References
1. Bit, S., Dey, P., Maji, A., & Khan, T. K. (2024). MRI-based mild cognitive impairment and alzheimer’s disease classification using an algorithm of combination of variational autoencoder and other machine learning classifiers. Journal of Alzheimer’s Disease Reports, 8(1), 1434–1452. https://doi.org/10.1177/25424823241290694
[doi]
2. Wittens, M. M. J., Denissen, S., Sima, D. M., Fransen, E., Niemantsverdriet, E., Bastin, C., Benoit, F., Bergmans, B., Bier, J.-C., de Deyn, P. P., Deryck, O., Hanseeuw, B., Ivanoiu, A., Picard, G., Ribbens, A., Salmon, E., Segers, K., Sieben, A., Struyfs, H., … Engelborghs, S. (2024). Brain age as a biomarker for pathological versus healthy ageing – A remember study. Alzheimer’s Research & Therapy, 16(1). https://doi.org/10.1186/s13195-024-01491-y
[doi]
3. Frisoni, G. B., Fox, N. C., Jack, C. R., Scheltens, P., & Thompson, P. M. (2010). The clinical use of structural MRI in alzheimer disease. Nature Reviews Neurology, 6(2), 67–77. https://doi.org/10.1038/nrneurol.2009.215
[doi]
4. Zhang, B., Lin, L., Wu, S., & Al-Masqari, Z. (2021). Multiple subtypes of alzheimer’s disease base on brain atrophy pattern. Brain Sciences, 11(2), 278. https://doi.org/10.3390/brainsci11020278
[doi]
5. Verdi, S., Kia, S. M., Yong, K. X. X., Tosun, D., Schott, J. M., Marquand, A. F., & Cole, J. H. (2023). Revealing individual neuroanatomical heterogeneity in alzheimer disease using neuroanatomical normative modeling. Neurology, 100(24). https://doi.org/10.1212/wnl.0000000000207298
[doi]
6. Li, B., Zhang, M., Riphagen, J., Morrison Yochim, K., Li, B., Liu, J., & Salat, D. H. (2020). Prediction of clinical and biomarker conformed alzheimer’s disease and mild cognitive impairment from multi-feature brain structural MRI using age-correction from a large independent lifespan sample. NeuroImage: Clinical, 28, 102387. https://doi.org/10.1016/j.nicl.2020.102387
[doi]
7. Klingenberg, M., Stark, D., Eitel, F., Budding, C., Habes, M., & Ritter, K. (2023). Higher performance for women than men in MRI-based alzheimer’s disease detection. Alzheimer’s Research & Therapy, 15(1). https://doi.org/10.1186/s13195-023-01225-6
[doi]
8. Petersen, R. C., Aisen, P. S., Beckett, L. A., Donohue, M. C., Gamst, A. C., Harvey, D. J., Jack, C. R., Jagust, W. J., Shaw, L. M., Toga, A. W., Trojanowski, J. Q., & Weiner, M. W. (2010). Alzheimer’s disease neuroimaging initiative (ADNI). Neurology, 74(3), 201–209. https://doi.org/10.1212/wnl.0b013e3181cb3e25
[doi]
9. Tombaugh, Tom N., and Nancy J. McIntyre. "The mini‐mental state examination: a comprehensive review." Journal of the American Geriatrics Society 40.9 (1992): 922-935.
10. O’Bryant, Sid E., et al. "Staging dementia using Clinical Dementia Rating Scale Sum of Boxes scores: a Texas Alzheimer's research consortium study." Archives of neurology 65.8 (2008): 1091-1095.
11. Perrin, R. J., Franklin, E. E., Bernhardt, H., Burns, A., Schwetye, K. E., Cairns, N. J., Baxter, M., Weiner, M. W., & Morris, J. C. (2024). The alzheimer’s disease neuroimaging initiative Neuropathology Core: An update. Alzheimer’s and Dementia, 20(11), 7859–7870. https://doi.org/10.1002/alz.14253
[doi]
12. Ricchi, Ilaria, et al. "The complementary role of automated brain volumetry to stratify ADNI participants within the ATN framework." Journal of Alzheimer’s Disease (2025): 13872877251339840.
13. Shaw, L. M., Vanderstichele, H., Knapik‐Czajka, M., Clark, C. M., Aisen, P. S., Petersen, R. C., Blennow, K., Soares, H., Simon, A., Lewczuk, P., Dean, R., Siemers, E., Potter, W., Lee, V. M. ‐Y., & Trojanowski, J. Q. (2009). Cerebrospinal fluid biomarker signature in alzheimer’s disease neuroimaging initiative subjects. Annals of Neurology, 65(4), 403–413. https://doi.org/10.1002/ana.21610
[doi]
14. Venkategowda et al. “Establishing Brain Reference Ranges in Indian Population: Comparison with Westerners and Validation for Parkinsonism Differential Diagnosis”. Submitted in parallel to the Annual Meeting of ISMRM 2025.
15. Breiman, Leo. "Random forests." Machine learning 45.1 (2001): 5-32.
16. Darst, Burcu F., Kristen C. Malecki, and Corinne D. Engelman. "Using recursive feature elimination in random forest to account for correlated variables in high dimensional data." BMC genetics 19.Suppl 1 (2018): 65.
17. Fabian, Pedregosa. "Scikit-learn: Machine learning in Python." Journal of machine learning research 12 (2011): 2825.
18. Di Noto, T., von Spiczak, J., Mannil, M., Gantert, E., Soda, P., Manka, R., & Alkadhi, H. (2019). Radiomics for distinguishing myocardial infarction from myocarditis at late gadolinium enhancement at MRI: Comparison with subjective visual analysis. Radiology: Cardiothoracic Imaging, 1(5). https://doi.org/10.1148/ryct.2019180026
[doi]
19. Tougui, I., Jilbab, A., & Mhamdi, J. E. (2021). Impact of the choice of cross-validation techniques on the results of machine learning-based diagnostic applications. Healthcare Informatics Research, 27(3), 189–199. https://doi.org/10.4258/hir.2021.27.3.189
[doi]
20. Jarholm, J. A., Bjørnerud, A., Dalaker, T. O., Akhavi, M. S., Kirsebom, B. E., Pålhaugen, L., Nordengen, K., Grøntvedt, G. R., Nakling, A., Kalheim, L. F., Almdahl, I. S., Tecelão, S., Fladby, T., & Selnes, P. (2023). Medial temporal lobe atrophy in predementia alzheimer’s disease: A longitudinal multi-site study comparing staging and a/t/N in a clinical research cohort. Journal of Alzheimer’s Disease, 94(1), 259–279. https://doi.org/10.3233/jad-221274
[doi]
21. Teipel, S. J., Bayer, W., Alexander, G. E., Zebuhr, Y., Teichberg, D., Kulic, L., Schapiro, M. B., Möller, H.-J., Rapoport, S. I., & Hampel, H. (2002). Progression of corpus callosum atrophy in alzheimer disease. Archives of Neurology, 59(2), 243. https://doi.org/10.1001/archneur.59.2.243
[doi]
22. Ott, B. R., Cohen, R. A., Gongvatana, A., Okonkwo, O. C., Johanson, C. E., Stopa, E. G., Donahue, J. E., & Silverberg, G. D. (2010). Brain ventricular volume and cerebrospinal fluid biomarkers of alzheimer’s disease. Journal of Alzheimer’s Disease, 20(2), 647–657. https://doi.org/10.3233/jad-2010-1406
[doi]