{"doi":"10.17760/d20581910","title":"Functional network dynamics of music listening and effects of age","abstract":"Music-Based Interventions (MBIs) have been used to aid in the treatment of Alzheimer's Disease and Related Dementias (ADRD) and other aging populations. However, music can be used as a form of neurostimulation throughout the lifespan (Global Council on Brain Health, 2020), and there is currently much still unknown about the functional network dynamics of the aging brain. Here, we review concepts relevant to the understanding of music in a biological context, and present three functional magnetic resonance imaging (fMRI) studies of music listening in aging populations without a clinical ADRD diagnosis.Chapter 2 shows preliminary results of an eight-week MBI involving daily music listening in older adults (OA, N=16). Subjects completed task and resting state fMRI scans immediately prior to and immediately following the intervention. The music listening task involved listening to 20 second musical clips, including researcher-selected popular western pieces, researcher-selected novel music, and participant-selected pieces. Participants then rated each piece on a 4-point scale for liking and familiarity between stimulus presentations. We found that more well-liked pieced and especially the participant-selected pieces resulted in increased activation of auditory, reward, and default-mode network (DMN) regions. We also found that following intervention, Auditory network seed-based connectivity (SBC) to medial prefrontal cortex (mPFC) was increased during music listening relative to pre-intervention, and this region had become more sensitive to well-liked and self-selected pieces. Chapters 3 and 4 compare task and resting state activity and connectivity in a sample of younger adults (YAs) and OAs (N =24 per group). Chapter 3 focuses on whole brain-univariate analyses, SBC analyses, and ROI-ROI analyses, for which 5 effects of task and group were both observed. Both age groups showed activation of functional systems related to musical preference in keeping with patterns observed in chapter 2. Additionally, YAs showed higher ROI-ROI connectivity among reward regions relative to OAs, as well as higher within-network SBC of auditory network. Furthermore, YAs, but not OAs, showed increased SBC between auditory network and mPFC during task as compared to rest, leading to similar effects of group (YA>OA) during the music, but not rest, conditions. Chapter 4 then focuses on graph theoretical connectivity measures, using multivariate analysis of covariance (MANCOVA) to observe effects of group and task on the functional connectome. We found that OAs show higher global efficiency and lower modularity relative to YAs, and that this effect is more pronounced in the music listening condition relative to rest. To close, we present a functional model of cognitive aging we refer to as the functional dynamic phase shift (FDPS) model. This model posits that human aging is characterized by a subtle yet observable shift in the functional connectome that is both present at baseline and highlighted by the presence of certain task demands. Evidence here supports the idea that OAs tend towards a connectome that is less modular and less efficient and the individual network level, but more globally interconnected. Future work may further test this model, as well as use it to compare these patterns to cognitive aging measures, providing neural and cognitive targets for future MBIs.--Author's abstract","journal":null,"year":2023,"id":414083,"datarank":0.0,"base_score":0.0,"endowment":0.0,"self_citation_contribution":0.0,"citation_network_contribution":0.0,"self_endowment_contribution":0.0,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":0,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9506,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2023-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":805582,"name":"Alexander Belden","orcid":"0000-0003-2285-4587","position":0,"is_corresponding":true}],"reference_count":198,"raw_metadata":null,"created_at":"2026-07-19T01:22:01.321790Z","pmid":null,"pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}