| VÝTVAROVÁ, Eva, Jan FOUSEK, Michal MIKL, Irena REKTOROVÁ and 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, p. 1-14. ISSN 1824-8039. Available from: https://dx.doi.org/10.22323/1.293.0018. |
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@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 and 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, p.~1-14. ISSN~1824-8039. Available from: https://dx.doi.org/10.22323/1.293.0018.
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