A review on brain age prediction in brain ageing_Journal of Biomedical Engineering

Authors：

 LIN Lan , WANG Jingxuan , FU Zhenrong , WU Xuetao , WU Shuicai

College of Life Science and Bio-engineering, Beijing University of Technology, Beijing 100124, P.R.China;

Corresponding author：

LIN Lan, Email: lanlin@bjut.edu.cn

Keywords：

brain age; prediction model; neuroimage; machine learning; brain ageing; convolution neural network

DOI：

10.7507/1001-5515.201804030

Video：

Export PDF Favorites Scan Get Citation

Abstract Full text Figures/Tables Video References Cited by

The human brain deteriorates as we age, and the rate and the trajectories of these changes significantly vary among brain regions and among individuals. Because neuroimaging data are potentially important indicators of individual's brain health, they are commonly used in brain age prediction. In this review, we summarize brain age prediction model from neuroimaging-based studies in the last ten years. The studies are categorized based on their image modalities and feature types. The results indicate that the prediction frameworks based on neuroimaging holds promise toward individualized brain age prediction. Finally, we addressed the challenges in brain age prediction and suggested some future research directions.

Citation： LIN Lan, WANG Jingxuan, FU Zhenrong, WU Xuetao, WU Shuicai. A review on brain age prediction in brain ageing. Journal of Biomedical Engineering, 2019, 36(3): 493-498. doi: 10.7507/1001-5515.201804030 Copy

1.	党俊武. 应对人口老龄化顶层设计刍议. 老龄科学研究, 2017, 5(1): 3-14.
2.	Valizadeh S A, Liem F, Merillat S A, et al. Identification of individual subjects on the basis of their brain anatomical features. Sci Rep, 2018, 8(1): 5611.
3.	Fan Yangteng, Fang Yawen, Chen Yaping, et al. Aging, cognition, and the brain: effects of age-related variation in white matter integrity on neuropsychological function. Aging Mental Health, 2018, 10: 1-9.
4.	彭飞, 耿左军, 朱青峰, 等. 健康成人大脑灰质体积年龄相关性变化的研究. 河北医药, 2015, 37(1): 28-31.
5.	Cole J H, Franke K. Predicting age using neuroimaging: innovative brain ageing biomarkers. Trends Neurosci, 2017, 40(12): 681-690.
6.	Cole J H, Ritchie S J, Bastin M E, et al. Brain age predicts mortality. Mol Psychiatry, 2018, 23(5): 1385-1392.
7.	Franke K, Ziegler G, Klöppel S, et al. Estimating the age of healthy subjects from T1-weighted MRI scans using kernel methods: exploring the influence of various parameters. Neuroimage, 2010, 50(3): 883-892.
8.	Cole J H, Underwood J, Caan M W, et al. Increased brain-predicted aging in treated HIV disease. Neurology, 2017, 88(14): 1349-1357.
9.	Koutsouleris N, Davatzikos C, Borgwardt S, et al. Accelerated brain aging in schizophrenia and beyond: a neuroanatomical marker of psychiatric disorders. Schizophr Bull, 2014, 40(5): 1140-1153.
10.	Pardoe H R, Cole J H, Blackmon K, et al. Structural brain changes in medically refractory focal epilepsy resemble premature brain aging. Epilepsy Res, 2017, 133: 28-32.
11.	Franke K, Gaser C, Manor B, et al. Advanced BrainAGE in older adults with type 2 diabetes mellitus. Front Aging Neurosci, 2013, 5(1): 90.
12.	Ronan L, Alexander-Bloch A F, Wagstyl K A, et al. Obesity associated with increased brain age from midlife. Neurobiol Aging, 2016, 47: 63-70.
13.	林岚, 张柏雯, 付振荣, 等. 高血压对大脑年龄估值差的影响. 中国医疗设备, 2015, 30(6): 7-11.
14.	Gaser C, Franke K, Kloeppel S A, et al. BrainAGE in mild cognitive impaired patients: predicting the conversion to Alzheimer's disease. PLoS One, 2013, 8(6): e67346-e67346.
15.	Cole J H, Annus T, Wilson L R, et al. Brain-predicted age in down syndrome is associated with b-amyloid deposition and cognitive decline. Neurobiol of Aging, 2017, 56: 41-49.
16.	Cole J H, Leech R, Sharp D J, et al. Prediction of brain age suggests accelerated atrophy after traumatic brain injury. Ann Neurol, 2015, 77(4): 571-581.
17.	Schnack H G, van Haren N E, Nieuwenhuis M, et al. Accelerated brain aging in schizophrenia: a longitudinal pattern recognition study. American Journal of Psychiatry, 2016, 173(6): 607-616.
18.	Steffener J, Habeck C, O'shea D, et al. Differences between chronological and brain age are related to education and self-reported physical activity. Neurobiol Aging, 2016, 40: 138-144.
19.	Su L, Wang L, Hu D. Predicting the age of healthy adults from structural MRI by sparse representation//International Conference on Intelligent Science and Intelligent Data Engineering, Nanjing: Springer-Verlag, 2012: 271-279.
20.	Cole J H, Rpk P, Tsagkrasoulis D, et al. Predicting brain age with deep learning from raw imaging data results in a reliable and heritable biomarker. Neuroimage, 2017, 163: 115-124.
21.	Wang Jieqiong, Dai Dai, Li Meng, et al. Human age estimation with surface-based features from MRI images//Lecture Notes in Computer Science, Berlin: Springer, 2012(7588): 111-118.
22.	Pardoe H R, Kuzniecky R. NAPR: a cloud-based framework for neuroanatomical age prediction. Neuroinformatics, 2018, 16(1): 43-49.
23.	Wang Bing, Pham T D. MRI-based age prediction using hidden Markov models. J Neurosci Methods, 2011, 199(1): 140-145.
24.	Khundrakpam B S, Tohka J, Evans A C. Prediction of brain maturity based on cortical thickness at different spatial resolutions. Neuroimage, 2015, 111: 350-359.
25.	Alam S B, Kobashi S. Simple brain atrophy quantification method using Mr images//2016 5th International Conference on Informatics, Electronics and Vision (ICIEV), 2016: 743-746.
26.	Fujimoto R, Ito K, Wu Kai, et al. Brain age estimation from T1-Weighted images using effective local features//2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2017: 3028-3031.
27.	Ball G, Adamson C, Beare R, et al. Modelling neuroanatomical variation during childhood and adolescence with neighbourhood-preserving embedding. Sci Rep, 2017, 7(1): 17796.
28.	Lancaster J, Lorenz R, Leech R, et al. Bayesian optimization for neuroimaging pre-processing in brain age classification and prediction. Front Aging Neurosci, 2018, 10(1): 13.
29.	林岚, 付振荣, 张柏雯, 等. DTI脑连接组在大脑疾病方面的研究进展. 中国医疗设备, 2015, 30(6): 1-6.
30.	Mwangi B, Hasan K M, Soares J C. Prediction of individual subject's age across the human lifespan using diffusion tensor imaging: a machine learning approach. Neuroimage, 2013, 75: 58-67.
31.	Lin Lan, Jin Cong, Fu Zhenrong, et al. Predicting healthy older adult's brain age based on structural connectivity networks using artificial neural networks. Comput Methods Programs Biomed, 2016, 125: 8-17.
32.	Dean I D, O'muircheartaigh J, Dirks H, et al. Estimating the age of healthy infants from quantitative myelin water fraction Maps. Hum Brain Mapp, 2015, 36(4): 1233-1244.
33.	林岚, 靳聪, 付振荣, 等. 健康老年人脑年龄预测: 基于尺度子配置模型的大脑连接组分析. 北京工业大学学报, 2015, (6): 955-960.
34.	Tian Lixia, Ma Lin. Microstructural changes of the human brain from early to mid-adulthood. Front Hum Neurosci, 2017, 11: 393.
35.	Tian Lixia, Ma Lin, Wang Linlin. Alterations of functional connectivities from early to middle adulthood: clues from multivariate pattern analysis of resting-state fMRI data. Neuroimage, 2016, 129: 389-400.
36.	Dosenbach N U, Nardos B, Cohen A L, et al. Prediction of individual brain maturity using fMRI. Science, 2010, 329(5997): 1358-1361.
37.	Li Hongming, Satterthwaite T D, Fan Yong. Brain age prediction based on resting-state functional connectivity patterns using convolutional neural networks//2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018), Washington: IEEE Computer Society, 2018: 101-104.
38.	Brown T T, Kuperman J M, Chung Y, et al. Neuroanatomical assessment of biological maturity. Current Biology, 2012, 22(18): 1693-1698.
39.	Erus G, Battapady H, Satterthwaite T D, et al. Imaging patterns of brain development and their relationship to cognition. Cerebral Cortex, 2015, 25(6): 1676-1684.
40.	Cherubini A, Caligiuri M E, Peran P A, et al. Importance of multimodal MRI in characterizing brain tissue and its potential application for individual age prediction. IEEE J Biomed Health Inform, 2016, 20(5): 1232-1239.
41.	Liem F, Varoquaux G, Kynast J, et al. Predicting brain-age from multimodal imaging data captures cognitive impairment. Neuroimage, 2017, 148: 179-188.
42.	Sabuncu M R, Konukoglu E. Clinical prediction from structural brain MRI scans: a Large-Scale empirical study. Neuroinformatics, 2015, 13(1): 31-46.
43.	田苗, 林岚, 张柏雯, 等. 深度学习在神经影像中的应用研究. 中国医疗设备, 2016, 31(12): 4-9.
44.	杨业, 汤艺, 彭微微, 等. 共情: 遗传-环境-内分泌-大脑机制. 科学通报, 2017, 62(32): 3729-3742.
45.	林岚, 张柏雯, 王婧璇, 等. 认知储备在大脑老化中的研究进展. 医疗卫生装备, 2017, 38(9): 93-98.
46.	Li Yongming, Liu Yuchuan, Wang Pin, et al. Dependency criterion based brain pathological age estimation of Alzheimer's disease patients with Mr scans. Biomed Eng Online, 2017, 16(1): 50.

1. 党俊武. 应对人口老龄化顶层设计刍议. 老龄科学研究, 2017, 5(1): 3-14.
2. Valizadeh S A, Liem F, Merillat S A, et al. Identification of individual subjects on the basis of their brain anatomical features. Sci Rep, 2018, 8(1): 5611.
3. Fan Yangteng, Fang Yawen, Chen Yaping, et al. Aging, cognition, and the brain: effects of age-related variation in white matter integrity on neuropsychological function. Aging Mental Health, 2018, 10: 1-9.
4. 彭飞, 耿左军, 朱青峰, 等. 健康成人大脑灰质体积年龄相关性变化的研究. 河北医药, 2015, 37(1): 28-31.
5. Cole J H, Franke K. Predicting age using neuroimaging: innovative brain ageing biomarkers. Trends Neurosci, 2017, 40(12): 681-690.
6. Cole J H, Ritchie S J, Bastin M E, et al. Brain age predicts mortality. Mol Psychiatry, 2018, 23(5): 1385-1392.
7. Franke K, Ziegler G, Klöppel S, et al. Estimating the age of healthy subjects from T1-weighted MRI scans using kernel methods: exploring the influence of various parameters. Neuroimage, 2010, 50(3): 883-892.
8. Cole J H, Underwood J, Caan M W, et al. Increased brain-predicted aging in treated HIV disease. Neurology, 2017, 88(14): 1349-1357.
9. Koutsouleris N, Davatzikos C, Borgwardt S, et al. Accelerated brain aging in schizophrenia and beyond: a neuroanatomical marker of psychiatric disorders. Schizophr Bull, 2014, 40(5): 1140-1153.
10. Pardoe H R, Cole J H, Blackmon K, et al. Structural brain changes in medically refractory focal epilepsy resemble premature brain aging. Epilepsy Res, 2017, 133: 28-32.
11. Franke K, Gaser C, Manor B, et al. Advanced BrainAGE in older adults with type 2 diabetes mellitus. Front Aging Neurosci, 2013, 5(1): 90.
12. Ronan L, Alexander-Bloch A F, Wagstyl K A, et al. Obesity associated with increased brain age from midlife. Neurobiol Aging, 2016, 47: 63-70.
13. 林岚, 张柏雯, 付振荣, 等. 高血压对大脑年龄估值差的影响. 中国医疗设备, 2015, 30(6): 7-11.
14. Gaser C, Franke K, Kloeppel S A, et al. BrainAGE in mild cognitive impaired patients: predicting the conversion to Alzheimer's disease. PLoS One, 2013, 8(6): e67346-e67346.
15. Cole J H, Annus T, Wilson L R, et al. Brain-predicted age in down syndrome is associated with b-amyloid deposition and cognitive decline. Neurobiol of Aging, 2017, 56: 41-49.
16. Cole J H, Leech R, Sharp D J, et al. Prediction of brain age suggests accelerated atrophy after traumatic brain injury. Ann Neurol, 2015, 77(4): 571-581.
17. Schnack H G, van Haren N E, Nieuwenhuis M, et al. Accelerated brain aging in schizophrenia: a longitudinal pattern recognition study. American Journal of Psychiatry, 2016, 173(6): 607-616.
18. Steffener J, Habeck C, O'shea D, et al. Differences between chronological and brain age are related to education and self-reported physical activity. Neurobiol Aging, 2016, 40: 138-144.
19. Su L, Wang L, Hu D. Predicting the age of healthy adults from structural MRI by sparse representation//International Conference on Intelligent Science and Intelligent Data Engineering, Nanjing: Springer-Verlag, 2012: 271-279.
20. Cole J H, Rpk P, Tsagkrasoulis D, et al. Predicting brain age with deep learning from raw imaging data results in a reliable and heritable biomarker. Neuroimage, 2017, 163: 115-124.
21. Wang Jieqiong, Dai Dai, Li Meng, et al. Human age estimation with surface-based features from MRI images//Lecture Notes in Computer Science, Berlin: Springer, 2012(7588): 111-118.
22. Pardoe H R, Kuzniecky R. NAPR: a cloud-based framework for neuroanatomical age prediction. Neuroinformatics, 2018, 16(1): 43-49.
23. Wang Bing, Pham T D. MRI-based age prediction using hidden Markov models. J Neurosci Methods, 2011, 199(1): 140-145.
24. Khundrakpam B S, Tohka J, Evans A C. Prediction of brain maturity based on cortical thickness at different spatial resolutions. Neuroimage, 2015, 111: 350-359.
25. Alam S B, Kobashi S. Simple brain atrophy quantification method using Mr images//2016 5th International Conference on Informatics, Electronics and Vision (ICIEV), 2016: 743-746.
26. Fujimoto R, Ito K, Wu Kai, et al. Brain age estimation from T1-Weighted images using effective local features//2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2017: 3028-3031.
27. Ball G, Adamson C, Beare R, et al. Modelling neuroanatomical variation during childhood and adolescence with neighbourhood-preserving embedding. Sci Rep, 2017, 7(1): 17796.
28. Lancaster J, Lorenz R, Leech R, et al. Bayesian optimization for neuroimaging pre-processing in brain age classification and prediction. Front Aging Neurosci, 2018, 10(1): 13.
29. 林岚, 付振荣, 张柏雯, 等. DTI脑连接组在大脑疾病方面的研究进展. 中国医疗设备, 2015, 30(6): 1-6.
30. Mwangi B, Hasan K M, Soares J C. Prediction of individual subject's age across the human lifespan using diffusion tensor imaging: a machine learning approach. Neuroimage, 2013, 75: 58-67.
31. Lin Lan, Jin Cong, Fu Zhenrong, et al. Predicting healthy older adult's brain age based on structural connectivity networks using artificial neural networks. Comput Methods Programs Biomed, 2016, 125: 8-17.
32. Dean I D, O'muircheartaigh J, Dirks H, et al. Estimating the age of healthy infants from quantitative myelin water fraction Maps. Hum Brain Mapp, 2015, 36(4): 1233-1244.
33. 林岚, 靳聪, 付振荣, 等. 健康老年人脑年龄预测: 基于尺度子配置模型的大脑连接组分析. 北京工业大学学报, 2015, (6): 955-960.
34. Tian Lixia, Ma Lin. Microstructural changes of the human brain from early to mid-adulthood. Front Hum Neurosci, 2017, 11: 393.
35. Tian Lixia, Ma Lin, Wang Linlin. Alterations of functional connectivities from early to middle adulthood: clues from multivariate pattern analysis of resting-state fMRI data. Neuroimage, 2016, 129: 389-400.
36. Dosenbach N U, Nardos B, Cohen A L, et al. Prediction of individual brain maturity using fMRI. Science, 2010, 329(5997): 1358-1361.
37. Li Hongming, Satterthwaite T D, Fan Yong. Brain age prediction based on resting-state functional connectivity patterns using convolutional neural networks//2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018), Washington: IEEE Computer Society, 2018: 101-104.
38. Brown T T, Kuperman J M, Chung Y, et al. Neuroanatomical assessment of biological maturity. Current Biology, 2012, 22(18): 1693-1698.
39. Erus G, Battapady H, Satterthwaite T D, et al. Imaging patterns of brain development and their relationship to cognition. Cerebral Cortex, 2015, 25(6): 1676-1684.
40. Cherubini A, Caligiuri M E, Peran P A, et al. Importance of multimodal MRI in characterizing brain tissue and its potential application for individual age prediction. IEEE J Biomed Health Inform, 2016, 20(5): 1232-1239.
41. Liem F, Varoquaux G, Kynast J, et al. Predicting brain-age from multimodal imaging data captures cognitive impairment. Neuroimage, 2017, 148: 179-188.
42. Sabuncu M R, Konukoglu E. Clinical prediction from structural brain MRI scans: a Large-Scale empirical study. Neuroinformatics, 2015, 13(1): 31-46.
43. 田苗, 林岚, 张柏雯, 等. 深度学习在神经影像中的应用研究. 中国医疗设备, 2016, 31(12): 4-9.
44. 杨业, 汤艺, 彭微微, 等. 共情: 遗传-环境-内分泌-大脑机制. 科学通报, 2017, 62(32): 3729-3742.
45. 林岚, 张柏雯, 王婧璇, 等. 认知储备在大脑老化中的研究进展. 医疗卫生装备, 2017, 38(9): 93-98.
46. Li Yongming, Liu Yuchuan, Wang Pin, et al. Dependency criterion based brain pathological age estimation of Alzheimer's disease patients with Mr scans. Biomed Eng Online, 2017, 16(1): 50.

Journal of Biomedical Engineering

A review on brain age prediction in brain ageing

Abstract Full text Figures/Tables Video References Cited by

Previous Article

Next Article

Format

Content