### Abstract

Language | English |
---|---|

Title of host publication | XIII Mediterranean Conference on Medical and Biological Engineering and Computing 2013 |

Publisher | Springer |

Pages | 783-786 |

Number of pages | 4 |

ISBN (Electronic) | 978-3-319-00846-2 |

ISBN (Print) | 978-3-319-00845-5 |

DOIs | |

Publication status | Published - 2014 |

### Publication series

Name | IFMBE Proceedings |
---|---|

Publisher | Springer |

Volume | 41 |

ISSN (Print) | 1680-0737 |

### Fingerprint

### Keywords

- Functional Connectivity
- Graph Theory
- Mathematical Cognition
- Mathematics
- Mutual Information

### Cite this

*XIII Mediterranean Conference on Medical and Biological Engineering and Computing 2013*(pp. 783-786). (IFMBE Proceedings; Vol. 41). Springer. https://doi.org/10.1007/978-3-319-00846-2_194

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*XIII Mediterranean Conference on Medical and Biological Engineering and Computing 2013.*IFMBE Proceedings, vol. 41, Springer, pp. 783-786. https://doi.org/10.1007/978-3-319-00846-2_194

**Studying Functional Brain Networks to Understand Mathematical Thinking: A Graph-Theoretical Approach.** / Bamparopoulos, Georgios; Klados, ManousosA. A.; Papathanasiou, Nikolaos; Antoniou, Ioannis; Micheloyannis, Sifis; Bamidis, PanagiotisD. D.

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

TY - GEN

T1 - Studying Functional Brain Networks to Understand Mathematical Thinking: A Graph-Theoretical Approach

AU - Bamparopoulos, Georgios

AU - Klados, ManousosA. A.

AU - Papathanasiou, Nikolaos

AU - Antoniou, Ioannis

AU - Micheloyannis, Sifis

AU - Bamidis, PanagiotisD. D.

PY - 2014

Y1 - 2014

N2 - Brain function during mathematical thinking is a common concern of scientists from different research fields. The study of functional brain networks extracted from electroencephalographic (EEG) signals using graph theory seems to meet the challenge of neuroscience to understand brain functioning in terms of dynamic flow of information among brain regions. Some studies have found differences between the basic arithmetic operations among brain regions; however, they have not explained the brain function during difficult mathematical tasks and complex mathematical processing. This study investigates the changes of the functional networks’ organization among different mathematical tasks. To this end, EEG data from 10 subjects were recorded during three different tasks, Number Looking which was served as the control situation, Simple Addition which refers to the addition of single-digit numbers and Difficult Multiplication with trials of two-digit multiplications. We analyzed weighted graphs so as to provide a more realistic representation of functional brain networks. Mutual information was employed to form the weights among the different channels, while various global and local graph indices were further examined. The results suggest that there are some statistically significant differences between graph theoretical indices among the different tasks and their range of values depend on the particular task.

AB - Brain function during mathematical thinking is a common concern of scientists from different research fields. The study of functional brain networks extracted from electroencephalographic (EEG) signals using graph theory seems to meet the challenge of neuroscience to understand brain functioning in terms of dynamic flow of information among brain regions. Some studies have found differences between the basic arithmetic operations among brain regions; however, they have not explained the brain function during difficult mathematical tasks and complex mathematical processing. This study investigates the changes of the functional networks’ organization among different mathematical tasks. To this end, EEG data from 10 subjects were recorded during three different tasks, Number Looking which was served as the control situation, Simple Addition which refers to the addition of single-digit numbers and Difficult Multiplication with trials of two-digit multiplications. We analyzed weighted graphs so as to provide a more realistic representation of functional brain networks. Mutual information was employed to form the weights among the different channels, while various global and local graph indices were further examined. The results suggest that there are some statistically significant differences between graph theoretical indices among the different tasks and their range of values depend on the particular task.

KW - Functional Connectivity

KW - Graph Theory

KW - Mathematical Cognition

KW - Mathematics

KW - Mutual Information

UR - https://link.springer.com/chapter/10.1007/978-3-319-00846-2_194

U2 - 10.1007/978-3-319-00846-2_194

DO - 10.1007/978-3-319-00846-2_194

M3 - Conference contribution

SN - 978-3-319-00845-5

T3 - IFMBE Proceedings

SP - 783

EP - 786

BT - XIII Mediterranean Conference on Medical and Biological Engineering and Computing 2013

PB - Springer

ER -