Abstract
Integration of imaging genetics data provides unprecedented opportunities for revealing biological mechanisms underpinning diseases and certain phenotypes. In this paper, a new model called attentive deep canonical correlation analysis (ADCCA) is proposed for the diagnosis of Alzheimer’s disease using multimodal brain imaging genetics data. ADCCA combines the strengths of deep neural networks, attention mechanisms, and canonical correlation analysis to integrate and exploit the complementary information from multiple data modalities. This leads to improved interpretability and strong multimodal feature learning ability. The ADCCA model is evaluated using the ADNI database with three imaging modalities (VBM-MRI, FDG-PET, and AV45-PET) and genetic SNP data. The results indicate that this approach can achieve outstanding performance and identify meaningful biomarkers for Alzheimer’s disease diagnosis. To promote reproducibility, the code has been made publicly available at https://github.com/rongzhou7/ADCCA.
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References
Ashburner, J., Friston, K.J.: Voxel-based morphometry-the methods. Neuroimage 11(6), 805–821 (2000)
Barshan, E., Fieguth, P.: Stage-wise training: An improved feature learning strategy for deep models. In: Feature extraction: modern questions and challenges, pp. 49–59. PMLR (2015)
Batmanghelich, N.K., Dalca, A., Quon, G., Sabuncu, M., Golland, P.: Probabilistic modeling of imaging, genetics and diagnosis. IEEE Trans. Med. Imaging 35(7), 1765–1779 (2016)
Benton, A., Khayrallah, H., Gujral, B., Reisinger, D.A., Zhang, S., Arora, R.: Deep generalized canonical correlation analysis. In: Proceedings of the 4th Workshop on Representation Learning for NLP (RepL4NLP-2019), pp. 1–6 (2019)
Catania, M., et al.: A novel bio-inspired strategy to prevent amyloidogenesis and synaptic damage in Alzheimer’s disease. Mol. Psych. 1–8 (2022)
Chicco, D., Jurman, G.: The advantages of the Matthews correlation coefficient (mcc) over f1 score and accuracy in binary classification evaluation. BMC Genomics 21, 1–13 (2020)
De Boer, P.T., Kroese, D.P., Mannor, S., Rubinstein, R.Y.: A tutorial on the cross-entropy method. Ann. Oper. Res. 134, 19–67 (2005)
Du, L., et al.: Identifying diagnosis-specific genotype-phenotype associations via joint multitask sparse canonical correlation analysis and classification. Bioinformatics 36, i371–i379 (2020)
Du, L., et al.: Detecting genetic associations with brain imaging phenotypes in Alzheimer’s disease via a novel structured SCCA approach. Med. Image Anal. 61, 101656 (2020)
Ghosal, S., et al.: Bridging imaging, genetics, and diagnosis in a coupled low-dimensional framework. In: Shen, D., et al. (eds.) Medical Image Computing and Computer Assisted Intervention – MICCAI 2019: 22nd International Conference, Shenzhen, China, October 13–17, 2019, Proceedings, Part IV, pp. 647–655. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-32251-9_71
Ghosal, S., et al.: A biologically interpretable graph convolutional network to link genetic risk pathways and imaging phenotypes of disease. In: ICLR (2022)
Hotelling, H.: Relations between two sets of variates. Biometrika 28(3/4), 321–377 (1936)
Hu, W., et al.: Adaptive sparse multiple canonical correlation analysis with application to imaging (epi) genomics study of schizophrenia. IEEE Trans. Biomed. Eng. 65(2), 390–399 (2017)
Jansen, I.E., et al.: Genome-wide meta-analysis identifies new loci and functional pathways influencing Alzheimer’s disease risk. Nat. Genet. 51(3), 404–413 (2019)
Kettenring, J.R.: Canonical analysis of several sets of variables. Biometrika 58(3), 433–451 (1971)
Kim, M., et al.: Multi-task learning based structured sparse canonical correlation analysis for brain imaging genetics. Med. Image Anal. 76, 102297 (2022)
Kokhlikyan, N., et al.: Captum: A unified and generic model interpretability library for pytorch. arXiv preprint arXiv:2009.07896 (2020)
LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521(7553), 436–444 (2015)
Liu, J., Calhoun, V.D.: A review of multivariate analyses in imaging genetics. Front. Neuroinform. 8, 29 (2014)
Moon, S., Hwang, J., Lee, H.: SDGCCA: supervised deep generalized canonical correlation analysis for multi-omics integration. J. Comput. Biol. 29(8), 892–907 (2022)
Mu, Y., Gage, F.H.: Adult hippocampal neurogenesis and its role in Alzheimer’s disease. Mol. Neurodegener. 6(1), 1–9 (2011)
Muller, S.G., et al.: The Alzheimer’s disease neuroimaging initiative. Neuroimaging Clin. 15(4), 869–877 (2005)
Shen, L., Thompson, P.M.: Brain imaging genetics: integrated analysis and machine learning. In: IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1–1. IEEE Computer Society (2021)
Tzourio-Mazoyer, N., et al.: Automated anatomical labeling of activations in SPM using a macroscopic anatomical parcellation of the MNI MRI single-subject brain. Neuroimage 15(1), 273–289 (2002)
Vaswani, A., et al.: Attention is all you need. Adv. Neural Inform. Process. Syst. 30 (2017)
Viding, E., Williamson, D.E., Forbes, E.E., Hariri, A.R.: The integration of neuroimaging and molecular genetics in the study of developmental cognitive neuroscience. MIT press (2008)
Wang, M.L., Shao, W., Hao, X.K., Zhang, D.Q.: Machine learning for brain imaging genomics methods: a review. Mach. Intell. Res. 20(1), 57–78 (2023)
Xin, Y., Sheng, J., Miao, M., Wang, L., Yang, Z., Huang, H.: A review of imaging genetics in Alzheimer’s disease. J. Clin. Neurosci. 100, 155–163 (2022)
Zhou, H., Zhang, Yu., Chen, B.Y., Shen, L., He, L.: Sparse interpretation of graph convolutional networks for multi-modal diagnosis of Alzheimer’s disease. In: Wang, L., Dou, Q., Fletcher, P.T., Speidel, S., Li, S. (eds.) Medical Image Computing and Computer Assisted Intervention – MICCAI 2022: 25th International Conference, Singapore, September 18–22, 2022, Proceedings, Part VIII, pp. 469–478. Springer, Cham (2022). https://doi.org/10.1007/978-3-031-16452-1_45
Zhu, Y., et al.: Graphene and graphene oxide: synthesis, properties, and applications. Adv. Mater. 22(35), 3906–3924 (2010)
Acknowledgements
This work is partially supported by the National Science Foundation (MRI-2215789 and IIS-1909879), National Institutes of Health (U01AG068057, U01AG-066833, R01LM013463, R01MH129694, and R21MH130956), Alzheimer’s Association grant (AARG-22-972541), and Lehigh’s grants under Accelerator (S00010293), CORE (001250), and FIG (FIGAWD35).
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Zhou, R., Zhou, H., Chen, B.Y., Shen, L., Zhang, Y., He, L. (2023). Attentive Deep Canonical Correlation Analysis for Diagnosing Alzheimer’s Disease Using Multimodal Imaging Genetics. In: Greenspan, H., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2023. MICCAI 2023. Lecture Notes in Computer Science, vol 14221. Springer, Cham. https://doi.org/10.1007/978-3-031-43895-0_64
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