Abstract
Background
The pattern of dopamine cell loss in Parkinson's disease is known to be prominent in the ventrolateral and caudal substantia nigra, but less severe in the dorsal and rostral region. Both diffusion tensor imaging and R2* relaxometry of the substantia nigra have been reported as potential markers for Parkinson's disease, but their relative ability to mark disease progression and differences in pathophysiological bases remains unclear.
Methods
High resolution T2-weigthed, R2*, and diffusion tensor imaging were obtained from 28 controls and 40 Parkinson's disease subjects [15 early-stage (disease duration≤1 year), 14 mid-stage (duration 2-5 years), and 11 late-stage (duration>5 years)]. Fractional anisotropy and R2* values in both rostral and caudal substantia nigra were obtained for all subjects, and clinical measures (disease duration; levodopa-equivalent daily dosage; “off”-drug Unified Parkinson's Disease Rating Scale motor score) were obtained for Parkinson's subjects.
Results
There was no correlation between fractional anisotropy and clinical measures, whereas R2* was strongly associated with disease progression. Compared to controls, fractional anisotropy in caudal substantia nigra was significantly decreased in Parkinson's disease patients of all stages, whereas in rostral substantia nigra it was decreased significantly only in the late-stage group. R2* in both substantia nigra regions was significantly increased in the mid-stage and late-stage, but not early-stage, of Parkinson's disease subjects.
Conclusions
These findings suggest that fractional anisotropy changes may mark early pathological changes in caudal substantia nigra, whereas the changes in R2* may more closely track Parkinson's disease's clinical progression after symptom onset.
Keywords: Parkinson's disease (PD), substantia nigra, diffusion tensor imaging (DTI), transverse relaxation rate (R2*), magnetic resonance imaging (MRI)
INTRODUCTION
Parkinson's disease (PD) is marked by the loss of dopaminergic neurons in the substantia nigra (SN) of the basal ganglia. The neuronal loss is known to occur primarily in the ventrolateral and caudal region of the SN pars compacta, with less severe pathology in the dorsal and rostral region.1 This pattern of cell loss was confirmed in another pathological study that used a different subdivision framework for the SN.2 Both the understanding of the PD-associated cell loss and the evaluation of potential neuroprotective therapies have been hindered by the lack of reliable and objective in vivo markers for cell loss and progression in living human subjects.
Magnetic resonance imaging (MRI) is widely available, and has been explored extensively to study pathological processes in PD.3-7 For instance, several MRI studies have demonstrated that effective transverse relaxation rate (R2*=1/T2*) in the SN is correlated with iron concentration in vivo,4,6,8 and is increased in the SN of PD patients.4,6,9,10 Concomitantly, efforts have been made to apply diffusion tensor imaging (DTI) approaches to measure microstructure disorganization due to PD-related pathology in the SN.5,7,9,11 Correlation between dopaminergic neuron loss and DTI changes has been reported in mice.12 Consistent with this, several human studies have demonstrated reduced FA values in the SN of PD patients.5,7,9,10
Despite the potential of these two promising MRI measurements to mark PD-related changes in the SN,13 there is neither clear understanding of their relevance to dopamine neuron death in the SN, nor the clinical signs of PD progression. This is critical for elucidating the pathophysiological underpinnings of these potential markers, and for evaluating their utility in both clinical and research settings. Recent data from our laboratory has suggested that R2* and FA changes in the SN are not correlated, and may reflect different aspects of PD-related pathological processes.10 To address these issues, the current study evaluated the regionally-specific changes of R2* and FA in rostral and caudal regions of the SN in PD subjects, and their relationship to clinical progression.
Methods
Subjects
Forty PD and 28 healthy control subjects were recruited from a tertiary movement disorders clinic (see Table 1 for detailed demographic information). Of these subjects, 16 with PD and 16 controls were data that was reported earlier.10 PD diagnosis was confirmed by a movement disorder specialist (XH) according to published criteria.14,15 Unified Parkinson's Disease Rating Scale-III motor scores (UPDRS-III) were obtained for each PD subject after withholding all PD medication overnight (~12 hr), this being an operational “off” stage .16 Levodopa-equivalent daily dose (LEDD) was estimated for PD subjects according to a published formula.17 All subjects were free of major acute medical issues such as liver, kidney, or thyroid abnormalities and/or deficiencies of B12 and folate, and all subjects had Mini Mental Status Exam (MMSE) scores >24. All subjects gave written informed consent that was reviewed and approved by the Penn State University Institutional Review Board, and was consistent with the Declaration of Helsinki.
Table 1.
Demographics of PD and control subjects.
Controls | PD | P-values | |||||
---|---|---|---|---|---|---|---|
Total | PDES Early stage (≤1 yr) | PDMS Mid-stage (2 to 5 yrs) | PDLS Later stage (≥ 6 yrs) | Group+ | Subgroup‡ | ||
Male/Female | 13/15 | 23/17 | 7/8 | 8/6 | 8/3 | 0.131 | 0.486 |
Age (years) | 59.8±7.0 | 60.8±8.2 | 60.2±10.1 | 59.2±6.0 | 63.4±7.9 | 0.602 | 0.548 |
Hoehn-Yahr stage (I/II/III) | - | 13/22/4 | 8/5/1 | 4/9/1 | 1/8/2 | - | - |
Disease duration (years) | - | 4.2±4.7 | 0.5±0.5 | 3.3±1.1 | 10.4±4.3 | - | - |
LEDD* (mg/day) | - | 528±401 | 278±225 | 456±199 | 960±444 | - | - |
UPDRS-III scores | - | 23.5±15.1 | 17.1±9.4 | 21.6±11.0 | 34.6±20.3 | - | - |
Age, UPDRS-III scores, disease duration and LEDD values represent by mean ± SD.
LEDD = levodopa-equivalent daily dosage.
Group difference between PD and Controls.
Group difference between subgroups of PD and Controls.
P-values are from two-simple t-tests, one-way ANOVA, and/or Fisher's exact tests as appropriate.
It is believed that the rate of nigrostriatal dopamine neurons and terminals loss in PD is greatest shortly after diagnosis, but this absolute rate decreases or even plateaus with disease progression. This slow-down phase typically occurs within 5-10 years of diagnosis18-20 and may be related to either fewer cells remaining or the presence of dopaminergic cells resistant to PD-related cell death.18,21 Thus, we categorized PD patients into three clinical stages: PDES - “early-stage,” newly diagnosed PD subjects within the first year of diagnosis; PDMS - “mid-stage,” the rapidly progressing stage 2-5 years following diagnosis; and PDL – “late-stage,” >5 years since initial diagnosis.
Data acquisition
All subjects were scanned with a 3.0 Tesla MRI system (Trio, Siemens Magnetom, Erlangen, Germany, 8-channel phased array head coil) with high-resolution T2-weighted, multi-gradient-echoes (T2* weighted), and diffusion tensor imaging (DTI) sequences. A fast-spin-echo sequence was used to obtain T2-weighted images with TR/TE=2500/316, FOV=256 mm × 256 mm, matrix=256 × 256, slice thickness=1 mm (with no gap), slice number=176. A multi-gradient-echo sequence was used to estimate the transverse relaxation rate, R2* (R2*=1/T2*). Six echoes with TE ranging from 7 to 47 ms and an interval of 8 ms were acquired with TR=54 ms, flip angle=20°, FOV=256 mm × 256 mm, matrix=256 × 256, slice thickness=1 mm (with no gap), slice number=64. The middle slice of the multiple gradient echoes images was placed on the line between the anterior and posterior commissures (AC and PC, respectively). For DTI, acquisition parameters were as follows: TR/TE=8300/82 ms, b value=1000 s/mm2, diffusion gradient directions=42 and 7 b=0 scans, FOV=256 mm × 256 mm, matrix=128 × 128, slice thickness=2 mm (with no gap), slice number=65.
Image processing and analysis
Generation of R2* and DTI maps
The magnitude images of multi-echo SWI images were used to generate R2* maps by employing a voxel-wise non-linear Levenberg-Marquardt algorithm to fit a monoexponential function with free baseline using an in-house MATLAB (The MathWorks, Inc., Natick, MA) tool. DTI image quality control and tensor reconstruction was done by using DTIPrep (Neuro Image Research and Analysis Laboratory, University of North Carolina, Chapel Hill, NC) that first checks DWI images for appropriate quality by calculating the inter-slice and inter-image intra-class correlation, and then corrects for the distortions induced by eddy currents and head motion.22 Then, diffusion tensor maps were estimated via weighted least squares.23 FA, mean diffusivity (MD), axial diffusivity (AD), and radial diffusivity (RD) maps were finally generated for subsequent analysis.
Segmentation of substantia nigra
The SN was delineated manually using ITK-SNAP 24 by an investigator (GD) blinded to subject diagnosis. To obtain a comparable region-of-interest (ROI) definition with pathological studies,2,25 individual high resolution T2-weighted images were reformatted by positioning the AC-PC line to the center of the image and then rotating the AC-PC line 35° superior to the PC point and along the mid-sagittal plane, as shown in Figure 1A. The 35° rotation was used to minimize the inclusion of the subthalamic nucleus that lays superior-anterolateral to the SN.26,27 The SN was defined as a hypo-intensity band between the red nucleus and cerebral peduncle in axial sections on multi-planar reformatted T2-weighted images as illustrated in Figure 1. Segmentation of the SN was started at one slice lower than the level of the red nucleus showing the largest radius and is shown as the yellow band in Figure 1A. The relationship of the SN, red nucleus, and subthalamic nucleus is depicted in Figure 1B. A total of six slices (from the rostral to caudal part of the SN) were used as the SN ROI (Figure 1C). The first three slices were treated as the rostral SN segment and the remaining three slices were considered the caudal segment. Figures 1D 1-6 show the six slices segmented for the SN starting with the most rostral slice (Figure 1D 1) and gradually growing the SN ROI to include the more caudal region (Figure 1D 2-6). All ROIs then were visually assessed by a second trained neuroscientist blinded to subject diagnosis to validate and fix potential mislabeling of the SN definition.
Figure 1.
Procedure used to segment the rostral and caudal regions of the SN. A: Illustration of the position of slices used for the definitin of ROIs. B and C: Illustration of the three dimesional relationship of the ROIs and the separation of the rostral and caudal regions of the SN. D(1-6): Illustration of the exact locations of the SN ROI.
After manual segmentation, the ROIs were mapped to R2* and FA maps, respectively, by co-registering the T2-weighted images to R2* maps and the B0 images of DTI data by an affine registration followed with a b-spline non-rigid registration implemented in 3D Slicer (www.slicer.org).28 R2* and DTI (FA, MD, AD, and RD) values in both the rostral and caudal segments of the SN of each subject were calculated using trimmed means (from the 5%-95% percentile) to reduce variability introduced by manual segmentation and imperfect registration processes.
Statistical Analysis
PD and Control demographics were compared using two-sample t-tests and Fisher's exact tests as appropriate. The association between clinical (disease duration, LEDD, and “off” drug UPDRS-III scores) and Regional MRI measures (R2* and DTI) was quantified via partial Pearson correlation coefficient analysis with adjustment for age. R2* and DTI values were compared separately between PD and control subjects by analysis of covariance (ANCOVA) with adjustments for age and gender. PD subgroups and control subjects also were compared using ANCOVA with adjustments for age and gender. All statistical analyses were performed using SAS 9.2 (SAS Institute Inc., Cary, NC, USA).
Results
Demographics
The demographic and clinical data are summarized in Table 1. Although there were no significant differences in age or gender between the overall PD group and control subjects or among controls and the PD subgroups, there were more males in the PD group and more females in the controls, reflecting the fact that PD is more common in males and many controls were spouses of PD patients. Because the exact effects of gender on PD and disease progression are unknown, we have adjusted for gender in all means comparisons as reported below.
DTI results
The PD subjects overall had significantly reduced FA values in both rostral and caudal segments of the SN compared to control subjects, the FA decrease in the caudal region seemed to be more robust than the rostral region [p=0.020 for rostral SN, p<0.001 for caudal SN, Table 2]. In addition, FA values showed a different pattern of change between the rostral and caudal regions of the SN across the PD subgroups. Namely, FA value decreases did not reach significance until the very late stage of PD (PDLS subgroup) for the rostral SN, whereas in the caudal region significant decreases were evident in the very early stage of the disease (PDES subgroup, see Table 2). Although there appears to be a linear trend of FA decreasing across PD subgroups (Figure 2), there was no statistically significant difference among PD subgroups. The results of analyses of MD, AD, and RD values are presented in Supplemental Table 1, and indicated that only RD in the caudal SN showed a significant increase in the overall PD group and the PDLS subgroup when compared to Controls.
Table 2.
FA and R2* values in Controls and PD patients
Mean (SD) | P-values | ||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Controls | PDT | PDE | PDM | PDL | Controls vs PDT | Controls vs PDES | Controls vs PDMS | Controls vs PDLS | PDES vs PDMS | PDES vs PDLS | PDMS vs PDLS | ||
DTI FA | Rostral | 0.518 (0.051) | 0.485 (0.057) | 0.493 (0.055) | 0.488 (0.057) | 0.472 (0.064) | 0.020 | 0.167 | 0.082 | 0.031 | 0.720 | 0.377 | 0.583 |
Caudal | 0.533 (0.044) | 0.488 (0.048) | 0.497 (0.055) | 0.490 (0.045) | 0.475 (0.043) | <0.001 | 0.022 | 0.007 | 0.003 | 0.669 | 0.369 | 0.616 | |
R2* (s-1) | Rostral | 30.2 (4.30) | 35.0 (5.58) | 33.4 (4.89) | 34.4 (6.09) | 37.7 (5.26) | <0.001 | 0.050 | 0.012 | 0.002 | 0.568 | 0.053 | 0.158 |
Caudal | 27.2 (4.07) | 31.8 (5.25) | 29.8 (4.78) | 31.7 (5.49) | 34.7 (4.56) | <0.001 | 0.094 | 0.004 | <0.001 | 0.246 | 0.013 | 0.152 |
PDT: total PD patients; PDES: early stage subgroup; PDMS: mid-stage subgroup; and PDLS: later stage subgroup.
Significant p-values are highlighted by italic bold font.
P-values are from separate ANCOVA with adjustment for age and gender.
Figure 2.
Correlations between clinical measurements and MRI measurements. The scatterplots depict the age-adjusted residuals. The shaded areas represent the 95% confidence interval for each regression line.
R2* results
PD subjects, as a whole, had significantly increased R2* values in both rostral and caudal segments of the SN compared to control subjects [p<0.001 in both segments, Table 2]. In contrast to the FA results, R2* values showed a similar pattern in both the rostral and caudal regions of the SN in PD subgroup analyses (see Figure 2). Namely, there was only a marginal increase in R2* values in the PDES subgroup [p=0.050 for rostral SN, p=0.094 for caudal SN], that then reached significance in the PDMS subgroup [p=0.012 for rostral SN, p=0.004 for caudal SN], and was even more significant in the PDLS subgroup [p<0.001 for both SN regions]. There also were significant differences between PDES and PDLS [p=0.013] subgroups in the caudal region of the SN, with a trend observed in the rostral region [p=0.053].
Relationship of imaging and clinical values
None of the clinical measurements were associated with FA values (third and fourth rows in Figure 3). Conversely, R2* values from the caudal region of the SN were significantly correlated with all three clinical measures (second row graphs in Figure 3), whereas R2* in the rostral area were correlated only with UPDRS-III scores (first row graphs in Figure 3).
Figure 3.
Comparison of R2* and FA values in different segments of the SN in PDs and Controls. Error bars represent the SEM. PDES - “early-stage,” are newly diagnosed PD subjects within the first year of diagnosis; PDMS - “mid-stage,” are those that are in the rapidly progressing stage, 2-5 years following diagnosis; and PDL – “late-stage,” are PD subjects who are >5 years since initial diagnosis.. Signficant differences between PD and control subjects are represented as: *: p<0.05; ***: p<0.001.
Discussion
In the past, DTI and R2* in the SN have each been hypothesized to be useful in assisting with the diagnosis of PD and gauging its progression.3-7 We previously found a lack of a correlation between R2* and FA, although combined R2* and FA measurements in the SN may increase the discrimination between PDs and Controls.10 One implication of these prior data is that that these two MRI measurements reflect different pathological processes in the SN. The results of the current study are consistent with this hypothesis, and are the first to show that SN FA and R2* changes follow different temporal and spatial (dorsal and caudal) patterns in PD. Namely, FA changes in the caudal SN were present at the time of disease diagnosis, but did not capture the clinical progression thereafter. Conversely, R2* changes in both SN regions were not obvious until later disease stages, but correlated significantly with all clinical measures.
Histopathological studies have shown that there is a specific temporal and spatial pattern of SN neuron loss in PD. Temporally, this loss follows an exponential course that gradually decreases with disease progression.1 Spatially, more than 90% of cells in the caudal region are lost at the onset of motor symptoms, whereas only ~50% are lost in the rostral region.1,2 In the present study, the FA changes are consistent with the spatial and temporal pattern of dopaminergic cell loss reported in pathological studies. Namely, FA changes were evident very early in the disease (around the time of diagnosis), most significantly in the caudal region of the SN, and seemed not to capture disease progression. The spatial finding of FA changes in early-stage PD is consistent with the results of a previous imaging study by Vaillancourt et al.7 that showed decreased FA values in the caudal, but not rostral, area of the SN in early-stage PD patients. In our study, SN FA changes did not correlate significantly with any clinical measurements as was noted earlier.9 The lack of a correlation between FA values and clinical measurements of disease progression may be due to the asymptotic nature of cell loss, and/or the limited FA sensitivity to detect subtle changes related to progressive loss of the remaining ~10% of neurons in the caudal region. Collectively, these data underscore the need for future testing of the hypothesis that FA changes in the SN may be a reflection of neuronal loss and consequential tissue microstructure damage in the SN, and may have potential for pre-symptomatic screening.
Our R2* data in the SN strongly discriminated PD and controls, consistent with previous studies.6,9,10 More interestingly, we found a significant correlation between SN R2* values and all clinical measurements of PD. This is consistent with the hypothesis that R2* in the SN may be used as a quantitative marker for disease progression.6,9 Unlike FA changes, the R2* changes in the SN did not follow the temporal and spatial patterns reported for dopaminergic cell death as delineated in past pathological studies.1,2 Temporally, R2* changes were not obvious in early-stage PD subjects, but only became significant later in the disease. Spatially, similar R2* changes were observed in both caudal and rostral regions of the SN. This pattern discrepancy between R2* and dopaminergic cell loss suggests that R2* values may not be a good marker for dopaminergic cell loss.
Although the exact mechanism of the R2* changes in PD is unknown, there are two potential explanations for the R2* value changes and its clinical associations in PD. First, it is possible that the R2* value may be an indicator of the oxidative stress related to excessive iron deposition,29-32 suggesting that R2* may be a surrogate marker for PD-related etiological mechanism(s). Alternatively, R2* is known to be altered by complex factors (e.g., ferric iron and amyloid plaques, ferritin and hemosiderin, and calcium deposition) that create magnetic field inhomogeneity.33-36 The presence of a significant correlation between SN R2* values and all clinical measurements after diagnosis also may suggest that R2* reflects iron homeostasis determined by PD-related iron accumulation although we cannot exclude epiphenomena such as treatment-induced metabolic changes. Increased iron content in the SN has been observed in the most severe PD cases, but not in milder or presymptomatic PD cases.37,38 The fact that we detected significant SN R2* changes only in later disease stages subjects is consistent with the hypothesis that R2* reflects SN iron accumulation. Neuromelanin plays an important role in iron homeostasis39 and an iron-neuromelanin interaction in the SN is observed in PD postmortem brain tissue.40,41 T1-weighted MRI was suggested to reflect SN neuromelanin content, and recently reported to change in a disease stage-dependent manner in PD.42 Combining these two MRI measurements in future studies may provide insight on the in vivo dynamic changes in iron homeostasis in the PD process.
Unlike previous studies,6,7 we developed a semi-automated protocol to define SN subregions on both R2* and FA images, one that provided a robust method for reducing the variability introduced by manual segmentation in different MRI modalities. Definition of the SN in MR images remains controversial.6,43-45 Instead of separating the SN into the pars compacta and pars reticulata, we delineated the rostral and caudal SN because this is most comparable to previous pathology studies.1,2 Moreover, in the data analysis, PD subjects were divided into three disease duration subgroups to capture better the known pattern of PD-related dopaminergic cell progression,1,20 thus providing increased power for detecting potential temporal correlations between these two MRI measurements and PD progression.
There are some limitations in the present study. For example, the sample size of the current study is relatively small for a cross-sectional observational study. The spatial resolution of the DTI images in the study is 2×2×2 mm, lower than that for the R2* images (1×1×1 mm). Therefore, the power of FA values in predicting PD may be underestimated, and may explain why we did not achieve as great a discrimination between PD and controls as did Vaillancourt et al.7 Emerging 7T MRI data may provide better spatial resolution and increased sensitivity to detect iron in the SN. Indeed, 7T SWI recently was effective in differentiating PDs from controls,46 and may be integrated into MRI biomarker research once it becomes more accessible.
In summary, this study is consistent with the hypothesis that FA and R2* values in the SN reflect different aspects of PD-related pathology and/or concomitant epiphenomena. As FA is thought to reflect the microstructural integrity of a region, its change in the caudal SN in early PD may indicate existing and significant PD-related microstructural alterations in this area, and may be useful for early PD detection. R2* values, on the other hand, may reflect iron homeostasis and/or other metabolic/biochemical changes occurring during disease progression in PD and may be useful as an objective marker for PD progression. Further studies with drug-naïve subjects, larger sample size and longitudinal design, and histopathological correlations are warranted to test these hypotheses and investigate the detailed pathological underpinnings of these two in vivo MRI markers in PD and their utility in clinical practice and research studies.
Supplementary Material
Acknowledgements
This work was supported by NS060722 (XH), and the HMC GCRC (NIH M01RR10732) and GCRC Construction Grant (C06RR016499), and the National Alliance for Medical Image Computing (NAMIC, NIH U54 EB005149, MS). We also would like to thank all the participants in the study, as well as the support of the study coordinator Ms. Brittany Harder and MRI technical support from Mr. Jeffery Vesek. We also thank Dr. Richard Mailman for his helpful comments on the manuscript.
Footnotes
Financial Disclosure: The authors have reported no disclosures.
Author Roles:
1. Research project: A. Conception, B. Organization, C. Execution;
2. Statistical Analysis: A. Design, B. Execution, C. Review and Critique;
3. Manuscript: A. Writing of the first draft, B. Review and Critique;
Guangwei Du: 1A, 1B, 1C, 2A, 2B, 3A.
Mechelle M. Lewis: 1B, 1C, 3A, 3B.
Suman Sen: 1B, 1C, 3B.
Jianli Wang: 1B, 1C, 2C, 3B
Michele L. Shaffer: 2A, 2C, 3B.
Martin Styner: 1A, 1B, 3B.
Qing Yang: 1A, 1B, 3B.
Xuemei Huang: 1A, 1B, 1C, 2A, 2C, 3B.
REFERENCES
- 1.Fearnley JM, Lees AJ. Ageing and Parkinson's disease: substantia nigra regional selectivity. Brain. 1991;114(Pt 5):2283–2301. doi: 10.1093/brain/114.5.2283. [DOI] [PubMed] [Google Scholar]
- 2.Damier P, Hirsch EC, Agid Y, et al. The substantia nigra of the human brain. II. Patterns of loss of dopamine-containing neurons in Parkinson's disease. Brain. 1999;122(Pt 8):1437–1448. doi: 10.1093/brain/122.8.1437. [DOI] [PubMed] [Google Scholar]
- 3.Gorell JM, Ordidge RJ, Brown GG, et al. Increased iron-related MRI contrast in the substantia nigra in Parkinson's disease. Neurology. 1995;45:1138–1143. doi: 10.1212/wnl.45.6.1138. [DOI] [PubMed] [Google Scholar]
- 4.Graham JM, Paley MN, Grunewald RA, et al. Brain iron deposition in Parkinson's disease imaged using the PRIME magnetic resonance sequence. Brain. 2000;123(Pt 12):2423–2431. doi: 10.1093/brain/123.12.2423. [DOI] [PubMed] [Google Scholar]
- 5.Chan LL, Rumpel H, Yap K, et al. Case control study of diffusion tensor imaging in Parkinson's disease. J Neurol Neurosurg Psychiatry. 2007;78:1383–1386. doi: 10.1136/jnnp.2007.121525. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 6.Martin WR, Wieler M, Gee M. Midbrain iron content in early Parkinson disease: a potential biomarker of disease status. Neurology. 2008;70:1411–1417. doi: 10.1212/01.wnl.0000286384.31050.b5. [DOI] [PubMed] [Google Scholar]
- 7.Vaillancourt DE, Spraker MB, Prodoehl J, et al. High-resolution diffusion tensor imaging in the substantia nigra of de novo Parkinson disease. Neurology. 2009;72:1378–1384. doi: 10.1212/01.wnl.0000340982.01727.6e. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 8.Langkammer C, Krebs N, Goessler W, et al. Quantitative MR imaging of brain iron: a postmortem validation study. Radiology. 2010;257:455–462. doi: 10.1148/radiol.10100495. [DOI] [PubMed] [Google Scholar]
- 9.Peran P, Cherubini A, Assogna F, et al. Magnetic resonance imaging markers of Parkinson's disease nigrostriatal signature. Brain. 2010 doi: 10.1093/brain/awq212. [DOI] [PubMed] [Google Scholar]
- 10.Du G, Lewis MM, Styner M, et al. Combined R2* and diffusion tensor imaging changes in the substantia nigra in Parkinson's disease. Mov Disord. 2011;26:1627–1632. doi: 10.1002/mds.23643. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 11.Menke RA, Scholz J, Miller KL, et al. MRI characteristics of the substantia nigra in Parkinson's disease: a combined quantitative T1 and DTI study. Neuroimage. 2009;47:435–441. doi: 10.1016/j.neuroimage.2009.05.017. [DOI] [PubMed] [Google Scholar]
- 12.Boska MD, Hasan KM, Kibuule D, et al. Quantitative diffusion tensor imaging detects dopaminergic neuronal degeneration in a murine model of Parkinson's disease. Neurobiol Dis. 2007;26:590–596. doi: 10.1016/j.nbd.2007.02.010. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 13.Lang AE, Mikulis D. A new sensitive imaging biomarker for Parkinson disease? Neurology. 2009;72:1374–1375. doi: 10.1212/01.wnl.0000343512.36654.41. [DOI] [PubMed] [Google Scholar]
- 14.Hughes AJ, Daniel SE, Kilford L, et al. Accuracy of clinical diagnosis of idiopathic Parkinson's disease: a clinico-pathological study of 100 cases. J Neurol Neurosurg Psychiatry. 1992;55:181–184. doi: 10.1136/jnnp.55.3.181. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 15.Hughes AJ, Daniel SE, Lees AJ. Improved accuracy of clinical diagnosis of Lewy body Parkinson's disease. Neurology. 2001;57:1497–1499. doi: 10.1212/wnl.57.8.1497. [DOI] [PubMed] [Google Scholar]
- 16.Langston JW, Widner H, Goetz CG, et al. Core assessment program for intracerebral transplantations (CAPIT). Mov Disord. 1992;7:2–13. doi: 10.1002/mds.870070103. [DOI] [PubMed] [Google Scholar]
- 17.Tomlinson CL, Stowe R, Patel S, et al. Systematic review of levodopa dose equivalency reporting in Parkinson's disease. Mov Disord. 2010;25:2649–2653. doi: 10.1002/mds.23429. [DOI] [PubMed] [Google Scholar]
- 18.Lee CS, Schulzer M, Mak EK, et al. Clinical observations on the rate of progression of idiopathic parkinsonism. Brain. 1994;117(Pt 3):501–507. doi: 10.1093/brain/117.3.501. [DOI] [PubMed] [Google Scholar]
- 19.Lee CS, Schulzer M, Fuente-Fernandez R, et al. Lack of regional selectivity during the progression of Parkinson disease: implications for pathogenesis. Arch Neurol. 2004;61:1920–1925. doi: 10.1001/archneur.61.12.1920. [DOI] [PubMed] [Google Scholar]
- 20.Lang AE. The progression of Parkinson disease: a hypothesis. Neurology. 2007;68:948–952. doi: 10.1212/01.wnl.0000257110.91041.5d. [DOI] [PubMed] [Google Scholar]
- 21.Lee CS, Schulzer M, Mak E, et al. Patterns of asymmetry do not change over the course of idiopathic parkinsonism: implications for pathogenesis. Neurology. 1995;45:435–439. doi: 10.1212/wnl.45.3.435. [DOI] [PubMed] [Google Scholar]
- 22.Liu, Zhexing, Wang, Yi, Gerig, Guido, Gouttard, Sylvain, Tao, Ran, Fletcher, Thomas, Styner, Martin Quality control of diffusion weighted images. Proc.SPIE. 2010;7628(76280J) doi: 10.1117/12.844748. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 23.Salvador R, Pena A, Menon DK, et al. Formal characterization and extension of the linearized diffusion tensor model. Hum Brain Mapp. 2005;24:144–155. doi: 10.1002/hbm.20076. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 24.Yushkevich PA, Piven J, Hazlett HC, et al. User-guided 3D active contour segmentation of anatomical structures: significantly improved efficiency and reliability. Neuroimage. 2006;31:1116–1128. doi: 10.1016/j.neuroimage.2006.01.015. [DOI] [PubMed] [Google Scholar]
- 25.Damier P, Hirsch EC, Agid Y, et al. The substantia nigra of the human brain. I. Nigrosomes and the nigral matrix, a compartmental organization based on calbindin D(28K) immunohistochemistry. Brain. 1999;122(Pt 8):1421–1436. doi: 10.1093/brain/122.8.1421. [DOI] [PubMed] [Google Scholar]
- 26.Vaillancourt DE, Spraker MB, Prodoehl J, et al. Effects of aging on the ventral and dorsal substantia nigra using diffusion tensor imaging. Neurobiol Aging. 2010 doi: 10.1016/j.neurobiolaging.2010.02.006. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 27.Massey LA, Yousry TA. Anatomy of the substantia nigra and subthalamic nucleus on MR imaging. Neuroimaging Clin N Am. 2010;20:7–27. doi: 10.1016/j.nic.2009.10.001. [DOI] [PubMed] [Google Scholar]
- 28.Rueckert D, Sonoda LI, Hayes C, et al. Nonrigid registration using free-form deformations: application to breast MR images. IEEE Trans Med Imaging. 1999;18:712–721. doi: 10.1109/42.796284. [DOI] [PubMed] [Google Scholar]
- 29.Halliwell B. Oxygen radicals as key mediators in neurological disease: fact or fiction? Ann Neurol. 1992;32(Suppl):S10–S15. doi: 10.1002/ana.410320704. [DOI] [PubMed] [Google Scholar]
- 30.Sofic E, Riederer P, Heinsen H, et al. Increased iron (III) and total iron content in post mortem substantia nigra of parkinsonian brain. J Neural Transm. 1988;74:199–205. doi: 10.1007/BF01244786. [DOI] [PubMed] [Google Scholar]
- 31.Mann VM, Cooper JM, Daniel SE, et al. Complex I, iron, and ferritin in Parkinson's disease substantia nigra. Ann Neurol. 1994;36:876–881. doi: 10.1002/ana.410360612. [DOI] [PubMed] [Google Scholar]
- 32.Zecca L, Youdim MB, Riederer P, et al. Iron, brain ageing and neurodegenerative disorders. Nat Rev Neurosci. 2004;5:863–873. doi: 10.1038/nrn1537. [DOI] [PubMed] [Google Scholar]
- 33.Meadowcroft MD, Connor JR, Smith MB, et al. MRI and histological analysis of beta-amyloid plaques in both human Alzheimer's disease and APP/PS1 transgenic mice. J Magn Reson Imaging. 2009;29:997–1007. doi: 10.1002/jmri.21731. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 34.Gossuin Y, Roch A, Muller RN, et al. Relaxation induced by ferritin and ferritin-like magnetic particles: the role of proton exchange. Magn Reson Med. 2000;43:237–243. doi: 10.1002/(sici)1522-2594(200002)43:2<237::aid-mrm10>3.0.co;2-5. [DOI] [PubMed] [Google Scholar]
- 35.Gossuin Y, Hautot D, Muller RN, et al. Looking for biogenic magnetite in brain ferritin using NMR relaxometry. NMR Biomed. 2005;18:469–472. doi: 10.1002/nbm.983. [DOI] [PubMed] [Google Scholar]
- 36.Haacke EM, Cheng NY, House MJ, et al. Imaging iron stores in the brain using magnetic resonance imaging. Magn Reson Imaging. 2005;23:1–25. doi: 10.1016/j.mri.2004.10.001. [DOI] [PubMed] [Google Scholar]
- 37.Dexter DT, Jenner P, Schapira AH, et al. Alterations in levels of iron, ferritin, and other trace metals in neurodegenerative diseases affecting the basal ganglia. The Royal Kings and Queens Parkinson's Disease Research Group. Ann Neurol. 1992;32(Suppl):S94–100. doi: 10.1002/ana.410320716. [DOI] [PubMed] [Google Scholar]
- 38.Riederer P, Sofic E, Rausch WD, et al. Transition metals, ferritin, glutathione, and ascorbic acid in parkinsonian brains. J Neurochem. 1989;52:515–520. doi: 10.1111/j.1471-4159.1989.tb09150.x. [DOI] [PubMed] [Google Scholar]
- 39.Zecca L, Casella L, Albertini A, et al. Neuromelanin can protect against iron-mediated oxidative damage in system modeling iron overload of brain aging and Parkinson's disease. J Neurochem. 2008;106:1866–1875. doi: 10.1111/j.1471-4159.2008.05541.x. [DOI] [PubMed] [Google Scholar]
- 40.Double KL, Gerlach M, Schunemann V, et al. Iron-binding characteristics of neuromelanin of the human substantia nigra. Biochem Pharmacol. 2003;66:489–494. doi: 10.1016/s0006-2952(03)00293-4. [DOI] [PubMed] [Google Scholar]
- 41.Faucheux BA, Martin ME, Beaumont C, et al. Neuromelanin associated redox-active iron is increased in the substantia nigra of patients with Parkinson's disease. J Neurochem. 2003;86:1142–1148. doi: 10.1046/j.1471-4159.2003.01923.x. [DOI] [PubMed] [Google Scholar]
- 42.Schwarz ST, Rittman T, Gontu V, et al. T1-weighted MRI shows stage-dependent substantia nigra signal loss in Parkinson's disease. Mov Disord. 2011;26:1633–1638. doi: 10.1002/mds.23722. [DOI] [PubMed] [Google Scholar]
- 43.Manova ES, Habib CA, Boikov AS, et al. Characterizing the mesencephalon using susceptibility-weighted imaging. AJNR Am J Neuroradiol. 2009;30:569–574. doi: 10.3174/ajnr.A1401. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 44.Oikawa H, Sasaki M, Tamakawa Y, et al. The substantia nigra in Parkinson disease: proton density-weighted spin-echo and fast short inversion time inversion-recovery MR findings. AJNR Am J Neuroradiol. 2002;23:1747–1756. [PMC free article] [PubMed] [Google Scholar]
- 45.Kwon DH, Kim JM, Oh SH, et al. Seven-tesla magnetic resonance images of the substantia nigra in Parkinson disease. Ann Neurol. 2012;71:267–277. doi: 10.1002/ana.22592. [DOI] [PubMed] [Google Scholar]
- 46.Lotfipour AK, Wharton S, Schwarz ST, et al. High resolution magnetic susceptibility mapping of the substantia nigra in Parkinson's disease. J Magn Reson Imaging. 2012;35:48–55. doi: 10.1002/jmri.22752. [DOI] [PubMed] [Google Scholar]
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