自监督方法concurrence可无先验发现生物信号间复杂依赖关系。
Measuring Dependencies between Biological Signals with Self-supervision, and its Limitations
- 通过时序对齐/错位片段区分判断信号依赖性
- 在fMRI、生理与行为信号中均能有效识别跨模态关系
- 无需调参或先验知识，适合多领域科学探索
衡量观测信号间的统计依赖是科学发现的核心工具。然而，生物系统常表现出复杂的非线性交互，现有方法需依赖先验知识才能捕捉。本文提出自监督方法concurrence，其思路源于：若两信号相关，则应能区分来自它们的时序对齐与错位片段。在fMRI、生理及行为信号上的实验表明，据我们所知，concurrence是首个能在如此广泛信号类型中暴露关系且无需手工调参或先验信息的方法，为跨领域科学发现提供有力工具。但由外部因素引发的依赖仍为开放问题，研究者需验证所揭示关系是否真正关联研究问题。
原文摘要 · Abstract (English)
Measuring the statistical dependence between observed signals is a primary tool for scientific discovery. However, biological systems often exhibit complex non-linear interactions that currently cannot be captured without a priori knowledge regarding the nature of dependence. We introduce a self-supervised approach, concurrence, which is inspired by the observation that if two signals are dependent, then one should be able to distinguish between temporally aligned vs. misaligned segments extracted from them. Experiments with fMRI, physiological and behavioral signals show that, to our knowledge, concurrence is the first approach that can expose relationships across such a wide spectrum of signals and extract scientifically relevant differences without ad-hoc parameter tuning or reliance on a priori information, providing a potent tool for scientific discoveries across fields. However, dependencies caused by extraneous factors remain an open problem, thus researchers should validate that exposed relationships truly pertain to the question(s) of interest.
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