Longitudinal neuromelanin changes in prodromal and early Parkinson’s disease in humans and rat model

Jean-Baptiste Pérot; Anthony Ruze; Rahul Gaurav; Sana Rebbah; Capucine Cadin; Arnaud Le Troter; Lucas Soustelle; Laura Mouton; Romain Valabrègue; Annabelle Parent; Graziella Mangone; François-Xavier Lejeune; Isabelle Arnulf; Jean-Christophe Corvol; Marie Vidailhet; Mathieu D Santin; Miquel Vila; Stéphane Lehéricy

doi:10.1093/braincomms/fcaf204

. 2025 May 28;7(3):fcaf204. doi: 10.1093/braincomms/fcaf204

Longitudinal neuromelanin changes in prodromal and early Parkinson’s disease in humans and rat model

Jean-Baptiste Pérot ^1,^✉, Anthony Ruze ², Rahul Gaurav ³, Sana Rebbah ⁴, Capucine Cadin ⁵, Arnaud Le Troter ⁶, Lucas Soustelle ⁷, Laura Mouton ⁸, Romain Valabrègue ⁹, Annabelle Parent ¹⁰, Graziella Mangone ¹¹, François-Xavier Lejeune ¹², Isabelle Arnulf ^13,¹⁴, Jean-Christophe Corvol ¹⁵, Marie Vidailhet ¹⁶, Mathieu D Santin ¹⁷, Miquel Vila ^18,^19,^20,²¹, Stéphane Lehéricy ^22,²³

¹ Paris Brain Institute – ICM, MOVIT Team, Sorbonne Université, Inserm U1127, CNRS 7225, Hôpital Pitié-Salpêtriere, Paris 75013, France

² Paris Brain Institute – ICM, Centre de NeuroImagerie de Recherche – CENIR, Sorbonne Université, Inserm U1127, CNRS 7225, Hôpital Pitié-Salpêtriere, Paris 75013, France

³ Paris Brain Institute – ICM, MOVIT Team, Sorbonne Université, Inserm U1127, CNRS 7225, Hôpital Pitié-Salpêtriere, Paris 75013, France

⁴ Paris Brain Institute – ICM, Data Analysis Core, Sorbonne Université, Inserm U1127, CNRS 7225, Hôpital Pitié-Salpêtriere, Paris 75013, France

⁵ Paris Brain Institute – ICM, MOVIT Team, Sorbonne Université, Inserm U1127, CNRS 7225, Hôpital Pitié-Salpêtriere, Paris 75013, France

⁶ Aix-Marseille Univ, CNRS, CRMBM, Marseille 13385, France

⁷ Aix-Marseille Univ, CNRS, CRMBM, Marseille 13385, France

⁸ Paris Brain Institute – ICM, Centre de NeuroImagerie de Recherche – CENIR, Sorbonne Université, Inserm U1127, CNRS 7225, Hôpital Pitié-Salpêtriere, Paris 75013, France

⁹ Paris Brain Institute – ICM, Centre de NeuroImagerie de Recherche – CENIR, Sorbonne Université, Inserm U1127, CNRS 7225, Hôpital Pitié-Salpêtriere, Paris 75013, France

¹⁰ Neurodegenerative Diseases Research Group, Vall d’Hebron Research Institute (VHIR)-Network Center for Biomedical Research in Neurodegenerative Diseases (CIBERNED), Barcelona 08035, Spain

¹¹ Paris Brain Institute – ICM, Centre de NeuroImagerie de Recherche – CENIR, Sorbonne Université, Inserm U1127, CNRS 7225, Hôpital Pitié-Salpêtriere, Paris 75013, France

¹² Paris Brain Institute – ICM, Data Analysis Core, Sorbonne Université, Inserm U1127, CNRS 7225, Hôpital Pitié-Salpêtriere, Paris 75013, France

¹³ Paris Brain Institute – ICM, MOVIT Team, Sorbonne Université, Inserm U1127, CNRS 7225, Hôpital Pitié-Salpêtriere, Paris 75013, France

¹⁴ Sleep Disorders Unit, Pitié-Salpêtrière Hospital, AP-HP, Paris 75013, France

¹⁵ Aligning Science Across Parkinson’s (ASAP) Collaborative Research Network, Chevy Chase, MD 20815, USA

¹⁶ Paris Brain Institute – ICM, Centre de NeuroImagerie de Recherche – CENIR, Sorbonne Université, Inserm U1127, CNRS 7225, Hôpital Pitié-Salpêtriere, Paris 75013, France

¹⁷ Paris Brain Institute – ICM, Centre de NeuroImagerie de Recherche – CENIR, Sorbonne Université, Inserm U1127, CNRS 7225, Hôpital Pitié-Salpêtriere, Paris 75013, France

¹⁸ Neurodegenerative Diseases Research Group, Vall d’Hebron Research Institute (VHIR)-Network Center for Biomedical Research in Neurodegenerative Diseases (CIBERNED), Barcelona 08035, Spain

¹⁹ Aligning Science Across Parkinson’s (ASAP) Collaborative Research Network, Chevy Chase, MD 20815, USA

²⁰ Institut de Neurociències (INc-UAB), Autonomous University of Barcelona (UAB), Barcelona 08193, Spain

²¹ Catalan Institution for Research and Advanced Studies (ICREA), Barcelona 08010, Spain

²² Paris Brain Institute – ICM, MOVIT Team, Sorbonne Université, Inserm U1127, CNRS 7225, Hôpital Pitié-Salpêtriere, Paris 75013, France

²³ Paris Brain Institute – ICM, Centre de NeuroImagerie de Recherche – CENIR, Sorbonne Université, Inserm U1127, CNRS 7225, Hôpital Pitié-Salpêtriere, Paris 75013, France

^✉

Correspondence to: Dr. Jean-Baptiste Pérot Paris Brain Institute, Hôpital Pitié-Salpêtriere, Paris 75013, France E-mail: jb.perot@gmail.com

PMCID: PMC12149847 PMID: 40496671

Abstract

Neuromelanin-sensitive MRI has been proposed as a biomarker of Parkinson’s disease pathology. However, the biological and physical origins of this contrast are debated. A recent rodent model of controlled neuromelanin accumulation in the substantia nigra has been developed and recapitulates several features of Parkinson’s disease. In this work, we first combined neuromelanin-sensitive-MRI and histology to study neuromelanin accumulation and neurodegeneration in a humanized rat model of Parkinson’s disease. Neuromelanin-sensitive-MRI signal changes were biphasic with an initial increase due to the accumulation of neuromelanin in dopaminergic neurons, followed signal decrease due to neurodegeneration. In healthy subjects and patients with isolated rapid eye movement sleep behaviour disorder, neuromelanin-sensitive-MRI signal increased initially and then decreased similarly as in rodents after reaching a similar maximum signal intensity in both groups. In early Parkinson’s disease and converted isolated rapid eye movement sleep behaviour disorder patients, neuromelanin-sensitive-MRI signal drop was greater than in healthy individuals. Results in animals and humans show that neuromelanin-sensitive-MRI is a marker of the intracellular neuromelanin accumulation and then of neuronal degeneration and originates mainly from T₁ reduction effect of neuromelanin.

Keywords: Parkinson’s disease

Pérot et al. report that longitudinal neuromelanin (NM)-sensitive MRI can detect NM accumulation and neurodegeneration in a rat model with controlled accumulation of NM. Characterization of this process and comparison with early and prodromal Parkinson’s disease patients showed that this contrast could allow better understanding of the progression of Parkinson’s disease.

Graphical Abstract

Introduction

Preferential degeneration of dopaminergic neurons in the substantia nigra (SN) and reduction in striatal dopamine are key features of Parkinson’s disease.¹ Dopaminergic neurons of the SN, and other Parkinson’s disease-vulnerable catecholaminergic neurons such as noradrenergic neurons of the locus coeruleus (LC), contain a polymeric pigment called neuromelanin (NM). Although a pathological hallmark of Parkinson’s disease is the accumulation of misfolded, aggregated α-synuclein, which is a major component of Lewy bodies, in the brain and in particular the SN,¹ several lines of evidence suggest that NM may also play an important role in Parkinson’s disease pathogenesis and in the preferential vulnerability of catecholaminergic neurons. In the SN of Parkinson’s disease patients, neuronal loss occurs in melanized neurons and cell loss directly correlates with the percentage of NM-pigmented neurons.² Studies have suggested a dual role of NM, protective at the early stage by binding toxins and redox active metals,³ and toxic once these systems have been exhausted.⁴ In addition, neuroinflammatory changes in Parkinson’s disease are highly localized within NM-containing areas¹ and extracellular NM released from dying neurons can activate microglia.^5,6 NM is also associated with α-synuclein that redistributes to the lipid component of NM at early Parkinson’s disease stages⁷ and becomes entrapped within NM granules.⁸ However, because NM is absent in most animal species, including rodents, its potential role in Parkinson’s disease pathogenesis has not been experimentally studied in vivo until the recent development of NM-producing animal models overexpressing human melanin-producing enzyme tyrosinase (hTyr) via an adeno-associated virus (AAV) vector.^9,10 AAV–hTyr-injected animals presented progressive intracellular build-up of human-like NM in the SN, with intracellular concentration reaching similar levels in 2 months old rats^9,11 and 4 months old macaques¹⁰ than in Parkinson’s disease patients, ultimately compromising neuronal function and viability. These results raised the hypothesis of a pathogenic threshold above which intracellular NM accumulation triggers in an age-dependent manner the main pathological features of Parkinson’s disease, including motor deficits, Lewy pathology and nigrostriatal neurodegeneration.¹² This hypothesis was further supported by histological analyses of the SN of subjects with Parkinson’s disease and incidental Lewy body disease who had increased intracellular NM levels in dopaminergic neurons compared with age-matched healthy controls.⁹

The most commonly used neuroimaging technique able to detect neuromelanized structures in the human brain is NM-sensitive MRI (NM-MRI). The origin of the NM-MRI signal is still debated but probably includes the T₁ reduction effect of NM, which is paramagnetic when bound to iron to form NM-iron complexes,^13,14 and the high density of water protons in the nucleus.^15,16 NM-MRI has been proposed as a potential biomarker of melanized neurons^17-19 and hence of nigral degeneration.^14,20,21 Histological studies in the human SN found that NM appeared at around 2–3 years, accumulated with age, and then, depending on the study, remained stable after the second decade of life²² or increased subsequently.²³ This evolutionary profile was clarified in healthy subjects using NM-MRI which showed a progressive increase in the NM signal until reaching a plateau around 50–60 years of age followed by a decrease beyond that age,²⁴ in line with the concept of a pathogenic threshold of NM accumulation. The authors hypothesized that combined effects of intracellular NM accumulation²⁵ and age-related neuronal death could explain this behaviour, although this remains to be proven. NM-MRI contrast-to-noise ratio (CNR) is reduced in the SN and LC of Parkinson’s disease patients and patients with isolated rapid eye movement (REM) sleep behaviour disorder (iRBD), a prodromal condition of parkinsonism.^26-28 Previous studies have suggested that these changes occur after a long prodromal phase preceding motor symptom onset by 10–20 years for putaminal dopamine uptake^29,30 and by 5–6 years for SN volume loss assessed using NM-MRI.^27,31 In Parkinson’s disease, death of the dopaminergic neurons in the SN is accelerated,³² and recent work suggested that NM accumulation could also be accelerated.⁹

Characterizing the NM-MRI contrast changes versus age and disease duration in normal and pathologic conditions could thus enable to better understand the progression of Parkinson’s disease pathology. While the characterization of NM concentration before the onset of the disease is not possible to date in Parkinson’s disease, it is possible in the AAV–hTyr rat model and in patients at prodromal stages of parkinsonism, e.g. iRBD. Here, we used NM-MRI in the AAV–hTyr rat model and in human healthy and Parkinson’s disease subjects to answer the following questions: (i) In the AAV–hTyr rat model of Parkinson’s disease, what is the temporal evolution of the NM-MRI signal and (ii) can we explain the modifications of this NM signal over time by the successive combination of NM accumulation followed by neurodegeneration of melanized neurons threshold in vivo? (iii) In healthy human subjects, do we reproduce such an evolution of the NM signal with a maximum around 50–60 years and a decline beyond this age? (iv) In patients with iRBD, is the maximum of NM-MRI contrast the same as in controls and how does NM-MRI evolve before and after conversion to parkinsonism? (v) In patients with iRBDs after conversion, is the slope of NM-MRI signal loss similar to that of Parkinson’s disease patients? In the present work, we used longitudinal quantitative MRI to characterize the NM-MRI contrast in vivo in the AAV–hTyr rat model, with histological confirmation, and we then compared these results with longitudinal NM-MRI from clinical dataset including healthy volunteers (HVs), iRBD, and Parkinson’s disease patients.

Materials and methods

Animal study

Animals

Forty 1-month-old Sprague–Dawley (CD) male rats were received from Charles Rivers Laboratories. After 2 weeks of acclimatization, rats were handled daily for 2 weeks to reduce the stress due to experimentation. Animals were housed by pairs with 12 h day/night cycle, water and food ad libitum, and nesting enrichment that was changed once a week, together with litter.

We conducted a longitudinal imaging study with this cohort of rats. MRI protocol was realized 2 weeks prior to the injection of AAV–hTyr as a baseline, and 1, 2, 4, and 8 months following the injection. Animals were 2 months old at the time of injection. Ten rats were euthanized after each time point for histological evaluation. Rats were randomly dispatched for euthanasia between timepoints before the start of the study. Figure 1 summarizes the study design.

**Study design of the longitudinal imaging experiment on AAV–hTyr rats cohort.** Forty rats were scanned with multiparametric MRI for NM imaging 1 month before, and 1, 2, 4, and 8 months after injection of viral vector expressing human tyrosinase. Ten animals were euthanized after each timepoint post injection for histological analysis.

Surgical procedures

Recombinant AAV vector serotype 2/1 expressing the human tyrosinase cDNA driven by the cytomegalovirus promoter (AAV–hTyr; concentration 9.48E + 12 gc/mL) were produced at the Viral Vector Production Unit of the Autonomous University of Barcelona (UPV-UAB, Spain). After the first MRI experiment, all rats were injected with AAV–hTyr in the right SN only. Detailed procedures are available in Supplementary material. A protocol is accessible from the Key Resource Table.

Preclinical MRI protocol

Rats were anaesthetized with inhalation of isoflurane (3% for induction, 1.5–2% for maintain) and placed inside a 11.7T magnet (Paravision 6.0.1, Bruker BioSpec; Bruker BioSpin, Ettlingen, Germany) with a birdcage resonator and a receive only head surface coil. The body temperature was monitored and regulated at 37°C during the whole procedure, including induction of anaesthesia. A 2D axial T₁-weighted NM-MRI sequence with no magnetization transfer (MT) preparation was acquired after installation as well as three 3D multi gradient echo sequences with varying flip angles (FA) and MT preparation for quantitative MRI [T_1, T₂*, QSM, macromolecular proton fraction (MPF)]. A transmit field (B₁⁺) map was acquired for bias correction using the Actual Flip Imaging method.^33,34 Power calibration was performed before each sequence to account for heating of electronic components. Two-dimensional multi-echo spin-echo sequence was added to the protocol, when possible, for T₂ mapping. Parameters of acquisitions are detailed in Supplementary material. A protocol is accessible from the Key Resource Table.

Image processing

Rat brain images were reconstructed from raw data using in-house MATLAB program. T₁ mapping was computed from MTOFF_FA6 and MTOFF_FA24 images using the Variable Flip Angle method.³⁵ R₂* was estimated by exponential fitting of the signal from the 16 echoes of MTOFF_FA6 image. QSM and MPF were computed from the three 3D multi gradient echo sequences (Supplementary material).

Multi-contrast template was then computed from MTOFF_FA6, R₁, R₂*, MPF, and QSM baseline images from 24 animals using antsMultivariateTemplateConstruction.sh from ANTS.³⁶ To ensure symmetry of the template, native and flipped versions over the left-right axis were given as inputs into the template construction. Following construction of the global template, a subject-specific template was computed for each subject using images from the different timepoints.

The ipsilateral SN was manually delineated at 1 month post injection (mpi) on individual NM-MRI images in native space, using contrast appearing after the injection. As no contrast was available for contralateral SN, this segmentation was co-registered to the global template and flipped on the left-right axis for segmentation of the contralateral SN pars compacta (SNpc). Segmentation was also performed using an atlas registration. We ensured that atlas-registered SN was colocalized with manually delineated SN. As this was the case for all animals, we did not exclude any animal based on this criterion. We used the manually delineated SN for further analyses because atlas-based segmentation included both SNpc and SN pars reticulata.

Ipsi- and contralateral segmentations were then coregistered to subject-specific template and to images of all time points using ANTS. Registration pipeline is available on Github (https://github.com/jbperot/NIPARK).

Tissue collection

Ten animals were euthanized after each MRI session for brain tissue collection. Rats were euthanized and perfused with 4% paraformaldehyde before brain extraction. Brains were then post-fixed for 3 days, cryoprotected, frozen and stored at −80°C until slicing with cryostat. A protocol is accessible from the Key Resource Table.

Immunostaining

Immunostaining of tyrosine hydroxylase (TH) was realized on brain slices using the MULTIVIEW PLUS IHC Kit (Enzo Life Science). Quantification of intracellular NM was realized with slides mounted in Mowiol 4-88 aqueous solution directly after unfreezing and three rinses in distilled water, with no staining. A protocol is accessible from the Key Resource Table.

Cell counting and extracellular NM quantification

After drying of mounting medium, slides were scanned using an Olympus Slideview VS200 slide scanner together with the Olyvia 3.3 software to obtain 20× high resolution micrographs of the samples.

For all quantifications, SNpc was manually drawn in both hemispheres on 12 TH-immunostained brain slices covering the whole region. As some brain slices may have been damaged and discarded, total counts were normalized by the number of retained slices. Animals were entirely discarded from the analysis when there was less than seven retained slices.

Scanned slices were then analysed by a specific artificial intelligence-assisted algorithm implemented as in Gonzalez-Sepulveda et al.¹¹ for the quantification of TH⁺/NM⁺ cells and extracellular NM aggregates in both ipsilateral and contralateral SNpc. For TH⁺ cells, we divided the number of cells ipsilateral by the number of cells contralateral to have a percentage of cells ipsilateral compared with contralateral. A protocol is accessible from the Key Resource Table.

Human study

Participants

All subjects were prospectively recruited in the longitudinal, prospective, observational, case–control ICEBERG study (ClinicalTrials.gov Identifier: NCT02305147, ethics committee approval: IRB of Paris VI, RCB 2014-A00725-42) at the Paris Brain Institute. This cohort included HVs, iRBD patients and Parkinson’s disease patients assessed using MRI at baseline (V1) and after 2 years (V2) and 4 years (V4). Patient inclusion criteria comprised a clinical diagnosis of iRBD or Parkinson’s disease, respectively performed by sleep neurologists and movement disorder specialists, 18–75 years of age, minimal or no cognitive disturbances (Mini-Mental State Examination score > 26/30) and, for Parkinson’s disease patients, time elapsed from the first appearance of motor symptoms (i.e. disease duration) < 4 years. Clinical examination is detailed in Supplementary material. Patients with Parkinson’s disease met the UK Parkinson’s disease Society Brain Bank criteria.³⁷ Patients with iRBD had a history of dream-enacting behaviours with (potentially) injurious movements and enhanced tonic chin muscle tone or complex behaviours during REM sleep but did not meet the criteria for Parkinson’s disease or dementia.³⁸ All participants gave written informed consent. The demographic and clinical characteristics of the subjects are presented in Table 1.

Table 1.

Demographic and clinical characteristics at the baseline visit (V1)

	HV	Unconverted iRBD	Converted	PD	P-value^a
	HV	Unconverted iRBD	iRBD	PD	P-value^a
N	52	29	7	145
Age (years)	61.7 ± 9.2 [41.1–77.4]	68.0 ± 5.8	65.3 ± 0.6	62.4 ± 1.0	<0.0001
Age (years)	61.7 ± 9.2 [41.1–77.4]	[54.0–80.3]	[64.3–66.1]	[60.4–64.8]	<0.0001
Sex					0.004
Male	26 (50%)	24 (82.8%)	7 (100%)	90 (62.1%)
Female	26 (50%)	5 (17.2%)	0 (0%)	55 (37.9%)
MDS-UPDRS part III (range 0–132)
OFF condition	5.3 ± 5.2	9.0 ± 4.8	10.9 ± 6.3	29.5 (7.8)	<0.0001
OFF condition	[0–28]	[2–18]	[2–20]	[10–62]
ON condition	N/A	N/A	N/A	26.1 ± 7.3
ON condition	N/A	N/A	N/A	[12–45] (n = 124)
Hoehn and Yahr (Scale 0–5)	0 (0–0) [0–3]	0 (0–0) [0–2]	0 (0–2) [0–2]	2 (2–2) [1–3]	<0.0001
Total MoCA (range 0–30)	28.1 ± 1.7	27.4 ± 2.1	26.4 ± 2.4	27.6 ± 2.0	0.09
Total MoCA (range 0–30)	[23–30]	[22–30] (n = 28)	[22–29]	[21–30]	0.09
Total MDRS (range 0–144)	140.0 ± 3.7	138.0 ± 5.7	137.7 ± 3.4	139.1 ± 4.2	0.18
Total MDRS (range 0–144)	[126–144]	[121–144] (n = 28)	[131–141]	[122–144]	0.18

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P-values were calculated from the following tests as appropriate: one-way ANOVA test with post hoc Tukey’s honest significant difference test, Kruskal–Wallis test with post hoc Dunn’s test using Bonferroni’s correction, and Fisher’s exact test with pairwise comparisons for proportions using Bonferroni’s correction. P-values in bold indicate significant differences between the groups (P < 0.05). HVs, healthy volunteers; iRBD, idiopathic REM sleep behaviour disorder; PD, Parkinson’s disease; MDS-UPDRS, Movement Disorder Society Unified Parkinson’s Disease Rating Scale; MoCA, Montreal Cognitive Assessment; MDRS, Mattis Dementia Rating Scale; N/A, not applicable.

^a Post hoc comparisons: Unconverted iRBD older than HV and Parkinson's disease; MDS-UPDRS part III and Hoehn and Yahr higher in Parkinson's disease compared with other groups; Males more represented in Unconverted iRBD compared with HV. Quantitative variables are summarized as mean ± standard deviation [min–max] (number of available values) or median (interquartile range) [min–max], and sex as counts and percentages.

Clinical MRI protocol

MRI was performed for all subjects on a Siemens Prisma 3T scanner (Siemens Healthineers) with a 64-channel head reception coil. Whole brain anatomic image was acquired with T₁-weighted 3D MP2RAGE. NM-sensitive MRI was acquired using T₁-weighted 2D turbo spin echo with no MT preparation as previously described.³⁰

Image co-registrations were performed using NiftyReg (v1.5.58). Region of interest segmentations were delineated using the FreeSurfer image viewer (v5.3.0).

Brain extraction and segmentation into grey matter, white matter and CSF was performed on whole-brain anatomical images from MP2RAGE sequence using Statistical Parametric Mapping software for MATLAB (SPM12).

NM-sensitive turbo spin echo images of the SN were coregistered to anatomical images using affine transformation. The SN was then manually segmented by two trained raters blind to the groups, as previously described.^27,39 SN volume was extracted and corrected by total intracranial volume. Normalized signal intensity (NSI) of the SN was computed in comparison with a background region including the tegmentum and superior cerebral peduncle. A protocol is accessible from the Key Resource Table.

Statistical analyses

All statistical analyses were conducted using R version 4.2.2 (R Development Core Team, https://www.r-project.org/). R₁, R₂, R₂*, MTR, MPF were included in the statistical analysis as the change rate between ipsilateral and contralateral as following (S_ipsi-S_contra)/S_contra. CNR was calculated from NM-MRI image as (S_ipsi-S_contra)/σ_background. QSM was analysed as (S_ipsi-S_contra) without normalization due to values very close to zero. Intracellular and extracellular NM were included as raw data, neurodegeneration as n_ipsi/n_contra ratio.

Longitudinal data were analysed by fitting separate linear mixed models (LMMs) to each imaging modality. To compare histological measurements over time, which consisted in independent observation points between the time points, we performed one-way ANOVA with time from injection as a factor. Correlations between the changes in NM-MRI and histological quantification were examined using Spearman’s rank correlation coefficients (Spearman’s rho).

The level of statistical was set at P or adjusted P < 0.05 (two-sided) for all tests.

Study approval

All animal studies were conducted according to the French regulation (EU Directive 2010/63/EU—French Act Rural Code R 214-87 to 126). The animal facility was approved by veterinarian inspectors (authorization B-751319) and complies with Standards for Humane Care and Use of Laboratory Animals of the Office of Laboratory Animal Welfare (CEEA-005). All procedures received approval from the ethical committee (APAFIS # 34975-2022012412181911 v9).

Results

AAV–hTyr rats

Age-dependent NM accumulation and neurodegeneration in AAV–hTyr rats

Previous studies showed that overexpression of hTyr in rat and non-human primate SN resulted in age-dependent production of human-like NM within nigral dopaminergic neurons associated with nigrostriatal neurodegeneration, Lewy body-like pathology and a Parkinson’s disease phenotype.^9-11 In this rat model, animals showed a steady increase in intracellular NM levels in dopaminergic neurons over time associated with neurodegenerative changes that started by 1 mpi but became significant by 4 mpi.⁹ Here, we first sought to reproduce these findings and then to characterize by NM-MRI the longitudinal changes in NM accumulation and neurodegeneration in the SN of AAV–hTyr rats.

One-month-old male rats received a single unilateral stereotaxic injection of AAV–hTyr above the right SN as described previously.⁹ As expected, following injection, rats showed a progressive accumulation of intracellular (Fig. 2A and D) and extracellular (Fig. 2B and E) NM in the ipsilateral SN. No NM⁺ cell nor extracellular NM aggregate was found in the contralateral SNpc in any rat, since rodents do not naturally produce this brain pigment. At 1 mpi intracellular NM optical density (OD) was already significantly different from zero, considered as the theoretical baseline level as regular rats lack NM (P < 0.01, one-sample, one-sided t-test). Intracellular NM OD was further increased at 4 mpi compared with 1 mpi (+48.7%, P < 0.05) and at 8 mpi compared with other time points (+73.9% versus 1 mpi, P < 0.001). In parallel to NM accumulation, nigral dopaminergic neurons started to degenerate in AAV–hTyr rats, as previously reported.⁹ At 1 mpi, AAV–hTyr rats already showed a non-significant tendency to dopaminergic neuronal loss, with a 21.7% reduction in the ratio of the number of TH⁺ neurons in the injected (i.e. melanized) versus contralateral (i.e. non-melanized) hemisphere (Fig. 2C and F). The number of TH⁺ cells was significantly reduced in the SN in the hemisphere ipsilateral to AAV–hTyr injection as shown by the decrease in the ratio of the number of TH⁺ neurons in the ipsilateral hemisphere divided by the number of TH⁺ neurons in the contralateral hemisphere at 4 mpi (−68.4%, P < 0.001) and 8 mpi (−74.8%, P < 0.001) (Fig. 2C). Following degeneration, NM from dying melanized neurons is released to the extracellular space, both in humans and AAV–hTyr rats.⁹ In agreement with this, extracellular aggregates appear more numerous at 2 mpi than at 1 mpi, although there is no significant main effect of time for extracellular NM (+40.0%, P = 0.3) (Fig. 2B and E).

**NM accumulation and neurodegeneration following AAV–hTyr injection in rat SN. (A)** OD of intracellular NM in ipsilateral SN of rats euthanized at different mpi (1 mpi: 3 rats, 2 mpi: 5 rats, 4 mpi: 6 rats, 8 mpi: 6 rats). One-way ANOVA F_Time = 632.8, Tukey’s *post hoc* test between timepoints *P < 0.05, ***P < 0.001. (B) Number of extracellular NM aggregates in ipsilateral SN of rats euthanized at different mpi (1 mpi: 3 rats, 2 mpi: 6 rats, 4 mpi: 3 rats, 8 mpi: 6 rats). One-way ANOVA F_Time = 72.13, Tukey’s *post hoc* test between timepoints *P < 0.05, ***P < 0.001. (C) Ratio of the number of tyrosine hydroxylase positive (TH⁺) cells in the ipsilateral (TH + ipsi) compared with the contralateral SN (TH + contra) in rats euthanized at different mpi (1 mpi: 3 rats, 2 mpi: 6 rats, 4 mpi: 3 rats, 8 mpi: 6 rats). Two-way ANOVA F_Time = 3.8, Tukey’s *post hoc* test between timepoints *P < 0.05, ***P < 0.001; F_side = 31.4, Tukey’s *post hoc* test between ipsi- and contralateral sides ^###P < 0.001 with Tukey’s *post hoc* test. Histological images of the SN ipsilateral to AAV–hTyr injection with TH staining showing. **(D)** intracellular NM (arrows) and **(E)** extracellular NM (arrows). **(F)** Histological images of the SN contralateral and ipsilateral to AAV–hTyr injection with TH staining showing reduced TH + neurons in the injected ipsilateral side and NM accumulation.

Biphasic age-dependent NM-MRI signal changes in AAV–hTyr rats

We next characterized the temporal evolution of the NM-MRI signal in AAV–hTyr rats. As histological analyses showed an accumulation of intracellular NM followed by a reduction of NM-containing neurons, we would expect NM signal intensity to follow a biphasic curve, with an initial signal increase followed by a secondary decrease after reaching a maximum.

AAV–hTyr rats were scanned longitudinally at baseline, 1, 2, 4 and 8 mpi using an 11.7T MRI system (Fig. 3A). As expected, AAV–hTyr rats showed an increase in ipsilateral SN NM-MRI CNR between baseline and 1 mpi (×11.58, P < 0.001) followed by a progressive decrease in CNR from 2 to 8 mpi (Fig. 3B). CNR was still higher at 2 mpi (×8.26, P < 0.001) and 4 mpi (×4.58, P < 0.05) compared with baseline. At 8 mpi, CNR was no longer significantly different from baseline levels. Compared with 1 mpi, CNR was significantly decreased at 2 mpi (−26.4%, P < 0.05), 4 mpi (−55.7%, P < 0.001) and 8 mpi (−63.0%, P < 0.001). CNR was also significantly decreased at 4 mpi (−39.7%, P < 0.05) and 8 mpi (−49.7%, P < 0.05) compared with 2 mpi. These results are in line with the concept that degeneration of melanized neurons occurs beyond a pathological NM threshold in vivo.

**Progression of NM contrast and correlation with NM accumulation and neurodegeneration.** (A) Representative NM-MRI image of an AAV–hTyr-injected rat at 1 mpi. Melanized SN is detected as an ipsilateral hyperintense area (arrow). (B) CNR between the ipsilateral and contralateral SN as function of time from AAV–hTyr injection. Grey dots represent single CNR values from individual rats. Black dots represent average value across rats for each timepoint. Continuous line and shading represent estimated marginal means and confidence intervals. LMM, χ² = 99.6, *post hoc* pairwise comparisons versus baseline using the FDR correction *P < 0.05 and ***P < 0.001, S_ipsi = signal in the ipsilateral SN, S_contra = signal in the contralateral SN. (C) Correlation matrix of CNR parameter with histological quantification between 1 and 8 mpi Spearman’s rho correlation coefficient. Significant correlations are highlighted. *P < 0.05, ***P < 0.001, Spearman’s correlation test.

Overall, our results showed that age-dependent NM-MRI signal changes in AAV–hTyr rats were first associated with NM accumulation and then with neurodegeneration of NM-containing neurons. The initial increase of NM-MRI CNR (Fig. 3B) paralleled the accumulation of intracellular (Fig. 2A) and extracellular NM (Fig. 2B) in these animals. From 1 mpi onwards, decreasing NM-MRI CNR signal positively correlated with the decreasing number of nigral dopaminergic neurons (r = 0.55, P < 0.05, Fig. 3C). In turn, the number of dopaminergic neurons correlated negatively with intracellular NM (r = −0.51, P < 0.05). Indirectly, this led to negative correlation between NM density and CNR during this degenerative phase (Fig. 3C).

NM-MRI is associated with T₁ reduction in AAV–hTyr rats

Although NM-MRI signal hyperintensity in humans is clearly observed in regions that contain melanized catecholaminergic neurons (notably SN and LC), the exact origin of the observed signal is still debated. Two hypotheses are most often put forward. The most widespread hypothesis attributes the elevation of the T₁ signal to the paramagnetic properties of NM when it is associated with metals such as iron.¹⁴ The main competing hypothesis is that the main source of NM-MRI contrast is the high water content of catecholaminergic neurons.^15,16 Notably several studies have reported that MT preparation increased the contrast between the SN and the surrounding brain tissue, which led several authors to consider that MT was the main source of contrast.^40,41 Our previous results showing an elevation of the T₁ signal associated with the presence of NM suggested that the elevation of signal was well linked to the presence of NM.³⁰ To further study the origin of the so-called NM-MRI signal, we studied in AAV–hTyr rats the correlations of this signal with other MRI parameters such as the longitudinal relaxation rate R₁ (= 1/T₁), the proportion of protons linked to macromolecules, evaluated by the MT ratio (MTR) and the (MPF, evaluated by the quantitative MT), and the presence of iron in the SN (evaluated by the transverse relaxation rates R₂ and R₂*, and quantitative susceptibility mapping QSM) (Fig. 4A–C).

**Progression of MT and longitudinal relaxation parameters in time. (A–C)** Representative brain images of (A) R₁, (B) MTR and (C) MPF. (**D–F)** Variation over time from AAV–hTyr injection of (D) R₁, (E) MTR, and (F) MPF parameters in the ipsilateral SN compared with the contralateral SN. Grey dots represent single values from individual rats. Black dots represent average value across rats for each time point. Continuous line and shading represent estimated marginal means with 95% confidence intervals. LMM, (D) χ² = 44.6, (E) χ² = 5.1, (F) χ² = 8.4; *post hoc* pairwise comparisons versus baseline using the FDR correction ***P < 0.001.

The variation over time of quantitative R₁ mapping between ipsilateral and contralateral SN was similar to that of NM-MRI CNR (Fig. 4D), highlighting the contribution of T₁ reduction effect of NM to the NM-MRI contrast. R₁ in the ipsilateral SN was significantly higher at 1 mpi (+13.2%, P < 0.001) and 2 mpi (+14.7%, P < 0.001) compared with the contralateral SN. In contrast, neither MTR nor MPF were significantly sensitive to NM accumulation (Fig. 4E and F). NM-MRI CNR correlated significantly with R₁ (r = 0.63, P < 0.001) but not with MPF or MTR. MTR was strongly correlated with MPF (r = 0.85, P < 0.001) and R₁ (r = −0.7, P < 0.001).

Iron-sensitive magnetic susceptibility changes in AAV–hTyr rats

Iron changes were assessed using QSM, R₂* and R₂ mapping (Fig. 5A–C). Magnetic susceptibility (QSM) increased progressively in AAV–hTyr rats (Fig. 5D), suggesting iron accumulation in the ipsilateral SN of these animals. Magnetic susceptibility difference between ipsilateral and contralateral SN was significantly increased between the baseline and 1 mpi (×3.28, P < 0.05), and 2, 4, and 8 mpi (×5.94, ×7.26, and ×7.87 respectively, P < 0.001). Variation of R₂* (Fig. 5E) and R₂ over time (Fig. 5F) between the ipsilateral and contralateral SN were not significantly different. Magnetic susceptibility was also correlated positively with R₂ (r = 0.66, P < 0.05).

**Progression of susceptibility and transverse relaxation parameters in time. (A–C)** Representative brain images of (A) QSM, (B) R₂* and (C) R₂. **(D–F)** Correlation of magnetic susceptibility parameters with CNR in AAV–hTyr rats. (D) Differences in QSM between ipsilateral and contralateral SN over time from injection. Variations in (E) R₂* and (F) R₂ parameters in the ipsilateral compared with the contralateral SN over time from injection. Grey dots represent single values from individual rats. Black dots represent average value across rats for each timepoint. Continuous line and shading represent estimated marginal means with 95% confidence intervals. LMM, (D) χ² = 32.2, (B) χ² = 5.5, (C) χ² = 2.5; *post hoc* pairwise comparisons versus baseline using the FDR correction *P < 0.05, ***P < 0.001.

Longitudinal NM-MRI in human subjects

Age-dependent NM-MRI signal changes in HVs and patients with iRBD

Longitudinal characterization of NM-MRI signal in a rat model with controlled NM accumulation may help us to better interpret the NM-MRI signal variations observed in humans. In healthy subjects, NM-MRI studies have shown an increase in the signal with age reaching a plateau around 50–60 years of age²⁴ corresponding to an accumulation of NM in histological analyses,²⁵ then a decrease beyond that age. We therefore first studied the variations of the NM-MRI signal in the SN of 52 HVs recruited in the ICEBERG study at the Paris Brain Institute. Subjects were aged between 41 and 77 years and had no current or prior history of psychiatric or neurological disorders. Longitudinal follow-up of the HV over a period of up to 4.5 years showed a parabolic inverted U-shape trajectory of the NM-MRI signal intensity in the SN with a maximum signal intensity of 100.2 reached at 54.9 years of age, followed by a decrease in signal intensity beyond this threshold (Fig. 6, black line) in line with a previous study.²⁴

**NM-MRI NSI in the SN as a function of age in years in HV (black), unconverted iRBD patients (cyan), converted iRBD patients (orange) and Parkinson’s disease patients (red).** NSI values were obtained by dividing each value by the mean NSI value (110.9) of the control subjects at the baseline visit (V1). Circles, triangles and squares represent values measured at baseline (V1), first follow-up (V2) and last follow-up (V4) visits respectively. Vertical dotted lines represent the mean ages of onset in Parkinson’s disease (red) and converted iRBD (orange). For the sake of comparison between groups and within the Parkinson’s disease group, all Parkinson’s disease patients’ ages were aligned at baseline with the mean age of onset to study the effect of disease duration as the actual age was meaningless. Solid curves were added to indicate the NSI evolution of each group using the ‘geom_smooth’ function of the ggplot2 R package with either a linear fit for Parkinson’s disease and converted iRBD patients or a quadratic fit for HV and unconverted iRBD patients. Dashed lines represent extrapolated prolongation of linear trajectory outside of observed age intervals. NSI loss in percentage (%) as indicated by the regression lines at the mean ages of onset was indicated for Parkinson’s disease and iRBD converted patients. Estimated marginal means of linear trends: converted iRBD compared with unconverted iRBD r = −0.537, SE = 0.056, P = 0.10; Parkinson’s disease compared with HV: r = −0.326, SE = 0.326, P < 0.0001; Parkinson’s disease compared with converted iRBD: r = −0.211, SE = 0.331, P = 0.53. NSI, normalized signal intensity; yo, years old; HV, healthy volunteers; iRBD, idiopathic REM sleep behaviour disorder; PD, Parkinson’s disease.

We then wanted to compare this evolution profile of the NM-MRI signal to that of patients at the prodromal stage of parkinsonism, i.e. iRBD, to determine whether this threshold was similar or increased compared with HV and at what age it was reached. We studied the signal evolution profile of 36 patients with polysomnography-confirmed iRBD included in the ICEBERG study and aged from 54 to 80 years. We found that iRBD patients who did not progress to parkinsonism during the study (n = 29 non converted iRBD patients) showed a parabolic trajectory similar to that of HV, delayed by 7.9 years, with a maximum of 100.0 reached at 62.8 years (Fig. 6, blue line). The maximum appeared therefore the same as in HV.

Age-dependent NM-MRI signal changes in converted iRBD and Parkinson’s disease patients

Finally, we analysed the NM-MRI evolution profile in 7 iRBD patients who converted to parkinsonism during the study and compared this profile to that of 145 idiopathic Parkinson’s disease patients and HV from the ICEBERG study. In converted iRBD patients (Fig. 6, orange line) the mean age of conversion was 66.4 years. At the time of conversion, iRBD patients had a 1.3% reduction in NM-MRI signal intensity compared with unconverted iRBD patients. Longitudinal progression of the NM-MRI signal was best fitted by a linear estimation (r = −0.537, SE = 0.056, P = 0.10). The back projection of this estimation met the curve of unconverted iRBD at the maximum (62.8 years).

The mean age at diagnosis in Parkinson’s disease patients was 60.9 years, 5.5 years earlier than that of converted iRBD patients. At the time of onset, Parkinson’s disease patients had a 1.2% reduction of NM-MRI signal intensity compared with the mean signal intensity in HV (Fig. 6, red line) similar to that of converted iRBD patients. Longitudinal imaging of Parkinson’s disease patients showed a decrease in NM-MRI signal intensity after onset. This decrease was better fitted by a linear estimate (r = −0.326, SE = 0.326, P < 0.0001), accelerated compared with HV whose decrease rate is ∼0.0007 for the year following the Parkinson’s disease onset. The back projection of the linear estimation met the HV curve near maximum (56.8 years). There was no significant difference between the CNR decrease in Parkinson’s disease and converted iRBD (r = −0.211, SE = 0.331, P = 0.53).

Discussion

This is the first longitudinal study tracking NM-MRI signal changes from the presymptomatic phase to the Parkinson’s disease stage with histological comparison in a humanized rat model producing NM and in prodromal parkinsonism. The results showed that NM-sensitive imaging made it possible to follow the progression of NM accumulation in the SN with a first initial phase of progressive increase in the levels of NM in nigral neurons until reaching a specific threshold followed by neurodegeneration beyond this threshold. The results obtained in humans show a similar signal evolution profile in healthy subjects confirming previous report²⁴ but also in parkinsonian patients at the prodromal stage of iRBD, although delayed by 7.9 years. In both the healthy and unconverted iRBD groups, the NM-MRI signal increased to a similar peak intensity in both groups, likely reflecting NM accumulation up to a similar intracellular content in dopamine neurons. Beyond this threshold, the signal intensity began to decrease, probably reflecting the occurrence of neurodegeneration. The NM-MRI signal drop linked to neurodegeneration was more accentuated in iRBD patients who had converted to Parkinson’s disease and in patients with Parkinson’s disease than in healthy individuals.

Biological origin of the NM-MRI contrast in the humanized rat model of Parkinson’s disease

Many studies have already reported a relationship between NM-MRI based signal intensity and the presence of NM in the human SN.^14,17-20,42 In the AAV–hTyr rat and macaque models, the accumulation of NM in the SN was associated with an increase in NM-MRI signal intensity.^9,10 In human specimens, previous studies showed that the area of high signal intensity in NM-MRI images matched with the location of the SNpc.^18,43,44 Positive correlation of NM-MRI signal intensity was reported with the density of NM-containing neurons in 12 regions of interest from the SN of 3 subjects¹⁷ and with the regional NM concentration from midbrain sections of 7 individuals with Parkinson’s disease or Parkinson’s disease-related syndromes.¹⁹ The results of the present study complement these data by precisely quantifying the longitudinal changes in NM-MRI signal in relation to NM accumulation in the SN in histological sections. NM-MRI CNR in the ipsilateral SN of AAV–hTyr rats showed a large and rapid increase after injection of the viral vector. At this stage, CNR increase was concomitant with the accumulation of intracellular NM without overt neuronal loss. Following this initial increase, CNR reached a plateau between 1 and 2 mpi during which the intracellular NM density continued to increase, the population of TH⁺ neurons started to decrease by ∼20–25%, and the accumulation of extracellular NM debris released from dying neurons was established. During this plateau phase, the increase in NM levels and cell loss compensated each other, explaining the relative stability of the signal. From 2 mpi onwards, CNR decreased continuously in parallel to cell death, reaching 80% reduction compared with the contralateral SN at 8 mpi. During this phase, the decrease in NM-MRI CNR despite the continuous increase in intracellular NM indicates that the reduction in the number of NM-containing neurons at regional level predominated over the progressive increase of NM at cellular level within the remaining, yet-to-degenerate neurons. Together, these results demonstrate that NM-MRI can follow these two successive phenomena.

Longitudinal progression of NM-MRI signal in healthy aged individuals

Together with previous histological data in humans, our results in the rat model of NM production allowed us to better interpret the evolution of the NM-MRI signal in healthy subjects. On the one hand, our results in HV confirmed those of a previous study²⁴ by showing an increase in the NM-MRI signal up to a maximum plateau between 50 and 60 years followed by a signal drop beyond that age. On the other hand, results confirmed the hypothesis that the initial NM-MRI signal increase in HV was due to the age-dependent increase in intracellular NM levels and that the following NM-MRI signal decrease was secondary to the appearance of overt age-related neurodegeneration. Our results are in agreement with histological studies in ageing humans, which showed that NM increased continuously through life²³ and that age-related loss of pigmented neurons occurred even in the absence of clinical Parkinson’s disease.^45,46 Our results thus support the hypothesis that loss of pigmented neurons occurs when a maximum threshold of intracellular NM is reached.¹² In healthy ageing individuals, this threshold is reached around fifty, according to NM-MRI, an age at which increased NM levels and neurodegeneration compete to explain the MRI signal of NM.

Longitudinal progression of NM-MRI signal in Parkinson’s disease

In idiopathic Parkinson’s disease patients, interpolations of NM-MRI signal changes suggested that maximum NM concentration was reached at the same age as in HV but that neurodegeneration was much more marked with an accelerated signal loss slope. In Parkinson’s disease patients, additional genetic and/or environmental factors, in combination with pathogenic NM levels, might thus intervene to explain the faster degeneration of vulnerable melanized neurons once the threshold is reached. In Parkinson’s disease, the temporal profile of nigrostriatal neurodegeneration has been most often described by a negative mono-exponential function by histological studies,^47,48 studies using radiotracers targeting the dopaminergic system^29,40,49-53 or by NM-MRI.^27,30 Some studies however have reported a linear decrease in dopamine function with advancing disease duration using DAT-SPECT^54-56 or NM-MRI.³¹

Longitudinal progression of NM-MRI signal in iRBD

In unconverted iRBD subjects, the CNR described a similar inverted U-shape curve to that of HV, reaching the same maximum threshold but delayed by 7.9 years. Moreover, iRBD patients who converted to Parkinson’s disease during the study showed accelerated decrease in NM-MRI signal like Parkinson’s disease patients but delayed by the same number of years. The later onset of NM-MRI signal changes in iRBD was consistent with the known later onset of parkinsonism in iRBD compared with idiopathic Parkinson’s disease.⁵⁷ This delay suggests a different mechanism of progression of SN neurodegeneration between Parkinson’s disease and iRBD patients. The majority of iRBD patients convert to parkinsonism within 10–15 years, including not only Parkinson’s disease but also dementia with Lewy bodies and rarely multiple system atrophy, with a phenoconversion rate of ∼6–8% per year.^37,57 There is strong evidence that iRBD also represents distinct, more severe subtypes of alpha-synucleinopathies.⁵⁸ In addition, the pattern of progression of the neurodegenerative process differs between iRBD and Parkinson’s disease patients, being more variable in Parkinson’s disease patients in which the first neurodegenerative changes occur either in the brainstem or the olfactory areas.^59-61 In the present study, the slope of the NM-MRI signal decrease seemed greater in iRBDs than Parkinson’s disease, but the difference was not significant, which could be due to the low number of converted iRBD subjects.

NM accumulation in the SN in Parkinson’s disease

Together, our results are supporting a role of NM in the pathogenesis of Parkinson’s disease. In rodents, hTyr overexpression produced NM, whose accumulation above a specific threshold compromised neuronal function and triggered neurodegeneration replicating a previous study.⁹ Interestingly, NM intracellular concentration correlated negatively with neuronal loss in these animals suggesting a relationship between these two phenomena. Supporting this concept, reduction of intracellular NM accumulation in AAV–hTyr rats, either by boosting NM cytosolic clearance with autophagy activator TFEB or by delaying age-dependent NM production through VMAT2-mediated enhancement of dopamine vesicular encapsulation, resulted in a major attenuation of their neurodegenerative phenotype, both at the behavioural and neuropathological level.^9,11 In humans, the average NM signal intensity in Parkinson’s disease and converted iRBD patients crossed the parabolic curves of HV and non-converting iRBD at their maximum, respectively, a maximum that was similar between both groups. Increased levels of NM contrast in the SN have been found in other pathologies, such as schizophrenia⁶² or addiction.⁶³ These pathologies are characterized by altered dopamine function in the absence of neurodegeneration. Further studies are needed to understand if the correlations we observed between NM accumulation and the pathology arise from a direct, causal role of NM accumulation, or an indirect role due to compensatory mechanisms of NM, e.g. binding to reactive oxidative species, or to simultaneous processes.

Physical origin of the NM-MRI contrast

The origin of the NM-MRI contrast is debated. It is usually attributed to T₁ shortening of NM when bound to metals and particularly iron.^13,14 Such effects may be mostly attributable to iron bound to NM, forming paramagnetic complexes, at least ex vivo.¹³ Alternatively, recent work has suggested that the high signal intensity observed in the LC containing melanized noradrenergic neurons was unlikely due to the presence of unique T₁-shortening molecules such as NM, but could instead be attributed to the high proton density of water whose T₁ was shortened by paramagnetic ions.¹⁶ Low MPF due to poor myelination of the SN may also be involved,⁶⁴ in which case NM-MRI hyperintensity would not actually come from NM but from a reduced MPF.^15,42 MT experiments also point towards a suppressing effect of signal from the surrounding tissues.¹³ In this context, our results indicate that the increase in NM-MRI signal in AAV–hTyr rats was mainly related to the T₁ reduction effect of accumulation of intracellular NM. In contrast, MT ratio did not show any contribution to the NM-MRI contrast in this model, possibly due to the lower difference with surrounding tissue in rats. MPF did not show a significant variation with time, however, at the latest timepoint, a tendency to decreased MPF was notable. A significant contribution of decreased MPF to the NM-MRI contrast is thus possible in the presence of high neurodegeneration.

Iron accumulation in the SN of AAV–hTyr rats

Iron-sensitive QSM increased over time in the SN of AAV–hTyr rats. QSM and R₂* increases have both been associated with iron accumulation with QSM being more sensitive than R₂* to detect iron changes.^65,66 In particular, QSM has been reported to be sensitive to NM-iron complexes,⁶⁷ which represent the main storage system of iron molecules in the SNpc, potentially protecting dopaminergic neurons from iron toxicity.^68,69 Conversely, R₂ correlates with ferric iron deposits⁶⁷ and was not significantly changed in AAV–hTyr rats.

Study limitations

This study presents several limitations. First, rat and human MRI data were not acquired at the same magnetic field, with strong differences in relaxometry parameters. Thus, while based on the same sequence structure, the origin of the NM-MRI contrast at 11.7T may not entirely be the same as 3T. Second, the brain structure and metabolism between the two species are not identical and the phenotype induced by unilateral nigral AAV–hTyr injections in rats do not replicate all the complexity of Parkinson’s disease. NM accumulates though enzymatic production in the dopaminergic neurons of the SN in AAV–hTyr rats. In humans, whether tyrosinase contributes to NM production remains uncertain, since this enzyme is only expressed at barely detectable levels in the human SNpc.^9,70-72 In this context, it is currently believed that NM is instead produced by spontaneous non-enzymatic dopamine auto-oxidation. However, experimentally induced increases in dopamine and/or oxidized dopamine in mice and rats, achieved either with chronic L-dopa treatment^73,74 or by genetically enhancing TH activity⁷⁵ are not sufficient by themselves to produce NM in these animals. In contrast, hTyr brain overexpression in rodents and non-human primates produces a neuronal pigment virtually analogous to human NM.^9-11,76,77 The similarities between Parkinson’s disease and the Parkinson’s disease-like changes reported previously in AAV–hTyr rats,⁹ combined with the changes in the NM-MRI profiles observed here, suggests that results obtained in AAV–hTyr rats could indeed serve to better understand changes observed in humans. However, the similarities between Parkinson’s disease and the Parkinson’s disease-like changes reported previously in AAV–hTyr rats,⁹ combined with the changes in the NM-MRI profiles observed here, suggested that results obtained in AAV–hTyr rats could indeed serve to better understand changes observed in humans. Furthermore, this model is the only possibility to observe NM-MRI in vivo with controlled NM accumulation. Third, the low number of iRBD patients who converted to parkinsonism may have decreased the accuracy of modelling NM-MRI signal loss in this group. However, the results obtained were in agreement with literature data, which reported a later onset of motor disorders in iRBD patients compared with Parkinson’s disease patients.⁵⁷ Fourth, the relatively short follow-up of the patients did not make it possible to precisely determine the type of decrease in the NM-MRI signal, i.e. mono-exponential or linear. However, this was not the focus of our study and a linear versus exponential fit would not significantly change the results reported here. Fifth, the SN was manually segmented in both rats and humans. Automatic segmentation could allow better reproducibility and avoid potential biases. Lastly, it would have been interesting to include an additional earlier time point (i.e. 0.5 mpi) in our AAV–hTyr rat studies, to observe the increase of CNR before neurodegeneration started. However, in practice it was not possible to scan so many animals in such a short time.

Conclusion

Longitudinal, quantitative in vivo characterization of the NM-MRI contrast in the AAV–hTyr rat model allowed the identification of the biological and physical origins of this signal. NM-MRI contrast was associated with T₁ reduction and was sensitive to NM accumulation in the first phase, and then to neurodegeneration of pigmented dopaminergic neurons. These results suggest a correlation between neurodegeneration and intracellular NM accumulation, followed with iron accumulation. Maximum of NM-MRI was reached later in patients with iRBD, and may explain the late onset of Parkinson’s disease conversion in iRBD patients. More studies are needed to better understand the different progression patterns in iRBD and Parkinson’s disease.

Supplementary Material

fcaf204_Supplementary_Data

fcaf204_supplementary_data.zip^{(246KB, zip)}

Acknowledgements

The authors would like to thank the HISTOMICS and PHENOPARC platforms of the Paris Brain Institute for their support. The B₁⁺ mapping SPGR prototype sequence used in experiments was provided by the CRMBM laboratory (Aix Marseille Univ, CNRS, CRMBM, Marseille, France).

Contributor Information

Jean-Baptiste Pérot, Paris Brain Institute – ICM, MOVIT Team, Sorbonne Université, Inserm U1127, CNRS 7225, Hôpital Pitié-Salpêtriere, Paris 75013, France.

Anthony Ruze, Paris Brain Institute – ICM, Centre de NeuroImagerie de Recherche – CENIR, Sorbonne Université, Inserm U1127, CNRS 7225, Hôpital Pitié-Salpêtriere, Paris 75013, France.

Rahul Gaurav, Paris Brain Institute – ICM, MOVIT Team, Sorbonne Université, Inserm U1127, CNRS 7225, Hôpital Pitié-Salpêtriere, Paris 75013, France.

Sana Rebbah, Paris Brain Institute – ICM, Data Analysis Core, Sorbonne Université, Inserm U1127, CNRS 7225, Hôpital Pitié-Salpêtriere, Paris 75013, France.

Capucine Cadin, Paris Brain Institute – ICM, MOVIT Team, Sorbonne Université, Inserm U1127, CNRS 7225, Hôpital Pitié-Salpêtriere, Paris 75013, France.

Arnaud Le Troter, Aix-Marseille Univ, CNRS, CRMBM, Marseille 13385, France.

Lucas Soustelle, Aix-Marseille Univ, CNRS, CRMBM, Marseille 13385, France.

Laura Mouton, Paris Brain Institute – ICM, Centre de NeuroImagerie de Recherche – CENIR, Sorbonne Université, Inserm U1127, CNRS 7225, Hôpital Pitié-Salpêtriere, Paris 75013, France.

Romain Valabrègue, Paris Brain Institute – ICM, Centre de NeuroImagerie de Recherche – CENIR, Sorbonne Université, Inserm U1127, CNRS 7225, Hôpital Pitié-Salpêtriere, Paris 75013, France.

Annabelle Parent, Neurodegenerative Diseases Research Group, Vall d’Hebron Research Institute (VHIR)-Network Center for Biomedical Research in Neurodegenerative Diseases (CIBERNED), Barcelona 08035, Spain.

Graziella Mangone, Paris Brain Institute – ICM, Centre de NeuroImagerie de Recherche – CENIR, Sorbonne Université, Inserm U1127, CNRS 7225, Hôpital Pitié-Salpêtriere, Paris 75013, France.

François-Xavier Lejeune, Paris Brain Institute – ICM, Data Analysis Core, Sorbonne Université, Inserm U1127, CNRS 7225, Hôpital Pitié-Salpêtriere, Paris 75013, France.

Isabelle Arnulf, Paris Brain Institute – ICM, MOVIT Team, Sorbonne Université, Inserm U1127, CNRS 7225, Hôpital Pitié-Salpêtriere, Paris 75013, France; Sleep Disorders Unit, Pitié-Salpêtrière Hospital, AP-HP, Paris 75013, France.

Jean-Christophe Corvol, Aligning Science Across Parkinson’s (ASAP) Collaborative Research Network, Chevy Chase, MD 20815, USA.

Marie Vidailhet, Paris Brain Institute – ICM, Centre de NeuroImagerie de Recherche – CENIR, Sorbonne Université, Inserm U1127, CNRS 7225, Hôpital Pitié-Salpêtriere, Paris 75013, France.

Mathieu D Santin, Paris Brain Institute – ICM, Centre de NeuroImagerie de Recherche – CENIR, Sorbonne Université, Inserm U1127, CNRS 7225, Hôpital Pitié-Salpêtriere, Paris 75013, France.

Miquel Vila, Neurodegenerative Diseases Research Group, Vall d’Hebron Research Institute (VHIR)-Network Center for Biomedical Research in Neurodegenerative Diseases (CIBERNED), Barcelona 08035, Spain; Aligning Science Across Parkinson’s (ASAP) Collaborative Research Network, Chevy Chase, MD 20815, USA; Institut de Neurociències (INc-UAB), Autonomous University of Barcelona (UAB), Barcelona 08193, Spain; Catalan Institution for Research and Advanced Studies (ICREA), Barcelona 08010, Spain.

Stéphane Lehéricy, Paris Brain Institute – ICM, MOVIT Team, Sorbonne Université, Inserm U1127, CNRS 7225, Hôpital Pitié-Salpêtriere, Paris 75013, France; Paris Brain Institute – ICM, Centre de NeuroImagerie de Recherche – CENIR, Sorbonne Université, Inserm U1127, CNRS 7225, Hôpital Pitié-Salpêtriere, Paris 75013, France.

Supplementary material

Supplementary material is available at Brain Communications online.

Funding

This project was funded by the EU Joint Programme Neurodegenerative Disease Research (JPND NIPARK, ANR-20-JPW2-0004-04), Instituto de Salud Carlos III, EU/Spain (AC20/00121 to M.V.) and Agence Nationale de la Recherche (to S.L.) and by the Aligning Science Across Parkinson’s through The Michael J. Fox Foundation for Parkinson Research, USA (ASAP-020505 to M.V.). The ICEBERG study was funded by grants from the Investissements d'Avenir, IAIHU-06 (Paris Institute of Neurosciences – IHU), ANR-11-INBS-0006, Fondation d’Entreprise EDF, Biogen Inc., JPND Control-PD (ANR-21-JPW2-0005-05), Fondation Thérèse and René Planiol, Fondation Saint Michel, Energipole (M. Mallart), M. Villain, and the Société Française de Médecine Esthétique (M. Legrand).

Competing interests

J.-C.C. has served in advisory boards for Alzprotect, Bayer, Biogen, Denali, Ferrer, Idorsia, iRegene, Prevail Therapeutic, Roche, Servier, Theranexus, UCB and received grants from Sanofi and the Michael J. Fox Foundation outside of this work. The other authors have no conflict of interests to declare.

Data availability

The data, code, protocols, and key lab materials used and generated in this study are listed in a Key Resource Table alongside their persistent identifiers at https://doi.org/10.5281/zenodo.14904521. Preprocessed rat brain images with segmentations and registrations are available on a separate Zenodo repository (https://doi.org/10.5281/zenodo.14581127). Processed data used for figures in this manuscript are available for download in Supplementary material.

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Associated Data

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

Supplementary Materials

fcaf204_Supplementary_Data

fcaf204_supplementary_data.zip^{(246KB, zip)}

Data Availability Statement

[fcaf204-B1] 1. Braak H, Tredici KD, Rüb U, et al. Staging of brain pathology related to sporadic Parkinson’s disease. Neurobiol Aging. 2003;24(2):197–211. [DOI] [PubMed] [Google Scholar]

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[fcaf204-B4] 4. Zecca L, Zucca FA, Albertini A, Rizzio E, Fariello RG. A proposed dual role of neuromelanin in the pathogenesis of Parkinson’s disease. Neurology. 2006;67(7 Suppl 2):S8–S11. [DOI] [PubMed] [Google Scholar]

[fcaf204-B5] 5. Beach TG, Sue LI, Walker DG, et al. Marked microglial reaction in normal aging human substantia nigra: Correlation with extraneuronal neuromelanin pigment deposits. Acta Neuropathol. 2007;114(4):419–424. [DOI] [PubMed] [Google Scholar]

[fcaf204-B6] 6. Zhang W, Phillips K, Wielgus AR, et al. Neuromelanin activates microglia and induces degeneration of dopaminergic neurons: Implications for progression of Parkinson’s disease. Neurotox Res. 2011;19(1):63–72. [DOI] [PMC free article] [PubMed] [Google Scholar]

[fcaf204-B7] 7. Halliday GM, Ophof A, Broe M, et al. Alpha-synuclein redistributes to neuromelanin lipid in the substantia nigra early in Parkinson’s disease. Brain J Neurol. 2005;128(Pt 11):2654–2664. [DOI] [PubMed] [Google Scholar]

[fcaf204-B8] 8. Fasano M, Giraudo S, Coha S, Bergamasco B, Lopiano L. Residual substantia nigra neuromelanin in Parkinson’s disease is cross-linked to alpha-synuclein. Neurochem Int. 2003;42(7):603–606. [DOI] [PubMed] [Google Scholar]

[fcaf204-B9] 9. Carballo-Carbajal I, Laguna A, Romero-Giménez J, et al. Brain tyrosinase overexpression implicates age-dependent neuromelanin production in Parkinson’s disease pathogenesis. Nat Commun. 2019;10(1):973. [DOI] [PMC free article] [PubMed] [Google Scholar]

[fcaf204-B10] 10. Chocarro J, Rico AJ, Ariznabarreta G, et al. Neuromelanin accumulation drives endogenous synucleinopathy in non-human primates. Brain J Neurol. 2023;146(12):5000–5014. [DOI] [PMC free article] [PubMed] [Google Scholar]

[fcaf204-B11] 11. Gonzalez-Sepulveda M, Compte J, Cuadros T, et al. In vivo reduction of age-dependent neuromelanin accumulation mitigates features of Parkinson’s disease. Brain. 2023;146(3):1040–1052. [DOI] [PMC free article] [PubMed] [Google Scholar]

[fcaf204-B12] 12. Vila M. Neuromelanin, aging, and neuronal vulnerability in Parkinson’s disease. Mov Disord. 2019;34(10):1440–1451. [DOI] [PMC free article] [PubMed] [Google Scholar]

[fcaf204-B13] 13. Trujillo P, Summers PE, Ferrari E, et al. Contrast mechanisms associated with neuromelanin-MRI. Magn Reson Med. 2017;78(5):1790–1800. [DOI] [PubMed] [Google Scholar]

[fcaf204-B14] 14. Sulzer D, Cassidy C, Horga G, et al. Neuromelanin detection by magnetic resonance imaging (MRI) and its promise as a biomarker for Parkinson’s disease. NPJ Parkinsons Dis. 2018;4:11. [DOI] [PMC free article] [PubMed] [Google Scholar]

[fcaf204-B15] 15. Watanabe T, Tan Z, Wang X, Martinez-Hernandez A, Frahm J. Magnetic resonance imaging of noradrenergic neurons. Brain Struct Funct. 2019;224(4):1609–1625. [DOI] [PMC free article] [PubMed] [Google Scholar]

[fcaf204-B16] 16. Watanabe T, Wang X, Tan Z, Frahm J. Magnetic resonance imaging of brain cell water. Sci Rep. 2019;9(1):5084. [DOI] [PMC free article] [PubMed] [Google Scholar]

[fcaf204-B17] 17. Kitao S, Matsusue E, Fujii S, et al. Correlation between pathology and neuromelanin MR imaging in Parkinson’s disease and dementia with Lewy bodies. Neuroradiology. 2013;55(8):947–953. [DOI] [PubMed] [Google Scholar]

[fcaf204-B18] 18. Keren NI, Taheri S, Vazey EM, et al. Histologic validation of locus coeruleus MRI contrast in post-mortem tissue. NeuroImage. 2015;113:235–245. [DOI] [PMC free article] [PubMed] [Google Scholar]

[fcaf204-B19] 19. Cassidy CM, Zucca FA, Girgis RR, et al. Neuromelanin-sensitive MRI as a noninvasive proxy measure of dopamine function in the human brain. Proc Natl Acad Sci U S A. 2019;116(11):5108–5117. [DOI] [PMC free article] [PubMed] [Google Scholar]

[fcaf204-B20] 20. Betts MJ, Kirilina E, Otaduy MCG, et al. Locus coeruleus imaging as a biomarker for noradrenergic dysfunction in neurodegenerative diseases. Brain J Neurol. 2019;142(9):2558–2571. [DOI] [PMC free article] [PubMed] [Google Scholar]

[fcaf204-B21] 21. Hwang KS, Langley J, Tripathi R, Hu XP, Huddleston DE. In vivo detection of substantia nigra and locus coeruleus volume loss in Parkinson’s disease using neuromelanin-sensitive MRI: Replication in two cohorts. PLoS One. 2023;18(4):e0282684. [DOI] [PMC free article] [PubMed] [Google Scholar]

[fcaf204-B22] 22. Halliday GM, Fedorow H, Rickert CH, et al. Evidence for specific phases in the development of human neuromelanin. J Neural Transm 2006;113(6):721–728. [DOI] [PubMed] [Google Scholar]

[fcaf204-B23] 23. Zecca L, Gallorini M, Schünemann V, et al. Iron, neuromelanin and ferritin content in the substantia nigra of normal subjects at different ages: Consequences for iron storage and neurodegenerative processes. J Neurochem. 2001;76(6):1766–1773. [DOI] [PubMed] [Google Scholar]

[fcaf204-B24] 24. Xing Y, Sapuan A, Dineen RA, Auer DP. Life span pigmentation changes of the substantia nigra detected by neuromelanin-sensitive MRI. Mov Disord. 2018;33(11):1792–1799. [DOI] [PMC free article] [PubMed] [Google Scholar]

[fcaf204-B25] 25. Fedorow H, Halliday GM, Rickert CH, et al. Evidence for specific phases in the development of human neuromelanin. Neurobiol Aging. 2006;27(3):506–512. [DOI] [PubMed] [Google Scholar]

[fcaf204-B26] 26. Nobileau A, Gaurav R, Chougar L, et al. Neuromelanin-sensitive magnetic resonance imaging changes in the locus coeruleus/subcoeruleus complex in patients with typical and atypical parkinsonism. Mov Disord. 2023;38(3):479–484. [DOI] [PubMed] [Google Scholar]

[fcaf204-B27] 27. Biondetti E, Gaurav R, Yahia-Cherif L, et al. Spatiotemporal changes in substantia nigra neuromelanin content in Parkinson’s disease. Brain J Neurol. 2020;143(9):2757–2770. [DOI] [PubMed] [Google Scholar]

[fcaf204-B28] 28. Gaurav R, Yahia-Cherif L, Pyatigorskaya N, et al. Longitudinal changes in neuromelanin MRI signal in Parkinson’s disease: A progression marker. Mov Disord. 2021;36(7):1592–1602. [DOI] [PMC free article] [PubMed] [Google Scholar]

[fcaf204-B29] 29. de la Fuente-Fernández R, Schulzer M, Kuramoto L, et al. Age-specific progression of nigrostriatal dysfunction in Parkinson’s disease. Ann Neurol. 2011;69(5):803–810. [DOI] [PubMed] [Google Scholar]

[fcaf204-B30] 30. Biondetti E, Santin MD, Valabrègue R, et al. The spatiotemporal changes in dopamine, neuromelanin and iron characterizing Parkinson’s disease. Brain J Neurol. 2021;144(10):3114–3125. [DOI] [PMC free article] [PubMed] [Google Scholar]

[fcaf204-B31] 31. Xing Y, Sapuan AH, Martín-Bastida A, et al. Neuromelanin-MRI to quantify and track nigral depigmentation in Parkinson’s disease: A multicenter longitudinal study using template-based standardized analysis. Mov Disord. 2022;37(5):1028–1039. [DOI] [PMC free article] [PubMed] [Google Scholar]

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PERMALINK

Longitudinal neuromelanin changes in prodromal and early Parkinson’s disease in humans and rat model

Jean-Baptiste Pérot

Anthony Ruze

Rahul Gaurav

Sana Rebbah

Capucine Cadin

Arnaud Le Troter

Lucas Soustelle

Laura Mouton

Romain Valabrègue

Annabelle Parent

Graziella Mangone

François-Xavier Lejeune

Isabelle Arnulf

Jean-Christophe Corvol

Marie Vidailhet

Mathieu D Santin

Miquel Vila

Stéphane Lehéricy

Abstract

Graphical Abstract

Graphical Abstract.

Introduction

Materials and methods

Animal study

Animals

Figure 1.

Surgical procedures

Preclinical MRI protocol

Image processing

Tissue collection

Immunostaining

Cell counting and extracellular NM quantification

Human study

Participants

Table 1.

Clinical MRI protocol

Statistical analyses

Study approval

Results

AAV–hTyr rats

Age-dependent NM accumulation and neurodegeneration in AAV–hTyr rats

Figure 2.

Biphasic age-dependent NM-MRI signal changes in AAV–hTyr rats

Figure 3.

NM-MRI is associated with T1 reduction in AAV–hTyr rats

Figure 4.

Iron-sensitive magnetic susceptibility changes in AAV–hTyr rats

Figure 5.

Longitudinal NM-MRI in human subjects

Age-dependent NM-MRI signal changes in HVs and patients with iRBD

Figure 6.

Age-dependent NM-MRI signal changes in converted iRBD and Parkinson’s disease patients

Discussion

Biological origin of the NM-MRI contrast in the humanized rat model of Parkinson’s disease

Longitudinal progression of NM-MRI signal in healthy aged individuals

Longitudinal progression of NM-MRI signal in Parkinson’s disease

Longitudinal progression of NM-MRI signal in iRBD

NM accumulation in the SN in Parkinson’s disease

Physical origin of the NM-MRI contrast

Iron accumulation in the SN of AAV–hTyr rats

Study limitations

Conclusion

Supplementary Material

Acknowledgements

Contributor Information

Supplementary material

Funding

Competing interests

Data availability

References

Associated Data

Supplementary Materials

Data Availability Statement

ACTIONS

PERMALINK

RESOURCES

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