Development and validation of an fMRI-informed EEG model of reward-related ventral striatum activation

Singer, Neomi; Poker, Gilad; Dunsky, Netta; Nemni, Shlomi; Balter, Shira Reznik; Doron, Maayan; Baker, Travis E.; Zatorre, Robert J.; Hendler, Talma

doi:10.1016/j.neuroimage.2023.120183

Cited by 2 publications

(4 citation statements)

References 89 publications

(136 reference statements)

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“…In this sense, a complete characterization of the music stimuli and their neurobehavioral effects is critical to better understand the effect associated with the music-based feedback interface. Study design should consider the neural correlates of music stimuli and their interaction with the target brain signal, neurofeedback, and reward mechanisms to optimize the feedback loop (Singer, Poker, et al, 2023). Ultimately, this comprehension may lead to the optimization of the framework or contribute to the translation to more naturalistic/clinical setups (e.g.…”

Section: Results and Evaluation Of Neurofeedback Successmentioning

confidence: 99%

“…Recent advances use fMRI-informed EEG models of the activation within a particular region, also known as electrical fingerprint. To develop this EEG-based model of the activation of subcortical structures, the authors use simultaneous EEG/fMRI data to model BOLD signals from these regions using spectro-temporal features from the EEG signal (Simões et al, 2020;Abreu, Simões, and Castelo-Branco, 2020;Singer, Poker, et al, 2023;Meir-Hasson et al, 2014;Lubianiker et al, 2019).…”

Section: Imaging Modalitiesmentioning

confidence: 99%

“…The solution to determine the signal sources, also known as the inverse problem, requires computationally intensive methods that were not addressed in these studies. Alternatives such as the electrical fingerprint have been proposed in the literature, but it is still not a standard solution (Singer, Poker, et al, 2023;Meir-Hasson et al, 2014). To improve the signal-to-noise ratio, multi-modal signal acquisition might also be used.…”

Section: Future Directionsmentioning

confidence: 99%

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Music in the loop: an overview of current neurofeedback methodologies using music

Sayal,

Direito,

Sousa

et al. 2024

Preprint

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How can music, a universal feature of human societies with the powerful ability to evoke meaningful emotions and influence mood, be used to control brain activity? Music therapy is a well-established discipline, but clinical neuroscience implications of its use remain to be established, in particular in the context of brain modulation. This literature review offers a comprehensive investigation into music-based neurofeedback studies, providing an overview of the underlying reasoning and methodological facets explored in previous music-based neurofeedback research. Through a critical assessment of the results obtained from thirteen studies, we delve into the methodologies and their limitations to propose potential hypotheses and approaches that could steer the development of innovative methodologies incorporating music-based neurofeedback. Our review is organized around three primary questions. Firstly, we address the question of why music is utilized as a stimulus instead of other alternatives, seeking to identify the primary motivations and any specific hypotheses on the use of music in these interventions. Secondly, we explore how music is integrated into the feedback loop, examining the various paradigms and stages of incorporating music as a central component. Lastly, we investigate the principal outcomes of the reviewed studies, aiming to identify the main findings and uncover potential neural correlates associated with the intervention. We found that most studies justify the choice of music as the neurofeedback interface given the link between music and emotion. The mechanistic explanation for the musical features used is generally absent, and there is no consensus regarding the imaging and/or behavioral success measure of NF. In sum, we discuss that when designing a music-NF study, one should consider the whole-brain neural correlates of music stimuli and their interaction with the target brain network and the reward mechanisms, to optimize the feedback intervention and test its specificity. This review intends to contribute as a valuable resource for researchers in the field, aiding in the development of a deeper understanding of music and neurofeedback and offering guidance for future investigations in this promising domain.

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Section: Results and Evaluation Of Neurofeedback Successmentioning

confidence: 99%

Section: Imaging Modalitiesmentioning

confidence: 99%

Section: Future Directionsmentioning

confidence: 99%

See 1 more Smart Citation

Music in the loop: an overview of current neurofeedback methodologies using music

Sayal,

Direito,

Sousa

et al. 2024

Preprint

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“…Nonetheless, their activity can be estimated using computational modeling. Singer et al ( 2023 ) used fMRI-informed EEG models of reward processing to decode activity from the ventral striatum using EEG and demonstrated good model performance across aesthetic and non aesthetic domains, as well as across subjects. Another possibility that could improve decoding of aesthetic appreciation would be to use EEG source localization algorithms with realistic headmodels and define reward areas as regions of interest.…”

Section: Discussionmentioning

confidence: 99%

Ecological decoding of visual aesthetic preference with oscillatory electroencephalogram features—A mini-review

Welter,

Lotte

2024

Front. Neuroergonomics

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In today's digital information age, human exposure to visual artifacts has reached an unprecedented quasi-omnipresence. Some of these cultural artifacts are elevated to the status of artworks which indicates a special appreciation of these objects. For many persons, the perception of such artworks coincides with aesthetic experiences (AE) that can positively affect health and wellbeing. AEs are composed of complex cognitive and affective mental and physiological states. More profound scientific understanding of the neural dynamics behind AEs would allow the development of passive Brain-Computer-Interfaces (BCI) that offer personalized art presentation to improve AE without the necessity of explicit user feedback. However, previous empirical research in visual neuroaesthetics predominantly investigated functional Magnetic Resonance Imaging and Event-Related-Potentials correlates of AE in unnaturalistic laboratory conditions which might not be the best features for practical neuroaesthetic BCIs. Furthermore, AE has, until recently, largely been framed as the experience of beauty or pleasantness. Yet, these concepts do not encompass all types of AE. Thus, the scope of these concepts is too narrow to allow personalized and optimal art experience across individuals and cultures. This narrative mini-review summarizes the state-of-the-art in oscillatory Electroencephalography (EEG) based visual neuroaesthetics and paints a road map toward the development of ecologically valid neuroaesthetic passive BCI systems that could optimize AEs, as well as their beneficial consequences. We detail reported oscillatory EEG correlates of AEs, as well as machine learning approaches to classify AE. We also highlight current limitations in neuroaesthetics and suggest future directions to improve EEG decoding of AE.

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Development and validation of an fMRI-informed EEG model of reward-related ventral striatum activation

Cited by 2 publications

References 89 publications

Music in the loop: an overview of current neurofeedback methodologies using music

Music in the loop: an overview of current neurofeedback methodologies using music

Ecological decoding of visual aesthetic preference with oscillatory electroencephalogram features—A mini-review

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