Friedman Eric J, Young Karl, Tremper Graham, Liang Jason, Landsberg Adam S, Schuff Norbert
International Computer Science Institute, Berkeley, CA, United States of America; Department of Computer Science, University of California, Berkeley, Berkeley, CA, United States of America.
Department of Radiology & Biomedical Imaging, University of California San Francisco, San Francisco, CA, United States of America; VA Medical Center, San Francisco, CA, United States of America.
PLoS One. 2015 Apr 16;10(4):e0124453. doi: 10.1371/journal.pone.0124453. eCollection 2015.
Directed network motifs are the building blocks of complex networks, such as human brain networks, and capture deep connectivity information that is not contained in standard network measures. In this paper we present the first application of directed network motifs in vivo to human brain networks, utilizing recently developed directed progression networks which are built upon rates of cortical thickness changes between brain regions. This is in contrast to previous studies which have relied on simulations and in vitro analysis of non-human brains. We show that frequencies of specific directed network motifs can be used to distinguish between patients with Alzheimer's disease (AD) and normal control (NC) subjects. Especially interesting from a clinical standpoint, these motif frequencies can also distinguish between subjects with mild cognitive impairment who remained stable over three years (MCI) and those who converted to AD (CONV). Furthermore, we find that the entropy of the distribution of directed network motifs increased from MCI to CONV to AD, implying that the distribution of pathology is more structured in MCI but becomes less so as it progresses to CONV and further to AD. Thus, directed network motifs frequencies and distributional properties provide new insights into the progression of Alzheimer's disease as well as new imaging markers for distinguishing between normal controls, stable mild cognitive impairment, MCI converters and Alzheimer's disease.
有向网络基序是复杂网络（如人类大脑网络）的构建模块，它捕捉了标准网络测量中未包含的深层连接信息。在本文中，我们首次将有向网络基序在活体中应用于人类大脑网络，利用了最近开发的基于脑区之间皮质厚度变化率构建的有向进展网络。这与以往依赖于对非人类大脑进行模拟和体外分析的研究形成对比。我们表明，特定有向网络基序的频率可用于区分阿尔茨海默病（AD）患者和正常对照（NC）受试者。从临床角度来看特别有趣的是，这些基序频率还可以区分在三年中保持稳定的轻度认知障碍（MCI）受试者和那些转化为AD的受试者（CONV）。此外，我们发现有向网络基序分布的熵从MCI到CONV再到AD逐渐增加，这意味着病理分布在MCI中更具结构性，但随着病情发展到CONV并进一步发展到AD，其结构性逐渐减弱。因此，有向网络基序频率和分布特性为阿尔茨海默病的进展提供了新的见解，同时也为区分正常对照、稳定的轻度认知障碍、MCI转化者和阿尔茨海默病提供了新的成像标志物。
