Revisiting Nonlinear Functional Brain Co-activations: Directed, Dynamic, and Delayed.

Ignacio Cifre,Maria T Miller Flores,Dante R Chialvo,Jeremi K Ochab,Lucia Penalba

doi:10.3389/fnins.2021.700171

Abstract

The center stage of neuro-imaging is currently occupied by studies of functional correlations between brain regions. These correlations define the brain functional networks, which are the most frequently used framework to represent and interpret a variety of experimental findings. In the previous study, we first demonstrated that the relatively stronger blood oxygenated level dependent (BOLD) activations contain most of the information relevant to understand functional connectivity, and subsequent work confirmed that a large compression of the original signals can be obtained without significant loss of information. In this study, we revisit the correlation properties of these epochs to define a measure of nonlinear dynamic directed functional connectivity (nldFC) across regions of interest. We show that the proposed metric provides at once, without extensive numerical complications, directed information of the functional correlations, as well as a measure of temporal lags across regions, overall offering a different and complementary perspective in the analysis of brain co-activation patterns. In this study, we provide further details for the computations of these measures and for a proof of concept based on replicating existing results from an Autistic Syndrome database, and discuss the main features and advantages of the proposed strategy for the study of brain functional correlations.

Highlights

The large scale dynamics of the brain exhibits a plethora of spatio-temporal patterns
We proposed that the timing of the brief epochs of relatively stronger blood oxygenated level dependent (BOLD) activations contain a great deal of functional connectivity (FC) information (Tagliazucchi et al, 2011, 2012)
The study is organized as follows: we describe the essence of the method, starting with the basic procedure to define the BOLD-triggered events followed by a description of the available correlation measures that allow a proper definition of the functional connectivity between the events, including a definition of directionality and temporal lag of the events

Summary

Introduction

The large scale dynamics of the brain exhibits a plethora of spatio-temporal patterns. Since the first description of voxel-wise correlation networks (Eguíluz et al , 2005), there has been a continuous interest in developing better ways to derive brain “networks” from fMRI time series data. Common to all is the identification of functional “nodes” [i.e., fMRI time series extracted from regions of interest (ROI)], functional edges (i.e., the cross-correlations), which allows for the subsequent graph analysis. An important methodological challenge has been always to define an adequate coarse graining of the brain imaging data to compress 1,000 of the so-called blood oxygenated level dependent time series. The usual analysis aims at the identification of bursts of correlated activity across certain regions, which requires extensive computations, complicated in part by the humongous size of the data sets.

Methods

Results

Conclusion