Theta oscillations integrate functionally segregated sub-regions of the medial prefrontal cortex

Reinforcement learning requires the dynamic interplay of several specialized networks distributed across the brain. A potential mechanism to establish accurate temporal coordination among these paths is through the synchronization of neuronal activity to a common rhythm of neuronal firing. Previous...

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Bibliographic Details
Published in:NeuroImage (Orlando, Fla.) Fla.), 2016-12, Vol.143, p.166-174
Main Authors: Mas-Herrero, Ernest, Marco-Pallarés, Josep
Format: Article
Language:English
Subjects:
Adult
Behavior
Brain Mapping - methods
Electroencephalography
Electroencephalography - methods
Feedback
Female
Government grants
Humans
Magnetic Resonance Imaging
Male
Medial prefrontal cortex
Prefrontal Cortex - diagnostic imaging
Prefrontal Cortex - physiology
Reinforcement (Psychology)
Reinforcement learning
Reward
Reward processing
Studies
Theta oscillations
Theta Rhythm - physiology
Young Adult
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Summary:Reinforcement learning requires the dynamic interplay of several specialized networks distributed across the brain. A potential mechanism to establish accurate temporal coordination among these paths is through the synchronization of neuronal activity to a common rhythm of neuronal firing. Previous EEG studies have suggested that theta oscillatory activity might be crucial in the integration of information from motivational and attentional paths that converge into the medial Prefrontal Cortex (mPFC) during reward-guided learning. However, due to the low spatial resolution of EEG, this hypothesis has not been directly tested. Here, by combining EEG and fMRI, we show that theta oscillations serve as common substrate for the engagement of separated sub-regions within the mPFC (the pre-Supplementary Motor Area and the dorsomedial Prefrontal Cortex), underlying different cognitive operations (encoding of outcome valence and unsigned prediction errors) through separate functional paths (the Salience and the Central Executive Networks).
ISSN:1053-8119
1095-9572
DOI:10.1016/j.neuroimage.2016.08.024