A quantitative neural network approach to understanding aging phenotypes.
A quantitative neural network approach to understanding aging phenotypes.
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DOI：
10.1016/j.arr.2014.02.001
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发表时间：
2014-05
影响因子：
13.1
通讯作者：
Rapp PR
中科院分区：
文献类型：
--
作者：
Ash JA;Rapp PR
神经认知老化的基础研究传统上采用还原论的方法来寻找认知保留与衰退的基础。然而，越来越多的证据表明，对大脑的网络水平的理解可以为复杂行为和功能障碍出现的结构和功能组织提供额外的新见解。使用图论作为表征神经网络的数学框架，最近的数据表明，结构和功能网络的改变可能有助于在晚期衰老的认知表型的个体差异。本文综述了定义健康和病理衰老表型中网络变化的文献，同时强调了在衰老表型中观察到的关键特征和模式的大量重叠。与目前在这一领域的努力相一致，在这里，我们概述了一种分析策略，试图更精确地量化图论指标，其目标是提高诊断灵敏度和预测精度的差异轨迹在神经认知老化。最终，这种方法可能会产生有用的措施，用于衡量潜在的预防性干预措施和疾病改善治疗的效果在衰老过程的早期。
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.