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The most successful obesity therapeutics, glucagon-like peptide-1 receptor (GLP1R) agonists, cause aversive responses such as nausea and vomiting 1 , 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 NTS GLP1R neurons triggers satiety in the absence of aversion, whereas activation of AP GLP1R neurons triggers strong aversion with food intake reduction. Anatomical and behavioural analyses revealed that NTS GLP1R and AP GLP1R 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 NTS GLP1R neurons as a population that could be selectively targeted to promote weight loss while avoiding the adverse side effects that limit treatment adherence. The neural circuits in the hindbrain that link satiety and aversion are shown to be separate, raising the possibility of developing obesity drugs without the common side effects of nausea and vomiting.

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). 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. 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, neural activity analyses demonstrated that acute AgRP neuron dynamics track the association between flavors and nutrients in both sexes. 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.

Objective: Continuous EEG (cEEG) monitoring is associated with lower mortality in critically ill patients, however it is underutilized due to the difficulty of manually interpreting prolonged streams of cEEG data. Here we present a novel real-time, machine learning-based alerting and monitoring system for epilepsy and seizures (RAMSES) that dramatically reduces the amount of manual EEG 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 validate RAMSES on cEEG recordings from 77 patients undergoing routine scalp ICU EEG monitoring. Results: On subjects with seizures we achieved >80% overall data reduction, while detecting a mean of 84% of seizures across all validation patients, with 19/27 patients achieving 100% seizure detection. On seizure free-patients, the majority of cEEG records, we reduced data requiring manual review by >83%. Conclusion: This study validates a platform for machine-learning assisted data reduction. Significance: This work represents a meaningful step towards improving utility and decreasing cost for cEEG monitoring We also make our high-quality annotated dataset of 77 ICU cEEG recordings public for others to validate and improve upon our methods.

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. Humans can easily uncover abstract associations. Here, the authors propose that higher-order associations arise from natural errors in learning and memory. They suggest that mental errors influence the humans’ representation of the world in significant and predictable ways.

From sequences of discrete events, humans build mental models of their world. Referred to as graph learning, the process produces a model encoding the graph of event-to-event transition probabilities. Recent evidence suggests that some networks are easier to learn than others, but the neural underpinnings of this effect remain unknown. Here we use fMRI to show that even over short timescales the network structure of a temporal sequence of stimuli determines the fidelity of event representations as well as the dimensionality of the space in which those representations are encoded: when the graph was modular as opposed to lattice-like, BOLD representations in visual areas better predicted trial identity and displayed higher intrinsic dimensionality. 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. How do humans build mental models from sequences of events? Using fMRI, Kahn et al. show that the network structure of a temporal sequence of stimuli determines the fidelity of event representations and the dimensionality of the representation space.

Humans naturally attend to patterns that emerge in our perceptual environments, building mental models that allow future experiences to be processed more effectively and efficiently. Perceptual events and statistical relations can be represented as nodes and edges in a graph. Recent work in graph learning has shown that human behavior is sensitive to graph topology, but little is known about how that topology might elicit distinct neural responses during learning. Here, we address this knowledge gap by applying time-resolved network analyses to fMRI data collected during a visuomotor graph learning task. We assess neural signatures of learning on two types of structures: modular and non-modular lattice graphs. We find that task performance is supported by a highly flexible visual system, relatively stable brain-wide community structure, cohesiveness within the dorsal attention, limbic, default mode, and subcortical systems, and an increasing degree of integration between the visual and ventral attention systems. Additionally, we find that the time-resolved connectivity of the limbic, default mode, temporoparietal, and subcortical systems is associated with enhanced performance on modular graphs but not on lattice-like graphs. These findings provide evidence for the differential processing of statistical patterns with distinct underlying graph topologies. Our work highlights the similarities between the neural correlates of graph learning and those of statistical learning.

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.

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. Combining ideas from information theory and reinforcement learning, we derive a maximum entropy (or minimum complexity) 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.