Participants

All methods were carried out in accordance with the approved guidelines from the Wake Forest University Medical Center Institutional Review Board. Participants were recruited by flyer and word of mouth and signed Informed Consent documents and experimental protocols approved by the Wake Forest Baptist Medical Center Institutional Review Board. We recruited 21 young adults (average age 24 ± 3.4 yrs; 13 female) based on their most preferred musical genre: classical (n = 5), country (n = 5), rap/hip hop (n = 5) and rock music (n = 6). The genre-ranking of the 11 genres before enrollment in the study was used to ensure we did not bias the study toward participants who only liked a certain genre. All participants were right-handed, English speaking, color-sighted, had normal hearing and were free from neurological disorders. Prior to scanning, participants completed a comprehensive questionnaire about formal musical training and a genre preference ranking of a total of eleven music genres (classical, country, gospel/blues, rap/hip hop, alternative, rock, Christian, folk, pop, metal, Broadway, jazz, classic rock). Prior to the scanning session, participants were asked to self-report a title of their most favorite song. Participants were instructed that their favorite song did not need to be specifically from their preferred music listening genre, merely that it was their favorite song. Participants provided the title of their favorite song to the experimenter prior to the scanning session. The favorite song was intentionally requested in order to analyze the functional brain connectivity responses separately from their preferred and non-preferred music.

The rationale for choosing to differentiate between overall preferred music and a favorite song was to determine if brain network responses might differ between the two. To clarify further, preferred music is a more broadly experienced musical listening phenomenon that can be envisioned as a preferred music listening overall experience, such as through on-line preferential music listening application streams such as Pandora or Groove Shark. On the other hand and often in contradiction to one's overall musical listening preferences, an individual's all-time favorite song may not coincide with an individual's preferred genre. Indeed, in this study, there were many of our participants who reported a favorite song that was outside of their most preferred music listening genre. For example, one of our participant's preferred music was classical music but their self-reported favorite song was a country song by Garth Brooks, a country music singer and artist. Each participant's favorite song title and any other recording specifics, such as the specific artist, conductor and/or year of performance, was provided to the experimenter prior to the participant's scanning session. Ten subjects reported having formal musical training. Eight had completed, or were in the process of completing, a university music degree. Eleven reported that they could read music fluently. For the favorite song, participants were simply to self-report and provide a title to their favorite song by being asked, “I want the name of your favorite song, the one that, ‘Rocks your world’, ‘Floats your boat’ and ‘You love this song’”. Further details, including the pre-reported favorite songs, are contained in the Supporting Information.

Stimuli

Six musical selections, hereafter referred to as songs, were presented pseudo-randomly to each participant in the MRI scanner while blood oxygen level dependent (BOLD) functional MRI (fMRI) data were collected. The six songs, each five minutes long, included four pre-selected songs considered iconic within each musical genre, an unfamiliar selection and a sixth personal favorite song. The five songs presented to every subject were Movement I from Symphony No. 5 by Beethoven (classical genre), “Water” by Brad Paisley (country genre), “OMG” by Usher (rap/hip hop genre), “Rock ‘N Roll All Nite” by KISS (rock genre) and “Spring Hall” by the Chinese Jinna Opera Band (unfamiliar genre). Favorite songs ranged from Rhinna & Eminem to Rachmaninov (for the list of favorite songs see Supporting Information).

Scanning Procedures

Prior to the scan session, participants were trained to use a Visual Analog Scale (VAS) to rate how much they liked/disliked each song selection. With this procedure, we were able measure subjective preference across participants54. During the scanning procedure each participant listened to six musical selections. Each selection was presented as a continuous audio clip of five minutes with no interruptions. The music genre clips included rock, rap, classical, country, an unfamiliar piece and their pre-reported favorite song. Before the onset of imaging and with the scanner on, participants had their headphones tested and the music volume adjusted. Prior to presentation of the musical selections, a five minute eyes-closed at-rest scan was acquired. During the entire musical portion of the scanning procedure, all participants had their eyes closed. Unbeknownst to the participants, songs were played for each participant in a randomized order based on an overarching scheme by genre preference from a pre-study music genre preference questionnaire. However, because the music preference questionnaire included rating scale choices of a total of eleven (11) genres, the 4 genre songs appeared as randomly presented to the participants. Each participant provided the title of their self-determined favorite song that was presented last. Participants rated their preference for each song from 1–10 using the visual analog scale (VAS) when each selection ended and before the next song was presented. The highest VAS score report was used to determine their top preferred music during the scanning session. The lowest VAS score was used to determine their least preferred music during the session. The favorite song, the title of which was provided by each participant to the experimenter during the screening session, was presented last. See Supporting Information for a discussion of possible ordering effects associated with the song presentation order.

MR scans were performed on a 1.5 T GE twin-speed LX scanner with birdcage 12-channel head coil (GE Medical Systems, Milwaukee, WI). For blood oxygenation level–dependent (BOLD) contrast, T2*- weighted functional images were acquired using a single-shot, gradient-recalled, echo-planar imaging sequence: TR/TE = 2000/40 ms, voxel size 3.75 × 3.75 × 5 mm3. To allow for all songs to be played in their entirety, songs were edited for a total continuous playing time of five minutes. 150 brain volumes were collected per MRI run and the first 6 volumes were not included in the network analyses, as this was the time during which the BOLD signal achieved steady-state.

Network Generation and Analysis

Network generation and analysis was performed using the fMRI time series data from each subject with eyes closed. Acquired images were motion corrected, spatially normalized to the MNI (Montreal Neurological Institute) space and re-sliced to 4 × 4 × 5 mm3 voxel size with an in-house processing script using FSL package (FMRIB-University of Oxford). Imaging data was filtered (0.00945-0.084 Hz) and head motion (6 rigid-body transformation parameters) and mean signal (whole-brain, white matter and ventricles) were regressed from the data to limit effects due to physiological noise55,56,57. Networks were then generated using a Pearson correlation with each voxel (~21,000) representing a network node. This produced a cross-correlation matrix containing the Pearson correlation coefficient representing the strength of association between each voxel pair.

The correlation matrix was then thresholded to generate a sparse network in keeping with other biological networks58. The threshold was based on a relationship between the number of nodes and the average node degree (K). This procedure ensured that comparisons across subjects were based on networks with comparable densities. Details of the thresholding procedure and the rationale for the chosen threshold are presented in the Supporting Information. Briefly, the threshold is based on S = log(N)/log(K). For the data presented here, a threshold of S = 2.5 was applied to the matrix, resulting in the binary adjacency matrix (A ij ). Once the complete adjacency matrix was generated for each subject, network statistics were calculated. The network statistics for each node were mapped back into 3D brain space to identify the spatial location of key network nodes and network communities. Networks were generated for each participant using the fMRI time series from each subject's six musical experiences. Results presented here are from the participants' favorite song and the highest and lowest rated songs from the five experimenter-selected genres. Therefore, we conducted three separate network calculations based on most and least preferred songs of the iconic music selections and one for their personal favorite song.

Network Properties

The analyses focused on four network properties: degree, global efficiency, local efficiency and community structure. Degree (K) is the number of edges or functional links for an individual node. Global efficiency (Eglob) is a measure of the distance (in network space) from a given node to every other node in the network59. Eglob is calculated as the average of the inverse of the shortest paths between node i and all other nodes in the network. This property is scaled ranging from 0 (indicating no path between nodes) to 1 (direct connection between nodes). Disparate regions of the brain can have high global efficiency if the path length for nodal communication is sufficiently short. Local efficiency (Eloc) is a measure of local neighborhood connectivity. Eloc is calculated by computing the global efficiency of a sub-graph of node i. In other words it measures the distance between all the neighbors of node i. This statistic also ranges between 0–1, with larger values indicating that the neighbors of a particular node are highly connected to each other. A detailed description of the properties calculated in this study may be found in multiple review articles20,21,55. Once network statistics were calculated, we evaluated the location within the brain of the nodes with the highest statistics. Visual depictions of network maps in brain space focused on “network hubs” heuristically defined as the top 20% of the nodes for each of the network statistics. Statistical analyses were performed by comparing the actual network statistics, with no arbitrary threshold, within specific regions-of-interest (ROIs) located in the precuneus and in the auditory cortex. The precuneus ROI was a 12 mm sphere centered at 0, -54, 34 in MNI space. The auditory ROI consisted of bilateral boxes (16 mm × 16 mm × 10 mm) centered at 54, -12, 2 and -52, -12, 2 in MNI space.

Community Structure

Community structure is a multivariate analysis that identifies collections of network nodes that are more interconnected with each other than they are with other network communities. The analysis used was based on modularity (Q) introduced by Newman and Girvan60. Details of the community structure analyses are presented in the Supporting Information. Briefly, hierarchical network partitioning was performed using the algorithm called QCut developed by Ruan and Zhang61. The optimal partition was identified using Q and was the basis of all further analyses. Due to the multivariate nature of community structure, it is not possible to simply identify a representative module for a group of individuals. However, scaled inclusivity (SI) is a statistic that can be used to determine the consistency of a given network community across individuals62. This analysis was used to evaluate the consistency of the community encompassing the precuneus and the community encompassing the auditory cortex under the three conditions (for detailed information see Supporting Information).