Investigating Community Detection Algorithms and their Capacity
as Markers of Brain Diseases

D 2017

Investigating Community Detection Algorithms and their Capacity as Markers of Brain Diseases

VÝTVAROVÁ, Eva, Jan FOUSEK, Michal MIKL, Irena REKTOROVÁ, Eva HLADKÁ et. al.

Základní údaje

Originální název

Investigating Community Detection Algorithms and their Capacity as Markers of Brain Diseases

Autoři

VÝTVAROVÁ, Eva (203 Česká republika, domácí), Jan FOUSEK (203 Česká republika, domácí), Michal MIKL (203 Česká republika, domácí), Irena REKTOROVÁ (203 Česká republika, domácí) a Eva HLADKÁ (203 Česká republika, domácí)

Vydání

Taipei; Taiwan, International Symposium on Grids and Clouds (ISGC) 2017. Academia Sinica, Taipei, Taiwan: Proceedings of Science, od s. 1-14, 14 s. 2017

Nakladatel

Sissa Medialab Srl

Další údaje

Jazyk

angličtina

Typ výsledku

Stať ve sborníku

Obor

10201 Computer sciences, information science, bioinformatics

Stát vydavatele

Itálie

Utajení

není předmětem státního či obchodního tajemství

Forma vydání

elektronická verze "online"

Kód RIV

RIV/00216224:14330/17:00098154

Organizační jednotka

Fakulta informatiky

ISSN

DOI

http://dx.doi.org/10.22323/1.293.0018

Klíčová slova anglicky

Classification (of information); Optimization; Population dynamics; Random variables

Příznaky

Mezinárodní význam, Recenzováno

Změněno: 18. 5. 2018 06:40, RNDr. Pavel Šmerk, Ph.D.

Anotace

V originále

In this paper, we present a workflow for evaluating resting-state brain functional connectivity with different community detection algorithms and their strengths to discriminate between health and Parkinson’s disease (PD) and mild cognitive impairment preceding Alzheimer’s disease (ADMCI). We further analyze the complexity of particular pipeline steps aiming to provide guidelines for both execution on computing infrastructure and further optimization efforts. On a dataset of 50 controls and 70 patients we measured an increased modularity coefficient with 81.8% accuracy of classifying PD versus controls and 76.2% accuracy of classifying ADMCI versus controls. Significantly higher modularity coefficient values were measured when the random matrix theory decomposition was adapted for network construction. These results were observed on networks of 82 nodes based on AAL atlas and 317 nodes based on multimodal parcellation atlas.

Návaznosti

MUNI/A/0897/2016, interní kód MU

Název: Rozsáhlé výpočetní systémy: modely, aplikace a verifikace VI.

Investor: Masarykova univerzita, Rozsáhlé výpočetní systémy: modely, aplikace a verifikace VI., DO R. 2020_Kategorie A - Specifický výzkum - Studentské výzkumné projekty

Citovat

VÝTVAROVÁ, Eva, Jan FOUSEK, Michal MIKL, Irena REKTOROVÁ a Eva HLADKÁ. Investigating Community Detection Algorithms and their Capacity as Markers of Brain Diseases. Online. In International Symposium on Grids and Clouds (ISGC) 2017. Academia Sinica, Taipei, Taiwan: Proceedings of Science. Taipei; Taiwan: Sissa Medialab Srl, 2017, s. 1-14. ISSN 1824-8039. Dostupné z: https://dx.doi.org/10.22323/1.293.0018.

@inproceedings{1394346,
   author = {Výtvarová, Eva and Fousek, Jan and Mikl, Michal and Rektorová, Irena and Hladká, Eva},
   address = {Taipei; Taiwan},
   booktitle = {International Symposium on Grids and Clouds (ISGC) 2017. Academia Sinica, Taipei, Taiwan: Proceedings of Science},
   doi = {http://dx.doi.org/10.22323/1.293.0018},
   keywords = {Classification (of information); Optimization; Population dynamics; Random variables},
   howpublished = {elektronická verze "online"},
   language = {eng},
   location = {Taipei; Taiwan},
   pages = {1-14},
   publisher = {Sissa Medialab Srl},
   title = {Investigating Community Detection Algorithms and their Capacity as Markers of Brain Diseases},
   year = {2017}
}

TY  - JOUR
ID  - 1394346
AU  - Výtvarová, Eva - Fousek, Jan - Mikl, Michal - Rektorová, Irena - Hladká, Eva
PY  - 2017
TI  - Investigating Community Detection Algorithms and their Capacity as Markers of Brain Diseases
PB  - Sissa Medialab Srl
CY  - Taipei; Taiwan
KW  - Classification (of information)
KW  - Optimization
KW  - Population dynamics
KW  - Random variables
N2  - In this paper, we present a workflow for evaluating resting-state brain functional connectivity with different community detection algorithms and their strengths to discriminate between health and Parkinson’s disease (PD) and mild cognitive impairment preceding Alzheimer’s disease (ADMCI). We further analyze the complexity of particular pipeline steps aiming to provide guidelines for both execution on computing infrastructure and further optimization efforts. On a dataset of 50 controls and 70 patients we measured an increased modularity coefficient with 81.8% accuracy of classifying PD versus controls and 76.2% accuracy of classifying ADMCI versus controls. Significantly higher modularity coefficient values were measured when the random matrix theory decomposition was adapted for network construction. These results were observed on networks of 82 nodes based on AAL atlas and 317 nodes based on multimodal parcellation atlas.
ER  -

VÝTVAROVÁ, Eva, Jan FOUSEK, Michal MIKL, Irena REKTOROVÁ a Eva HLADKÁ. Investigating Community Detection Algorithms and their Capacity as Markers of Brain Diseases. Online. In \textit{International Symposium on Grids and Clouds (ISGC) 2017. Academia Sinica, Taipei, Taiwan: Proceedings of Science}. Taipei; Taiwan: Sissa Medialab Srl, 2017, s.~1-14. ISSN~1824-8039. Dostupné z: https://dx.doi.org/10.22323/1.293.0018.

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