Statistical Perspective on Functional and Causal Neural Connectomics: The Time-Aware PC Algorithm

TL;DR

This paper introduces the Time-Aware PC (TPC) algorithm, a novel method for inferring causal functional connectomes from neural time series data, accounting for the dynamic and causal nature of neuronal interactions.

Contribution

The paper adapts the PC algorithm for neural time series, enabling non-parametric, causality-reflecting inference of neural connectomes with properties suitable for dynamic data.

Findings

TPC accurately infers causal connectivity in simulated data.

Demonstrates effectiveness on benchmark datasets.

Successfully applied to mouse visual cortex recordings.

Abstract

The representation of the flow of information between neurons in the brain based on their activity is termed the causal functional connectome. Such representation incorporates the dynamic nature of neuronal activity and causal interactions between them. In contrast to connectome, the causal functional connectome is not directly observed and needs to be inferred from neural time series. A popular statistical framework for inferring causal connectivity from observations is the directed probabilistic graphical modeling. Its common formulation is not suitable for neural time series since was developed for variables with independent and identically distributed static samples. In this work, we propose to model and estimate the causal functional connectivity from neural time series using a novel approach that adapts directed probabilistic graphical modeling to the time series scenario. In particular, we develop the Time-Aware PC (TPC) algorithm for estimating the causal functional connectivity, which adapts the PC algorithm a state-of-the-art method for statistical causal inference. We show that the model outcome of TPC has the properties of reflecting causality of neural interactions such as being non-parametric, exhibits the directed Markov property in a time-series setting, and is predictive of the consequence of counterfactual interventions on the time series. We demonstrate the utility of the methodology to obtain the causal functional connectome for several datasets including simulations, benchmark datasets, and recent multi-array electro-physiological recordings from the mouse visual cortex.