AI 中文总结
该研究发现盲源分离（BSS）作为钙瞬变去噪步骤，会扭曲行为解码与连接性分析，其对合成基准和斑马鱼记录的测试显示，BSS会降低图恢复性能、改变连接性密度，并非中性清理步骤。
AI 中文摘要
去噪常被视为钙瞬变分析的技术前置步骤，但它会重新定义用于行为解码和因果结构学习的变量。本研究因一次带有可见伪影的可检查运动神经元记录而开展：去除与伪影相关的盲源分离（BSS）成分也改变了目标帧外的轨迹动态。我们因此测试，BSS去噪是否能在已提取的钙瞬变上保留行为和因果证据。在具有已知滞后图的合成基准中，原始受污染轨迹可保留图恢复能力，而去除成分的BSS变体在所有估计器族中使中位F1值降至0。随后，我们评估了FastICA、Infomax、SOBI和JADE这四种BSS方法在四个带有尾部行为的幼鱼斑马鱼v2a网状脊髓神经元（v2a-RSNs）记录上的表现。BSS有时可改善行为解码，但增益取决于鱼类、方法和保留的聚类；经折叠审计比较，匹配的PCA和低通对照常与BSS相当或优于BSS。连接性效应更一致：从BSS清理后的轨迹推断的c-GC和c-GC*图比原始轨迹图密集得多。因此，BSS应被视为对所测过程的干预，而非中性清理步骤。
英文摘要
Denoising is often treated as a technical prelude to calcium transients analysis, but it can redefine the variables used for behavior decoding and causal structure learning. An inspectable motorneuron recording with a visible artefact motivated this study: removing artefact-linked blind source separation (BSS) components also changed trace dynamics outside the targeted frame. We therefore tested whether BSS denoising preserves behavior and causal evidence on already extracted calcium transients. In a synthetic benchmark with known lagged graphs, raw corrupted traces retained graph recovery, whereas component-removing BSS variants collapsed median graph F1 to 0 across estimator families. We then evaluated four BSS methods: FastICA, Infomax, SOBI, and JADE, on four larval zebrafish recordings of v2a reticulospinal neurons (v2a-RSNs) with tail behavior. BSS sometimes improved behavior decoding, but gains depended on fish, method and retained clusters; matched PCA and low-pass controls often matched or exceeded BSS in fold-audited comparisons. Connectivity effects were more consistent: c-GC and c-GC* graphs inferred from BSS-cleaned traces were much denser than raw-trace graphs. BSS should therefore be treated as an intervention on the measured process, not a neutral cleanup step.
