Abstract
Alzheimer’s disease (AD) is a neurodegenerative disorder that is beginning with amyloidosis, followed by neuronal loss and deterioration in structure, function, and cognition. The accumulation of amyloid-\(\beta \) in the brain, measured through 18F-florbetapir (AV45) positron emission tomography (PET) imaging, has been widely used for early diagnosis of AD. However, the relationship between amyloid-\(\beta \) accumulation and AD pathophysiology remains unclear, and causal inference approaches are needed to uncover how amyloid-\(\beta \) levels can impact AD development. In this paper, we propose a Graph-VCNet for estimating the individual treatment effect with continuous treatment levels using a graph convolutional neural network. We highlight the potential of causal inference approaches, including Graph-VCNet, for measuring the regional causal connections between amyloid-\(\beta \) accumulation and AD pathophysiology, which may serve as a robust tool for early diagnosis and tailored care.
H. Dai—Contributed equally to this paper.
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References
An, L., et al.: Multi-level canonical correlation analysis for standard-dose pet image estimation. IEEE Trans. Image Process. 25(7), 3303–3315 (2016). https://doi.org/10.1109/TIP.2016.2567072
Bica, I., Jordon, J., van der Schaar, M.: Estimating the effects of continuous-valued interventions using generative adversarial networks. Adv. Neural. Inf. Process. Syst. 33, 16434–16445 (2020)
Camus, V., et al.: Using pet with 18f-av-45 (florbetapir) to quantify brain amyloid load in a clinical environment. Eur. J. Nucl. Med. Molecul. Imag. 39(4), 621–631 (2012)
Chipman, H.A., George, E.I., McCulloch, R.E.: Bart: Bayesian additive regression trees. Annal. Appl. Statist. 4(1), 266–298 (2010)
Chu, Z., Rathbun, S.L., Li, S.: Matching in selective and balanced representation space for treatment effects estimation. In: Proceedings of the 29th ACM International Conference on Information and Knowledge Management, pp. 205–214 (2020)
Ge, Q., et al.: Tracer-specific reference tissues selection improves detection of 18f-fdg, 18f-florbetapir, and 18f-flortaucipir pet SUVR changes in Alzheimer’s disease. Hum. Brain Mapp. 43(7), 2121–2133 (2022)
Hansen, B.B.: The prognostic analogue of the propensity score. Biometrika 95(2), 481–488 (2008)
Hassanpour, N., Greiner, R.: Counterfactual regression with importance sampling weights. In: IJCAI, pp. 5880–5887 (2019)
Hirano, K., Imbens, G.W.: The propensity score with continuous treatments. In: Applied Bayesian Modeling and Causal Inference from Incomplete-Data Perspectives, vol. 226164, pp. 73–84 (2004)
Jack, C.R., et al.: Rates of \(\upbeta \)-amyloid accumulation are independent of hippocampal neurodegeneration. Neurology 82(18), 1605 (2014)
Johansson, F., Shalit, U., Sontag, D.: Learning representations for counterfactual inference. In: International Conference on Machine Learning, pp. 3020–3029. PMLR (2016)
Landau, S.M., et al.: The Alzheimer’s disease neuroimaging initiative: amyloid pet imaging in Alzheimer’s disease: a comparison of three radiotracers. Eur. J. Nucl. Med. Mol. Imaging 41, 1398–1407 (2014)
Li, Q., et al.: The Alzheimer’s disease neuroimaging initiative: Aberrant connectivity in mild cognitive impairment and Alzheimer disease revealed by multimodal neuroimaging data. Neurodegener. Dis. 18, 5–18 (2018)
Li, Q., et al.: Multi-modal discriminative dictionary learning for Alzheimer’s disease and mild cognitive impairment. Comput. Methods Prog. Biomed. 150, 1–8 (2017)
Miller, M.B., et al.: Somatic genomic changes in single Alzheimer’s disease neurons. Nature 604 (2022)
Morgan, S.L., Winship, C.: Counterfactuals and Causal Inference. Cambridge University Press (2015)
Nie, L., Ye, M., Liu, Q., Nicolae, D.: Vcnet and functional targeted regularization for learning causal effects of continuous treatments. arXiv preprint arXiv:2103.07861 (2021)
Ossenkoppele, R., et al.: Amyloid and tau pet-positive cognitively unimpaired individuals are at high risk for future cognitive decline. Nat. Med. 28, 2381–2387 (2022)
Rubin, D.B.: Estimating causal effects of treatments in randomized and nonrandomized studies. J. Educ. Psychol. 66(5), 688 (1974)
Schwab, P., Linhardt, L., Bauer, S., Buhmann, J.M., Karlen, W.: Learning counterfactual representations for estimating individual dose-response curves. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, pp. 5612–5619 (2020)
Shalit, U., Johansson, F.D., Sontag, D.: Estimating individual treatment effect: generalization bounds and algorithms. In: International Conference on Machine Learning, pp. 3076–3085. PMLR (2017)
Stuart, E.A.: Matching methods for causal inference: a review and a look forward. Statist. Sci. Rev. J. Inst. Math. Statist. 25(1), 1 (2010)
Wager, S., Athey, S.: Estimation and inference of heterogeneous treatment effects using random forests. J. Am. Stat. Assoc. 113(523), 1228–1242 (2018)
Yao, L., Li, S., Li, Y., Huai, M., Gao, J., Zhang, A.: Representation learning for treatment effect estimation from observational data. Adv. Neural Inf. Process. Syst. 31 (2018)
Yao, L., et al.: Concept-level model interpretation from the causal aspect. IEEE Trans. Knowl. Data Eng. (2022)
Zhang, Y., Zhang, H., Lipton, Z.C., Li, L.E., Xing, E.: Exploring transformer backbones for heterogeneous treatment effect estimation. In: NeurIPS ML Safety Workshop (2022)
Zhu, X., Suk, H.-I., Shen, D.: Multi-modality canonical feature selection for Alzheimer’s disease diagnosis. In: Golland, P., Hata, N., Barillot, C., Hornegger, J., Howe, R. (eds.) MICCAI 2014. LNCS, vol. 8674, pp. 162–169. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-10470-6_21
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Dai, H. et al. (2023). Graph-Based Counterfactual Causal Inference Modeling for Neuroimaging Analysis. In: Woo, J., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2023 Workshops. MICCAI 2023. Lecture Notes in Computer Science, vol 14394. Springer, Cham. https://doi.org/10.1007/978-3-031-47425-5_19
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