Cape Town - 2026 ISMRM-ISMRT Annual Meeting and Exhibition
9 May 2026 – 14 May 2026 · Cape Town, South Africa
465-04-009 ISMRM Abstract

Staging of Alzheimer’s Disease Using Multi-Regional Volumetric Z-Scores and Cognitive Assessments

Accepted
Lina Bacha 1,2,3, Punith Bidarakka Venkategowda4,5, Keerthi Prabhu M4, Jean-Philippe Thiran2, Jonathan A Disselhorst1,2,3, Bénédicte Maréchal1,2,3, Tommaso Di Noto1,2,3
1Swiss Innovation Hub, Siemens Healthineers International AG, Lausanne, Switzerland
2LTS5, Ecole Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
3Department of Radiology, Lausanne University Hospital (CHUV) and University of Lausanne (UNIL), Lausanne, Switzerland
4Magnetic Resonance, Siemens Healthineers India, Bangalore, India
5International Institute of Information Technology Bangalore, Bangalore, India
Presenting Author: Lina Bacha

Synopsis

Motivation:
Goals:
Approach:
Results:
Full abstract & presentation

The full text, figures, and any recorded presentation for this abstract are not shown here. Log in if you are a member or registered attendee with access.

Full abstracts, figures, and presentations for Cape Town - 2026 ISMRM-ISMRT Annual Meeting and Exhibition are available to registered attendees. This content becomes freely available to the public roughly two years after the meeting.

To request or purchase access, contact the ISMRM Central Office at info@ismrm.org.

Log in

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]

Cite this abstract