Assessing imbalance in signed brain networks
评估带符号脑网络的不平衡性AI 解读 ↓
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评估带符号脑网络的不平衡性
- 研究问题
- 能否从多个单元的动态活动推断静态正负连接，并据此衡量脑网络的受挫与不平衡程度？所得模块是否符合统计平衡结构？
- 方法与思路
- 作者以最大熵方法建立保留部分经验属性的基准，用一致或相反取值的显著性构造正负边，再以带符号随机块模型分析模块。
- 材料陈述的贡献
- 摘要称脑网络存在受挫现象，负子图贡献主要来自皮层下区域；模块分析结果与宽松平衡理论的统计版本相吻合。
- 阅读关注点
- 应核对最大熵约束、边检验及多重比较处理、受挫程度定义，以及时间依赖和脑区划分如何影响连边与模块选择。
- 阅读边界
- 这是 arXiv 预印本，同行评审与期刊接收未核实；摘要未给出数据细节和不确定性结果，动态相似性构建的连接不能直接解释为因果关系。
原文摘要
Many complex systems - be they financial, natural, or social - are composed of units - such as stocks, neurons, or agents - whose joint activity can be represented as a multivariate time series. An issue of both practical and theoretical importance concerns the possibility of inferring the presence of a static relationship between any two units solely from their dynamic behaviour. The present contribution aims at tackling such an issue within the framework of traditional hypothesis testing: briefly speaking, our suggestion is that of linking any two units if behaving in a sufficiently similar way. To achieve such a goal, we project a multivariate time series onto a signed graph by (1) comparing the empirical properties of the former with those expected under a suitable benchmark and (2) linking any two units with a positive (negative) edge in case the corresponding series shares a significantly large number of concordant (discordant) values. To define our benchmarks, we adopt an information-theoretic approach that is rooted into the constrained maximisation of Shannon entropy, a procedure inducing an ensemble of multivariate time series that preserves some of the empirical properties on average, while randomising everything else. We showcase the possible applications of our method by addressing one of the most timely issues in the domain of neurosciences, i.e. that of determining if brain networks are frustrated or not, and, if so, to what extent. As our results suggest, this is indeed the case, with the major contribution to the underlying negative subgraph coming from the subcortical regions (and, to a lesser extent, from the limbic ones). At the mesoscopic level, the minimisation of the Bayesian information criterion, instantiated with the signed stochastic block model, reveals that brain regions gather into modules aligning with the statistical variant of the relaxed balance theory.