Point process analysis of large-scale brain fMRI dynamics

TL;DR

This paper demonstrates that brain dynamics during resting state fMRI can be effectively characterized using a point process approach based on discrete BOLD signal events, simplifying analysis while capturing key functional networks.

Contribution

It introduces a novel point process method to analyze resting state fMRI data, revealing that large amplitude BOLD peaks suffice to extract meaningful brain network information.

Findings

Resting state networks can be derived from point process events.

Brain activity exhibits avalanches with consistent dynamical properties.

Discrete event analysis captures essential brain dynamics comparable to traditional methods.

Abstract

Functional magnetic resonance imaging (fMRI) techniques have contributed significantly to our understanding of brain function. Current methods are based on the analysis of \emph{gradual and continuous} changes in the brain blood oxygenated level dependent (BOLD) signal. Departing from that approach, recent work has shown that equivalent results can be obtained by inspecting only the relatively large amplitude BOLD signal peaks, suggesting that relevant information can be condensed in \emph{discrete} events. This idea is further explored here to demonstrate how brain dynamics at resting state can be captured just by the timing and location of such events, i.e., in terms of a spatiotemporal point process. As a proof of principle, we show that the resting state networks (RSN) maps can be extracted from such point processes. Furthermore, the analysis uncovers avalanches of activity which are ruled by the same dynamical and statistical properties described previously for neuronal events at smaller scales. Given the demonstrated functional relevance of the resting state brain dynamics, its representation as a discrete process might facilitate large scale analysis of brain function both in health and disease.