Spatial Neural Networks and their Functional Samples: Similarities and Differences

TL;DR

This paper introduces a spatial neural network model to analyze functional brain networks derived from EEG data, demonstrating how mesoscopic dynamics can reflect underlying microscopic spatial network structures under certain conditions.

Contribution

The study develops a novel simulation framework linking spatial neural networks with functional samples, enabling better understanding of brain network dynamics.

Findings

Functional networks can accurately estimate spatial network features under specific conditions.

The model links mesoscopic EEG signals to microscopic neural structures.

Sampling conditions critically influence the fidelity of network measurements.

Abstract

Models of neural networks have proven their utility in the development of learning algorithms in computer science and in the theoretical study of brain dynamics in computational neuroscience. We propose in this paper a spatial neural network model to analyze the important class of functional networks, which are commonly employed in computational studies of clinical brain imaging time series. We developed a simulation framework inspired by multichannel brain surface recordings (more specifically, EEG -- electroencephalogram) in order to link the mesoscopic network dynamics (represented by sampled functional networks) and the microscopic network structure (represented by an integrate-and-fire neural network located in a 3D space -- hence the term spatial neural network). Functional networks are obtained by computing pairwise correlations between time-series of mesoscopic electric potential dynamics, which allows the construction of a graph where each node represents one time-series. The spatial neural network model is central in this study in the sense that it allowed us to characterize sampled functional networks in terms of what features they are able to reproduce from the underlying spatial network. Our modeling approach shows that, in specific conditions of sample size and edge density, it is possible to precisely estimate several network measurements of spatial networks by just observing functional samples.