The first year of a new era.

What happened when eLife decided to eliminate accept/reject decisions after peer review?

Distributional reinforcement learning in prefrontal cortex.

The prefrontal cortex is crucial for learning and decision-making. Classic reinforcement learning (RL) theories center on learning the expectation of potential rewarding outcomes and explain a wealth of neural data in the prefrontal cortex. Distributional RL, on the other hand, learns the full distribution of rewarding outcomes and better explains dopamine responses. In the present study, we show that distributional RL also better explains macaque anterior cingulate cortex neuronal responses, suggesting that it is a common mechanism for reward-guided learning.

Generative replay underlies compositional inference in the hippocampal-prefrontal circuit.

Human reasoning depends on reusing pieces of information by putting them together in new ways. However, very little is known about how compositional computation is implemented in the brain. Here, we ask participants to solve a series of problems that each require constructing a whole from a set of elements. With fMRI, we find that representations of novel constructed objects in the frontal cortex and hippocampus are relational and compositional. With MEG, we find that replay assembles elements into compounds, with each replay sequence constituting a hypothesis about a possible configuration of elements. The content of sequences evolves as participants solve each puzzle, progressing from predictable to uncertain elements and gradually converging on the correct configuration. Together, these results suggest a computational bridge between apparently distinct functions of hippocampal-prefrontal circuitry and a role for generative replay in compositional inference and hypothesis testing.

PET-measured human dopamine synthesis capacity and receptor availability predict trading rewards and time-costs during foraging.

Foraging behavior requires weighing costs of time to decide when to leave one reward patch to search for another. Computational and animal studies suggest that striatal dopamine is key to this process; however, the specific role of dopamine in foraging behavior in humans is not well characterized. We use positron emission tomography (PET) imaging to directly measure dopamine synthesis capacity and D1 and D2/3 receptor availability in 57 healthy adults who complete a computerized foraging task. Using voxelwise data and principal component analysis to identify patterns of variation across PET measures, we show that striatal D1 and D2/3 receptor availability and a pattern of mesolimbic and anterior cingulate cortex dopamine function are important for adjusting the threshold for leaving a patch to explore, with specific sensitivity to changes in travel time. These findings suggest a key role for dopamine in trading reward benefits against temporal costs to modulate behavioral adaptions to changes in the reward environment critical for foraging.

The Navajo Nation Healthy Diné Nation Act: Community Support of a 2% Tax on Unhealthy Foods.

CONTEXT: The Healthy Diné Nation Act (HDNA) of 2014 included a 2% tax on foods of little-to-no-nutritious value ("junk foods") on the Navajo Nation. The law was the first ever in the United States and any Indigenous nation worldwide with a population at a high risk for common nutrition-related conditions. To date, research on community support for food tax legislation among Indigenous nations is entirely lacking. OBJECTIVE: To assess the extent of support for the HDNA and factors associated with support including sociodemographic variables, knowledge of the HDNA, nutrition intake, and pricing preferences. DESIGN: Cross-sectional survey. SETTING: The Navajo Nation. PARTICIPANTS: A total of 234 Navajo Nation community members across 21 communities. OUTCOME MEASURES: The percentage of participants who were supportive of the HDNA. RESULTS: Participants were 97% Navajo, on average middle-aged, 67% reported an income below $25 000 annually, and 69.7% were female. Half of the respondents said they "support" (37.4%) or "strongly support" (13.0%) the tax, while another 35% of people said they were neutral or somewhat supportive; 15% did not support the tax. Participants with higher income ( P = .025) and education ( P = .026) and understanding of the legislation ( P < .001 for "very well" vs "not at all") had increased odds of greater support, as did people who believed that the HDNA would make Navajo people healthier (vs not, P < .001). Age, gender, language, and reported nutrition intake (healthy or unhealthy) were not associated with HDNA support, but participants willing to pay 5% or 12%-15% higher prices for fast food and soda had increased odds of greater support ( P values range from .023 to <.001). CONCLUSIONS: The majority of Navajo community members surveyed were moderately supportive of the Navajo Nation tax on unhealthy foods. Higher income and education and understanding of the law were associated with greater support, but nutrition intake was not.

The human brain reactivates context-specific past information at event boundaries of naturalistic experiences.

Although we perceive the world in a continuous manner, our experience is partitioned into discrete events. However, to make sense of these events, they must be stitched together into an overarching narrative-a model of unfolding events. It has been proposed that such a stitching process happens in offline neural reactivations when rodents build models of spatial environments. Here we show that, while understanding a natural narrative, humans reactivate neural representations of past events. Similar to offline replay, these reactivations occur in the hippocampus and default mode network, where reactivations are selective to relevant past events. However, these reactivations occur, not during prolonged offline periods, but at the boundaries between ongoing narrative events. These results, replicated across two datasets, suggest reactivations as a candidate mechanism for binding temporally distant information into a coherent understanding of ongoing experience.

Replay and compositional computation.

Replay in the brain has been viewed as rehearsal or, more recently, as sampling from a transition model. Here, we propose a new hypothesis: that replay is able to implement a form of compositional computation where entities are assembled into relationally bound structures to derive qualitatively new knowledge. This idea builds on recent advances in neuroscience, which indicate that the hippocampus flexibly binds objects to generalizable roles and that replay strings these role-bound objects into compound statements. We suggest experiments to test our hypothesis, and we end by noting the implications for AI systems which lack the human ability to radically generalize past experience to solve new problems.

A cognitive map for value-guided choice in ventromedial prefrontal cortex.

The prefrontal cortex is crucial for economic decision-making and representing the value of options. However, how such representations facilitate flexible decisions remains unknown. We reframe economic decision-making in prefrontal cortex in line with representations of structure within the medial temporal lobe because such cognitive map representations are known to facilitate flexible behaviour. Specifically, we framed choice between different options as a navigation process in value space. Here we show that choices in a 2D value space defined by reward magnitude and probability were represented with a grid-like code, analogous to that found in spatial navigation. The grid-like code was present in ventromedial prefrontal cortex (vmPFC) local field potential theta frequency and the result replicated in an independent dataset. Neurons in vmPFC similarly contained a grid-like code, in addition to encoding the linear value of the chosen option. Importantly, both signals were modulated by theta frequency - occurring at theta troughs but on separate theta cycles. Furthermore, we found sharp-wave ripples - a key neural signature of planning and flexible behaviour - in vmPFC, which were modulated by accuracy and reward. These results demonstrate that multiple cognitive map-like computations are deployed in vmPFC during economic decision-making, suggesting a new framework for the implementation of choice in prefrontal cortex.

Peer review without gatekeeping.

eLife is changing its editorial process to emphasize public reviews and assessments of preprints by eliminating accept/reject decisions after peer review.

Complementary task representations in hippocampus and prefrontal cortex for generalizing the structure of problems.

Humans and other animals effortlessly generalize prior knowledge to solve novel problems, by abstracting common structure and mapping it onto new sensorimotor specifics. To investigate how the brain achieves this, in this study, we trained mice on a series of reversal learning problems that shared the same structure but had different physical implementations. Performance improved across problems, indicating transfer of knowledge. Neurons in medial prefrontal cortex (mPFC) maintained similar representations across problems despite their different sensorimotor correlates, whereas hippocampal (dCA1) representations were more strongly influenced by the specifics of each problem. This was true for both representations of the events that comprised each trial and those that integrated choices and outcomes over multiple trials to guide an animal's decisions. These data suggest that prefrontal cortex and hippocampus play complementary roles in generalization of knowledge: PFC abstracts the common structure among related problems, and hippocampus maps this structure onto the specifics of the current situation.

How to build a cognitive map.

Learning and interpreting the structure of the environment is an innate feature of biological systems, and is integral to guiding flexible behaviors for evolutionary viability. The concept of a cognitive map has emerged as one of the leading metaphors for these capacities, and unraveling the learning and neural representation of such a map has become a central focus of neuroscience. In recent years, many models have been developed to explain cellular responses in the hippocampus and other brain areas. Because it can be difficult to see how these models differ, how they relate and what each model can contribute, this Review aims to organize these models into a clear ontology. This ontology reveals parallels between existing empirical results, and implies new approaches to understand hippocampal-cortical interactions and beyond.

A whole genome sequencing study of moderate to severe asthma identifies a lung function locus associated with asthma risk.

Genome-wide association studies (GWAS) have identified many common variant loci associated with asthma susceptibility, but few studies investigate the genetics underlying moderate-to-severe asthma risk. Here, we present a whole-genome sequencing study comparing 3181 moderate-to-severe asthma patients to 3590 non-asthma controls. We demonstrate that asthma risk is genetically correlated with lung function measures and that this component of asthma risk is orthogonal to the eosinophil genetics that also contribute to disease susceptibility. We find that polygenic scores for reduced lung function are associated with younger asthma age of onset. Genome-wide, seven previously reported common asthma variant loci and one previously reported lung function locus, near THSD4, reach significance. We replicate association of the lung function locus in a recently published GWAS of moderate-to-severe asthma patients. We additionally replicate the association of a previously reported rare (minor allele frequency < 1%) coding variant in IL33 and show significant enrichment of rare variant burden in genes from common variant allergic disease loci. Our findings highlight the contribution of lung function genetics to moderate-to-severe asthma risk, and provide initial rare variant support for associations with moderate-to-severe asthma risk at several candidate genes from common variant loci.

Genome-wide association study identifies kallikrein 5 in type 2 inflammation-low asthma.

BACKGROUND: Clinical studies of type 2 (T2) cytokine-related neutralizing antibodies in asthma have identified a substantial subset of patients with low levels of T2 inflammation who do not benefit from T2 cytokine neutralizing antibody treatment. Non-T2 mechanisms are poorly understood in asthma but represent a redefined unmet medical need. OBJECTIVE: We sought to gain a better understanding of genetic contributions to T2-low asthma. METHODS: We utilized an unbiased genome-wide association study of patients with moderate to severe asthma stratified by T2 serum biomarker periostin. We also performed additional expression and biological analysis for the top genetic hits. RESULTS: We identified a novel protective single nucleotide polymorphism at chr19q13.41, which is selectively associated with T2-low asthma and establishes Kallikrein-related peptidase 5 (KLK5) as the causal gene mediating this association. Heterozygous carriers of the single nucleotide polymorphisms have reduced KLK5 expression. KLK5 is secreted by human bronchial epithelial cells and elevated in asthma bronchial alveolar lavage. T2 cytokines IL-4 and IL-13 downregulate KLK5 in human bronchial epithelial cells. KLK5, dependent on its catalytic function, induces epithelial chemokine/cytokine expression. Finally, overexpression of KLK5 in airway or lack of an endogenous KLK5 inhibitor, SPINK5, leads to spontaneous airway neutrophilic inflammation. CONCLUSION: Our data identify KLK5 to be the causal gene at a novel locus at chr19q13.41 associated with T2-low asthma.

Reinforcement learning: Dopamine ramps with fuzzy value estimates.

A new study in reinforcement learning theory shows that extending the temporal difference algorithm to unbiased learning under state uncertainty explains the observed ramping behaviour of dopamine neurons.

Decoding cognition from spontaneous neural activity.

In human neuroscience, studies of cognition are rarely grounded in non-task-evoked, 'spontaneous' neural activity. Indeed, studies of spontaneous activity tend to focus predominantly on intrinsic neural patterns (for example, resting-state networks). Taking a 'representation-rich' approach bridges the gap between cognition and resting-state communities: this approach relies on decoding task-related representations from spontaneous neural activity, allowing quantification of the representational content and rich dynamics of such activity. For example, if we know the neural representation of an episodic memory, we can decode its subsequent replay during rest. We argue that such an approach advances cognitive research beyond a focus on immediate task demand and provides insight into the functional relevance of the intrinsic neural pattern (for example, the default mode network). This in turn enables a greater integration between human and animal neuroscience, facilitating experimental testing of theoretical accounts of intrinsic activity, and opening new avenues of research in psychiatry.

The Navajo Nation Healthy Diné Nation Act: A Description of Community Wellness Projects Funded by a 2% Tax on Minimal-to-No-Nutritious-Value Foods.

CONTEXT: To promote the health of the Navajo people, the Navajo Nation passed the Healthy Diné Nation Act (HDNA) in 2014. The HDNA included a 2% tax on "minimal-to-no-nutritional-value" foods and waived 5% sales tax on healthy foods, the first such policy in the United States and any sovereign Tribal nation. Uniquely aligned with Tribal government structures, revenue was directly allocated to 110 small local government entities (Chapters) for self-determined wellness projects. OBJECTIVE: To characterize HDNA-funded wellness projects, test for variation in project type, and funding amount over time by region and community size. DESIGN: Longitudinal study assessing funded wellness projects from tax inception through 2019. SETTING: The Navajo Nation. PARTICIPANTS: One hundred ten Navajo Nation Chapters receiving funding for self-determined wellness projects. OUTCOME MEASURES: The categories and specific types of wellness projects and funding over 4 years by region and community size. RESULTS: Of revenue collected in 2015-2018, more than 99.1% was disbursed through 2019 ($4.6 million, $13 385 annually per community) across 1315 wellness projects (12 per community). The built recreational environment category received 38.6% of funds, equipment/supplies 16.5%, instruction 15.7%, food and water initiatives 14.0%, and social events 10.2%. Most common specific projects were walking trails ($648 470), exercise equipment ($585 675), food for events ($288 879), playgrounds ($287 471), and greenhouses ($275 554). Only the proportion allocated to instruction changed significantly over time (increased 2% annually, P = .02). Smaller communities (population <1000) allocated significantly higher proportions to traditional, agricultural, and intergenerational projects and less to the built environment. CONCLUSIONS: Through 2019, more than 99% of HDNA revenue was successfully disbursed to 110 rural, Tribal communities. Communities chose projects related to promoting the built recreational environment, agriculture, and fitness/nutrition education, with smaller communities emphasizing cultural and intergenerational projects. These findings can inform other indigenous nations considering similar policies and funding distributions.

ADAR and hnRNPC deficiency synergize in activating endogenous dsRNA-induced type I IFN responses.

Cytosolic double-stranded RNA (dsRNA) initiates type I IFN responses. Endogenous retroelements, notably Alu elements, constitute a source of dsRNA. Adenosine-to-inosine (A-to-I) editing by ADAR induces mismatches in dsRNA and prevents recognition by MDA5 and autoinflammation. To identify additional endogenous dsRNA checkpoints, we conducted a candidate screen in THP-1 monocytes and found that hnRNPC and ADAR deficiency resulted in synergistic induction of MDA5-dependent IFN responses. RNA-seq analysis demonstrated dysregulation of Alu-containing introns in hnRNPC-deficient cells via utilization of unmasked cryptic splice sites, including introns containing ADAR-dependent A-to-I editing clusters. These putative MDA5 ligands showed reduced editing in the absence of ADAR, providing a plausible mechanism for the combined effects of hnRNPC and ADAR. This study contributes to our understanding of the control of repetitive element-induced autoinflammation and suggests that patients with hnRNPC-mutated tumors might maximally benefit from ADAR inhibition-based immunotherapy.

Rigorous review and editorial oversight of clinical preprints.

Research in many different areas of medicine will benefit from new approaches to peer review and publishing.

Temporally delayed linear modelling (TDLM) measures replay in both animals and humans.

There are rich structures in off-task neural activity which are hypothesized to reflect fundamental computations across a broad spectrum of cognitive functions. Here, we develop an analysis toolkit - temporal delayed linear modelling (TDLM) - for analysing such activity. TDLM is a domain-general method for finding neural sequences that respect a pre-specified transition graph. It combines nonlinear classification and linear temporal modelling to test for statistical regularities in sequences of task-related reactivations. TDLM is developed on the non-invasive neuroimaging data and is designed to take care of confounds and maximize sequence detection ability. Notably, as a linear framework, TDLM can be easily extended, without loss of generality, to capture rodent replay in electrophysiology, including in continuous spaces, as well as addressing second-order inference questions, for example, its temporal and spatial varying pattern. We hope TDLM will advance a deeper understanding of neural computation and promote a richer convergence between animal and human neuroscience.