Directly Exploring the Neural Correlates of Feedback-Related Reward Saliency and Valence During Real-Time fMRI-Based Neurofeedback

Direito, Bruno; Ramos, Manuel Mesa; Pereira, João; Sayal, Alexandre; Sousa, Teresa

doi:10.3389/fnhum.2020.578119

Cited by 8 publications

(6 citation statements)

References 42 publications

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“…Ultimately, we pave the way for developing music-based neurorehabilitative strategies (such as neurofeedback): the inherent ability of music to modulate emotions and regulate mood could indicate that it is a strong candidate for the interface between the participants and their brain activity. Our previous findings support the strategic role of task complexity and feedback valence on the modulation of the network nodes involved in monitoring and feedback control, key variables in neurofeedback framework optimization [46].…”

Section: Discussionsupporting

confidence: 83%

Decoding Musical Valence And Arousal: Exploring The Neural Correlates Of Music Evoked Emotions And The Role Of Expressivity Features

Sayal,

Guedes,

Almeida

et al. 2024

Preprint

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Music can convey basic emotions, such as joy and sadness, and more complex ones, such as tenderness or nostalgia. Its effects on emotion regulation and reward have attracted much attention in cognitive and affective neuroscience. Understanding the underlying neural mechanisms of music-evoked emotions could guide the development of novel technological and individually-tuned neurorehabilitation music-based therapies. This study aims to unravel the relationship between the classification of music excerpts regarding perceived affective states and their associated neural correlates, as measured by fMRI. We used valence and arousal to classify both the stimuli and the affective states perceived by the participants. We acquired fMRI data from 20 participants while listening to 96 musical excerpts a priori classified into four quadrants, considering the valence-arousal model. We first characterized the neural correlates resulting from a GLM analysis of the quadrants defined by valence (positive, negative) and arousal (high, low). Our results highlight the role of neocortical regions, most noticeably the music-specific sub-regions of the auditory cortex and thalamus, as well as regions from the reward network such as the amygdala. Using multivoxel activity patterns corresponding to the four quadrants representation of core affect, we were able to create a computational model that decodes the quadrant corresponding to the music with significant accuracy, well above a stringent chance level. We further analyzed a set of musical features using regression analysis and explored how they relate to brain activity in valence-, arousal-, reward-, and auditory-related ROIs. The results emphasize the role of expressive features in emotion-related networks. These results contribute to the definition of a relation between music and the neural substrate of music listening and emotions, which is key to developing novel music-based neurorehabilitation strategies.

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Section: Discussionsupporting

confidence: 83%

Decoding Musical Valence And Arousal: Exploring The Neural Correlates Of Music Evoked Emotions And The Role Of Expressivity Features

Sayal,

Guedes,

Almeida

et al. 2024

Preprint

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“…Using functional neuroimaging, brain regions that have been consistently implicated in neurofeedback or real-time self-modulation include the anterior insula (cognitive control, self-awareness), and the basal ganglia (sensory motor integration, implicit learning), independent of the targeted region-of-interest ( Emmert et al, 2016 ). There are different networks involved in neurofeedback in cognitive and affective functions ( Trambaiolli et al, 2021 ); this is seen particularly in fMRI-based studies that allow better spatial definition of the structures involved during cognition and affective processes ( Paret et al, 2019 ; Skottnik et al, 2019 ; Direito et al, 2021 ).…”

Section: Significance Of Working Memory and Improvementmentioning

confidence: 99%

Sharpening Working Memory With Real-Time Electrophysiological Brain Signals: Which Neurofeedback Paradigms Work?

Jiang

Jessee

Hoyng

et al. 2022

Front. Aging Neurosci.

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Growing evidence supports the idea that the ultimate biofeedback is to reward sensory pleasure (e.g., enhanced visual clarity) in real-time to neural circuits that are associated with a desired performance, such as excellent memory retrieval. Neurofeedback is biofeedback that uses real-time sensory reward to brain activity associated with a certain performance (e.g., accurate and fast recall). Working memory is a key component of human intelligence. The challenges are in our current limited understanding of neurocognitive dysfunctions as well as in technical difficulties for closed-loop feedback in true real-time. Here we review recent advancements of real time neurofeedback to improve memory training in healthy young and older adults. With new advancements in neuromarkers of specific neurophysiological functions, neurofeedback training should be better targeted beyond a single frequency approach to include frequency interactions and event-related potentials. Our review confirms the positive trend that neurofeedback training mostly works to improve memory and cognition to some extent in most studies. Yet, the training typically takes multiple weeks with 2–3 sessions per week. We review various neurofeedback reward strategies and outcome measures. A well-known issue in such training is that some people simply do not respond to neurofeedback. Thus, we also review the literature of individual differences in psychological factors e.g., placebo effects and so-called “BCI illiteracy” (Brain Computer Interface illiteracy). We recommend the use of Neural modulation sensitivity or BCI insensitivity in the neurofeedback literature. Future directions include much needed research in mild cognitive impairment, in non-Alzheimer’s dementia populations, and neurofeedback using EEG features during resting and sleep for memory enhancement and as sensitive outcome measures.

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“…Under the conditions of our experiment, the green bar feedback does not seem to have a positive impact on our non-volitional neurofeedback paradigm. The effect and use of neurofeedback has been a topic of research [57,58], though not focused on non-volitional neurofeedback.…”

Section: Oerrp Classificationmentioning

confidence: 99%

A new error-monitoring brain–computer interface based on reinforcement learning for people with autism spectrum disorders

et al. 2022

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Objective: Brain-computer interfaces (BCIs) are emerging as promising cognitive training tools in neurodevelopmental disorders, as they combine the advantages of traditional computerized interventions with real-time tailored feedback. We propose a gamified BCI based on non-volitional neurofeedback for cognitive training, aiming at reaching a neurorehabilitation tool for application in Autism Spectrum Disorders (ASD). Approach: The BCI consists of an emotional facial expression paradigm (EFP) controlled by an intelligent agent that makes correct and wrong actions, while the user observes and judges the agent’s actions. The agent learns through reinforcement learning (RL) an optimal strategy if the participant generates error-related potentials (ErrPs) upon incorrect agent actions. We hypothesize that this training approach will allow not only the agent to learn but also the BCI user, by participating through implicit error scrutiny in the process of learning through operant conditioning, making it of particular interest for disorders where error monitoring processes are altered/compromised such as in ASD. In this paper, the main goal is to validate the whole methodological BCI approach and assess whether it is feasible enough to move on to clinical experiments. A control group of 10 neurotypical participants and one participant with ASD tested the proposed BCI approach. Main Results: We achieved an online balanced-accuracy in ErrPs detection of 81.6% and 77.1%, respectively for 2 different game modes. Additionally, all participants achieved an optimal RL strategy for the agent at least in one of the test sessions. Significance: The ErrP classification results and the possibility of successfully achieving an optimal learning strategy, show the feasibility of the proposed methodology, which allows to move towards clinical experimentation with ASD participants to assess the effectiveness of the approach as hypothesized.

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Directly Exploring the Neural Correlates of Feedback-Related Reward Saliency and Valence During Real-Time fMRI-Based Neurofeedback

Cited by 8 publications

References 42 publications

Decoding Musical Valence And Arousal: Exploring The Neural Correlates Of Music Evoked Emotions And The Role Of Expressivity Features

Decoding Musical Valence And Arousal: Exploring The Neural Correlates Of Music Evoked Emotions And The Role Of Expressivity Features

Sharpening Working Memory With Real-Time Electrophysiological Brain Signals: Which Neurofeedback Paradigms Work?

A new error-monitoring brain–computer interface based on reinforcement learning for people with autism spectrum disorders

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