Network inference - with confidence - from multivariate time series

TL;DR

This paper presents a systematic method for inferring functional connectivity networks from multivariate time series, providing both the network structure and uncertainty quantification, validated on simulated and real epileptic data.

Contribution

It introduces a principled approach that quantifies uncertainty in network edges, improving reliability of network inference from noisy data.

Findings

Accurate and robust edge detection in simulated data.

Effective uncertainty quantification in real epileptic seizure data.

Method outperforms traditional threshold-based approaches.

Abstract

Networks - collections of interacting elements or nodes - abound in the natural and manmade worlds. For many networks, complex spatiotemporal dynamics stem from patterns of physical interactions unknown to us. To infer these interactions, it is common to include edges between those nodes whose time series exhibit sufficient functional connectivity, typically defined as a measure of coupling exceeding a pre-determined threshold. However, when uncertainty exists in the original network measurements, uncertainty in the inferred network is likely, and hence a statistical propagation-of-error is needed. In this manuscript, we describe a principled and systematic procedure for the inference of functional connectivity networks from multivariate time series data. Our procedure yields as output both the inferred network and a quantification of uncertainty of the most fundamental interest: uncertainty in the number of edges. To illustrate this approach, we apply our procedure to simulated data and electrocorticogram data recorded from a human subject during an epileptic seizure. We demonstrate that the procedure is accurate and robust in both the determination of edges and the reporting of uncertainty associated with that determination.