The Impact of Diverse Preprocessing Pipelines on Brain Functional Connectivity

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216224%3A14330%2F17%3A00095053" target="_blank" >RIV/00216224:14330/17:00095053 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.23919/EUSIPCO.2017.8081690" target="_blank" >http://dx.doi.org/10.23919/EUSIPCO.2017.8081690</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.23919/EUSIPCO.2017.8081690" target="_blank" >10.23919/EUSIPCO.2017.8081690</a>

Jazyk výsledku

angličtina

Název v původním jazyce

The Impact of Diverse Preprocessing Pipelines on Brain Functional Connectivity

Popis výsledku v původním jazyce

Brain functional connectivity measured by functional magnetic resonance imaging was shown to be influenced by preprocessing procedures. We aim to describe this influence separately for different preprocessing factors and in 20 different most used preprocessing pipelines. We evaluate the effects of slice-timing correction and physiological noise filtering by RETROICOR, diverse levels of motion correction, and white matter, cerebrospinal fluid, and global signal filtering. With usage of three datasets, we show the impact on global metrics of restingstate functional brain networks and their reliability. We show negative effect of RETROICOR on reliability of metrics and disrupting effect of global signal regression on network topology. We do not support the use of slice-timing correction because it does not significantly influence any of the measured features. We also show that the selected types of preprocessing may affect averaged node strength, normalized clustering coefficient, normalized characteristic path length and modularity.

Název v anglickém jazyce

The Impact of Diverse Preprocessing Pipelines on Brain Functional Connectivity

Popis výsledku anglicky

Brain functional connectivity measured by functional magnetic resonance imaging was shown to be influenced by preprocessing procedures. We aim to describe this influence separately for different preprocessing factors and in 20 different most used preprocessing pipelines. We evaluate the effects of slice-timing correction and physiological noise filtering by RETROICOR, diverse levels of motion correction, and white matter, cerebrospinal fluid, and global signal filtering. With usage of three datasets, we show the impact on global metrics of restingstate functional brain networks and their reliability. We show negative effect of RETROICOR on reliability of metrics and disrupting effect of global signal regression on network topology. We do not support the use of slice-timing correction because it does not significantly influence any of the measured features. We also show that the selected types of preprocessing may affect averaged node strength, normalized clustering coefficient, normalized characteristic path length and modularity.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

30103 - Neurosciences (including psychophysiology)

Projekt

Výsledek vznikl pri realizaci vícero projektů. Více informací v záložce Projekty.

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2017

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

25th European Signal Processing Conference (EUSIPCO), Kos, Greece.

ISBN

9780992862671

ISSN

2076-1465

e-ISSN

—

Počet stran výsledku

5

Strana od-do

2644-2648

Název nakladatele

IEEE

Místo vydání

Kos, Greece

Místo konání akce

Kos, Greece

Datum konání akce

1. 1. 2017

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

000426986000534