Longitudinal Changes in Auditory and Reward Systems Following Receptive Music-Based Intervention in Older Adults

Abstract

Listening to pleasurable music is known to engage the brain's reward system, but little is known about how this engagement develops over time. Here we show for the first time that brain network connectivity can change longitudinally as a result of a personalized receptive music-based intervention (MBI) in cognitively unimpaired older adults. Using a combination of whole-brain regression, seed-based connectivity analysis, and representational similarity analysis (RSA), we compared fMRI responses during a simple music listening task in older adults before and after an eight-week personalized music listening program. Participants rated self-selected and researcher-selected musical excerpts on liking and familiarity. Parametric effects of liking, familiarity, and selection showed significant activation of auditory, reward, default mode, and sensorimotor areas both pre- and post-intervention. Seed-based connectivity comparing pre- and post-intervention showed a significant increase in functional connectivity between auditory regions and the medial prefrontal cortex (mPFC), and this auditory-mPFC connectivity was modulated by participant liking and familiarity ratings. RSA showed significant representations of selection and novelty in auditory regions at both time-points, and an increase in striatal representation of musical stimuli following intervention. Taken together, results show a sensitivity of auditory, reward, default, and sensorimotor regions to individual differences in music familiarity and liking, as well as a shift in brain network dynamics following the personalized MBI. Results show how regular music listening can provide an auditory channel towards the mPFC, thus offering a potential neural mechanism supporting healthy brain aging.

Musicians and non-musicians’ consonant/dissonant perception investigated by EEG and fMRI

“…6), illuminates more from the network perspective: overall, musicians recruit bilateral midline and lateralized brain networks (mostly around mid-to-superior temporal, inferior frontal, and posterior parietal/precuneus areas) by frequency ratio model; whereas non-musicians more heavily along the orbitofrontal, the rostral ACC network by freq-difference model, when both groups were able to be captured by the all-different models. While there is currently no available publication documenting the use of Representational (Dis-)Similarity Analysis in consonant judgments on musicians or non-musicians, there are 1 case report of a professional singer/composer (Levitin & Grafton, 2016) and another preprint on music listening intervention with healthy older adults (Quinci et al, 2021). As a useful analysis toolkit, RSA models (Fig.…”

Functional Organization of Auditory and Reward Systems in Aging

“…Young adults were recruited from Northeastern University for a single-session study in return for course credit. Older adults were recruited as part of a longitudinal music-based intervention study that included an MRI, blood draw, battery of neuropsychological tests, and series of surveys (as described separately in Quinci et al, 2022). For the present analyses, we only used pre-intervention fMRI data from the older adults.…”

“…Sagittal slices (0.8 mm thick, anterior to posterior) were acquired covering the whole brain (TR = 2400 ms, TE = 2.55 ms, flip angle = 8°, FOV= 256, voxel size = 0.8 x 0.8 x 0.8 mm 3 ) (as described in Quinci et al, 2022).…”

“…The copyright holder for this preprint this version posted January 2, 2023. ; https://doi.org/10.1101/2023.01.01.522417 doi: bioRxiv preprint (Salimpoor et al, 2011;Zatorre, 2015). Music listening habits also vary by personality traits, with different musical preferences mapping onto specific personality traits (Rentfrow et al, 2011).…”

“…Even within the same individual, differences in musical reward processing can arise based on the listener’s familiarity with the piece, autobiographical memories related to the piece, and one’s overall appreciation for the intentionality of the performer or composer (Alluri et al, 2017; Aydogan et al, 2018; Janata, 2009). Agency in music listening also impacts the processing of musical information, with self-selection of musical stimuli leading to more active engagement for listeners (Quinci et al, 2022), possibly due to predictive processes that can be more strongly engaged when listening to self-selected music (Vuust et al, 2022).…”