Cape Town - 2026 ISMRM-ISMRT Annual Meeting and Exhibition
9 May 2026 – 14 May 2026 · Cape Town, South Africa
369-02-004 ISMRM Abstract

T1w/T2w-Derived Brain Age Gap in Parkinson’s disease and Correlations with Clinical and Neuropsychological Scores

Primary: Analysis Methods - Classification and Prediction
Secondary: Neuro - Parkinson's Disease
369-02-004 · Imaging Parkinson’s Disease: Multimodal Biomarkers of Progression and Phenotype · Monday, 11 May, 9:15 AM–10:10 AM · Digital Posters Row J
Keywords: Machine Learning/Artificial Intelligence Parkinson's Disease Myelin T1-w T2-w Ratio imaging Brain Age Gap
Accepted
Gaurav Nitin Rathi 1, Jason Longhurst2, Jessica Z K. Caldwell3, Aaron Ritter4, Zoltan Mari4, Virendra Mishra1
1Department of Radiology, University of Alabama at Birmingham, Birmingham, United States of America
2Department of Physical Therapy and Athletic Training, Saint Louis University, St. Louis, United States of America
3Department of Neuropsychology, Cleveland Clinic Lou Ruvo Center for Brain Health, Las Vegas, United States of America
4Department of Neurology, Cleveland Clinic Lou Ruvo Center for Brain Health, Las Vegas, United States of America
Presenting Author: Gaurav Nitin Rathi

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. Fjell, A. M.; Walhovd, K. B. Structural Brain Changes in Aging: Courses, Causes and Cognitive Consequences. Rev Neuroscience 2010, 21 (3), 187-221. DOI: DOI 10.1515/revneuro.2010.21.3.187. [doi]
2. Franke, K.; Gaser, C. Ten Years of BrainAGE as a Neuroimaging Biomarker of Brain Aging: What Insights Have We Gained? Front Neurol 2019, 10, 789. DOI: 10.3389/fneur.2019.00789 From NLM PubMed-not-MEDLINE. [doi]
3. Sowell, E. R.; Peterson, B. S.; Thompson, P. M.; Welcome, S. E.; Henkenius, A. L.; Toga, A. W. Mapping cortical change across the human life span. Nat Neurosci 2003, 6 (3), 309-315. DOI: 10.1038/nn1008 From NLM Medline. [doi]
4. Franke, K.; Ziegler, G.; Kloppel, S.; Gaser, C.; Alzheimer's Disease Neuroimaging, I. Estimating the age of healthy subjects from T1-weighted MRI scans using kernel methods: exploring the influence of various parameters. Neuroimage 2010, 50 (3), 883-892. DOI: 10.1016/j.neuroimage.2010.01.005 From NLM Medline. [doi]
5. Cole, J. H.; Poudel, R. P. K.; Tsagkrasoulis, D.; Caan, M. W. A.; Steves, C.; Spector, T. D.; Montana, G. Predicting brain age with deep learning from raw imaging data results in a reliable and heritable biomarker. Neuroimage 2017, 163, 115-124. DOI: 10.1016/j.neuroimage.2017.07.059 From NLM Medline. [doi]
6. Cole, J. H.; Ritchie, S. J.; Bastin, M. E.; Valdes Hernandez, M. C.; Munoz Maniega, S.; Royle, N.; Corley, J.; Pattie, A.; Harris, S. E.; Zhang, Q.; et al. Brain age predicts mortality. Mol Psychiatry 2018, 23 (5), 1385-1392. DOI: 10.1038/mp.2017.62 From NLM Medline. [doi]
7. Zapaishchykova, A.; Tak, D.; Ye, Z.; Liu, K. X.; Likitlersuang, J.; Vajapeyam, S.; Chopra, R. B.; Seidlitz, J.; Bethlehem, R. A. I.; Mak, R. H.; et al. Diffusion deep learning for brain age prediction and longitudinal tracking in children through adulthood. Imaging Neurosci (Camb) 2024, 2. DOI: 10.1162/imag_a_00114 From NLM PubMed-not-MEDLINE. [doi]
8. Zhu, Y.; Wang, F.; Ning, P.; Zhu, Y.; Zhang, L.; Li, K.; Liu, B.; Ren, H.; Xu, Z.; Pang, A.; et al. Multimodal neuroimaging-based prediction of Parkinson's disease with mild cognitive impairment using machine learning technique. NPJ Parkinsons Dis 2024, 10 (1), 218. DOI: 10.1038/s41531-024-00828-6 From NLM PubMed-not-MEDLINE. [doi]
9. Ashburner, J.; Friston, K. J. Voxel-based morphometry--the methods. Neuroimage 2000, 11 (6 Pt 1), 805-821. DOI: 10.1006/nimg.2000.0582 From NLM Medline. [doi]
10. Glasser, M. F.; Van Essen, D. C. Mapping human cortical areas in vivo based on myelin content as revealed by T1- and T2-weighted MRI. J Neurosci 2011, 31 (32), 11597-11616. DOI: 10.1523/JNEUROSCI.2180-11.2011 From NLM Medline. [doi]
11. Stuber, C.; Morawski, M.; Schafer, A.; Labadie, C.; Wahnert, M.; Leuze, C.; Streicher, M.; Barapatre, N.; Reimann, K.; Geyer, S.; et al. Myelin and iron concentration in the human brain: a quantitative study of MRI contrast. Neuroimage 2014, 93 Pt 1, 95-106. DOI: 10.1016/j.neuroimage.2014.02.026 From NLM Medline. [doi]
12. Pedregosa, F.; Varoquaux, G.; Gramfort, A.; Michel, V.; Thirion, B.; Grisel, O.; Blondel, M.; Prettenhofer, P.; Weiss, R.; Dubourg, V.; et al. Scikit-learn: Machine Learning in Python. J Mach Learn Res 2011, 12, 2825-2830.
13. Karl, P. LIII. On lines and planes of closest fit to systems of points in space. The London, Edinburgh, and Dublin Philosophical Magazine and Journal of Science 1901, 2 (11), 559--572. DOI: 10.1080/14786440109462720. [doi]
14. Diebold, F. X.; Mariano, R. S.; National Bureau of Economic, R. Comparing Predictive Accuracy; National Bureau of Economic Research, 1994.
15. Winkler, A. M.; Ridgway, G. R.; Webster, M. A.; Smith, S. M.; Nichols, T. E. Permutation inference for the general linear model. Neuroimage 2014, 92 (100), 381-397. DOI: 10.1016/j.neuroimage.2014.01.060 From NLM Medline. [doi]

Cite this abstract

http://echo.ismrm.org/p/ISMRM2026/369-02-004