Blind Source Separation Can Distort Behavior and Connectivity Analyses of Calcium Transients

2026-08-01

AI Summary

Blind source separation (BSS) is commonly employed as a neutral denoising step in calcium transient analysis, yet it may inadvertently interfere with behavioral decoding and causal connectivity inference. This study systematically evaluates four BSS algorithms—FastICA, Infomax, SOBI, and JADE—on both synthetic data and real zebrafish v2a-RSN recordings, benchmarking them against PCA and low-pass filtering using conditional Granger causality (c-GC/c-GC*) for connectivity inference. Results indicate that while BSS occasionally enhances behavioral decoding performance, such improvements are inconsistent. More critically, BSS consistently induces significant densification of inferred causal graphs, reducing graph recovery F1 scores to zero in synthetic datasets. These findings demonstrate that BSS acts as an interventional rather than an unbiased preprocessing step, thereby challenging prevailing analytical paradigms in the field.

Abstract

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.

Problem

• Blind Source Separation
• Calcium Imaging
• Behavior Decoding
• Functional Connectivity
• Denoising

Innovation

• Blind Source Separation
• Calcium Imaging Denoising
• Causal Connectivity
• Behavior Decoding
• Neural Data Preprocessing