This week our class took a trip to the Philharmonie de Paris where we explored the Electro Exhibition titled Kraftwerk to Daft Punk. I was especially excited to see this exhibit because I love electronically produced music and it is prevalent in most of my daily activities. Personally, I like listening to more chill, “low-fi hip hop beats” type of music when I’m studying (ODESZA is my go to, see playlist clouds), and more upbeat and rave-like music while I run (Illenium is a personal favorite, see playlist run). Learning about the history and origins of electro to techno music and the cultural significance from clubs to raves increased my appreciation for the music I listen to daily.
Album cover for A Moment Apart by ODESZA, 10/10 would recommend for study music
While I also like other types of music for other occasions, one music genre I will not listen to is country (except maybe Old Town Road). To me, country music just isn’t enjoyable and being in the south at Emory for the past 3 years hasn’t changed my opinion. However, my music taste had me thinking, what’s the neuroscience behind music preference? Is there a difference in my brain when I listen to songs I like and dislike compared to someone who loves country music but hates electronic music? A study conducted by Wilkins et al. uses network science on the brain to see the connectivity between brain regions when we listen to our favorite song, songs that we like, and songs that we dislike (Wilkins et al., 2014). Network science in neuroscience is an emerging field that studies the brain as a complex network by mapping, recording, and analyzing the interactions between different brain regions (Bassett and Sporns, 2017). In this study, 21 young adults with different music genre preferences and different music backgrounds were asked to listen to five iconic songs categorized in the genres classical, country, rap/hip hop, rock, an unfamiliar genre such as Chinese opera, and their personally selected favorite song. While the subjects listened to the full song, functional magnetic resonance imaging (fMRI) data was collected. fMRI is a technique that measures brain activity by detecting changes in blood flow.
During the fMRI scan, subjects were also asked to rate whether they liked or disliked the song being played on a scale. Four network science statistics, degree, global efficiency, local efficiency and community structure, were used to quantify the data. Degree distribution is a measure of how many other nodes a specific node is connected to, thus the greater the degree distribution, the more connections there are from a specific area. Global efficiency (Eglob) is a measure of distance between one node and another, thus a greater Eglob indicates shorter distance from one node to the rest of the network (Bassett and Sporns, 2017). Local efficiency (Eloc) is like global efficiency but on a smaller scale that measures local connectivity (Bassett and Sporns, 2017). Lastly, community structure identifies the nodes that are more connected to each other rather than to other parts of the brain.
Results showed that in all participants, the default mode network (DMN) and the precuneus in particular have the highest degree nodes in the brain when listening to the songs, regardless of genre preference. The DMN is a network of brain regions including the precuneus that is involved in introspection and reprocessing of memories (Greicis et al., 2003).
fMRI of brain regions in the DMN
There is also significantly higher global efficiency in the precuneus when subjects listened to songs they liked compared to songs they disliked, meaning there were closer connections within the precuneus when subjects listened to songs they liked. There is no significant difference in local efficiency between liked, disliked, and favorite song condition. Additionally, there was a greater dissociation/fewer connections between the precuneus and another brain region in the DMN in the dislike condition compared to the like condition. The authors did not state whether this difference in community structure was significant or not, but this information could have strengthened the authors’ hypothesis that there are differences in neural activity when we listen to music we like and music we don’t like.
Differences in community structure between Like and Dislike conditions. In the Like condition there is more connectivity between the precuneus and other parts of the brain compared to the Dislike condition.
Overall, there is a difference in brain connectivity when I listen to electronic music compared to when I listen to country music and this same activity is present in someone else who likes listening to country music but not electronic music. The exact reason for this connectivity difference is not yet known but the fact that we now know that there is an association between brain connectivity patterns and music preference brings us closer to understanding the neuroscience of music preference. So I guess one thing I can say I have in common with those who like country music is that we have similar neural connectivity when we listen to the music we like.
References
Bassett, D. S., & Sporns, O. (2017). Network neuroscience. Nature Neuroscience, 20, 353-364.
Greicius, M. D., Krasnow, B., Reiss, A. L., & Menon, V. (2003). Functional connectivity in the resting brain: A network analysis of the default mode hypothesis. PNAS, 100(1), 253-258.
Wilkins, R. W., Hodges, D. A., Laurienti, P. J., Steen, M., & Burdette, J. H. (2014). Network science and the effects of music preference on functional brain connectivity: From Beethoven to Eminem. Scientific Reports, 4.
https://en.wikipedia.org/wiki/Default_mode_network#/media/File:Default_mode_network-WRNMMC.jpg
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