-
Ageing research reviews · May 2014
ReviewA quantitative neural network approach to understanding aging phenotypes.
- Jessica A Ash and Peter R Rapp.
- 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.
- Ageing Res. Rev. 2014 May 1;15:44-50.
AbstractBasic 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.Published by Elsevier B.V.
Notes
Knowledge, pearl, summary or comment to share?You can also include formatting, links, images and footnotes in your notes
- Simple formatting can be added to notes, such as
*italics*
,_underline_
or**bold**
. - Superscript can be denoted by
<sup>text</sup>
and subscript<sub>text</sub>
. - Numbered or bulleted lists can be created using either numbered lines
1. 2. 3.
, hyphens-
or asterisks*
. - Links can be included with:
[my link to pubmed](http://pubmed.com)
- Images can be included with:
![alt text](https://bestmedicaljournal.com/study_graph.jpg "Image Title Text")
- For footnotes use
[^1](This is a footnote.)
inline. - Or use an inline reference
[^1]
to refer to a longer footnote elseweher in the document[^1]: This is a long footnote.
.