Cognitive Neurodynamics

, Volume 9, Issue 5, pp 495–508 | Cite as

Localization of neural efficiency of the mathematically gifted brain through a feature subset selection method

Research Article

Abstract

Based on the neural efficiency hypothesis and task-induced EEG gamma-band response (GBR), this study investigated the brain regions where neural resource could be most efficiently recruited by the math-gifted adolescents in response to varying cognitive demands. In this experiment, various GBR-based mental states were generated with three factors (level of mathematical ability, task complexity, and short-term learning) modulating the level of neural activation. A feature subset selection method based on the sequential forward floating search algorithm was used to identify an “optimal” combination of EEG channel locations, where the corresponding GBR feature subset could obtain the highest accuracy in discriminating pairwise mental states influenced by each experiment factor. The integrative results from multi-factor selections suggest that the right-lateral fronto–parietal system is highly involved in neural efficiency of the math-gifted brain, primarily including the bilateral superior frontal, right inferior frontal, right-lateral central and right temporal regions. By means of the localization method based on single-trial classification of mental states, new GBR features and EEG channel-based brain regions related to mathematical giftedness were identified, which could be useful for the brain function improvement of children/adolescents in mathematical learning through brain–computer interface systems.

Keywords

Neural efficiency Math-gifted adolescents Numerical inductive reasoning EEG Gamma-band response Feature subset selection 

Notes

Acknowledgments

This work was supported in part by the National Natural Science Foundation of China under Grant 31130025, the National Basic Research Program of China under Grant 2015CB351704, the National Natural Science Foundation of China under Grant 61375118, and the Program for New Century Excellent Talents in Universities of China under Grant NCET-12-0115. The authors would like to thank the anonymous reviewers and editors for their thoughtful comments and suggestions.

References

  1. Alexander JE, O’Boyle MW, Benbow CP (1996) Developmentally advanced EEG alpha power in gifted male and female adolescents. Int J Psychophysiol 23:25–31CrossRefPubMedGoogle Scholar
  2. Blankertz B, Lemm S, Treder M, Haufe S, Müller KR (2011) Single-trial analysis and classification of ERP components: a tutorial. NeuroImage 56:814–825CrossRefPubMedGoogle Scholar
  3. Chein JM, Schneider W (2005) Neuroimaging studies of practice-related change: fMRI and meta-analytic evidence of a domain-general control network for learning. Cogn Brain Res 25:607–623CrossRefGoogle Scholar
  4. Chen SC, Huang CK, Chen JF, Su SB (2012) The relationship between attention assessment and EEG control. IPCBEE 34:27–31Google Scholar
  5. Desco M, Navas-Sanchez FJ, Sanchez-Gonzalez J, Reig S, Robles O, Franco C (2011) Mathematically gifted adolescents use more extensive and more bilateral areas of the fronto–parietal network than controls during executive functioning and fluid reasoning tasks. Neuroimage 57:281–292CrossRefPubMedGoogle Scholar
  6. Doppelmayr M, Klimesch W, Hödlmoser K, Sauseng P, Gruber W (2005) Intelligence related upper alpha desynchronization in a semantic memory task. Brain Res Bull 66:171–177CrossRefPubMedGoogle Scholar
  7. Dyson M, Sepulveda F, Gan JQ (2010) Localisation of cognitive tasks used in EEG-based BCIs. Clin Neurophysiol 121:1481–1493CrossRefPubMedGoogle Scholar
  8. Escolano C, Aguilar M, Minguez J (2011) EEG-based upper alpha neurofeedback training improves working memory performance. EMBC, pp 2327–2330Google Scholar
  9. Fitzgibbon SP, Pope KJ, Mackenzie L, Clark CR, Willoughby JO (2004) Cognitive tasks augment gamma EEG power. Clin Neurophysiol 115:1802–1809CrossRefPubMedGoogle Scholar
  10. Gaetz W, Liu C, Zhu H, Bloy L, Roberts TP (2013) Evidence for a motor gamma-band network governing response interference. Neuroimage 74:245–253CrossRefPubMedGoogle Scholar
  11. Gardner HE (1985) Frames of mind: the theory of multiple intelligences. Basic Books, New YorkGoogle Scholar
  12. Gramfort A, Papadopoulo T, Olivi E, Clerc M (2010) OpenMEEG: opensource software for quasistatic bioelectromagnetics. Biomed Eng Online 9:45PubMedCentralCrossRefPubMedGoogle Scholar
  13. Gruber T, Keil A, Muller MM (2001) Modulation of induced gamma band responses and phase synchrony in a paired associate learning task in the human EEG. Neurosci Lett 316:29–32CrossRefPubMedGoogle Scholar
  14. Haier RJ, Siegel BV, Nuechterlein KH, Hazlett E, Wu JC, Paek J (1988) Cortical glucose metabolic rate correlates of abstract reasoning and attention studied with positron emission tomography. Intelligence 12:199–217CrossRefGoogle Scholar
  15. Haier RJ, Siegel B, Tang C, Abel L, Buchsbaum MS (1992) Intelligence and changes in regional cerebral glucose metabolic rate following learning. Intelligence 16:415–426CrossRefGoogle Scholar
  16. Harmon-Jones E, Gable PA, Peterson CK (2010) The role of asymmetric frontal cortical activity in emotion-related phenomena: a review and update. Biol Psychol 84:451–462CrossRefPubMedGoogle Scholar
  17. Herrmann CS, Frund I, Lenz D (2010) Human gamma-band activity: a review on cognitive and behavioral correlates and network models. Neurosci Biobehav Rev 34:981–992CrossRefPubMedGoogle Scholar
  18. Hoppe C, Fliessbach K, Stausberg S, Stojanovic J, Trautner P, Elger CE (2012) A key role for experimental task performance: effects of math talent, gender and performance on the neural correlates of mental rotation. Brain Cogn 78:14–27CrossRefPubMedGoogle Scholar
  19. Howard MW, Rizzuto DS, Caplan JB (2003) Gamma oscillations correlate with working memory load in humans. Cereb Cortex 13:1369–1374CrossRefPubMedGoogle Scholar
  20. Jaušovec N (1996) Differences in EEG alpha activity related to giftedness. Intelligence 23:159–173CrossRefGoogle Scholar
  21. Jaušovec N, Jaušovec K (2004) Intelligence related differences in induced brain activity during the performance of memory tasks. Personal Individ Differ 36:597–612CrossRefGoogle Scholar
  22. Jia XQ, Liang PP, Lu J, Yang YH, Zhong N, Li KC (2011) Common and dissociable neural correlates associated with component processes of inductive reasoning. Neuroimage 56:2292–2299CrossRefPubMedGoogle Scholar
  23. Klimesch W, Sauseng P, Hanslmayr S (2007) EEG alpha oscillations: the inhibition-timing hypothesis. Brain Res Rev 53:63–88CrossRefPubMedGoogle Scholar
  24. Lachaux JP, Fonlupt P, Kahane P, Minotti L, Hoffmann D, Bertrand O (2007) Relationship between task-related gamma oscillations and BOLD signal: new insights from combined fMRI and intracranial EEG. Hum Brain Mapp 28:1368–1375CrossRefPubMedGoogle Scholar
  25. Larson GE, Haier RJ, Lacasse L, Hazen K (1995) Evaluation of a “mental effort” hypothesis for correlations between cortical metabolism and intelligence. Intelligence 21:267–278CrossRefGoogle Scholar
  26. Li X, Morita K, Robinson HPC (2011) Impact of gamma-oscillatory inhibition on the signal transmission of a cortical pyramidal neuron. Cogn Neurodyn 5:241–251PubMedCentralCrossRefPubMedGoogle Scholar
  27. Liu T, Xiao T, Shi J, Zhao D (2011) Response preparation and cognitive control of highly intelligent children: a Go-Nogo event-related potential study. Neuroscience 180:122–128CrossRefPubMedGoogle Scholar
  28. Livne NL, Milgram RM (2006) Academic versus creative abilities in mathematics: two components of the same construct. Creat Res J 18:199–212CrossRefGoogle Scholar
  29. Lu SF, Liang PP, Yang YH, Li KC (2010) Recruitment of the pre-motor area in human inductive reasoning: an fMRI study. Cogn Syst Res 11:74–80CrossRefGoogle Scholar
  30. Muller MM, Gruber T, Keil A (2000) Modulation of induced gamma band activity in the human EEG by attention and visual information processing. Int J Psychophysiol 38:283–299CrossRefPubMedGoogle Scholar
  31. Neubauer AC, Fink A (2003) Fluid intelligence and neural efficiency: effects of task complexity and sex. Personal Individ Differ 35:811–827CrossRefGoogle Scholar
  32. Neubauer AC, Fink A (2008) Intelligence and neural efficiency: a review and new data. Int J Psychophysiol 69:168–169CrossRefGoogle Scholar
  33. Neubauer AC, Fink A (2009) Intelligence and neural efficiency. Neurosci Biobehav Rev 33:1004–1023CrossRefPubMedGoogle Scholar
  34. Neubauer AC, Sange G, Pfurtscheller G (1999) Psychometric intelligence and event-related desynchronisation during performance of a letter matching task. In: Pfurtscheller G, da Silva FHL (eds) Event-related desynchronization (ERD) and related oscillatory EEG-phenomena of the awake brain. Elsevier, Amsterdam, pp 219–231Google Scholar
  35. Neubauer AC, Fink A, Schrausser DG (2002) Intelligence and neural efficiency: the influence of task content and sex on the brain-IQ relationship. Intelligence 30:515–536CrossRefGoogle Scholar
  36. Neubauer AC, Grabner RH, Freudenthaler HH, Beckmann JF, Guthke H (2004) Intelligence and individual differences in becoming neurally efficient. Acta Psychol 116:55–74CrossRefGoogle Scholar
  37. Neubauer AC, Grabner RH, Fink A, Neuper C (2005) Intelligence and neural efficiency: further evidence of the influence of task content and sex on the brain-IQ relationship. Cogn Brain Res 25:217–225CrossRefGoogle Scholar
  38. O’Boyle MW, Cunnington R, Silk TJ, Vaughan D, Jackson G, Syngeniotis A (2005) Mathematically gifted male adolescents activate a unique brain network during mental rotation. Cogn Brain Res 25:583–587CrossRefGoogle Scholar
  39. Prescott J, Gavrilescu M, Cunnington R, O’Boyle MW, Egan GF (2010) Enhanced brain connectivity in math-gifted adolescents: an fMRI study using mental rotation. Cogn Neurosci 1:277–288CrossRefPubMedGoogle Scholar
  40. Pudil P, Novovičová J, Kittler J (1994) Floating search methods in feature selection. Pattern Recognit Lett 15:1119–1125CrossRefGoogle Scholar
  41. Qu J, Wang R, Yan C, Du Y (2014) Oscillations and synchrony in a cortical neural network. Cogn Neurodyn 8:157–166PubMedCentralCrossRefPubMedGoogle Scholar
  42. Ray S, Niebur E, Hsiao SS, Sinai A, Crone NE (2008) High-frequency gamma activity (80–150 Hz) is increased in human cortex during selective attention. Clin Neurophysiol 119:116–133PubMedCentralCrossRefPubMedGoogle Scholar
  43. Rypma B, Berger JS, Prabhakaran V, Bly BM, Kimberg DY, Biswal BB (2006) Neural correlates of cognitive efficiency. Neuroimage 33:969–979CrossRefPubMedGoogle Scholar
  44. Schoenberg PL, Speckens AE (2015) Multi-dimensional modulations of α and γ cortical dynamics following mindfulness-based cognitive therapy in Major Depressive Disorder. Cogn Neurodyn 9:13–29Google Scholar
  45. Simos PG, Papanikolaou E, Sakkalis E, Micheloyannis S (2002) Modulation of gamma-band spectral power by cognitive task complexity. Brain Topogr 14:191–196CrossRefPubMedGoogle Scholar
  46. Sternberg RJ (2003) Giftedness according to the theory of successful intelligence. In: Colangelo N, Davis GA (eds) Handbook of gifted education. Allyn and Bacon, Boston, pp 88–99Google Scholar
  47. Tadel F, Baillet S, Mosher JC, Pantazis D, Leahy RM (2011) Brainstorm: a user-friendly application for MEG/EEG analysis. Comput Intell Neurosci 2011:8CrossRefGoogle Scholar
  48. Tanji K, Suzuki K, Delorme A, Shamoto H, Nakasato N (2005) High-frequency gamma-band activity in the basal temporal cortex during picture-naming and lexical-decision tasks. J Neurosci 25:3287–3293CrossRefPubMedGoogle Scholar
  49. Wartenburger I, Heekeren HR, Preusse F, Kramer J, van der Meer E (2009) Cerebral correlates of analogical processing and their modulation by training. Neuroimage 48:291–302CrossRefPubMedGoogle Scholar
  50. Xu X, Wang R (2014) Neurodynamics of up and down transitions in a single neuron. Cogn Neurodyn 8:509–515CrossRefGoogle Scholar
  51. Zhang JH, Peng XD, Liu H (2013a) Classifying human operator functional state based on electrophysiological and performance measures and fuzzy clustering method. Cogn Neurodyn 7:477–494PubMedCentralCrossRefPubMedGoogle Scholar
  52. Zhang L, Wang H, Gan JQ (2013b) EEG-based cortical localization of neural efficiency related to mathematical giftedness. ICONIP, pp 25–32Google Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2015

Authors and Affiliations

  1. 1.Key Lab of Child Development and Learning Science of Ministry of Education, Research Center for Learning ScienceSoutheast UniversityNanjingChina
  2. 2.School of Computer Science and Electronic EngineeringUniversity of EssexColchesterUK

Personalised recommendations