Research Progress for the Analysis of Images and Genetic Features in Alzheimer′s Disease
Han Liting1,2, Yao Xufeng2,3*, Jin Yu1,2, Zhao Congyi1,2, Huang Gang2,3
1(School of Medical Instrument and Food Engineering, University of Shanghai for Science and Technology, Shanghai 200082, China) 2(College of Medical Imaging, Shanghai University of Medicine and Health Sciences, Shanghai 201308, China) 3(Shanghai Key Laboratory of Molecular Imaging, Shanghai University of Medicine and Health Sciences, Shanghai 201308, China)
Abstract:Alzheimer′s disease (AD) is one of the most common neurodegenerative diseases, and its phenotype has shown susceptible to genetic factors. In recent years, with the wide application of multimodal brain imaging and high-throughput genomics in medical imaging, it has become a new hotspot to explore the association analysis between images and genes by means of data mining and mathematical modeling. Till now, the combined analysis of images and genetic characteristics has been used to study AD and has made significant progress in the early diagnosis, classification, and prognostic analysis. This article first summarized the imaging and genetic features, then explained the application of statistics and machine learning (ML) methods in the joint analysis of image gene features, and finally summarized and proposed its development perspectives.
韩立婷, 姚旭峰, 金宇, 赵从义, 黄钢. 影像与基因特征分析方法在阿尔茨海默病中的研究进展[J]. 中国生物医学工程学报, 2022, 41(4): 485-492.
Han Liting, Yao Xufeng, Jin Yu, Zhao Congyi, Huang Gang. Research Progress for the Analysis of Images and Genetic Features in Alzheimer′s Disease. Chinese Journal of Biomedical Engineering, 2022, 41(4): 485-492.
[1] Huang M, Yang W, Feng Q, et al. Longitudinal measurement and hierarchical classification framework for the prediction of Alzheimer′s disease[J]. Scientific Reports, 2017, 7(1):1-13. [2] Hou Y, Dan X, Babbar M, et al. Ageing as a risk factor for neurodegenerative disease[J]. Nat Rev Neurol 15, 2019, 15(10):565-581. [3] Kim BH, Choi YH, Yang JJ, et al. Identification of Novel genes associated with cortical thickness in Alzheimer′s disease: systems biology approach to neuroimaging endophenotype[J]. Journal of Alzheimer′s Disease, 2020, 75(2):531- 545. [4] Sørensen L, Igel C, Liv Hansen N, et al. Early detection of Alzheimer′s disease using MRI hippocampal texture[J]. Human Brain Mapping, 2016, 37(3):1148-1161. [5] Nakamura A, Kaneko N, Villemagne VL, et al. High performance plasma amyloid-β biomarkers for Alzheimer′s disease[J]. Nature, 2018, 554(7691): 249-254. [6] Elsheikh SSM, Chimusa ER, Mulder NJ, et al. Genome-wide association study of brain connectivity changes for Alzheimer′s disease[J]. Scientific Reports, 2020, 10(1):1-16. [7] Knutson KA, Pan W. Integrating brain imaging endophenotypes with GWAS for Alzheimer′s disease[J]. Quantitative Biology, 2020:1-16. [8] Wang H, Yang J, Schneider JA, et al. Genome-wide interaction analysis of pathological hallmarks in Alzheimer′s disease[J]. Neurobiology of Aging, 2020,93:61-68. [9] Falk M, Hausmann M, Lukasova E, et al. Determining Omics spatiotemporal dimensions using exciting new nanoscopy techniques to assess complex cell responses to DNA damage: part A-radiomics[J]. Crit Rev Eukaryot Gene Expr, 2014, 24(3):205-223. [10] Christopher L, Napolioni V, Khan RR, et al. A variant in PPP4R3A protects against Alzheimer-related metabolic decline[J]. Annals of neurology, 2017, 82(6): 900-911. [11] Coon KD, Myers AJ, Craig DW, et al. A high-density whole-genome association study reveals that APOE is the major susceptibility gene for sporadic late-onset Alzheimer′s disease[J]. The Journal of Clinical Psychiatry, 2007, 68(4): 613-618. [12] Lambert JC, Ibrahim-Verbaas CA, Harold D, et al. Meta-analysis of 74,046 individuals identifies 11 new susceptibility loci for Alzheimer′s disease[J]. Nature genetics, 2013, 45(12): 1452-1458. [13] Marioni RE, Harris SE, Zhang Q, et al. GWAS on family history of Alzheimer′s disease[J]. Translational Psychiatry, 2018, 8: 1-7. [14] Jansen IE, Savage JE, Watanabe K, et al. Genome-wide meta-analysis identifies new loci and functional pathways influencing Alzheimer′s disease risk[J]. Nat Genet 2019; 51: 404-413. [15] Jombart T, Pontier D, Dufour AB. Genetic markers in the playground of multivariate analysis[J]. Heredity, 2009, 102(4): 330-341. [16] Hashimoto R, Ohi K, Yamamori H, et al. Imaging genetics and psychiatric disorders[J]. Current Molecular Medicine, 2015, 15 (2):168-175. [17] Yao Xiaohui, Shan Cong, Yan Jingwen, et al. Regional imaging genetic enrichment analysis[J], Bioinformatics, 2020, 36(8):2554-2560. [18] Lee G, Lee HY, Ko ES, et al. Radiomics and imaging genomics in precision medicine[J], Precis Future Med, 2017, 1:10-31. [19] Bonham LW, Sirkis DW, Hess CP, et al. The radiogenomics of late-onset Alzheimer disease[J], Topics in Magnetic Resonance Imaging, 2019, 28(6):325-334. [20] Habes M, Toledo JB, Resnick SM, et al. Relationship between APOE genotype and structural MRI measures throughout adulthood in the study of health in Pomerania population-based cohort[J]. American Journal of Neuroradiology, 2016, 37(9):1636-1642. [21] Stein JL, Hua X, Lee S, et al. Voxelwise genome-wide association study (vGWAS)[J]. NeuroImage, 2010, 53(3):1160-1174. [22] Huang Meiyan, Deng Chunyan, Yu Yuwei, et al. Spatial correlations exploitation based on nonlocal voxel-wise GWAS for biomarker detection of AD. NeuroImage: Clinical, 2019, 21:101642. [23] Larson NB, Jenkins GD, Larson MC, et al. Kernel canonical correlation analysis for assessing gene–gene interactions and application to ovarian cancer[J]. European Journal of Human Genetics, 2014, 22(1):126-131. [24] Andrew G, Arora R, Bilmes J, et al. Deep canonical correlation analysis[C]//Proceedings of the 30th International Conference on Machine Learning. Atlanta: PMLR, 2013:1247-1255. [25] Grellmann C, Bitzer S, Neumann J, et al. Comparison of variants of canonical correlation analysis and partial least squares for combined analysis of MRI and genetic data[J]. Neuroimage, 2015, 107:289-310. [26] Liu J, Calhoun VD. A review of multivariate analyses in imaging genetics[J]. Frontiers in Neuroinformatics, 2014, 8:29. [27] Du L, Liu K, Yao X, et al. Detecting genetic associations with brain imaging phenotypes in Alzheimer′s disease via a novel structured SCCA approach[J]. Medical Image Analysis, 2020, 61:101656. [28] Zhou T, Thung KH, Liu M, et al. Brain-wide genome-wide association study for Alzheimer′s disease via joint projection learning and sparse regression model[J]. IEEE Transactions on Biomedical Engineering, 2018, 66(1):165-175. [29] Lu ZH, Khondker Z, Ibrahim JG, et al. Bayesian longitudinal low-rank regression models for imaging genetic data from longitudinal studies[J]. Neuroimage, 2017, 149:305-322. [30] Zhu X, Suk HI, Huang H, et al. Low-rank graph-regularized structured sparse regression for identifying genetic biomarkers[J]. IEEE Transactions on Big Data, 2017, 3(4):405-414. [31] Soheili-Nezhad S, Jahanshad N, Guelfi S, et al. Alzheimer′s Disease Neuroimaging. Imaging genomics discovery of a new risk variant for Alzheimer′s disease in the postsynaptic SHARPIN gene[J]. Human Brain Mapping, 2020, 41(13):3737-3748. [32] Martinez-Torteya A, Rodriguez-Rojas JA, Celaya-Padilla JM, et al. Magnetization-prepared rapid acquisition with gradient echo magnetic resonance imaging signal and texture features for the prediction of mild cognitive impairment to Alzheimer′s disease progression[J]. Journal of Medical Imaging, 2014, 1:031005. [33] Oriol JD, Vallejo EE, Estrada K, et al. Benchmarking machine learning models for late-onset Alzheimer′s disease prediction from genomic data[J]. BMC bioinformatics, 2019, 20(1):1-17. [34] Burgos N, Colliot O. Machine learning for classification and prediction of brain diseases: recent advances and upcoming challenges[J]. Current Opinion in Neurology, 2020, 33(4):439-450. [35] Dukart J, Sambataro F, Bertolino A. Accurate prediction of conversion to Alzheimer′s disease using imaging, genetic, and neuropsychological biomarkers[J]. Journal of Alzheimer′s Disease, 2016, 49(4):1143-1159. [36] Zhang Z, Huang H, Shen D. Integrative analysis of multi-dimensional imaging genomics data for Alzheimer′s disease prediction[J]. Frontiers in Aging Neuroscience, 2014, 6:260. [37] Peng J, An L, Zhu X, et al. Structured sparse kernel learning for imaging genetics based Alzheimer′s disease diagnosis[C]//International Conference on Medical Image Computing and Computer-Assisted Intervention. Cham: Springer, 2016:70-78. [38] Lee G, Kang B, Nho K, et al. MildInt: deep learning-based multimodal longitudinal data integration framework[J]. Frontiers in Genetics, 2019, 10: 617. [39] Valliani AA, Ranti D, Oermann EK. Deeplearning and neurology: a systematic review[J]. Neurology and Therapy, 2019, 8(2):351-365. [40] Ning K, Chen B, Sun F, et al. Classifying Alzheimer′s disease with brain imaging and genetic data using a neural network framework[J]. Neurobiology of Aging, 2018, 68: 151-158. [41] Pelka O, Friedrich CM, Nensa F, et al. Sociodemographic data and APOE-ε4 augmentation for MRI-based detection of amnestic mild cognitive impairment using deep learning systems[J]. PLoS ONE, 2020, 15(9): e0236868. [42] Zhou T, Thung KH, Zhu X, et al. Effective feature learning and fusion of multimodality data using stage-wise deep neural network for dementia diagnosis[J]. Human Brain Mapping, 2019, 40(3):1001-1016. [43] Wachinger C, Nho K, Saykin AJ, et al. A longitudinal imaging genetics study of neuroanatomical asymmetry in Alzheimer′s disease[J]. Biological Psychiatry, 2018, 84(7):522-530. [44] Tabarestani S, Aghili M, Shojaie M, et al. Longitudinal Prediction Modeling of Alzheimer Disease using Recurrent Neural Networks[C]//2019 IEEE EMBS International Conference on Biomedical & Health Informatics (BHI). Chicago: IEEE, 2019:1-4. [45] Du Lei, Liu Kefei, Zhu Lei, et al. Identifying progressive imaging genetic patterns via multi-task sparse canonical correlation analysis: a longitudinal study of the ADNI cohort[J]. Bioinformatics, 2019, 35(14):i474-i483.