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Value-based decision–making involves multiple cortical and subcortical brain areas, but the distributed nature of neurophysiological activity underlying economic choices in the human brain remains largely unexplored. Specifically, the nature of the neurophysiological representation of reward-guided choices, as well as whether they are represented in a subset of reward-related regions or in a more distributed fashion, is unknown. Here, we hypothesize that reward choices, as well as choice-related computations (win probability, risk), are primarily represented in high-frequency neural activity reflecting local cortical processing and that they are highly distributed throughout the human brain, engaging multiple brain regions. To test these hypotheses, we used intracranial recordings from multiple areas (including orbitofrontal, lateral prefrontal, parietal, cingulate cortices as well as subcortical regions such as the hippocampus and amygdala) from neurosurgical patients of both sexes playing a decision-making game. We show that high-frequency activity (HFA; ɣ and HFA) represents both individual choice-related computations (e.g., risk, win probability) and choice information with different prevalence and regional representation. Choice-related computations are locally and unevenly present in multiple brain regions, whereas choice information is widely distributed and more prevalent and appears later across all regions examined. These results suggest brain-wide reward processing, with local HFA reflecting the coalescence of choice-related information into a final choice, and shed light on the distributed nature of neural activity underlying economic choices in the human brain. 

Abstract Sensorimotor integration in the trunk system is poorly understood despite its importance for functional recovery after neurological injury. To address this, a series of mapping studies were performed in the rat. First, the receptive fields (RFs) of cells recorded from thoracic dorsal root ganglia were identified. Second, the RFs of cells recorded from trunk primary sensory cortex (S1) were used to assess the extent and internal organization of trunk S1. Finally, the trunk motor cortex (M1) was mapped using intracortical microstimulation to assess coactivation of trunk muscles with hindlimb and forelimb muscles, and integration with S1. Projections from trunk S1 to trunk M1 were not anatomically organized, with relatively weak sensorimotor integration between trunk S1 and M1 compared to extensive integration between hindlimb S1/M1 and trunk M1. Assessment of response latency and anatomical tracing suggest that trunk M1 is abundantly guided by hindlimb somatosensory information that is derived primarily from the thalamus. Finally, neural recordings from awake animals during unexpected postural perturbations support sensorimotor integration between hindlimb S1 and trunk M1, providing insight into the role of the trunk system in postural control that is useful when studying recovery after injury.