A Graph theoretical approach to study the organization of the cortical networks during different mathematical tasks.
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....
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2013
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oai:doaj.org-article:a6e6c86264714d059d03b799003cecf22021-11-18T08:58:52ZA Graph theoretical approach to study the organization of the cortical networks during different mathematical tasks.1932-620310.1371/journal.pone.0071800https://doaj.org/article/a6e6c86264714d059d03b799003cecf22013-01-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/23990992/?tool=EBIhttps://doaj.org/toc/1932-6203The 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.Manousos A KladosKassia KanatsouliIoannis AntoniouFabio BabiloniVassiliki TsirkaPanagiotis D BamidisSifis MicheloyannisPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 8, Iss 8, p e71800 (2013) |
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Medicine R Science Q Manousos A Klados Kassia Kanatsouli Ioannis Antoniou Fabio Babiloni Vassiliki Tsirka Panagiotis D Bamidis Sifis Micheloyannis A Graph theoretical approach to study the organization of the cortical networks during different mathematical tasks. |
| description |
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. |
| format |
article |
| author |
Manousos A Klados Kassia Kanatsouli Ioannis Antoniou Fabio Babiloni Vassiliki Tsirka Panagiotis D Bamidis Sifis Micheloyannis |
| author_facet |
Manousos A Klados Kassia Kanatsouli Ioannis Antoniou Fabio Babiloni Vassiliki Tsirka Panagiotis D Bamidis Sifis Micheloyannis |
| author_sort |
Manousos A Klados |
| title |
A Graph theoretical approach to study the organization of the cortical networks during different mathematical tasks. |
| title_short |
A Graph theoretical approach to study the organization of the cortical networks during different mathematical tasks. |
| title_full |
A Graph theoretical approach to study the organization of the cortical networks during different mathematical tasks. |
| title_fullStr |
A Graph theoretical approach to study the organization of the cortical networks during different mathematical tasks. |
| title_full_unstemmed |
A Graph theoretical approach to study the organization of the cortical networks during different mathematical tasks. |
| title_sort |
graph theoretical approach to study the organization of the cortical networks during different mathematical tasks. |
| publisher |
Public Library of Science (PLoS) |
| publishDate |
2013 |
| url |
https://doaj.org/article/a6e6c86264714d059d03b799003cecf2 |
| work_keys_str_mv |
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| _version_ |
1718421102660681728 |