Organizational principles of the cerebral cortex predict cognitive decline in Alzheimer’s Disease
Poster No:
150
Submission Type:
Abstract Submission
Authors:
Arianna Menardi1, Ceren Saglam2, Beatrice La Rocca2, Francesco Alberti3, Diego Cecchin4, Annalena Venneri5, Annachiara Cagnin1, Antonino Vallesi1
Institutions:
1Department of Neuroscience, University of Padua, Padua, Italy, 2Department of General Psychology, University of Padua, Padua, Italy, 3Université Paris Citè, Paris, Île-de-France, 4Department of Medicine (DIMED), University Hospital of Padua, Padua, Italy, 5Department of Medicine and Surgery, University of Parma, Parma, Italy
First Author:
Co-Author(s):
Introduction:
Dysfunction in Default Mode Network (DMN) regions has been proposed as an early indicator of Alzheimer's disease (AD) (Badhwar et al., 2017). However, the specific subclinical functional and structural changes within the DMN that predict future cognitive deterioration remain unclear. This study evaluates the value of a multimodal approach to trace the spread of altered brain activity along the mild cognitive impairment (MCI) to AD continuum. Specifically, we investigated changes in spontaneous fluctuations of individual functional connectivity, hypothesizing these may serve as early biomarkers of neural dysfunction tied to episodic memory decline. To this aim, we used connectivity gradients, which reduce the complexity of neuroimaging data by identifying latent dimensions that explain covariance patterns in connectivity profiles (Margulies et al., 2016). Regions are mapped along these gradients based on the similarity of their activity patterns. Additionally, the mathematical framework of graph theory was used to assess the network's efficiency in optimizing information transfer while maintaining local specialization (Bassett et al., 2006). This study aimed to combine gradients, graph theory, and volumetric indices to create a unified framework for understanding the relationship between cognitive decline and structural and functional brain changes.
Methods:
We analyzed functional and structural MRI data from the Alzheimer's Disease Neuroimaging Initiative (ADNI), including 184 healthy controls (HC), 321 MCI participants, and 71 AD patients. Episodic memory performance (encoding, retrieval, recall) was measured using the Embic Digital Cognitive Biomarker scale at baseline and after two years. Global cognitive function was assessed using Mini-Mental State Examination (MMSE) scores. Structural MRI data provided indices of cortical thickness, gyrification, and sulcal depth for DMN regions based on the Schaefer parcellation (Schaefer et al., 2018). Resting-state functional data were used to calculate graph theory metrics of betweenness centrality, participation coefficient and nodal strength, indicating the centrality and connectivity of DMN regions. Functional connectivity gradients were also computed, and regional dispersion-measured as the Euclidean distance from the network's centroid in gradient space-was used to estimate the loss of similarity in within-network connectivity (Bethlehem et al., 2020).
Results:
At the whole-network level, greater DMN dispersion was linked to poorer global cognitive performance (r = -0.12, p = 0.007) and effectively distinguished HC from MCI/AD groups (F(2) = 4.3, p = 0.007). At the regional level, interactions between nodal dispersion and MMSE scores significantly predicted encoding and retrieval performance at baseline and the two-year follow-up. Most of these regions included the temporal and cingulate cortices, angular gyrus, precuneus and all consistently showed increased steepness of the interaction slope as a function of poorer global performance scores. Importantly, the number of DMN regions where baseline dispersion predicted recall performance two years later was nearly double the number associated with baseline scores. Regions with increased dispersion also showed lower cortical thickness and degree strength, indicating both functional and structural vulnerability.
Conclusions:
DMN dispersion reflects reduced covariance in communication among network regions and may signal early disruption of neural pathways. The degree of dispersion effectively differentiates healthy individuals from those with pathological cognitive decline and correlates with the severity of impairment. At the regional level, dispersion appears predictive of long-term recall performance and is associated with concurrent structural and functional alterations. To our knowledge, this is the first study to explore DMN dispersion in conjunction with graph theory and volumetric indices across the AD spectrum.
Disorders of the Nervous System:
Neurodegenerative/ Late Life (eg. Parkinson’s, Alzheimer’s) 1
Higher Cognitive Functions:
Higher Cognitive Functions Other
Learning and Memory:
Long-Term Memory (Episodic and Semantic)
Lifespan Development:
Aging
Modeling and Analysis Methods:
fMRI Connectivity and Network Modeling 2
Keywords:
Aging
Cognition
Degenerative Disease
FUNCTIONAL MRI
Memory
STRUCTURAL MRI
Other - Alzheimer's Disease, Network Analysis, Functional Gradients, Multimodality
1|2Indicates the priority used for review
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