PhD Scientific Days 2021

NE_I_L: Neurosciences I. Lectures

Increased Multifractal Functional Connectivity in the Brain During Visual Pattern Recognition

Előadó neve

Stylianou, Orestis

Előadó munkahelye

Department of Physiology Semmelweis University

Előadó telefonszáma

+36702731655

Előadó e-mail címe

orestisstylianou@rocketmail.com

Az előadás címe

Increased Multifractal Functional Connectivity in the Brain During Visual Pattern Recognition

Szerző(k) neve és munkahelye

Orestis Stylianou, Department of Physiology, Semmelweis University

Bemutatás módja

Szóbeli

Szekció

Neurosciences I. Lectures

Language of the presentation

English

Preferred session

Neurosciences

Összefoglaló szövege

Introduction: The human brain is a complex system encompassing distant neuronal populations interconnected via a dense axonal grid. Functional brain networks emerge within this anatomical network, which provide the neurophysiological basis for higher order brain functions. The functional connectivity (FC) between the nodes of these networks is evaluated based on the statistical interdependence of the brain activity recorded from different localizations. Here we examined the often-ignored scale-free coupled dynamics using a bivariate focus-based multifractal (BFMF) analysis.

Aims: Our goal was to investigate if the multifractal FC reorganizes during a visual pattern recognition task of varying difficulty and whether it associates with task performance (accuracy – ACC, reaction time – RT).

Methods: 58 young, healthy volunteers were recruited. Before the task, 3 min of eyesclosed (EC) and 3 min of eyes-open (EO) resting-state EEG was recorded from 14 brain regions. The task consisted of 30 trials of 3 difficulty levels (Easy, Medium, Hard). To assess multifractal FC, we estimated generalized Hurst (H) exponents by BFMF analysis of preprocessed EEG data, which characterizes global [H(2)] and local [ΔH15] scaling behavior of coupled dynamics. An H(2) and ΔH15 network for every participant and state was constructed. The topology of every network was described by the weighted node degree (D) – calculated as the sum of connection weights for each region – and their average for the whole network (𝐷̅).

Results: D increased during task, but no differences were found within the 3 different task states. We also observed regional variability of D in all networks. Finally, positive correlations were seen between 𝐷̅ and RT but not between 𝐷̅ and ACC.

Conclusions: Previously, we showed that there is substantial regional variability in the multifractal FC in EC, here we see that similar motifs emerged during task as well. A similar visual pattern recognition paradigm was found to increase scale-specific FC, agreeing with the increased scale-free FC observed here in the task states. Additionally, these topological changes i) indicated increased FC during task that were not influenced by the level of difficulty and ii) were significantly associated with RT.

Funding: We gratefully acknowledge the financial support from the Department of Physiology.

University and Doctoral School

Semmelweis University, Doctoral School of Theoretical and Translational Medicine

Supervisor

Peter Mukli, MD, PhD

Publication of my abstract

I give consent to the publication of my abstract on the website of the congress.

Kind

Szabad

Status

elfogadva

Accepted presentation method

szóbeli

Előadás fájl jóváhagyás

nem rendelkezett róla

Előadó

4605

Start

10:25

End

10:40

Authors (legacy)

Orestis Stylianou, Department of Physiology, Semmelweis University