Network structural change point detection and reconstruction for balanced neuronal networks

TL;DR

This paper introduces a novel framework combining change point detection and correlation analysis to reconstruct and analyze dynamic neuronal networks with structural changes, applicable to large-scale brain data.

Contribution

It develops a unified method for detecting structural change points and reconstructing neuronal networks, accommodating topology and strength changes in sparse data.

Findings

Effective in large-scale simulations

Handles topology and strength changes

Maintains accuracy with sparse data

Abstract

Understanding brain dynamics and functions critically depends on knowledge of the network connectivity among neurons. However, the complexity of brain structural connectivity, coupled with continuous modifications driven by synaptic plasticity, makes its direct experimental measurement particularly challenging. Conventional connectivity inference methods based on neuronal recordings often assumes a static underlying structural connectivity and requires stable statistical features of neural activities, making them unsuitable for reconstructing structural connectivity that undergoes changes. To fulfill the needs of reconstructing networks undergoing potential structural changes, we propose a unified network reconstruction framework that combines connectivity-induced change point detection (CPD) with pairwise time-delayed correlation coefficient (TDCC) method. For general neuronal networks in balanced regimes, we develop a theoretical analysis for discriminating changes in structural connectivity based on the fluctuation of neuronal voltage time series. We then demonstrate a pairwise TDCC method to reconstruct the network using spike train recordings segmented at the detected change points. We show the effectiveness of our CPD-TDCC network reconstruction using large-scale network simulations with multiple neuronal models. Crucially, our method accommodates networks with changes in both network topologies and synaptic coupling strengths while retaining accuracy even with sparsely sampled subnetwork data, achieving a critical advancement for practical applications in real experimental situations. Our CPD-TDCC framework addresses the critical gap in network reconstruction by accounting connectivity-induced changes points, potentially offering a valuable tool for studying structure and dynamics in the cortical brain.