School of Informatics, Computing & Engineering, Indiana University, Bloomington, IN, 47405, USA.
Department of Psychological & Brain Sciences, Indiana University, Bloomington, IN, 47405, USA.
Commun Biol. 2023 Apr 24;6(1):451. doi: 10.1038/s42003-023-04843-w.
One of the most well-established tools for modeling the brain is the functional connectivity network, which is constructed from pairs of interacting brain regions. While powerful, the network model is limited by the restriction that only pairwise dependencies are considered and potentially higher-order structures are missed. Here, we explore how multivariate information theory reveals higher-order dependencies in the human brain. We begin with a mathematical analysis of the O-information, showing analytically and numerically how it is related to previously established information theoretic measures of complexity. We then apply the O-information to brain data, showing that synergistic subsystems are widespread in the human brain. Highly synergistic subsystems typically sit between canonical functional networks, and may serve an integrative role. We then use simulated annealing to find maximally synergistic subsystems, finding that such systems typically comprise ≈10 brain regions, recruited from multiple canonical brain systems. Though ubiquitous, highly synergistic subsystems are invisible when considering pairwise functional connectivity, suggesting that higher-order dependencies form a kind of shadow structure that has been unrecognized by established network-based analyses. We assert that higher-order interactions in the brain represent an under-explored space that, accessible with tools of multivariate information theory, may offer novel scientific insights.
用于模拟大脑的最成熟的工具之一是功能连接网络，它是由相互作用的大脑区域对构建而成的。虽然该网络模型功能强大，但它受到限制，只能考虑成对的依赖性，而可能会错过更高阶的结构。在这里，我们探讨了多元信息论如何揭示人类大脑中的高阶依赖性。我们首先对 O 信息进行了数学分析，从理论和数值上分析了它与先前建立的复杂性信息论度量之间的关系。然后，我们将 O 信息应用于脑数据，表明协同子系统在人类大脑中广泛存在。高度协同的子系统通常位于典型的功能网络之间，可能起到整合作用。然后，我们使用模拟退火找到最大协同的子系统，发现这样的系统通常包含大约 10 个大脑区域，这些区域来自多个典型的大脑系统。虽然普遍存在，但当考虑成对的功能连接时，高度协同的子系统是不可见的，这表明高阶依赖性形成了一种未被已有的基于网络的分析所识别的潜在结构。我们断言，大脑中的高阶相互作用代表了一个尚未被充分探索的领域，通过多元信息论的工具可以获得这些领域，这可能会提供新的科学见解。
