A quantitative neural network approach to understanding aging phenotypes
| Autor: | Jessica A. Ash, Peter R. Rapp |
|---|---|
| Rok vydání: | 2014 |
| Předmět: |
Adult
Aging Artificial neural network Models Neurological Graph theory Cognition Disease Middle Aged Biochemistry Phenotype Article Developmental psychology Functional networks Young Adult Neurology Basic research Humans Nerve Net Psychology Molecular Biology Neurocognitive Aged Biotechnology Cognitive psychology |
| Zdroj: | Ageing Research Reviews. 15:44-50 |
| ISSN: | 1568-1637 |
| DOI: | 10.1016/j.arr.2014.02.001 |
| Popis: | Basic research on neurocognitive aging has traditionally adopted a reductionist approach in the search for the basis of cognitive preservation versus decline. However, increasing evidence suggests that a network level understanding of the brain can provide additional novel insight into the structural and functional organization from which complex behavior and dysfunction emerge. Using graph theory as a mathematical framework to characterize neural networks, recent data suggest that alterations in structural and functional networks may contribute to individual differences in cognitive phenotypes in advanced aging. This paper reviews literature that defines network changes in healthy and pathological aging phenotypes, while highlighting the substantial overlap in key features and patterns observed across aging phenotypes. Consistent with current efforts in this area, here we outline one analytic strategy that attempts to quantify graph theory metrics more precisely, with the goal of improving diagnostic sensitivity and predictive accuracy for differential trajectories in neurocognitive aging. Ultimately, such an approach may yield useful measures for gauging the efficacy of potential preventative interventions and disease modifying treatments early in the course of aging. |
| Databáze: | OpenAIRE |
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