DMN and TPN Connectivity Alterations in Alzheimer’s Disease: A rsfMRI Study in Humans and Rodents
Poster No:
236
Submission Type:
Abstract Submission
Authors:
Sushil Bohara1, Shella Keilholz1
Institutions:
1Biomedical Engineering, Emory University and Georgia Tech, Atlanta, GA
First Author:
Co-Author:
Introduction:
Alzheimer's disease (AD) is a neurodegenerative disorder characterized by progressive cognitive decline and disrupted brain network connectivity [6]. Early indicators of AD pathology include altered connectivity in the Default Mode Network (DMN), a network active during rest [3]. The Task-Positive Network (TPN), which supports cognitive task engagement, also shows connectivity changes in AD. This alteration weakens the typical anti-correlation between the DMN and TPN [8]. Rodent models like the TgF344-AD rat replicate key features of AD, making it possible to study network-level changes over time [4]. By exploring DMN and TPN connectivity in both human and rodent cohorts, this study seeks to improve our understanding of AD-related network disruptions.
Methods:
Humans
Resting-state fMRI data were collected from 20 AD patients and 20 healthy patients through the Alzheimer's Disease Neuroimaging Initiative (ADNI). Both groups were sex-matched. fMRI preprocessing was done using the default pipeline on the CONN toolbox with the Brainnetome Atlas (246 ROIs) applied and grouped into Yeo's 7 networks. This resulted in 36 DMN ROIs and 30 TPN ROIs.
Rodents
Resting-state fMRI data from 20 TgF344-AD rats and 20 wild-type (WT) rats were collected using a 9.4T Bruker MRI system. Both groups were sex-matched. Preprocessing was done on a rodent whole-brain fMRI toolbox with the SIGMA atlas (59 ROIs) applied. The DMN for rodents was identified based on literature [5], resulting in 18 DMN ROIs. The TPN is not well defined in rodents, so an exploratory approach was used to identify 8 TPN ROIs [7].
Network Connectivity Matrix
For both human and rodent data, pairwise Pearson correlation coefficients were calculated between the BOLD time-series of each ROI. To normalize the distribution of correlation values, Fisher z-transformation was applied to each correlation matrix. Group-level connectivity matrices were generated by averaging the z-transformed matrices. This is composed of beta values representing the average connectivity strength within each group. Results were unthresholded.
Resting-state fMRI data were collected from 20 AD patients and 20 healthy patients through the Alzheimer's Disease Neuroimaging Initiative (ADNI). Both groups were sex-matched. fMRI preprocessing was done using the default pipeline on the CONN toolbox with the Brainnetome Atlas (246 ROIs) applied and grouped into Yeo's 7 networks. This resulted in 36 DMN ROIs and 30 TPN ROIs.
Rodents
Resting-state fMRI data from 20 TgF344-AD rats and 20 wild-type (WT) rats were collected using a 9.4T Bruker MRI system. Both groups were sex-matched. Preprocessing was done on a rodent whole-brain fMRI toolbox with the SIGMA atlas (59 ROIs) applied. The DMN for rodents was identified based on literature [5], resulting in 18 DMN ROIs. The TPN is not well defined in rodents, so an exploratory approach was used to identify 8 TPN ROIs [7].
Network Connectivity Matrix
For both human and rodent data, pairwise Pearson correlation coefficients were calculated between the BOLD time-series of each ROI. To normalize the distribution of correlation values, Fisher z-transformation was applied to each correlation matrix. Group-level connectivity matrices were generated by averaging the z-transformed matrices. This is composed of beta values representing the average connectivity strength within each group. Results were unthresholded.
Results:
Our preliminary findings show distinct patterns of network connectivity changes in AD across both human and rodent models. In the human data, we observed reduced connectivity within the DMN in AD patients compared to healthy controls, particularly in regions such as Temporal Gyrus, and Temporal Sulcus (Fig 1A). Conversely, Task-Positive Network (TPN) connectivity showed increased connectivity in AD patients, notably in regions such as the Superior Parietal Lobe and Fusiform Gyrus (Fig 1B). In the rodent data, TgF344-AD rats at 6 months of age showed an increase in DMN and TPN connectivity compared to wild-type (WT) rats, suggesting an early compensatory mechanism (Fig 2A) [2]. However, in 15-month-old TgF344-AD rats DMN and TPN connectivity significantly decreased, reflecting a disruption like that observed in human AD patients (Fig 2B).
Conclusions:
This study highlights the changing nature of network connectivity disruptions in Alzheimer's disease (AD). Particularly, rodent models demonstrated early hyper-connectivity within DMN and TPN regions at 6 months of age, which corresponds to early adulthood in humans and suggests a potential compensatory mechanism. By 15 months, equivalent to adulthood, this hyper-connectivity diminished. These findings emphasize the importance of age in interpreting functional connectivity changes [1], as compensatory mechanisms may vary depending on the disease stage. Understanding these age-dependent patterns could aid in identifying early biomarkers for AD and developing interventions that target compensatory mechanisms to slow disease progression.
Disorders of the Nervous System:
Neurodegenerative/ Late Life (eg. Parkinson’s, Alzheimer’s) 1
Modeling and Analysis Methods:
Connectivity (eg. functional, effective, structural) 2
fMRI Connectivity and Network Modeling
Neuroinformatics and Data Sharing:
Brain Atlases
Novel Imaging Acquisition Methods:
BOLD fMRI
Keywords:
ANIMAL STUDIES
FUNCTIONAL MRI
Open Data
Structures
Systems
Other - Alzheimer; Network Connectivity
1|2Indicates the priority used for review
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Please indicate below if your study was a "resting state" or "task-activation” study.
Healthy subjects only or patients (note that patient studies may also involve healthy subjects):
Was this research conducted in the United States?
Are you Internal Review Board (IRB) certified? Please note: Failure to have IRB, if applicable will lead to automatic rejection of abstract.
Were any human subjects research approved by the relevant Institutional Review Board or ethics panel? NOTE: Any human subjects studies without IRB approval will be automatically rejected.
Were any animal research approved by the relevant IACUC or other animal research panel? NOTE: Any animal studies without IACUC approval will be automatically rejected.
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Provide references using APA citation style.
[2] Behfar, Q. (2020). Graph theory analysis reveals resting-state compensatory mechanisms in healthy aging and prodromal Alzheimer's disease. Frontiers in Aging Neuroscience, 12, 576627. https://doi.org/10.3389/fnagi.2020.576627
[3] Brier, M. R.(2012). Loss of intranetwork and internetwork resting state functional connections with Alzheimer’s disease progression. Journal of Neuroscience, 32, 8890–8899.
[4] De Waegenaere, S.(2024). Early altered directionality of resting brain network state transitions in the TgF344-AD rat model of Alzheimer’s disease. Frontiers in Human Neuroscience, 18. https://doi.org/10.3389/fnhum.2024.1379923
[5] Gozzi, A., & Schwarz, A. J. (2015). Large-scale functional connectivity networks in the rodent brain. NeuroImage, 127, 496–509. https://doi.org/10.1016/j.neuroimage.2015.12.017
[6] Kumar, A., & Singh, A. (2015). A review on Alzheimer’s disease pathophysiology and its management: An update. Pharmacological Reports, 67, 195–203.
[7] Peeters, L. M. (2020). Cholinergic modulation of the default mode like network in rats. iScience, 23(9), 101455. https://doi.org/10.1016/j.isci.2020.101455
[8] Xu, N. (2022). Functional connectivity of the brain across rodents and humans. Frontiers in Neuroscience, 16. https://doi.org/10.3389/fnins.2022.816331