Predictors of coupling between structural and functional cortical networks in normal aging.
Rafael Romero-Garcia1, Mercedes Atienza, Jose L Cantero
1Laboratory of Functional Neuroscience, Spanish Network of Excellence for Research on Neurodegenerative Diseases (CIBERNED), University Pablo de Olavide, Seville, Spain.
Human Brain Mapping
|September 13, 2013
Summary
Aging brains prioritize information segregation over integration, impacting cognitive function. Local connections and network modularity in functional networks are key, suggesting potential for early detection of neurodegeneration.
Area of Science:
- Neuroscience
- Cognitive Aging
- Network Science
Background:
- Understanding neocortical organization is crucial for cognitive function.
- Aging impacts structure-function relationships in cortical networks, but topological determinants are unclear.
Purpose of the Study:
- Investigate how network connections and node properties predict functional-structural connectivity coupling in healthy elderly individuals.
- Elucidate the role of network topology in normal aging of the mammalian neocortex.
Main Methods:
- Derived connectivity networks from correlations of cortical thickness and glucose consumption during resting state.
- Analyzed local-direct connections and node modularity in functional and structural networks.
Main Results:
- Local-direct connections and functional network modularity significantly determined coupling between cortical networks in aging.
- Aging appears to constrain structural networks by functional properties favoring information segregation over integration.
- Observed increased connectivity between sensorimotor/memory networks and decreased connectivity in fronto-parietal executive networks.
Conclusions:
- Functional topological properties, particularly information segregation, constrain the aging structural network.
- Aging-related deficits in functional integration may underlie observed network changes.
- Findings offer insights into aging-related failures in neocortical organization and potential for early neurodegenerative disease detection.
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