Mcclellanrossen3286

Perilipin 5 (PLIN5) is a lipid-droplet-associated protein that coordinates intracellular lipolysis in highly oxidative tissues and is thought to regulate lipid metabolism in response to phosphorylation by protein kinase A (PKA). We sought to identify PKA phosphorylation sites in PLIN5 and assess their functional relevance in cultured cells and the livers of mice. We detected phosphorylation on S155 and identified S155 as a functionally important site for lipid metabolism. Expression of phosphorylation-defective PLIN5 S155A in Plin5 null cells resulted in decreased rates of lipolysis and triglyceride-derived fatty acid oxidation. FLIM-FRET analysis of protein-protein interactions showed that PLIN5 S155 phosphorylation regulates PLIN5 interaction with adipose triglyceride lipase at the lipid droplet, but not with α-β hydrolase domain-containing 5. Re-expression of PLIN5 S155A in the liver of Plin5 liver-specific null mice reduced lipolysis compared with wild-type PLIN5 re-expression, but was not associated with other changes in hepatic lipid metabolism. Furthermore, glycemic control was impaired in mice with expression of PLIN5 S155A compared with mice expressing PLIN5. Together, these studies demonstrate that PLIN5 S155 is required for PKA-mediated lipolysis and builds on the body of evidence demonstrating a critical role for PLIN5 in coordinating lipid and glucose metabolism.Recent behavioral evidence implicates reward prediction errors (RPEs) as a key factor in the acquisition of episodic memory. Yet, important neural predictions related to the role of RPEs in episodic memory acquisition remain to be tested. Humans (both sexes) performed a novel variable-choice task where we experimentally manipulated RPEs and found support for key neural predictions with fMRI. Our results show that in line with previous behavioral observations, episodic memory accuracy increases with the magnitude of signed (i.e., better/worse-than-expected) RPEs (SRPEs). Neurally, we observe that SRPEs are encoded in the ventral striatum (VS). Crucially, we demonstrate through mediation analysis that activation in the VS mediates the experimental manipulation of SRPEs on episodic memory accuracy. In particular, SRPE-based responses in the VS (during learning) predict the strength of subsequent episodic memory (during recollection). Furthermore, functional connectivity between task-relevant processing areas (i.e., face-selective areas) and hippocampus and ventral striatum increased as a function of RPE value (during learning), suggesting a central role of these areas in episodic memory formation. Our results consolidate reinforcement learning theory and striatal RPEs as key factors subtending the formation of episodic memory.SIGNIFICANCE STATEMENT Recent behavioral research has shown that reward prediction errors (RPEs), a key concept of reinforcement learning theory, are crucial to the formation of episodic memories. In this study, we reveal the neural underpinnings of this process. Using fMRI, we show that signed RPEs (SRPEs) are encoded in the ventral striatum (VS), and crucially, that SRPE VS activity is responsible for the subsequent recollection accuracy of one-shot learned episodic memory associations.Impulsive decisions arise from preferring smaller but sooner rewards compared with larger but later rewards. How neural activity and attention to choice alternatives contribute to reward decisions during temporal discounting is not clear. Here we probed (1) attention to and (2) neural representation of delay and reward information in humans (both sexes) engaged in choices. We studied behavioral and frequency-specific dynamics supporting impulsive decisions on a fine-grained temporal scale using eye tracking and MEG recordings. In one condition, participants had to decide for themselves but pretended to decide for their best friend in a second prosocial condition, which required perspective taking. Hence, conditions varied in the value for themselves versus that pretending to choose for another person. Stronger impulsivity was reliably found across three independent groups for prosocial decisions. Eye tracking revealed a systematic shift of attention from the delay to the reward information and differences in ess in self-choices compared with the prosocial condition. selleck chemical Eye movement and MEG recordings revealed how participants represent choice options most evident for options with high subjective value. These results advance our understanding of neural mechanisms underlying decision-making in humans.The human cortex encodes information in complex networks that can be anatomically dispersed and variable in their microstructure across individuals. Using simulations with neural network models, we show that contemporary statistical methods for functional brain imaging-including univariate contrast, searchlight multivariate pattern classification, and whole-brain decoding with L1 or L2 regularization-each have critical and complementary blind spots under these conditions. We then introduce the sparse-overlapping-sets (SOS) LASSO-a whole-brain multivariate approach that exploits structured sparsity to find network-distributed information-and show in simulation that it captures the advantages of other approaches while avoiding their limitations. When applied to fMRI data to find neural responses that discriminate visually presented faces from other visual stimuli, each method yields a different result, but existing approaches all support the canonical view that face perception engages localized areas in posterie show that four widespread statistical approaches to functional brain imaging have critical blind spots in this scenario and use simulations with neural network models to illustrate why. We then introduce a new approach designed specifically to find radically distributed representations in neural networks. In simulation and in fMRI data collected in the well studied domain of face perception, the new approach discovers extensive signal missed by the other methods-suggesting that prior functional imaging work may have significantly underestimated the degree to which neurocognitive representations are distributed and variable across individuals.