Classification of fMRI data using Dynamic Time Warping based functional connectivity analysis

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F16%3A10324421" target="_blank" >RIV/00216208:11320/16:10324421 - isvavai.cz</a>

Výsledek na webu

<a href="http://ieeexplore.ieee.org/document/7760247/" target="_blank" >http://ieeexplore.ieee.org/document/7760247/</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/EUSIPCO.2016.7760247" target="_blank" >10.1109/EUSIPCO.2016.7760247</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Classification of fMRI data using Dynamic Time Warping based functional connectivity analysis

Popis výsledku v původním jazyce

The synchronized spontaneous low frequency fluctuations of the BOLD signal, as captured by functional MRI measurements, is known to represent the functional connections of different brain areas. The aforementioned MRI measurements result in high-dimensional time series, the dimensions of which correspond to the activity of different brain regions. Recently we have shown that Dynamic Time Warping (DTW) distance can be used as a similarity measure between BOLD signals of brain regions [1] as an alternative of the traditionally used correlation coefficient. We have characterized the new metric's stability in multiple measurements, and between subjects in homogenous groups. In this paper we investigated the DTW metric's sensitivity and demonstrated that DTW-based models outperform correlation-based models in resting-state fMRI data classification tasks. Additionally, we show that functional connectivity networks resulting from DTW-based models as compared to the correlation-based models are more stable and sensitive to differences between healthy subjects and patient groups.

Název v anglickém jazyce

Classification of fMRI data using Dynamic Time Warping based functional connectivity analysis

Popis výsledku anglicky

The synchronized spontaneous low frequency fluctuations of the BOLD signal, as captured by functional MRI measurements, is known to represent the functional connections of different brain areas. The aforementioned MRI measurements result in high-dimensional time series, the dimensions of which correspond to the activity of different brain regions. Recently we have shown that Dynamic Time Warping (DTW) distance can be used as a similarity measure between BOLD signals of brain regions [1] as an alternative of the traditionally used correlation coefficient. We have characterized the new metric's stability in multiple measurements, and between subjects in homogenous groups. In this paper we investigated the DTW metric's sensitivity and demonstrated that DTW-based models outperform correlation-based models in resting-state fMRI data classification tasks. Additionally, we show that functional connectivity networks resulting from DTW-based models as compared to the correlation-based models are more stable and sensitive to differences between healthy subjects and patient groups.

Druh

D - Stať ve sborníku

CEP obor

IN - Informatika

OECD FORD obor

—

Projekt

—

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2016

Kód důvěrnosti údajů

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

Název statě ve sborníku

24th European Signal Processing Conference, EUSIPCO 2016

ISBN

978-0-9928626-5-7

ISSN

2219-5491

e-ISSN

—

Počet stran výsledku

5

Strana od-do

245-249

Název nakladatele

IEEE

Místo vydání

NEW YORK, NY 10017 USA

Místo konání akce

Budapest, Hungary

Datum konání akce

29. 8. 2016

Typ akce podle státní příslušnosti

EUR - Evropská akce

Kód UT WoS článku

000391891900049