ArticleNeuroImage2023
Development and validation of an fMRI-informed EEG model of reward-related ventral striatum activation.
Article in NeuroImage, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 1 of them a synthesis that pooled it.
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
10 citing papers in PubMed, 1 synthesis or guideline pooled it, 19 citations in OpenAlex.
- Music in the loop: a systematic review of current neurofeedback methodologies using music.Frontiers in neuroscience · 2025Pooled it
- Towards precision psychiatry: initial foundations and future directions.Nature reviews. Drug discovery · 2026Review
- An fMRI-informed EEG model of the amygdala is associated with salience network dynamics during naturalistic emotional stimulation.Molecular psychiatry · 2026Article
- A chill brain-music interface for enhancing music chills with personalized playlists.iScience · 2026Article
- Immersive metaverse art as a psychological intervention for depression and anxiety: a narrative review and multilevel model integrating engagement, cultural identity, and neurocognitive mechanisms.Frontiers in psychology · 2026Review
- Reward System EEG-fMRI-Pattern Neurofeedback for Major Depressive Disorder with Anhedonia: A Multicenter Pilot Study.Brain sciences · 2025Article
- Neural networks and foundation models: two strategies for EEG-to-fMRI prediction.Frontiers in systems biology · 2025Article
- Amygdala self-neuromodulation capacity as a window for process-related network recruitment.Philosophical transactions of the Royal Society of London. Series B, Biological sciences · 2024Article
- Ecological decoding of visual aesthetic preference with oscillatory electroencephalogram features-A mini-review.Frontiers in neuroergonomics · 2024Review
- From surface to depth: Using deep learning to predict striatal fMRI reward signaling from EEG.Imaging neuroscience (Cambridge, Mass.)Article
Corrections and comments
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Authors and funding
10 authors at 4 institutions in 3 countries.
Funding
Abstract
Reward processing is essential for our mental-health and well-being. In the current study, we developed and validated a scalable, fMRI-informed EEG model for monitoring reward processing related to activation in the ventral-striatum (VS), a significant node in the brain's reward system. To develop this EEG-based model of VS-related activation, we collected simultaneous EEG/fMRI data from 17 healthy individuals while listening to individually-tailored pleasurable music - a highly rewarding stimulus known to engage the VS. Using these cross-modal data, we constructed a generic regression model for predicting the concurrently acquired Blood-Oxygen-Level-Dependent (BOLD) signal from the VS using spectro-temporal features from the EEG signal (termed hereby VS-related-Electrical Finger Print; VS-EFP). The performance of the extracted model was examined using a series of tests that were applied on the original dataset and, importantly, an external validation dataset collected from a different group of 14 healthy individuals who underwent the same EEG/FMRI procedure. Our results showed that the VS-EFP model, as measured by simultaneous EEG, predicted BOLD activation in the VS and additional functionally relevant regions to a greater extent than an EFP model derived from a different anatomical region. The developed VS-EFP was also modulated by musical pleasure and predictive of the VS-BOLD during a monetary reward task, further indicating its functional relevance. These findings provide compelling evidence for the feasibility of using EEG alone to model neural activation related to the VS, paving the way for future use of this scalable neural probing approach in neural monitoring and self-guided neuromodulation.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.