When you play an instrument for the first time, not only are you terrible at it, but playing even the smallest semblance of a song requires an enormous amount of effort. Along these lines, consider walking; although it is also an extraordinarily complex activity requiring the careful coordination of numerous muscles, almost no mental effort is needed. Using the example of learning the piano, William James wrote, “Habit simplifies the movements required to achieve a given result, makes them more accurate and diminishes fatigue.”1 Not only are trained activities easier, our brain has a way of adapting to make them require less mental effort. A recent contribution in Nature Neuroscience by Peter Strick's group at the University of Pittsburgh has illustrated a part of how this happens.
This adaptive phenomenon, the transitioning of high cognitive demand motor tasks guided by sensory input, to low cognitive demand motor tasks orchestrated by highly integrated circuits using anticipatory planning, has been studied for decades.2,3 As James put it, “A strictly voluntary act has to be guided by idea, perception, and volition, throughout its whole course. In a habitual action, mere sensation is a sufficient guide, and the upper regions of brain and mind are set comparatively free.” Interestingly, this adaptation not only results in behavioral changes, clear anatomic changes have been seen as well. The primary motor and premotor areas necessary to play musical instruments were found to be larger in professional musicians when compared to amateurs.4,5 Similarly, the sensory and motor representations for the body parts necessary for skilled performance occupy a larger than normal fraction of M1 and S1 in professional musicians.6,7
fMRI studies in particular have turned up an interesting correlate of expertise; in motor areas, compared to untrained tasks, highly trained tasks produce a smaller, more focused increase in the BOLD signal.8-12 Yet, while multiple studies have validated this result, its implications for processing within motor areas are not entirely clear. Some researchers have postulated that the lower BOLD signal reflects a more efficient motor system that requires less neural firing, while others claim that the lower activation signal is due to the decreased attention during trained tasks.9,11,13-15 Until now, no study has directly compared brain metabolism with neural activity.16-20
To address this question, Picard and colleagues utilized a combination of radiolabeled 2-deoxyglucose (2DG) and single neuron recordings in macaques to investigate this adaptation phenomenon.21 2DG is a modified glucose molecule that is taken up by cells, but unable to be broken down through glycolysis. Therefore, radiolabeled 2DG can serve as a marker of glucose uptake, and correlates with the average cellular metabolic activity. As metabolic and vascular processes are coupled, 2DG uptake parallels the changes seen on fMRI.22
The investigators trained ten macaques to perform 2 types of hand motor experiments: a task guided by random visual input, and a task involving execution of a planned set of movements when prompted. The former task type was referred to as “visually guided,” the latter, “internally generated.” The macaques had practiced both tasks equally, for at least a year. At the end of the study, each macaque was infused with 2DG while performing 1 of the 2 tasks. After 35 to 45 minutes of task performance they were sacrificed and their brains were examined. The level of 2DG (eg, glucose uptake) was analyzed in the premotor and motor areas specific to the hand and arm, and the levels of 2DG in those areas were compared between the macaques that performed “visually guided” and “internally generated” tasks. Interestingly, the level of 2DG in macaques performing “internally generated” tasks was significantly less than those performing “visually guided” tasks, a finding suggesting that less metabolic activity had occurred during the “internally generated” tasks (Figure 1A).
In general, 2DG uptake is also correlated with the average neuronal activity over the period of time it is available extracellularly.23 However, the investigators found no difference in the average firing rates for “visually guided” compared to “internally generated” tasks, despite the striking difference in 2DG levels. Furthermore, 2DG uptake correlated with area-specific firing in the macaque that performed a visually guided task, but not the macaque performing an internally generated task (Figure 1B). Together, these findings implied that while neural firing is the same between activities, metabolic demand decreases when performing highly practiced internally generated tasks compared to visually guided tasks.
As 2DG uptake is thought to be strongly associated with presynaptic activity, it is likely that decreased synaptic activity is required to activate neurons in circuits governing highly practiced internally generated motor activity. The adaptation process that occurs over years of practice causes motor circuits to become more efficient in their use of metabolic resources. Compared to an amateur, the neurons of a concert pianist's brain don't fire any less when playing a sonata; they just fire more efficiently.
This result is in concordance with our current knowledge of how the brain addresses energy expenditure.24,25 When presented with 2 tasks of equal reward, humans tend to prefer the task requiring less cognitive demand. The popularity of reality television attests to this. However, despite its shortcomings, this pattern of behavior reflects a necessary and conserved mechanism for the efficient allocation of resources; Picard and colleagues have shown that the way to Carnegie Hall ends in a surprisingly efficient state.
1. James W. The Principles of Psychology. New York, NY: H. Holt; 1918.
2. Adams JA. Historical review and appraisal of research on the learning, retention, and transfer of human motor skills. Psychol Bull. 1987;101(1):41–74.
3. Proctor RW, Dutta A. Skill Acquisition and Human Performance. Thousand Oaks, CA: Sage Publications, Inc; 1995:442.
4. Amunts K, Schlaug G, Jäncke L, et al.. Motor cortex and hand motor skills: structural compliance in the human brain. Hum Brain Mapp. 1997;5(3):206–215.
5. Gaser C, Schlaug G. Brain structures differ between musicians and non-musicians. J Neurosci. 2003;23(27):9240–9245.
6. Elbert T, Pantev C, Wienbruch C, Rockstroh B, Taub E. Increased cortical representation of the fingers of the left hand in string players. Science. 1995;270(5234):305–307.
7. Schwenkreis P, El Tom S, Ragert P, Pleger B, Tegenthoff M, Dinse HR. Assessment of sensorimotor cortical representation asymmetries and motor skills in violin players. Eur J Neurosci. 2007;26(11):3291–3302.
8. Hund-Georgiadis M, von Cramon DY. Motor-learning-related changes in piano players and non-musicians revealed by functional magnetic-resonance signals. Exp Brain Res. 1999;125(4):417–425.
9. Krings T, Töpper R, Foltys H, et al.. Cortical activation patterns during complex motor tasks in piano players and control subjects. A functional magnetic resonance imaging study. Neurosci Lett. 2000;278(3):189–193.
10. Jäncke L, Shah NJ, Peters M. Cortical activations in primary and secondary motor areas for complex bimanual movements in professional pianists. Cogn Brain Res. 2000;10(1-2):177–183.
11. Haslinger B, Erhard P, Altenmüller E, et al.. Reduced recruitment of motor association areas during bimanual coordination in concert pianists. Hum Brain Mapp. 2004;22(3):206–215.
12. Meister I, Krings T, Foltys H, et al.. Effects of long-term practice and task complexity in musicians and nonmusicians performing simple and complex motor tasks: Implications for cortical motor organization. Hum Brain Mapp. 2005;25(3):345–352.
13. Floyer-Lea A, Matthews PM. Changing brain networks for visuomotor control with increased movement Automaticity. J Neurophysiol. 2004;92(4):2405–2412.
14. Landau SM, D'Esposito M. Sequence learning in pianists and nonpianists: an fMRI study of motor expertise. Cogn Affective, Behav Neurosci. 2006;6(3):246–259.
15. Xiong J, Ma L, Wang B, et al.. Long-term motor training induced changes in regional cerebral blood flow in both task and resting states. NeuroImage. 2009;45(1):75–82.
16. Li CS, Padoa-Schioppa C, Bizzi E. Neuronal correlates of motor performance and motor learning in the primary motor cortex of monkeys adapting to an external force field. Neuron. 2001;30(2):593–607.
17. Paz R, Boraud T, Natan C, Bergman H, Vaadia E. Preparatory activity in motor cortex reflects learning of local visuomotor skills. Nat Neurosci. 2003;6(8):882–890.
18. Ben-Shaul Y, Drori R, Asher I, Stark E, Nadasdy Z, Abeles M. Neuronal activity in motor cortical areas reflects the sequential context of movement. J Neurophysiol. 2004;91(4):1748–1762.
19. Lu X, Ashe J. Anticipatory activity in primary motor cortex codes memorized movement sequences. Neuron. 2005;45(6):967–973.
20. Matsuzaka Y, Picard N, Strick PL. Skill representation in the primary motor cortex after long-term practice. J Neurophysiol. 2007;97(2):1819–1832.
21. Picard N, Matsuzaka Y, Strick PL. Extended practice of a motor skill is associated with reduced metabolic activity in M1. Nat Neurosci. 2013;16(9):1340–1347.
22. Magistretti PJ. Neuron—glia metabolic coupling and plasticity. J Exp Biol. 2006;209(12):2304–2311.
23. Sokoloff L, Reivich M, Kennedy C, et al.. The [14c]deoxyglucose method for the measurement of local cerebral glucose utilization: theory, procedure, and normal values in the conscious and anesthetized albino rat1. J Neurochem. 1977;28(5):897–916.
24. Kool W, McGuire JT, Rosen ZB, Botvinick MM. Decision making and the avoidance of cognitive demand. J Exp Psychol Gen. 2010;139(4):665–682.
25. Shenhav A, Botvinick MM, Cohen JD. The expected value of control: an integrative theory of anterior cingulate cortex function. Neuron. 2013;79(2):217–240.