Abstract
Brain age prediction using neuroimaging data has shown great potential as an indicator of overall brain health and successful aging, as well as a disease biomarker. Deep learning models have been established as reliable and efficient brain age estimators, being trained to predict the chronological age of healthy subjects. In this paper, we investigate the impact of a pre-training step on deep learning models for brain age prediction. More precisely, instead of the common approach of pre-training on natural imaging classification, we propose pre-training the models on brain-related tasks, which led to state-of-the-art results in our experiments on ADNI data. Furthermore, we validate the resulting brain age biomarker on images of patients with mild cognitive impairment and Alzheimer’s disease. Interestingly, our results indicate that better-performing deep learning models in terms of brain age prediction on healthy patients do not result in more reliable biomarkers.
Data used in preparation of this article were obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database (adni.loni.usc.edu). As such, the investigators within the ADNI contributed to the design and implementation of ADNI and/or provided data but did not participate in analysis or writing of this report. A complete listing of ADNI investigators can be found at: http://adni.loni.usc.edu/wp-content/uploads/how_to_apply/ADNI_Acknowledgement_List.pdf.
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Notes
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In contrast to natural imaging datasets, as medical imaging requires an expensive procedure and legal authorization from each subject.
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At the end of each epoch, we compute the average over the 5 latest results, including the current one.
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The results of the tests on CN-MCI and CN-AD are available in our code repository https://github.com/gama-ufsc/brain-age.
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Pacheco, B.M., de Oliveira, V.H.R., Antunes, A.B.F., Pedro, S.D.S., Silva, D., for the Alzheimer’s Disease Neuroimaging Initiative. (2023). Does Pre-training on Brain-Related Tasks Results in Better Deep-Learning-Based Brain Age Biomarkers?. In: Naldi, M.C., Bianchi, R.A.C. (eds) Intelligent Systems. BRACIS 2023. Lecture Notes in Computer Science(), vol 14196. Springer, Cham. https://doi.org/10.1007/978-3-031-45389-2_13
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