Dataset Information

Transfer Entropy as a Measure of Brain Connectivity: A Critical Analysis With the Help of Neural Mass Models.

ABSTRACT: Objective: Assessing brain connectivity from electrophysiological signals is of great relevance in neuroscience, but results are still debated and depend crucially on how connectivity is defined and on mathematical instruments utilized. Aim of this work is to assess the capacity of bivariate Transfer Entropy (TE) to evaluate connectivity, using data generated from simple neural mass models of connected Regions of Interest (ROIs). Approach: Signals simulating mean field potentials were generated assuming two, three or four ROIs, connected via excitatory or by-synaptic inhibitory links. We investigated whether the presence of a statistically significant connection can be detected and if connection strength can be quantified. Main Results: Results suggest that TE can reliably estimate the strength of connectivity if neural populations work in their linear regions, and if the epoch lengths are longer than 10 s. In case of multivariate networks, some spurious connections can emerge (i.e., a statistically significant TE even in the absence of a true connection); however, quite a good correlation between TE and synaptic strength is still preserved. Moreover, TE appears more robust for distal regions (longer delays) compared with proximal regions (smaller delays): an approximate a priori knowledge on this delay can improve the procedure. Finally, non-linear phenomena affect the assessment of connectivity, since they may significantly reduce TE estimation: information transmission between two ROIs may be weak, due to non-linear phenomena, even if a strong causal connection is present. Significance: Changes in functional connectivity during different tasks or brain conditions, might not always reflect a true change in the connecting network, but rather a change in information transmission. A limitation of the work is the use of bivariate TE. In perspective, the use of multivariate TE can improve estimation and reduce some of the problems encountered in the present study.

SUBMITTER: Ursino M

PROVIDER: S-EPMC7292208 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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Transfer Entropy as a Measure of Brain Connectivity: A Critical Analysis With the Help of Neural Mass Models.

Ursino Mauro M Ricci Giulia G Magosso Elisa E

Frontiers in computational neuroscience 20200605

<b>Objective:</b> Assessing brain connectivity from electrophysiological signals is of great relevance in neuroscience, but results are still debated and depend crucially on how connectivity is defined and on mathematical instruments utilized. Aim of this work is to assess the capacity of bivariate Transfer Entropy (TE) to evaluate connectivity, using data generated from simple neural mass models of connected Regions of Interest (ROIs). <b>Approach:</b> Signals simulating mean field potentials w ...[more]

PMID: 32581756

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Project description:BACKGROUND:The use of functional connectivity magnetic resonance imaging (fcMRI) in research involving preterm infants is relatively new, and its feasibility in this population is not fully established. However, fcMRI images reveal functional neural connections that may be useful in establishing the mechanisms of neuroprotective interventions in preterm infants. OBJECTIVE:The aim of this study was to determine the feasibility of using fcMRI to measure differences in functional neural connections in nursing intervention studies. METHODS:A pilot study was conducted as part of a longitudinal, randomized controlled trial (RCT) testing the effect of a feeding intervention on neurodevelopmental and clinical outcomes of preterm infants randomly assigned to one of two groups: a patterned feeding experience (PFE) group and a usual feeding care (UFC) group. The fcMRIs were done at term-equivalent age. Visual, motor, and default mode networks were analyzed. RESULTS:Seven infants were studied (four were in the PFE group, and three were in the UFC group). Participants were selected sequentially from the parent RCT. Clear images were obtained from all participants. Differences were noted among PFE and UFC infants: Infants receiving PFE were hyperconnective in the default mode (caudate, anterior cingulate cortex, and precuneus) and motor networks (middle temporal and middle occipital areas) and hypoconnective in others areas of the default mode (hippocampal and lingual regions) and motor networks (precentral and superior frontal cortices) relative to UFC infants. No differences were noted in visual networks. DISCUSSION:The feasibility of using fcMRI at term-equivalent age in preterm infants who participated in an RCT on the effect of a nursing intervention was shown. Differences in connectivity among infants by group were detected. Further research is needed to show the benefit of fcMRI in studies of preterm infants given the costs of the procedure as well as the uncertain relationship of this early outcome measure to long-term neurodevelopment.

Dataset Information

Transfer Entropy as a Measure of Brain Connectivity: A Critical Analysis With the Help of Neural Mass Models.

Publications

Transfer Entropy as a Measure of Brain Connectivity: A Critical Analysis With the Help of Neural Mass Models.

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