ArticleImaging neuroscience (Cambridge, Mass.)2026
From surface to depth: Using deep learning to predict striatal fMRI reward signaling from EEG.
Article in Imaging neuroscience (Cambridge, Mass.), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Reward processing is critical for motivation, learning, and decision making. It involves a network centered on the fronto-striatal circuit, with the ventral striatum (VS) playing a pivotal role. While functional magnetic resonance imaging (fMRI) has been instrumental in mapping subcortical VS reward signals, its cost and limited accessibility hinder broader clinical applications. In this study, we adapted a convolutional autoencoder deep learning (DL) model to reconstruct VS blood-oxygen-level-dependent (BOLD) activity from task-based electroencephalography (EEG) data, both recorded during a two-choice gambling task known to elicit reward-related activation in the VS. The model was trained on consecutive EEG-fMRI data from 19 healthy participants, allowing it to identify patterns that generalize across individuals. Results show that the DL model significantly outperforms linear baseline models in predicting VS activity: across leave-one-out folds, the mean correlation between the DL-derived and the ground truth VS BOLD signal was
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