Milisav Filip, Bazinet Vincent, Betzel Richard F, Misic Bratislav
Montréal Neurological Institute, McGill University, Montreal, Quebec, Canada.
Department of Neuroscience, University of Minnesota, Minneapolis, MN, USA.
Nat Comput Sci. 2025 Jan;5(1):48-64. doi: 10.1038/s43588-024-00735-z. Epub 2024 Dec 10.
Scientific discovery in connectomics relies on network null models. The prominence of network features is conventionally evaluated against null distributions estimated using randomized networks. Modern imaging technologies provide an increasingly rich array of biologically meaningful edge weights. Despite the prevalence of weighted graph analysis in connectomics, randomization models that only preserve binary node degree remain most widely used. Here we propose a simulated annealing procedure for generating randomized networks that preserve weighted degree (strength) sequences. We show that the procedure outperforms other rewiring algorithms and generalizes to multiple network formats, including directed and signed networks, as well as diverse real-world networks. Throughout, we use morphospace representation to assess the sampling behavior of the algorithm and the variability of the resulting ensemble. Finally, we show that accurate strength preservation yields different inferences about brain network organization. Collectively, this work provides a simple but powerful method to analyze richly detailed next-generation connectomics datasets.
连接组学中的科学发现依赖于网络零模型。网络特征的显著性通常是根据使用随机网络估计的零分布来评估的。现代成像技术提供了越来越丰富的具有生物学意义的边权重阵列。尽管加权图分析在连接组学中很普遍，但仅保留二元节点度的随机化模型仍然是使用最广泛的。在这里，我们提出了一种模拟退火程序，用于生成保留加权度（强度）序列的随机网络。我们表明，该程序优于其他重新布线算法，并可推广到多种网络格式，包括有向和带符号网络，以及各种真实世界的网络。在整个过程中，我们使用形态空间表示来评估算法的采样行为和所得集合的变异性。最后，我们表明准确的强度保留会对脑网络组织产生不同的推断。总的来说，这项工作提供了一种简单而强大的方法来分析丰富详细的下一代连接组学数据集。
