Melo, B. | bioRxiv Science

bioRxiv2022

Transcranial electrical stimulation modulates emotional experience and metabolites in the prefrontal cortex in a donation task

In this study we investigated whether transcranial Direct Current Stimulation (tDCS) applied to two prefrontal cortex regions, the ventromedial prefrontal cortex (vmPFC, anode) and right dorsolateral prefrontal cortex (DLPFC, cathode) can be used to induce changes in self-reported emotions and modulate metabolite concentrations under both electrodes. We employed in vivo quantitative MR Spectroscopy (MRS) in healthy adult participants and quantified the changes in GABA and Glx complex (Glutamate and Glutamine) before and after five sessions of tDCS stimulation. tDCS was delivered at 2mA intensity for 20 minutes for the active group and 1 minute for the sham group. tDCS was applied over five days while participants were engaged in a charitable donation task, known to engage the reward network. We observed increased levels of GABA in vmPFC, but not in DLPFC. Glx levels were decreased in both vmPFC and DLPFC. We found that self-reported happiness increased significantly over time only in the active group. There was a decrease in self-reported guiltiness in both active and sham groups. Altogether, the results indicate that self-reported emotions can be modulated by prefrontal stimulation. This may be because the changes in GABA and Glx concentrations following repeated stimulation induce remote changes in the reward network through interactions with other metabolites, previously thought to be unreachable with noninvasive stimulation techniques.

neuroscience↗

bioRxiv2020

Classification of complex emotions using EEG and virtual environment: proof of concept and therapeutic implication

During the last decades, neurofeedback training for emotional self-regulation has received significant attention from both the scientific and clinical communities. However, most studies have focused on broader emotional states such as "negative vs. positive", primarily due to our poor understanding of the functional anatomy of more complex emotions at the electrophysiological level. Our proof-of-concept study aims at investigating the feasibility of classifying two complex emotions that have been implicated in mental health, namely tenderness and anguish, using features extracted from the electroencephalogram (EEG) signal in healthy participants. Electrophysiological data were recorded from fourteen participants during a block-designed experiment consisting of emotional self-induction trials combined with a multimodal virtual scenario. For the within-subject classification, the linear Support Vector Machine was trained with two sets of samples: random cross-validation of the sliding windows of all trials; and 2) strategic cross-validation, assigning all the windows of one trial to the same fold. Spectral features, together with the frontal-alpha asymmetry, were extracted using Complex Morlet Wavelet analysis. Classification results with these features showed an accuracy of 79.3% on average when doing random cross-validation, and 73.3% when applying strategic cross-validation. We extracted a second set of features from the amplitude time-series correlation analysis, which significantly enhanced random cross-validation accuracy while showing similar performance to spectral features when doing strategic cross-validation. These results suggest that complex emotions show distinct electrophysiological correlates, which paves the way for future EEG-based, real-time neurofeedback training of complex emotional states. Significance statementThere is still little understanding about the correlates of high-order emotions (i.e., anguish and tenderness) in the physiological signals recorded with the EEG. Most studies have investigated emotions using functional magnetic resonance imaging (fMRI), including the real-time application in neurofeedback training. However, concerning the therapeutic application, EEG is a more suitable tool with regards to costs and practicability. Therefore, our proof-of-concept study aims at establishing a method for classifying complex emotions that can be later used for EEG-based neurofeedback on emotion regulation. We recorded EEG signals during a multimodal, near-immersive emotion-elicitation experiment. Results demonstrate that intraindividual classification of discrete emotions with features extracted from the EEG is feasible and may be implemented in real-time to enable neurofeedback.

neuroscience↗