Altered Hierarchical Organization of the Brain in Young-Onset Alzheimer’s Disease
Presented During:
Wednesday, June 25, 2025: 5:45 PM - 7:00 PM
Brisbane Convention & Exhibition Centre
Room:
M3 (Mezzanine Level)
Poster No:
148
Submission Type:
Abstract Submission
Authors:
Seda Sacu1, Jonathan Schott2, Adeel Razi3
Institutions:
1Central Institute of Mental Health, Mannheim, Germany, 2University College London, London, United Kingdom, 3Monash University, Melbourne, Australia
First Author:
Co-Author(s):
Introduction:
Young-onset Alzheimer's Disease is a rare form of Alzheimer's Disease characterized by early symptom onset (< 65 years) and more aggressive clinical course (Mendez, 2019). Previous literature reported altered connectivity within the default-mode network in patients with young-onset Alzheimer's Disease (Gour et al., 2014; Lehmann et al., 2013; Singh et al., 2023), which is a large-scale brain network associated with episodic memory and self-awareness (Buckner et al., 2005). Despite the importance of the default-mode network in Alzheimer's Disease pathology and cognitive functioning, little is known about how the default-mode network interacts with other cognitive networks, such as salience and dorsal attention network, in patients with young-onset Alzheimer's Disease.
Methods:
The case-control study was conducted with 26 patients with young-onset Alzheimer's Disease and 24 age-matched healthy controls. Using the predefined spatial masks (Shirer et al., 2012), we performed constrained independent component analysis (Lin et al., 2010) to identify networks of interest. We chose 11 regions of interest from three networks: Default-mode network (medial prefrontal cortex, precuneus, bilateral angular gyri), salience network (bilateral insula, dorsal anterior cingulate cortex), and dorsal attention network (bilateral inferior frontal gyri and bilateral inferior parietal lobe). Regional time-series were extracted within 8 mm sphere of group-level peak coordinates. Resting-state effective connectivity within and between networks was assessed using spectral dynamic causal modeling (Friston et al., 2014). Parametric empirical Bayes (Friston et al., 2016) was then performed to characterise group differences in effective connectivity. To identify hierarchical architecture, we computed mean connectivity strength between networks and corresponding posterior probability by taking into account uncertainty in these estimates.
Results:
Healthy controls exhibited excitatory connections between the regions belonging to the same networks (Figure 1A, free energy with vs. without > 0.95). The salience network and dorsal attention network exerted inhibitory influences on the default-mode network (Figure 1A, free energy with vs. without > 0.95), suggesting a hierarchical organization where the default-mode network activity is modulated by task-positive networks. Compared to healthy controls, patients with young-onset Alzheimer's disease had lower inhibitory influences from the salience network and dorsal attention network to the default-mode network as well as from the salience network to dorsal attention network (Figure 1C and Figure 2C, free energy with vs. without > 0.95).
·Figure 1. Effective connectivity within and between networks.
·Figure 2. Summary of between network effective connectivity.
Conclusions:
The current study provided evidence for hierarchical organization among large-scale brain networks in healthy controls, which was impaired in young-onset Alzheimer's Disease. The hierarchical organization plays a critical role in maintaining the balance between task-positive and task-negative systems, which is essential for efficient and adaptive brain functioning. Future research should focus on elucidating the relationship between hierarchical brain network organization and cognitive functioning, both in health and in disease, to better understand its role in supporting complex mental processes and its potential as a target for intervention.
Disorders of the Nervous System:
Neurodegenerative/ Late Life (eg. Parkinson’s, Alzheimer’s) 1
Lifespan Development:
Aging
Modeling and Analysis Methods:
fMRI Connectivity and Network Modeling 2
Task-Independent and Resting-State Analysis
Novel Imaging Acquisition Methods:
BOLD fMRI
Keywords:
Aging
Degenerative Disease
FUNCTIONAL MRI
1|2Indicates the priority used for review
Abstract Information
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Provide references using APA citation style.
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Friston, K. J., Litvak, V., Oswal, A., Razi, A., Stephan, K. E., Van Wijk, B. C. M., Ziegler, G., & Zeidman, P. (2016). Bayesian model reduction and empirical Bayes for group (DCM) studies. NeuroImage. https://doi.org/10.1016/j.neuroimage.2015.11.015
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