Decoding Gender and Musical Expertise From Brain Responses to Music: A Comparison of Functional Connectivity Measures

Abstract

Individual differences, encompassing a wide array of physical, cognitive, and emotional characteristics such as age, gender, personality, musical expertise, and empathy, play a pivotal role in shaping our interactions with the world around us. These differences, stable over time and across situations, not only influence our end goals but also the processes through which we perceive and interact with them. Recent research in neuroscience has brought to light that each individual possesses unique and fundamentally stable functional brain connections. These connections remain consistent, irrespective of the task at hand, suggesting that functional brain networks could potentially be employed as a measure of stable individual traits. Such an approach could revolutionize personalized medicine, offering tailored therapeutic interventions based on an individual’s unique brain signature. In the context of music, distinct patterns in functional brain networks related to individual differences become crucial. Music, as a continuous task, offers a naturalistic paradigm to explore these patterns. This study aims to bridge the gap in the existing literature by examining how brain responses to music may vary based on individual characteristics, such as gender and musical expertise. Furthermore, previous studies have predominantly focused on Pearson correlation, which captures linear relationships but may not encapsulate the full complexity of functional brain networks. Therefore, our study explores various functional connectivity (FC) measures. By comparing these measures, we aim to understand which ones are most suitable for a given study, ensuring that the chosen measures capture the intricacies of FC effectively. The study utilized data from the ”Tunteet” project, which involved multiple fMRI scans and behavioral tests. A total of 36 participants underwent fMRI scanning while listening to three distinct 8-minute-long musical pieces representing different musical styles. Their responses were analyzed using various temporal and spectral FC measures. The aim was to compare these FC measures to identify the one that captured the most variation associated with gender and musical expertise. Subsequently, a binary Support Vector Machine (SVM) was employed to classify distinct population groupings. Our results align with previous research, suggesting that musical preferences can indeed be considered as a distinct personality trait. This was particularly evident in the differences in liking ratings specific to gender and musical expertise. We also found that Coherence, a spectral-domain FC measure, captured the maximum variation for musical stimuli. However, when classifying individuals based on gender and musical expertise, a composite measure, which combined all measures obtained by concatenation followed by a feature selection procedure, outperformed any single measure. This composite measure consistently achieved better results, suggesting that each FC measure captures different aspects of the relationship between brain regions. In summary, our research offers a fresh perspective on the interplay between musical preferences, gender, musical expertise, and brain responses. By leveraging diverse functional connectivity measures, we’ve shed light on the complexities of functional brain networks, paving the way for future research in this domain.