自发脑类器官活动中涌现的拓扑结构
Emergent topological structure in spontaneous brain-organoid activity
AI总结：
研究将持久同调应用于人类和小鼠皮质类器官自发活动的MEA记录，通过构建加权网络并表征其拓扑结构，发现一阶同调在多数数据集显著高于零模型，拓扑丰富度随网络大小增长，二阶同调仅在较大网络中显著，证明能解析神经记录中的结构化拓扑。
AI中文摘要：
神经活动通常被认为是在嵌入高维状态空间的低维结构上组织起来的。持久同调直接从成对相关模式中读取这种结构，而无需预先假设哪些变量是相关的。我们将持久同调应用于人类（兰开斯特）和小鼠（帕斯卡）皮质类器官自发活动的微电极阵列（MEA）记录，这些记录涵盖26至234个同时分类的单元，并询问拓扑数据分析是否能在神经记录实际提供的节点数上解析结构。在相关空间中构建加权网络并通过Vietoris-Rips过滤对其进行表征，我们发现18个数据集中有14个数据集的一阶同调（\(H_1\)，环）显著高于速率和群体保持零模型。这种环结构占据了一个非冗余核心：它对随机去除单元具有鲁棒性，但通过有针对性地去除携带它的单元而被破坏。拓扑丰富度随着网络大小而增长，二阶同调（\(H_2\)）仅在较大的网络中显著高于零模型。这些结果表明，持久同调能够在实验实际提供的尺度上解析神经记录中的结构化拓扑。
英文摘要：
Neural activity is widely held to organize on low-dimensional structure embedded in a high-dimensional state space. Persistent homology reads such structure directly from the pattern of pairwise correlations, without assuming in advance which variables are relevant. We apply persistent homology to microelectrode-array (MEA) recordings of spontaneous activity from human (Lancaster) and mouse (Paşca) cortical organoids, spanning $26$--$234$ simultaneously sorted units, and ask whether topological data analysis resolves structure at the node counts that neural recordings actually deliver. Building weighted networks in correlation space and characterizing them by Vietoris--Rips filtration, we find that the first homology ($H_1$, loops) rises significantly above a rate- and population-preserving null in $14$ of $18$ datasets. This loop structure occupies a non-redundant core: it is robust to random removal of units yet disrupted by targeted removal of the units that carry it. Topological richness grows with network size, and second homology ($H_2$) emerges significantly above the null only in the larger networks. These results show that persistent homology resolves structured topology in neural recordings at the scale experiments actually deliver.
