Standard

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. Vol. 449 Springer Nature, 2016. p. 265-271 (Advances in Intelligent Systems and Computing; Vol. 449).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Harvard

Zavyalova, V, Knyazeva, I, Ushakov, V, Poyda, A, Makarenko, N, Malakhov, D & Velichkovsky, B 2016, Dynamic clustering of connections between fMRI resting state networks: A comparison of two methods of data analysis. in Biologically Inspired Cognitive Architectures BICA for Young Scientists - Proceedings of the 1st International Early Research Career Enhancement School, FIERCES 2016. vol. 449, Advances in Intelligent Systems and Computing, vol. 449, Springer Nature, pp. 265-271. https://doi.org/10.1007/978-3-319-32554-5_34

APA

Zavyalova, V., Knyazeva, I., Ushakov, V., Poyda, A., Makarenko, N., Malakhov, D., & Velichkovsky, B. (2016). Dynamic clustering of connections between fMRI resting state networks: A comparison of two methods of data analysis. In Biologically Inspired Cognitive Architectures BICA for Young Scientists - Proceedings of the 1st International Early Research Career Enhancement School, FIERCES 2016 (Vol. 449, pp. 265-271). (Advances in Intelligent Systems and Computing; Vol. 449). Springer Nature. https://doi.org/10.1007/978-3-319-32554-5_34

Vancouver

Zavyalova V, Knyazeva I, Ushakov V, Poyda A, Makarenko N, Malakhov D et al. Dynamic clustering of connections between fMRI resting state networks: A comparison of two methods of data analysis. In Biologically Inspired Cognitive Architectures BICA for Young Scientists - Proceedings of the 1st International Early Research Career Enhancement School, FIERCES 2016. Vol. 449. Springer Nature. 2016. p. 265-271. (Advances in Intelligent Systems and Computing). https://doi.org/10.1007/978-3-319-32554-5_34

Author

Zavyalova, Victoria ; Knyazeva, Irina ; Ushakov, Vadim ; Poyda, Alexey ; Makarenko, Nikolay ; Malakhov, Denis ; Velichkovsky, Boris. / Dynamic clustering of connections between fMRI resting state networks : A comparison of two methods of data analysis. Biologically Inspired Cognitive Architectures BICA for Young Scientists - Proceedings of the 1st International Early Research Career Enhancement School, FIERCES 2016. Vol. 449 Springer Nature, 2016. pp. 265-271 (Advances in Intelligent Systems and Computing).

BibTeX

@inproceedings{b0e2cd4a2d1b4ba9a7023d061ec16cd2,
title = "Dynamic clustering of connections between fMRI resting state networks: A comparison of two methods of data analysis",
abstract = "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.",
keywords = "Correlation matrix, Dynamical network clustering, FMRI, Independent component analysis, Resting state, Topological data analysis",
author = "Victoria Zavyalova and Irina Knyazeva and Vadim Ushakov and Alexey Poyda and Nikolay Makarenko and Denis Malakhov and Boris Velichkovsky",
year = "2016",
doi = "10.1007/978-3-319-32554-5_34",
language = "English",
isbn = "9783319325538",
volume = "449",
series = "Advances in Intelligent Systems and Computing",
publisher = "Springer Nature",
pages = "265--271",
booktitle = "Biologically Inspired Cognitive Architectures BICA for Young Scientists - Proceedings of the 1st International Early Research Career Enhancement School, FIERCES 2016",
address = "Germany",

}

RIS

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