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Cognitive Processing

, Volume 20, Issue 4, pp 507–513 | Cite as

Hand motor learning in a musical context and prefrontal cortex hemodynamic response: a functional near-infrared spectroscopy (fNIRS) study

  • Rafael Alves Heinze
  • Patricia Vanzella
  • Guilherme Augusto Zimeo Morais
  • João Ricardo SatoEmail author
Short Communication

Abstract

Due to movement automatization, the engagement of high-order cognitive processing during the motor execution of a task is expected to decrease over repetitions and practice. In this study, we assessed single session changes in the prefrontal hemodynamic signals in response to training a piano chord progression in an ecological experimental setting. We acquired functional near-infrared spectroscopy signals from 15 subjects without any previous experience on playing keyboard instruments. Our findings were that oxygenated hemoglobin changes at orbitofrontal cortex followed an inverted U-shaped curve over task execution, while the subjects’ performance presented a steady slope. These results suggest an initial executive function engagement followed by facilitation of motor execution over time.

Keywords

fNIRS Learning Cognitive control Piano Motor execution Prefrontal cortex Hemodynamics 

Notes

Acknowledgements

The authors are grateful to Jackson Cionek (BrainLatam) and NIRx for technological support. R.A.H. received a scholarship of PIBIC-CNPq (Brazil). J.R.S. is supported by grants FAPESP (Sao Paulo Research Foundation, Brazil) 2018/04654-9 and 2018/21934-5.

References

  1. Anderson JR (1982) Acquisition of cognitive skill. Psychol Rev 89(4):369–406CrossRefGoogle Scholar
  2. Balardin J, Morais G, Furucho R, Trambaiolli L, Sato J (2017a) Impact of communicative head movements on the quality of functional near-infrared spectroscopy signals: negligible effects for affirmative and negative gestures and consistent artifacts related to raising eyebrows. J Biomed Opt 22(4):046010.  https://doi.org/10.1117/1.jbo.22.4.046010 CrossRefGoogle Scholar
  3. Balardin J, Zimeo Morais G, Furucho R, Trambaiolli L, Vanzella P, Biazoli C, Sato J (2017b) Imaging brain function with functional near-infrared spectroscopy in unconstrained environments. Front Hum Neurosci.  https://doi.org/10.3389/fnhum.2017.00258 CrossRefPubMedPubMedCentralGoogle Scholar
  4. Beilock SL, Carr TH, MacMahon C, Starkes JL (2002) When paying attention becomes counterproductive: impact of divided versus skill-focused attention on novice and experienced performance of sensorimotor skills. J Exp Psychol Appl 8(1):6–16.  https://doi.org/10.1037/1076-898x.8.1.6 CrossRefPubMedGoogle Scholar
  5. de Lima-Pardini A, Zimeo Morais G, Balardin J, Coelho D, Azzi N, Teixeira L, Sato J (2017) Measuring cortical motor hemodynamics during assisted stepping—an fNIRS feasibility study of using a walker. Gait Posture 56:112–118.  https://doi.org/10.1016/j.gaitpost.2017.05.018 CrossRefPubMedGoogle Scholar
  6. Delpy D, Cope M, Zee P, Arridge S, Wray S, Wyatt J (1988) Estimation of optical pathlength through tissue from direct time of flight measurement. Phys Med Biol 33(12):1433–1442.  https://doi.org/10.1088/0031-9155/33/12/008 CrossRefPubMedGoogle Scholar
  7. Eversheim U, Bock O (2001) Evidence for processing stages in skill acquisition: a dual-task study. Learn Mem 8(4):183–189.  https://doi.org/10.1101/lm.39301 CrossRefPubMedPubMedCentralGoogle Scholar
  8. Fishburn F, Norr M, Medvedev A, Vaidya C (2014) Sensitivity of fNIRS to cognitive state and load. Front Hum Neurosci 8:76CrossRefGoogle Scholar
  9. Gupta A, Vig L, Noelle D (2012) A neurocomputational approach to automaticity in motor skill learning. Biol Inspired Cogn Archit 2:1–12Google Scholar
  10. Hatakenaka M, Miyai I, Mihara M, Sakoda S, Kubota K (2007) Frontal regions involved in learning of motor skill—a functional NIRS study. NeuroImage 34(1):109–116.  https://doi.org/10.1016/j.neuroimage.2006.08.014 CrossRefPubMedGoogle Scholar
  11. Jackson P, Lafleur M, Malouin F, Richards C, Doyon J (2003) Functional cerebral reorganization following motor sequence learning through mental practice with motor imagery. NeuroImage 20(2):1171–1180.  https://doi.org/10.1016/s1053-8119(03)00369-0 CrossRefPubMedGoogle Scholar
  12. Jäncke L, Shah NJ, Peters M (2000) Cortical activations in primary and secondary motor areas for complex bimanual movements in professional pianists. Cogn Brain Res 10(1–2):177–183CrossRefGoogle Scholar
  13. Jurcak V, Tsuzuki D, Dan I (2007) (2007) 10/20, 10/10, and 10/5 systems revisited: their validity as relative head-surface-based positioning systems. NeuroImage 34(4):1600–1611CrossRefGoogle Scholar
  14. Kirilina E, Yu N, Jelzow A, Wabnitz H, Jacobs AM, Tachtsidis I (2013) Identifying and quantifying main components of physiological noise in functional near infrared spectroscopy on the prefrontal cortex. Front Hum Neurosci 7:864PubMedPubMedCentralGoogle Scholar
  15. Kirschner PA (2002) Cognitive load theory: implications of cognitive load theory on the design of learning. Learn Instr 12:1–10CrossRefGoogle Scholar
  16. Koeneke S, Lutz K, Herwig U, Ziemann U, Jäncke L (2006) Extensive training of elementary finger tapping movements changes the pattern of motor cortex excitability. Exp Brain Res 174(2):199–209CrossRefGoogle Scholar
  17. Krings T, Töpper R, Foltys H, Erberich S, Sparing R, Willmes K, Thron A (2000) Cortical activation patterns during complex motor tasks in piano players and control subjects. A functional magnetic resonance imaging study. Neurosci Lett 278(3):189–193CrossRefGoogle Scholar
  18. Leff D, Elwell C, Orihuela-Espina F, Atallah L, Delpy D, Darzi A, Yang G (2008) Changes in prefrontal cortical behaviour depend upon familiarity on a bimanual co-ordination task: an fNIRS study*. NeuroImage 39(2):805–813.  https://doi.org/10.1016/j.neuroimage.2007.09.032 CrossRefPubMedGoogle Scholar
  19. Leff DR, Orihuela-Espina F, Elwell CE, Athanasiou T, Delpy DT, Darzi AWE, Yang G (2011) Assessment of the cerebral cortex during motor task behaviours in adults: a systematic review of functional near infrared spectroscopy (fNIRS) studies. NeuroImage 54(4):2922–2936CrossRefGoogle Scholar
  20. Ono Y, Nomoto Y, Tanaka S, Sato K, Shimada S, Tachibana A, Bronner S, Noah J (2014) Frontotemporal oxyhemoglobin dynamics predict performance accuracy of dance simulation gameplay: temporal characteristics of top-down and bottom-up cortical activities. NeuroImage 85:461–470CrossRefGoogle Scholar
  21. Ono Y, Noah J, Zhang X, Nomoto Y, Suzuki T, Shimada S, Tachibana A, Bronner S, Hirsch J (2015) Motor learning and modulation of prefrontal cortex: an fNIRS assessment. J Neural Eng 12(6):066004CrossRefGoogle Scholar
  22. Pascual-Leone A, Nguyet D, Cohen LG, Brasil-Neto JP, Cammarota A, Hallett M (1995) Modulation of muscle responses evoked by transcranial magnetic stimulation during the acquisition of new fine motor skills. J Neurophysiol 74(3):1037–1045CrossRefGoogle Scholar
  23. Poldrack R, Sabb F, Foerde K, Tom S, Asarnow R, Bookheimer S, Knowlton B (2005) The neural correlates of motor skill automaticity. J Neurosci 25(22):5356–5364.  https://doi.org/10.1523/JNEUROSCI.3880-04.2005 CrossRefPubMedPubMedCentralGoogle Scholar
  24. Smith E, Jonides J (1999) Storage and executive processes in the frontal lobes. Science 283(5408):1657–1661CrossRefGoogle Scholar
  25. Solovey E, Girouard A, Chauncey K, Hirshfield L, Sassaroli A, Zheng F, Fantini S, Jacob R (2009) Using fNIRS brain sensing in realistic HCI settings. In: Proceedings of the 22nd annual ACM symposium on user interface software and technology—UIST’09Google Scholar
  26. Tachtsidis I, Scholkmann F (2016) False positives and false negatives in functional near-infrared spectroscopy: issues, challenges, and the way forward. Neurophotonics 3(3):031405CrossRefGoogle Scholar
  27. Villringer A (1997) Non-invasive optical spectroscopy and imaging of human brain function. Trends Neurosci 20(10):435–442CrossRefGoogle Scholar
  28. Wan C, Schlaug G (2010) Music making as a tool for promoting brain plasticity across the life span. Neuroscientist 16(5):566–577CrossRefGoogle Scholar
  29. Wickens C (2008) Multiple resources and mental workload. Hum Factors J Hum Factors Ergon Soc 50(3):449–455CrossRefGoogle Scholar
  30. Wu T, Kansaku K, Hallett M (2004) How self-initiated memorized movements become automatic: a functional MRI study. J Neurophysiol 91(4):1690–1698CrossRefGoogle Scholar
  31. Yücel MA, Selb J, Aasted CM, Lin P, Borsook D, Becerra L, Boas DA (2016) Mayer waves reduce the accuracy of estimated hemodynamic response functions in functional near-infrared spectroscopy. Biomed Opt Express 7(8):3078–3088CrossRefGoogle Scholar
  32. Yuksel B, Afergan D, Peck E, Griffin G, Harrison L, Chen N, Chang R, Jacob R (2015) BRAAHMS: a novel adaptive musical interface based on users’ cognitive state. In: NIME, pp 136–139Google Scholar
  33. Yuksel BF, Oleson KB, Harrison L, Peck EM, Afergan D, Chang R, Jacob RJ (2016) Learn piano with BACh: an adaptive learning interface that adjusts task difficulty based on brain state. In: Proceedings of the 2016 CHI conference on human factors in computing systems, pp 5372–5384Google Scholar

Copyright information

© Marta Olivetti Belardinelli and Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Center of Mathematics, Computing and CognitionUniversidade Federal do ABCSão Bernardo do CampoBrazil

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