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.

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.

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.

Shared Cortex-Cerebellum Dynamics in the Execution and Learning of a Motor Task.

Throughout mammalian neocortex, layer 5 pyramidal (L5) cells project via the pons to a vast number of cerebellar granule cells (GrCs), forming a fundamental pathway. Yet, it is unknown how neuronal dynamics are transformed through the L5→GrC pathway. Here, by directly comparing premotor L5 and GrC activity during a forelimb movement task using dual-site two-photon Ca2+ imaging, we found that in expert mice, L5 and GrC dynamics were highly similar. L5 cells and GrCs shared a common set of task-encoding activity patterns, possessed similar diversity of responses, and exhibited high correlations comparable to local correlations among L5 cells. Chronic imaging revealed that these dynamics co-emerged in cortex and cerebellum over learning: as behavioral performance improved, initially dissimilar L5 cells and GrCs converged onto a shared, low-dimensional, task-encoding set of neural activity patterns. Thus, a key function of cortico-cerebellar communication is the propagation of shared dynamics that emerge during learning.

Reassessing the role of urban green space in air pollution control.

The assumption that vegetation improves air quality is prevalent in scientific, popular, and political discourse. However, experimental and modeling studies show the effect of green space on air pollutant concentrations in urban settings is highly variable and context specific. We revisited the link between vegetation and air quality using satellite-derived changes of urban green space and air pollutant concentrations from 2,615 established monitoring stations over Europe and the United States. Between 2010 and 2019, stations recorded declines in ambient NO2, (particulate matter) PM10, and PM2.5 (average of -3.14% y-1), but not O3 (+0.5% y-1), pointing to the general success of recent policy interventions to restrict anthropogenic emissions. The effect size of total green space on air pollution was weak and highly variable, particularly at the street scale (15 to 60 m radius) where vegetation can restrict ventilation. However, when isolating changes in tree cover, we found a negative association with air pollution at borough to city scales (120 to 16,000 m) particularly for O3 and PM. The effect of green space was smaller than the pollutant deposition and dispersion effects of meteorological drivers including precipitation, humidity, and wind speed. When averaged across spatial scales, a one SD increase in green space resulted in a 0.8% (95% CI: -3.5 to 2%) decline in air pollution. Our findings suggest that while urban greening may improve air quality at the borough-to-city scale, the impact is moderate and may have detrimental street-level effects depending on aerodynamic factors like vegetation type and urban form.

Combining environmental DNA and visual surveys can inform conservation planning for coral reefs.

Environmental DNA (eDNA) metabarcoding has the potential to revolutionize conservation planning by providing spatially and taxonomically comprehensive data on biodiversity and ecosystem conditions, but its utility to inform the design of protected areas remains untested. Here, we quantify whether and how identifying conservation priority areas within coral reef ecosystems differs when biodiversity information is collected via eDNA analyses or traditional visual census records. We focus on 147 coral reefs in Indonesia's hyper-diverse Wallacea region and show large discrepancies in the allocation and spatial design of conservation priority areas when coral reef species were surveyed with underwater visual techniques (fishes, corals, and algae) or eDNA metabarcoding (eukaryotes and metazoans). Specifically, incidental protection occurred for 55% of eDNA species when targets were set for species detected by visual surveys and 71% vice versa. This finding is supported by generally low overlap in detection between visual census and eDNA methods at species level, with more overlap at higher taxonomic ranks. Incomplete taxonomic reference databases for the highly diverse Wallacea reefs, and the complementary detection of species by the two methods, underscore the current need to combine different biodiversity data sources to maximize species representation in conservation planning.

Distinct replay signatures for prospective decision-making and memory preservation.

Theories of neural replay propose that it supports a range of functions, most prominently planning and memory consolidation. Here, we test the hypothesis that distinct signatures of replay in the same task are related to model-based decision-making ("planning") and memory preservation. We designed a reward learning task wherein participants utilized structure knowledge for model-based evaluation, while at the same time had to maintain knowledge of two independent and randomly alternating task environments. Using magnetoencephalography and multivariate analysis, we first identified temporally compressed sequential reactivation, or replay, both prior to choice and following reward feedback. Before choice, prospective replay strength was enhanced for the current task-relevant environment when a model-based planning strategy was beneficial. Following reward receipt, and consistent with a memory preservation role, replay for the alternative distal task environment was enhanced as a function of decreasing recency of experience with that environment. Critically, these planning and memory preservation relationships were selective to pre-choice and post-feedback periods, respectively. Our results provide support for key theoretical proposals regarding the functional role of replay and demonstrate that the relative strength of planning and memory-related signals are modulated by ongoing computational and task demands.

Avoiding an unjust transition to sustainability: An equity metric for spatial conservation planning.

The need for rapid and ambitious conservation and restoration is widely acknowledged, yet concern exists that the widespread reallocation of land to nature would disproportionately affect the world's poor. Conservation and restoration may limit nutrition and livelihood options and thus negatively affect social development objectives. Although much research looks into global-scale scenarios and planning of conservation and restoration, spatial evaluations of these trade-offs in terms of equity remain limited. We fill this gap by identifying areas where conservation or restoration under different future scenarios and prioritization maps expand nature into landscapes that likely support land-dependent communities in their local food security. By contrasting the expansion of nature into areas supporting land-dependent communities vs. places where the food system is supported by regional to global markets, we highlight the need for disaggregated indicators that reflect the diversity of human land-use needs in order to identify more equitable pathways. Conservation prioritizations were found to result in more equitable land-use outcomes than the land-use outcomes of widely used socioeconomic scenarios. Accounting for differentiated social impacts in model-based conservation and restoration planning and global scale scenario assessment can help achieve a more inclusive transition to sustainability as well as reduce barriers to meaningful change.

Humans plan for the near future to walk economically on uneven terrain.

Humans experience small fluctuations in their gait when walking on uneven terrain. The fluctuations deviate from the steady, energy-minimizing pattern for level walking and have no obvious organization. But humans often look ahead when they walk, and could potentially plan anticipatory fluctuations for the terrain. Such planning is only sensible if it serves some an objective purpose, such as maintaining constant speed or reducing energy expenditure, that is also attainable within finite planning capacity. Here, we show that humans do plan and perform optimal control strategies on uneven terrain. Rather than maintaining constant speed, they make purposeful, anticipatory speed adjustments that are consistent with minimizing energy expenditure. A simple optimal control model predicts economical speed fluctuations that agree well with experiments with humans (N = 12) walking on seven different terrain profiles (correlated with model [Formula: see text] , [Formula: see text] all terrains). Participants made repeatable speed fluctuations starting about six to eight steps ahead of each terrain feature (up to ±7.5 cm height difference each step, up to 16 consecutive features). Nearer features matter more, because energy is dissipated with each succeeding step's collision with ground, preventing momentum from persisting indefinitely. A finite horizon of continuous look-ahead and motor working space thus suffice to practically optimize for any length of terrain. Humans reason about walking in the near future to plan complex optimal control sequences.

Biocultural vulnerability exposes threats of culturally important species.

There are growing calls for conservation frameworks that, rather than breaking the relations between people and other parts of nature, capture place-based relationships that have supported social-ecological systems over the long term. Biocultural approaches propose actions based on biological conservation priorities and cultural values aligned with local priorities, but mechanisms that allow their global uptake are missing. We propose a framework to globally assess the biocultural status of specific components of nature that matter to people and apply it to culturally important species (CIS). Drawing on a literature review and a survey, we identified 385 wild species, mostly plants, which are culturally important. CIS predominate among Indigenous peoples (57%) and ethnic groups (21%). CIS have a larger proportion of Data-Deficient species (41%) than the full set of International Union for Conservation of Nature (IUCN) species (12%), underscoring the disregard of cultural considerations in biological research. Combining information on CIS biological conservation status (IUCN threatened status) and cultural status (language vitality), we found that more CIS are culturally Vulnerable or Endangered than they are biologically and that there is a higher share of bioculturally Endangered or Vulnerable CIS than of either biologically or culturally Endangered CIS measured separately. Bioculturally Endangered or Vulnerable CIS are particularly predominant among Indigenous peoples, arguably because of the high levels of cultural loss among them. The deliberate connection between biological and cultural values, as developed in our "biocultural status" metric, provides an actionable way to guide decisions and operationalize global actions oriented to enhance place-based practices with demonstrated long-term sustainability.

Mapping the connectivity-conflict interface to inform conservation.

Balancing the competing, and often conflicting, needs of people and wildlife in shared landscapes is a major challenge for conservation science and policy worldwide. Connectivity is critical for wildlife persistence, but dispersing animals may come into conflict with people, leading to severe costs for humans and animals and impeding connectivity. Thus, conflict mitigation and connectivity present an apparent dilemma for conservation. We present a framework to address this dilemma and disentangle the effects of barriers to animal movement and conflict-induced mortality of dispersers on connectivity. We extend random-walk theory to map the connectivity-conflict interface, or areas where frequent animal movement may lead to conflict and conflict in turn impedes connectivity. We illustrate this framework with the endangered Asian elephant Elephas maximus, a species that frequently disperses out of protected areas and comes into conflict with humans. We mapped expected movement across a human-dominated landscape over the short- and long-term, accounting for conflict mortality. Natural and conflict-induced mortality together reduced expected movement and connectivity among populations. Based on model validation, our conflict predictions that explicitly captured animal movement better explained observed conflict than a model that considered distribution alone. Our work highlights the interaction between connectivity and conflict and enables identification of location-specific conflict mitigation strategies that minimize losses to people, while ensuring critical wildlife movement between habitats. By predicting where animal movement and humans collide, we provide a basis to plan for broad-scale conservation and the mutual well-being of wildlife and people in shared landscapes.

Climate oscillation impacts on water supply augmentation planning.

Climate oscillations ranging from years to decades drive precipitation variability in many river basins globally. As a result, many regions will require new water infrastructure investments to maintain reliable water supply. However, current adaptation approaches focus on long-term trends, preparing for average climate conditions at mid- or end-of-century. The impact of climate oscillations, which bring prolonged and variable but temporary dry periods, on water supply augmentation needs is unknown. Current approaches for theory development in nature-society systems are limited in their ability to realistically capture the impacts of climate oscillations on water supply. Here, we develop an approach to build middle-range theory on how common climate oscillations affect low-cost, reliable water supply augmentation strategies. We extract contrasting climate oscillation patterns across sub-Saharan Africa and study their impacts on a generic water supply system. Our approach integrates climate model projections, nonstationary signal processing, stochastic weather generation, and reinforcement learning-based advances in stochastic dynamic control. We find that longer climate oscillations often require greater water supply augmentation capacity but benefit more from dynamic approaches. Therefore, in settings with the adaptive capacity to revisit planning decisions frequently, longer climate oscillations do not require greater capacity. By building theory on the relationship between climate oscillations and least-cost reliable water supply augmentation, our findings can help planners target scarce resources and guide water technology and policy innovation. This approach can be used to support climate adaptation planning across large spatial scales in sectors impacted by climate variability.

Neural Correlates of Online Action Preparation.

When performing movements in rapid succession, the brain needs to coordinate ongoing execution with the preparation of an upcoming action. Here we identify the processes and brain areas involved in this ability of online preparation. Human participants (both male and female) performed pairs of single-finger presses or three-finger chords in rapid succession, while 7T fMRI was recorded. In the overlap condition, they could prepare the second movement during the first response and in the nonoverlap condition only after the first response was completed. Despite matched perceptual and movement requirements, fMRI revealed increased brain activity in the overlap condition in regions along the intraparietal sulcus and ventral visual stream. Multivariate analyses suggested that these areas are involved in stimulus identification and action selection. In contrast, the dorsal premotor cortex, known to be involved in planning upcoming movements, showed no discernible signs of heightened activity. This observation suggests that the bottleneck during simultaneous action execution and preparation arises at the level of stimulus identification and action selection, whereas movement planning in the premotor cortex can unfold concurrently with the execution of a current action without requiring additional neural activity.

A marginal structural model for normal tissue complication probability.

The goal of radiation therapy for cancer is to deliver prescribed radiation dose to the tumor while minimizing dose to the surrounding healthy tissues. To evaluate treatment plans, the dose distribution to healthy organs is commonly summarized as dose-volume histograms (DVHs). Normal tissue complication probability (NTCP) modeling has centered around making patient-level risk predictions with features extracted from the DVHs, but few have considered adapting a causal framework to evaluate the safety of alternative treatment plans. We propose causal estimands for NTCP based on deterministic and stochastic interventions, as well as propose estimators based on marginal structural models that impose bivariable monotonicity between dose, volume, and toxicity risk. The properties of these estimators are studied through simulations, and their use is illustrated in the context of radiotherapy treatment of anal canal cancer patients.

US foreign aid restrictions and maternal and children's health: Evidence from the "Mexico City Policy".

This paper analyzes the link between foreign aid for family planning services and a broad set of health outcomes. More specifically, it documents the harmful effects of the so-called "Mexico City Policy" (MCP), which restricts US funding for nongovernmental organizations that provide abortion-related services abroad. First enacted in 1985, the MCP is implemented along partisan lines; it is enforced only when a Republican administration is in office and quickly rescinded when a Democrat wins the presidency. Although previous research has shown that MCP causes significant disruption to family planning programs worldwide, its consequences for health outcomes, such as mortality and HIV rates, remain underexplored. The independence of the MCP's implementation from the situation in recipient countries allows us to systematically study its impact. Using country-level data from 134 countries between 1990 and 2015, we first show that the MCP is associated with higher maternal and child mortality and HIV incidence rates. These effects are magnified by dependence on US aid while mitigated by funds from non-US donors. Next, we complement these results using individual-level data from 30 low- and middle-income countries and show that, under the MCP, women have less access to modern contraception and are less exposed to information on family planning and AIDS via in-person channels. Moreover, pregnant women are more likely to report that their pregnancy is not desired. Our findings highlight the importance of mitigating the harmful effects of MCP by redesigning or counteracting this policy.

The causal effect of a family planning intervention on women's contraceptive use and birth spacing.

Studies have suggested that improving access to family planning (FP) may improve contraceptive use and reduce fertility. However, high-quality evidence, particularly from randomized implementation trials, of the effect of FP programs and interventions on longer-term fertility and birth spacing is lacking. We conduct a nonblinded, randomized, controlled trial to assess the causal impact of improved access to FP on contraceptive use and pregnancy spacing in Lilongwe, Malawi. A total of 2,143 married women aged 18 to 35 who were either pregnant or had recently given birth were recruited through home visits between September 2016 and January 2017 and were randomly assigned to an intervention arm or a control arm. The intervention arm received four services over a 2-y period: 1) up to six FP counseling sessions; 2) free transportation to an FP clinic; 3) free FP services at the clinic or financial reimbursement for FP services obtained elsewhere; and 4) treatment for contraceptive-related side effects. Contraceptive use after 2 y of intervention exposure increased by 5.9 percentage points, mainly through an increased use of contraceptive implants. The intervention group's hazard of pregnancy was 43.5% lower 24 mo after the index birth. Our results highlight the positive impact of increased access to FP on a woman's contraceptive use. In addition, we show that exposure to the FP intervention led to a prolongation of birth intervals among intervention women relative to control women and increased her control over birth spacing and postpartum fertility, which, in turn, may contribute to her longer-term health and well-being.

Chemistry-informed molecular graph as reaction descriptor for machine-learned retrosynthesis planning.

Infusing "chemical wisdom" should improve the data-driven approaches that rely exclusively on historical synthetic data for automatic retrosynthesis planning. For this purpose, we designed a chemistry-informed molecular graph (CIMG) to describe chemical reactions. A collection of key information that is most relevant to chemical reactions is integrated in CIMG:NMR chemical shifts as vertex features, bond dissociation energies as edge features, and solvent/catalyst information as global features. For any given compound as a target, a product CIMG is generated and exploited by a graph neural network (GNN) model to choose reaction template(s) leading to this product. A reactant CIMG is then inferred and used in two GNN models to select appropriate catalyst and solvent, respectively. Finally, a fourth GNN model compares the two CIMG descriptors to check the plausibility of the proposed reaction. A reaction vector is obtained for every molecule in training these models. The chemical wisdom of reaction propensity contained in the pretrained reaction vectors is exploited to autocategorize molecules/reactions and to accelerate Monte Carlo tree search (MCTS) for multistep retrosynthesis planning. Full synthetic routes with recommended catalysts/solvents are predicted efficiently using this CIMG-based approach.

Cortical Patterns Shift from Sequence Feature Separation during Planning to Integration during Motor Execution.

Performing sequences of movements from memory and adapting them to changing task demands is a hallmark of skilled human behavior, from handwriting to playing a musical instrument. Prior studies showed a fine-grained tuning of cortical primary motor, premotor, and parietal regions to motor sequences: from the low-level specification of individual movements to high-level sequence features, such as sequence order and timing. However, it is not known how tuning in these regions unfolds dynamically across planning and execution. To address this, we trained 24 healthy right-handed human participants (14 females, 10 males) to produce four five-element finger press sequences with a particular finger order and timing structure in a delayed sequence production paradigm entirely from memory. Local cortical fMRI patterns during preparation and production phases were extracted from separate No-Go and Go trials, respectively, to tease out activity related to these perimovement phases. During sequence planning, premotor and parietal areas increased tuning to movement order or timing, regardless of their combinations. In contrast, patterns reflecting the unique integration of sequence features emerged in these regions during execution only, alongside timing-specific tuning in the ventral premotor, supplementary motor, and superior parietal areas. This was in line with the participants' behavioral transfer of trained timing, but not of order to new sequence feature combinations. Our findings suggest a general informational state shift from high-level feature separation to low-level feature integration within cortical regions for movement execution. Recompiling sequence features trial-by-trial during planning may enable flexible last-minute adjustment before movement initiation.SIGNIFICANCE STATEMENT Musicians and athletes can modify the timing and order of movements in a sequence trial-by-trial, allowing for a vast repertoire of flexible behaviors. How does the brain put together these high-level sequence features into an integrated whole? We found that, trial-by-trial, the control of sequence features undergoes a state shift from separation during planning to integration during execution across a network of motor-related cortical areas. These findings have implications for understanding the hierarchical control of skilled movement sequences, as well as how information in brain areas unfolds across planning and execution.

A Short Route for Reach Planning between Human V6A and the Motor Cortex.

In the macaque monkey, area V6A, located in the medial posterior parietal cortex, contains cells that encode the spatial position of a reaching target. It has been suggested that during reach planning this information is sent to the frontal cortex along a parieto-frontal pathway that connects V6A-premotor cortex-M1. A similar parieto-frontal network may also exist in the human brain, and we aimed here to study the timing of this functional connection during planning of a reaching movement toward different spatial positions. We probed the functional connectivity between human area V6A (hV6A) and the primary motor cortex (M1) using dual-site, paired-pulse transcranial magnetic stimulation with a short (4 ms) and a longer (10 ms) interstimulus interval while healthy participants (18 men and 18 women) planned a visually-guided or a memory-guided reaching movement toward positions located at different depths and directions. We found that, when the stimulation over hV6A is sent 4 ms before the stimulation over M1, hV6A inhibits motor-evoked potentials during planning of either rightward or leftward reaching movements. No modulations were found when the stimulation over hV6A was sent 10 ms before the stimulation over M1, suggesting that only short medial parieto-frontal routes are active during reach planning. Moreover, the short route of hV6A-premotor cortex-M1 is active during reach planning irrespectively of the nature (visual or memory) of the reaching target. These results agree with previous neuroimaging studies and provide the first demonstration of the flow of inhibitory signals between hV6A and M1.SIGNIFICANCE STATEMENT All our dexterous movements depend on the correct functioning of the network of brain areas. Knowing the functional timing of these networks is useful to gain a deeper understanding of how the brain works to enable accurate arm movements. In this article, we probed the parieto-frontal network and demonstrated that it takes 4 ms for the medial posterior parietal cortex to send inhibitory signals to the frontal cortex during reach planning. This fast flow of information seems not to be dependent on the availability of visual information regarding the reaching target. This study opens the way for future studies to test how this timing could be impaired in different neurological disorders.

Single-Trial Dynamics of Competing Reach Plans in the Human Motor Periphery.

Contemporary motor control theories propose competition between multiple motor plans before the winning command is executed. While most competitions are completed before movement onset, movements are often initiated before the competition has been resolved. An example of this is saccadic averaging, wherein the eyes land at an intermediate location between two visual targets. Behavioral and neurophysiological signatures of competing motor commands have also been reported for reaching movements, but debate remains about whether such signatures attest to an unresolved competition, arise from averaging across many trials, or reflect a strategy to optimize behavior given task constraints. Here, we recorded EMG activity from an upper limb muscle (m. pectoralis) while 12 (8 female) participants performed an immediate response reach task, freely choosing between one of two identical and suddenly presented visual targets. On each trial, muscle recruitment showed two distinct phases of directionally tuned activity. In the first wave, time-locked ∼100 ms of target presentation, muscle activity was clearly influenced by the nonchosen target, reflecting a competition between reach commands that was biased in favor of the ultimately chosen target. This resulted in an initial movement intermediate between the two targets. In contrast, the second wave, time-locked to voluntary reach onset, was not biased toward the nonchosen target, showing that the competition between targets was resolved. Instead, this wave of activity compensated for the averaging induced by the first wave. Thus, single-trial analysis reveals an evolution in how the nonchosen target differentially influences the first and second wave of muscle activity.SIGNIFICANCE STATEMENT Contemporary theories of motor control suggest that multiple motor plans compete for selection before the winning command is executed. Evidence for this is found in intermediate reach movements toward two potential target locations, but recent findings have challenged this notion by arguing that intermediate reaching movements reflect an optimal response strategy. By examining upper limb muscle recruitment during a free-choice reach task, we show early recruitment of a suboptimal averaged motor command to the two targets that subsequently transitions to a single motor command that compensates for the initially averaged motor command. Recording limb muscle activity permits single-trial resolution of the dynamic influence of the nonchosen target through time.