Cape Town - 2026 ISMRM-ISMRT Annual Meeting and Exhibition
9 May 2026 – 14 May 2026 · Cape Town, South Africa
271-01-048 / 605-03-006 ISMRM Abstract

Limbic Neurometabolic Network Segregation Underlies Cognitive Resilience in Alzheimer’s Disease

Accepted
Wenli Li 1, Miao Zhang2, Yibo Zhao3, Yudu Li3,4,5, Wen Jin3,6, Yaoyu Zhang1, Yue Guan1, Wenqi Zhang1, Zhi-Pei Liang3,6, Yao Li1
1National Engineering Research Center of Advanced Magnetic Resonance Technologies for Diagnosis and Therapy, School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China
2Department of Nuclear Medicine, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
3Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Champaign, United States of America
4Department of Bioengineering, University of Illinois at Urbana-Champaign, Champaign, United States of America
5National Center for Supercomputing Applications, University of Illinois at Urbana-Champaign, Champaign, United States of America
6Electrical and Computer Engineering, University of Illinois at Urbana-Champaign, Champaign, United States of America
Presenting Author: Wenli Li

Synopsis

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References

1. Anand C, Abdelnour F, Sipes B, et al. Selective vulnerability and resilience to Alzheimer's disease tauopathy as a function of genes and the connectome. Brain. 2025;148(10):3679-3693. doi: 10.1101/2024.03.04.583403. [doi]
2. Pereira JB, Ossenkoppele R, Palmqvist S, et al. Amyloid and tau accumulate across distinct spatial networks and are differentially associated with brain connectivity. Elife. 2019;8:e50830. doi: 10.7554/eLife.50830. [doi]
3. Vogel JW, Iturria-Medina Y, Strandberg OT, et al. Spread of pathological tau proteins through communicating neurons in human Alzheimer’s disease. Nature Communications. 2020;11(1):2612. doi: 10.1038/s41467-020-15701-2. [doi]
4. Braak H, Braak E. Neuropathological stageing of Alzheimer-related changes. Acta Neuropathologica. 1991;82(4):239-259. doi: 10.1007/BF00308809. [doi]
5. Nestor PJ, Fryer TD, Smielewski P, Hodges JR. Limbic hypometabolism in Alzheimer's disease and mild cognitive impairment. Annals of Neurology. 2003;54(3):343-351. doi: 10.1002/ana.10669. [doi]
6. Schneider JA, Arvanitakis Z, Leurgans SE, Bennett DA. The neuropathology of probable Alzheimer disease and mild cognitive impairment. Annals of Neurology. 2009;66(2):200-208. doi: 10.1002/ana.21706. [doi]
7. Zissimopoulos J, Crimmins E, Clair PS. The value of delaying Alzheimer’s disease onset. De Gruyter; 2015:25-39. doi: 10.1515/fhep-2014-0013. [doi]
8. Bullmore E, Sporns O. The economy of brain network organization. Nature Reviews Neuroscience. 2012;13(5):336-349. doi: 10.1038/nrn3214. [doi]
9. Wig GS. Segregated systems of human brain networks. Trends in Cognitive Sciences. 2017;21(12):981-996. doi: 10.1016/j.tics.2017.09.006. [doi]
10. Ewers M, Luan Y, Frontzkowski L, et al. Segregation of functional networks is associated with cognitive resilience in Alzheimer’s disease. Brain. 2021;144(7):2176-2185. doi: 10.1093/brain/awab112. [doi]
11. Qiu T, Liu ZQ, Rheault F, et al. Structural white matter properties and cognitive resilience to tau pathology. Alzheimer's & Dementia. 2024;20(5):3364-3377. doi: 10.1002/alz.13776. [doi]
12. Lam F, Li Y, Guo R, Clifford B, Liang Z-P. Ultrafast magnetic resonance spectroscopic imaging using SPICE with learned subspaces. Magnetic Resonance in Medicine. 2020;83(2):377-390. doi: 10.1002/mrm.27980. [doi]
13. Lam F, Ma C, Clifford B, Johnson CL, Liang Z-P. High‐resolution 1H‐MRSI of the brain using SPICE: data acquisition and image reconstruction. Magnetic Resonance in Medicine. 2016;76(4):1059-1070. doi: 10.1002/mrm.26019. [doi]
14. Liang Z-P. Spatiotemporal imaging with partially separable functions. IEEE; 2007:988-991. doi: 10.1109/NFSI-ICFBI.2007.4387720. [doi]
15. Zhao Y, Li Y, Jin W, et al. Ultrafast J-resolved magnetic resonance spectroscopic imaging for high-resolution metabolic brain imaging. Nature Biomedical Engineering. 2025:1-13. doi: 10.1038/s41551-025-01418-4. [doi]
16. Li Y, Lam F, Clifford B, Liang Z-P. A subspace approach to spectral quantification for MR spectroscopic imaging. IEEE Transactions on Biomedical Engineering. 2017;64(10):2486-2489. doi: 10.1109/TBME.2017.2741922. [doi]
17. Ma C, Lam F, Johnson CL, Liang Z-P. Removal of nuisance signals from limited and sparse 1H MRSI data using a union‐of‐subspaces model. Magnetic Resonance in Medicine. 2016;75(2):488-497. doi: 10.1002/mrm.25635. [doi]
18. Schaefer A, Kong R, Gordon EM, et al. Local-global parcellation of the human cerebral cortex from intrinsic functional connectivity MRI. Cerebral Cortex. 2018;28(9):3095-3114. doi: 10.1093/cercor/bhx179. [doi]
19. Li W, Tang Y, Peng L, Wang Z, Hu S, Gao X. The reconfiguration pattern of individual brain metabolic connectome for Parkinson's disease identification. MedComm. 2023;4(4):e305. doi: 10.1002/mco2.305. [doi]
20. Wang J, He Y. Toward individualized connectomes of brain morphology. Trends in Neurosciences. 2024;47(2):106-119. doi: 10.1016/j.tins.2023.11.011. [doi]

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