sibmedСибирский научный медицинский журналСибирский научный медицинский журнал2410-25122410-2520ИЦиГ СО РАН10.18699/SSMJ20260204sibmed-2821Research ArticleОБЗОРЫREVIEWSВозможности методов нейровизуализации в диагностике болезни Паркинсона: обзор предметного поляPotential of modern neuroimaging methods in diagnostics of Parkinson’s disease: a review of the subject fieldhttps://orcid.org/0009-0002-0729-5182ФадеевА. И.FadeevA. I.

Фадеев Александр Игоревич

192019, г. Санкт-Петербург, ул. Бехтерева, 3

Alexandr I. Fadeev

192019, Saint Petersburg, Bekhtereva st., 3

fadeev.al.ig@yandex.ruhttps://orcid.org/0000-0003-1534-4490ЛукинаЛ. В.LukinaL. V.

Лукина Лариса Викторовна, к.м.н.

192019, г. Санкт-Петербург, ул. Бехтерева, 3

Larisa V. Lukina, candidate of medical sciences

192019, Saint Petersburg, Bekhtereva st., 3

larisalu@yandex.ruhttps://orcid.org/0000-0002-7087-0437АнаньеваН. И.AnanyevaN. I.

Ананьева Наталия Исаевна, д.м.н.

191014, г. Санкт-Петербург, ул. Маяковского, 12 

Natalia I. Ananyeva, doctor of medical sciences

191014, Saint Petersburg, Mayakovskogo st., 12

ananieva_n@mail.ruhttps://orcid.org/0000-0002-7700-2704МихайловВ. А.MikhailovV. A.

Михайлов Владимир Алексеевич, д.м.н.

192019, г. Санкт-Петербург, ул. Бехтерева, 3

Vladimir A. Mikhailov, doctor of medical sciences

192019, Saint Petersburg, Bekhtereva st., 3

vladmikh@yandex.ruhttps://orcid.org/0009-0000-2793-6798РаднаеваС. М.RadnaevaS. M.

Раднаева Сэсэгма Мижитовна

192019, г. Санкт-Петербург, ул. Бехтерева, 3

Sesegma M. Radnaeva

192019, Saint Petersburg, Bekhtereva st., 3

sesegma.1996@mail.ruНациональный медицинский исследовательский центр психиатрии и неврологии им. В.М. Бехтерева Минздрава РоссииРоссияV.M. Bekhterev National Medical Research Center for Psychiatry and Neurology of Minzdrav of RussiaRussian FederationНациональный медицинский исследовательский центр им. В.А. Алмазова Минздрава РоссииРоссияAlmazov National Medical Research Centre of Minzdrav of RussiaRussian Federation2026050520264623244Copyright © Фадеев А.И., Лукина Л.В., Ананьева Н.И., Михайлов В.А., Раднаева С.М., 20262026Фадеев А.И., Лукина Л.В., Ананьева Н.И., Михайлов В.А., Раднаева С.М.Fadeev A.I., Lukina L.V., Ananyeva N.I., Mikhailov V.A., Radnaeva S.M.Данная работа распространяется под лицензией Creative Commons Attribution 4.0.This work is licensed under a Creative Commons Attribution 4.0 License.https://sibmed.elpub.ru/jour/article/view/2821

В статье представлен обзор литературы, посвященный методам визуализации у пациентов с болезнью Паркинсона (БП). Рассматриваются патогенез и паттерны нейровизуализации при различных методиках исследования. Обозреваются различные методы исследования головного мозга. БП – одно из наиболее распространенных нейродегенеративных заболеваний, характеризующихся прогрессирующей гибелью дофаминергических нейронов. Ранняя диагностика БП имеет решающее значение для своевременного применения лекарственных интервенций, замедления прогрессирования заболевания и обеспечения качества жизни пациентов. В данной статье рассматриваются современные методы нейровизуализации, применяемые для диагностики БП, включая структурные и функциональные подходы. Особое внимание уделено методам, позволяющим визуализировать патологические изменения в базальных ганглиях и дофаминергических путях. В обзоре проанализированы возможности и ограничения таких методик, как позитронно-эмиссионная компьютерная томография (КТ), однофотонная эмиссионная КТ, нативное КТ-исследование головного мозга (без контрастного усиления), МРТ (SWI, T2*), МРТ-морфометрия на основе вокселов (VBM), визуализация переноса намагниченности (MTI), метод артериальной спин-метки (ASL) для оценки перфузии, функциональная МРТ покоя, транскраниальная сонография, с точки зрения их чувствительности, специфичности и доступности. Представлены как традиционные, так и перспективные технологии, находящиеся на этапе клинической валидации. Обобщение данных по применению различных методов визуализации позволяет выработать наиболее эффективные подходы к диагностике БП и формирует основу для дальнейших исследований в этой области.

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.

болезнь Паркинсонаголовной мозгМРТМРТ-морфометрияпозитронно-эмиссионная компьютерная томографияоднофотонная эмиссионная компьютерная томографияУЗИтранскраниальная сонографияParkinson’s diseasebrainmagnetic resonance imagingvoxel-based morphometrypositron emission tomographysingle photon emission computed tomographyultrasoundИсследование выполнено в рамках государственного задания ФГБУ НМИЦ психиатрии и неврологии им. В.М. Бехтерева Минздрава России на 2024–2026 гг. (XSOZ 2024 0014)The study was carried out within the framework of the state assignment of the Federal State Budgetary Institution of the Russian Federation National Medical Research Center of PN named after V.M. Bekhterev of the Ministry of Health of the Russian Federation for 2024-2026 (XSOZ 2024 0014).Referencesde Lau L.M., Breteler M.M. Epidemiology of Parkinson’s disease. Lancet Neurol. 2006;5(6):525535. doi: 10.1016/S1474-4422(06)70471-9de Lau L.M., Breteler M.M. Epidemiology of Parkinson’s disease. Lancet Neurol. 2006;5(6):525535. doi: 10.1016/S1474-4422(06)70471-9Pringsheim 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.25945Pringsheim 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.25945Sy M.A.C., Fernandez H.H. Pharmacological treatment of early motor manifestations of Parkinson disease (PD). Neurotherapeutics. 2020;17(4):13311338. doi: 10.1007/s13311-020-00924-4Sy M.A.C., Fernandez H.H. Pharmacological treatment of early motor manifestations of Parkinson disease (PD). Neurotherapeutics. 2020;17(4):13311338. doi: 10.1007/s13311-020-00924-4Luo 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.2024.1498756Luo 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.2024.1498756Su 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-2024080952Su 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-2024080952Иллариошкин С.Н., Левин О.С. Руководство по диагностике и лечению болезни Паркинсона. М.: ИПК Парето-Принт, 2017. 336 с.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].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)00030-2Tolosa 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)00030-2Sapronova 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: 10.31550/1727-2378-2021-20-5-33-38Sapronova 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: 10.31550/1727-2378-2021-20-5-33-38Berg 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_38Berg 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_38Lingor 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: 10.5772/18987Lingor 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: 10.5772/18987Crespo-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-190949Crespo-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-190949Zhang 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.2024.1407860Zhang 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.2024.1407860Иллариошкин С.Н., Чечеткин А.О., Федотова Е.Ю. Транскраниальная сонография при экстрапирамидных заболеваниях. М.: АТМО, 2014. 176 с.Illarioshkin S.N., Chechetkin A.O., Fedotova E.Yu. Transcranial sonography in extrapyramidal diseases. Moscow: ATMO, 2014. 176 p. [In Russian].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-yHaller 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-yМоскаленко А.Н., Филатов А.С., Федотова Е.Ю., Коновалов Р.Н., Иллариошкин С.Н. Визуальный анализ нигросомы-1 в дифференциальной диагностике болезни Паркинсона и эссенциального тремора. Вестн. РГМУ. 2022;(1):13–20. doi: 10.24075/vrgmu.2022.002Moskalenko 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.2022.002Stacy M., Jankovic J. Differential diagnosis of Parkinson’s disease and the parkinsonism plus syndromes. Neurol. Clin. 1992;10(2):341–359.Stacy M., Jankovic J. Differential diagnosis of Parkinson’s disease and the parkinsonism plus syndromes. Neurol. Clin. 1992;10(2):341–359.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.2018.05.027Schwarz 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.2018.05.027Jin 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. 2019;10:100. doi: 10.3389/fneur.2019.00100Jin 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.2019.00100Brammerloh 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.212696Brammerloh 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.212696Панова Л.В., Панова А.Ю. Доступность современных медицинских технологий в России и странах Европы. Экономическая социология. 2020;20(5):385–397. doi: 10.17323/1726-3247-2020-5-58-93Panova 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: 10.17323/1726-3247-2020-5-58-93Schwarz 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.0093814Schwarz 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.0093814Grossman 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.14.2.8190954Grossman 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.14.2.8190954Tambasco 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.xTambasco 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.xEckert 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.2003.08.028Eckert 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.2003.08.028Goto 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.2021-0096Goto 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.2021-0096Gama 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/s0004-282x2010000300001Gama 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/s0004-282x2010000300001Wilson 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.2019.01.020Wilson 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.2019.01.020Yau 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/s41467-017-02416-0Yau 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/s41467-017-02416-0Solana-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.2020.105793Solana-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.2020.105793Yu 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.2019.1103Yu 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.2019.1103Gao 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.2017.08.028Gao 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.2017.08.028Li 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.A8665Li 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.A8665Pisarkova 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: 10.1145/3584202.3584305Pisarkova 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: 10.1145/3584202.3584305Manchev 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.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.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/s12149-012-0577-4Gerasimou 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/s12149-012-0577-4Cummings 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.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.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.0000000000000766Gayed 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.0000000000000766Soriano 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/S151689132005000700017Soriano 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/S151689132005000700017Tripathi 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/s00234-012-1132-7Tripathi 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/s00234-012-1132-7Zhao 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)70020-7Zhao 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)70020-7Ahn 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/s13550-024-01121-6Ahn 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/s13550-024-01121-6Fernandez-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.2011.10.033Fernandez-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.2011.10.033Melzer 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/awq377Melzer 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/awq377Heim 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/s00702-0171717-8Heim 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/s00702-0171717-8Holtbernd F., Eidelberg D. The utility of neuroimaging in the differential diagnosis of parkinsonian syndromes. Semin. Neurol. 2014;34(2):202209. doi: 10.1055/s-0034-1381733Holtbernd F., Eidelberg D. The utility of neuroimaging in the differential diagnosis of parkinsonian syndromes. Semin. Neurol. 2014;34(2):202209. doi: 10.1055/s-0034-1381733Teune 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.2014.06.007Teune 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.2014.06.007Detre 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.23581Detre 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.23581Fernandez-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.2011.10.033Fernandez-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.2011.10.033Madhyastha 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.26128Madhyastha 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.26128Pelizzari 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.2020.00562Pelizzari 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.2020.00562Logothetis, N.K., Wandell B.A. Interpreting the BOLD Signal. Annu. Rev. Physiol. 2004;66(1):735–769. doi: 10.1146/annurev.physiol.66.082602.092845Logothetis, N.K., Wandell B.A. Interpreting the BOLD Signal. Annu. Rev. Physiol. 2004;66(1):735–769. doi: 10.1146/annurev.physiol.66.082602.092845Friston 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.1910350312Friston 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.1910350312Gu 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/s10072-022-06272-9Gu 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/s10072-022-06272-9Nyatega 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.2022.1027907Nyatega 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

The authors declare that there are no conflicts of interest present.