小柯机器人

功能性大脑网络揭示空间和时间的自相关关系
2023-04-27 15:10

美国耶鲁大学John D. Murray研究组的一项最新研究发现功能性大脑网络反映空间和时间自相关。2023年4月24日出版的《自然—神经科学》发表了这项成果。

为了探究复杂测量是否可以更简单,研究人员使用网络神经科学的复杂拓扑测量检查了静息状态下功能磁共振成像(rs-fMRI)数据。

研究表明空间和时间自相关是可靠的统计量,可以解释网络拓扑的众多度量。具有目的匹配的空间和时间自相关代理时间序列几乎捕获了这些拓扑测量中所有可靠的单个和区域差异。衰老过程中的网络拓扑变化受空间自相关驱动,多种5-羟色胺能药物诱导产生与时间自相关相同的变化。对广泛使用的复杂性测量方法的简化解释可能有助于将它们与神经生物学联系起来。

据悉,神经科学领域的高通量实验方法导致了测量复杂相互作用和多维模式技术爆炸式的增长。然而,对现有复杂测量是否能简化成更简单的低维统计,这在很大程度上是未知的。

附:英文原文

Title: Functional brain networks reflect spatial and temporal autocorrelation

Author: Shinn, Maxwell, Hu, Amber, Turner, Laurel, Noble, Stephanie, Preller, Katrin H., Ji, Jie Lisa, Moujaes, Flora, Achard, Sophie, Scheinost, Dustin, Constable, R. Todd, Krystal, John H., Vollenweider, Franz X., Lee, Daeyeol, Anticevic, Alan, Bullmore, Edward T., Murray, John D.

Issue&Volume: 2023-04-24

Abstract: High-throughput experimental methods in neuroscience have led to an explosion of techniques for measuring complex interactions and multi-dimensional patterns. However, whether sophisticated measures of emergent phenomena can be traced back to simpler, low-dimensional statistics is largely unknown. To explore this question, we examined resting-state functional magnetic resonance imaging (rs-fMRI) data using complex topology measures from network neuroscience. Here we show that spatial and temporal autocorrelation are reliable statistics that explain numerous measures of network topology. Surrogate time series with subject-matched spatial and temporal autocorrelation capture nearly all reliable individual and regional variation in these topology measures. Network topology changes during aging are driven by spatial autocorrelation, and multiple serotonergic drugs causally induce the same topographic change in temporal autocorrelation. This reductionistic interpretation of widely used complexity measures may help link them to neurobiology.

DOI: 10.1038/s41593-023-01299-3

Source: https://www.nature.com/articles/s41593-023-01299-3

Nature Neuroscience:《自然—神经科学》,创刊于1998年。隶属于施普林格·自然出版集团,最新IF:28.771
官方网址:https://www.nature.com/neuro/
投稿链接:https://mts-nn.nature.com/cgi-bin/main.plex


本期文章:《自然—神经科学》:Online/在线发表

分享到:

0