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Abstract

Objective

To investigate hemodynamic and blood oxygen metabolism and their associations with disease progression, dopaminergic transporter (DAT) activity, and glucose uptake in patients with Parkinson’s disease (PD).

Methods

This cross-sectional study included 73 patients with PD (mean age: 61.10 years) and 67 healthy controls (mean age: 58.99 years). Oxygen metabolism parameters—deoxygenated hemoglobin (C), oxygen extraction fraction (OEF), deoxygenated cerebral blood volume (dCBV), and R2* were measured using qMRI. DAT availability and glucose metabolism were assessed using PET with [18F]FP-CIT and [18F]FDG, respectively. Regional analyses were conducted using standardized brain atlases.

Results

Compared with the controls patients with PD exhibited elevated C, OEF and R2* in the substantia nigra, whereas C and dCBV levels were reduced in the bilateral caudate nucleus and frontal cortex ( < 0.05). The Hoehn-Yahr (H-Y) 2.5–3 subgroup exhibited higher levels of C and OEF in the left putamen than the H-Y 1-2 subgroup ( < 0.05). In the H-Y 1-2 subgroup, C, OEF, and R2* correlated with UPDRS scores in the substantia nigra and red nucleus ( < 0.05). In advanced stages (H-Y stages 2.5-3), significant correlations were observed in the striatal structures/the left dorsolateral putamen/posterior right caudate ( < 0.05). OEF and R2* values were positively correlated with glucose metabolism in the left putamen and right caudate. ( < 0.05).

Conclusion

qMRI demonstrated alterations in hemodynamics and oxygen metabolism in patients with PD, particularly within the nigrostriatal system, suggesting that metabolic indicators could serve as supplementary biomarkers for diagnosing and monitoring the progression of PD.

© 2025 The Author(s). Published by Bentham Science Publishers.
This is an open access article published under CC BY 4.0 https://creativecommons.org/licenses/by/4.0/legalcode
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2025-12-02
2025-12-19

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References

  1. Vidovi&#263; M.; Rikalovic, M.G. Alpha-Synuclein aggregation pathway in Parkinson’s disease: Current status and novel therapeutic approaches. Cells 2022 11 11 1732 10.3390/cells11111732 35681426
    [Google Scholar]
  2. Tanner C.M. Ostrem J.L. Parkinson’s disease. N. Engl. J. Med. 2024 391 5 442 452 10.1056/NEJMra2401857 39083773
    [Google Scholar]
  3. Munhoz R.P. Tumas V. Pedroso J.L. Silveira-Moriyama L. The clinical diagnosis of Parkinson’s disease. Arq Neuropsiquiatr. 2024 82 6 001 010 10.1055/s‑0043‑1777775 38325391
    [Google Scholar]
  4. Brooker S.M. Gonzalez-Latapi P. Biomarkers in Parkinson’s disease. Neurol. Clin. 2025 43 2 229 248 10.1016/j.ncl.2024.12.005 40185520
    [Google Scholar]
  5. Morris H.R. Spillantini M.G. Sue C.M. Williams-Gray C.H. The pathogenesis of Parkinson’s disease. Lancet 2024 403 10423 293 304 10.1016/S0140‑6736(23)01478‑2 38245249
    [Google Scholar]
  6. Bellini G. D’Antongiovanni, V.; Palermo, G.; Antonioli, L.; Fornai, M.; Ceravolo, R.; Bernardini, N.; Derkinderen, P.; Pellegrini, C. α-Synuclein in Parkinson’s disease: From bench to bedside. Med. Res. Rev. 2025 45 3 909 946 10.1002/med.22091 39704040
    [Google Scholar]
  7. Vekrellis K. Emmanouilidou, E.; Xilouri, M.; Stefanis, L. α-Synuclein in Parkinson’s disease: 12 years later. Cold Spring Harb. Perspect. Med. 2024 14 11 a041645 10.1101/cshperspect.a041645 39349314
    [Google Scholar]
  8. Xiang J. Zhang Z. Wu S. Ye K. Positron emission tomography tracers for synucleinopathies. Mol. Neurodegener. 2025 20 1 1 10.1186/s13024‑024‑00787‑9 39757220
    [Google Scholar]
  9. Simuni T. Chahine L.M. Poston K. Brumm M. Buracchio T. Campbell M. Chowdhury S. Coffey C. Concha-Marambio L. Dam T. DiBiaso P. Foroud T. Frasier M. Gochanour C. Jennings D. Kieburtz K. Kopil C.M. Merchant K. Mollenhauer B. Montine T. Nudelman K. Pagano G. Seibyl J. Sherer T. Singleton A. Stephenson D. Stern M. Soto C. Tanner C.M. Tolosa E. Weintraub D. Xiao Y. Siderowf A. Dunn B. Marek K. A biological definition of neuronal α-synuclein disease: Towards an integrated staging system for research. Lancet Neurol. 2024 23 2 178 190 10.1016/S1474‑4422(23)00405‑2 38267190
    [Google Scholar]
  10. Negi S. Khurana N. Duggal N. The misfolding mystery: α-synuclein and the pathogenesis of Parkinson’s disease. Neurochem. Int. 2024 177 105760 10.1016/j.neuint.2024.105760 38723900
    [Google Scholar]
  11. Payne T. Burgess T. Bradley S. Roscoe S. Sassani M. Dunning M.J. Hernandez D. Scholz S. McNeill A. Taylor R. Su L. Wilkinson I. Jenkins T. Mortiboys H. Bandmann O. Multimodal assessment of mitochondrial function in Parkinson’s disease. Brain 2024 147 1 267 280 10.1093/brain/awad364 38059801
    [Google Scholar]
  12. Watanabe H. Shima S. Kawabata K. Mizutani Y. Ueda A. Ito M. Brain network and energy imbalance in Parkinson’s disease: linking ATP reduction and α-synuclein pathology. Front. Mol. Neurosci. 2025 17 1507033 10.3389/fnmol.2024.1507033 39911281
    [Google Scholar]
  13. Mehra S. Sahay, S.; Maji, S.K. α-Synuclein misfolding and aggregation: Implications in Parkinson’s disease pathogenesis. Biochim. Biophys. Acta. Proteins Proteomics 2019 1867 10 890 908 10.1016/j.bbapap.2019.03.001 30853581
    [Google Scholar]
  14. Anderson F.L. Biggs K.E. Rankin B.E. Havrda M.C. NLRP3 inflammasome in neurodegenerative disease. Transl. Res. 2023 252 21 33 10.1016/j.trsl.2022.08.006 35952982
    [Google Scholar]
  15. Badanjak K. Fixemer S. Smajić S.; Skupin, A.; Grünewald, A. The contribution of microglia to neuroinflammation in Parkinson’s disease. Int. J. Mol. Sci. 2021 22 9 4676 10.3390/ijms22094676 33925154
    [Google Scholar]
  16. Li H.Y. Liu D.S. Zhang Y.B. Rong H. Zhang X.J. The interaction between alpha-synuclein and mitochondrial dysfunction in Parkinson’s disease. Biophys. Chem. 2023 303 107122 10.1016/j.bpc.2023.107122 37839353
    [Google Scholar]
  17. Chakrabarti S. Bisaglia M. Oxidative stress and neuroinflammation in Parkinson’s disease: The role of dopamine oxidation products. Antioxidants 2023 12 4 955 10.3390/antiox12040955 37107329
    [Google Scholar]
  18. Guo M. Ji X. Liu J. Hypoxia and Alpha-Synuclein: Inextricable link underlying the pathologic progression of Parkinson’s disease. Front. Aging Neurosci. 2022 14 919343 10.3389/fnagi.2022.919343 35959288
    [Google Scholar]
  19. Watts M.E. Pocock R. Claudianos C. Brain energy and oxygen metabolism: Emerging role in normal function and disease. Front. Mol. Neurosci. 2018 11 216 10.3389/fnmol.2018.00216 29988368
    [Google Scholar]
  20. Zhao T. Wang B. Liang W. Cheng S. Wang B. Cui M. Shou J. Accuracy of 18F-FDG PET Imaging in differentiating Parkinson’s disease from atypical Parkinsonian syndromes: A systematic review and meta-analysis. Acad. Radiol. 2024 31 11 4575 4594 10.1016/j.acra.2024.08.016 39183130
    [Google Scholar]
  21. Gujral J. Gandhi O.H. Singh S.B. Ahmed M. Ayubcha C. Werner T.J. Revheim M.E. Alavi A. PET, SPECT, and MRI imaging for evaluation of Parkinson’s disease. Am. J. Nucl. Med. Mol. Imaging 2024 14 6 371 390 10.62347/AICM8774 39840378
    [Google Scholar]
  22. Prange S. Theis H. Banwinkler M. van Eimeren T. Molecular imaging in Parkinsonian disorders—what’s new and hot? Brain Sci. 2022 12 9 1146 10.3390/brainsci12091146 36138882
    [Google Scholar]
  23. Dai C. Tan C. Zhao L. Liang Y. Liu G. Liu H. Zhong Y. Liu Z. Mo L. Liu X. Chen L. Glucose metabolism impairment in Parkinson’s disease. Brain Res. Bull. 2023 199 110672 10.1016/j.brainresbull.2023.110672 37210012
    [Google Scholar]
  24. Walker Z. Gandolfo F. Orini S. Garibotto V. Agosta F. Arbizu J. Bouwman F. Drzezga A. Nestor P. Boccardi M. Altomare D. Festari C. Nobili F. Clinical utility of FDG PET in Parkinson’s disease and atypical parkinsonism associated with dementia. Eur. J. Nucl. Med. Mol. Imaging 2018 45 9 1534 1545 10.1007/s00259‑018‑4031‑2 29779045
    [Google Scholar]
  25. Lindner T. Bolar D.S. Achten E. Barkhof F. Bastos-Leite A.J. Detre J.A. Golay X. Günther M. Wang D.J.J. Haller S. Ingala S. Jäger H.R. Jahng G.H. Juttukonda M.R. Keil V.C. Kimura H. Ho M.L. Lequin M. Lou X. Petr J. Pinter N. Pizzini F.B. Smits M. Sokolska M. Zaharchuk G. Mutsaerts H.J.M.M. Current state and guidance on arterial spin labeling perfusion MRI in clinical neuroimaging. Magn. Reson. Med. 2023 89 5 2024 2047 10.1002/mrm.29572 36695294
    [Google Scholar]
  26. Zhao Y. Wen J. Cross A.H. Yablonskiy D.A. On the relationship between cellular and hemodynamic properties of the human brain cortex throughout adult lifespan. Neuroimage 2016 133 417 429 10.1016/j.neuroimage.2016.03.022 26997360
    [Google Scholar]
  27. Ulrich X. Yablonskiy D.A. Separation of cellular and BOLD contributions to T2* signal relaxation. Magn. Reson. Med. 2016 75 2 606 615 10.1002/mrm.25610 25754288
    [Google Scholar]
  28. Biondetti E. Cho J. Lee H. Cerebral oxygen metabolism from MRI susceptibility. Neuroimage 2023 276 120189 10.1016/j.neuroimage.2023.120189 37230206
    [Google Scholar]
  29. Mamah D. Wen J. Luo J. Ulrich X. Barch D.M. Yablonskiy D. Subcomponents of brain T2* relaxation in schizophrenia, bipolar disorder and siblings: A Gradient Echo Plural Contrast Imaging (GEPCI) study. Schizophr. Res. 2015 169 1-3 36 45 10.1016/j.schres.2015.10.004 26603058
    [Google Scholar]
  30. Xiang B. Wen J. Cross A.H. Yablonskiy D.A. Single scan quantitative gradient recalled echo MRI for evaluation of tissue damage in lesions and normal appearing gray and white matter in multiple sclerosis. J. Magn. Reson. Imaging 2019 49 2 487 498 10.1002/jmri.26218 30155934
    [Google Scholar]
  31. Qiu J. Wu K. Zhu M. Chen C.Y. Luo Y. Liu Y. Wen J. Using quantitative MRI to study the association of isocitrate dehydrogenase (IDH) status with oxygen metabolism and cellular structure changes in glioma. Eur. J. Radiol. 2022 155 110502 10.1016/j.ejrad.2022.110502 36049408
    [Google Scholar]
  32. Kothapalli S.V.V.N. Benzinger T.L. Aschenbrenner A.J. Perrin R.J. Hildebolt C.F. Goyal M.S. Fagan A.M. Raichle M.E. Morris J.C. Yablonskiy D.A. Quantitative gradient Echo MRI identifies dark matter as a new imaging biomarker of neurodegeneration that precedes tissue atrophy in early Alzheimer’s disease. J. Alzheimers Dis. 2022 85 2 905 924 10.3233/JAD‑210503 34897083
    [Google Scholar]
  33. Emre M. Aarsland D. Brown R. Burn D.J. Duyckaerts C. Mizuno Y. Broe G.A. Cummings J. Dickson D.W. Gauthier S. Goldman J. Goetz C. Korczyn A. Lees A. Levy R. Litvan I. McKeith I. Olanow W. Poewe W. Quinn N. Sampaio C. Tolosa E. Dubois B. Clinical diagnostic criteria for dementia associated with Parkinson’s disease. Mov. Disord. 2007 22 12 1689 1707 10.1002/mds.21507 17542011
    [Google Scholar]
  34. Martin W.R.W. Wieler M. Gee M. Midbrain iron content in early Parkinson disease. Neurology 2008 70 16_part_2 1411 1417 10.1212/01.wnl.0000286384.31050.b5 18172063
    [Google Scholar]
  35. Akamatsu G. Ikari Y. Ohnishi A. Nishida H. Aita K. Sasaki M. Yamamoto Y. Sasaki M. Senda M. Automated PET-only quantification of amyloid deposition with adaptive template and empirically pre-defined ROI. Phys. Med. Biol. 2016 61 15 5768 5780 10.1088/0031‑9155/61/15/5768 27405579
    [Google Scholar]
  36. Martín-Bastida A. Lao-Kaim N.P. Roussakis A.A. Searle G.E. Xing Y. Gunn R.N. Schwarz S.T. Barker R.A. Auer D.P. Piccini P. Relationship between neuromelanin and dopamine terminals within the Parkinson’s nigrostriatal system. Brain 2019 142 7 2023 2036 10.1093/brain/awz120 31056699
    [Google Scholar]
  37. Wang W. Presynaptic terminal integrity is associated with glucose metabolism in Parkinson’s disease. Eur. J. Nucl. Med. Mol. Imaging 2024 52 4 1510 1519 10.1007/s00259‑024‑06993‑3 39572432
    [Google Scholar]
  38. Chu J.S. Liu T.H. Wang K.L. Han C.L. Liu Y.P. Michitomo S. Zhang J.G. Fang T. Meng F.G. The metabolic activity of caudate and prefrontal cortex negatively correlates with the severity of idiopathic Parkinson’s disease. Aging Dis. 2019 10 4 847 853 10.14336/AD.2018.0814 31440389
    [Google Scholar]
  39. Delva A. Van Weehaeghe D. Koole M. Van Laere K. Vandenberghe W. Loss of presynaptic terminal integrity in the substantia nigra in early Parkinson’s disease. Mov. Disord. 2020 35 11 1977 1986 10.1002/mds.28216 32767618
    [Google Scholar]
  40. Claassen J.A.H.R. Thijssen D.H.J. Panerai R.B. Faraci F.M. Regulation of cerebral blood flow in humans: Physiology and clinical implications of autoregulation. Physiol. Rev. 2021 101 4 1487 1559 10.1152/physrev.00022.2020 33769101
    [Google Scholar]
  41. Vu E.L. Brown C.H. Brady K.M. Hogue C.W. Monitoring of cerebral blood flow autoregulation: Physiologic basis, measurement, and clinical implications. Br. J. Anaesth. 2024 132 6 1260 1273 10.1016/j.bja.2024.01.043 38471987
    [Google Scholar]
  42. Jiang D. Lu H. Cerebral oxygen extraction fraction MRI: Techniques and applications. Magn. Reson. Med. 2022 88 2 575 600 10.1002/mrm.29272 35510696
    [Google Scholar]
  43. Bright M.G. Croal P.L. Blockley N.P. Bulte D.P. Multiparametric measurement of cerebral physiology using calibrated fMRI. Neuroimage 2019 187 128 144 10.1016/j.neuroimage.2017.12.049 29277404
    [Google Scholar]
  44. Kudo K. Liu T. Murakami T. Goodwin J. Uwano I. Yamashita F. Higuchi S. Wang Y. Ogasawara K. Ogawa A. Sasaki M. Oxygen extraction fraction measurement using quantitative susceptibility mapping: Comparison with positron emission tomography. J. Cereb. Blood Flow Metab. 2016 36 8 1424 1433 10.1177/0271678X15606713 26661168
    [Google Scholar]
  45. Yang A. Zhuang H. Du L. Liu B. Lv K. Luan J. Hu P. Chen F. Wu K. Shu N. Shmuel A. Ma G. Wang Y. Evaluation of whole-brain oxygen metabolism in Alzheimer’s disease using QSM and quantitative BOLD. Neuroimage 2023 282 120381 10.1016/j.neuroimage.2023.120381 37734476
    [Google Scholar]
  46. Huber L. Uludağ K.; Möller, H.E. Non-BOLD contrast for laminar fMRI in humans: CBF, CBV, and CMRO2. Neuroimage 2019 197 742 760 10.1016/j.neuroimage.2017.07.041 28736310
    [Google Scholar]
  47. Engle J. Saberi P. Bain P. Ikram A. Selim M. Soman S. Oxygen extraction fraction (OEF) values and applications in neurological diseases. Neurol. Sci. 2024 45 7 3007 3020 10.1007/s10072‑024‑07362‑6 38367153
    [Google Scholar]
  48. Huang Z. Jiang C. Li L. Xu Q. Ge J. Li M. Guan Y. Wu J. Wang J. Zuo C. Yu H. Wu P. Correlations between dopaminergic dysfunction and abnormal metabolic network activity in REM sleep behavior disorder. J. Cereb. Blood Flow Metab. 2020 40 3 552 562 10.1177/0271678X19828916 30741074
    [Google Scholar]
  49. Coukos R. Krainc D. Key genes and convergent pathogenic mechanisms in Parkinson disease. Nat. Rev. Neurosci. 2024 25 6 393 413 10.1038/s41583‑024‑00812‑2 38600347
    [Google Scholar]
  50. Flores-Ponce X. Velasco I. Dopaminergic neuron metabolism: Relevance for understanding Parkinson’s disease. Metabolomics 2024 20 6 116 10.1007/s11306‑024‑02181‑4 39397188
    [Google Scholar]
  51. Matsui H. Takahashi R. Current trends in basic research on Parkinson’s disease: From mitochondria, lysosome to α-synuclein. J. Neural Transm. 2024 131 6 663 674 10.1007/s00702‑024‑02774‑2 38613675
    [Google Scholar]
  52. Guan X. Lancione M. Ayton S. Dusek P. Langkammer C. Zhang M. Neuroimaging of Parkinson’s disease by quantitative susceptibility mapping. Neuroimage 2024 289 120547 10.1016/j.neuroimage.2024.120547 38373677
    [Google Scholar]
  53. Mitchell T. Lehéricy S. Chiu S.Y. Strafella A.P. Stoessl A.J. Vaillancourt D.E. Emerging neuroimaging biomarkers across disease stage in Parkinson disease. JAMA Neurol. 2021 78 10 1262 1272 10.1001/jamaneurol.2021.1312 34459865
    [Google Scholar]
  54. Pyatigorskaya N. Sanz-Morère C.B. Gaurav R. Biondetti E. Valabregue R. Santin M. Yahia-Cherif L. Lehéricy S. Iron imaging as a diagnostic tool for Parkinson’s disease: A systematic review and meta-analysis. Front. Neurol. 2020 11 366 10.3389/fneur.2020.00366 32547468
    [Google Scholar]
  55. Yao Z. Jiao Q. Du X. Jia F. Chen X. Yan C. Jiang H. Ferroptosis in Parkinson’s disease —— The iron-related degenerative disease. Ageing Res. Rev. 2024 101 102477 10.1016/j.arr.2024.102477 39218077
    [Google Scholar]
  56. Lee H. Wehrli F.W. Whole-brain 3D mapping of oxygen metabolism using constrained quantitative BOLD. Neuroimage 2022 250 118952 10.1016/j.neuroimage.2022.118952 35093519
    [Google Scholar]
  57. Wobst P. Wenzel R. Kohl M. Obrig H. Villringer A. Linear aspects of changes in deoxygenated hemoglobin concentration and cytochrome oxidase oxidation during brain activation. Neuroimage 2001 13 3 520 530 10.1006/nimg.2000.0706 11170817
    [Google Scholar]
  58. Toronov V. Walker S. Gupta R. Choi J.H. Gratton E. Hueber D. Webb A. The roles of changes in deoxyhemoglobin concentration and regional cerebral blood volume in the fMRI BOLD signal. Neuroimage 2003 19 4 1521 1531 10.1016/S1053‑8119(03)00152‑6 12948708
    [Google Scholar]
  59. Nikparast F. Ganji Z. Danesh Doust M. Faraji R. Zare H. Brain pathological changes during neurodegenerative diseases and their identification methods: How does QSM perform in detecting this process? Insights Imaging 2022 13 1 74 10.1186/s13244‑022‑01207‑6 35416533
    [Google Scholar]
  60. Rocha E.M. De Miranda B. Sanders L.H. Alpha-synuclein: Pathology, mitochondrial dysfunction and neuroinflammation in Parkinson's disease. Neurobiol. Dis0 2018 109 Pt B 249 257 10.1016/j.nbd.2017.04.004 28400134
    [Google Scholar]
  61. Moradi Vastegani S. Nasrolahi A. Ghaderi S. Belali R. Rashno M. Farzaneh M. Khoshnam S.E. Mitochondrial dysfunction and Parkinson’s disease: Pathogenesis and therapeutic strategies. Neurochem. Res. 2023 48 8 2285 2308 10.1007/s11064‑023‑03904‑0 36943668
    [Google Scholar]
  62. Ryan B.J. Bengoa-Vergniory N. Williamson M. Kirkiz E. Roberts R. Corda G. Sloan M. Saqlain S. Cherubini M. Poppinga J. Bogetofte H. Cioroch M. Hester S. Wade-Martins R. REST protects dopaminergic neurons from mitochondrial and α-Synuclein Oligomer pathology in an alpha synuclein overexpressing BAC-Transgenic mouse model. J. Neurosci. 2021 41 16 3731 3746 10.1523/JNEUROSCI.1478‑20.2021 33563726
    [Google Scholar]
  63. Sohrabi T. Mirzaei-Behbahani B. Zadali R. Pirhaghi M. Morozova-Roche L.A. Meratan A.A. Common mechanisms underlying α-Synuclein-induced mitochondrial dysfunction in Parkinson’s disease. J. Mol. Biol. 2023 435 12 167992 10.1016/j.jmb.2023.167992 36736886
    [Google Scholar]
  64. Burtscher J. Duderstadt Y. Gatterer H. Burtscher M. Vozdek R. Millet G.P. Hicks A.A. Ehrenreich H. Kopp M. Hypoxia sensing and responses in Parkinson’s disease. Int. J. Mol. Sci. 2024 25 3 1759 10.3390/ijms25031759 38339038
    [Google Scholar]
  65. Gao Y. Zhang J. Tang T. Liu Z. Hypoxia pathways in Parkinson’s disease: From pathogenesis to therapeutic targets. Int. J. Mol. Sci. 2024 25 19 10484 10.3390/ijms251910484 39408813
    [Google Scholar]
  66. Regoni M. Valtorta F. Sassone J. Dopaminergic neuronal death via necroptosis in Parkinson’s disease: A review of the literature. Eur. J. Neurosci. 2024 59 6 1079 1098 10.1111/ejn.16136 37667848
    [Google Scholar]
  67. Nakamura K. Shiroto Y. Tamura Y. Koyama K. Takeuchi K. Amanuma M. Nagasawa T. Ozawa S. An increase in the deoxygenated hemoglobin concentration induced by a working memory task during the refractory period in the hemodynamic response in the human cerebral cortex. Neurosci. Lett. 2020 714 134531 10.1016/j.neulet.2019.134531 31586697
    [Google Scholar]
  68. Meles S.K. Teune L.K. de Jong B.M. Dierckx R.A. Leenders K.L. Metabolic imaging in Parkinson disease. J. Nucl. Med. 2017 58 1 23 28 10.2967/jnumed.116.183152 27879372
    [Google Scholar]
  69. Wang X. Zhang J. Yuan Y. Li T. Zhang L. Ding J. Jiang S. Li J. Zhu L. Zhang K. Cerebral metabolic change in Parkinson’s disease patients with anxiety: A FDG-PET study. Neurosci. Lett. 2017 653 202 207 10.1016/j.neulet.2017.05.062 28579485
    [Google Scholar]
  70. Campanelli F. Natale G. Marino G. Ghiglieri V. Calabresi P. Striatal glutamatergic hyperactivity in Parkinson’s disease. Neurobiol. Dis. 2022 168 105697 10.1016/j.nbd.2022.105697 35314319
    [Google Scholar]
  71. Han Q.Q. Le W. NLRP3 inflammasome-mediated neuroinflammation and related mitochondrial impairment in Parkinson’s disease. Neurosci. Bull. 2023 39 5 832 844 10.1007/s12264‑023‑01023‑y 36757612
    [Google Scholar]
  72. Rong D. Hu C.P. Yang J. Guo Z. Liu W. Yu M. Consistent abnormal activity in the putamen by dopamine modulation in Parkinson’s disease: A resting-state neuroimaging meta-analysis. Brain Res. Bull. 2024 210 110933 10.1016/j.brainresbull.2024.110933 38508469
    [Google Scholar]
  73. Johansson M.E. Cameron I.G.M. Van der Kolk N.M. de Vries N.M. Klimars E. Toni I. Bloem B.R. Helmich R.C. Aerobic exercise alters brain function and structure in Parkinson’s disease: A randomized controlled trial. Ann. Neurol. 2022 91 2 203 216 10.1002/ana.26291 34951063
    [Google Scholar]
  74. Wang J. Zhang J.R. Zang Y.F. Wu T. Consistent decreased activity in the putamen in Parkinson’s disease: A meta-analysis and an independent validation of resting-state fMRI. Gigascience 2018 7 6 giy071 10.1093/gigascience/giy071 29917066
    [Google Scholar]
  75. Sotoudeh H. Sarrami A.H. Wang J. Saadatpour Z. Razaei A. Gaddamanugu S. Choudhary G. Shafaat O. Singhal A. Susceptibility-weighted imaging in neurodegenerative disorders: A review. J. Neuroimaging 2021 31 3 459 470 10.1111/jon.12841 33624404
    [Google Scholar]
  76. Lim S.J. Suh C.H. Shim W.H. Kim S.J. Diagnostic performance of T2* gradient echo, susceptibility-weighted imaging, and quantitative susceptibility mapping for patients with multiple system atrophy-parkinsonian type: A systematic review and meta-analysis. Eur. Radiol. 2022 32 1 308 318 10.1007/s00330‑021‑08174‑4 34272590
    [Google Scholar]
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Evaluation of Hemodynamic and Blood Oxygen Metabolism Alterations in Parkinson's Disease Using Quantitative MRI
Bentham Science Publishers ; https://doi.org/10.2174/011570159X385539250706094824
/content/journals/cn/10.2174/011570159X385539250706094824
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  • Article Type:     Research Article
Keywords: deoxygenated cerebral blood volume ; striatum ; quantitative MRI ; Parkinson's disease ; substantia nigra ; oxygen metaboliDsm

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