Causal Network Inference for Neural Ensemble Activity

Rong Chen

doi:10.1007/s12021-020-09505-4

Abstract

Interactions among cellular components forming a mesoscopic scale brain network (microcircuit) display characteristic neural dynamics. Analysis of microcircuits provides a system-level understanding of the neurobiology of health and disease. Causal discovery aims to detect causal relationships among variables based on observational data. A key barrier in causal discovery is the high dimensionality of the variable space. A method called Causal Inference for Microcircuits (CAIM) is proposed to reconstruct causal networks from calcium imaging or electrophysiology time series. CAIM combines neural recording, Bayesian network modeling, and neuron clustering. Validation experiments based on simulated data and a real-world reaching task dataset demonstrated that CAIM accurately revealed causal relationships among neural clusters.

Highlights

Increasing experimental and computational evidence supports the existence of a specific pattern of connectivity among adjacent neurons during cognition and emotion (Yoshimura and Callaway 2005; Yoshimura et al 2005; Song et al 2005; Ko et al 2013; Litwin-Kumar and Doiron 2012)
A dynamic Bayesian networks (DBNs) is defined as a pair, (B1, B→), where B1 is a Bayesian network defining the baseline probability distribution; and B→ defines the transition probability P(Yt + 1 | Yt)
We propose a causal discovery method called Causal Inference for Microcircuits (CAIM) that is based on DBNs

Summary

Introduction

Increasing experimental and computational evidence supports the existence of a specific pattern of connectivity among adjacent neurons during cognition and emotion (Yoshimura and Callaway 2005; Yoshimura et al 2005; Song et al 2005; Ko et al 2013; Litwin-Kumar and Doiron 2012). A microcircuit lies at the heart of the information processing capability of the brain. It carries out a specific computation of a region. Network analysis (or connectivity analysis) methods for neural signals can be classified as synchrony analysis and causal discovery. Causal discovery (or effective connectivity analysis) aims to infer cause-effect relations among variables based on observational data. An important framework of causal discovery is based on conditional independence (Spirtes et al 2001). This framework considers the dependence between two variables X and Y given a set of variables Z.

Methods

Results

Conclusion