TY - JOUR
T1 - Revisiting Nonlinear Functional Brain Co-activations
T2 - Directed, Dynamic, and Delayed
AU - Cifre, Ignacio
AU - Miller Flores, Maria T.
AU - Penalba, Lucia
AU - Ochab, Jeremi K.
AU - Chialvo, Dante R.
N1 - Funding Information:
This work was supported by the MICINN (Spain) grant PSI2017-82397-R, the National Science Centre (Poland) grant DEC-2015/17/D/ST2/03492 and the Foundation for Polish Science (FNP) project Bio-inspired Artificial Neural Networks grant POIR.04.04.00-00-14DE/18-00, and by CONICET (Argentina) and Escuela de Ciencia y Tecnología, UNSAM. Work conducted under the auspice of the Jagiellonian University-UNSAM Cooperation Agreement.
Publisher Copyright:
© Copyright © 2021 Cifre, Miller Flores, Penalba, Ochab and Chialvo.
PY - 2021/10/12
Y1 - 2021/10/12
N2 - 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.
AB - 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.
KW - autism (ASD)
KW - dynamic functional connectivity
KW - fMRI
KW - functional connectivity
KW - resting state networks
UR - http://www.scopus.com/inward/record.url?scp=85117957792&partnerID=8YFLogxK
U2 - 10.3389/fnins.2021.700171
DO - 10.3389/fnins.2021.700171
M3 - Article
AN - SCOPUS:85117957792
SN - 1662-4548
VL - 15
JO - Frontiers in Neuroscience
JF - Frontiers in Neuroscience
M1 - 700171
ER -