Relationship Between Brain Iron Deposition and Mitochondrial Dysfunction in Idiopathic Parkinson’s Disease

doi:10.21203/rs.3.rs-929923/v1

Background

The underlying pathophysiology of Parkinson's disease is complex, involving different molecular pathways, including brain iron deposition and mitochondrial dysfunction. At a molecular level, these disease mechanisms are likely interconnected. Therefore, they offer potential strategies for disease-modifying treatments. We aimed to investigate subcortical brain iron deposition as a potential predictor of the bioenergetic status in patients with idiopathic Parkinson’s disease.

Methods

Thirty patients with idiopathic Parkinson's disease underwent multimodal MR imaging (T1, susceptibility-weighted imaging, SWI) and ³¹Phosphorus Magnetic Resonance Spectroscopy Imaging. Contrast-to-noise ratios based on the SWI images of the putamen, caudate, globus pallidus, and thalamus for each hemisphere were used in a multiple linear regression model to predict in vivo metabolites.

Results

Subcortical brain iron deposition, particularly in the putamen and globus pallidus, was highly predictive of the region-specific amount of high-energy-containing phosphorus metabolites in our subjects.

Conclusions

Our study suggests that brain iron deposition but not the variability of individual volumetric measurements are highly predictive of mitochondrial impairment in vivo. These findings offer the opportunity, e.g., by using chelating therapies, to improve mitochondrial bioenergetics in patients with idiopathic Parkinson's disease.

Neurology

Parkinson disease (PD)

Mitochondria

Iron

Various molecular disease mechanisms are associated with nigral and extranigral neurodegeneration in patients with Parkinson's disease (PD), often determining disease onset and progression.¹ Two such molecular alterations involve brain iron homeostasis and mitochondrial function disturbances.² Although the concept of mitochondrial dysfunction involves distinct pathophysiological aspects, e.g. impaired mitophagy and altered mitochondrial dynamics, the final common pathway is bioenergetic depletion.³ The underlying idea that iron metabolism changes and mitochondrial disturbances are relevant for the disease development refers to the initial, environmental agent-related studies, involving MPTP, 6-OH-DOPA, rotenone, or paraquat) that impair mitochondrial homeostasis.⁴ in vivo models have furthered our understanding of these environmental agents, revealing increased iron deposition in subcortical brain structures following mitochondrial impairment.⁵ Subcortical brain iron deposition in PD has been extensively studied using neuroimaging and post-mortem brain examinations.^6–9 Histopathological investigations have demonstrated that iron deposition is mainly localized to the mitochondria on a subcellular level, stressing the importance of this organelle in regulating intracellular iron metabolism.² Mitochondria are responsible for the macromolecular iron integration in metalloproteins, Fe-Sulfur clusters, or heme groups.^10,11 Previous reports highlighted that increased oxidative stress (e.g., by the inhibition of complex I of the electron transport chain) leads to an impaired assembly of Fe-Sulfur clusters, forcing the mitochondria to import even more iron.¹² This action might be a self-promoting mechanism as the resulting disproportion of divalent and trivalent iron could increase oxidative stress.¹³ Based on these in vitro interactions, the aggravating effects of iron and mitochondrial dyshomeostasis would be reasonable to study for the potential development of disease-modifying treatment strategies.^14–16 In this context, specific chelating agents cross the blood-brain barrier, e.g. deferoxamine or deferiprone, might thus rescue iron-overloaded mitochondria by cellular iron redistribution.^14,17 To the best of our knowledge, non-invasive studies combining brain iron deposition (by SWI) and bioenergetic depletion (by ³¹Phosphorus Magnetic Resonance Spectroscopy Imaging) have not yet been performed in PD. Therefore, our primary hypothesis was to test whether (i) subcortical brain iron deposition or (ii) the individual volumetric measurements are predictive of bioenergetic depletion in patients with PD. The combination of these two imaging modalities might not only help to recapitulate in vitro and preclinical in vivo findings to understand disease pathophysiology in human subjects but might also serve as a measure of patient stratification in future clinical trials.

Recruitment and clinical assessment

The present study and all subsequent experimental procedures have been performed in accordance with the revised version of the Declaration of Helsinki. Before the enrollment of the first study participant, this study has been approved by the ethics committee of the University of Lübeck (AZ 18_945). Thirty right-handed patients with PD were enrolled of whom 19 (63.3%) were male and eleven (36.7%) female with a mean age of 62.5 ± 9.4 years and a disease duration of 6.9 ± 5.0 years. The disease severity is characterized by MDS-UPDRS-I (7.3 ± 4.1), MDS-UPDRS-II (8.8 ± 6.8), MDS-UPDRS-III (24.5 ± 13.2), MDS-UPDRS-IV (5.1 ± 3.3) scores, and a Hoehn and Yahr Stage of 2.1 ± 0.8. Our study subjects took a levodopa equivalent daily dose (LEDD) of 680 ± 465 mg/d. All study participants gave written informed consent before participation in this study. We confirmed the diagnosis of PD following the MDS clinical diagnostic criteria as evaluated by trained movement disorders specialists (JP, HH, NB).¹⁸ The clinical examination included the general patient history and demographic data, potential MRI contraindications, concomitant illnesses, medication including LEDD, and a standardized clinical assessment following the MDS-UPDRS protocol.¹⁹ All patients were regularly taking antiparkinsonian medication, were in the ON state, not fasting, and rested for at least one hour before the start of the imaging procedure.

MR sequences and analyses

All MRI measurements were performed on a 3T Siemens MAGNETOM Skyra Magnetic Resonance Imaging scanner. T1 imaging. We employed a three-dimensional T1-weighted MP-RAGE sequence (64-channel head/neck coil) for structural imaging following subsequent imaging parameters: 1×1×1 mm³ voxel size; 192×256×256 mm³ field of view; TR = 1900 ms; TE = 2.44 ms; TI = 900 ms; flip angle 9°; GRAPPA acceleration factor 2 along A/P phase-encoding direction, total scan time 4 minutes and 33 seconds). All acquired neuroanatomical images were evaluated by consulting neuroradiologists to preclude relevant brain lesions. Susceptibility-Weighted Imaging. We performed SWI using a standard Siemens 3D high-spatial-resolution fully velocity corrected gradient-echo MRI sequence with the following image parameters: voxel size 0.9 x 0.9 x 1.5 mm³; 220 x 220 x 120 mm³ field of view; TR 27 ms; TE 20 ms; flip angle 15°; transversal orientation; phase-encoding direction R/L; total scan time 4 minutes and 54 seconds). ³¹P-MRSI. We used a dual tuned quadrature head coil ¹H/³¹P (RAPID Biomedical) for 3T to acquire 3D chemical shift imaging (CSI) data in accordance with the subsequent image protocol parameters: voxel size 30 x 30 x 30 mm³; 240 x 240 x 240 mm³ field of view; TR 2000 ms; TE 2.3 ms; 6-fold weighted averaging; flip angle 50°; Hamming filtering (width 100%); Nuclear overhauser effect disabled; WALTZ-4 decoupling, total scan time 8 minutes and 4 seconds. Field of view and CSI grid placement is highlighted in Fig. 1. Neuroimaging analyses. We performed volumetric measurements of subcortical brain structures on T1 native space images following the segmentation via the well-established FIRST suite of the FMRIB software library (v6.0).^20,21 We have chosen the respective options to derive only the left- and right-sided segmentation of the putamen, caudate, globus pallidus, and thalamus. Images of all subjects were manually controlled for segmentation errors before the subsequent statistical analysis. We calculated the subcortical volumes based on the derived segmentations, known voxel size, and voxel number employing standard functions of fslmaths and fslstats (FSLUTILS suite). SWI images were linearly coregistered to the native space T1 images of the respective subjects using the FMRIB’s Linear Image Registration Tool.^22,23 For the computation of SWI mean voxel intensities, we used the T1-derived segmentation masks and standard functions of fslmaths and fslstats. To standardize the voxel intensities of subcortical SWI measures, we expressed the mean voxel intensities as the contrast-to-noise ratio (CNR) referenced to a localized CSF signal. Therefore, we created spheres with a diameter of 4 mm in the MNI space for the lateral ventricles neighboring the subcortical structures of interest (right: x: 93, y: 132, z: 82; left: x: 85, y: 132, z: 82) by fslmaths (as highlighted for the axial plane in Fig. 1). We non-linearly normalized the native space T1 images of each subject to the MNI152_T1_1mm template using the FMRIB's Non-Linear Image Registration Tool (FNIRT) suite following the recommendations by Andersson et al.²⁴ We transferred the CSF spheres from the standard space to the individual native space for each subject using the invwarp function of FNIRT. Correct sphere placement was manually controlled for each subject, and fslmaths and fslstats standard functions computed the mean voxel intensity and the standard deviation (SD) for the CSF signal. After data extraction, we calculated the CNR for each subcortical ROI following the equation: (mean_ROI-mean_CSF)/SD_CSF. Here, lower CNR values indicate increased iron deposition, and higher CNR values decreased iron deposition. ³¹P-MRSI spectra were fitted in the time domain using the AMARES (advanced method for accurate, robust, and efficient spectral fitting) algorithm²⁵ as implemented in the OXSA toolbox²⁶ for Matlab®. The AMARES algorithm was developed for the evaluation of MR spectra and allows imposing prior knowledge and boundary conditions to constrain the fit. Initial values for the peak positions, i.e., chemical shifts relative to PCr (0.0 ppm), of the phosphorous metabolites were taken from Ren et al.²⁷ Due to homonuclear ³¹P-³¹P J-coupling the 𝛼- and 𝛾ATP signals are split into duplets, and the 𝛽ATP signal is split into a 1-2-1 triplet. The J-coupling constant was constrained to 16 Hz.²⁸ After fitting the spectra, we determined the area under each signal and calculated metabolite ratios ((𝛽ATP + PCr)/iP, 𝛽ATP/iP, and PCr/iP) for each voxel of interest (VOI) to account for the high degree of within-spectra autocorrelation of metabolites and to standardize the potentially differing alimentary intake of phosphorus-containing nutrients. The combination of 𝛽ATP and PCr is a frequently used approach, as both metabolites form a highly dynamic equilibrium in vivo. The VOI placement is highlighted in Fig. 1 (Panel A.II). Based on the intra-individual differing rostral brain length and resulting imprecise localization of distinct subcortical brain structures within the CSI grid, rostrally neighboring (but hemispherically different) VOIs were averaged for subsequent analyses.

Statistical analysis

We computed all statistical analyses using GraphPad Prism (version 9.0.0 88) on a MacOS Mojave (version 10.14.6) workstation. Demographics and clinical characteristics are reported as mean ± SD. Initially, we analyzed whether hemispherical side differences for distinct subcortical voxels (for ³¹P-MRSI) or neuroanatomical structures (SWI CNR or volumetric measures) were present using paired sample t-tests. As the presence of hemispherical side differences may foster our findings' interpretability, these analyses were exploratory and are subsequently reported as uncorrected p-values. We computed six multiple linear regression models to test our predefined hypotheses: The three metabolite ratios served as the dependent variables, the CNR values, or the volumetric measures of the four selected subcortical brain structures served as the independent variables. These results were corrected for multiple comparisons (n = 6) applying the Bonferroni correction, resulting in an adjusted significance level (P_adj) of P_adj≤.0083. For significant findings on an uncorrected statistical level, we also calculated parameter estimates and the goodness of fit for our multiple regression models to enhance our results' interpretability. In addition, we used a combination of graphical and numerical diagnostics to test the validity of prior assumptions for multiple regression models (absence of multicollinearity, normality of residuals, and the presence of homoscedasticity), which are highlighted in Fig. 2, Supplementary Fig. 1, Table 1, and Supplementary Table 1. To explore potential relationships between clinical parameters and PD-related brain changes, we performed correlation analyses for demographic and clinical data with our neuroimaging derived parameters. For a possible influence of the sex of our study participants on neuroimaging measures, we performed logistic regressions. We calculated Pearson's correlation for age, disease duration, MDS-UPDRS subscores, Hoehn and Yahr scale, and the LEDD²⁹ with our neuroimaging markers. These exploratory analyses (Pearson’s correlations) were illustrated via a heatmap (consisting of correlation coefficients. To decrease the number of performed tests and for the sake of enhanced interpretability, hemispherically averaged neuroimaging parameters served here as the correlation targets.

Table 1. Summary of multiple regression model results of (𝛽ATP+PCr)/iP vs. SWI CNR values

Analysis of Variance	SS	DF	MS	F	p-value

Regression	48.79	4	12.20	F(4,57) = 41.39	P < .0001****
SWI: putamen (CNR)	15.46	1	15.46	F(1,57) = 52.46	P < .0001****
SWI: caudate (CNR)	2.00	1	2.00	F(1,57) = 6.78	P = .0117*
SWI: globus pallidus (CNR)	2.98	1	2.98	F(1,57) = 10.10	P = .0024**
SWI: thalamus (CNR)	0.79	1	0.79	F(1,57) = 2.68	P = .1074
Residual	16.80	57	0.29
Total	65.58	61

Parameter Estimates	Variable	Estimate	SE	95% CI	T	p-value

β0	Intercept	1.36	0.31	0.74; 1.98	4.39	P < .0001****
β1	SWI: putamen (CNR)	4.44	0.61	3.21; 5,67	7.24	P < .0001****
β2	SWI: caudate (CNR)	0.27	0.10	0.06; 0.48	2.60	P = .0117*
β3	SWI: globus pallidus (CNR)	0.19	0.06	0.07; 0,31	3.18	P = .0024**
β4	SWI: thalamus (CNR)	0.10	0.05	0.00; 0.18	1.64	P = .1074

Goodness of Fit

DF	57
Multiple R	0.86
R²	0.74
R²_adj	0.73
SS	0.74
RMSE	0.11

Multicollinearity	Variable	VIF	R² with other variables

β0	Intercept
β1	SWI: putamen (CNR)	1.59	0.37
β2	SWI: caudate (CNR)	1.18	0.15
β3	SWI: globus pallidus (CNR)	1.29	0.22
β4	SWI: thalamus (CNR)	1.21	0.17

Normality of Residuals	Statistics	p-value	Passed normality test (𝛂 = .05)?

Anderson–Darling(42)	0.58	P = 0.13	Yes
D'Agostino–Pearson omnibus(43)	2.23	P = 0.33	Yes
Shapiro–Wilk(44)	0.97	P = 0.16	Yes
Kolmogorov–Smirnov (distance)(45)	0.08	P > .10	Yes

The table summarizes the multiple regression model of (𝛽ATP+PCr)/iP vs. SWI CNR values, including descriptive analyses. Apart from the overall significance test of the model, parameter estimates, goodness of fit, and necessary assumptions for multiple regression models were tested (absence of multicollinearity and normality of residuals). */**/***/***: significance levels (*: P ≤ 0.05, **: P ≤ 0.01, ***: P ≤ 0.001; ****: P ≤ 0.0001). CI: confidence interval. CNR: contrast-to-noise ratio. DF: degrees of freedom. MS: mean square. R²: coefficient of determination. R²_adj: adjusted coefficient of determination. RMSE: root mean square error. SE: standard error. SS: sum of squares. SWI: susceptibility-weighted imaging. T: t-statistic. F: F-Statistic. VIF: variance inflation factor.

Hemispherical side differences in subcortical brain bioenergetics, iron deposition, and volumetric measures

We observed that the ³¹P-MRSI-derived metabolite ratios were significantly lower in the right hemisphere (Fig. 2) inclusive of (𝛽ATP + PCr)/iP (t(30) = 5.58, P < 0.0001) (right 5.9 ± 2.6 vs. left 9.9 ± 3.3), 𝛽ATP/iP and PCr/iP of the (𝛽ATP + PCr)/iP ratio with significant side differences for 𝛽ATP/iP (t(30) = 6.96, P < 0.0001, right: 1.7 ± .6, left: 3.1 ± 1.3) and PCr/iP (t(30) = 4.07, P < 0.001, right: 4.1 ± 2.4, left: 6.9 ± 2.8). The logistic regressions showed that lateralization of brain energy metabolite levels was not driven by handedness or the more severely affected body side of the study participants. The CNR values derived from the SWI images showed similar but less pronounced side differences for the caudate (t(30) = 2.4, P = 0.025, right: 3.4 ± .5, left: 3.2 ± 0.5) but not for the other regions of interest. Volumetric measures revealed only significant side differences for the caudate (t(30) = 2.32, P = 0.027, right: 2.2 ± 0.8, left: 1.9 ± 0.6).

Subcortical brain iron deposition, but not the individual volumetric measurements, predicts the bioenergetic status of each hemisphere

Based on three metabolite ratios and two independent variable sets, we computed six multiple regression models. We observed a highly significant association for the model of (𝛽ATP + PCr)/iP vs. the CNR values (putamen, caudate, globus pallidus, and thalamus) (P < 0.0001) (see Table 1). Here, the parameter estimates for the CNR of the putamen (P < 0.0001), the caudate (P = 0.0117), and globus pallidus (P = 0.0024) significantly contributed to the prediction of our model. The overall goodness of fit resulted in a high adjusted coefficient of determination R²_adj of 0.74. The independent variables did not show a relevant degree of multicollinearity with variance inflation factors ranging from 1.18 to 1.59 and R² (among included variables) of only maximal 0.37 (putamen). Diagnostic tests for the normality of residuals were all passed (Table 1, Fig. 3: QQ plot). Furthermore, the present multiple linear regression model showed homoscedasticity (Fig. 3: Homoscedasticity plot), the residuals themselves were not predictive of the dependent variable (Fig. 3: Residual plot), and the selected parameters were not concerningly intertwined (Fig. 3: Parameter covariance matrix). For illustrative purposes, the second (uncorrected) significant (P = 0.0393, R²_adj=0.10) regression model (PCr/iP vs. CNR) is listed in the Supplementary Material (Supplementary Table 1, Supplementary Fig. 1). We could describe no significant findings or trends for the 𝛽ATP/iP ratio vs. CNR regression model and the regression models with volumetric measures as independent variables.

Neither subcortical brain iron deposition nor the individual volumetric measurements correlate with age, disease duration, or MDS-UPDRS-III

Figure 4 summarizes the exploratory Pearson’s correlations of our neuroimaging measures with demographic and clinical data. The logistic regressions with sex were negative, suggesting that sex did not confound on metabolite ratios and imaging findings. Neither SWI nor T1 imaging findings correlated with demographic or clinical data. Furthermore, we performed additional exploratory correlations due to the lack of a significant relationship with age, disease duration, and MDS-UPDRS-III. Interestingly, the SWI CNR values seemed to be of minor relevance to characterize the disease state with correlation coefficients ranging between ± 0.40. The same could be observed for the ³¹P-MRSI metabolite ratios.

To the best of our knowledge, this study reports first the potential interconnectedness of bioenergetic disturbances and brain iron deposition level in patients with PD using in vivo neuroimaging. Here, subcortical brain iron deposition, particularly in the putamen and globus pallidus, was highly predictive of the overall amount of high-energy containing phosphates in our subjects. We observed no association with the individual volumetric measurements, highlighting the potential of ³¹P-MRSI and iron-weighted imaging as pathophysiology-orientated biomarkers. Our findings suggest that brain iron deposition is related to mitochondrial impairment in vivo. However, we could not determine a causal relationship between them. Future studies should address whether these findings might indicate therapeutic advancements to improve mitochondrial bioenergetics in patients by administering chelating agents.

The observed hemispherical differences in brain energy metabolism and iron distribution were unexpected findings. Previous reports suggest that the lateralization of distinct SWI findings is present in patients with PD or Multiple System Atrophy with predominant parkinsonism (MSA-P).³⁰ In line with our findings, the putaminal tracer uptake of [(123)I]β-carboxymethyoxy-3-β-(4-iodophenyl)tropane PET indicated that the right hemisphere is predominantly affected in PD, being potentially related to handedness.³¹ Hemispheric side differences, in particular those concerning the role of the dominant hemisphere, could also be predictive for individual symptom presentation and disease progression.^32,33 Furthermore, the distribution of striatal dopamine content shows an asymmetric distribution in prodromal PD, being relevant for the subsequent motor symptom onset in patients with PD.³⁴ Nevertheless, it remains elusive whether these previous findings would also result in bioenergetic alterations and should be considered in future multimodal imaging studies. However, the observed hemispherical differences yield important implications for future studies: ³¹P-MRSI studies often record a global signal (e.g., by using surface head coils), which might miss lateralized differences concerning an individual's brain anatomy.³⁵ Frequent brain iron deposition in midbrain or brainstem structures of diseased individuals (such as in the Substantia nigra of patients with PD) might also be a potential limitation, which could be assessed by iron-weighted neuroimaging.^36–38 In contrast, ³¹P-MRSI-mediated examination of in vivo bioenergetics is substantially hampered in these brain structures by the relatively low spatial resolution and insufficient tissue homogeneity to yield satisfactory spectral quality for metabolite quantification. Interestingly, our neuroimaging measures were only marginally associated with the phenotype. In particular, the SWI measures are in contrast to previous reports where the iron deposition was related to disease duration or severity.⁹ This finding implicates the need for longitudinal studies that could address whether brain iron deposition is a consequence or rather a primary driver of neurodegeneration. The latter would be especially relevant as patients with increased brain iron deposition in early disease stages could benefit the most from targeted treatment strategies. Given the likely complexity of one individual's disease pathophysiology, it would be crucial to stratify patients by their outweighing etiology to stratify them and subsequently sustain clinical trial success in the future. To better understand the temporal dynamics of brain iron deposition and mitochondrial dysfunction in the prolonged process of neurodegeneration, longitudinal studies would thus be necessary. Such studies would substantially benefit from the use of quantitative MRI methods, potentially improving the multi-site reliability of the upcoming findings.^8,39 As a limitation, SWI is also sensitive to compounds other than iron, e.g. calcium, potentially distorting the local magnetic field and generating image contrast.⁴⁰ The combination of quantitative susceptibility imaging and relaxometry (e.g., by multiparameter mapping) might thus provide more information on the role of brain iron deposition in neurodegenerative disorders.³⁹ The combination of different iron-sensitive MRI methodologies might also lead to the specific detection of divalent and trivalent iron atoms in vivo, as preliminarily demonstrated in a phantom MRI study.⁴¹ Especially for the investigation of mitochondrial dysfunction in patients with PD, the role of divalent and trivalent iron in the production of reactive oxygen species might provide more detailed insights into the underlying biology of PD and could be used to map individual treatment responses to oxidative stress-targeted treatment regimes.

In this study, we demonstrated that subcortical brain iron deposition is highly predictive of mitochondrial impairment in patients with PD in vivo. Our findings highlight the interconnectedness of two important pathophysiological hallmarks of this disorder that were previously implicated by in-vitro and post-mortem experiments. Our preliminary experimental data support the potential use of chelating agents in individualized treatments for patients with PD. However, longitudinal studies are required to address the temporal aspects of the course of the disease and identify the window of opportunity for personalized therapies.

PD: Parkinson’s disease

SWI: Susceptibility-weighted imaging

LEDD: Levodopa equivalent daily dose

CNR: Contrast-to-noise ratio

FNIRT: FMRIB’s Non-Linear Image Registration Tool

SD: Standard deviation

VOI: Voxel of interest

CSI: Chemical shift imaging

MSA-P Multiple System Atrophy with predominant parkinsonism

Ethics approval and consent to participate

This study has been approved by the ethics committee of the University of Lübeck (AZ 18_945). All participants gave written informed consent for participation.

Consent for publication

All enrolled study participants gave written informed consent for the publication of anonymized study-related data.

Availability of data and materials

The datasets used and/or analyzed during the present study are available from the corresponding author on reasonable request.

Competing interests

The authors declare that they have no competing interests.

Funding

JP received funding from the Parkinson’s Foundation (Miami, Florida, USA), the Deutsche Parkinsongesellschaft (Berlin, Germany), and the Deutsche Forschungsgemeinschaft via the Clinician Scientist School Lübeck (DFG-GEPRIS 413535489). NB received funding from the DFG (BR4328.2-1 (FOR2488), GRK1957), the Collaborative Center for X-linked Dystonia-Parkinsonism and the Else-Kröner Fresenius-Stiftung (HA17_2017).

Authors’ contributions

JP designed the study, recruited study participants, performed the clinical and neuroimaging assessments, performed the neuroimaging and statistical analyses, and drafted the manuscript.

MG established and improved the ³¹P-MRSI measurements, performed the neuroimaging analyses, and reviewed the manuscript.

FG recruited study participants, performed the clinical and neuroimaging assessments, and reviewed the manuscript.

SK recruited study participants, performed the clinical and neuroimaging assessments, and reviewed the manuscript.

BE recruited study participants, performed the clinical and neuroimaging assessments, and reviewed the manuscript.

MK designed the study, ensured study participant recruitment, and reviewed the manuscript.

HH recruited study participants, performed the clinical and neuroimaging assessments, and reviewed the manuscript.

CK designed the study, ensured study participant recruitment, and reviewed the manuscript.

NB designed the study, ensured study participant recruitment, organized the study, wrote the first draft, and critically reviewed the manuscript.

All authors read and approved the final manuscript.

Acknowledgements

We thank Dr. Nathalie Schell and Dr. Alexander Balck for their support in patient recruitment.

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31PMRSIvsSWISupplementsMOME.docx

Relationship Between Brain Iron Deposition and Mitochondrial Dysfunction in Idiopathic Parkinson’s Disease

Status:

Journal Publication

Version 1

Abstract

Figures

Background

Methods

Recruitment and clinical assessment

MR sequences and analyses

Statistical analysis

Results

Hemispherical side differences in subcortical brain bioenergetics, iron deposition, and volumetric measures

Discussion

Conclusions

Abbreviations

Declarations

References

Supplementary Files

Status:

Journal Publication

Version 1