TY - JOUR
T1 - Development and validation of an fMRI-informed EEG model of reward-related ventral striatum activation
AU - Singer, Neomi
AU - Poker, Gilad
AU - Dunsky-Moran, Netta
AU - Nemni, Shlomi
AU - Reznik Balter, Shira
AU - Doron, Maayan
AU - Baker, Travis
AU - Dagher, Alain
AU - Zatorre, Robert J.
AU - Hendler, Talma
N1 - Publisher Copyright:
© 2023
PY - 2023/8/1
Y1 - 2023/8/1
N2 - 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.
AB - 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.
KW - Electrical finger-print
KW - Musical pleasure
KW - One-class EEG model
KW - Reward
KW - Simultaneous EEG-fMRI
KW - Ventral striatum
UR - http://www.scopus.com/inward/record.url?scp=85161802557&partnerID=8YFLogxK
U2 - 10.1016/j.neuroimage.2023.120183
DO - 10.1016/j.neuroimage.2023.120183
M3 - ???researchoutput.researchoutputtypes.contributiontojournal.article???
C2 - 37225112
AN - SCOPUS:85161802557
SN - 1053-8119
VL - 276
JO - NeuroImage
JF - NeuroImage
M1 - 120183
ER -