Wired for Music: How Musical Training Shapes the Brain

The Tidldibab is a 60,000-year-old Neanderthal flute. Carved from a cave bear femur, it was discovered in Slovenia in 1995, and is emblematic of the ubiquity of music in human societies. Its precisely spaced holes suggest that Neanderthals were not just capable of producing sound, but of creating music – long before the invention of written language. Music has served as a tool for social bonding, cultural preservation through oral tradition, and the communication of social and political ideals to mass audiences. Given its pervasiveness, one might expect that music and its attendant rituals confer an evolutionary advantage, either at the individual or societal level. Writing in his seminal work The Descent of Man, and Selection in Relation to Sex, Darwin speculated that “some early progenitor of man, probably first used his voice in producing true musical cadences, that is in singing, as do some of the gibbon-apes at the present day” (Darwin, 1871/2009). In a different direction, Steven Brown’s theory of musilanguage argues that the evolution of music is closely intertwined  with that of human language (Brown, 1999). Precisely how music influences the individual remains an area of active investigation at the intersection of sociology, cognitive science, and neurobiology. This essay explores how musical training – from early education through lifelong practice – shapes the brain and enhances cognitive development.

Music holds a significant place in the American education system, with school bands, orchestras, and choirs forming a cornerstone of the school community. Research has shown that musical education provides measurable benefits for children. One study (Kirschner & Tomasello, 2010)  demonstrated that joint music-making significantly enhanced prosocial behavior in 4-year-old children. In a controlled experiment with 96 participants, children who engaged in musical activities were more likely to help spontaneously (p < .01) and to solve problems cooperatively, compared to a non-musical control. The study suggests that music serves an adaptive purpose by fostering social bonding, enhancing empathy, and promoting cooperation and prosociality. In another study (Schellenberg, 2011), musically trained children aged 9–12 exhibited significantly higher Full Scale IQ (FSIQ) scores than untrained peers, with music training accounting for 15.2% of the variance in FSIQ, even after controlling for demographic factors.

Musical education and practice also produces measurable effects at the anatomical level. A study (Schlaug et al., 2009) examined the impact of 29 months of instrumental music training on specific subareas of the corpus callosum (CC), which connect motor-related areas of the two brain hemispheres. Thirty-one children aged 5–7 were divided into high-practicing, low-practicing, and control groups, based on weekly practice time. Initially, no CC size differences were observed among the groups. However, after 29 months, the high-practicing group exhibited differences in the anterior midbody of the CC (p = 0.02), which links premotor and supplementary motor areas. The amount the children practiced each week predicted changes in this CC subregion. These findings suggest that intense musical practice, rather than innate differences, is responsible for these changes in the brain.

These effects extend beyond childhood, becoming even more pronounced in professional musicians. Unlike children who are just beginning their musical journey, professional musicians dedicate years to mastering their craft through formal training and extensive practice. These skills, developed through long-term commitment, can be traced to observable changes in parts of the brain responsible for auditory, visual and motor functions. A study (Gaser & Schlaug, 2003) investigated structural brain differences among 20 professional musicians, 20 amateur musicians, and 40 non-musicians, using voxel-based morphometry. This study revealed significant gray matter volume increases in motor, auditory, and visual-spatial regions, particularly in the left inferior temporal gyrus (which processes visual stimuli), left Heschl’s gyrus (which processes sound), and the cerebellum (responsible for balance and coordination). Increased gray matter volume correlated with musician status and practice intensity; professional musicians exhibited the highest volume, followed by amateurs and then non-musicians. Another study (Leipold et al., 2021), involving 52 musicians with absolute pitch, 51 musicians without absolute pitch, and 50 non-musicians, found that musicianship is correlated with significant changes in interhemispheric and intrahemispheric connectivity, particularly in the planum temporale, a region responsible for auditory and language processing. This increased connectivity suggests that musical training enhances communication within and between different parts of the brain.

Musical experience has also been shown to confer neuroprotective effects in aging. One study (Rus-Oswald et al., 2022) investigated the preservation of structural and functional cortical features in elderly professional musicians compared to elderly non-musicians and young musicians. Using high-resolution MRI and fMRI, the study involved 16 elderly musicians, 15 elderly non-musicians, and 16 young musicians. Results indicated that while overall gray matter declined with age, elderly musicians maintained increased gyrification in the auditory cortex, enhancing the function of an important area for processing auditory information. This suggests that sustained musical engagement throughout life may help preserve brain function, allowing musicians to maintain cognitive health into old age.

Music has been part of our experience since the dawn of our species.. From the Neanderthal flute to K-pop, music and musical practice are deeply rooted in human culture. The studies mentioned above demonstrate the social and neuroanatomical effects of musical practice, starting in children, and continuing through adulthood and into old age. Affected areas include the corpus callosum, cerebellum, and auditory cortex. It would be of interest to conduct longitudinal studies that follow individuals from childhood into old age to better understand how lifelong musical engagement shapes cognitive development and aging trajectories. Studies that integrate neuroimaging with real-time neural activity during musical performance could offer insight into the dynamic processes underlying creativity, emotion, and memory in musicians and non-musicians alike. Continued research into the impact of music on the brain may not only inform education and therapy, but also deepen our understanding of cognition and consciousness.

References

Brown, S. (1999). The "musilanguage" model of music evolution. In N. L. Wallin, B. Merker, & S. Brown (Eds.), The origins of music (pp. 271–301). MIT Press.

Darwin, C. (2009). The descent of man, and selection in relation to sex. Part 2. New York University Press ; Chesham. (Original work published 1871)

Gaser, C., & Schlaug, G. (2003). Brain Structures Differ between Musicians and Non-Musicians. The Journal of Neuroscience, 23(27), 9240–9245. https://doi.org/10.1523/jneurosci.23-27-09240.2003

Kirschner, S., & Tomasello, M. (2010). Joint music making promotes prosocial behavior in 4-year-old children. Evolution and Human Behavior, 31(5), 354–364. https://doi.org/10.1016/j.evolhumbehav.2010.04.004

Leipold, S., Klein, C., & Jäncke, L. (2021). Musical Expertise Shapes Functional and Structural Brain Networks Independent of Absolute Pitch Ability. The Journal of Neuroscience, 41(11), 2496–2511. https://doi.org/10.1523/jneurosci.1985-20.2020

Rus-Oswald, O. G., Benner, J., Reinhardt, J., Bürki, C., Christiner, M., Hofmann, E., Schneider, P., Stippich, C., Kressig, R. W., & Blatow, M. (2022). Musicianship-Related Structural and Functional Cortical Features Are Preserved in Elderly Musicians. Frontiers in Aging Neuroscience, 14. https://doi.org/10.3389/fnagi.2022.807971

Schellenberg, E. G. (2011). Examining the association between music lessons and intelligence. British Journal of Psychology, 102(3), 283–302. https://doi.org/10.1111/j.2044-8295.2010.02000.x

Schlaug, G., Forgeard, M., Zhu, L., Norton, A., Norton, A., & Winner, E. (2009). Training-induced Neuroplasticity in Young Children. Annals of the New York Academy of Sciences, 1169(1), 205–208. https://doi.org/10.1111/j.1749-6632.2009.04842.x