Measuring Dependencies between Biological Signals with Self-supervision, and its Limitations
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.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
Concurrence: A dependence criterion for time series, applied to biological data
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 withou…
SPAR: Self-supervised Placement-Aware Representation Learning for Multi-Node IoT Systems
This work develops the underpinnings of self-supervised placement-aware representation learning given spatially-distributed (multi-view and multimodal) sensor observations, motivated by the need to represent external env…
Activity RecognitionHuman Activity RecognitionRepresentation LearningDivisive Normalization: Justification and Effectiveness as Efficient Coding Transform
Divisive normalization (DN) has been advocated as an effective nonlinear {\em efficient coding} transform for natural sensory signals with applications in biology and engineering. In this work, we aim to establish a conn…
Kernel Self-Attention in Deep Multiple Instance Learning
Not all supervised learning problems are described by a pair of a fixed-size input tensor and a label. In some cases, especially in medical image analysis, a label corresponds to a bag of instances (e.g. image patches), …
Medical Image AnalysisMultiple Instance Learningwhole slide imagesModeling Multivariate Biosignals With Graph Neural Networks and Structured State Space Models
Multivariate biosignals are prevalent in many medical domains, such as electroencephalography, polysomnography, and electrocardiography. Modeling spatiotemporal dependencies in multivariate biosignals is challenging due …
ClassificationGraph Neural NetworkGraph structure learningSeizure Detection+2