A Graph theoretical approach to study the organization of the cortical networks during different mathematical tasks.

Manousos A. Klados, Kassia Kanatsouli, Ioannis Antoniou, Fabio Babiloni, Vassiliki Tsirka, Panagiotis D. Bamidis, Sifis Micheloyannis

Research output: Contribution to journalArticle

Abstract

The two core systems of mathematical processing (subitizing and retrieval) as well as their functionality are already known and published. In this study we have used graph theory to compare the brain network organization of these two core systems in the cortical layer during difficult calculations. We have examined separately all the EEG frequency bands in healthy young individuals and we found that the network organization at rest, as well as during mathematical tasks has the characteristics of Small World Networks for all the bands, which is the optimum organization required for efficient information processing. The different mathematical stimuli provoked changes in the graph parameters of different frequency bands, especially the low frequency bands. More specific, in Delta band the induced network increases it's local and global efficiency during the transition from subitizing to retrieval system, while results suggest that difficult mathematics provoke networks with higher cliquish organization due to more specific demands. The network of the Theta band follows the same pattern as before, having high nodal and remote organization during difficult mathematics. Also the spatial distribution of the network's weights revealed more prominent connections in frontoparietal regions, revealing the working memory load due to the engagement of the retrieval system. The cortical networks of the alpha brainwaves were also more efficient, both locally and globally, during difficult mathematics, while the fact that alpha's network was more dense on the frontparietal regions as well, reveals the engagement of the retrieval system again. Concluding, this study gives more evidences regarding the interaction of the two core systems, exploiting the produced functional networks of the cerebral cortex, especially for the difficult mathematics.

LanguageEnglish
Article numbere71800
JournalPLoS ONE
Volume8
Issue number8
DOIs
Publication statusPublished - 2013

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Mathematics
Frequency bands
Brain Waves
cerebral cortex
Small-world networks
Automatic Data Processing
Short-Term Memory
Cerebral Cortex
Graph theory
Electroencephalography
Spatial distribution
spatial distribution
Brain
Efficiency
brain
Weights and Measures
mathematics
Data storage equipment
Processing

Bibliographical note

© 2013 Klados et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits
unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Cite this

Klados, M. A., Kanatsouli, K., Antoniou, I., Babiloni, F., Tsirka, V., Bamidis, P. D., & Micheloyannis, S. (2013). A Graph theoretical approach to study the organization of the cortical networks during different mathematical tasks. PLoS ONE, 8(8), [e71800]. https://doi.org/10.1371/journal.pone.0071800
Klados, Manousos A. ; Kanatsouli, Kassia ; Antoniou, Ioannis ; Babiloni, Fabio ; Tsirka, Vassiliki ; Bamidis, Panagiotis D. ; Micheloyannis, Sifis. / A Graph theoretical approach to study the organization of the cortical networks during different mathematical tasks. In: PLoS ONE. 2013 ; Vol. 8, No. 8.
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Klados, MA, Kanatsouli, K, Antoniou, I, Babiloni, F, Tsirka, V, Bamidis, PD & Micheloyannis, S 2013, 'A Graph theoretical approach to study the organization of the cortical networks during different mathematical tasks.' PLoS ONE, vol. 8, no. 8, e71800. https://doi.org/10.1371/journal.pone.0071800

A Graph theoretical approach to study the organization of the cortical networks during different mathematical tasks. / Klados, Manousos A.; Kanatsouli, Kassia; Antoniou, Ioannis; Babiloni, Fabio; Tsirka, Vassiliki; Bamidis, Panagiotis D.; Micheloyannis, Sifis.

In: PLoS ONE, Vol. 8, No. 8, e71800, 2013.

Research output: Contribution to journalArticle

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