Skip to main content
NCBI home page
Wiley Open Access Collection logoLink to Wiley Open Access Collection
. 2025 Jun 23;40(10):2129–2138. doi: 10.1002/mds.30265

Substantia Nigra Iron Deposition in Lewy Body Disease: A Magnetic Resonance Imaging and Neuropathology Study

Patricia Diaz‐Galvan 1, Scott A Przybelski 2, Timothy G Lesnick 2, Melissa E Murray 3, Aivi Nguyen 4, Ross R Reichard 4, Dennis W Dickson 4, Daisuke Ono 4, Matthew L Senjem 1,5, Christopher G Schwarz 1, Jeffrey Gunter 1, Val Lowe 1, Leah K Forsberg 6, Julie A Fields 7, Rodolfo Savica 6, Jonathan Graff‐Radford 6, Vijay K Ramanan 6, David Jones 6, Hugo Botha 6, Erik K St Louis 6,8,9, David Knopman 6, Gregory S Day 10, Neill Graff‐Radford 10, Walter Kremers 2, Ronald C Petersen 6, Clifford R Jack Jr 1, Tanis J Ferman 11, Bradley F Boeve 6, Kejal Kantarci 1,✉
PMCID: PMC12354321  PMID: 40545947

Abstract

Objective

To compare iron deposition in the substantia nigra (SN) as measured with quantitative susceptibility mapping (QSM) on antemortem magnetic resonance imaging (MRI) between individuals with and without Lewy‐related pathology at autopsy.

Methods

We performed a retrospective cohort study including 54 participants who underwent autopsy and antemortem MRI with QSM. Cases were classified as Lewy body disease (LBD)‐present (n = 13) if they had Lewy‐related pathology and LBD‐absent (n = 41) if they did not have Lewy‐related pathology. QSM was calculated for the whole SN and the two subregions: pars compacta (SNc) and pars reticulata (SNr). Nonparametric Wilcoxon rank‐sum tests were used to compare SN QSM values between LBD‐present and LBD‐absent cases. Area under the receiver operating characteristic (ROC) curve (AUC) analyses tested the accuracy of SN QSM values to distinguish the two groups. Associations of QSM values in the SN and its subregions with clinical features were tested with Spearman's correlations.

Results

The LBD‐present group had higher QSM values in the SNc (P = 0.008) than the LBD‐absent group with no differences in SNr. QSM values of the SNc distinguished LBD‐present and LBD‐absent cases with good accuracy (AUC = 0.74) and correlated with the presence of parkinsonism and parkinsonism severity.

Conclusions

This study provides neuropathological confirmation of the utility of SNc QSM as an in vivo biomarker of Lewy‐related pathology. Imaging evidence of abnormal iron deposition in the SNc could potentially serve as a biomarker for inclusion in emerging research frameworks aimed at defining individuals with LBD based on their biological characteristics. Ultimately, this could facilitate more precise diagnoses and guide treatment strategies in LBD. © 2025 The Author(s). Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

Keywords: quantitative susceptibility mapping, substantia nigra, iron deposition, Lewy body disease, MRI

Lewy body disease (LBD) refers to the abnormal deposition of aggregated α‐synuclein in neuronal cell bodies and cell processes (Lewy bodies and Lewy neurites). 1 LBD often overlaps with other neurodegenerative pathologies associated with neuronal degeneration affecting different pathways. For example, patients with dementia with Lewy bodies (DLB) and Parkinson's disease (PD) have neurodegeneration in the nigrostriatal pathway with abnormal iron deposition. 2 Iron‐sensitive biomarkers that reflect pathological involvement in key regions of the nigrostriatal pathway may have utility in detecting pathology associated with LBD during life. 3

Quantitative susceptibility mapping (QSM) is an advanced magnetic resonance imaging (MRI) technique that provides in vivo estimation of regional iron deposition by reconstructing magnetic susceptibility sources from field perturbations. 4 , 5 In patients with PD and DLB, QSM has been shown to be a sensitive measure of increased iron content in the substantia nigra (SN). 6 , 7 , 8 The SN is a key region in the midbrain with extensive connections with the basal ganglia. It is divided into two regions based on morphology and function: the SN pars compacta (SNc) and the SN pars reticulata (SNr). The SNc, located in the ventral part of the SN, is particularly involved in the production of dopamine. This region naturally contains a high iron concentration, which tends to show an abnormal increase associated with LBD pathology. 9 , 10 While QSM demonstrated promising results as a potential biomarker of SN integrity in clinical cohorts of patients with PD and DLB, this has not yet been pathologically validated, limiting appreciation of the sensitivity and specificity of this putative LBD biomarker.

The objective of this study was to investigate the magnetic susceptibility in the SN using QSM in individuals who were found to have LBD at autopsy. We compared the SN and the subregions SNc and SNr on antemortem MRI between participants with and without LBD, which was confirmed at autopsy. We also tested the ability of QSM measures in the SN and its subregions to discriminate between cases with and without LBD at autopsy. Finally, we determined the association of QSM in the SN and its subregions with demographic and clinical characteristics, including the presence and severity of parkinsonism.

Methods

Participants

Participants were drawn from two cohorts: the Mayo Clinic Alzheimer's Disease Research Center (ADRC), which is a referral‐based cohort, and the Mayo Clinic Study of Aging (MCSA), which is a population‐based cohort from Olmsted County, Minnesota, USA. 11 We included participants who underwent an antemortem MRI examination and autopsy at the Mayo Clinic ADRC Neuropathology Core from 2018 to 2022 (n = 55). Inclusion was carried out independently of the clinical diagnosis, solely based on the availability of MRI and autopsy data. Cases with concurrent AD or cerebrovascular neuropathology were included except for one case with a substantial load of microbleeds, which interfered with the calculation of QSM values in the SN.

Participants (n = 54) were evaluated with a clinical interview (participant and informant), completion of symptom questionnaires, neurological examination, and neuropsychological assessment. Further details on clinical assessments are reported in previous publications. 11 , 12 Briefly, parkinsonism was based on the neurologic examination as having at least two of the four cardinal features (tremors, rigidity, bradykinesia, and postural instability). The Unified Parkinson Disease Rating Scale‐Part III (UPDRS‐III) was used to quantify parkinsonism severity and was not used in the diagnostic process. 13 Visual hallucinations had to be fully formed, not restricted to a single episode, and not related to another medical issue, dementia, or treatment. The four‐item Mayo Fluctuation Scale was used to determine the presence of cognitive fluctuations. 14 Finally, probable rapid eye movement (REM) sleep behavior disorder (pRBD) was deemed present when sleep symptoms met the minimal diagnostic criteria for RBD diagnosis according to the International Classification of Sleep Disorders‐II. 15 We used the Mini‐Mental State Examination (MMSE) 16 to assess patients' global cognitive status, and the Clinical Dementia Rating® Sum‐of‐Boxes (CDR‐SB) 17 as a non‐cognitive assessment of dementia severity.

Neuropathological Examinations

At death, each case underwent standardized neuropathological examination by an expert neuropathologist (D.W.D., M.E.M., A.N., R.R.R.) blinded to the MRI and QSM results. Lewy‐related pathology was immunohistochemically assessed with the rabbit NACP antibody (1:3000 dilution, rabbit polyclonal; Mayo Clinic's antibody) against amino acids 98–115 of the α‐synuclein protein to determine topographic distribution. 18 Cases were classified into LBD‐present based on α‐synuclein burden in brainstem only (brainstem Lewy body disease: BLBD), brainstem and limbic regions (transitional Lewy body disease: TLBD), and brainstem, limbic, and neocortical regions (diffuse Lewy body disease: DLBD). The LBD‐absent group included cases who were not categorized as BLBD, TLBD, or DLBD. Persons with AD and amygdala‐only Lewy‐related pathology were categorized as LBD‐absent because this typically represents advanced AD. 19

Aβ plaques and neurofibrillary tangles (NFTs) were evaluated in all cases with a modified Bielschowsky silver stain or thioflavin‐S, as recommended by National Institute on Aging–Alzheimer's Association (NIA‐AA) criteria. 20 , 21 The NIA‐AA score was determined by a four‐point semiquantitative assessment: 0 = none, 1 = sparse, 2 = moderate, and 3 = frequent. As previously described, 22 thioflavin‐S microscopy was used to assign Thal amyloid phase. 23 Aβ immunohistochemistry in the neocortex, hippocampus, basal ganglia, and cerebellum was used to assign Thal amyloid phase as follows: phase 1 = neocortex, phase 2 = CA1/subiculum, phase 3 = basal ganglia or dentate fascia of the hippocampus, phase 4 = midbrain or CA4 of the hippocampus, and phase 5 = cerebellum. The distribution of NFT‐tau pathology was used to assign a Braak NFT stage. 24

MRI Acquisition

All participants underwent MRI using a 3 Tesla scanner (Siemens PRISMA; Siemens, Erlangen, Germany) equipped with a 64‐channel phased array head coil. Anatomical segmentation and labeling were conducted through a three‐dimensional (3D) high‐resolution T1‐weighted magnetization‐prepared rapid acquisition gradient echo (MPRAGE). Magnetic resonance phase measurements utilized for quantitative susceptibility mapping (QSM) calculations were obtained through a bipolar multi‐echo 3D gradient‐recalled echo (GRE) sequence with five echoes (TR/TE1/ΔTE = 28/6.71/3.91 ms, field of view = 200 × 200 mm, voxel size = 0.5 × 0.5 × 1.8 mm3, NEX = 1, flip angle = 15, bandwidth = 280 Hz/px). 25

MRI Analysis and QSM Calculation

The T1 MRI scan of each participant was segmented and corrected for intensity inhomogeneity using the unified segmentation algorithm, which was implemented in SPM12. The algorithm used tissue priors and settings derived from the Mayo Clinic Adult Lifespan Template (MCALT) accessible at https://www.nitrc.org/projects/mcalt/. Advanced normalization tools (ANTs) nonlinear registration was used to register ROI masks of SN from the MCALT space to subject native space as previously described. 26 The ROI of bilateral SN was segmented based on the DISTAL atlas. 27 Based on this atlas, we manually generated a second atlas of the SN differentiating two key anatomical ROIs of SN, namely SNc and SNr,7 displayed in Supplemental Figure S1. The TIV and SN masks were realigned to the respective magnitude and phase image by applying an affine registration between the T1 and the magnitude image corresponding to the first echo of the multi‐echo GRE. Next, we generated QSM images using the STI Suite software. 28 First, Laplacian‐based phase unwrapping was used to unwrap the phase from each echo. 29 The reconstructed phase image was then calculated by summing all unwrapped phase images for all echoes. V‐SHARP28 filtering was applied to remove the bias field with the magnitude and total intracranial volume mask as additional inputs, and then QSM was calculated by using the iterative least squares decomposition method. 30 Finally, ROI masks of bilateral SN, SNc, and SNr were used to obtain magnetic susceptibility values from the respective QSM images for each participant.

Statistical Analysis

Participant characteristics were described with medians and interquartile ranges (IQRs) for continuous variables and counts and proportions (%) for categorical variables. Prior to analysis, data distributions were assessed for normality. As the data were not normally distributed, we employed nonparametric statistical methods. Differences in demographic and clinical characteristics between LBD‐present and LBD‐absent groups were evaluated using the Wilcoxon rank‐sum test for continuous variables and the chi‐squared test for categorical variables. To assess differences in QSM values within the SN and its subregions across pathological LBD groups, we used a rank analysis of covariance (ANCOVA), which is robust to outliers and deviations from normality. These models were adjusted for age and time from MRI to death. Receiver‐operating characteristic (ROC) curves were generated for QSM metrics in the substantia nigra (SN) and its subregions, including the pars compacta (SNc) and pars reticulata (SNr). Area under the curve (AUC) values were computed to assess the discriminative performance of each regional QSM metric in differentiating LBD‐present from LBD‐absent cases. Associations between QSM values in the SN and its subregions and clinical features were assessed using Spearman's rank correlation coefficients.

Results

Participant Characteristics

Demographic and clinical characteristics of the participants at the time of the MRI are summarized in Table 1. The LBD‐present and LBD‐absent groups did not differ in sex, age at imaging or death, time from imaging to death, education, or MMSE scores at the time of imaging. The LBD‐present group had lower CDR‐SB scores than the LBD‐absent group (P = 0.011), and higher parkinsonism severity at the time of the scan.

TABLE 1.

Demographic, clinical, and pathological characteristics of the study participants

Characteristic LBD‐present LBD‐absent P‐value
(n = 13) (n = 41)
Age at MRI, years 80.5 (74.8, 82.6) 76.4 (69.8, 83.2) 0.51
Age at death, years 82.2 (75.5, 84.8) 79.3 (72.5, 84.7) 0.55
Time between MRI and death, years 1.4 (1.1, 2.7) 2.0 (1.3, 2.7) 0.34
Males, n (%) 9 (69) 23 (56) 0.40
APOE ε4 carrier, n (%) 8 (67) 21 (55) 0.49
Education, years 14.0 (12.0, 14.0) 14.0 (12.0, 16.0) 0.46
MMSE at time of scan 22.0 (18.0, 24.0) 24.0 (19.5, 28.0) 0.38
CDR‐SB 8.0 (4.5, 12.0) 3.5 (0.0, 7.2) 0.011
Braak NFT stage 5.0 (2.0, 5.0) 5.0 (3.0, 6.0) 0.33
Thal Aβ phase 4.0 (4.0, 5.0) 4.0 (2.0, 5.0) 0.28
Hallucinations, n (%) 6 (50) 2 (6) <0.001
Fluctuations, n (%) 4 (44) 0 (0) 0.005
Parkinsonism, n (%) 11 (85) 10 (29) <0.001
pRBD, n (%) 7 (54) 4 (11) 0.002
UPDRS‐III, total score at time of scan 26.0 (9.0, 33.0) 0.0 (0.0, 4.5) 0.005
Open in a new tab

Note: Median (interquartile range) are listed for the continuous variables and count (percentage, %) for the categorical variables. P‐values for differences between pathological groups come from a Wilcoxon two‐sample rank‐sum test for continuous variables and a chi‐square test for categorical variables.

Abbreviations: Aβ, β‐amyloid; APOE e4, Apolipoprotein E epsilon 4; CDR‐SB, Clinical Dementia Rating® Sum‐of‐Boxes; LBD, Lewy body disease; MMSE, Mini‐Mental State Examination; MRI, magnetic resonance imaging; NFT, neurofibrillary tangles; pRBD, probable rapid eye movement (REM) sleep behavior disorder; UPDRS‐III, Unified Parkinson's Disease Rating Scale‐Part III (3rd edition).

Antemortem clinical diagnoses for the LBD‐present and LBD‐absent groups are displayed in Figure 1A. Most patients in the LBD‐present group fulfilled the clinical criteria for probable DLB (n = 7). There were five cases in the LBD‐present group who fulfilled the criteria for probable AD dementia, but all of them also had, at least, one core clinical feature of DLB. One LBD‐present case had a diagnosis of PD. In the LBD‐absent group, most cases fulfilled the clinical criteria for probable AD dementia (n = 15), were cognitively unimpaired (n = 11), or had mild cognitive impairment (n = 6). The rest of the cases had primary progressive aphasia (n = 3), posterior cortical atrophy (n = 2), progressive supranuclear palsy (n = 1), or frontotemporal dementia (n = 1).

FIG. 1.

FIG. 1

Open in a new tab

Frequency of clinical (A) and pathological (B) diagnoses among the Lewy body disease (LBD)‐present and LBD‐absent groups. DLB, dementia with lewy bodies; LBD, Lewy body disease; PD, Parkinson's disease; AD, Alzheimer's disease‐related pathology; PSP, progressive supranuclear palsy; CU, cognitively unimpaired; FTD, frontotemporal dementia; lvPPA, logopenic variant primary progressive aphasia; MCI, mild cognitive impairment; PPA, primary progressive aphasia; PCA, posterior cortical atrophy; DLBD, diffuse Lewy body disease; BLBD, brainstem Lewy body disease; TLBD, transitional Lewy body disease; FTLD, frontotemporal lobar degeneration‐related tauopathy; CBD, corticobasal degeneration.

Pathological diagnoses for the LBD‐present and LBD‐absent groups are displayed in Figure 1B. The LBD‐present group was mostly composed of cases with DLBD (n = 10), two cases were classified as TLBD, and only one case was classified as BLBD. The LBD‐absent group was mostly composed of cases with a range of AD pathology (Table 1). According to the NIA‐AA classification of Alzheimer's disease neuropathological change (ADNC), 21 cases were classified as having high likelihood of AD, seven as having intermediate likelihood of AD, and two had low likelihood of AD. Seven cases did not have any neurodegenerative pathology. One case had corticobasal degeneration, two cases had frontotemporal lobar degeneration (FTLD)‐related taupathy, and one had progressive supranuclear palsy (PSP).

In terms of AD pathology, most cases in the LBD‐present group (n = 8, 62%) showed high Aβ Thal phase and Braak NFT stage, similar to those in the LBD‐absent group (Table 1).

Susceptibility in the SN in the Pathological Groups

Box plots of median susceptibility in the SN across cases are displayed in Figure 2. The LBD‐present group had higher QSM values in the SN than the LBD‐absent group (P = 0.030). In the subregions of the SN, this difference was present in the SNc (P = 0.008) but not the SNr (P = 0.530) after adjusting for age and time from MRI to death interval. QSM of the SN showed moderate accuracy in distinguishing LBD‐present and LBD‐absent cases (AUC = 0.70; P = 0.012; Fig. 3). Specifically, QSM in the SNc showed fair accuracy in distinguishing the groups (AUC = 0.74, P = 0.006; Fig. 3), while QSM in the SNr was unable to effectively differentiate between LBD‐present and LBD‐absent groups (AUC = 0.56 P = 0.250; Fig. 3) Pairwise comparisons of AUCs were conducted to assess significant differences in discriminative performance across regions. The results showed no significant difference between the SN and SNc (P = 0.25) or between the SN and SNr (P = 0.32). However, the AUC for the SNc was significantly higher than that of the SNr (P = 0.047), suggesting superior discriminatory capacity of the SNc in differentiating LBD‐present cases.

FIG. 2.

FIG. 2

Open in a new tab

Box plots of quantitative susceptibility mapping (QSM) in the substantia nigra across the Lewy body disease (LBD)‐present and LBD‐absent groups. Box plots show the magnetic susceptibility from the whole substantia nigra (SN), from the substantia nigra pars compacta (SNc), and from the substantia nigra pars reticulata (SNr) in LBD‐present group (yellow) and LBD‐absent group (purple).

FIG. 3.

FIG. 3

Open in a new tab

Accuracy of quantitative susceptibility mapping (QSM) in the substantia nigra (SN) in identifying Lewy body disease (LBD) pathology. The graph shows receiver‐operating characteristic (ROC) curves of QSM values in the SN, in the substantia nigra pars compacta (SNc), and the substantia nigra pars reticulata (SNr) for the discrimination of LBD‐present versus LBD‐absent cases.

Correlation between QSM and Clinical Features

Median magnetic susceptibility in the SNc was higher in participants with (vs without) parkinsonism (P = 0.045; Fig. 4A). Additionally, we selected individuals with symptoms of parkinsonism by including those with a combined UPDRS score higher than zero (n = 24). Correlation analysis showed that higher UPDRS scores correlated with increased median magnetic susceptibility in the SN (Rho = 0.417; P = 0.034) and SNc (Rho = 0.545; P = 0.004; Fig. 4B). Importantly, when restricting the analysis to the subset of individuals with confirmed Lewy body pathology (LBD‐present, n = 11), we still observed a positive correlation between UPDRS scores and susceptibility in the SN (Rho = 0.578; P = 0.039), particularly in the SNc (Rho = 0.583; P = 0.036). Consistently, there was a non‐significant correlation between UPDRS‐III scores and susceptibility in the SNr (Rho = 0.297; P = 0.320). Presence of other core symptoms of DLB (RBD, visual hallucinations, or cognitive fluctuations) were not associated with the magnetic susceptibility in the SN, SNc, and SNr. Furthermore, MMSE scores and the presence of AD‐related pathology did not correlate with QSM in the SN, SNc, and SNr.

FIG. 4.

FIG. 4

Open in a new tab

Box plots of differences in quantitative susceptibility mapping (QSM) values in the substantia nigra between participants with parkinsonism and no‐parkinsonism (A) and correlation of QSM values in the substantia nigra with total score in the Unified Parkinsonism Disease Rating Scale‐Part III (UPDRS‐III) (B). LBD, Lewy body disease.

Case Studies

Two cases illustrate the utility of measuring magnetic susceptibility in the SN as an indicator of LBD pathology (Supplemental Fig. S2). Participant 1 was an 82‐year‐old man with DLB. Neuropathological examination revealed diffuse LBD and low likelihood AD pathology (Thal phase 1, Braak NFT stage II). QSM values consistently revealed elevated susceptibility in the SNc and SNr. Participant 2 was a 59‐year‐old man with probable AD. Neuropathological examination revealed high likelihood AD pathology (Thal phase 5; Braak NFT stage VI) without LBD. QSM values indicated very low susceptibility in the SNc and SNr.

Three cases highlight the limitations of elevated magnetic susceptibility in the SN as a specific marker of LBD pathology (Supplemental Fig. S2). Participant 3 had low susceptibility in the SN despite evidence of LBD at autopsy (ie, false‐negative). Participant 3 was a 90‐year‐old man clinically diagnosed with probable AD and corresponding high burden of AD neuropathological change (Thal phase 5; Braak NFT stage V). LBD pathology was detected in the brainstem but not in the limbic or neocortical regions, so he was classified as BLBD. Participants 4 and 5 had very high QSM values in the SN in the absence of LBD (ie, false‐positive). Participant 4 was a 73‐year‐old man who was clinically diagnosed with DLB, but his neuropathological examination indicated PSP without AD co‐pathology (Thal phase 0, Braak NFT stage III). Participant 5 was a 64‐year‐old woman with behavioral variant frontotemporal dementia. Her neuropathological examination revealed tau pathology, consistent with frontotemporal lobar degeneration.

Discussion

We investigated magnetic susceptibility in the SN with QSM, an MRI biomarker for increased brain tissue iron content, in a retrospective cohort of individuals with antemortem MRI and postmortem neuropathological examinations. Elevated magnetic susceptibility in the SN during life effectively distinguished participants with and without LBD at autopsy. Elevated magnetic susceptibility in the LBD‐present group was found in the SNc, but not in the SNr. Moreover, magnetic susceptibility in the SNc could distinguish between LBD‐present and LBD‐absent cases with fair accuracy and it was associated with the presence and severity of parkinsonism.

In this study, we used QSM to quantify total magnetic susceptibility, which is associated with tissue iron burden. In neuronal α‐synucleinopathies, elevated magnetic susceptibility in the SN is considered a potential biomarker of neurodegeneration with increased iron content. 6 , 7 , 8 Previous studies have shown elevated magnetic susceptibility in the SN in patients with DLB and PD, which could be detected even at the prodromal clinical stages with highest levels observed in cases with dementia. 6 , 7 , 31 , 32 Furthermore, increased magnetic susceptibility in the SN using QSM have shown a high diagnostic performance for the discrimination between patients with PD and controls. 33 Consistently, two new research frameworks envision the inclusion of imaging biomarkers of SN with iron‐sensitive MRI for the biological definition of syndromes with Lewy body‐related pathology. 3 , 34 Studies so far have included clinical cohorts of patients, which limits the exploration of the association between QSM and pathological diagnosis of LBD. In this study, the combination of clinical and neuropathological findings and imaging data provide a more robust assessment of the ability of QSM in the detection of LBD.

Our data showing that elevated magnetic susceptibility in the SNc was able to discriminate between cases with LBD from cases without LBD is consistent with observations suggesting that intracellular aggregates of α‐synuclein closely interact with iron burden in the SNc. There is evidence that iron radicals in the SN may have a role in α‐synuclein aggregation. Proteins of α‐synuclein have binding affinity to ferrous and ferric iron, 35 which can induce or promote greater aggregation of α‐synuclein protein in key brain regions with high iron metabolic requirements such as the SN. 36 , 37 There is also evidence suggesting that abnormal depositions of α‐synuclein in the SN may promote dysregulation of iron metabolism, leading to iron accumulation and eventually toxicity and death of dopaminergic neurons in the SN. 38 , 39 Although it is still under discussion whether iron accumulation in the SN is the cause or consequence of cell death, there seems to be an intricate and close interplay between α‐synuclein and iron.

In the current study, our findings also indicate a greater vulnerability of SNc to abnormal iron deposition. This is consistent with a recent QSM study in PD which demonstrated a disparate ventral to dorsal distribution pattern of iron content in the SN, at both early and late stages of PD6, suggesting a higher vulnerability to iron deposition in the dorsal part of the SN, where SNc is located. Vulnerability of SNc to iron deposition is expected considering the morphological and physiological attributes of SNc. SNc contains the pigmented, neuromelanin‐dense dopaminergic neurons, which are vulnerable in LBD‐related disorders (for a recent review, see Wise et al., 2022 40 ). Conversely, the SNr has fewer neuromelanin‐containing neurons and is largely comprised of inhibitory GABAergic neurons. 41 Therefore, our findings support current data indicating higher vulnerability of SNc to iron dysregulation in LBD compared to SNr.

Furthermore, the association between increased iron burden in the SNc and LBD pathology seems to also occur at a region‐specific level. A close exploration of our results suggests that elevated magnetic susceptibility in the SNc occurs with the advancing LBD pathology in the midbrain. This became evident when we investigated a false‐negative case in the LBD‐present group with low SNc QSM values. This LBD‐present case had LBD in the midbrain that had not spread to the hemispheric cortices (ie, brainstem only), suggesting that QSM may be less effective in detecting iron dysregulation in the SN at early (brainstem) pathological stages compared to later stages of LBD, when there is spread to hemispheric cortices. Further analysis of the neuronal integrity in the SN may reveal the level of involvement where QSM may be effective in detecting the neurodegenerative changes in LBD. The two false‐positive cases with autopsy‐confirmed PSP and frontotemporal lobar degeneration also highlight that elevated QSM may not be specific to LBD and may also occur in taupathies. This finding is not surprising, recognizing that iron metabolism may be dysregulated in other neurodegenerative diseases, influencing susceptibility in the SN. 42 , 43 , 44

There was a positive correlation between magnetic susceptibility in the SNc and parkinsonism detected on physical exam and quantified by the UPDRS‐III. These findings are consistent with previous studies showing a positive relationship between magnetic susceptibility in the SN and clinical progression of PD based on the Hoehn & Yahr stage system, and between magnetic susceptibility in the SN and UPDRS‐III scores in patients with PD. 45 , 46 This positive correlation of elevated magnetic susceptibility in the SNc and the presence of symptoms of parkinsonism contrasts with other well‐established biomarkers of neurodegeneration in LBD, including measures of dopamine transporter loss in the putamen and caudate using ioflupane(123I) single photon emission computed tomography (SPECT). Studies with ioflupane(123I) SPECT suggest a very modest correlation of putaminal or caudate dopamine transporter availability with UPDRS score in patients with PD. 47 , 48 Furthermore, abnormal dopamine transporter uptake in the basal ganglia has been found in DLB patients independently of the presence of parkinsonism. 49 , 50 It has been hypothesized that UPDRS and dopamine transporter imaging with SPECT measure overlapping yet distinct aspects of LBD. 51 Moreover, imaging measures and UPDRS might manifest at different stages of the neurodegeneration in LBD considering that the earliest clinical motor manifestations in LBD‐related disorders occur at the point of at least 50% loss of dopaminergic transporter binding. 52 However, QSM in the SN seems to reflect neurodegenerative processes associated with the loss of dopaminergic neurons in the SN, which results in the manifestation of clinical motor symptoms of parkinsonism. Therefore, QSM in the SN could be a complimentary imaging biomarker of neurodegeneration in LBD, which may better correlate with or even predict the onset of motor manifestations and symptomatic progression. Further histopathological studies are needed to elucidate the mechanisms that link elevated QSM values in the SN with clinical motor outcome assessments.

One of the limitations of this study is that participants were unbalanced between groups. The relatively small sample size of the LBD‐present group (n = 13) raises questions of generalizability, recognizing that results may be impacted by influential cases. Furthermore, patients were clinically advanced at the time of neuroimaging; thus, it is unclear whether results can be applied to patients at early stages where diagnostic applications of biomarkers may be greater. This is a common challenge in autopsy studies where cases are usually at a later stage of neurodegenerative diseases when included. Another limitation of the study is the variability in the time interval between the MRI scan and the participants' death, a common issue in studies that correlate antemortem imaging with postmortem pathology. To account for this, we used a rank ANCOVA, adjusting our analyses for the time between the MRI scan and death. Although rank ANCOVA allowed us to account for potential differences in variations of time interval between MRI acquisition and death, this approach assumes that there is a linear relationship between this time interval and pathological changes, although this assumption may not always be true. Therefore, our findings should be interpreted cautiously. Future studies with larger sample sizes and a longitudinal design are needed to further assess the potential of elevated QSM values in the SN as an early and longitudinal biomarker for Lewy body‐related pathology. An additional challenge in neuropathological retrospective cohort studies is the limited representation of non‐White participants, which limits the generalization of the findings. Future research must prioritize the inclusion of diverse participants in imaging and neuropathological research to gain insights into this topic across other representative groups. Finally, our results highlight the importance of using MRI atlases that differentiate subregions of the SN. However, our ROI results in the SN need to be validated in other cohorts and apply to other standardized atlases. We also acknowledge that the Ewert et al. atlas, which was used to define the SNc and SNr in this study, relies on T1‐weighted images. This approach may not fully capture all the intricate details of these subregions, particularly when distinguishing it from surrounding structures. Future studies may benefit from a more precise delineation of the SNc and SNr.

With the emergence of research frameworks to biologically define neuronal α‐synuclein disease, the validation of in vivo biomarkers of LBD is becoming one of the outstanding research questions in the field. In this study, we used QSM to measure magnetic susceptibility in the SN of participants with and without autopsy‐confirmed LBD. Overall, our results provide neuropathological confirmation of the relevance of QSM of the SNc as an in vivo biomarker of underlying Lewy‐related pathology, which is associated with clinical outcomes of parkinsonism. Although abnormal magnetic susceptibility in the SNc may occasionally be influenced by other neurodegenerative processes, such as tau pathology, our findings underscore that it predominantly reflects LBD, particularly within the midbrain. This could ultimately aid in the biological characterization of neuronal α‐synuclein disease, leading to more accurate diagnoses and improved treatment guidance.

Author Roles

(1) Research Project: A. Conception and Design, B. Organization, C. Execution, D. Data Acquisition and Analysis; (2) Statistical Analysis: A. Design, B. Execution, C. Review and Critique; (3) Manuscript Preparation: A. Writing of the First Draft, B. Review and Critique; (4) A. Figure Preparation.

P.D.‐G.: 1A, 1B, 1C, 2A, 2C, 3A, 4A.

S.A.P.: 2A, 2B, 2C, 3B, 4A

T.G.L.: 2A, 2B, 2C, 3B.

M.E.M: 1D, 3B.

A.N.: 1D, 3B.

R.R.R.: 1D, 3B.

D.W.D.: 1D, 3B.

D.O.: 1D, 3B.

M.L.S.: 1D, 3B.

C.G.S.: 1D, 3B.

J.G.: 1D, 3B.

V.L.: 1D, 3B.

L.K.F.: 1D, 3B.

J.A.F.: 1D, 3B.

R.S.: 1D, 3B.

J.G.‐R.: 1D, 3B.

V.K.R.: 1D, 3B.

D.J.: 1D, 3B.

H.B.: 1D, 3B.

E.K.St.L.: 1D, 3B.

D.K.: 1D, 3B.

G.S.D.: 1D, 3B.

N.G.‐R.: 1D, 3B.

W.K.: 1D, 3B.

R.C.P.: 1D.

C.R.J.: 1D.

T.J.F.: 1D, 3B.

B.F.B.: 1A, 1B, 1C, 1D, 3B.

K.K.: 1A, 1B, 1C, 2C, 3B, 4A.

Full Financial Disclosures for the Preceding 12 Months

P.D.‐G., S.A.P., T.G.L., M.E.M., A.N., R.R.R., D.W.D., L.K.F., R.S., H.B., E.K.St.L., G.S.D., W.K., D.O., J.G., J.A.F., D.T.J., and N.R.G.‐R. report no disclosures relevant to this article. C.G.S. receives research support from the National Institutes of Health (NIH). J.G.‐R. serves on the editorial board for Neurology and receives research support from NIH. M.L.S. owns or has owned stock in medical‐related companies, unrelated to the current work, within the past 36 months: Align Technology, Inc., Inovio Pharmaceuticals, Inc., Mesa Laboratories, Inc., Johnson and Johnson, LHC Group, Inc., Natus Medical Inc., and Varex Imaging Corporation. D.S.K. serves on a Data Safety Monitoring Board (DSMB) for the DIAN study, has served on a DSMB for a tau therapeutic for Biogen but received no personal compensation, is a site investigator in the Biogen aducanumab trials, is an investigator in clinical trials sponsored by Lilly Pharmaceuticals and the University of Southern California, serves as a consultant for Samus Therapeutics, Roche, Magellan Health, and Alzeca Biosciences but receives no personal compensation, and receives research support from the NIH. V.K.R. receives research funding from the NIH and the Mangurian Foundation for Lewy body disease research and has provided educational content for Medscape unrelated to this work. T.J.F. receives funding from the Mangurian Foundation for Lewy body disease research and NIH. V.L. serves as a consultant for Bayer Schering Pharma, Piramal Life Sciences, Life Molecular Imaging, Eisai Inc., AVID Radiopharmaceuticals, and Merck Research and receives research support from GE Healthcare, Siemens Molecular Imaging, AVID Radiopharmaceuticals, and the NIH (National Institute on Aging [NIA], National Cancer Institute [NCI]). C.R.J. has consulted for Lily and serves on an independent data monitoring board for Roche and as a speaker for Eisai, but he receives no personal compensation from any commercial entity. He receives research support from NIH and the Alexander Family Alzheimer's Disease Research Professorship of the Mayo Clinic. R.C.P. serves as a consultant for Roche, Inc., Merck, Inc., Biogen, Inc., Eisai, Inc., Genentech, Inc., and Nestle, Inc., served on a DSMB for Genentech, receives royalties from Oxford University Press and UpToDate, and receives NIH funding. B.F.B. has served as an investigator for clinical trials sponsored by Biogen and Alector. He receives royalties from the publication of a book entitled Behavioral Neurology of Dementia (Cambridge Medicine, 2017). He serves on the Scientific Advisory Board of the Tau Consortium. He receives research support from NIH, the Mayo Clinic Dorothy and Harry T. Mangurian Jr. Lewy Body Dementia Program, the Little Family Foundation, and the Ted Turner and Family Foundation LBD Functional Genomics Program. E.K.L. has no disclosures related to this work. He has served as a Co‐Investigator on K. Kantarci research grant (DLB U01) and as a NAPS Co‐Investigator. JK.K. consults for Biogen, receives research support from Avid Radiopharmaceuticals and Eli Lilly, and receives funding from NIH and Alzheimer's Drug Discovery Foundation.

Supporting information

Figure S1. Regions of interest of the substantia nigra (SN) (A) and representative quantitative susceptibility mapping (QSM) in pathological groups (B). Regions of interest (ROIs) of the SN are overlaid on the MCALT template. Left image shows the template for the whole SN (in red) and the right image shows the SN segmented into SN pars compacta (SNc; in yellow) and SN par reticulata (SNr; in blue). Representative QSM images were from a participant with probable dementia with Lewy bodies (DLB) classified as LBD‐present at autopsy (man, 82 years old) and a participant with probable Alzheimer's disease (AD) classified as LBD‐absent at autopsy (man, 59 years old). The upper row shows QSM images in the ventral level of the SN (inferior to the red nucleus) and the lower row shows QSM images in the dorsal level of the SN (adjacent to the red nucleus).

Figure S2. Examples of cases with antemortem quantitative susceptibility mapping (QSM) on magnetic resonance imaging (MRI) and postmortem neuropathological examination. Two cases illustrate the accuracy of QSM in the substantia nigra (SN) to discriminate between LBD‐present (participant 1) and LBD‐absent (participant 2) cases; the false‐negative case with low susceptibility in the SN despite the evidence of LBD at autopsy (participant 3); and two cases were false‐positives with high susceptibility in the SN despite the absence of LBD at autopsy (participants 4 and 5). Age, clinical diagnosis before death, pathological diagnosis, and QSM values in the SN are presented alongside each set of MRI QSM images.

MDS-40-2129-s001.docx (793.6KB, docx)

Acknowledgments

The authors especially thank the patients and their family members for participating in this research. This study is supported by National Institutes of Health (NIH) grants U01 NS100620, P50 AG016574, P30 AG 062677, R34 AG056639, U19 AG71754, and U01 AG006786; Foundation Dr. Corinne Schulerand; The Harry T. Mangurian Jr. Foundation; The Elsie and Marvin Dekelboum Family Foundation; GHR Foundation; Mayo Medical Foundation for Education and Research; Little Family Foundation; and Lewy Body Disease Functional Genomics Program.

Relevant conflicts of interest/financial disclosures: The authors especially thank the patients and their family members for participating in this research. This study is supported by National Institutes of Health (NIH) grants U01 NS100620, P50 AG016574, P30 AG 062677, R34 AG056639, U19 AG71754, and U01 AG006786; Foundation Dr. Corinne Schulerand; the Mangurian Foundation for Lewy Body Research; The Elsie and Marvin Dekelboum Family Foundation; GHR Foundation; Mayo Medical Foundation for Education and Research; Little Family Foundation; and Lewy Body Disease Functional Genomics Program. The authors declare no conflicts of interest.

Funding agencies: None.

Data Availability Statement

Anonymized data not published within this article will be made available by request from any qualified investigator.

References

  • 1. Spillantini MG, Schmidt ML, Lee VM, Trojanowski JQ, Jakes R, Goedert M. Alpha‐synuclein in Lewy bodies. Nature 1997;388(6645):839–840. [DOI] [PubMed] [Google Scholar]
  • 2. Levi S, Tiranti V. Neurodegeneration with brain iron accumulation disorders: valuable models aimed at understanding the pathogenesis of iron deposition. Pharmaceuticals (Basel) 2019;12(1):27. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 3. Höglinger GU, Adler CH, Berg D, et al. A biological classification of Parkinson's disease: the SynNeurGe research diagnostic criteria. Lancet Neurol 2024;23(2):191–204. [DOI] [PubMed] [Google Scholar]
  • 4. Wang Y, Liu T. Quantitative susceptibility mapping (QSM): decoding MRI data for a tissue magnetic biomarker. Magn Reson Med 2015;73(1):82–101. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 5. Haacke EM, Liu S, Buch S, Zheng W, Wu D, Ye Y. Quantitative susceptibility mapping: current status and future directions. Magn Reson Imaging 2015;33(1):1–25. [DOI] [PubMed] [Google Scholar]
  • 6. Bergsland N, Zivadinov R, Schweser F, Hagemeier J, Lichter D, Guttuso T Jr. Ventral posterior substantia nigra iron increases over 3 years in Parkinson's disease. Mov Disord 2019;34(7):1006–1013. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 7. Chen Q, Boeve BF, Forghanian‐Arani A, et al. MRI quantitative susceptibility mapping of the substantia nigra as an early biomarker for Lewy body disease. J Neuroimaging 2021;31(5):1020–1027. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 8. Zhang D, Yao J, Sun J, et al. Quantitative susceptibility mapping and free water imaging of substantia nigra in Parkinson's disease. J Parkinsons Dis 2022;12(8):2469–2478. [DOI] [PubMed] [Google Scholar]
  • 9. Lhermitte J, Kraus WM, McAlpine D. Original papers: on the occurrence of abnormal deposits of iron in the brain in parkinsonism with special reference to its localisation . J Neurol Psychopathol 1924;5(19):195–208. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 10. Wang EW, Brown GL, Lewis MM, et al. Susceptibility magnetic resonance imaging correlates with glial density and tau in the substantia nigra pars compacta. Mov Disord 2023;38(3):464–473. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 11. Roberts RO, Geda YE, Knopman DS, et al. The Mayo Clinic study of aging: design and sampling, participation, baseline measures and sample characteristics. Neuroepidemiology 2008;30(1):58–69. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 12. Kantarci K, Lowe VJ, Boeve BF, et al. Multimodality imaging characteristics of dementia with Lewy bodies. Neurobiol Aging 2012;33(9):2091–2105. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 13. Fahn SER. The unified Parkinson's disease rating scale. In: Fahn SMC, Calne DB, Goldstein M, eds. Recent Developments in Parkinson's Disease. Florham Park, NJ: Macmillan Healthcare Information; 1987:153–163. [Google Scholar]
  • 14. Ferman TJ, Smith GE, Boeve BF, et al. DLB fluctuations: specific features that reliably differentiate DLB from AD and normal aging. Neurology 2004;27;62(2):181–187. [DOI] [PubMed] [Google Scholar]
  • 15. Medicine AAoS . International Classification of Sleep Disorders‐2: diagnostic and coding manual, second edition (ICSD‐2); 2005:51–55.
  • 16. Folstein MF, Folstein SE, McHugh PR. Mini‐mental state. A practical method for grading the cognitive state of patients for the clinician. J Psychiatr Res 1975;12(3):189–198. [DOI] [PubMed] [Google Scholar]
  • 17. Hughes CP, Berg L, Danziger WL, Coben LA, Martin RL. A new clinical scale for the staging of dementia. Br J Psychiatry 1982;140:566–572. [DOI] [PubMed] [Google Scholar]
  • 18. McKeith IG, Boeve BF, Dickson DW, et al. Diagnosis and management of dementia with Lewy bodies: Fourth consensus report of the DLB Consortium. Neurology 2017. 4;89(1):88–100. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 19. Uchikado H, Lin WL, DeLucia MW, Dickson DW. Alzheimer disease with amygdala Lewy bodies: a distinct form of alpha‐synucleinopathy. J Neuropathol Exp Neurol 2006;65(7):685–697. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 20. Hyman BT, Phelps CH, Beach TG, et al. National Institute on Aging‐Alzheimer's Association guidelines for the neuropathologic assessment of Alzheimer's disease. Alzheimers Dement 2012;8(1):1–13. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 21. Montine TJ, Phelps CH, Beach TG, et al. National Institute on Aging‐Alzheimer's Association guidelines for the neuropathologic assessment of Alzheimer's disease: a practical approach. Acta Neuropathol 2012;123(1):1–11. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 22. Murray ME, Lowe VJ, Graff‐Radford NR, et al. Clinicopathologic and 11C‐Pittsburgh compound B implications of Thal amyloid phase across the Alzheimer's disease spectrum. Brain 2015;138(Pt 5):1370–1381. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 23. Thal DR, Rüb U, Orantes M, Braak H. Phases of a beta‐deposition in the human brain and its relevance for the development of AD. Neurology 2002;58(12):1791–1800. [DOI] [PubMed] [Google Scholar]
  • 24. Braak H, Braak E. Neuropathological stageing of Alzheimer‐related changes. Acta Neuropathol 1991;82(4):239–259. [DOI] [PubMed] [Google Scholar]
  • 25. Cogswell PM, Wiste HJ, Senjem ML, et al. Associations of quantitative susceptibility mapping with Alzheimer's disease clinical and imaging markers. NeuroImage 2021;224:117433. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 26. Avants BB, Epstein CL, Grossman M, Gee JC. Symmetric diffeomorphic image registration with cross‐correlation: evaluating automated labeling of elderly and neurodegenerative brain. Med Image Anal 2008;12(1):26–41. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 27. Ewert S, Plettig P, Li N, et al. Toward defining deep brain stimulation targets in MNI space: a subcortical atlas based on multimodal MRI, histology and structural connectivity. NeuroImage 2018;170:271–282. [DOI] [PubMed] [Google Scholar]
  • 28. Li W, Avram AV, Wu B, Xiao X, Liu C. Integrated Laplacian‐based phase unwrapping and background phase removal for quantitative susceptibility mapping. NMR Biomed 2014;27(2):219–227. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 29. Schofield MA, Zhu Y. Fast phase unwrapping algorithm for interferometric applications. Opt Lett 2003;28(14):1194–1196. [DOI] [PubMed] [Google Scholar]
  • 30. Li W, Wu B, Liu C. Quantitative susceptibility mapping of human brain reflects spatial variation in tissue composition. NeuroImage 2011;55(4):1645–1656. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 31. Chen M, Wang Y, Zhang C, et al. Free water and iron content in the substantia nigra at different stages of Parkinson's disease. Eur J Radiol 2023;167:111030. [DOI] [PubMed] [Google Scholar]
  • 32. Thomas GEC, Hannaway N, Zarkali A, Shmueli K, Weil RS. Longitudinal associations of magnetic susceptibility with clinical severity in Parkinson's disease. Mov Disord 2024;39(3):546–559. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 33. Ghassaban K, He N, Sethi SK, et al. Regional high iron in the substantia nigra differentiates Parkinson's disease patients from healthy controls. Front Aging Neurosci 2019;11:106. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 34. Simuni T, Chahine LM, Poston K, et al. A biological definition of neuronal α‐synuclein disease: towards an integrated staging system for research. Lancet Neurol 2024;23(2):178–190. [DOI] [PubMed] [Google Scholar]
  • 35. Peng Y, Wang C, Xu HH, Liu YN, Zhou F. Binding of alpha‐synuclein with Fe(III) and with Fe(II) and biological implications of the resultant complexes. J Inorg Biochem 2010;104(4):365–370. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 36. Kostka M, Högen T, Danzer KM, et al. Single particle characterization of iron‐induced pore‐forming alpha‐synuclein oligomers. J Biol Chem 2008;283(16):10992–11003. [DOI] [PubMed] [Google Scholar]
  • 37. Levin J, Högen T, Hillmer AS, et al. Generation of ferric iron links oxidative stress to α‐synuclein oligomer formation. J Parkinsons Dis 2011;1(2):205–216. [DOI] [PubMed] [Google Scholar]
  • 38. Davies P, Moualla D, Brown DR. Alpha‐synuclein is a cellular ferrireductase. PLoS One 2011;6(1):e15814. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 39. Patel D, Xu C, Nagarajan S, et al. Alpha‐synuclein inhibits Snx3‐retromer‐mediated retrograde recycling of iron transporters in S. cerevisiae and C. elegans models of Parkinson's disease. Hum Mol Genet 2018;27(9):1514–1532. [DOI] [PubMed] [Google Scholar]
  • 40. Wise RM, Wagener A, Fietzek UM, et al. Interactions of dopamine, iron, and alpha‐synuclein linked to dopaminergic neuron vulnerability in Parkinson's disease and neurodegeneration with brain iron accumulation disorders. Neurobiol Dis 2022;175:105920. [DOI] [PubMed] [Google Scholar]
  • 41. Lee CR, Tepper JM. Morphological and physiological properties of parvalbumin‐ and calretinin‐containing γ‐aminobutyric acidergic neurons in the substantia nigra. J Comp Neurol 2007;500(5):958–972. [DOI] [PubMed] [Google Scholar]
  • 42. Ito K, Ohtsuka C, Yoshioka K, et al. Differential diagnosis of parkinsonism by a combined use of diffusion kurtosis imaging and quantitative susceptibility mapping. Neuroradiology 2017;59(8):759–769. [DOI] [PubMed] [Google Scholar]
  • 43. Sjöström H, Surova Y, Nilsson M, et al. Mapping of apparent susceptibility yields promising diagnostic separation of progressive supranuclear palsy from other causes of parkinsonism. Sci Rep 2019;9(1):6079. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 44. Santillo AF, Skoglund L, Lindau M, et al. Frontotemporal dementia‐amyotrophic lateral sclerosis complex is simulated by neurodegeneration with brain iron accumulation. Alzheimer Dis Assoc Disord 2009;23(3):298–300. [DOI] [PubMed] [Google Scholar]
  • 45. An H, Zeng X, Niu T, et al. Quantifying iron deposition within the substantia nigra of Parkinson's disease by quantitative susceptibility mapping. J Neurol Sci 2018;386:46–52. [DOI] [PubMed] [Google Scholar]
  • 46. Langkammer C, Pirpamer L, Seiler S, et al. Quantitative susceptibility mapping in Parkinson's disease. PLoS One 2016;11(9):e0162460. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 47. Walker RW, Walker Z. Dopamine transporter single photon emission computerized tomography in the diagnosis of dementia with Lewy bodies. Mov Disord 2009;24(Suppl. 2):S754–S759. [DOI] [PubMed] [Google Scholar]
  • 48. Simuni T, Siderowf A, Lasch S, et al. Longitudinal change of clinical and biological measures in early Parkinson's disease: Parkinson's progression markers initiative cohort. Mov Disord 2018;33(5):771–782. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 49. Walker Z, Jaros E, Walker RW, et al. Dementia with Lewy bodies: a comparison of clinical diagnosis, FP‐CIT single photon emission computed tomography imaging and autopsy. J Neurol Neurosurg Psychiatry 2007;78(11):1176–1181. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 50. McKeith I, O'Brien J, Walker Z, et al. Sensitivity and specificity of dopamine transporter imaging with 123I‐FP‐CIT SPECT in dementia with Lewy bodies: a phase III, multicentre study. Lancet Neurol 2007;6(4):305–313. [DOI] [PubMed] [Google Scholar]
  • 51. Simuni T, Caspell‐Garcia C, Coffey CS, et al. Baseline prevalence and longitudinal evolution of non‐motor symptoms in early Parkinson's disease: the PPMI cohort. J Neurol Neurosurg Psychiatry 2018;89(1):78–88. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 52. Kordower JH, Olanow CW, Dodiya HB, et al. Disease duration and the integrity of the nigrostriatal system in Parkinson's disease. Brain 2013;136(Pt 8):2419–2431. [DOI] [PMC free article] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Figure S1. Regions of interest of the substantia nigra (SN) (A) and representative quantitative susceptibility mapping (QSM) in pathological groups (B). Regions of interest (ROIs) of the SN are overlaid on the MCALT template. Left image shows the template for the whole SN (in red) and the right image shows the SN segmented into SN pars compacta (SNc; in yellow) and SN par reticulata (SNr; in blue). Representative QSM images were from a participant with probable dementia with Lewy bodies (DLB) classified as LBD‐present at autopsy (man, 82 years old) and a participant with probable Alzheimer's disease (AD) classified as LBD‐absent at autopsy (man, 59 years old). The upper row shows QSM images in the ventral level of the SN (inferior to the red nucleus) and the lower row shows QSM images in the dorsal level of the SN (adjacent to the red nucleus).

Figure S2. Examples of cases with antemortem quantitative susceptibility mapping (QSM) on magnetic resonance imaging (MRI) and postmortem neuropathological examination. Two cases illustrate the accuracy of QSM in the substantia nigra (SN) to discriminate between LBD‐present (participant 1) and LBD‐absent (participant 2) cases; the false‐negative case with low susceptibility in the SN despite the evidence of LBD at autopsy (participant 3); and two cases were false‐positives with high susceptibility in the SN despite the absence of LBD at autopsy (participants 4 and 5). Age, clinical diagnosis before death, pathological diagnosis, and QSM values in the SN are presented alongside each set of MRI QSM images.

MDS-40-2129-s001.docx (793.6KB, docx)

Data Availability Statement

Anonymized data not published within this article will be made available by request from any qualified investigator.

Articles from Movement Disorders are provided here courtesy of Wiley

RESOURCES