Music-to My Brain: Modulation of Reward System Activity via Musical Neurofeedback

preprint OA: closed
Full text JSON View at publisher
Full text 1,880 characters · extracted from oa-doi-fallback · click to expand
Abstract Music robustly engages the human reward system, yet whether this engagement can be harnessed for volitional self-neuromodulation remains unknown. We developed a musical neurofeedback approach that enables individuals to control a validated, fMRI-informed EEG marker of ventral striatal activity. Personalized pleasurable music served as feedback, becoming increasingly rewarding through acoustic manipulation as regulation improved, thereby creating a positive feedback loop. Across three double-blind, sham-controlled studies (N=80; two with repeated training), contingent neurofeedback enabled participants to upregulate this EEG signal reflecting ventral striatal activity; in studies with repeated training, this learning generalized to no-feedback contexts. In the neurofeedback (but not sham) group, regulation success correlated with self-reported hedonic capacity, indicating behavioral relevance. Pre-post fMRI further showed that improvements in ventral striatal BOLD self-regulation were associated with EEG-based regulation performance, supporting the EEG measure as a marker of ventral striatal modulation. Mechanistically, neurofeedback training enhanced functional connectivity between right auditory cortex and ventral striatum during listening to trained music, with stronger effects in the neurofeedback than in the sham group, demonstrating experience-dependent modification of auditory-reward pathways. Together, these findings reveal a mechanism for music-based reward self-regulation and offer a potential scalable, personalized approach for targeting reward dysfunction such as anhedonia. Competing Interest Statement T.H. is the Chief Medical Scientist and Chair of the advisory board in GrayMatters Health. T.H., N.S., R.J.Z., A.D. and M.F.F have a filed patent related to the topic of this paper in the United States Patent and Trademark Office.

Text is read by the "Ask this paper" AI Q&A widget below. Extraction quality varies by source — PMC NXML preserves structure cleanly, OA-HTML may include some navigation residue, and OA-PDF can have broken hyphenation. The publisher copy (via DOI) is the canonical version.

My notes (saved in your browser only)

Ask this paper AI returns verbatim quotes from the full text · source: oa-doi-fallback

Answers must be backed by verbatim quotes from this paper's full text. Hallucinated quotes are dropped automatically; if no verbatim passage answers the question, we say so. How this works

Citation neighborhood (no data yet)

We don't have any in-corpus citations linked to this paper yet. This is a recent paper (2026) — citers typically take a year or two to land, and the OpenAlex reference graph may still be filling in.

Source provenance

europepmc
last seen: 2026-05-20T01:45:00.602351+00:00
unpaywall
last seen: 2026-06-15T06:18:04.506796+00:00