Potential of modern neuroimaging methods in diagnostics of Parkinson’s disease: a review of the subject field

The article presents a literature review on imaging methods in patients with Parkinson’s disease (PD). The pathogenesis and patterns of neuroimaging in various research methods are considered. Various methods of brain research are reviewed. PD is one of the most common neurodegenerative diseases characterized by progressive loss of dopaminergic neurons. Early diagnosis of PD is critical for timely use of drug interventions, slowing disease progression and ensuring the quality of life of patients. This article reviews modern neuroimaging methods used to diagnose PD, including structural and functional approaches. Particular attention is paid to methods that allow visualization of pathological changes in the basal ganglia and dopaminergic pathways. The review analyzes the capabilities and limitations of such techniques as positron emission tomography, single photon emission computed tomography (CT), non-contrast CT scan of the brain, magnetic resonance imaging (MRI) with specific sequences (susceptibility-weighted imaging (SWI), T2* weighted imaging (T2*)), voxel-based morphometry, magnetization transfer imaging, arterial spin labeling (ASL) for perfusion assessment, resting-state functional MRI, transcranial sonography, in terms of their sensitivity, specificity and availability. Both traditional and promising technologies at the stage of clinical validation are presented. Generalization of data on the use of various visualization methods allows us to develop the most effective approaches to the diagnosis of PD and forms the basis for further research in this area.

1. de Lau L.M., Breteler M.M. Epidemiology of Parkinson’s disease. Lancet Neurol. 2006;5(6):525535. doi: 10.1016/S1474-4422(06)70471-9

2. Pringsheim T., Jette N., Frolkis A., Steeves T.D. The prevalence of Parkinson’s disease: a systematic review and meta-analysis. J. Mov. Disord. 2014;29(13):1583–1590. doi: 10.1002/mds.25945

3. Sy M.A.C., Fernandez H.H. Pharmacological treatment of early motor manifestations of Parkinson disease (PD). Neurotherapeutics. 2020;17(4):13311338. doi: 10.1007/s13311-020-00924-4

4. Luo Y., Qiao L., Li M., Wen X., Zhang W., Li X. Global, regional, national epidemiology and trends of Parkinson’s disease from 1990 to 2021: findings from the Global Burden of Disease Study 2021. Front. Aging Neurosci. 2025;10(16):1498756. doi: 10.3389/fnagi.2024.1498756

5. Su D., Cui Y., He C., Yin P., Bai R., Zhu J., Lam J.S.T., Zhang J., Yan R., Zheng X., Wu J., Zhao D., Wang A., Zhou M., Feng T. Projections for prevalence of Parkinson’s disease and its driving factors in 195 countries and territories to 2050: modelling study of Global Burden of Disease Study 2021. BMJ Glob. Health. 2025;388:e080952. doi:10.1136/bmj-2024080952

6. Illarioshkin S.N., Levin O.S. Guide to the diagnosis and treatment of Parkinson’s disease. Moscow: IPK Pareto-Print, 2017. 336 p. [In Russian].

7. Tolosa E., Garrido A., Scholz S.W., Poewe W. Challenges in the diagnosis of Parkinson’s disease. Lancet Neurol. 2021;20(5):385–397. doi: 10.1016/S1474-4422(21)00030-2

8. Sapronova M.R., Dmitrenko D.V., Shnaider N.A., Molgachev A.A. Diagnostics of Parkinson’s disease. Part 2. Possibilities of structural neuroimaging. Doctor.Ru. 2021;20(5):33–38. doi: 10.31550/1727-2378-2021-20-5-33-38

9. Berg D. Transcranial sonography in the early and differential diagnosis of Parkinson’s disease. J. Neural. Transm. Supplt. 2006;70:249–254. doi: 10.1007/978-3-211-45295-0_38

10. Lingor P., Liman J., Kallenberg K., Sahlmann C.-O., Bähr M. Diagnosis and differential diagnosis of Parkinson’s disease. In: Diagnosis and Treatment of Parkinson’s Disease. Ed. A.K. Rana. InTech, 2011;120. doi: 10.5772/18987

11. Crespo-Cuevas A.M., Lоpez-Cancio E., Cаceres C., Gonzаlez A., Ispierto L., Hernаndez-Perez M., Matarо M., Planas A., Canento T., Martin L., Arenillas J.F., Alvarez R., Vilas D. Third ventricle width assessed by transcranial sonography as predictor of long-term cognitive impairment. J. Alzheimers Dis. 2020;73(2):741–749. doi: 10.3233/JAD-190949

12. Zhang Y.Y., Jiang X.H., Zhu P.P., Zhuo W.Y., Liu L.B. Advancements in understanding substantia nigra hyperechogenicity via transcranial sonography in Parkinson’s disease and its clinical implications. Front. Neurol. 2024;18(15):1407860. doi: 10.3389/fneur.2024.1407860

13. Illarioshkin S.N., Chechetkin A.O., Fedotova E.Yu. Transcranial sonography in extrapyramidal diseases. Moscow: ATMO, 2014. 176 p. [In Russian].

14. Haller S., Badoud S., Nguyen D., Barnaure I., Montandon M.L., Lovblad K.O., Burkhard P.R. Differentiation between Parkinson disease and other forms of Parkinsonism using support vector machine analysis of susceptibility-weighted imaging (SWI): Initial results. Eur. Radiol. 2013;23(1):12–19. doi: 10.1007/s00330-012-2579-y

15. Moskalenko A.N., Filatov A.S., Fedotova E.Yu., Konovalov R.N., Illarioshkin S.N. Visual analysis of nigrosoma-1 in the differential diagnosis of Parkinson’s disease and essential tremor. Vestnik Rossiiskogo Gosudarstvennogo meditsinskogo universiteta = Bulletin of the Russian State Medical University. 2022;(1):1320. [In Russian]. doi: 10.24075/vrgmu.2022.002

16. Stacy M., Jankovic J. Differential diagnosis of Parkinson’s disease and the parkinsonism plus syndromes. Neurol. Clin. 1992;10(2):341–359.

17. Schwarz S.T., Mougin O., Xing Y., Blazejewska A., Bajaj N., Auer D.P., Gowland P. Parkinson’s disease related signal change in the nigrosomes 1–5 and the substantia nigra using T2* weighted 7T MRI. Neuroimage Clin. 2018;24(19):683–689. doi: 10.1016/j.nicl.2018.05.027

18. Jin L., Wang J., Wang C., Lian D., Zhou Y., Zhang Y., Lv M., Li Y., Huang Z., Cheng X., Fei G., Liu K., Zeng M., Zhong C. Combined visualization of nigrosome-1 and neuromelanin in the substantia nigra using 3T MRI for the differential diagnosis of essential tremor and de novo Parkinson’s disease. Front. Neurol. ;10:100. doi: 10.3389/fneur.2019.00100

19. Brammerloh M., Kirilina E., Alkemade A., Bazin P.L., Jantzen C., Jäger C., Herrler A., Pine K.J., Gowland P.A., Morawski M., Forstmann B.U., Weiskopf N. Swallow tail sign: revisited. Radiology. 2022;305(3):674–677. doi: 10.1148/radiol.212696

20. Panova L.V., Panova A.Yu. Availability of modern medical technologies in Russia and European countries. Ekonomicheskaya sotsiologiya = Economic Sociology. 2020;20(5):385–397. [In Russian]. doi: 10.17323/1726-3247-2020-5-58-93

21. Schwarz S.T., Afzal M., Morgan P.S., Bajaj N., Gowland P.A., Auer D.P. The ‘swallow tail’ appearance of the healthy nigrosome – a new accurate test of Parkinson’s disease: a case-control and retrospective cross-sectional MRI study at 3T. PLoS ONE. 2014;9(4):e93814. doi: 10.1371/journal.pone.0093814

22. Grossman R.I., Gomori J.M., Ramer K.N., Lexa F.J., Schnall M.D. Magnetization transfer: theory and clinical applications in neuroradiology. RadioGraphics. 1994;14(2):279–290. doi: 10.1148/radiographics.14.2.8190954

23. Tambasco N., Belcastro V., Sarchielli P., Floridi P., Pierguidi L., Menichetti C., Castrioto A., Chiarini P., Parnetti L., Eusebi P., Calabresi P., Rossi A. A magnetization transfer study of mild and advanced Parkinson’s disease. Eur. J. Neurol. 2011;18(3):471477. doi: 10.1111/j.1468-1331.2010.03184.x

24. Eckert T., Sailer M., Kaufmann J., Schrader C., Peschel T., Bodammer N., Heinze H.J., Schoenfeld M.A. Differentiation of idiopathic Parkinson’s disease, multiple system atrophy, progressive supranuclear palsy, and healthy controls using magnetization transfer imaging. Neuroimage. 2004;21(1):229235. doi: 10.1016/j.neuroimage.2003.08.028

25. Goto M., Abe O., Hagiwara A., Fujita S., Kamagata K., Hori M., Aoki S., Osada T., Konishi S., Masutani Y., Sakamoto H., Sakano Y., Kyogoku S., Daida H. Advantages of using both voxel- and surface-based morphometry in cortical morphology analysis: a review of various applications. Magn. Reson. Med. Sci. 2022;21(1):41–57. doi: 10.2463/mrms.rev.2021-0096

26. Gama R.L., Tаvora D.F., Bomfim R.C., Silva C.E., Bruin V.M., Bruin P.F. Morphometry MRI in the differential diagnosis of parkinsonian syndromes. Arq. Neuropsiquiatr. 2010;68(3):333–338. doi: 10.1590/s0004-282x2010000300001

27. Wilson H., Niccolini F., Pellicano C., Politis M. Cortical thinning across Parkinson’s disease stages and clinical correlates. J. Neurol. Sci. 2019;15(398):31–38. doi: 10.1016/j.jns.2019.01.020

28. Yau Y., Zeighami Y., Baker T.E., Larcher K., Vainik U., Dadar M., Fonov V.S., Hagmann P., Griffa A., Misiс B., Collins D.L., Dagher A. Network connectivity determines cortical thinning in early Parkinson’s disease progression. Nat. Commun. 2018;9(1):12. doi: 10.1038/s41467-017-02416-0

29. Solana-Lavalle G., Rosas-Romero R. Classification of PPMI MRI scans with voxel-based morphometry and machine learning to assist in the diagnosis of Parkinson’s disease. Comput. Methods Programs Biomed. 2020;198:105793. doi: 10.1016/j.cmpb.2020.105793

30. Yu L., Ying F., Yi-Fang Z., Jian-Ping H., Xiao-Zhen L., Nai-Qing C., Qiang W., Yi-Jing Z., Yi L., Dai-Rong C., Ning W. Six visual rating scales as a biomarker for monitoring atrophied brain volume in Parkinson’s disease. Aging. Dis. 2020;11(5):10821090. doi: 10.14336/AD.2019.1103

31. Gao Y., Nie K., Huang B., Mei M., Guo M., Xie S., Huang Z., Wang L., Zhao J., Zhang Y., Wang L. Changes of brain structure in Parkinson’s disease patients with mild cognitive impairment analyzed via VBM technology. Neurosci. Lett. 2017;29(658):121132. doi: 10.1016/j.neulet.2017.08.028

32. Li Y., Yuan T., Gao L., Sun W., Du X., Sun Z., Fan K., Qiu R., Zhang Y. The value of quantitative susceptibility mapping and morphometry in the differential diagnosis of parkinsonism. AJNR Am. J. Neuroradiol. 2025;46(7):1429–1438. doi: 10.3174/ajnr.A8665

33. Pisarkova V., Lopukhova E., Yamileva A., Kovtunenko A., Voronkov G., Grakhova E., Kutluyarov R., Bilyalov A. Machine learning methods for medical diagnostics based on a multimodal approach: a brief review. ICFNDS ‘22: Proceedings of the 6th International Conference on Future. Tashkent, 2023;679–683. doi: 10.1145/3584202.3584305

34. Manchev L., Mancheva-Ganeva V., Manchev I., Traikova N. Clinical and computed tomography studies of Parkinson’s disease. Int. J. Sci. Res. (Raipur). 2013;4(6):1463–1467.

35. Gerasimou G., Costa D.C., Papanastasiou E., Bostanjiopoulou S. SPECT study with I-123-Ioflupane (DaTSCAN) in patients with essential tremor: is there any correlation with Parkinson’s disease? Ann. Nucl. Med. 2012;26(4):337–344. doi: 10.1007/s12149-012-0577-4

36. Cummings J.L., Fine M.J., Grachev I.D., Jarecke C.R., Johnson M.K., Kuo P.H., Schaecher K.L., Oberdorf J.A., Rezak M., Riley D.E., Truong D. Effective and efficient diagnosis of parkinsonism: the role of dopamine transporter SPECT imaging with ioflupane I-123 injection (DaTscan™). Am. J. Manag. Care. 2014;20(5):97–109.

37. Gayed I., Joseph U., Fanous M., Wan D., Schiess M., Ondo W., Won K.S. The impact of DaTscan in the diagnosis of Parkinson disease. Clin. Nucl. Med. 2015;40(5):390–393. doi: 10.1097/RLU.0000000000000766

38. Soriano C.A., Garcia V.A.M., Cortes R.M., Rodado M.S., Poblete G.V.M., Ruiz S.S., Talavera R.M.P., Vaamonde C.J. 123-I ioflupane (Datscan) presynaptic nigrostriatal imaging in patients with movement disorders. Braz. Arch. Biol. Technol. 2005;48(2):115–125. doi: 10.1590/S151689132005000700017

39. Tripathi M., Dhawan V., Peng S., Kushwaha S., Batla A., Jaimini A., D’Souza M.M., Sharma R., Saw S., Mondal A. Differential diagnosis of parkinsonian syndromes using F-18 fluorodeoxyglucose positron emission tomography. Neuroradiology. 2013;55(4):483–492. doi: 10.1007/s00234-012-1132-7

40. Zhao P., Zhang B., Gao S. 18F-FDG PET study on the idiopathic Parkinson’s disease from several parkinsonian-plus syndromes. Parkinsonism Relat. Disord. 2012;18(1):60–62. doi: 10.1016/S1353-8020(11)70020-7

41. Ahn J.H., Kim M.H., Lee K., Oh K., Lim H., Kil H.S., Kwon S.J., Choi J.Y., Chi D.Y., Lee Y.J. Preclinical evaluation of [18F] FP-CIT, the radiotracer targeting dopamine transporter for diagnosing Parkinson’s disease: pharmacokinetic and efficacy analysis. Eur. J. Nucl. Med. Mol. Imaging. 2024;14(1):59. doi: 10.1186/s13550-024-01121-6

42. Fernandez-Seara M.A., Mengual E., Vidorreta M., Aznarez-Sanado M., Loayza F.R., Villagra F., Irigoyen J., Pastor M.A. Cortical hypoperfusion in Parkinson’s disease assessed using arterial spin labeled perfusion MRI. Neuroimage. 2012;59(3):2743–2750. doi: 10.1016/j.neuroimage.2011.10.033

43. Melzer T.R., Watts R., MacAskill M.R., Pearson J.F., Rüeger S., Pitcher T.L., Livingston L., Graham C., Keenan R., Shankaranarayanan A., Alsop D.C., Dalrymple-Alford J.C., Anderson T.J. Arterial spin labelling reveals an abnormal cerebral perfusion pattern in Parkinson’s disease. Brain. 2011;134(3):845–855. doi: 10.1093/brain/awq377

44. Heim B., Krismer F., de Marzi R., Seppi K. Magnetic resonance imaging for the diagnosis of Parkinson’s disease. J. Neural. Transm. 2017;124(8):915–964. doi: 10.1007/s00702-0171717-8

45. Holtbernd F., Eidelberg D. The utility of neuroimaging in the differential diagnosis of parkinsonian syndromes. Semin. Neurol. 2014;34(2):202209. doi: 10.1055/s-0034-1381733

46. Teune L.K., Renken R.J., de Jong B.M., Willemsen A.T., van Osch M.J., Roerdink J.B., Dierckx R.A., Leenders K.L. Parkinson’s disease-related perfusion and glucose metabolic brain patterns identified with PCASL-MRI and FDG-PET imaging. Neuroimage Clin. 2014;5:240–244. doi: 10.1016/J.Nicl.2014.06.007

47. Detre J.A., Rao H., Wang D.J., Chen Y.F., Wang Z. Applications of arterial spin labeled MRI in the brain. Magn. Reson. Imaging. 2012;35(5):10261037. doi: 10.1002/jmri.23581

48. Fernandez-Seara M.A., Mengual E., Vidorreta M., Aznarez-Sanado M., Loayza F.R., Villagra F., Irigoyen J., Pastor M.A. Cortical hypoperfusion in Parkinson’s disease assessed using arterial spin labeled perfusion MRI. Neuroimage. 2012;59(3):2743–2750. doi: 10.1016/j.neuroimage.2011.10.033

49. Madhyastha T.M., Askren M.K., Boord P., Zhang J., Leverenz J.B., Grabowski T.J. Cerebral perfusion and cortical thickness indicate cortical involvement in mild Parkinson’s disease. J. Mov. Disord. 2015;30(14):1893–1900. doi: 10.1002/mds.26128

50. Pelizzari L., Di Tella S., Rossetto F., Lagana M.M., Bergsland N., Pirastru A., Meloni M., Nemni R., Baglio F. Parietal perfusion alterations in Parkinson’s disease patients without dementia. Front. Neurol. 2020;11:562. doi: 10.3389/fneur.2020.00562

51. Logothetis, N.K., Wandell B.A. Interpreting the BOLD Signal. Annu. Rev. Physiol. 2004;66(1):735–769. doi: 10.1146/annurev.physiol.66.082602.092845

52. Friston K.J., Williams S., Howard R., Frackowiak R.S., Turner R. Movement-Related effects in fMRI time-series. Magn. Reson. Med. 1996;35(3):346–355. doi: 10.1002/mrm.1910350312

53. Gu L., Shu H., Xu H., Wang Y. Functional brain changes in Parkinson’s disease: a whole brain ALE study. J. Neurol. Sci. 2022;43(10):5909–5916. doi: 10.1007/s10072-022-06272-9

54. Nyatega Ch.O., Li Q., Adamu M.J., Kawuwa H.B. Gray matter, white matter and cerebrospinal fluid abnormalities in Parkinson’s disease: A voxel-based morphometry study. Front. Psychiatry. 2022;13:1027907. doi: 10.3389/fpsyt.2022.1027907

55.