Cape Town - 2026 ISMRM-ISMRT Annual Meeting and Exhibition
9 May 2026 – 14 May 2026 · Cape Town, South Africa
531-02-003 / 531-02-003 ISMRM Abstract

Triple-Network Effective Connectivity Predicts Dementia Conversion in Cognitively Normal Adults

Accepted
Zhen Xuen Brandon Low1, Lucy Vivash2, Terence J O'Brien2,3,4, Adeel Razi1, Meng Law1,2,5, Ben Sinclair 1
1Monash University, Melbourne, Australia
2Department of Neuroscience, School of Translational Medicine, Monash University, Melbourne, Australia
3The Alfred, Australia
4Neurology, Alfred Hospital, Melbourne, Australia
5Alfred Health, Melbourne, Australia
Presenting Author: Ben Sinclair

Synopsis

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References

1. Menon, V. (2011). Large-scale brain networks and psychopathology: a unifying triple network model. Trends in Cognitive Sciences, 15(10), 483-506. https://doi.org/https://doi.org/10.1016/j.tics.2011.08.003 [doi]
2. Razi, A., Kahan, J., Rees, G., & Friston, K. J. (2015). Construct validation of a DCM for resting state fMRI. Neuroimage, 106, 1-14. https://doi.org/10.1016/j.neuroimage.2014.11.027 [doi]
3. Ingannato, A., Bagnoli, S., Mazzeo, S., Giacomucci, G., Bessi, V., Ferrari, C., Sorbi, S., & Nacmias, B. (2024). Plasma GFAP, NfL and pTau 181 detect preclinical stages of dementia [Original Research]. Frontiers in Endocrinology, Volume 15 - 2024. https://doi.org/10.3389/fendo.2024.1375302 [doi]
4. Katabathula, S., Wang, Q., & Xu, R. (2021). Predict Alzheimer’s disease using hippocampus MRI data: a lightweight 3D deep convolutional network model with visual and global shape representations. Alzheimer's Research & Therapy, 13(1), 104. https://doi.org/10.1186/s13195-021-00837-0 [doi]
5. Qiang, Y.-X., You, J., He, X.-Y., Guo, Y., Deng, Y.-T., Gao, P.-Y., Wu, X.-R., Feng, J.-F., Cheng, W., & Yu, J.-T. (2024). Plasma metabolic profiles predict future dementia and dementia subtypes: a prospective analysis of 274,160 participants. Alzheimer's Research & Therapy, 16(1), 16. https://doi.org/10.1186/s13195-023-01379-3 [doi]
6. Wang, X., Shi, Z., Qiu, Y., Sun, D., & Zhou, H. (2024). Peripheral GFAP and NfL as early biomarkers for dementia: longitudinal insights from the UK Biobank. BMC Medicine, 22(1), 192. https://doi.org/10.1186/s12916-024-03418-8 [doi]
7. Almgren, H., Van de Steen, F., Kühn, S., Razi, A., Friston, K., & Marinazzo, D. (2018). Variability and reliability of effective connectivity within the core default mode network: A multi-site longitudinal spectral DCM study. Neuroimage, 183, 757-768. https://doi.org/10.1016/j.neuroimage.2018.08.053 [doi]
8. Dunn, C. J., Duffy, S. L., Hickie, I. B., Lagopoulos, J., Lewis, S. J. G., Naismith, S. L., & Shine, J. M. (2014). Deficits in episodic memory retrieval reveal impaired default mode network connectivity in amnestic mild cognitive impairment. NeuroImage: Clinical, 4, 473-480. https://doi.org/https://doi.org/10.1016/j.nicl.2014.02.010 [doi]
9. Krönke, K. M., Wolff, M., Shi, Y., Kräplin, A., Smolka, M. N., Bühringer, G., & Goschke, T. (2020). Functional connectivity in a triple-network saliency model is associated with real-life self-control. Neuropsychologia, 149, 107667. https://doi.org/10.1016/j.neuropsychologia.2020.107667 [doi]

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