Current - Issue
Year 2026 · Volume 5 · Issue 3
Original Article
Voxel-Level Brain Age Mapping Via Explainable Deep Learning - A Multi-Disease Framework for Characterizing Alzheimer’s And Parkinson’s Pathologies
Shobha P1
Sandhya K S2
Hemanth K3
1 2 Assistant professor, Department of computer Application, JSS Science and Technology University, Mysore, Karnataka, India. 3 Assistant Professor, Vidyavardhaka First Grade College PG Centre, Mysore, Karnataka, India.
Published Online: September-December 2026
Pages: 84-89
Cite this article
↗ https://www.doi.org/10.59256/indjcst.20260503011
References
1. Calabresi, P., Mechelli, A., Natale, G., Volpicelli-Daley, L., Di Lazzaro, G., & Ghiglieri, V. (2023). Alpha-synuclein in Parkinson’s disease and other synucleinopathies: From overt neurodegeneration back to early synaptic dysfunction. Cell Death & Disease, 14, 176. https://doi.org/10.1038/s41419-023-05672-9
2. Cole, J. H., & Franke, K. (2017). Predicting age using neuroimaging: Innovative brain ageing biomarkers. Trends in Neurosciences, 40(12), 681–690. https://doi.org/10.1016/j.tins.2017.10.001
3. Cole, J. H., Poudel, R. P. K., Tsagkrasoulis, D., Caan, M. W. A., Steves, C., Spector, T. D., & Montana, G. (2017). Predicting brain age with deep learning from raw imaging data results in a reliable and heritable biomarker. NeuroImage, 163, 115–124. https://doi.org/10.1016/j.neuroimage.2017.07.059
4. Fraser, M. A., Shaw, M. E., & Cherbuin, N. (2015). A systematic review and meta-analysis of longitudinal hippocampal atrophy in healthy human ageing. NeuroImage, 112, 364–374. https://doi.org/10.1016/j.neuroimage.2015.03.035
5. Goedert, M., Spillantini, M. G., Del Tredici, K., & Braak, H. (2013). 100 years of Lewy pathology. Nature Reviews Neurology, 9, 13–24. https://doi.org/10.1038/nrneurol.2012.242
6. Jack, C. R., Jr., Bennett, D. A., Blennow, K., Carrillo, M. C., Dunn, B., Haeberlein, S. B., Holtzman, D. M., Jagust, W., Jessen, F., Karlawish, J., Liu, E., Molinuevo, J. L., Montine, T., Phelps, C., Rankin, K. P., Rowe, C. C., Scheltens, P., Siemers, E., Snyder, H. M., & Silverberg, N. (2018). NIA-AA research framework: Toward a biological definition of Alzheimer’s disease. Alzheimer’s & Dementia, 14(4), 535–562. https://doi.org/10.1016/j.jalz.2018.02.018
7. Jenkinson, M., Bannister, P., Brady, M., & Smith, S. (2002). Improved optimization for the robust and accurate linear registration and motion correction of brain images. NeuroImage, 17(2), 825–841. https://doi.org/10.1006/nimg.2002.1132
8. Morra, J. H., Tu, Z., Apostolova, L. G., Green, A. E., Avedissian, C., Madsen, S. K.,Parikshak, N., Toga, A. W., Jack, C. R., Jr., Schuff, N., Weiner, M. W., & Thompson, P.M. (2009). Automated mapping of hippocampal atrophy in 1-year repeat MRI data from 490 subjects with Alzheimer’s disease, mild cognitive impairment, and elderly controls. NeuroImage, 45(Suppl. 1), S3–S15.https://doi.org/10.1016/j.neuroimage.2008.10.043
9. Park, H., Kam, T.-I., Dawson, V. L., & Dawson, T. M. (2025). α-Synuclein pathology as a target in neurodegenerative diseases. Nature Reviews Neurology, 21, 32–47. https://doi.org/10.1038/s41582-024-01043-w
10. Parkinson’s Progression Markers Initiative. (n.d.). Research documents and study information. https://www.ppmi-info.org/
11. Selvaraju, R. R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., & Batra, D. (2017). Grad-CAM: Visual explanations from deep networks via gradient-based localization. In Proceedings of the IEEE International Conference on Computer Vision (pp. 618–626). https://doi.org/10.1109/ICCV.2017.74
12. Tustison, N. J., Avants, B. B., Cook, P. A., Zheng, Y., Egan, A., Yushkevich, P. A., & Gee, J. C. (2010). N4ITK: Improved N3 bias correction. IEEE Transactions on Medical Imaging, 29(6), 1310–1320. https://doi.org/10.1109/TMI.2010.2046908
2. Cole, J. H., & Franke, K. (2017). Predicting age using neuroimaging: Innovative brain ageing biomarkers. Trends in Neurosciences, 40(12), 681–690. https://doi.org/10.1016/j.tins.2017.10.001
3. Cole, J. H., Poudel, R. P. K., Tsagkrasoulis, D., Caan, M. W. A., Steves, C., Spector, T. D., & Montana, G. (2017). Predicting brain age with deep learning from raw imaging data results in a reliable and heritable biomarker. NeuroImage, 163, 115–124. https://doi.org/10.1016/j.neuroimage.2017.07.059
4. Fraser, M. A., Shaw, M. E., & Cherbuin, N. (2015). A systematic review and meta-analysis of longitudinal hippocampal atrophy in healthy human ageing. NeuroImage, 112, 364–374. https://doi.org/10.1016/j.neuroimage.2015.03.035
5. Goedert, M., Spillantini, M. G., Del Tredici, K., & Braak, H. (2013). 100 years of Lewy pathology. Nature Reviews Neurology, 9, 13–24. https://doi.org/10.1038/nrneurol.2012.242
6. Jack, C. R., Jr., Bennett, D. A., Blennow, K., Carrillo, M. C., Dunn, B., Haeberlein, S. B., Holtzman, D. M., Jagust, W., Jessen, F., Karlawish, J., Liu, E., Molinuevo, J. L., Montine, T., Phelps, C., Rankin, K. P., Rowe, C. C., Scheltens, P., Siemers, E., Snyder, H. M., & Silverberg, N. (2018). NIA-AA research framework: Toward a biological definition of Alzheimer’s disease. Alzheimer’s & Dementia, 14(4), 535–562. https://doi.org/10.1016/j.jalz.2018.02.018
7. Jenkinson, M., Bannister, P., Brady, M., & Smith, S. (2002). Improved optimization for the robust and accurate linear registration and motion correction of brain images. NeuroImage, 17(2), 825–841. https://doi.org/10.1006/nimg.2002.1132
8. Morra, J. H., Tu, Z., Apostolova, L. G., Green, A. E., Avedissian, C., Madsen, S. K.,Parikshak, N., Toga, A. W., Jack, C. R., Jr., Schuff, N., Weiner, M. W., & Thompson, P.M. (2009). Automated mapping of hippocampal atrophy in 1-year repeat MRI data from 490 subjects with Alzheimer’s disease, mild cognitive impairment, and elderly controls. NeuroImage, 45(Suppl. 1), S3–S15.https://doi.org/10.1016/j.neuroimage.2008.10.043
9. Park, H., Kam, T.-I., Dawson, V. L., & Dawson, T. M. (2025). α-Synuclein pathology as a target in neurodegenerative diseases. Nature Reviews Neurology, 21, 32–47. https://doi.org/10.1038/s41582-024-01043-w
10. Parkinson’s Progression Markers Initiative. (n.d.). Research documents and study information. https://www.ppmi-info.org/
11. Selvaraju, R. R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., & Batra, D. (2017). Grad-CAM: Visual explanations from deep networks via gradient-based localization. In Proceedings of the IEEE International Conference on Computer Vision (pp. 618–626). https://doi.org/10.1109/ICCV.2017.74
12. Tustison, N. J., Avants, B. B., Cook, P. A., Zheng, Y., Egan, A., Yushkevich, P. A., & Gee, J. C. (2010). N4ITK: Improved N3 bias correction. IEEE Transactions on Medical Imaging, 29(6), 1310–1320. https://doi.org/10.1109/TMI.2010.2046908
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