Результаты исследований: Публикации в книгах, отчётах, сборниках, трудах конференций › статья в сборнике материалов конференции › научная › Рецензирование
Dynamic clustering of connections between fMRI resting state networks : A comparison of two methods of data analysis. / Zavyalova, Victoria; Knyazeva, Irina; Ushakov, Vadim; Poyda, Alexey; Makarenko, Nikolay; Malakhov, Denis; Velichkovsky, Boris.
Biologically Inspired Cognitive Architectures BICA for Young Scientists - Proceedings of the 1st International Early Research Career Enhancement School, FIERCES 2016. Том 449 Springer Nature, 2016. стр. 265-271 (Advances in Intelligent Systems and Computing; Том 449).Результаты исследований: Публикации в книгах, отчётах, сборниках, трудах конференций › статья в сборнике материалов конференции › научная › Рецензирование
}
TY - GEN
T1 - Dynamic clustering of connections between fMRI resting state networks
T2 - A comparison of two methods of data analysis
AU - Zavyalova, Victoria
AU - Knyazeva, Irina
AU - Ushakov, Vadim
AU - Poyda, Alexey
AU - Makarenko, Nikolay
AU - Malakhov, Denis
AU - Velichkovsky, Boris
PY - 2016
Y1 - 2016
N2 - In the present paper we describe an approach to the dynamical clustering of fMRI resting state networks and their connections, in which we use two known mathematical methods for data analysis: topological data analysis and k-means method. With these two methods we found about 4 stable states in group analysis. Dynamics of these states is characterized by periods of stability (blocks) with subsequent transition to another state. Topological data analysis method allowed us to find some regularity in subsequent transitions between blocks of states for individuals but it was not shown that the regularity repeats in all subjects. Topological method gives smoother distribution of dynamic states comparing to k-means method, highlighting about 4 dominant states in percentage, while k-means method gives 1–2 such states.
AB - In the present paper we describe an approach to the dynamical clustering of fMRI resting state networks and their connections, in which we use two known mathematical methods for data analysis: topological data analysis and k-means method. With these two methods we found about 4 stable states in group analysis. Dynamics of these states is characterized by periods of stability (blocks) with subsequent transition to another state. Topological data analysis method allowed us to find some regularity in subsequent transitions between blocks of states for individuals but it was not shown that the regularity repeats in all subjects. Topological method gives smoother distribution of dynamic states comparing to k-means method, highlighting about 4 dominant states in percentage, while k-means method gives 1–2 such states.
KW - Correlation matrix
KW - Dynamical network clustering
KW - FMRI
KW - Independent component analysis
KW - Resting state
KW - Topological data analysis
UR - http://www.scopus.com/inward/record.url?scp=84964005670&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-32554-5_34
DO - 10.1007/978-3-319-32554-5_34
M3 - Conference contribution
AN - SCOPUS:84964005670
SN - 9783319325538
VL - 449
T3 - Advances in Intelligent Systems and Computing
SP - 265
EP - 271
BT - Biologically Inspired Cognitive Architectures BICA for Young Scientists - Proceedings of the 1st International Early Research Career Enhancement School, FIERCES 2016
PB - Springer Nature
ER -
ID: 9326489