Neuro-PC: Causal Functional Connectivity from Neural Dynamics

TL;DR

This paper introduces Neuro-PC, a novel algorithm that infers causal functional connectivity from neural time series, enabling better understanding of neural pathways and responses to stimuli.

Contribution

Neuro-PC adapts the PC algorithm for causal inference in neural dynamics, providing a new tool for analyzing causal relationships in neural recordings.

Findings

Validated on simulated neural networks with various interactions.

Applied to mouse visual cortex data to map causal neural pathways.

Used causal connectome features to quantify neural response similarities.

Abstract

Functional connectome extends the anatomical connectome by capturing the relations between neurons according to their activity and interactions. When these relations are causal, the functional connectome maps how neural activity flows within neural circuits and provides the possibility for inference of functional neural pathways, such as sensory-motor-behavioral pathways. While there exist various information approaches for non-causal estimations of the functional connectome, approaches that characterize the causal functional connectivity - the causal relationships between neuronal time series, are scarce. In this work, we develop the Neuro-PC algorithm which is a novel methodology for inferring the causal functional connectivity between neurons from multi-dimensional time series, such as neuronal recordings. The core of our methodology relies on a novel adaptation of the PC algorithm, a state-of-the-art method for statistical causal inference, to the multi-dimensional time-series of neural dynamics. We validate the performance of the method on network motifs with various interactions between their neurons simulated using continuous-time artificial network of neurons. We then consider the application of the method to obtain the causal functional connectome for recent multi-array electrophysiological recordings from the mouse visual cortex in the presence of different stimuli. We show how features of the mapping can be used for quantification of the similarities between neural responses subject to different stimuli.