A brain connectivity characterization of children with different levels of mathematical achievement based on graph metrics.

Publisher: Public Library of Science Country of Publication: United States NLM ID: 101285081 Publication Model: eCollection Cited Medium: Internet ISSN: 1932-6203 (Electronic) Linking ISSN: 19326203 NLM ISO Abbreviation: PLoS One Subsets: MEDLINE

Original Publication: San Francisco, CA : Public Library of Science

Mathematics* ; Signal Processing, Computer-Assisted*; Brain/*physiology ; Electroencephalography/*methods; Achievement ; Child ; Computer Graphics ; Humans ; Machine Learning

Recent studies aiming to facilitate mathematical skill development in primary school children have explored the electrophysiological characteristics associated with different levels of arithmetic achievement. The present work introduces an alternative EEG signal characterization using graph metrics and, based on such features, a classification analysis using a decision tree model. This proposal aims to identify group differences in brain connectivity networks with respect to mathematical skills in elementary school children. The methods of analysis utilized were signal-processing (EEG artifact removal, Laplacian filtering, and magnitude square coherence measurement) and the characterization (Graph metrics) and classification (Decision Tree) of EEG signals recorded during performance of a numerical comparison task. Our results suggest that the analysis of quantitative EEG frequency-band parameters can be used successfully to discriminate several levels of arithmetic achievement. Specifically, the most significant results showed an accuracy of 80.00% (α band), 78.33% (δ band), and 76.67% (θ band) in differentiating high-skilled participants from low-skilled ones, averaged-skilled subjects from all others, and averaged-skilled participants from low-skilled ones, respectively. The use of a decision tree tool during the classification stage allows the identification of several brain areas that seem to be more specialized in numerical processing.
Competing Interests: The authors have declared that no competing interests exist.

Epilepsia. 2014 Dec;55(12):1910-7. (PMID: 25330985)
Front Neuroinform. 2018 Mar 15;12:7. (PMID: 29599714)
Front Hum Neurosci. 2017 Feb 17;11:70. (PMID: 28261078)
Clin Neurophysiol Pract. 2017 Oct 14;2:193-200. (PMID: 30214995)
Exp Brain Res. 2009 Sep;198(1):59-83. (PMID: 19626316)
Neuroreport. 2005 Nov 7;16(16):1769-73. (PMID: 16237324)
PLoS One. 2015 Sep 22;10(9):e0138685. (PMID: 26394404)
Neuroimage. 2005 Apr 15;25(3):838-49. (PMID: 15808984)
Neuroimage. 2010 Sep;52(3):1059-69. (PMID: 19819337)
PLoS Biol. 2006 May;4(5):e125. (PMID: 16594732)
Neuroimage. 2005 Sep;27(3):553-63. (PMID: 15921932)
IEEE Trans Biomed Eng. 2012 Nov;59(11):2979-85. (PMID: 22249595)
Int J Psychophysiol. 2007 Sep;65(3):252-60. (PMID: 17586077)
J Neural Eng. 2018 Jun;15(3):031005. (PMID: 29488902)
Electroencephalogr Clin Neurophysiol. 1997 Sep;103(3):395-404. (PMID: 9305288)
Nat Commun. 2015 Jan 23;6:6026. (PMID: 25613599)
Electroencephalogr Clin Neurophysiol. 1986 Aug;64(2):123-43. (PMID: 2424729)
J Neurophysiol. 1999 Dec;82(6):3095-107. (PMID: 10601444)
Brain Cogn. 2018 Jul;124:57-63. (PMID: 29747149)
J Cogn Neurosci. 2015 Aug;27(8):1471-91. (PMID: 25803596)
Neuroimage. 2007 Nov 1;38(2):346-56. (PMID: 17851092)
Neuroimage Clin. 2018;20:1255-1265. (PMID: 30389345)
Clin Neurophysiol. 1999 Mar;110(3):469-86. (PMID: 10363771)
Psychophysiology. 2000 Mar;37(2):163-78. (PMID: 10731767)
Behav Neurol. 2018 Jan 11;2018:4638903. (PMID: 29670667)
Dev Neuropsychol. 2019 Apr-Jun;44(3):325-338. (PMID: 30864846)
Electroencephalogr Clin Neurophysiol. 1998 May;106(5):416-23. (PMID: 9680154)
Neuroimage. 2011 Aug 1;57(3):1205-11. (PMID: 21620978)
Soc Neurosci. 2016;11(1):49-59. (PMID: 25833090)
Neuroimage. 2011 Feb 1;54(3):2382-93. (PMID: 20946958)
Int J Psychophysiol. 2017 Dec;122:24-31. (PMID: 28479367)
Neuroreport. 2006 Apr 24;17(6):587-91. (PMID: 16603917)
Environ Health Perspect. 2017 Apr;125(4):746-752. (PMID: 27385187)
J Int Neuropsychol Soc. 2016 Feb;22(2):105-19. (PMID: 26888611)
Cogn Affect Behav Neurosci. 2017 Aug;17(4):724-736. (PMID: 28474293)
Conf Proc IEEE Eng Med Biol Soc. 2014;2014:1881-4. (PMID: 25570345)
Neural Netw. 2005 Sep;18(7):998-1005. (PMID: 15990276)
Brain Imaging Behav. 2017 Apr;11(2):473-485. (PMID: 26960946)
Curr Opin Neurobiol. 2005 Oct;15(5):614-21. (PMID: 16146688)
Neuroimage. 2010 Jan 1;49(1):1006-17. (PMID: 19666127)
Nat Rev Neurosci. 2008 Apr;9(4):278-91. (PMID: 18334999)
Neuroscience. 2016 Mar 1;316:143-50. (PMID: 26724581)
Dialogues Clin Neurosci. 2013 Sep;15(3):247-62. (PMID: 24174898)
J Neurosci Methods. 2007 Oct 15;166(1):41-52. (PMID: 17698205)
Physiol Meas. 2005 Feb;26(1):R15-39. (PMID: 15742873)
Brain Res. 2015 Nov 19;1627:189-200. (PMID: 26385418)
Int J Neurosci. 1998 Jul;95(1-2):63-75. (PMID: 9845017)
IEEE Trans Neural Syst Rehabil Eng. 2013 Mar;21(2):225-32. (PMID: 23314778)
Brain Topogr. 2013 Apr;26(2):303-14. (PMID: 23053602)
Pediatr Neurol. 2017 Feb;67:7-22. (PMID: 28065825)
Electroencephalogr Clin Neurophysiol. 1975 Nov;39(5):526-30. (PMID: 52448)
Pediatr Neurol. 2001 May;24(5):337-42. (PMID: 11516606)
Neurol Res. 1999 Mar;21(2):139-52. (PMID: 10100200)
Int J Psychophysiol. 2005 Mar;55(3):313-21. (PMID: 15708644)
Front Hum Neurosci. 2016 Sep 14;10:454. (PMID: 27683547)
J Zhejiang Univ Sci B. 2005 Dec;6(12):1213-9. (PMID: 16358382)
J Exp Child Psychol. 2018 Feb;166:232-250. (PMID: 28946044)
Electroencephalogr Clin Neurophysiol. 1994 Jan;90(1):40-57. (PMID: 7509273)
Dev Cogn Neurosci. 2018 Apr;30:280-290. (PMID: 28268177)
Int J Psychophysiol. 2012 Aug;85(2):135-44. (PMID: 22732350)
Neuroimage. 2019 Apr 1;189:574-580. (PMID: 30682537)
Neuroimage. 2008 Jul 1;41(3):985-97. (PMID: 18457962)

Date Created: 20200118 Date Completed: 20200424 Latest Revision: 20200424

20221213

PMC6968862

10.1371/journal.pone.0227613

31951604