Abstract
The aging brain is characterized by a decline in physical and mental capacities and susceptibility to neurological disorders. Magnetic resonance imaging (MRI) has proven to be a critical tool in detecting age-related structural and morphological changes. One of the effective biomarkers for healthy aging is the difference between predicted brain age and chronological brain age (Predicted Age Difference: PAD). While deep learning networks give accurate brain age predictions, they are highly affected by confounding factors like variable noise levels from different MRI scanners/sites, gender, etc. This study focuses on the development of an algorithm leveraging deformation fields with debiasing in T1-W MRI images obtained from the OpenBHB dataset to learn representation vectors that capture the biological variability (age). To achieve this, we explore the use of learnable deformation fields combined with a contrast invariant training method (SynthMorph). We evaluate the accuracy of our method on the large publicly available dataset, OpenBHB, which consists of MRI scans from multiple sites and scanners and compare it with a standard available method, ResNet.
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References
Baecker, L., et al.: Brain age prediction: a comparison between machine learning models using region-and voxel-based morphometric data. Hum. Brain Mapp. 42(8), 2332–2346 (2021)
Beheshti, I., Ganaie, M.A., Paliwal, V., Rastogi, A., Razzak, I., Tanveer, M.: Predicting brain age using machine learning algorithms: a comprehensive evaluation. IEEE J. Biomed. Health Inform. 26, 1432–1440 (2021)
Cetin Karayumak, S., Kubicki, M., Rathi, Y.: Harmonizing diffusion MRI data across magnetic field strengths. In: Frangi, A.F., Schnabel, J.A., Davatzikos, C., Alberola-López, C., Fichtinger, G. (eds.) MICCAI 2018. LNCS, vol. 11072, pp. 116–124. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-00931-1_14
Cheng, J., et al.: Brain age estimation from MRI using cascade networks with ranking loss. IEEE Trans. Med. Imaging 40, 3400–3412 (2021)
Cherubini, A., Caligiuri, M.E., Péran, P., Sabatini, U., Cosentino, C., Amato, F.: Importance of multimodal MRI in characterizing brain tissue and its potential application for individual age prediction. IEEE J. Biomed. Health Inform. 20, 1232–1239 (2016)
Cole, J.H., et al.: Predicting brain age with deep learning from raw imaging data results in a reliable and heritable biomarker. Neuroimage 163, 115–124 (2016)
Cole, J.H., et al.: Brain age predicts mortality. Mol. Psych. 23, 1385–1392 (2017)
Dufumier, B., Grigis, A., Victor, J., Ambroise, C., Frouin, V., Duchesnay, E.: Openbhb: a large-scale multi-site brain mri data-set for age prediction and debiasing. NeuroImage 263 (2022)
de Fátima Machado Dias, M., de Carvalho, P., Duarte, J.V., Castelo-Branco, M.: Deformation fields: a new source of information to predict brain age. J. Neural Eng. 19 (2022)
Fortin, J.P., et al.: Harmonization of multi-site diffusion tensor imaging data. Neuroimage 161, 149–170 (2017)
Franke, K., Ziegler, G., Klöppel, S., Gaser, C., Initiative, A.D.N., et al.: Estimating the age of healthy subjects from t1-weighted MRI scans using kernel methods: exploring the influence of various parameters. Neuroimage 50(3), 883–892 (2010)
Fu, J., Tzortzakakis, A., Barroso, J., Westman, E., Ferreira, D., Moreno, R.: Generative aging of brain images with diffeomorphic registration. ArXiv abs/ arXiv: 2205.15607 (2022)
Ganaie, M.A., Tanveer, M., Beheshti, I.: Brain age prediction with improved least squares twin svr. IEEE J. Biomed. Health Inform. 27, 1661–1669 (2022)
Hoffmann, M., Billot, B., Greve, D.N., Iglesias, J.E., Fischl, B., Dalca, A.V.: Synthmorph: learning contrast-invariant registration without acquired images. IEEE Trans. Med. Imaging 41(3), 543–558 (2021)
Jonsson, B.A., et al.: Brain age prediction using deep learning uncovers associated sequence variants. Nat. Commun. 10 (2019)
Jónsson, B.A., et al.: Brain age prediction using deep learning uncovers associated sequence variants. Nat. Commun. 10(1), 1–10 (2019)
Kassani, P.H., Gossmann, A., Ping Wang, Y.: Multimodal sparse classifier for adolescent brain age prediction. IEEE J. Biomed. Health Inform. 24, 336–344 (2019)
Kaufmann, T., et al.: Common brain disorders are associated with heritable patterns of apparent aging of the brain. Nat. Neurosci. 22(10), 1617–1623 (2019)
Koutsouleris, N., et al.: Accelerated brain aging in schizophrenia and beyond: a neuroanatomical marker of psychiatric disorders. Schizophr. Bull. 40(5), 1140–1153 (2014)
Liem, F., et al.: Predicting brain-age from multimodal imaging data captures cognitive impairment. Neuroimage 148, 179–188 (2017)
Mishra, S., Beheshti, I., Khanna, P.: A review of neuroimaging-driven brain age estimation for identification of brain disorders and health conditions. IEEE Rev. Biomed. Eng. 16, 371–385 (2021)
Ning, L., et al.: Muti-shell diffusion MRI harmonisation and enhancement challenge (MUSHAC): progress and results. In: Bonet-Carne, E., Grussu, F., Ning, L., Sepehrband, F., Tax, C.M.W. (eds.) MICCAI 2019. MV, pp. 217–224. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-05831-9_18
Salih, A., et al.: A new scheme for the assessment of the robustness of explainable methods applied to brain age estimation. In: 2021 IEEE 34th International Symposium on Computer-Based Medical Systems (CBMS), pp. 492–497. IEEE (2021)
Valizadeh, S., Hänggi, J., Mérillat, S., Jäncke, L.: Age prediction on the basis of brain anatomical measures. Hum. Brain Mapp. 38(2), 997–1008 (2017)
Xiong, M., Lin, L., Jin, Y., Kang, W., Wu, S., Sun, S.: Comparison of machine learning models for brain age prediction using six imaging modalities on middle-aged and older adults. Sensors (Basel, Switzerland) 23 (2023)
Zavaliangos-Petropulu, A., et al.: Diffusion MRI indices and their relation to cognitive impairment in brain aging: the updated multi-protocol approach in adni3. Front. Neuroinform. 13, 2 (2019)
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Aqil, K.H., Kulkarni, T., Jayakumar, J., Ram, K., Sivaprakasam, M. (2023). Confounding Factors Mitigation in Brain Age Prediction Using MRI with Deformation Fields. In: Rekik, I., Adeli, E., Park, S.H., Cintas, C., Zamzmi, G. (eds) Predictive Intelligence in Medicine. PRIME 2023. Lecture Notes in Computer Science, vol 14277. Springer, Cham. https://doi.org/10.1007/978-3-031-46005-0_6
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DOI: https://doi.org/10.1007/978-3-031-46005-0_6
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