Research progress on intelligent brain age prediction methods in diagnosis of Parkinson’s disease_Journal of Biomedical Engineering

Authors：

ZHENG Shuangchang ^1,2 , ZHANG Yulei ³ , ZHANG Qingyi ^1,2 , HAO Zezhou ^1,2 , YANG Yanling ^1,2 , ZHOU Liang ¹ ,  YAO Xufeng ¹

1. Jiading District Central Hospital Affiliated to College of Medical Imaging, Shanghai University of Medicine and Health Sciences, Shanghai 201318, P. R. China;
2. School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai 200093, P. R. China;
3. Department of Radiation Oncology, Shanghai Public Health Clinical Center, Shanghai 201508, P. R. China;

Corresponding?author：

Keywords：

Parkinson’s disease; Brain age prediction; Machine learning; Deep learning

DOI：

10.7507/1001-5515.202504010

Video：

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With the increasing aging worldwide, the age-related neurodegenerative diseases are becoming more and more prevalent. Brain age, as a critical biological marker for assessing normal brain aging and indicating disease progression, has been widely applied in the early diagnosis and evaluation of neurodegenerative diseases such as Parkinson’s disease (PD). This paper systematically elaborates on three types of methods for PD brain age prediction: statistical methods, traditional Machine learning (ML), and Deep learning (DL), from the perspectives of methodological overview and clinical application of PD brain age predication. For the first aspect, the PD brain age prediction workflow, statistical methods, ML methods, and DL methods are sequentially outlined; in the second aspect, the current clinical application status of the three types of PD brain age prediction methods is introduced. Finally, a summary and outlook are provided. This review not only provides important references for research on PD brain age prediction, but also offers novel approaches for evaluating human brain health, thus holding significant scientific and clinical value.

Citation： ZHENG Shuangchang, ZHANG Yulei, ZHANG Qingyi, HAO Zezhou, YANG Yanling, ZHOU Liang, YAO Xufeng. Research progress on intelligent brain age prediction methods in diagnosis of Parkinson’s disease. Journal of Biomedical Engineering, 2026, 43(2): 421-427. doi: 10.7507/1001-5515.202504010 Copy

1.	Meng Q, Yu W. The impact of population aging on economic growth. Adv Econ Manag Polit Sci, 2024, 140(1): 109-116.
2.	Chen X, Giles J, Yao Y, et al. The path to healthy ageing in China: a Peking University-Lancet commission. Lancet, 2022, 400(10367): 1967-2006.
3.	Chen C, Xiong X, Tang G. A decomposition study on the factors influencing China’s total fertility rate changes between 1990 and 2020. Sci Rep, 2024, 14: 28176.
4.	Green G S, Fujita M, Yang H S, et al. Cellular communities reveal trajectories of brain ageing and Alzheimer’s disease. Nature, 2024, 633(8030): 634-645.
5.	Herdy J R, Mertens J, Gage F H. Neuronal senescence may drive brain aging. Science, 2024, 384(6703): 1404-1406.
6.	Gaser C, Kalc P, Cole J H. A perspective on brain-age estimation and its clinical promise. Nat Comput Sci, 2024, 4(10): 744-751.
7.	張玉蕾, 姚旭峰, 吳韜. 影像學預測腦齡研究進展. 中國介入影像與治療學, 2024, 21(9): 561-564.
8.	Zhu J, Cui Y, Zhang J. Temporal trends in the prevalence of Parkinson’s disease from 1980 to 2023: a systematic review and meta-analysis. Lancet Healthy Longev, 2024, 5(7): e464-e479.
9.	Xiao B, Zhou Z, Chao Y, et al. Pathogenesis of Parkinson’s disease. Neurol Clin, 2025, 43(2): 185-207.
10.	Kulcsarova K, Skorvanek M, Postuma R, et al. Defining Parkinson’s disease: past and future. J Parkinsons Dis, 2024, 14(S2): S257-S271.
11.	Gibson L L, Weintraub D, Lemmen R, et al. Risk of dementia in Parkinson’s disease: a systematic review and meta-analysis. Mov Disord, 2024, 39(10): 1697-1709.
12.	Rajamani N, Friedrich H, Butenko K, et al. Deep brain stimulation of symptom-specific networks in Parkinson’s disease. Nat Commun, 2024, 15(1): 4662.
13.	Meng Y, Tang T, Wang J, et al. The correlation of orthostatic hypotension in Parkinson disease with the disease course and severity and its impact on quality of life. Medicine (Baltimore), 2024, 103(19): e38169.
14.	Gallagher J, Gochanour C, Caspell-Garcia C, et al. Long-term dementia risk in Parkinson disease. Neurology, 2024, 103(5): e209699.
15.	Zhang Z, Jiang R, Zhang C, et al. Robust brain age estimation based on sMRI via nonlinear age-adaptive ensemble learning. IEEE Trans Neural Syst Rehabil Eng, 2022, 30: 2146-2156.
16.	Sone D, Beheshti I, Tagai K, et al. Neuropsychiatric symptoms and neuroimaging-based brain age in mild cognitive impairment and early dementia: a multicenter study. Psychiatry Clin Neurosci, 2025, 79(4): 158-164.
17.	Kalc P, Dahnke R, Hoffstaedter F, et al. BrainAGE: revisited and reframed machine learning workflow. Hum Brain Mapp, 2024, 45(3): e26632.
18.	Schinz D, Schmitz-Koep B, Tahedl M, et al. Lower cortical thickness and increased brain aging in adults with cocaine use disorder. Front Psychiatry, 2023, 14: 1266770.
19.	Valizadeh S A, H?nggi J, Mérillat S, et al. Age prediction on the basis of brain anatomical measures. Hum Brain Mapp, 2017, 38(2): 997-1008.
20.	Fraza C, Bu?ková B R, Johansson M E, et al. Understanding individual neurodegenerative progression in Parkinson’s disease through normative modelling. Sci Rep, 2025, 15: 36659.
21.	Bethlehem R A I, Seidlitz J, White S R, et al. Brain charts for the human lifespan. Nature, 2022, 604: 525-533.
22.	Xifra-Porxas A, Ghosh A, Mitsis G D, et al. Estimating brain age from structural MRI and MEG data: insights from dimensionality reduction techniques. NeuroImage, 2021, 231: 117822.
23.	de Lange A G, Anatürk M, Suri S, et al. Multimodal brain-age prediction and cardiovascular risk: the Whitehall II MRI sub-study. NeuroImage, 2020, 222: 117292.
24.	Massett R J, Maher A S, Imms P E, et al. Regional neuroanatomic effects on brain age inferred using magnetic resonance imaging and ridge regression. J Gerontol A Biol Sci Med Sci, 2023, 78(6): 872-881.
25.	Liu X, Beheshti I, Zheng W, et al. Brain age estimation using multi-feature-based networks. Comput Biol Med, 2022, 143: 105285.
26.	Beheshti I, Ganaie M A, Paliwal V, et al. Predicting brain age using machine learning algorithms: a comprehensive evaluation. IEEE J Biomed Health Inform, 2022, 26(4): 1432-1440.
27.	Beheshti I, Mishra S, Sone D, et al. T1-weighted MRI-driven brain age estimation in Alzheimer’s disease and Parkinson’s disease. Aging Dis, 2020, 11(3): 618-628.
28.	Beheshti I, Booth S, Ko J H, et al. Differences in brain aging between sexes in Parkinson’s disease. NPJ Parkinsons Dis, 2024, 10(1): 35.
29.	Eickhoff C R, Hoffstaedter F, Caspers J, et al. Advanced brain ageing in Parkinson’s disease is related to disease duration and individual impairment. Brain Commun, 2021, 3(3): fcab191.
30.	Hofmann S M, Beyer F, Lapuschkin S, et al. Towards the interpretability of deep learning models for multi-modal neuroimaging: finding structural changes of the ageing brain. NeuroImage, 2022, 261: 119504.
31.	He S, Feng Y, Grant P E, et al. Deep relation learning for regression and its application to brain age estimation. IEEE Trans Med Imaging, 2022, 41(9): 2304-2317.
32.	Gupta U, Lam P K, Steeg G V, et al. Improved brain age estimation with slice-based set networks//2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI). Nice: IEEE, 2021: 840-844.
33.	Li X, Hao Z, Li D, et al. Brain age prediction via cross-stratified ensemble learning. NeuroImage, 2024, 299: 120825.
34.	Zhang X, Pan Y, Wu T, et al. Brain age prediction using interpretable multi-feature-based convolutional neural network in mild traumatic brain injury. NeuroImage, 2024, 297: 120751.
35.	Cheng J, Liu Z, Guan H, et al. Brain age estimation from MRI using cascade networks with ranking loss. IEEE Trans Med Imaging, 2021, 40(12): 3400-3412.
36.	Peng H, Gong W, Beckmann C F, et al. Accurate brain age prediction with lightweight deep neural networks. Med Image Anal, 2021, 68: 101871.
37.	Nguyen H D, Clément M, Mansencal B, et al. Brain structure ages-a new biomarker for multi-disease classification. Hum Brain Mapp, 2024, 45(1): e26558.
38.	Chen C L, Kuo M C, Wu W C, et al. Advanced brain aging in multiple system atrophy compared to Parkinson’s disease. Neuroimage Clin, 2022, 34: 102997.
39.	Chen C L, Cheng S Y, Montaser-Kouhsari L, et al. Advanced brain aging in Parkinson’s disease with cognitive impairment. NPJ Parkinsons Dis, 2024, 10: 62.
40.	Li C, Cui Y, Luo N, et al. Trans-ResNet: integrating transformers and CNNs for Alzheimer’s disease classification//2022 IEEE 19th International Symposium on Biomedical Imaging (ISBI). Kolkata: IEEE, 2022: 1-5.
41.	Chen M, Wang Y, Shi Y, et al. Brain age prediction based on quantitative susceptibility mapping using the segmentation transformer. IEEE J Biomed Health Inform, 2024, 28: 1012-1021.
42.	He S, Grant P E, Ou Y, et al. Global-local transformer for brain age estimation. IEEE Trans Med Imaging, 2022, 41: 213-224.
43.	Zhong F, He K, Ji M, et al. Optimizing vitiligo diagnosis with ResNet and swin transformer deep learning models: a study on performance and interpretability. Sci Rep, 2024, 14: 9127.

1. Meng Q, Yu W. The impact of population aging on economic growth. Adv Econ Manag Polit Sci, 2024, 140(1): 109-116.
2. Chen X, Giles J, Yao Y, et al. The path to healthy ageing in China: a Peking University-Lancet commission. Lancet, 2022, 400(10367): 1967-2006.
3. Chen C, Xiong X, Tang G. A decomposition study on the factors influencing China’s total fertility rate changes between 1990 and 2020. Sci Rep, 2024, 14: 28176.
4. Green G S, Fujita M, Yang H S, et al. Cellular communities reveal trajectories of brain ageing and Alzheimer’s disease. Nature, 2024, 633(8030): 634-645.
5. Herdy J R, Mertens J, Gage F H. Neuronal senescence may drive brain aging. Science, 2024, 384(6703): 1404-1406.
6. Gaser C, Kalc P, Cole J H. A perspective on brain-age estimation and its clinical promise. Nat Comput Sci, 2024, 4(10): 744-751.
7. 張玉蕾, 姚旭峰, 吳韜. 影像學預測腦齡研究進展. 中國介入影像與治療學, 2024, 21(9): 561-564.
8. Zhu J, Cui Y, Zhang J. Temporal trends in the prevalence of Parkinson’s disease from 1980 to 2023: a systematic review and meta-analysis. Lancet Healthy Longev, 2024, 5(7): e464-e479.
9. Xiao B, Zhou Z, Chao Y, et al. Pathogenesis of Parkinson’s disease. Neurol Clin, 2025, 43(2): 185-207.
10. Kulcsarova K, Skorvanek M, Postuma R, et al. Defining Parkinson’s disease: past and future. J Parkinsons Dis, 2024, 14(S2): S257-S271.
11. Gibson L L, Weintraub D, Lemmen R, et al. Risk of dementia in Parkinson’s disease: a systematic review and meta-analysis. Mov Disord, 2024, 39(10): 1697-1709.
12. Rajamani N, Friedrich H, Butenko K, et al. Deep brain stimulation of symptom-specific networks in Parkinson’s disease. Nat Commun, 2024, 15(1): 4662.
13. Meng Y, Tang T, Wang J, et al. The correlation of orthostatic hypotension in Parkinson disease with the disease course and severity and its impact on quality of life. Medicine (Baltimore), 2024, 103(19): e38169.
14. Gallagher J, Gochanour C, Caspell-Garcia C, et al. Long-term dementia risk in Parkinson disease. Neurology, 2024, 103(5): e209699.
15. Zhang Z, Jiang R, Zhang C, et al. Robust brain age estimation based on sMRI via nonlinear age-adaptive ensemble learning. IEEE Trans Neural Syst Rehabil Eng, 2022, 30: 2146-2156.
16. Sone D, Beheshti I, Tagai K, et al. Neuropsychiatric symptoms and neuroimaging-based brain age in mild cognitive impairment and early dementia: a multicenter study. Psychiatry Clin Neurosci, 2025, 79(4): 158-164.
17. Kalc P, Dahnke R, Hoffstaedter F, et al. BrainAGE: revisited and reframed machine learning workflow. Hum Brain Mapp, 2024, 45(3): e26632.
18. Schinz D, Schmitz-Koep B, Tahedl M, et al. Lower cortical thickness and increased brain aging in adults with cocaine use disorder. Front Psychiatry, 2023, 14: 1266770.
19. Valizadeh S A, H?nggi J, Mérillat S, et al. Age prediction on the basis of brain anatomical measures. Hum Brain Mapp, 2017, 38(2): 997-1008.
20. Fraza C, Bu?ková B R, Johansson M E, et al. Understanding individual neurodegenerative progression in Parkinson’s disease through normative modelling. Sci Rep, 2025, 15: 36659.
21. Bethlehem R A I, Seidlitz J, White S R, et al. Brain charts for the human lifespan. Nature, 2022, 604: 525-533.
22. Xifra-Porxas A, Ghosh A, Mitsis G D, et al. Estimating brain age from structural MRI and MEG data: insights from dimensionality reduction techniques. NeuroImage, 2021, 231: 117822.
23. de Lange A G, Anatürk M, Suri S, et al. Multimodal brain-age prediction and cardiovascular risk: the Whitehall II MRI sub-study. NeuroImage, 2020, 222: 117292.
24. Massett R J, Maher A S, Imms P E, et al. Regional neuroanatomic effects on brain age inferred using magnetic resonance imaging and ridge regression. J Gerontol A Biol Sci Med Sci, 2023, 78(6): 872-881.
25. Liu X, Beheshti I, Zheng W, et al. Brain age estimation using multi-feature-based networks. Comput Biol Med, 2022, 143: 105285.
26. Beheshti I, Ganaie M A, Paliwal V, et al. Predicting brain age using machine learning algorithms: a comprehensive evaluation. IEEE J Biomed Health Inform, 2022, 26(4): 1432-1440.
27. Beheshti I, Mishra S, Sone D, et al. T1-weighted MRI-driven brain age estimation in Alzheimer’s disease and Parkinson’s disease. Aging Dis, 2020, 11(3): 618-628.
28. Beheshti I, Booth S, Ko J H, et al. Differences in brain aging between sexes in Parkinson’s disease. NPJ Parkinsons Dis, 2024, 10(1): 35.
29. Eickhoff C R, Hoffstaedter F, Caspers J, et al. Advanced brain ageing in Parkinson’s disease is related to disease duration and individual impairment. Brain Commun, 2021, 3(3): fcab191.
30. Hofmann S M, Beyer F, Lapuschkin S, et al. Towards the interpretability of deep learning models for multi-modal neuroimaging: finding structural changes of the ageing brain. NeuroImage, 2022, 261: 119504.
31. He S, Feng Y, Grant P E, et al. Deep relation learning for regression and its application to brain age estimation. IEEE Trans Med Imaging, 2022, 41(9): 2304-2317.
32. Gupta U, Lam P K, Steeg G V, et al. Improved brain age estimation with slice-based set networks//2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI). Nice: IEEE, 2021: 840-844.
33. Li X, Hao Z, Li D, et al. Brain age prediction via cross-stratified ensemble learning. NeuroImage, 2024, 299: 120825.
34. Zhang X, Pan Y, Wu T, et al. Brain age prediction using interpretable multi-feature-based convolutional neural network in mild traumatic brain injury. NeuroImage, 2024, 297: 120751.
35. Cheng J, Liu Z, Guan H, et al. Brain age estimation from MRI using cascade networks with ranking loss. IEEE Trans Med Imaging, 2021, 40(12): 3400-3412.
36. Peng H, Gong W, Beckmann C F, et al. Accurate brain age prediction with lightweight deep neural networks. Med Image Anal, 2021, 68: 101871.
37. Nguyen H D, Clément M, Mansencal B, et al. Brain structure ages-a new biomarker for multi-disease classification. Hum Brain Mapp, 2024, 45(1): e26558.
38. Chen C L, Kuo M C, Wu W C, et al. Advanced brain aging in multiple system atrophy compared to Parkinson’s disease. Neuroimage Clin, 2022, 34: 102997.
39. Chen C L, Cheng S Y, Montaser-Kouhsari L, et al. Advanced brain aging in Parkinson’s disease with cognitive impairment. NPJ Parkinsons Dis, 2024, 10: 62.
40. Li C, Cui Y, Luo N, et al. Trans-ResNet: integrating transformers and CNNs for Alzheimer’s disease classification//2022 IEEE 19th International Symposium on Biomedical Imaging (ISBI). Kolkata: IEEE, 2022: 1-5.
41. Chen M, Wang Y, Shi Y, et al. Brain age prediction based on quantitative susceptibility mapping using the segmentation transformer. IEEE J Biomed Health Inform, 2024, 28: 1012-1021.
42. He S, Grant P E, Ou Y, et al. Global-local transformer for brain age estimation. IEEE Trans Med Imaging, 2022, 41: 213-224.
43. Zhong F, He K, Ji M, et al. Optimizing vitiligo diagnosis with ResNet and swin transformer deep learning models: a study on performance and interpretability. Sci Rep, 2024, 14: 9127.

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