Dissociable hindbrain GLP1R circuits for satiety and aversion.

The most successful obesity therapeutics, glucagon-like peptide-1 receptor (GLP1R) agonists, cause aversive responses such as nausea and vomiting1,2, effects that may contribute to their efficacy. Here, we investigated the brain circuits that link satiety to aversion, and unexpectedly discovered that the neural circuits mediating these effects are functionally separable. Systematic investigation across drug-accessible GLP1R populations revealed that only hindbrain neurons are required for the efficacy of GLP1-based obesity drugs. In vivo two-photon imaging of hindbrain GLP1R neurons demonstrated that most neurons are tuned to either nutritive or aversive stimuli, but not both. Furthermore, simultaneous imaging of hindbrain subregions indicated that area postrema (AP) GLP1R neurons are broadly responsive, whereas nucleus of the solitary tract (NTS) GLP1R neurons are biased towards nutritive stimuli. Strikingly, separate manipulation of these populations demonstrated that activation of NTSGLP1R neurons triggers satiety in the absence of aversion, whereas activation of APGLP1R neurons triggers strong aversion with food intake reduction. Anatomical and behavioural analyses revealed that NTSGLP1R and APGLP1R neurons send projections to different downstream brain regions to drive satiety and aversion, respectively. Importantly, GLP1R agonists reduce food intake even when the aversion pathway is inhibited. Overall, these findings highlight NTSGLP1R neurons as a population that could be selectively targeted to promote weight loss while avoiding the adverse side effects that limit treatment adherence.

Network structure influences the strength of learned neural representations.

Human experience is built upon sequences of discrete events. From those sequences, humans build impressively accurate models of their world. This process has been referred to as graph learning, a form of structure learning in which the mental model encodes the graph of event-to-event transition probabilities [1], [2], typically in medial temporal cortex [3]-[6]. Recent evidence suggests that some network structures are easier to learn than others [7]-[9], but the neural properties of this effect remain unknown. Here we use fMRI to show that the network structure of a temporal sequence of stimuli influences the fidelity with which those stimuli are represented in the brain. Healthy adult human participants learned a set of stimulus-motor associations following one of two graph structures. The design of our experiment allowed us to separate regional sensitivity to the structural, stimulus, and motor response components of the task. As expected, whereas the motor response could be decoded from neural representations in postcentral gyrus, the shape of the stimulus could be decoded from lateral occipital cortex. The structure of the graph impacted the nature of neural representations: when the graph was modular as opposed to lattice-like, BOLD representations in visual areas better predicted trial identity in a held-out run and displayed higher intrinsic dimensionality. Our results demonstrate that even over relatively short timescales, graph structure determines the fidelity of event representations as well as the dimensionality of the space in which those representations are encoded. More broadly, our study shows that network context influences the strength of learned neural representations, motivating future work in the design, optimization, and adaptation of network contexts for distinct types of learning over different timescales.

AgRP neuron activity promotes associations between sensory and nutritive signals to guide flavor preference.

Objective: The learned associations between sensory cues (e.g., taste, smell) and nutritive value (e.g., calories, post-ingestive signaling) of foods powerfully influences our eating behavior [1], but the neural circuits that mediate these associations are not well understood. Here, we examined the role of agouti-related protein (AgRP)-expressing neurons - neurons which are critical drivers of feeding behavior [2; 3] - in mediating flavor-nutrient learning (FNL). Methods: Because mice prefer flavors associated with AgRP neuron activity suppression [4], we examined how optogenetic stimulation of AgRP neurons during intake influences FNL, and used fiber photometry to determine how endogenous AgRP neuron activity tracks associations between flavors and nutrients. Results: We unexpectedly found that tonic activity in AgRP neurons during FNL potentiated, rather than prevented, the development of flavor preferences. There were notable sex differences in the mechanisms for this potentiation. Specifically, in male mice, AgRP neuron activity increased flavor consumption during FNL training, thereby strengthening the association between flavors and nutrients. In female mice, AgRP neuron activity enhanced flavor-nutrient preferences independently of consumption during training, suggesting that AgRP neuron activity enhances the reward value of the nutrient-paired flavor. Finally, in vivo neural activity analyses demonstrated that acute AgRP neuron dynamics track the association between flavors and nutrients in both sexes. Conclusions: Overall, these data (1) demonstrate that AgRP neuron activity enhances associations between flavors and nutrients in a sex-dependent manner and (2) reveal that AgRP neurons track and update these associations on fast timescales. Taken together, our findings provide new insight into the role of AgRP neurons in assimilating sensory and nutritive signals for food reinforcement.

AgRP neuron activity promotes associations between sensory and nutritive signals to guide flavor preference.

OBJECTIVE: The learned associations between sensory cues (e.g., taste, smell) and nutritive value (e.g., calories, post-ingestive signaling) of foods powerfully influences our eating behavior [1], but the neural circuits that mediate these associations are not well understood. Here, we examined the role of agouti-related protein (AgRP)-expressing neurons - neurons which are critical drivers of feeding behavior [2; 3] - in mediating flavor-nutrient learning (FNL). METHODS: Because mice prefer flavors associated with AgRP neuron activity suppression [4], we examined how optogenetic stimulation of AgRP neurons during intake influences FNL, and used fiber photometry to determine how endogenous AgRP neuron activity tracks associations between flavors and nutrients. RESULTS: We unexpectedly found that tonic activity in AgRP neurons during FNL potentiated, rather than prevented, the development of flavor preferences. There were notable sex differences in the mechanisms for this potentiation. Specifically, in male mice, AgRP neuron activity increased flavor consumption during FNL training, thereby strengthening the association between flavors and nutrients. In female mice, AgRP neuron activity enhanced flavor-nutrient preferences independently of consumption during training, suggesting that AgRP neuron activity enhances the reward value of the nutrient-paired flavor. Finally, in vivo neural activity analyses demonstrated that acute AgRP neuron dynamics track the association between flavors and nutrients in both sexes. CONCLUSIONS: Overall, these data (1) demonstrate that AgRP neuron activity enhances associations between flavors and nutrients in a sex-dependent manner and (2) reveal that AgRP neurons track and rapidly update these associations. Taken together, our findings provide new insight into the role of AgRP neurons in assimilating sensory and nutritive signals for food reinforcement.

A Full-Stack Application for Detecting Seizures and Reducing Data During Continuous Electroencephalogram Monitoring.

Continuous electroencephalogram monitoring is associated with lower mortality in critically ill patients; however, it is underused due to the resource-intensive nature of manually interpreting prolonged streams of continuous electroencephalogram data. Here, we present a novel real-time, machine learning-based alerting and monitoring system for epilepsy and seizures that dramatically reduces the amount of manual electroencephalogram review. METHODS: We developed a custom data reduction algorithm using a random forest and deployed it within an online cloud-based platform, which streams data and communicates interactively with caregivers via a web interface to display algorithm results. We developed real-time, machine learning-based alerting and monitoring system for epilepsy and seizures on continuous electroencephalogram recordings from 77 patients undergoing routine scalp ICU electroencephalogram monitoring and tested it on an additional 20 patients. RESULTS: We achieved a mean seizure sensitivity of 84% in cross-validation and 85% in testing, as well as a mean specificity of 83% in cross-validation and 86% in testing, corresponding to a high level of data reduction. This study validates a platform for machine learning-assisted continuous electroencephalogram analysis and represents a meaningful step toward improving utility and decreasing cost of continuous electroencephalogram monitoring. We also make our high-quality annotated dataset of 97 ICU continuous electroencephalogram recordings public for others to validate and improve upon our methods.

Abstract representations of events arise from mental errors in learning and memory.

Humans are adept at uncovering abstract associations in the world around them, yet the underlying mechanisms remain poorly understood. Intuitively, learning the higher-order structure of statistical relationships should involve complex mental processes. Here we propose an alternative perspective: that higher-order associations instead arise from natural errors in learning and memory. Using the free energy principle, which bridges information theory and Bayesian inference, we derive a maximum entropy model of people's internal representations of the transitions between stimuli. Importantly, our model (i) affords a concise analytic form, (ii) qualitatively explains the effects of transition network structure on human expectations, and (iii) quantitatively predicts human reaction times in probabilistic sequential motor tasks. Together, these results suggest that mental errors influence our abstract representations of the world in significant and predictable ways, with direct implications for the study and design of optimally learnable information sources.