Concurrence: A dependence criterion for time series, applied to biological data
This provides a new tool for scientists analyzing biological systems to detect dependencies without prior knowledge, though it appears incremental as it builds on existing dependence measurement concepts.
The authors tackled the problem of measuring statistical dependence in biological time series with complex non-linear interactions by introducing a criterion called concurrence, which uses classifier performance on aligned vs. misaligned segments, and demonstrated its applicability across fMRI, physiological, and behavioral data without requiring large datasets or parameter tuning.
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 or large datasets. We introduce a criterion for dependence, whereby two time series are deemed dependent if one can construct a classifier that distinguishes between temporally aligned vs. misaligned segments extracted from them. We show that this criterion, concurrence, is theoretically linked with dependence, and can become a standard approach for scientific analyses across disciplines, as it can expose relationships across a wide spectrum of signals (fMRI, physiological and behavioral data) without ad-hoc parameter tuning or large amounts of data.