Cape Town - 2026 ISMRM-ISMRT Annual Meeting and Exhibition
9 May 2026 – 14 May 2026 · Cape Town, South Africa
460-02-010 ISMRM Abstract

Explainable Brain Age Prediction with White Matter Fiber Orientation Distributions Using Rotation-Equivariant CNNs

Primary: Neuro - Aging
Secondary: Analysis Methods - Classification and Prediction
460-02-010 · From Bench to Bedside: All About the Brain in Health and Alzheimer’s Disease · Tuesday, 12 May, 9:15 AM–10:10 AM · Digital Posters Row A
Keywords: Diffusion MRI Explainable AI Brain age Geometric deep learning Fiber orientation
Accepted
Chenyang Fan 1, Robert D Sanders2, Sharon L Naismith3, Fernando Calamante1,4, Jinglei Lv5
1Biomedical Engineering, The University of Sydney, Sydney, Australia
2Sydney Medical School/Central Clinical School, The University of Sydney, Sydney, Australia
3Leonard P Ullman Chair in Psychology, School of Psychology, The University of Sydney, Sydney, Australia
4Sydney Imaging, The University of Sydney, Sydney, Australia
5Sydney Medical School and Brain and Mind Centre, The University of Sydney, Sydney, Australia
Presenting Author: Chenyang Fan

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. Weiler, M., Geiger, M., Welling, M., Boomsma, W. & Cohen, T. 3D Steerable CNNs: Learning Rotationally Equivariant Features in Volumetric Data. Preprint at https://doi.org/10.48550/arXiv.1807.02547 (2018). [doi]
2. Glasser, M. F. et al. The Minimal Preprocessing Pipelines for the Human Connectome Project. Neuroimage 80, 105–124 (2013).
3. Tournier, J.-D. et al. MRtrix3: A fast, flexible and open software framework for medical image processing and visualisation. Neuroimage 202, 116137 (2019).
4. Basser, P. J., Mattiello, J. & Lebihan, D. Estimation of the Effective Self-Diffusion Tensor from the NMR Spin Echo. Journal of Magnetic Resonance, Series B 103, 247–254 (1994).
5. Jeurissen, B., Tournier, J.-D., Dhollander, T., Connelly, A. & Sijbers, J. Multi-tissue constrained spherical deconvolution for improved analysis of multi-shell diffusion MRI data. NeuroImage 103, 411–426 (2014).
6. Peng, H., Gong, W., Beckmann, C. F., Vedaldi, A. & Smith, S. M. Accurate brain age prediction with lightweight deep neural networks. Medical Image Analysis 68, 101871 (2021).
7. Jeurissen, B., Leemans, A., Tournier, J.-D., Jones, D. K. & Sijbers, J. Investigating the prevalence of complex fiber configurations in white matter tissue with diffusion magnetic resonance imaging. Hum Brain Mapp 34, 2747–2766 (2013).
8. Wasserthal, J., Neher, P. & Maier-Hein, K. H. TractSeg - Fast and accurate white matter tract segmentation. NeuroImage 183, 239–253 (2018).
9. Smith, S. M., Vidaurre, D., Alfaro-Almagro, F., Nichols, T. E. & Miller, K. L. Estimation of brain age delta from brain imaging. NeuroImage 200, 528–539 (2019).
10. Raffelt, D. A. et al. Investigating white matter fibre density and morphology using fixel-based analysis. Neuroimage 144, 58–73 (2017).
11. Wen, J. et al. The genetic architecture of multimodal human brain age. Nat Commun 15, 2604 (2024).
12. de Lange, A.-M. G. et al. Multimodal brain-age prediction and cardiovascular risk: The Whitehall II MRI sub-study. NeuroImage 222, 117292 (2020).
13. Beck, D. et al. White matter microstructure across the adult lifespan: A mixed longitudinal and cross-sectional study using advanced diffusion models and brain-age prediction. NeuroImage 224, 117441 (2021).
14. Richie-Halford, A., Yeatman, J. D., Simon, N. & Rokem, A. Multidimensional analysis and detection of informative features in human brain white matter. PLoS Comput Biol 17, e1009136 (2021).
15. Nemmi, F., Levardon, M. & Péran, P. Brain‐age estimation accuracy is significantly increased using multishell free‐water reconstruction. Hum Brain Mapp 43, 2365–2376 (2022).
16. Salih, A. et al. Brain age estimation at tract group level and its association with daily life measures, cardiac risk factors and genetic variants. Scientific Reports 11, (2021).
17. Tønnesen, S. et al. Brain Age Prediction Reveals Aberrant Brain White Matter in Schizophrenia and Bipolar Disorder: A Multisample Diffusion Tensor Imaging Study. Biological Psychiatry: Cognitive Neuroscience and Neuroimaging 5, 1095–1103 (2020).
18. Chen, C.-L. et al. Generalization of diffusion magnetic resonance imaging–based brain age prediction model through transfer learning. NeuroImage 217, 116831 (2020).
19. Cole, J. H. Multimodality neuroimaging brain-age in UK biobank: relationship to biomedical, lifestyle, and cognitive factors. Neurobiology of Aging 92, 34–42 (2020).
20. Richard, G. et al. Assessing distinct patterns of cognitive aging using tissue-specific brain age prediction based on diffusion tensor imaging and brain morphometry. PeerJ 6, e5908 (2018).
21. Feng, Y. et al. BundleAGE: Predicting White Matter Age using Along-Tract Microstructural Profiles from Diffusion MRI. bioRxiv 2024.08.16.608347 (2024) doi:10.1101/2024.08.16.608347. [doi]
22. Yang, A. C., Tsai, S.-J., Liu, M.-E., Huang, C.-C. & Lin, C.-P. The Association of Aging with White Matter Integrity and Functional Connectivity Hubs. Front. Aging Neurosci. 8, (2016).
23. Douet, V. & Chang, L. Fornix as an imaging marker for episodic memory deficits in healthy aging and in various neurological disorders. Front Aging Neurosci 6, 343 (2015).

Cite this abstract

http://echo.ismrm.org/p/ISMRM2026/460-02-010