleus. For the premanifest group only correlation showed no significant relationship between predicted years to onset and iron in the caudate nucleus or putamen. Individual iron levels versus a measure of disease load, namely burden of disease pathology (Penney et al., 1997), are displayed for individual gene carriers in Fig. 4. Discussion This study's main findings show increased levels of iron, as demonstrated by higher MFC values, in caudate nucleus and putamen in patients with early HD. MFC values were not significantly different to controls in premanifest gene carriers. For clarity and consistency with other studies, we will refer to changes in the measured irondependent quantity, namely MFC values, as changes in iron (Bartzokis et al., 1999; Hallgren and Sourander, 1958; Vymazal et al., 2007). The iron levels in the putamen and caudate nucleus are not related to the volume of these structures. Furthermore, iron levels appear to predict the disease state (premanifest or early manifest) of HD gene carriers. 562 E.M. Dumas et al. / NeuroImage 61 (2012) 558–564 Fig. 2. MFC group averages for controls, premanifest and manifest HD. All images were registered to standard space and averaged. The resulting average is a visual display of the values shown in Table 2, however more localised information is present in this visual display. High MFC values are found in the subcortical grey matter structures, known to correspond with high iron concentrations. In early HD the caudate nucleus and putamen show significantly higher MFC values. This is in line with in vivo (Bartzokis et al., 1999, 2007) and ex vivo (Chen et al., 1993; Dexter et al., 1991) findings. The relative MFC values in the healthy controls correspond to exvivo-determined levels of iron, whereby the globus pallidus showed highest values for all participant groups, which is in agreement with previous studies of both ageing and neurodegeneration (Bartzokis and Tishler, 2000; Drayer et al., 1986; Hallgren and Sourander, 1958). No significant differences in iron were observed between premanifest gene carriers and healthy controls. The only other report in premanifest gene carriers found elevated iron in the globus pallidus (Jurgens et al., 2010). However, this report was based on a group Table 3 Logistic regression model for predicting disease state (premanifest vs. early manifest) in all gene carrying participants. Total model Caudate nucleus Putamen Level of statistical significance (p value) χ2 DF p MFC value Volume 33.3 40 5 5 b 0.0005 b 0.0005 0.02 0.04 0.01 0.01 Table shows only those predictors that significantly contributed to the model. MFC values represent average value per subcortical nucleus. DF = degrees of freedom. Volumes represent total volume of left and right nucleus per subcortical structure. with less stringent inclusion criteria of individuals, namely without overt motor signs as opposed to a UHDRS cut-off point of 5. The correlation which Jurgens et al. (2010) found between the number of hypointense pixels and total motor score, demonstrates that all of the participants with higher iron levels would have been classified as having early manifest HD under our criteria (Jurgens et al., 2010). It is certainly possible that the iron levels are not high enough for MFC, or any other existing imaging techniques, to pick up small changes in premanifest gene carriers, or that abnormal iron depositions do not occur until later in the disease process. The volumetric decreases of the caudate nucleus and putamen in premanifest gene carriers found in this present study are in line with previous reports (Aylward et al., 1997; Paulsen et al., 2006; van den Bogaard et al., 2010). This study has demonstrated that the individual iron content of the caudate nucleus and putamen independently predicts disease state in gene carriers (premanifest versus early manifest), also when taking into consideration the predictive value of other commonly related factors, such as volume, CAG repeat length or age. Furthermore the iron content in the putamen and caudate nucleus is not directly related to the volume of these structures, and there is no interaction effect present that explains disease state. Therefore, our results indicate that volumetric decrease and iron increase are two independent processes in HD, with iron accumulation not occurring as a direct result of volume decrease (Fig. 4). This is an important finding as it reiterates the need for increasing our understanding of E.M. Dumas et al. / NeuroImage 61 (2012) 558–564 563 Fig. 3. Individual MFC values set out against volumetric data for caudate nucleus (A) and putamen (B). the pathophysiological processes related to iron in HD. When considering this conclusion it is important to exclude the possibility of epiphenomenonological results, especially in early HD. We addressed this by accounting for those factors most commonly found to explain the variance between premanifest and manifest gene carriers (Langbehn et al., 2004; Penney et al., 1997). We propose that iron levels may be regarded as a marker of disease state, as iron does not differentiate those prior to disease onset from controls, but does distinguish between premanifest gene carriers and those in the earliest stages of HD. Reproduction and longitudinal evaluation of potential Fig. 4. Burden of disease pathology scores ((CAG — 35.5) ⁎ age) versus MFC values for all gene carriers (premanifest + manifest) in the caudate nucleus and putamen. iron accumulation may demonstrate the capacity to predict symptom progression or, as previously suggested, the age of disease onset (Bartzokis et al., 2004b). Iron content in early HD was not found to be higher than in controls in the amygdala, hippocampus, nucleus accumbens or thalamus. This both replicates and also adds to previous findings in which no differences were found in the hippocampus and thalamus (Bartzokis et al., 2007). We have measured a pattern of elevated iron in early HD that could be toxic or an accelerated process of normal ageing. However, oxidative stress related mechanisms, as a result of higher free radicals cannot be excluded (Ischiropoulos and Beckman, 2003). It may be that iron depositions primarily affect nuclei that consist of the same types of cells, such as the medium spiny neurons common to the putamen and caudate nucleus, or as demonstrated in HD patients and mice, the microglia (Simmons et al., 2007). Many hypotheses on the role of iron in neurodegeneration have been formulated but the exact mechanisms remains unclear (Berg and Youdim, 2006; Brass et al., 2006; Zecca et al., 2004). Globus pallidus iron depositions showed an increase in the early HD group as compared to controls: however, this was not statistically significant. This may be related to the sequence parameters, and not due to an intrinsic absence of higher iron levels, especially as another study in manifest HD (Bartzokis et al., 2007) found higher iron in the globus pallidus. The chosen ASE parameters could be suboptimal for measurements in areas of very high iron concentration as a result of the pronounced signal reduction. As a result a higher standard deviation can be expected in the MFC maps within the globus pallidus, which was indeed the case. Future research at ultra high field strength (7 Tesla and above) could increase the sensitivity of the MFC method. We believe that the technique used in our study is a more direct measure of iron than conventional T2-, T2⁎- and SWI based methods that have been used extensively to study the distribution of iron in the brain (Gelman et al., 1999; Haacke et al., 2010; Schenck and Zimmerman, 2004), due primarily to the robustness with respect to any altered water concentrations. In the study by Bartzokis et al. T2 relaxation values were obtained by scanning patients at two different field strengths and significant differences were found in iron 564 E.M. Dumas et al. / NeuroImage 61 (2012) 558–564 concentration of the putamen, globus pallidus, and caudate nucleus in manifest HD patients (Bartzokis et al., 2007). This is comparable to the results of our study. Vymazal et al. measured T2 relaxation values at a single field strength and found changes in the globus pallidus and white matter only in HD patients (Vymazal et al., 2007). The discrepancy between these and our results can potentially be explained by the sensitivity of T2 measurements to water content, which is thought to change during disease progression due to, for example, breakdown of the structural integrity of myelin (Bartzokis et al., 2004a, 2007; Gelman et al., 1999). Although we cannot definitively exclude all potential sources of magnetic field variation (Wu et al., 2009), we believe that based on ex-vivo studies (Schenck and Zimmerman, 2004; Simmons et al., 2007) the changes observed in our and previous studies are related primarily to increased iron. An advantage of MFC measurements is that the macroscopic component of the field inhomogeneities can be separated from the microscopic components, which is a confounding factor for T2⁎ based techniques. A possible limitation could be the influence of other sources of magnetic field variation, such as the cavernous sinus or skull. However all possible steps were taken to prevent this influence. After correction a remainder of macroscopic field variations is still present located laterally near skull-tissue interface and around large vessels. However, upon visual inspection, the macroscopic contribution of these regions around the basal ganglia is adequately corrected. In conclusion, we have demonstrated that patients with early HD have higher magnetic field inhomogeneities in the caudate nucleus and putamen. This is not found in premanifest gene carriers. The iron content of the caudate nucleus, putamen seems to independently predict disease state in HD gene carriers. Furthermore, we have demonstrated that increased iron accumulation is an independent disease process not related to structural atrophy. References Aquino, D., Bizzi, A., Grisoli, M., Garavaglia, B., Bruzzone, M.G., Nardocci, N., Savoiardo, M., Chiapparini, L., 2009. Age-related iron deposition in the basal ganglia: quantitative analysis in healthy subjects. Radiology 252, 165–172. Aylward, E.H., Li, Q., Stine, O.C., Ranen, N., Sherr, M., Barta, P.E., Bylsma, F.W., Pearlson, G.D., Ross, C.A., 1997. Longitudinal change in basal ganglia volume in patients with Huntington's disease. Neurology 48, 394–399. Aylward, E.H., Codori, A.M., Rosenblatt, A., Sherr, M., Brandt, J., Stine, O.C., Barta, P.E., Pearlson, G.D., Ross, C.A., 2000. Rate of caudate atrophy in presymptomatic and symptomatic stages of Huntington's disease. Mov. Disord. 15, 552–560. Bartzokis, G., Tishler, T.A., 2000. MRI evaluation of basal ganglia ferritin iron and neurotoxicity in Alzheimer's and Huntington's disease. Cell. Mol. Biol. 46, 821–833. Bartzokis, G., Cummings, J., Perlman, S., Hance, D.B., Mintz, J., 1999. Increased basal ganglia iron levels in Huntington disease. Arch. Neurol. 56, 569–574. Bartzokis, G., Sultzer, D., Lu, P.H., Nuechterlein, K.H., Mintz, J., Cummings, J.L., 2004a. Heterogeneous age-related breakdown of white matter structural integrity: implications for cortical “disconnection” in aging and Alzheimer's disease. Neurobiol. Aging 25, 843–851. Bartzokis, G., Tishler, T.A., Shin, I.S., Lu, P.H., Cummings, J.L., 2004b. Brain ferritin iron as a risk factor for age at onset in neurodegenerative diseases. Ann. N. Y. Acad. Sci. 1012, 224–236. Bartzokis, G., Lu, P.H., Tishler, T.A., Fong, S.M., Oluwadara, B., Finn, J.P., Huang, D., Bordelon, Y., Mintz, J., Perlman, S., 2007. Myelin breakdown and iron changes in Huntington's disease: pathogenesis and treatment implications. Neurochem. Res. 32, 1655–1664. Berg, D., Youdim, M.B., 2006. Role of iron in neurodegenerative disorders. Top. Magn. Reson. Imaging 17, 5–17. Brass, S.D., Chen, N.K., Mulkern, R.V., Bakshi, R., 2006. Magnetic resonance imaging of iron deposition in neurological disorders. Top. Magn. Reson. Imaging 17, 31–40. Chen, J.C., Hardy, P.A., Kucharczyk, W., Clauberg, M., Joshi, J.G., Vourlas, A., Dhar, M., Henkelman, R.M., 1993. MR of human postmortem brain tissue: correlative study between T2 and assays of iron and ferritin in Parkinson and Huntington disease. AJNR Am. J. Neuroradiol. 14, 275–281. Dexter, D.T., Carayon, A., Javoyagid, F., Agid, Y., Wells, F.R., Daniel, S.E., Lees, A.J., Jenner, P., Marsden, C.D., 1991. Alterations in the levels of iron, ferritin and other tracemetals in Parkinson's-Disease and other neurodegenerative diseases affecting the basal ganglia. Brain 114, 1953–1975. Drayer, B., Burger, P., Darwin, R., Riederer, S., Herfkens, R., Johnson, G.A., 1986. MRI of brain iron. AJR Am. J. Roentgenol. 147, 103–110. Ge, Y., Jensen, J.H., Lu, H., Helpern, J.A., Miles, L., Inglese, M., Babb, J.S., Herbert, J., Grossman, R.I., 2007. Quantitative assessment of iron accumulation in the deep gray matter of multiple sclerosis by magnetic field correlation imaging. AJNR Am. J. Neuroradiol. 28 (9), 1639–1644. Gelman, N., Gorell, J.M., Barker, P.B., Savage, R.M., Spickler, E.M., Windham, J.P., Knight, R.A., 1999. MR imaging of human brain at 3.0 T: preliminary report on transverse relaxation rates and relation to estimated iron content. Radiology 210, 759–767. Graham, J.M., Paley, M.N., Grunewald, R.A., Hoggard, N., Griffiths, P.D., 2000. Brain iron deposition in Parkinson's disease imaged using the PRIME magnetic resonance sequence. Brain 123 (Pt 12), 2423–2431. Haacke, E.M., Xu, Y., Cheng, Y.C., Reichenbach, J.R., 2004. Susceptibility weighted imaging (SWI). Magn. Reson. Med. 52, 612–618. Haacke, E.M., Chengb, N.Y.C., House, M.J., Liu, Q., Neelavalli, J., Ogg, R.J., Khan, A., Ayaz, M., Kirsch, W., Obenaus, A., 2005. Imaging iron stores in the brain using magnetic resonance imaging. Magn. Reson. Imaging 23, 1–25. Haacke, E.M., Miao, Y.W., Liu, M.J., Habib, C.A., Katkuri, Y., Liu, T., Yang, Z.H., Lang, Z.J., Hu, J.N., Wu, J.L., 2010. Correlation of putative iron content as represented by changes in R2* and phase with age in deep gray matter of healthy adults. J. Magn. Reson. Imaging 32, 561–576. Haller, S., Bartsch, A., Nguyen, D., Rodriguez, C., Emch, J., Gold, G., Lovblad, K.O., Giannakopoulos, P., 2010. Cerebral microhemorrhage and iron deposition in mild cognitive impairment: susceptibility-weighted MR imaging assessment. Radiology 257 (3), 764–773. Hallgren, B., Sourander, P., 1958. The effect of age on the non-haemin iron in the human brain. J. Neurochem. 3, 41–51. Ischiropoulos, H., Beckman, J.S., 2003. Oxidative stress and nitration in neurodegeneration: cause, effect, or association? J. Clin. Invest. 111, 163–169. Jensen, J.H., Chandra, R., Ramani, A., Lu, H., Johnson, G., Lee, S.P., Kaczynski, K., Helpern, J.A., 2006. Magnetic field correlation imaging. Magn. Reson. Med. 55, 1350–1361. Jensen, J.H., Szulc, K., Hu, C., Ramani, A., Lu, H., Xuan, L., Falangola, M.F., Chandra, R., Knopp, E.A., Schenck, J., Zimmerman, E.A., Helpern, J.A., 2009. Magnetic field correlation as a measure of iron-generated magnetic field inhomogeneities in the brain. Magn. Reson. Med. 61, 481–485. Jurgens, C.K., Jasinschi, R., Ekin, A., Witjes-Ane, M.N., Middelkoop, H., van der Grond, J., Roos, R., 2010. MRI T2 hypointensities in basal ganglia of premanifest Huntington's disease. PLoS Curr. 2. 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. Patenaude, B., Smith, S.M., Kennedy, D.N., Jenkinson, M., 2011. A Bayesian model of shape and appearance for subcortical brain segmentation. Neuroimage 56, 907–922. 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. Penney, J.B., Vonsattel, J.P., MacDonald, M.E., Gusella, J.F., Myers, R.H., 1997. CAG repeat number governs the development rate of pathology in Huntington's disease. Ann. Neurol. 41, 689–692. Raz, E., Jensen, J.H., Ge, Y., Babb, J.S., Miles, L., Reaume, J., Grossman, R.I., Inglese, M., 2011. Brain iron quantification in mild traumatic brain injury: a magnetic field correlation study. AJNR Am. J. Neuroradiol. 32 (10), 1851–1856 (2011 Nov-Dec). Roos, R.A.C., Bots, G.T.A.M., 1983. Nuclear-membrane indentations in Huntington'sChorea. J. Neurol. Sci. 61, 37–47. Roos, R.A.C., Pruyt, J.F.M., Devries, J., Bots, G.T., 1985. Neuronal distribution in the putamen in Huntington's-Disease. J. Neurol. Neurosurg. Psychiatry 48, 422–425. Schenck, J.F., Zimmerman, E.A., 2004. High-field magnetic resonance imaging of brain iron: birth of a biomarker? NMR Biomed. 17, 433–445. Shoulson, I., Fahn, S., 1979. Huntington disease: clinical care and evaluation. Neurology 29, 1–3. Simmons, D.A., Casale, M., Alcon, B., Pham, N., Narayan, N., Lynch, G., 2007. Ferritin accumulation in dystrophic microglia is an early event in the development of Huntington's disease. Glia 55, 1074–1084. Stankiewicz, J.M., Brass, S.D., 2009. Role of iron in neurotoxicity: a cause for concern in the elderly? Curr. Opin. Clin. Nutr. Metab. Care 12, 22–29. Tabrizi, S.J., Langbehn, D.R., Leavitt, B.R., Roos, R.A., Durr, A., Craufurd, D., Kennard, C., Hicks, S.L., Fox, N.C., Scahill, R.I., Borowsky, B., Tobin, A.J., Rosas, H.D., Johnson, H., Reilmann, R., Landwehrmeyer, B., Stout, J.C., 2009. Biological and clinical manifestations of Huntington's disease in the longitudinal TRACK-HD study: crosssectional analysis of baseline data. Lancet Neurol. 8, 791–801. van den Bogaard, S.J., Dumas, E.M., Acharya, T.P., Johnson, H., Langbehn, D.R., Scahill, R.I., Tabrizi, S.J., van Buchem, M.A., van der Grond, J., Roos, R.A., 2010. Early atrophy of pallidum and accumbens nucleus in Huntington's disease. J. Neurol. 258, 412–420. Vonsattel, J.P.G., DiFiglia, M., 1998. Huntington disease. J. Neuropathol. Exp. Neurol. 57, 369–384. Vymazal, J., Klempir, J., Jech, R., Zidovska, J., Syka, M., Ruzicka, E., Roth, J., 2007. MR relaxometry in Huntington's disease: correlation between imaging, genetic and clinical parameters. J. Neurol. Sci. 263, 20–25. Wu, Z., Mittal, S., Kish, K., Yu, Y., Hu, J., Haacke, E.M., 2009. Identification of calcification with MRI using susceptibility-weighted imaging: a case study. J. Magn. Reson. Imaging 29, 177–182. Xu, X., Wang, Q., Zhang, M., 2008. Age, gender, and hemispheric differences in iron deposition in the human brain: an in vivo MRI study. Neuroimage 40, 35–42. Zecca, L., Youdim, M.B.H., Riederer, P., Connor, J.R., Crichton, R.R., 2004. Iron, brain ageing and neurodegenerative disorders. Nat. Rev. Neurosci. 5, 863–873.