Journal
BRAIN
Volume 139, Issue -, Pages 1295-1309Publisher
OXFORD UNIV PRESS
DOI: 10.1093/brain/awv409
Keywords
OFC; iEEG; reward probability; experienced value; risk
Categories
Funding
- Universite de Lyon, within the program Investissements d'Avenir [LABEX ANR-11-LABEX-0042, ANR-11-IDEX-0007]
- Pari Mutuel Urbain (PMU)
- EURIAS Fellowship Programme
- European Commission (Marie-Sklodowska-Curie Actions - COFUND Programme - FP7)
- Institute for Advanced Study 'Hanse-Wissenschaftskolleg'
- [ANR-14-CE13-0006-01]
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The orbitofrontal cortex is known to carry information regarding expected reward, risk and experienced outcome. Yet, due to inherent limitations in lesion and neuroimaging methods, the neural dynamics of these computations has remained elusive in humans. Here, taking advantage of the high temporal definition of intracranial recordings, we characterize the neurophysiological signatures of the intact orbitofrontal cortex in processing information relevant for risky decisions. Local field potentials were recorded from the intact orbitofrontal cortex of patients suffering from drug-refractory partial epilepsy with implanted depth electrodes as they performed a probabilistic reward learning task that required them to associate visual cues with distinct reward probabilities. We observed three successive signals: (i) around 400 ms after cue presentation, the amplitudes of the local field potentials increased with reward probability; (ii) a risk signal emerged during the late phase of reward anticipation and during the outcome phase; and (iii) an experienced value signal appeared at the time of reward delivery. Both the medial and lateral orbitofrontal cortex encoded risk and reward probability while the lateral orbitofrontal cortex played a dominant role in coding experienced value. The present study provides the first evidence from intracranial recordings that the human orbitofrontal cortex codes reward risk both during late reward anticipation and during the outcome phase at a time scale of milliseconds. Our findings offer insights into the rapid mechanisms underlying the ability to learn structural relationships from the environment.
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