Dataset Information

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

ACCESS DATA

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]

PMID: 25282614

Similar Datasets

Project description:Background: Parkinson’s disease (PD) is a progressive, degenerative disease with early-stage pathology hypothesized to manifest in brainstem regions. Vocal deficits, including soft monotone speech, result in significant clinical and quality of life issues and are present in 90% of PD patients; yet, the underlying pathology mediating these significant voice deficits is unknown. The Pink1-/- rat is a valid model of early-onset PD that presents with analogous vocal communication deficits (e.g. reduced loudness). Previous work shows abnormal α-synuclein protein aggregation in the periaqueductal gray (PAG), a brain region critical and necessary to the modulation of mammalian vocal behavior. In this study, we used high-throughput RNA sequencing to examine gene expression within the PAG of both male and female Pink1-/- rats as compared to age-matched wildtype controls. We used a bioinformatic approach to (1) test the hypothesis that loss of Pink1 in the PAG will influence expression of genes that interact with Pink1, (2) highlight other key genes that relate to Mendelian PD, and (3) catalog molecular targets important for the production of rat vocalizations. Results: Knockout of the Pink1 gene resulted in differentially expressed genes for both male and female rats. Pathway analysis highlighted several significant metabolic pathways. Weighted gene co-expression network analysis (WGCNA) was used to identify gene nodes and their interactions in (A) males, (B) females, and (C) combined-sexes datasets. For each analysis, within the module containing the Pink1 gene, Pink1 itself was the central node with the highest number of interactions with other genes including solute carriers, glutamate metabotropic receptors, and genes associated with protein localization. Strong connections between Pink1 and Krt2 and Hfe were found in both males and female datasets. In females a number of modules were significantly correlated with vocalization traits. Of interest, gene enrichment of the key vocal Cyan module in females showed a significant number of neuromodulatory genes including Nts, Slc6a4, Slc10a4, Ndnf, and Gpr160, that may be responsible for the modulation of rat vocalizations. The top biological pathways within the female Cyan vocal module included the regulation of membrane depolarization, indolalkylamine metabolic process, and monoamine transport. Conclusions: Overall, this work supports the premise that gene expression changes in the PAG may contribute to the vocal behavioral and deficits observed in this PD rat model. Additionally, this dataset identifies genes that represent new therapeutic targets for PD voice disorders.

Dataset Information

Representation of aversive prediction errors in the human periaqueductal gray.

Publications

Representation of aversive prediction errors in the human periaqueductal gray.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure