CD4 Helper and CD8 Cytotoxic T Cell Differentiation.

A fundamental question in developmental immunology is how bipotential thymocyte precursors generate both CD4+ helper and CD8+ cytotoxic T cell lineages. The MHC specificity of αß T cell receptors (TCRs) on precursors is closely correlated with cell fate-determining processes, prompting studies to characterize how variations in TCR signaling are linked with genetic programs establishing lineage-specific gene expression signatures, such as exclusive CD4 or CD8 expression. The key transcription factors ThPOK and Runx3 have been identified as mediating development of helper and cytotoxic T cell lineages, respectively. Together with increasing knowledge of epigenetic regulators, these findings have advanced our understanding of the transcription factor network regulating the CD4/CD8 dichotomy. It has also become apparent that CD4+ T cells retain developmental plasticity, allowing them to acquire cytotoxic activity in the periphery. Despite such advances, further studies are necessary to identify the molecular links between TCR signaling and the nuclear machinery regulating expression of ThPOK and Runx3.

Brain-wide neural activity underlying memory-guided movement.

Behavior relies on activity in structured neural circuits that are distributed across the brain, but most experiments probe neurons in a single area at a time. Using multiple Neuropixels probes, we recorded from multi-regional loops connected to the anterior lateral motor cortex (ALM), a circuit node mediating memory-guided directional licking. Neurons encoding sensory stimuli, choices, and actions were distributed across the brain. However, choice coding was concentrated in the ALM and subcortical areas receiving input from the ALM in an ALM-dependent manner. Diverse orofacial movements were encoded in the hindbrain; midbrain; and, to a lesser extent, forebrain. Choice signals were first detected in the ALM and the midbrain, followed by the thalamus and other brain areas. At movement initiation, choice-selective activity collapsed across the brain, followed by new activity patterns driving specific actions. Our experiments provide the foundation for neural circuit models of decision-making and movement initiation.

Learning attentional templates for value-based decision-making.

Attention filters sensory inputs to enhance task-relevant information. It is guided by an "attentional template" that represents the stimulus features that are currently relevant. To understand how the brain learns and uses templates, we trained monkeys to perform a visual search task that required them to repeatedly learn new attentional templates. Neural recordings found that templates were represented across the prefrontal and parietal cortex in a structured manner, such that perceptually neighboring templates had similar neural representations. When the task changed, a new attentional template was learned by incrementally shifting the template toward rewarded features. Finally, we found that attentional templates transformed stimulus features into a common value representation that allowed the same decision-making mechanisms to deploy attention, regardless of the identity of the template. Altogether, our results provide insight into the neural mechanisms by which the brain learns to control attention and how attention can be flexibly deployed across tasks.

Hippocampal neurons construct a map of an abstract value space.

The hippocampus is thought to encode a "cognitive map," a structural organization of knowledge about relationships in the world. Place cells, spatially selective hippocampal neurons that have been extensively studied in rodents, are one component of this map, describing the relative position of environmental features. However, whether this map extends to abstract, cognitive information remains unknown. Using the relative reward value of cues to define continuous "paths" through an abstract value space, we show that single neurons in primate hippocampus encode this space through value place fields, much like a rodent's place neurons encode paths through physical space. Value place fields remapped when cues changed but also became increasingly correlated across contexts, allowing maps to become generalized. Our findings help explain the critical contribution of the hippocampus to value-based decision-making, providing a mechanism by which knowledge of relationships in the world can be incorporated into reward predictions for guiding decisions.

Modularity and robustness of frontal cortical networks.

Neural activity underlying short-term memory is maintained by interconnected networks of brain regions. It remains unknown how brain regions interact to maintain persistent activity while exhibiting robustness to corrupt information in parts of the network. We simultaneously measured activity in large neuronal populations across mouse frontal hemispheres to probe interactions between brain regions. Activity across hemispheres was coordinated to maintain coherent short-term memory. Across mice, we uncovered individual variability in the organization of frontal cortical networks. A modular organization was required for the robustness of persistent activity to perturbations: each hemisphere retained persistent activity during perturbations of the other hemisphere, thus preventing local perturbations from spreading. A dynamic gating mechanism allowed hemispheres to coordinate coherent information while gating out corrupt information. Our results show that robust short-term memory is mediated by redundant modular representations across brain regions. Redundant modular representations naturally emerge in neural network models that learned robust dynamics.

Representational geometry of perceptual decisions in the monkey parietal cortex.

Lateral intraparietal (LIP) neurons represent formation of perceptual decisions involving eye movements. In circuit models for these decisions, neural ensembles that encode actions compete to form decisions. Consequently, representation and readout of the decision variables (DVs) are implemented similarly for decisions with identical competing actions, irrespective of input and task context differences. Further, DVs are encoded as partially potentiated action plans through balance of activity of action-selective ensembles. Here, we test those core principles. We show that in a novel face-discrimination task, LIP firing rates decrease with supporting evidence, contrary to conventional motion-discrimination tasks. These opposite response patterns arise from similar mechanisms in which decisions form along curved population-response manifolds misaligned with action representations. These manifolds rotate in state space based on context, indicating distinct optimal readouts for different tasks. We show similar manifolds in lateral and medial prefrontal cortices, suggesting similar representational geometry across decision-making circuits.

Striosomes Mediate Value-Based Learning Vulnerable in Age and a Huntington's Disease Model.

Learning valence-based responses to favorable and unfavorable options requires judgments of the relative value of the options, a process necessary for species survival. We found, using engineered mice, that circuit connectivity and function of the striosome compartment of the striatum are critical for this type of learning. Calcium imaging during valence-based learning exhibited a selective correlation between learning and striosomal but not matrix signals. This striosomal activity encoded discrimination learning and was correlated with task engagement, which, in turn, could be regulated by chemogenetic excitation and inhibition. Striosomal function during discrimination learning was disturbed with aging and severely so in a mouse model of Huntington's disease. Anatomical and functional connectivity of parvalbumin-positive, putative fast-spiking interneurons (FSIs) to striatal projection neurons was enhanced in striosomes compared with matrix in mice that learned. Computational modeling of these findings suggests that FSIs can modulate the striosomal signal-to-noise ratio, crucial for discrimination and learning.

Constant Sub-second Cycling between Representations of Possible Futures in the Hippocampus.

Cognitive faculties such as imagination, planning, and decision-making entail the ability to represent hypothetical experience. Crucially, animal behavior in natural settings implies that the brain can represent hypothetical future experience not only quickly but also constantly over time, as external events continually unfold. To determine how this is possible, we recorded neural activity in the hippocampus of rats navigating a maze with multiple spatial paths. We found neural activity encoding two possible future scenarios (two upcoming maze paths) in constant alternation at 8 Hz: one scenario per ∼125-ms cycle. Further, we found that the underlying dynamics of cycling (both inter- and intra-cycle dynamics) generalized across qualitatively different representational correlates (location and direction). Notably, cycling occurred across moving behaviors, including during running. These findings identify a general dynamic process capable of quickly and continually representing hypothetical experience, including that of multiple possible futures.

Behavior- and Modality-General Representation of Confidence in Orbitofrontal Cortex.

Every decision we make is accompanied by a sense of confidence about its likely outcome. This sense informs subsequent behavior, such as investing more-whether time, effort, or money-when reward is more certain. A neural representation of confidence should originate from a statistical computation and predict confidence-guided behavior. An additional requirement for confidence representations to support metacognition is abstraction: they should emerge irrespective of the source of information and inform multiple confidence-guided behaviors. It is unknown whether neural confidence signals meet these criteria. Here, we show that single orbitofrontal cortex neurons in rats encode statistical decision confidence irrespective of the sensory modality, olfactory or auditory, used to make a choice. The activity of these neurons also predicts two confidence-guided behaviors: trial-by-trial time investment and cross-trial choice strategy updating. Orbitofrontal cortex thus represents decision confidence consistent with a metacognitive process that is useful for mediating confidence-guided economic decisions.

Cerebellar Neurodynamics Predict Decision Timing and Outcome on the Single-Trial Level.

Goal-directed behavior requires the interaction of multiple brain regions. How these regions and their interactions with brain-wide activity drive action selection is less understood. We have investigated this question by combining whole-brain volumetric calcium imaging using light-field microscopy and an operant-conditioning task in larval zebrafish. We find global, recurring dynamics of brain states to exhibit pre-motor bifurcations toward mutually exclusive decision outcomes. These dynamics arise from a distributed network displaying trial-by-trial functional connectivity changes, especially between cerebellum and habenula, which correlate with decision outcome. Within this network the cerebellum shows particularly strong and predictive pre-motor activity (>10 s before movement initiation), mainly within the granule cells. Turn directions are determined by the difference neuroactivity between the ipsilateral and contralateral hemispheres, while the rate of bi-hemispheric population ramping quantitatively predicts decision time on the trial-by-trial level. Our results highlight a cognitive role of the cerebellum and its importance in motor planning.

Patterns of Early p21 Dynamics Determine Proliferation-Senescence Cell Fate after Chemotherapy.

Chemotherapy is designed to induce cell death. However, at non-lethal doses, cancer cells can choose to remain proliferative or become senescent. The slow development of senescence makes studying this decision challenging. Here, by analyzing single-cell p21 dynamics before, during, and days after drug treatment, we link three distinct patterns of early p21 dynamics to final cell fate. Surprisingly, while high p21 expression is classically associated with senescence, we find the opposite at early times during drug treatment: most senescence-fated cells express much lower p21 levels than proliferation-fated cells. We demonstrate that these dynamics lead to a p21 "Goldilocks zone" for proliferation, in which modest increases of p21 expression can lead to an undesirable increase of cancer cell proliferation. Our study identifies a counter-intuitive role for early p21 dynamics in the cell-fate decision and pinpoints a source of proliferative cancer cells that can emerge after exposure to non-lethal doses of chemotherapy.

Primate Amygdala Neurons Simulate Decision Processes of Social Partners.

By observing their social partners, primates learn about reward values of objects. Here, we show that monkeys' amygdala neurons derive object values from observation and use these values to simulate a partner monkey's decision process. While monkeys alternated making reward-based choices, amygdala neurons encoded object-specific values learned from observation. Dynamic activities converted these values to representations of the recorded monkey's own choices. Surprisingly, the same activity patterns unfolded spontaneously before partner's choices in separate neurons, as if these neurons simulated the partner's decision-making. These "simulation neurons" encoded signatures of mutual-inhibitory decision computation, including value comparisons and value-to-choice conversions, resulting in accurate predictions of partner's choices. Population decoding identified differential contributions of amygdala subnuclei. Biophysical modeling of amygdala circuits showed that simulation neurons emerge naturally from convergence between object-value neurons and self-other neurons. By simulating decision computations during observation, these neurons could allow primates to reconstruct their social partners' mental states.

Area-Specificity and Plasticity of History-Dependent Value Coding During Learning.

Decision making is often driven by the subjective value of available options, a value which is formed through experience. To support this fundamental behavior, the brain must encode and maintain the subjective value. To investigate the area specificity and plasticity of value coding, we trained mice in a value-based decision task and imaged neural activity in 6 cortical areas with cellular resolution. History- and value-related signals were widespread across areas, but their strength and temporal patterns differed. In expert mice, the retrosplenial cortex (RSC) uniquely encoded history- and value-related signals with persistent population activity patterns across trials. This unique encoding of RSC emerged during task learning with a strong increase in more distant history signals. Acute inactivation of RSC selectively impaired the reward-history-based behavioral strategy. Our results indicate that RSC flexibly changes its history coding and persistently encodes value-related signals to support adaptive behaviors.

Dendritic Integration of Sensory Evidence in Perceptual Decision-Making.

Perceptual decisions require the accumulation of sensory information to a response criterion. Most accounts of how the brain performs this process of temporal integration have focused on evolving patterns of spiking activity. We report that subthreshold changes in membrane voltage can represent accumulating evidence before a choice. αß core Kenyon cells (αßc KCs) in the mushroom bodies of fruit flies integrate odor-evoked synaptic inputs to action potential threshold at timescales matching the speed of olfactory discrimination. The forkhead box P transcription factor (FoxP) sets neuronal integration and behavioral decision times by controlling the abundance of the voltage-gated potassium channel Shal (KV4) in αßc KC dendrites. αßc KCs thus tailor, through a particular constellation of biophysical properties, the generic process of synaptic integration to the demands of sequential sampling.

Neural Control of Naturalistic Behavior Choices.

In the natural world, animals make decisions on an ongoing basis, continuously selecting which action to undertake next. In the lab, however, the neural bases of decision processes have mostly been studied using artificial trial structures. New experimental tools based on the genetic toolkit of model organisms now make it experimentally feasible to monitor and manipulate neural activity in small subsets of neurons during naturalistic behaviors. We thus propose a new approach to investigating decision processes, termed reverse neuroethology. In this approach, experimenters select animal models based on experimental accessibility and then utilize cutting-edge tools such as connectomes and genetically encoded reagents to analyze the flow of information through an animal's nervous system during naturalistic choice behaviors. We describe how the reverse neuroethology strategy has been applied to understand the neural underpinnings of innate, rapid decision making, with a focus on defensive behavioral choices in the vinegar fly Drosophila melanogaster.

EGFR Ligands Differentially Stabilize Receptor Dimers to Specify Signaling Kinetics.

Epidermal growth factor receptor (EGFR) regulates many crucial cellular programs, with seven different activating ligands shaping cell signaling in distinct ways. Using crystallography and other approaches, we show how the EGFR ligands epiregulin (EREG) and epigen (EPGN) stabilize different dimeric conformations of the EGFR extracellular region. As a consequence, EREG or EPGN induce less stable EGFR dimers than EGF-making them partial agonists of EGFR dimerization. Unexpectedly, this weakened dimerization elicits more sustained EGFR signaling than seen with EGF, provoking responses in breast cancer cells associated with differentiation rather than proliferation. Our results reveal how responses to different EGFR ligands are defined by receptor dimerization strength and signaling dynamics. These findings have broad implications for understanding receptor tyrosine kinase (RTK) signaling specificity. Our results also suggest parallels between partial and/or biased agonism in RTKs and G-protein-coupled receptors, as well as new therapeutic opportunities for correcting RTK signaling output.

Neural and Molecular Mechanisms of Biological Embedding of Social Interactions.

Animals operate in complex environments, and salient social information is encoded in the nervous system and then processed to initiate adaptive behavior. This encoding involves biological embedding, the process by which social experience affects the brain to influence future behavior. Biological embedding is an important conceptual framework for understanding social decision-making in the brain, as it encompasses multiple levels of organization that regulate how information is encoded and used to modify behavior. The framework we emphasize here is that social stimuli provoke short-term changes in neural activity that lead to changes in gene expression on longer timescales. This process, simplified-neurons are for today and genes are for tomorrow-enables the assessment of the valence of a social interaction, an appropriate and rapid response, and subsequent modification of neural circuitry to change future behavioral inclinations in anticipation of environmental changes. We review recent research on the neural and molecular basis of biological embedding in the context of social interactions, with a special focus on the honeybee.

Inferring Macroscale Brain Dynamics via Fusion of Simultaneous EEG-fMRI.

Advances in the instrumentation and signal processing for simultaneously acquired electroencephalography and functional magnetic resonance imaging (EEG-fMRI) have enabled new ways to observe the spatiotemporal neural dynamics of the human brain. Central to the utility of EEG-fMRI neuroimaging systems are the methods for fusing the two data streams, with machine learning playing a key role. These methods can be dichotomized into those that are symmetric and asymmetric in terms of how the two modalities inform the fusion. Studies using these methods have shown that fusion yields new insights into brain function that are not possible when each modality is acquired separately. As technology improves and methods for fusion become more sophisticated, the future of EEG-fMRI for noninvasive measurement of brain dynamics includes mesoscale mapping at ultrahigh magnetic resonance fields, targeted perturbation-based neuroimaging, and using deep learning to uncover nonlinear representations that link the electrophysiological and hemodynamic measurements.

Neurophysiology of Human Perceptual Decision-Making.

The discovery of neural signals that reflect the dynamics of perceptual decision formation has had a considerable impact. Not only do such signals enable detailed investigations of the neural implementation of the decision-making process but they also can expose key elements of the brain's decision algorithms. For a long time, such signals were only accessible through direct animal brain recordings, and progress in human neuroscience was hampered by the limitations of noninvasive recording techniques. However, recent methodological advances are increasingly enabling the study of human brain signals that finely trace the dynamics of the unfolding decision process. In this review, we highlight how human neurophysiological data are now being leveraged to furnish new insights into the multiple processing levels involved in forming decisions, to inform the construction and evaluation of mathematical models that can explain intra- and interindividual differences, and to examine how key ancillary processes interact with core decision circuits.

Studying lineage decision-making in vitro: emerging concepts and novel tools.

Correct and timely lineage decisions are critical for normal embryonic development and homeostasis of adult tissues. Therefore, the search for fundamental principles that underlie lineage decision-making lies at the heart of developmental biology. Here, we review attempts to understand lineage decision-making as the interplay of single-cell heterogeneity and gene regulation. Fluctuations at the single-cell level are an important driving force behind cell-state transitions and the creation of cell-type diversity. Gene regulatory networks amplify such fluctuations and define stable cell types. They also mediate the influence of signaling inputs on the lineage decision. In this review, we focus on insights gleaned from in vitro differentiation of embryonic stem cells. We discuss emerging concepts, with an emphasis on transcriptional regulation, dynamical aspects of differentiation, and functional single-cell heterogeneity. We also highlight some novel tools to study lineage decision-making in vitro.