Diffusional kurtosis imaging of cingulate fibers in Parkinson disease: Comparison with conventional diffusion tensor imaging

Magnetic Resonance Imaging 31 (2013) 1501–1506

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Diffusional kurtosis imaging of cingulate fibers in Parkinson disease: Comparison with conventional diffusion tensor imaging Koji Kamagata a,⁎, Hiroyuki Tomiyama b, Yumiko Motoi b, Masayoshi Kano b, Osamu Abe c, Kenji Ito a, Keigo Shimoji a, Michimasa Suzuki a, Masaaki Hori a, Atsushi Nakanishi a, Ryohei Kuwatsuru a, Keisuke Sasai a, Shigeki Aoki a, Nobutaka Hattori b a b c

Department of Radiology, Juntendo University School of Medicine, 2-1-1 Hongo, Bunkyo-ku, Tokyo 113-8421, Japan Department of Neurology, Juntendo University School of Medicine, 2-1-1 Hongo, Bunkyo-ku, Tokyo 113-8421, Japan Department of Radiology, Nihon University School of Medicine, 30-1 Oyaguchi-Kamicho, Itabashi-ku, Tokyo 173-8610, Japan

a r t i c l e

i n f o

Article history: Received 21 February 2013 Revised 17 June 2013 Accepted 22 June 2013 Keywords: Parkinson disease Diffusional kurtosis imaging Diffusion tensor imaging Cingulate fiber Tract-specific analysis

a b s t r a c t Objective: The pathological changes in Parkinson disease begin in the brainstem; reach the limbic system and ultimately spread to the cerebral cortex. In Parkinson disease (PD) patients, we evaluated the alteration of cingulate fibers, which comprise part of the limbic system, by using diffusional kurtosis imaging (DKI). Methods: Seventeen patients with PD and 15 age-matched healthy controls underwent DKI with a 3-T MR imager. Diffusion tensor tractography images of the anterior and posterior cingulum were generated. The mean kurtosis (MK) and conventional diffusion tensor parameters measured along the images in the anterior and posterior cingulum were compared between the groups. Receiver operating characteristic (ROC) analysis was also performed to compare the diagnostic abilities of the MK and conventional diffusion tensor parameters. Results: The MK and fractional anisotropy (FA) in the anterior cingulum were significantly lower in PD patients than in healthy controls. The area under the ROC curve was 0.912 for MK and 0.747 for FA in the anterior cingulum. MK in the anterior cingulum had the best diagnostic performance (mean cutoff, 0.967; sensitivity, 0.87; specificity, 0.94). Conclusions: DKI can detect alterations of the anterior cingulum in PD patients more sensitively than can conventional diffusion tensor imaging. Use of DKI can be expected to improve the ability to diagnose PD. Crown Copyright © 2013 Published by Elsevier Inc. All rights reserved.

1. Introduction Parkinson disease (PD)—the most common human neurodegenerative disorder after Alzheimer disease [1]—is classically characterized by resting tremor, slowness of initial movement, rigidity, and general postural instability. PD has a prevalence of approximately 1% of the population older than 60 years. The primary pathologic changes involve loss of midbrain dopaminergic neurons in association with the presence of α-synuclein-immunoreactive inclusions in the cytoplasm of neurons (Lewy bodies) and within neuronal processes (Lewy neurites) elsewhere [2–5]. The presence of Lewyrelated aggregations is required for neuropathologic confirmation of the clinical diagnosis. These intracerebral neuropathological changes begin in the dorsal motor nucleus of the vagal nerve and anterior olfactory nucleus; spread to the limbic system and forebrain; and finally reach the neocortex [2–5]. ⁎ Corresponding author. Tel.: +81 3 3813 3111; fax: +81 3 5684 0476. E-mail address: [email protected] (K. Kamagata).

Thus far, conventional magnetic resonance imaging (MRI) has been unsuccessful in evaluating these pathophysiologic changes in PD, even in cases of long disease duration. In contrast, advanced MRI techniques such as diffusion tensor imaging (DTI) have enabled the assessment of changes in PD at the microstructural level in vivo [6–11]. Specifically, DTI has enabled researchers to assess white matter integrity and thus demonstrate disruption of neural tracts. In a recent study, Vaillancour et al. reported that PD patients could be completely distinguished from controls on the basis of reduced fractional anisotropy (FA) values in the caudal part of the substantia nigra [10]. In another study, DTI using region-of-interest (ROI) analyses revealed changes in FA in the cingulate fibers in PD patients relative to controls [12]. Karagulle et al., by using statistical parametric mapping analysis in conjunction with DTI, observed decreased FA bilaterally in the frontal lobes, including the supplementary and presupplementary motor areas as well as the cingulate fibers, in PD patients relative to controls [8]. Similarly, Kamagata et al. found reduced FA in the cingulate fiber tracts in PD patients relative to controls [6]. However, in a recent study using tract-based

0730-725X/$ – see front matter. Crown Copyright © 2013 Published by Elsevier Inc. All rights reserved. http://dx.doi.org/10.1016/j.mri.2013.06.009

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all patients remained free of atypical parkinsonism and continued responding satisfactorily to antiparkinsonian therapy. Fifteen healthy subjects were recruited from the general population as control subjects and carefully matched in age to the patients. Individuals with any history of hypertension, diabetes mellitus, cardiovascular disease, stroke, brain tumor, epilepsy, PD, dementia, depression, drug abuse, or head trauma were excluded as controls.

spatial statistics, Hattori et al. reported that FA values were not significantly altered in the cerebral white matter in PD patients without dementia relative to control subjects [11,13]. Wiltshire et al. used DTI to investigate the cingulum and corpus callosum in Parkinson disease patients, Parkinson disease patients with mild dementia, and healthy controls [14]. However, they found no changes in the cingulum or corpus callosum. The findings from DTI studies of PD are thus controversial. In conventional DTI, water is assumed to undergo Gaussian diffusion. However, water in biological tissues is restricted by its interactions with other molecules and cell membranes; consequently, the assumption of Gaussian water diffusion may be inadequate to describe the actual diffusion process in biological tissues. Diffusional kurtosis imaging (DKI) is a new and promising diffusion imaging technique [15,16] that extends DTI to the quantification of nonGaussian water diffusion. In addition to conventional DTI parameters such as mean diffusivity (MD) and FA, an additional parameter related to the non-Gaussian diffusion profile, called the mean kurtosis (MK), is obtained in DKI, whereby a higher MK value indicates a more restricted diffusion environment. Recent studies have reported that, relative to conventional DTI, DKI improves the sensitivity in detecting developmental and pathological neural changes for conditions such as age-related degeneration, cerebral infarctions, PD, attention-deficit hyperactivity disorder, gliomas, multiple sclerosis, and spondylotic myelopathy [15,17–26]. Wang et al. reported increased mean kurtosis (MK) values in the basal ganglia and substantia nigra [22]. They found that the mean kurtosis for the ipsilateral substantia nigra had the best diagnostic performance relative to the conventional diffusion tensor parameter (sensitivity, 0.92; specificity, 0.87). According to Braak et al. [27], pathological characteristics are divided into six subgroups depending on the locations of the deposition of Lewy bodies. In Braak staging, the deposition of Lewy bodies in the anterior cingulum is classified as Stage 5—a relatively early stage for the neocortex. Furthermore diffusion abnormalities in cingulate fibers have been reported in some DTI study [6,8,12]. Therefore, we used DKI—a non-Gaussian diffusion model—to quantify microstructural changes in the cingulate fibers and to compare these data with conventional DTI parameters. The purpose of this study was to examine the usefulness of DKI in the diagnosis of PD.

The brains of all patients were examined with a 3-T Magnetic resonance imaging unit (Achieva; Philips Healthcare, Best, the Netherlands) and an 8-channel- array (receiving) head coil for sensitivity-encoding parallel imaging. Regular structural images such as T1-weighted spin-echo images, T2-weighted turbo spinecho images, and fluid-attenuated inversion recovery images were obtained before acquisition of diffusion tensor images. DKI data were acquired with a spin-echo EPI sequence with 20 isotropic diffusion gradient directions. For each diffusion gradient direction, DKI images were acquired with 3 values of b (0, 1000, and 2000 s/mm 2). The sequence parameters were: image orientation, axial; TR, 7041 ms; TE, 70 ms; diffusion gradient pulse duration (δ), 13.3 ms; diffusion gradient separation (Δ), 45.3 ms; NEX, 1; field of view, 240 mm; matrix, 80 × 80; thickness, 3 mm; number of slices, 50; and imaging time, 6 min 26 s. Diffusion tensor and kurtosis analyses were performed by using the free software dTV II FZRx and Volume-One 1.81 (http://www. volume-one.org), developed by Masutani et al. (University of Tokyo; diffusion tensor visualizer available at http://www.ut-radiology. umin.jp/people/masutani/dTV.htm) [6,28,29], on an independent Windows PC. First, FA and MD maps based on the conventional mono-exponential model were calculated. Because the kurtosis image data included those for multiple values of b, the FA and apparent diffusion coefficient (ADC) could be calculated by using part of the diffusion kurtosis data. Next, mean DK maps were obtained (Fig. 1). Details of their calculation procedure were as previously described [16,19,30]. Moreover, as described in previous papers [16,30], the DK for a single direction can be determined by acquiring data at three or more b values (including b = 0) and fitting them to Eq. (1):

2. Materials and methods

ln½SðbÞ ¼ ln½Sð0Þ–b  Dapp þ 1=6  b2  D2app  Kapp

2.1. Subjects

where Dapp is the apparent diffusion coefficient for the given direction and Kapp is the apparent kurtosis coefficient, which is dimensionless.

The study was approved by an institutional review board, and informed consent was obtained from all participants before evaluation. The demographic characteristics of the subjects are shown in Table 1. In all PD patients the disease had been diagnosed by neurologists and fulfilled the UK Parkinson’s Disease Society Brain Bank criteria. PD was staged according to the Hoehn and Yahr scale. All PD patients were taking levodopa at the time of the MR imaging and clinical examination. Eighteen months or more after scanning,

Table 1 Demographic characteristics of subjects.

Sex, male:female Age in years, mean (SD) Disease duration in years, mean (SD) Hoehn-Yahr stage (SD) Levodopa dosage mg/day, median (SD)

CN (n = 15) PD (n = 17)

P value

10:5 64.0 (12.7) NA 0 0

NS (0.430) NS (0.80) NA NA NA

9:8 65.0 (9.3) 6.7 (4.6) 2.7 (0.7) 464.2 (175.0)

Note: CN indicates normal controls; PD, Parkinson disease; NA, not applicable; NS, not significant (P N 0.05); SD, standard deviation.

2.2. MR Imaging

ð1Þ

2.3. DTI image processing using tract-specific analysis (TSA) We created color-coded maps by using 21 sets of images (20 sets of images with b = 1000 s/mm2, 1 set of images with b = 0 s/mm2). On the color maps, red, green, and blue were assigned to the left-right, anteroposterior, and craniocaudal directions, respectively [31]. Fiber tracts were based on fiber assignments made by using the continuous tracking approach [32] to obtain a 3D tract reconstruction. Identification of fiber tracts was initiated by placing a “seed” and a “target” area in anatomic regions through which the particular fibers were expected to course [33]. We performed diffusion tensor tractography of the anterior and posterior cingulum. The FA threshold for tracking was set at 0.18, and the stop length was set at 160 steps, in accordance with a previous report by Yasmin et al. [34]. The bending angle of the tract was not allowed to exceed 45°. Tract measurements were performed by 2 of the authors (K.S., K.K.), who were blinded to the disease status of the subjects. Tractography of the anterior cingulum was performed from the seed ROI in the anterior part of the cingulum to the target ROI in the middle of the

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Fig. 1. Axial color-coded maps (A), fractional anisotropy (B), and mean kurtosis (C) maps of a PD subject at the level of the cingulum (yellow arrows). All images are according to radiological convention, i.e., the left hemisphere of the brain corresponds to the right side of the image.

Fig. 2. Diffusion tensor tractography images of the right cingulum, and voxels included in diffusion tensor tractography images of the anterior cingulum. A, To determine coronal sections at the level of the genu and the middle of the corpus callosum, a sagittal section of the color map was used. B, The seed ROI, including the entire area of the cingulum (light blue area), was drawn manually on a coronal section of the color map at the level of the genu of the corpus callosum. C, The target ROI, including the entire area of the cingulum (purple area), was drawn manually on a coronal section of the color map at the level of the center of the corpus callosum in the sagittal plane. D, Tractography of the right anterior cingulum was generated from the seed ROI (light blue line) to the target region of interest (purple line). E, The anterior cingulum were defined as those between the seed and target ROIs. Voxelization was performed along the right anterior cingulum between the seed and target ROIs (blue voxels), and FA values in coregistered voxels were calculated.

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cingulum, and that of the posterior cingulum was performed from the seed ROI in the posterior part of the cingulum to the target ROI in the middle of the cingulum. Details of the procedure were as in our previous report [6]. To the best of our ability, we visually confirmed that the images obtained after we had performed the fiber tracking did not include any cerebrospinal fluid or cerebral cortices (i.e., the cingulate cortex). The trackline voxelization function of the dTV II software voxelizes the tracking line of the white matter tract by using the original tensor parameters. In this study, anterior and posterior cingulum was defined as the cingulum between the seed and target ROIs. The anterior and posterior cingulum was voxelized between the seed and target ROIs (Fig. 2E), and values for MD, FA, axial diffusivity (AD), radial diffusivity (RD), and MK in coregistered voxels were calculated. 2.4. Statistical analysis All statistical analyses were performed with the Statistical Package for the Social Sciences for Windows, Release 20.0 (SPSS, Chicago, Illinois). Statistical analysis of demographic and clinical data was conducted with Student’s t-test for continuous variables and the χ 2 test for categorical data. The criterion of statistical significance was P b 0.05. Student’s t-test was used to compare the averaged values of MK, MD, FA, AD, and RD between PD patients and healthy controls. A Bonferroni correction was applied for the number of comparisons (n = 2: [anterior cingulum, posterior cingulum], setting the level of significance at P b 0.05/2 = 0.025). Inter-rater reliability was assessed by using Pearson correlation coefficients. For each diffusion index, receiver operating characteristic (ROC) curves were used to determine the cutoff values associated with optimal sensitivity and specificity for distinguishing PD patients from control subjects. The areas under the ROC curve were used to compare the overall diagnostic performance of the diffusion indices in anterior cingulum. The Pearson correlation test was used to investigate correlations between the diffusion indices and disease severity. 3. Results Age (P b 0.43, Student’s t-test) and sex distribution (P b 0.80, χ 2) did not differ between PD patients and healthy controls (Table 1). Reproducibility was expressed as an inter-rater correlation coefficient: the coefficients in the anterior and posterior cingulum, respectively, were 0.91 and 0.83 for the FA analysis; 0.86 and 0.83 for the MD analysis; 0.77 and 0.76 for the AD analysis; 0.88 and 0.88 for the RD analysis; and 0.87 and 0.86 for the MK analysis. No significant differences in diffusion indices were seen between the right and left anterior or posterior cingulum (P b 0.05). Therefore, the averaged values were used for further statistical analyses. The measured diffusion indices of the anterior and posterior cingulum were not significantly correlated with the disease duration or Hoehn and Yahr stage (P N 0.05) in PD patients. The mean diffusion indices for the patients and control subjects are shown in Table 2. FA and MK are dimensionless, whereas MD, AD, and RD are expressed in units of 1000 mm 2/s. MK and FA in the anterior cingulum were significantly lower in PD patients than in healthy controls (MK: P = 0.000056,FA: P = 0.017; Table 2). MD, AD, and RD in the anterior cingulum did not differ significantly between PD patients and healthy controls. No significant diffusion differences were seen in the posterior cingulum between the groups (Table 2). Among the tensor-derived indices, only the difference in FA reached significance in the anterior cingulum. However, there was a large overlap in FA values between the two diagnostic groups. The sensitivity and specificity of MK, FA, MD, AD, and RD at each optimal cutoff point for the anterior cingulum are summarized in Table 3. The

Table 2 Comparison of DT/DK parameters in patients and control subjects. CN Anterior cingulum MK 1.01 FA 0.46 MD 0.72 AD 1.11 RD 0.52 Posterior cingulum MK 1.02 FA 0.50 MD 0.69 AD 1.13 RD 0.48

PD

P value

± ± ± ± ±

0.06 0.09 0.04 0.13 0.05

0.92 0.39 0.72 1.03 0.56

± ± ± ± ±

0.05 0.06 0.04 0.07 0.05

0.000056⁎ 0.017⁎ 0.99 0.04 0.05

± ± ± ± ±

0.08 0.08 0.04 0.10 0.05

1.03 0.52 0.70 1.16 0.47

± ± ± ± ±

0.09 0.07 0.04 0.07 0.06

0.76 0.47 0.50 0.26 0.81

Note: CN indicates healthy controls; PD, Parkinson's disease; MK, mean kurtosis; FA, fractional anisotropy; MD, mean diffusivity; AD, axial diffusivity; RD, radial diffusivity. Values are means ± SD. FA and MK are dimensionless. Mean, axial, and radial diffusivity values are given in 1000 mm2/s. *Significant difference between groups.

best diagnostic performance was achieved with MK in the anterior cingulum: at a cutoff MK of 0.968, the sensitivity and specificity were 0.87 and 0.94, respectively. The areas under the ROC curve for MK are presented in Fig. 3.

4. Discussion Although DTI has recently yielded substantial insights into brain microstructure in PD [6,8–10,12], the findings of these DTI studies are controversial. The anterior cingulum is a part of the brain in which pathological alterations occur relatively early in PD, and diffusion abnormalities in cingulate fibers have been reported in some DTI study [6,8,12]. Therefore, we examined cingulate fibers by using DKI, which extends DTI to the quantification of non-Gaussian water diffusion. Two findings were confirmed in this study. First, we demonstrated that MK and FA in the anterior cingulum were significantly lower in PD patients than in healthy controls. Second, the best diagnostic performance was achieved with MK in the anterior cingulum. MK in the anterior cingulum may be useful as a biomarker for the diagnosis of PD. Although Wang et al. evaluated the basal nuclei and substantia nigra by using DKI in PD patients, to the best of our knowledge our study is the first to evaluate white matter by using DKI in PD patients. Moreover, whereas Wang et al. used the ROI method in their research [26], we used tract-specific analysis. Diffusion tensor tractography is a tool that is becoming widely used to study human white matter anatomy [32,35–39]. This technology allows researchers to visualize the trajectories of specific white matter fiber bundles and measure diffusion parameters more precisely than with manually drawn ROIs [40].

Table 3 Diagnostic sensitivity and specificity of DT/DK parameters in anterior cingulum.

MK FA MD AD RD

Cutoff value

Sensitivity

Specificity

0.9680 0.3875 0.7125 0.9975 0.5475

0.867 0.800 0.467 0.867 0.400

0.941 0.647 0.647 0.529 0.412

Data are diagnostic sensitivity and specificity values achieved with the best cutoff DT/ DK parameter in the anterior CFTs. MK, mean kurtosis; FA, fractional anisotropy; MD, mean diffusivity; AD, axial diffusivity; RD, Radial diffusivity.

K. Kamagata et al. / Magnetic Resonance Imaging 31 (2013) 1501–1506

Fig. 3. Receiver operating characteristic plots from the mean, axial, and radial diffusivity values (MD, AD, and RD, respectively), mean kurtosis (MK), and FA. MK had the greatest sensitivity and specificity.

The decreased FA values in the anterior cingulum of PD patients agree with the results of our previous study [6]. In the Braak staging scheme, the deposition of Lewy bodies on the anterior cingulum is classified as Stage 5, which is a relatively early stage for the neocortex. Aggregates called Lewy neurites [41,42] are also found in parallel with Lewy bodies in PD [43]. Lewy pathologies, such as Lewy neurites, are believed to spread to the adjacent white matter in addition to the anterior cingulate cortex. Pale neurites, which are the earliest form of Lewy pathology, have also been reported to be deposited in axial fibers or nerve projections, inducing axonal swelling in the former [42]. Increased MK has been reported in cerebral infarctions, consistent with axonal swelling or beading that leads to dead space for local diffusion. Unlike the focal axonal swelling in cerebral infarctions, the axonal swelling in PD occurs in a larger range without beading [42]. Reduction in local fiber density—or, more generally, disturbed fiber integrity—is expected to result in a decrease in FA as a measure of diffusion directionality and an increase in MD as a measure of overall water diffusion. Changes in AD and RD values might be more specific: previous work in animal models has described a correlation between axonal loss and decreasing AD, as well as a correlation between an increase in RD and demyelination or increasing axon diameter [44–46]. The decrease in FA (i.e. disturbed fiber integrity) can be attributed to neuronal loss and a reduction in anisotropy due to the deposition of axonal Lewy neurites. Moreover, although the change was not significant, RD was slightly increased, and AD was slightly decreased, in PD patients compared with healthy controls. The reduction in AD may have been caused by axonal loss or the slower diffusion of water molecules axially owing to the presence of axonal deposits. The elevation in RD may have been due to an increase in axonal diameter as a result of axonal swelling or demyelination. Explaining what causes the reduction in MK is difficult. As a result of neuronal loss, deposition of axonal Lewy neurites, and concomitant axonal swelling, the state of the nerve structure could be simpler than one in which normal axons are densely arranged. The lack of change in MD may have occurred because of a lack of change in overall water diffusion as an integrative change in the cingulum of PD patients.

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Although conventional DTI supposes that water displays Gaussian diffusion behavior in an unrestricted environment, in biological tissues it is restricted by interactions with other molecules or cell membranes and thus often displays non-Gaussian diffusion behavior. As kurtosis is a measure of the deviation of the diffusion profile from a Gaussian distribution, DKI analyses quantify the degree of diffusion restriction or tissue complexity. Therefore, DKI would appear appropriate for analyzing the human brain. Our results suggest that the assessment of non-Gaussian directional diffusion by using DKI may provide more sensitive metrics of changes in tissue microstructure than conventional DTI. Our study had a number of limitations. First, we evaluated only MK in the full kurtosis tensor and did not evaluate axial kurtosis (AK) or radial kurtosis (RK). According to Jensen et al. [16], in order to determine the full diffusional kurtosis tensor, DKI must be measured in at least 15 different diffusion gradient directions. In consideration of their report, we acquired DKI data with 20 isotropic diffusion gradient directions. However, the AK and RK data, which were obtained from the DKI data set with 20 diffusion gradient directions, were too noisy. Therefore, we judged that these data would not withstand analysis. Twenty diffusion gradient directions may be insufficient to determine the full diffusional kurtosis tensor; the sufficient number, although unclear, warrants examination. Second, because the PD diagnoses were not histopathologically confirmed, the possibility of misdiagnosis remains. However, the validity of the diagnoses is strengthened by the observation that, after being followed for 1 year, all patients continued to respond satisfactorily to antiparkinsonian therapy and remained free of atypical parkinsonisms. Third, the small size of our samples may have limited the comparison of disease severity and DT/DK parameters. Fourth, the resolution of the DTI in this study was low, with the voxel size being 3 mm isotropic. After fiber tracking, we visually confirmed that cerebrospinal fluid or cerebral cortices (i.e., the cingulate cortex) were not included in the images obtained. However, the images may still have included the cingulate cortex because of partial volume effects. Therefore, partial volume effects may have influenced the results of this study. Finally, the seed and target ROIs were drawn manually, and the reproducibility of measurements was unclear. However, all ROIs were drawn by 2 of the authors, rater bias was prevented by blinding, and the interclass correlation coefficients were 0.76–0.92. In conclusion, DKI can detect alterations of the anterior cingulate fibers in PD patients more sensitively than can conventional DTI and can thus be expected to improve the diagnostic ability of PD. Acknowledgments We thank Nozomi Hamasaki and Syuji Sato, MR imaging technologists, for their skillful performance in data acquisition; Toshino Suzuki, Tomomi Okamura, and Yasmin Hasina for their research assistance; and Yuriko Suzuki and Masaru Takashima, Philips Healthcare for their technical assistance. We also thank Narisumi Cho for administrative assistance. This work is supported by a Grant-in-Aid for Scientific Research on Innovative Areas (Comprehensive Brain Science Network) from the Ministry of Education, Science, Sports, and Culture of Japan, and MEXT/JSPS KAKENHI Grant Number 24591787. References [1] de Lau LM, Breteler MM. Epidemiology of Parkinson's disease. Lancet Neurol 2006;5:525–35. [2] Braak H, Del Tredici K. Invited article: nervous system pathology in sporadic Parkinson disease. Neurology 2008;70:1916–25. [3] Braak H, Del Tredici K, Bratzke H, Hamm-Clement J, Sandmann-Keil D, Rub U. Staging of the intracerebral inclusion body pathology associated with idiopathic

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