Why ELA works despite binarization

Determine the mechanisms and conditions under which the Ising model–based energy landscape analysis (ELA) remains effective after binarizing continuous multivariate time series into binary activity patterns. Establish theoretical and empirical justification for how binarization preserves essential dynamical information, and characterize the data properties (e.g., distributional features, correlations, sampling) that permit successful ELA despite the loss of amplitude information.

Background

In the tutorial review, the authors note that ELA begins by binarizing each variable’s time series, which inherently discards amplitude information. They observe that binarization is more clearly justified when individual variables exhibit bimodal distributions over time, but such bimodality is uncommon across many domains.

Despite this apparent information loss, numerous studies have reported successful applications of ELA, raising a fundamental methodological question about why binarization does not degrade performance in practice. The authors explicitly state that the reason for this success is unknown, highlighting a gap in theoretical understanding and practical guidance for when binarization is appropriate.