A model for classification based on the functional connectivity pattern dynamics of the brain

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F16%3A10324420" target="_blank" >RIV/00216208:11320/16:10324420 - isvavai.cz</a>

Výsledek na webu

<a href="http://ieeexplore.ieee.org/document/7838066/" target="_blank" >http://ieeexplore.ieee.org/document/7838066/</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/ENIC.2016.037" target="_blank" >10.1109/ENIC.2016.037</a>

Jazyk výsledku

angličtina

Název v původním jazyce

A model for classification based on the functional connectivity pattern dynamics of the brain

Popis výsledku v původním jazyce

Synchronized spontaneous low frequency fluctuations of the so called BOLD signal, as measured by functional Magnetic Resonance Imaging (fMRI), are known to represent the functional connections of different brain areas. Dynamic Time Warping (DTW) distance can be used as a similarity measure between BOLD signals of brain regions as an alternative of the traditionally used correlation coefficient and the usage of the DTW algorithm has further advantages: beside the DTW distance, the algorithm generates the warping path, i.e. the time-delay function between the compared two time-series. In this paper, we propose to use the relative length of the warping path as classification feature and demonstrate that the warping path itself carries important information when classifying patients according to cannabis addiction. We discuss biomedical relevance of our findings as well.

Název v anglickém jazyce

A model for classification based on the functional connectivity pattern dynamics of the brain

Popis výsledku anglicky

Synchronized spontaneous low frequency fluctuations of the so called BOLD signal, as measured by functional Magnetic Resonance Imaging (fMRI), are known to represent the functional connections of different brain areas. Dynamic Time Warping (DTW) distance can be used as a similarity measure between BOLD signals of brain regions as an alternative of the traditionally used correlation coefficient and the usage of the DTW algorithm has further advantages: beside the DTW distance, the algorithm generates the warping path, i.e. the time-delay function between the compared two time-series. In this paper, we propose to use the relative length of the warping path as classification feature and demonstrate that the warping path itself carries important information when classifying patients according to cannabis addiction. We discuss biomedical relevance of our findings as well.

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

2016 Third European Network Intelligence Conference (ENIC)

ISBN

978-1-5090-3455-0

ISSN

—

e-ISSN

—

Počet stran výsledku

6

Strana od-do

203-208

Název nakladatele

IEEE

Místo vydání

New York, NY, USA

Místo konání akce

Wrocław, Poland

Datum konání akce

5. 9. 2016

Typ akce podle státní příslušnosti

EUR - Evropská akce

Kód UT WoS článku

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