Cape Town - 2026 ISMRM-ISMRT Annual Meeting and Exhibition
9 May 2026 – 14 May 2026 · Cape Town, South Africa
460-02-014 ISMRM Abstract

Connected component longitudinal features from white matter hyperintensities for the prediction of cognitive change

Primary: Neuro - Aging
Secondary: Analysis Methods - Classification and Prediction
460-02-014 · 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: White Matter Hyperintensities Predictive model Cognitive Function Random Forest Feature extraction
Accepted
Sang Hun Chung 1, Steven Cen2, Elizabeth Joe3, Vasilis Z Marmarelis4, Helena Chui3, Lirong Yan1,5
1Department of Radiology, Northwestern University Feinberg School of Medicine, Chicago, United States of America
2Radiology, Keck School of Medicine, University of Southern California, Los Angeles, United States of America
3Department of Neurology, Keck School of Medicine, University of Southern California, Los Angeles, United States of America
4Alfred E. Mann Department of Biomedical Engineering, University of Southern California, Los Angeles, United States of America
5Biomedical Engineering, Northwestern University, Chicago, United States of America
Presenting Author: Sang Hun Chung

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References

1. Debette, Stéphanie, and HS20660506 Markus. "The clinical importance of white matter hyperintensities on brain magnetic resonance imaging: systematic review and meta-analysis." Bmj 341 (2010).
2. Prins, Niels D., and Philip Scheltens. "White matter hyperintensities, cognitive impairment and dementia: an update." Nature Reviews Neurology 11.3 (2015): 157-165.
3. Promjunyakul, Nutta-on, et al. "Baseline NAWM structural integrity and CBF predict periventricular WMH expansion over time." Neurology 90.24 (2018): e2119-e2126.
4. Wang, Xin, et al. "Characterizing the penumbras of white matter hyperintensities in patients with cerebral small vessel disease." Japanese Journal of Radiology 41.9 (2023): 928-937.
5. Maillard, Pauline, et al. "Coevolution of white matter hyperintensities and cognition in the elderly." Neurology 79.5 (2012): 442-448.
6. De Groot, Marius, et al. "Changes in normal-appearing white matter precede development of white matter lesions." Stroke 44.4 (2013): 1037-1042.
7. Maillard, Pauline, et al. "White matter hyperintensities and their penumbra lie along a continuum of injury in the aging brain." Stroke 45.6 (2014): 1721-1726.
8. Schmidt, Paul, et al. "Automated segmentation of changes in FLAIR-hyperintense white matter lesions in multiple sclerosis on serial magnetic resonance imaging." NeuroImage: Clinical 23 (2019): 101849.
9. Lesions were segmented by the lesion prediction algorithm (Schmidt, 2017, Chapter 6.1) as implemented in the LST toolbox version 3.0.0 (www.statistical-modelling.de/lst.html) for SPM.
10. Wellcome Trust Centre for Neuroimaging. (2014). Statistical Parametric Mapping (SPM12) [Computer software]. University College London. Retrieved from https://www.fil.ion.ucl.ac.uk/spm/
11. Almgren, Hannes, et al. "Machine learning-based prediction of longitudinal cognitive decline in early Parkinson’s disease using multimodal features." Scientific reports 13.1 (2023): 13193.

Cite this abstract

http://echo.ismrm.org/p/ISMRM2026/460-02-014