Cape Town - 2026 ISMRM-ISMRT Annual Meeting and Exhibition
9 May 2026 – 14 May 2026 · Cape Town, South Africa
465-04-001 ISMRM Abstract

Signed Network Analysis Reveals Compensatory Responses of Default Mode Network Functional Connectivity to Amyloid Deposition

Primary: Brain Function and fMRI - fMRI Analysis
Secondary: Neuro - Alzheimer's Disease
465-04-001 · Image Analysis for Alzheimer's and Parkinson's · Tuesday, 12 May, 2:35 PM–3:30 PM · Digital Posters Row F
Keywords: Alzheimer's Disease Rs-fMRI Graphy theory Amyloid PET Signed Network Analysis
Accepted
Hasan Jafari 1, Ali Reihanian1, Gloria C Chiang2, Tracy A Butler2, Sudhin Shah2, Seyed Javad Moosania Zare3, Liangdong Zhou2, Yi Li2, Seyed Hani Hojjati2
1Arak University, Arak, Iran (Islamic Republic of)
2Department of Radiology, Weill Cornell Medicine, New York, United States of America
3Babol Noshirvani University of Technology, Iran (Islamic Republic of)
Presenting Author: Hasan Jafari

Synopsis

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References

1. J. A. Hardy and G. A. Higgins, ‘Alzheimer’s disease: The amyloid cascade hypothesis’, 1992. doi: 10.1126/science.1566067. [doi]
2. A. Hafkemeijer, J. van der Grond, and S. A. R. B. Rombouts, ‘Imaging the default mode network in aging and dementia’, 2012. doi: 10.1016/j.bbadis.2011.07.008. [doi]
3. S. H. Hojjati, F. Feiz, S. Ozoria, and Q. R. Razlighi, ‘Topographical Overlapping of the Amyloid-β and Tau Pathologies in the Default Mode Network Predicts Alzheimer’s Disease with Higher Specificity’, Journal of Alzheimer’s Disease, vol. 83, no. 1, pp. 407–421, 2021, doi: 10.3233/JAD-210419. [doi]
4. E. Bullmore and O. Sporns, ‘Complex brain networks: Graph theoretical analysis of structural and functional systems’, 2009. doi: 10.1038/nrn2575. [doi]
5. S. Hojjati, A. Ebrahimzadeh. of neuroscience, and undefined 2017, ‘Predicting conversion from MCI to AD using resting-state fMRI, graph theoretical approach and SVM’, Elsevier, Accessed: Jan. 21, 2021. [Online]. doi: 10.1016/j.jneumeth.2017.03.006 [doi]
6. S. H. Hojjati, A. Ebrahimzadeh, A. Khazaee, and A. Babajani-Feremi, ‘Predicting conversion from MCI to AD using resting-state fMRI, graph theoretical approach and SVM’, J Neurosci Methods, vol. 282, 2017, doi: 10.1016/j.jneumeth.2017.03.006. [doi]
7. S. H. Hojjati, A. Ebrahimzadeh, A. Khazaee, and A. Babajani-Feremi, ‘Predicting conversion from MCI to AD by integrating rs-fMRI and structural MRI’, Comput Biol Med, vol. 102, 2018, doi: 10.1016/j.compbiomed.2018.09.004. [doi]
8. L. Zhan et al., ‘The significance of negative correlations in brain connectivity’, Journal of Comparative Neurology, vol. 525, no. 15, 2017, doi: 10.1002/cne.24274. [doi]
9. A. Fornito, A. Zalesky, and M. Breakspear, ‘Graph analysis of the human connectome: Promise, progress, and pitfalls’, Neuroimage, vol. 80, 2013, doi: 10.1016/j.neuroimage.2013.04.087. [doi]
10. S. H. Hojjati et al., ‘Inter-network functional connectivity increases by beta-amyloid and may facilitate the early stage of tau accumulation’, Neurobiol Aging, vol. 148, pp. 16–26, Apr. 2025, doi: 10.1016/j.neurobiolaging.2025.01.005. [doi]
11. O. Esteban et al., ‘fmriprep’, Software, 2018.
12. O. Esteban et al., ‘fMRIPrep: a robust preprocessing pipeline for functional MRI’, Nat Methods, vol. 16, no. 1, 2019, doi: 10.1038/s41592-018-0235-4. [doi]
13. F. Heider, ‘Attitudes and Cognitive Organization’, Journal of Psychology: Interdisciplinary and Applied, vol. 21, no. 1, 1946, doi: 10.1080/00223980.1946.9917275. [doi]
14. D. Cartwright and F. Harary, ‘Structural balance: a generalization of Heider’s theory’, Psychol Rev, vol. 63, no. 5, 1956, doi: 10.1037/h0046049. [doi]
15. S. Aref, A. J. Mason, and M. C. Wilson, ‘A modeling and computational study of the frustration index in signed networks’, Networks, vol. 75, no. 1, 2020, doi: 10.1002/net.21907. [doi]
16. B. Zhang and S. Horvath, ‘A general framework for weighted gene co-expression network analysis’, Stat Appl Genet Mol Biol, vol. 4, no. 1, 2005, doi: 10.2202/1544-6115.1128. [doi]
17. G. Costantini and M. Perugini, ‘Generalization of clustering coefficients to signed correlation networks’, PLoS One, vol. 9, no. 2, 2014, doi: 10.1371/journal.pone.0088669. [doi]
18. G. Thedchanamoorthy, M. Piraveenan, D. Kasthuriratna, and U. Senanayake, ‘Node assortativity in complex networks: An alternative approach’, in Procedia Computer Science, 2014. doi: 10.1016/j.procs.2014.05.229. [doi]
19. LLC. Gurobi Optimization, ‘Gurobi Optimizer Reference Manual’, https://www.gurobi.com/ documentation/9.0/refman/index.html, aufgerufen am 27.10.2020, 2020.
20. S. H. Hojjati et al., ‘Remote Associations Between Tau and Cortical Amyloid-β Are Stage-Dependent’, 2024. doi: 10.3233/JAD-231362. [doi]
21. S. H. Hojjati et al., ‘Distinct and joint effects of low and high levels of Aβ and tau deposition on cortical thickness’, Neuroimage Clin, vol. 38, p. 103409, Apr. 2023, doi: 10.1016/j.nicl.2023.103409. [doi]
22. S. H. Hojjati et al., ‘Distinct and joint effects of low and high levels of Aβ and tau deposition on cortical thickness’, 2022. doi: 10.1101/2022.09.09.22279694. [doi]
23. S. H. Hojjati et al., ‘Increased between-network connectivity: A risk factor for tau elevation and disease progression’, Neurosci Lett, vol. 840, Sep. 2024, doi: 10.1016/j.neulet.2024.137943. [doi]

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http://echo.ismrm.org/p/ISMRM2026/465-04-001