Skip to main content
SpringerLink
Log in

Spatiotemporal patterns of brain iron-oxygen metabolism in patients with Parkinson’s disease

  • Neuro
  • Published:
European Radiology Aims and scope Submit manuscript

  • 3 Altmetric

Abstract

Objectives

Iron deposition and mitochondrial dysfunction are closely associated with the genesis and progression of Parkinson’s disease (PD). This study aims to extract susceptibility and oxygen extraction fraction (OEF) values of deep grey matter (DGM) to explore spatiotemporal progression patterns of brain iron-oxygen metabolism in PD.

Methods

Ninety-five PD patients and forty healthy controls (HCs) were included. Quantitative susceptibility mapping (QSM) and OEF maps were computed from MRI multi-echo gradient echo data. Analysis of covariance (ANCOVA) was used to compare mean susceptibility and OEF values in DGM between early-stage PD (ESP), advanced-stage PD (ASP) patients and HCs. Then Granger causality analysis on the pseudo-time-series of MRI data was applied to assess the causal effect of early altered nuclei on iron content and oxygen extraction in other DGM nuclei.

Results

The susceptibility values in substantia nigra (SN), red nucleus, and globus pallidus (GP) significantly increased in PD patients compared with HCs, while the iron content in GP did not elevate obviously until the late stage. The mean OEF values for the caudate nucleus, putamen, and dentate nucleus were higher in ESP patients than in ASP patients or/and HCs. We also found that iron accumulation progressively expands from the midbrain to the striatum. These alterations were correlated with clinical features and improved AUC for early PD diagnosis to 0.824.

Conclusions

Abnormal cerebral iron deposition and tissue oxygen utilization in PD measured by QSM and OEF maps could reflect pathological alterations in neurodegenerative processes and provide valuable indicators for disease identification and management.

Clinical relevance statement

Noninvasive assessment of cerebral iron-oxygen metabolism may serve as clinical evidence of pathological changes in PD and improve the validity of diagnosis and disease monitoring.

Key Points

• Quantitative susceptibility mapping and oxygen extraction fraction maps indicated the cerebral pathology of abnormal iron accumulation and oxygen metabolism in Parkinson’s disease.

• Iron deposition is mainly in the midbrain, while altered oxygen metabolism is concentrated in the striatum and cerebellum.

• The susceptibility and oxygen extraction fraction values in subcortical nuclei were associated with clinical severity.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Similar content being viewed by others

Abbreviations

ASP:

Advanced-stage Parkinson’s disease

CaSCNs:

Causal structural covariance network

CAU:

Caudate nucleus

CMRO2:

Cerebral oxygen consumption

DGM:

Deep grey matter

DN:

Dentate nucleus

ESP:

Early-stage Parkinson’s disease

FDG-PET:

Fluorodeoxyglucose positron emission tomography

GCA:

Granger causality analysis

GP:

Globus pallidus

HCs:

Healthy controls

H-Y stage:

Hoehn & Yahr stage

LEDD:

Levodopa equivalent daily dose

mGRE:

Multi-echo gradient echo

MMSE:

Mini-Mental State Examination

OEF:

Oxygen extraction fraction

PD:

Parkinson’s disease

PUT:

Putamen

QSM:

Quantitative susceptibility mapping

RN:

Red nucleus

ROI:

Region of interest

SN:

Substantia nigra

UPDRS-III:

The third part of the Unified Parkinson’s Disease Rating Scale

References

  1. Maiti P, Manna J, Dunbar GL (2017) Current understanding of the molecular mechanisms in Parkinson’s disease: targets for potential treatments. Transl Neurodegener 6:28

    Article  PubMed  PubMed Central  Google Scholar 

  2. Dauer W, Przedborski S (2003) Parkinson’s disease: mechanisms and models. Neuron 39:889–909

    Article  CAS  PubMed  Google Scholar 

  3. Albrecht F, Ballarini T, Neumann J, Schroeter ML (2019) FDG-PET hypometabolism is more sensitive than MRI atrophy in Parkinson’s disease: a whole-brain multimodal imaging meta-analysis. Neuroimage Clin 21:101594

    Article  PubMed  Google Scholar 

  4. Meles SK, Renken RJ, Pagani M et al (2020) Abnormal pattern of brain glucose metabolism in Parkinson’s disease: replication in three European cohorts. Eur J Nucl Med Mol Imag 47:437–450

    Article  CAS  Google Scholar 

  5. Wang JY, Zhuang QQ, Zhu LB et al (2016) Meta-analysis of brain iron levels of Parkinson’s disease patients determined by postmortem and MRI measurements. Sci Rep 6:36669

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Guan X, Xu X, Zhang M (2017) Region-specific iron measured by MRI as a biomarker for Parkinson’s disease. Neurosci Bull 33:561–567

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Haacke EM, Cheng NY, House MJ et al (2005) Imaging iron stores in the brain using magnetic resonance imaging. Magn Reson Imaging 23:1–25

    Article  CAS  PubMed  Google Scholar 

  8. Sofic E, Paulus W, Jellinger K, Riederer P, Youdim MB (1991) Selective increase of iron in substantia nigra zona compacta of parkinsonian brains. J Neurochem 56:978–982

    Article  CAS  PubMed  Google Scholar 

  9. Götz ME, Double K, Gerlach M, Youdim MB, Riederer P (2004) The relevance of iron in the pathogenesis of Parkinson’s disease. Ann N Y Acad Sci 1012:193–208

    Article  PubMed  Google Scholar 

  10. Ward RJ, Zucca FA, Duyn JH, Crichton RR, Zecca L (2014) The role of iron in brain ageing and neurodegenerative disorders. Lancet Neurol 13:1045–1060

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Langkammer C, Schweser F, Krebs N et al (2012) Quantitative susceptibility mapping (QSM) as a means to measure brain iron? A post mortem validation study. Neuroimage 62:1593–1599

    Article  PubMed  Google Scholar 

  12. Wu B, Li W, Guidon A, Liu C (2012) Whole brain susceptibility mapping using compressed sensing. Magn Reson Med 67:137–147

    Article  PubMed  Google Scholar 

  13. Acosta-Cabronero J, Cardenas-Blanco A, Betts MJ et al (2017) The whole-brain pattern of magnetic susceptibility perturbations in Parkinson’s disease. Brain 140:118–131

    Article  PubMed  Google Scholar 

  14. He N, Ling H, Ding B et al (2015) Region-specific disturbed iron distribution in early idiopathic Parkinson’s disease measured by quantitative susceptibility mapping. Hum Brain Mapp 36:4407–4420

    Article  PubMed  PubMed Central  Google Scholar 

  15. Guan X, Xuan M, Gu Q et al (2017) Regionally progressive accumulation of iron in Parkinson’s disease as measured by quantitative susceptibility mapping. NMR Biomed 30:4

  16. Wu P, Wang J, Peng S et al (2013) Metabolic brain network in the Chinese patients with Parkinson’s disease based on 18F-FDG PET imaging. Parkinsonism Relat Disord 19:622–627

    Article  PubMed  Google Scholar 

  17. Borghammer P, Cumming P, Østergaard K et al (2012) Cerebral oxygen metabolism in patients with early Parkinson’s disease. J Neurol Sci 313:123–128

    Article  CAS  PubMed  Google Scholar 

  18. Cho J, Zhang S, Kee Y et al (2020) Cluster analysis of time evolution (CAT) for quantitative susceptibility mapping (QSM) and quantitative blood oxygen level-dependent magnitude (qBOLD)-based oxygen extraction fraction (OEF) and cerebral metabolic rate of oxygen (CMRO2) mapping. Magn Reson Med 83:844–857

    Article  CAS  PubMed  Google Scholar 

  19. Cho J, Lee J, An H, Goyal MS, Su Y, Wang Y (2021) Cerebral oxygen extraction fraction (OEF): comparison of challenge-free gradient echo QSM+qBOLD (QQ) with (15)O PET in healthy adults. J Cereb Blood Flow Metab 41:1658–1668

    Article  CAS  PubMed  Google Scholar 

  20. Postuma RB, Berg D, Stern M et al (2015) MDS clinical diagnostic criteria for Parkinson’s disease. Mov Disord 30:1591–1601

    Article  PubMed  Google Scholar 

  21. Tomlinson CL, Stowe R, Patel S, Rick C, Gray R, Clarke CE (2010) Systematic review of levodopa dose equivalency reporting in Parkinson’s disease. Mov Disord 25:2649–2653

    Article  PubMed  Google Scholar 

  22. Liu Z, Spincemaille P, Yao Y, Zhang Y, Wang Y (2018) MEDI+0: Morphology enabled dipole inversion with automatic uniform cerebrospinal fluid zero reference for quantitative susceptibility mapping. Magn Reson Med 79:2795–2803

    Article  PubMed  Google Scholar 

  23. Liu T, Wisnieff C, Lou M, Chen W, Spincemaille P, Wang Y (2013) Nonlinear formulation of the magnetic field to source relationship for robust quantitative susceptibility mapping. Magn Reson Med 69:467–476

    Article  PubMed  Google Scholar 

  24. Liu T, Khalidov I, de Rochefort L et al (2011) A novel background field removal method for MRI using projection onto dipole fields (PDF). NMR Biomed 24:1129–1136

    Article  PubMed  PubMed Central  Google Scholar 

  25. Cho J, Kee Y, Spincemaille P et al (2018) Cerebral metabolic rate of oxygen (CMRO2) mapping by combining quantitative susceptibility mapping (QSM) and quantitative blood oxygenation level-dependent imaging (qBOLD). Magn Reson Med 80:1595–1604

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Cho J, Spincemaille P, Nguyen TD, Gupta A, Wang Y (2021) Temporal clustering, tissue composition, and total variation for mapping oxygen extraction fraction using QSM and quantitative BOLD. Magn Reson Med 86:2635–2646

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Zhang Y, Wei H, Cronin MJ, He N, Yan F, Liu C (2018) Longitudinal atlas for normative human brain development and aging over the lifespan using quantitative susceptibility mapping. Neuroimage 171:176–189

    Article  PubMed  Google Scholar 

  28. Zhang Z, Liao W, Xu Q et al (2017) Hippocampus-associated causal network of structural covariance measuring structural damage progression in temporal lobe epilepsy. Hum Brain Mapp 38:753–766

    Article  PubMed  Google Scholar 

  29. Berding G, Odin P, Brooks DJ et al (2001) Resting regional cerebral glucose metabolism in advanced Parkinson’s disease studied in the off and on conditions with [(18)F]FDG-PET. Mov Disord 16:1014–1022

    Article  CAS  PubMed  Google Scholar 

  30. Kordower JH, Olanow CW, Dodiya HB et al (2013) Disease duration and the integrity of the nigrostriatal system in Parkinson’s disease. Brain 136:2419–2431

    Article  PubMed  PubMed Central  Google Scholar 

  31. Herman S, Djaldetti R, Mollenhauer B, Offen D (2022) CSF-derived extracellular vesicles from patients with Parkinson’s disease induce symptoms and pathology. Brain 146:209–224

  32. Zhang Y, Larcher KM, Misic B, Dagher A (2017) Anatomical and functional organization of the human substantia nigra and its connections. eLife 6:e26653

  33. Lin TP, Carbon M, Tang C et al (2008) Metabolic correlates of subthalamic nucleus activity in Parkinson’s disease. Brain 131(Pt 5):1373–1380

  34. Tang BL (2020) Glucose, glycolysis, and neurodegenerative diseases. J Cell Physiol 235:7653–7662

    Article  CAS  PubMed  Google Scholar 

  35. Ko JH, Lerner RP, Eidelberg D (2015) Effects of levodopa on regional cerebral metabolism and blood flow. Mov Disord 30:54–63

    Article  CAS  PubMed  Google Scholar 

  36. Zang Z, Song T, Li J et al (2022) Modulation effect of substantia nigra iron deposition and functional connectivity on putamen glucose metabolism in Parkinson’s disease. Hum Brain Mapp 43:3735–3744

    Article  PubMed  PubMed Central  Google Scholar 

  37. Cheng HC, Ulane CM, Burke RE (2010) Clinical progression in Parkinson disease and the neurobiology of axons. Ann Neurol 67:715–725

    Article  PubMed  PubMed Central  Google Scholar 

  38. Lewis MM, Du G, Kidacki M et al (2013) Higher iron in the red nucleus marks Parkinson’s dyskinesia. Neurobiol Aging 34:1497–1503

    Article  CAS  PubMed  Google Scholar 

  39. He N, Huang P, Ling H et al (2017) Dentate nucleus iron deposition is a potential biomarker for tremor-dominant Parkinson’s disease. NMR Biomed 30:4

  40. Peralta M, Baxter JSH, Khan AR, Haegelen C, Jannin P (2020) Striatal shape alteration as a staging biomarker for Parkinson’s disease. Neuroimage Clin 27:102272

    Article  PubMed  PubMed Central  Google Scholar 

  41. Hou Y, Zhang L, Ou R et al (2022) Motor progression marker for newly diagnosed drug-naïve patients with Parkinson’s disease: a resting-state functional MRI study. Hum Brain Mapp 44:901–913

  42. Kosyakovsky J, Fine JM, Frey WH, 2nd, Hanson LR (2021) Mechanisms of intranasal deferoxamine in neurodegenerative and neurovascular disease. Pharmaceuticals (Basel) 14:95

  43. Novak P, Pimentel Maldonado DA, Novak V (2019) Safety and preliminary efficacy of intranasal insulin for cognitive impairment in Parkinson disease and multiple system atrophy: a double-blinded placebo-controlled pilot study. PLoS One 14:e0214364

    Article  CAS  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

The authors thank all the PD patients and healthy control subjects who participated in this study.

Funding

This study was supported by the Regional Innovation and Development Joint Fund of the National Natural Science Foundation of China (U22A20354).

Author information

Authors and Affiliations

  1. Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, No.1095, Jiefang Avenue, Wuhan, 430030, China

    Su Yan, Jun Lu, Yuanhao Li, Shun Zhang & Wenzhen Zhu

  2. Department of CT & MRI, The First Affiliated Hospital, College of Medicine, Shihezi University, 107 North Second Road, Shihezi, China

    Jun Lu

  3. Department of Biomedical Engineering, University at Buffalo, The State University of New York, Buffalo, NY, USA

    Junghun Cho

  4. Department of Radiology, Weill Cornell Medicine, New York, NY, USA

    Yi Wang

  5. Department of Biomedical Engineering, Cornell University, Ithaca, NY, USA

    Yi Wang

Authors
  1. Su Yan
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jun Lu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Yuanhao Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Junghun Cho
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Shun Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Wenzhen Zhu
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Yi Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Wenzhen Zhu.

Ethics declarations

Guarantor

The scientific guarantor of this publication is Professor Wenzhen Zhu.

Conflict of interest

The authors of this manuscript declare no relationships with any companies whose products or services may be related to the subject matter of the article.

Statistics and biometry

No complex statistical methods were necessary for this paper.

Informed consent

Written informed consent was obtained from all subjects (patients) in this study.

Ethical approval

Institutional Review Board approval was obtained.

Methodology

• retrospective

• cross-sectional study

• performed at one institution

Additional information

Publisher's note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Yan, S., Lu, J., Li, Y. et al. Spatiotemporal patterns of brain iron-oxygen metabolism in patients with Parkinson’s disease. Eur Radiol 34, 3074–3083 (2024). https://doi.org/10.1007/s00330-023-10283-1

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00330-023-10283-1

Keywords

Search

Navigation