Unknown

Dataset Information

0

Representation of aversive prediction errors in the human periaqueductal gray.


ABSTRACT: Pain is a primary driver of learning and motivated action. It is also a target of learning, as nociceptive brain responses are shaped by learning processes. We combined an instrumental pain avoidance task with an axiomatic approach to assessing fMRI signals related to prediction errors (PEs), which drive reinforcement-based learning. We found that pain PEs were encoded in the periaqueductal gray (PAG), a structure important for pain control and learning in animal models. Axiomatic tests combined with dynamic causal modeling suggested that ventromedial prefrontal cortex, supported by putamen, provides an expected value-related input to the PAG, which then conveys PE signals to prefrontal regions important for behavioral regulation, including orbitofrontal, anterior mid-cingulate and dorsomedial prefrontal cortices. Thus, pain-related learning involves distinct neural circuitry, with implications for behavior and pain dynamics.

SUBMITTER: Roy M 

PROVIDER: S-EPMC4213247 | biostudies-literature | 2014 Nov

REPOSITORIES: biostudies-literature

altmetric image

Publications

Representation of aversive prediction errors in the human periaqueductal gray.

Roy Mathieu M   Shohamy Daphna D   Daw Nathaniel N   Jepma Marieke M   Wimmer G Elliott GE   Wager Tor D TD  

Nature neuroscience 20141005 11


Pain is a primary driver of learning and motivated action. It is also a target of learning, as nociceptive brain responses are shaped by learning processes. We combined an instrumental pain avoidance task with an axiomatic approach to assessing fMRI signals related to prediction errors (PEs), which drive reinforcement-based learning. We found that pain PEs were encoded in the periaqueductal gray (PAG), a structure important for pain control and learning in animal models. Axiomatic tests combined  ...[more]

Similar Datasets

| S-EPMC8424891 | biostudies-literature
| S-EPMC3801046 | biostudies-literature
| S-EPMC4821794 | biostudies-literature
2020-05-21 | GSE150939 | GEO
2016-03-21 | GSE58803 | GEO
| S-EPMC9259957 | biostudies-literature
| S-EPMC5860873 | biostudies-literature
| S-EPMC6867375 | biostudies-literature