bioRxivNeurosciencePreprint
Theta-Band Temporal Interference Stimulation Targeting the dACC Modulates Neural Gain of Prediction Error Encoding
Yatong WeniD, Mo WangiD, Xinwen DongiD, Mateusz Kazimierz GolaiD, Quanying LiuiD, Yiheng TuiD, Yonghui Li
Abstract
BackgroundPrediction error (PE) signals encode discrepancies between expected and actual outcomes and are linked to medial frontal theta dynamics in the dorsal anterior cingulate cortex (dACC). However, causal evidence that dACC theta mechanisms contribute to PE gain in humans remains limited. Transcranial temporal interference stimulation (tTIS) enables non-invasive modulation of deep cortical targets and provides a strategy to probe dACC theta circuitry. ObjectiveTo determine whether theta-band tTIS targeting the dACC modulates the neural gain of prediction error encoding during reward processing. MethodsThirty-four participants with elevated food addiction-related symptoms were randomized in a single-blind, sham-controlled pre-post design to active theta-band tTIS or sham stimulation. Under satiety, participants completed a food incentive delay task while EEG was recorded before and after stimulation. Feedback-related negativity (FRN) was analyzed at mean and single-trial levels using mixed-effects models with behavioral and Rescorla-Wagner model-derived prediction errors. Stimulus-preceding negativity (SPN) indexed anticipatory processing. ResultsTheta-band tTIS selectively modulated feedback-stage prediction error encoding. It altered the trial-level FRN-prediction error slope, with pre-to-post changes differing between groups for both behavioral ({beta} = 1.74, p = .049) and model-derived prediction errors ({beta} = 1.77, p = .048). At the mean ERP level, FRN became more negative after active stimulation (t16 = 2.75, p = .014), whereas SPN was unaffected. ConclusionTheta-band tTIS targeting the dACC modulates neural sensitivity to prediction errors during reward feedback, providing causal evidence that medial frontal theta dynamics regulate the neural gain of prediction error encoding.
- organism: human
- assay: electronic health records
- research focus: differential expression
- approach: statistical model
- Citations
- 0
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