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Review
Research progress of functional MRI radiomics in Parkinson's disease
CUI Jiaqi  YU Qian'e  ZHANG Tijiang 

Cite this article as: CUI J Q, YU Q E, ZHANG T J. Research progress of functional MRI radiomics in Parkinson's disease[J]. Chin J Magn Reson Imaging, 2025, 16(6): 132-138. DOI:10.12015/issn.1674-8034.2025.06.020.


[Abstract] Parkinson's disease (PD) constitutes an age-related neurodegenerative disorder characterized by progressive deterioration, imposing substantial burdens on patients and their families while generating significant strain on healthcare resources. Early diagnosis and therapeutic intervention are crucial for mitigating disease progression; however, current diagnostic approaches demonstrate superior sensitivity for motor symptoms compared to non-motor manifestations, and the underlying neuropathophysiological mechanisms remain incompletely elucidated. Biomarker discovery represents a pivotal research priority. Whereas conventional imaging diagnostics rely on visual assessment of low-dimensional data, radiomics employs high-throughput computational methodologies to extract high-dimensional features from medical images, thereby enriching PD research with quantifiable markers. This technique has been extensively implemented across multiple domains, including presymptomatic detection, subtype classification, progression monitoring, and outcome prognostication. However, existing reviews have relatively few studies on radiomics based on functional magnetic resonance imaging (fMRI) in PD, lacking systematic and comprehensive sorting and in-depth analysis. This article aims to provide a new perspective for the diagnosis and treatment research of PD in the future, systematically sort out the research progress of fMRI radiomics in PD, analyze the current challenges and propose the future development directions, expecting to promote the further development of this field.
[Keywords] Parkinson's disease;neurodegenerative disease;magnetic resonance imaging;functional magnetic resonance imaging;radiomics

CUI Jiaqi   YU Qian'e   ZHANG Tijiang*  

Department of Radiology, Affiliated Hospital of Zunyi Medical University, Medical Imaging Center of Guizhou Province, Zunyi 563003, China

Corresponding author: ZHANG T J, E-mail: tijzhang@163.com

Conflicts of interest   None.

Received  2025-03-08
Accepted  2025-06-10
DOI: 10.12015/issn.1674-8034.2025.06.020
Cite this article as: CUI J Q, YU Q E, ZHANG T J. Research progress of functional MRI radiomics in Parkinson's disease[J]. Chin J Magn Reson Imaging, 2025, 16(6): 132-138. DOI:10.12015/issn.1674-8034.2025.06.020.

[1]
BEN-SHLOMO Y, DARWEESH S, LLIBRE-GUERRA J, et al. The epidemiology of Parkinson's disease[J]. Lancet, 2024, 403(10423): 283-292. DOI: 10.1016/s0140-6736(23)01419-8.
[2]
LI Y M, FAN G G. A multimodal MRI study of Parkinson's disease and Parkinson's syndrome[J]. Chin J Radiol, 2023, 57(8): 941-942. DOI: 10.3760/cma.j.cn112149-20230613-00404.
[3]
JU Y, WANG S. Progress of rs-fMRI combined with machine learning in the gut-brain axis[J]. Chin J Magn Reson Imag, 2023, 14(5): 171-174, 180. DOI: 10.12015/issn.1674-8034.2023.05.030.
[4]
SUN J, CONG C, LI X, et al. Identification of Parkinson's disease and multiple system atrophy using multimodal PET/MRI radiomics[J]. Eur Radiol, 2024, 34(1): 662-672. DOI: 10.1007/s00330-023-10003-9.
[5]
XIA H, LUAN X, BAO Z, et al. A multi-cohort study of the hippocampal radiomics model and its associated biological changes in Alzheimer's Disease[J/OL]. Transl Psychiatry, 2024, 14(1): 111 [2025-03-08]. https://doi.org/10.1038/s41398-024-02836-9. DOI: 10.1038/s41398-024-02836-9.
[6]
LIU P, WANG H, ZHENG S, et al. Parkinson's Disease Diagnosis Using Neostriatum Radiomic Features Based on T2-Weighted Magnetic Resonance Imaging[J/OL]. Front Neurol, 2020, 11: 248 [2025-03-08]. https://doi.org/10.3389/fneur.2020.00248. DOI: 10.3389/fneur.2020.00248.
[7]
TUPE-WAGHMARE P, RAJAN A, PRASAD S, et al. Radiomics on routine T1-weighted MRI can delineate Parkinson's disease from multiple system atrophy and progressive supranuclear palsy[J]. Eur Radiol, 2021, 31(11): 8218-8227. DOI: 10.1007/s00330-021-07979-7.
[8]
WANG F, WANG J, SHEN Y, et al. Iron Dyshomeostasis and Ferroptosis: A New Alzheimer's Disease Hypothesis?[J/OL]. Front Aging Neurosci, 2022, 14: 830569 [2025-03-08]. https://doi.org/10.3389/fnagi.2022.830569. DOI: 10.3389/fnagi.2022.830569.
[9]
AWASTHI A, MAPARU K, SINGH S. Ferroptosis role in complexity of cell death: unrevealing mechanisms in Parkinson's disease and therapeutic approaches[J]. Inflammopharmacology, 2025: 1-17. DOI: 10.1007/s10787-025-01672-7.
[10]
SU D, ZHANG Z, ZHANG Z, et al. Distinctive Pattern of Metal Deposition in Neurologic Wilson Disease: Insights From 7T Susceptibility-Weighted Imaging[J/OL]. Neurology, 2024, 102(12): e209478 [2025-03-08]. https://doi.org/10.1212/wnl.0000000000209478. DOI: 10.1212/wnl.0000000000209478.
[11]
YAN Y, WANG Z, WEI W, et al. Correlation of brain iron deposition and freezing of gait in Parkinson's disease: a cross-sectional study[J]. Quantitative imaging in medicine and surgery, 2023, 13(12): 7961-7972. DOI: 10.21037/qims-23-267.
[12]
MOHAMMADI S, GHADERI S. Parkinson's disease and Parkinsonism syndromes: Evaluating iron deposition in the putamen using magnetic susceptibility MRI techniques - A systematic review and literature analysis[J/OL]. Heliyon, 2024, 10(7): e27950 [2025-03-08]. https://doi.org/10.1016/j.heliyon.2024.e27950. DOI: 10.1016/j.heliyon.2024.e27950.
[13]
PAN J, KANG J, WANG X, et al. Research progress of anterior multi-system atrophy[J]. Chinese Journal of Geriatrics, 2024, 43(9): 1191-1195. DOI: 10.3760/cma.j.issn.0254-9026.2024.09.016.
[14]
HOSSEINZADEH M, GORJI A, FATHI JOUZDANI A, et al. Prediction of Cognitive decline in Parkinson's Disease using clinical and DAT SPECT Imaging features, and Hybrid Machine Learning systems[J/OL]. Diagnostics (Basel), 2023, 13(10): 1691 [2025-03-08]. https://doi.org/10.3390/diagnostics13101691. DOI: 10.3390/diagnostics13101691.
[15]
SALMANPOUR M R, REZAEIJO S M, HOSSEINZADEH M, et al. Deep versus Handcrafted Tensor Radiomics Features: Prediction of Survival in Head and Neck Cancer Using Machine Learning and Fusion Techniques[J/OL]. Diagnostics (Basel), 2023, 13(10): 1696 [2025-03-08]. https://doi.org/10.3390/diagnostics13101696. DOI: 10.3390/diagnostics13101696.
[16]
HU X, SUN X, HU F, et al. Multivariate radiomics models based on (18)F-FDG hybrid PET/MRI for distinguishing between Parkinson's disease and multiple system atrophy[J]. Eur J Nucl Med Mol Imaging, 2021, 48(11): 3469-3481. DOI: 10.1007/s00259-021-05325-z.
[17]
BU S, PANG H, LI X, et al. Multi-parametric radiomics of conventional T1 weighted and susceptibility-weighted imaging for differential diagnosis of idiopathic Parkinson's disease and multiple system atrophy[J/OL]. BMC Med Imaging, 2023, 23(1): 204 [2025-03-08]. https://doi.org/10.1186/s12880-023-01169-1. DOI: 10.1186/s12880-023-01169-1.
[18]
XU Y, HUANG X, GENG X, et al. Meta-analysis of iron metabolism markers levels of Parkinson's disease patients determined by fluid and MRI measurements[J/OL]. J Trace Elem Med Biol, 2023, 78: 127190 [2025-03-08]. https://doi.org/10.1016/j.jtemb.2023.127190. DOI: 10.1016/j.jtemb.2023.127190.
[19]
LIU C, LI W, TONG K A, et al. Susceptibility-weighted imaging and quantitative susceptibility mapping in the brain[J]. J Magn Reson Imaging, 2015, 42(1): 23-41. DOI: 10.1002/jmri.24768.
[20]
LANGKAMMER C, KREBS N, GOESSLER W, et al. Quantitative MR imaging of brain iron: a postmortem validation study[J]. Radiology, 2010, 257(2): 455-462. DOI: 10.1148/radiol.10100495.
[21]
CUI Y S, FENG T. Progress in structural imaging of nigrostriatal lesions in Parkinson's disease[J]. Natl Med J China, 2022, 102(21): 1625-1630. DOI: 10.3760/cma.j.cn112137-20211111-02516.
[22]
JIN J, SU D, ZHANG J, et al. Iron deposition in subcortical nuclei of Parkinson's disease: A meta-analysis of quantitative iron-sensitive magnetic resonance imaging studies[J]. Chin Med J (Engl), 2025, 138(6): 678-692. DOI: 10.1097/CM9.0000000000003167.
[23]
KANG J J, CHEN Y, XU G D, et al. Combining quantitative susceptibility mapping to radiomics in diagnosing Parkinson's disease and assessing cognitive impairment[J]. Eur Radiol, 2022, 32(10): 6992-7003. DOI: 10.1007/s00330-022-08790-8.
[24]
BETROUNI N, LOPES R, DEFEBVRE L, et al. Texture features of magnetic resonance images: A marker of slight cognitive deficits in Parkinson's disease[J]. Mov Disord, 2020, 35(3): 486-494. DOI: 10.1002/mds.27931.
[25]
CAO X, WANG X, XUE C, et al. A Radiomics Approach to Predicting Parkinson's Disease by Incorporating Whole-Brain Functional Activity and Gray Matter Structure[J/OL]. Front Neurosci, 2020, 14: 751 [2025-03-08]. https://doi.org/10.3389/fnins.2020.00751. DOI: 10.3389/fnins.2020.00751.
[26]
JELLINGER K A. Pathobiology of Cognitive Impairment in Parkinson Disease: Challenges and Outlooks[J/OL]. Int J Mol Sci, 2023, 25(1): 498 [2025-03-08]. https://doi.org/10.3390/ijms25010498. DOI: 10.3390/ijms25010498.
[27]
FILIP P, MANA J, LASICA A, et al. Structural and microstructural predictors of cognitive decline in deep brain stimulation of subthalamic nucleus in Parkinson's disease[J/OL]. Neuroimage Clin, 2024, 42: 103617 [2025-03-08]. https://doi.org/10.1016/j.nicl.2024.103617. DOI: 10.1016/j.nicl.2024.103617.
[28]
YU Q, LI Q, FANG W, et al. Disorganized resting-state functional connectivity between the dorsal attention network and intrinsic networks in Parkinson's disease with freezing of gait[J]. Eur J Neurosci, 2021, 54(7): 6633-6645. DOI: 10.1111/ejn.15439.
[29]
SHI D, YAO X, LI Y, et al. Classification of Parkinson's disease using a region-of-interest- and resting-state functional magnetic resonance imaging-based radiomics approach[J]. Brain Imaging Behav, 2022, 16(5): 2150-2163. DOI: 10.1007/s11682-022-00685-y.
[30]
SHI D, ZHANG H, WANG G, et al. Machine Learning for Detecting Parkinson's Disease by Resting-State Functional Magnetic Resonance Imaging: A Multicenter Radiomics Analysis[J/OL]. Front Aging Neurosci, 2022, 14: 806828 [2025-03-08]. https://doi.org/10.3389/fnagi.2022.806828. DOI: 10.3389/fnagi.2022.806828.
[31]
GORE S, DHOLE A, KUMBHAR S, et al. Radiomics for Parkinson's disease classification using advanced texture-based biomarkers[J/OL]. MethodsX, 2023, 11: 102359 [2025-03-08]. https://doi.org/10.1016/j.mex.2023.102359. DOI: 10.1016/j.mex.2023.102359.
[32]
PANG H, YU Z, YU H, et al. Multimodal striatal neuromarkers in distinguishing parkinsonian variant of multiple system atrophy from idiopathic Parkinson's disease[J]. CNS Neurosci Ther, 2022, 28(12): 2172-2182. DOI: 10.1111/cns.13959.
[33]
BAGGIO H C, ABOS A, SEGURA B, et al. Cerebellar resting-state functional connectivity in Parkinson's disease and multiple system atrophy: Characterization of abnormalities and potential for differential diagnosis at the single-patient level[J/OL]. Neuroimage Clin, 2019, 22: 101720 [2025-03-08]. https://doi.org/10.1016/j.nicl.2019.101720. DOI: 10.1016/j.nicl.2019.101720.
[34]
ZHANG X, CAO X, XUE C, et al. Aberrant functional connectivity and activity in Parkinson's disease and comorbidity with depression based on radiomic analysis[J/OL]. Brain Behav, 2021, 11(5): e02103 [2025-03-08]. https://doi.org/10.1002/brb3.2103. DOI: 10.1002/brb3.2103.
[35]
HU X, SONG X, LI E, et al. Altered Resting-State Brain Activity and Connectivity in Depressed Parkinson's Disease[J/OL]. PLoS One, 2015, 10(7): e0131133 [2025-03-08]. https://doi.org/10.1371/journal.pone.0131133. DOI: 10.1371/journal.pone.0131133.
[36]
GUO M, LIU H, GAO L, et al. A radiomics approach for predicting gait freezing in Parkinson's disease based on resting-state functional magnetic resonance imaging indices: a cross-sectional study[J/OL]. Neural Regen Res, 2024 [2025-03-08]. https://doi.org/10.4103/NRR.NRR-D-23-01392. DOI: 10.4103/NRR.NRR-D-23-01392.
[37]
FASANO A, HERMAN T, TESSITORE A, et al. Neuroimaging of Freezing of Gait[J]. J Parkinsons Dis, 2015, 5(2): 241-254. DOI: 10.3233/jpd-150536.
[38]
SONG W, RAZA H K, LU L, et al. Functional MRI in Parkinson's disease with freezing of gait: a systematic review of the literature[J]. Neurol Sci, 2021, 42(5): 1759-1771. DOI: 10.1007/s10072-021-05121-5.
[39]
CARANOVA M, SOARES J F, BATISTA S, et al. A systematic review of microstructural abnormalities in multiple sclerosis detected with NODDI and DTI models of diffusion-weighted magnetic resonance imaging[J]. Magn Reson Imaging, 2023, 104: 61-71. DOI: 10.1016/j.mri.2023.09.010.
[40]
SHIH Y C, TSENG W I, MONTASER-KOUHSARI L. Recent advances in using diffusion tensor imaging to study white matter alterations in Parkinson's disease: A mini review[J/OL]. Front Aging Neurosci, 2022, 14: 1018017 [2025-03-08]. https://doi.org/10.3389/fnagi.2022.1018017. DOI: 10.3389/fnagi.2022.1018017.
[41]
LI J, LIU X, WANG X, et al. Diffusion tensor imaging radiomics for diagnosis of Parkinson's disease[J/OL]. Brain Sciences, 2022, 12(7): 851 [2025-03-08]. https://doi.org/10.3390/brainsci12070851. DOI: 10.3390/brainsci12070851.
[42]
YASAKA K, KAMAGATA K, OGAWA T, et al. Parkinson's disease: deep learning with a parameter-weighted structural connectome matrix for diagnosis and neural circuit disorder investigation[J]. Neuroradiology, 2021, 63(9): 1451-62. DOI: 10.1007/s00234-021-02648-4.
[43]
WANG J, LIU X, WANG X, et al. Alterations of white matter tracts and topological properties of structural networks in hemifacial spasm[J/OL]. NMR Biomed, 2022, 35(9): e4756 [2025-03-08]. https://doi.org/10.1002/nbm.4756. DOI: 10.1002/nbm.4756.
[44]
PANAHI M, HOSSEINI M S. Multi-modality radiomics of conventional T1 weighted and diffusion tensor imaging for differentiating Parkinson's disease motor subtypes in early-stages[J/OL]. Sci Rep, 2024, 14(1): 20708 [2025-03-08]. https://doi.org/10.1038/s41598-024-71860-y. DOI: 10.1038/s41598-024-71860-y.
[45]
JIAN Y, PENG J, WANG W, et al. Prediction of cognitive decline in Parkinson's disease based on MRI radiomics and clinical features: A multicenter study[J/OL]. CNS Neurosci Ther, 2024, 30(6): e14789 [2025-03-08]. https://doi.org/10.1111/cns.14789. DOI: 10.1111/cns.14789.
[46]
BLEDSOE I O, STEBBINS G T, MERKITCH D, et al. White matter abnormalities in the corpus callosum with cognitive impairment in Parkinson disease[J/OL]. Neurology, 2018, 91(24): e2244-e2255 [2025-03-08]. https://doi.org/10.1212/wnl.0000000000006646. DOI: 10.1212/wnl.0000000000006646.
[47]
GORGES M, MÜLLER H P, LIEPELT-SCARFONE I, et al. Structural brain signature of cognitive decline in Parkinson's disease: DTI-based evidence from the LANDSCAPE study[J/OL]. Ther Adv Neurol Disord, 2019, 12: 1756286419843447 [2025-03-08]. https://doi.org/10.1177/1756286419843447. DOI: 10.1177/1756286419843447.
[48]
WANG K, WU G. Whole-volume diffusion kurtosis magnetic resonance (MR) imaging histogram analysis of non-small cell lung cancer: correlation with histopathology and degree of tumor differentiation[J/OL]. Clin Radiol, 2024, 79(8): e1072-e1080 [2025-03-08]. https://doi.org/10.1016/j.crad.2024.04.018. DOI: 10.1016/j.crad.2024.04.018.
[49]
WANG J J, LIN W Y, LU C S, et al. Parkinson disease: diagnostic utility of diffusion kurtosis imaging[J]. Radiology, 2011, 261(1): 210-217. DOI: 10.1148/radiol.11102277.
[50]
ZHANG N, ZHAO W, SHANG S, et al. Diffusion Kurtosis Imaging in Diagnosing Parkinson's Disease: A Preliminary Comparison Study Between Kurtosis Metric and Radiomic Features[J]. Acad Radiol, 2025, 32(2): 922-929. DOI: 10.1016/j.acra.2024.07.001.
[51]
WANG J, BI Q, GONG W, et al. Histogram analysis of diffusion kurtosis imaging of deep brain nuclei in Parkinson's disease with different motor subtypes[J/OL]. Clin Radiol, 2023, 78(12): e966-e974 [2025-03-08]. https://doi.org/10.1016/j.crad.2023.09.008. DOI: 10.1016/j.crad.2023.09.008.
[52]
ZHAO K, GAO A, GAO E, et al. Multiple diffusion metrics in differentiating solid glioma from brain inflammation[J/OL]. Front Neurosci, 2023, 17: 1320296 [2025-03-08]. https://doi.org/10.3389/fnins.2023.1320296. DOI: 10.3389/fnins.2023.1320296.
[53]
LIN K, CIDAN W, QI Y, et al. Glioma grading prediction using multiparametric magnetic resonance imaging-based radiomics combined with proton magnetic resonance spectroscopy and diffusion tensor imaging[J]. Med Phys, 2022, 49(7): 4419-4429. DOI: 10.1002/mp.15648.
[54]
ZHANG J, WU Y, WANG Y, et al. Diffusion-weighted imaging and arterial spin labeling radiomics features may improve differentiation between radiation-induced brain injury and glioma recurrence[J]. Eur Radiol, 2023, 33(5): 3332-3342. DOI: 10.1007/s00330-022-09365-3.
[55]
CHEN Z, BI S, SHAN Y, et al. Multiparametric hippocampal signatures for early diagnosis of Alzheimer's disease using (18)F-FDG PET/MRI Radiomics[J/OL]. CNS Neurosci Ther, 2024, 30(4): e14539 [2025-03-08]. https://doi.org/10.1111/cns.14539. DOI: 10.1111/cns.14539.
[56]
WANG Z, SHEN Y, ZHANG X, et al. Prognostic value of multi-PLD ASL radiomics in acute ischemic stroke[J/OL]. Front Neurol, 2024, 15: 1544578 [2025-03-08]. https://doi.org/10.3389/fneur.2024.1544578. DOI: 10.3389/fneur.2024.1544578.

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