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September's Featured Paper!

Midbrain dopamine neurons compute inferred and cached value prediction errors in a common framework

Elife. 2016 Mar 7;5. pii: e13665.

Brian F Sadacca, Joshua L Jones, and Geoffrey Schoenbaum

Midbrain dopamine neurons have been proposed to signal reward prediction errors as defined in temporal difference (TD) learning algorithms. While these models have been extremely powerful in interpreting dopamine activity, they typically do not use value derived through inference in computing errors. This is important because much real world behavior - and thus many opportunities for error-driven learning - is based on such predictions. Here, we show that error-signaling rat dopamine neurons respond to the inferred, model-based value of cues that have not been paired with reward and do so in the same framework as they track the putative cached value of cues previously paired with reward. This suggests that dopamine neurons access a wider variety of information than contemplated by standard TD models and that, while their firing conforms to predictions of TD models in some cases, they may not be restricted to signaling errors from TD predictions.

You can read more about this paper at PubMed.

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Featured paper of the Month!

Constituents and functional implications of the rat default mode network

Proc Natl Acad Sci U.S.A. 2016 Aug 2;113(31):E4541-7. Epub 2016 Jul 20.

Li-Ming Hsu, Xia Liang, Hong Gu, Julia K. Brynildsen, Jennifer A. Stark, Jessica A. Ash, Ching-Po Lin, Hanbing Lu, Peter R. Rapp, Elliot A. Stein, and Yihong Yang

The default mode network (DMN) has been suggested to support a variety of self-referential functions in humans and has been fractionated into subsystems based on distinct responses to cognitive tasks and functional connectivity architecture. Such subsystems are thought to reflect functional hierarchy and segregation within the network. Because preclinical models can inform translational studies of neuropsychiatric disorders, partitioning of the DMN in nonhuman species, which has previously not been reported, may inform both physiology and pathophysiology of the human DMN. In this study, we sought to identify constituents of the rat DMN using resting-state functional MRI (rs-fMRI) and diffusion tensor imaging. After identifying DMN using a group-level independent-component analysis on the rs-fMRI data, modularity analyses fractionated the DMN into an anterior and a posterior subsystem, which were further segregated into five modules. Diffusion tensor imaging tractography demonstrates a close relationship between fiber density and the functional connectivity between DMN regions, and provides anatomical evidence to support the detected DMN subsystems. Finally, distinct modulation was seen within and between these DMN subcomponents using a neurocognitive aging model. Taken together, these results suggest that, like the human DMN, the rat DMN can be partitioned into several subcomponents that may support distinct functions. These data encourage further investigation into the neurobiological mechanisms of DMN processing in preclinical models of both normal and disease states.

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The National Institute on Drug Abuse (NIDA), is part of the National Institutes of Health (NIH), the principal biomedical and behavioral research agency of the United States Government. NIH is a component of the U.S. Department of Health and Human Services.

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