Format

Send to

Choose Destination
Ageing Res Rev. 2014 May;15:44-50. doi: 10.1016/j.arr.2014.02.001. Epub 2014 Feb 15.

A quantitative neural network approach to understanding aging phenotypes.

Author information

1
Laboratory of Behavioral Neuroscience, Neurocognitive Aging Section, National Institute on Aging, Biomedical Research Center, 251 Bayview Blvd, Baltimore, MD 21224, USA. Electronic address: jessica.ash@nih.gov.
2
Laboratory of Behavioral Neuroscience, Neurocognitive Aging Section, National Institute on Aging, Biomedical Research Center, 251 Bayview Blvd, Baltimore, MD 21224, USA.

Abstract

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.

KEYWORDS:

Graph theory; Neural networks; Neurocognitive aging

PMID:
24548925
PMCID:
PMC4624105
DOI:
10.1016/j.arr.2014.02.001
[Indexed for MEDLINE]
Free PMC Article

Supplemental Content

Full text links

Icon for Elsevier Science Icon for PubMed Central
Loading ...
Support Center