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The hippocampus is a mammalian brain structure that expresses spatial representations1 and is crucial for navigation2,3. Navigation, in turn, intricately depends on locomotion; however, current accounts suggest a dissociation between hippocampal spatial representations and the details of locomotor processes. Specifically, the hippocampus is thought to represent mainly higher-order cognitive and locomotor variables such as position, speed and direction of movement4-7, whereas the limb movements that propel the animal can be computed and represented primarily in subcortical circuits, including the spinal cord, brainstem and cerebellum8-11. Whether hippocampal representations are actually decoupled from the detailed structure of locomotor processes remains unknown. To address this question, here we simultaneously monitored hippocampal spatial representations and ongoing limb movements underlying locomotion at fast timescales. We found that the forelimb stepping cycle in freely behaving rats is rhythmic and peaks at around 8 Hz during movement, matching the approximately 8 Hz modulation of hippocampal activity and spatial representations during locomotion12. We also discovered precisely timed coordination between the time at which the forelimbs touch the ground ('plant' times of the stepping cycle) and the hippocampal representation of space. Notably, plant times coincide with hippocampal representations that are closest to the actual position of the nose of the rat, whereas between these plant times, the hippocampal representation progresses towards possible future locations. This synchronization was specifically detectable when rats approached spatial decisions. Together, our results reveal a profound and dynamic coordination on a timescale of tens of milliseconds between central cognitive representations and peripheral motor processes. This coordination engages and disengages rapidly in association with cognitive demands and is well suited to support rapid information exchange between cognitive and sensory-motor circuits.

Organisms must learn new strategies to adapt to changing environments. Activity in different neurons often exhibits synchronization that can dynamically enhance their communication and might create flexible brain states that facilitate changes in behavior. We studied the role of gamma-frequency (~40 Hz) synchrony between prefrontal parvalbumin (PV) interneurons in mice learning multiple new cue-reward associations. Voltage indicators revealed cell-type-specific increases of cross-hemispheric gamma synchrony between PV interneurons when mice received feedback that previously learned associations were no longer valid. Disrupting this synchronization by delivering out-of-phase optogenetic stimulation caused mice to perseverate on outdated associations, an effect not reproduced by in-phase stimulation or out-of-phase stimulation at other frequencies. Gamma synchrony was specifically required when new associations used familiar cues that were previously irrelevant to behavioral outcomes, not when associations involved new cues or for reversing previously learned associations. Thus, gamma synchrony is indispensable for reappraising the behavioral salience of external cues.

How predictable are life trajectories? We investigated this question with a scientific mass collaboration using the common task method; 160 teams built predictive models for six life outcomes using data from the Fragile Families and Child Wellbeing Study, a high-quality birth cohort study. Despite using a rich dataset and applying machine-learning methods optimized for prediction, the best predictions were not very accurate and were only slightly better than those from a simple benchmark model. Within each outcome, prediction error was strongly associated with the family being predicted and weakly associated with the technique used to generate the prediction. Overall, these results suggest practical limits to the predictability of life outcomes in some settings and illustrate the value of mass collaborations in the social sciences.

ABSTRACT Neurodata Without Borders: Neurophysiology (NWB:N) is a data standard for neurophysiology, providing neuroscientists with a common standard to share, archive, use, and build common analysis tools for neurophysiology data. With NWB:N version 2.0 (NWB:N 2.0) we made significant advances towards creating a usable standard, software ecosystem, and vibrant community for standardizing neurophysiology data. In this manuscript we focus in particular on the NWB:N data standard schema and present advances towards creating an accessible data standard for neurophysiology.

Scholars have argued that populists disproportionately benefit from social media, and there is evidence that they attract more engagement than other politicians and parties in several countries. We systematically evaluate the relationship between populism and online popularity using a novel dataset covering over four hundred parties active on Facebook and Twitter in thirty European countries between 2010 and 2020. We use Bayesian hierarchical models to assess the relationship between populism and three types of engagement on each platform. We find that populists received more engagement than other parties on Facebook, but there was no consistent relationship on Twitter. Among these parties, only right-wing populist parties had any widespread advantages. Our results highlight considerable heterogeneity across countries and over time, providing new insight into the scope and scale of populists’ online popularity. This includes evidence that left-wing and centrist parties attracted more engagement in a handful of countries. We evaluate several explanations for these results, finding that negative macroeconomic indicators and increased migration are positively correlated with populist engagement, while internet and social media adoption have mixed impacts that benefit right-wing populists. [ABSTRACT FROM AUTHOR]

Thalamic relay neurons have well-characterized dual firing modes: bursting and tonic spiking. Studies in brain slices have led to a model in which rhythmic synchronized spiking (phasic firing) in a population of relay neurons leads to hyper-synchronous oscillatory cortico-thalamo-cortical rhythms that result in absence seizures. This model suggests that blocking thalamocortical phasic firing would treat absence seizures. However, recent in vivo studies in anesthetized animals have questioned this simple model. Here we resolve this issue by developing a real-time, mode-switching approach to drive thalamocortical neurons into or out of a phasic firing mode in two freely behaving genetic rodent models of absence epilepsy. Toggling between phasic and tonic firing in thalamocortical neurons launched and aborted absence seizures, respectively. Thus, a synchronous thalamocortical phasic firing state is required for absence seizures, and switching to tonic firing rapidly halts absences. This approach should be useful for modulating other networks that have mode-dependent behaviors.

Anxiety-related conditions are among the most difficult neuropsychiatric diseases to treat pharmacologically, but respond to cognitive therapies. There has therefore been interest in identifying relevant top-down pathways from cognitive control regions in medial prefrontal cortex (mPFC). Identification of such pathways could contribute to our understanding of the cognitive regulation of affect, and provide pathways for intervention. Previous studies have suggested that dorsal and ventral mPFC subregions exert opposing effects on fear, as do subregions of other structures. However, precise causal targets for top-down connections among these diverse possibilities have not been established. Here we show that the basomedial amygdala (BMA) represents the major target of ventral mPFC in amygdala in mice. Moreover, BMA neurons differentiate safe and aversive environments, and BMA activation decreases fear-related freezing and high-anxiety states. Lastly, we show that the ventral mPFC-BMA projection implements top-down control of anxiety state and learned freezing, both at baseline and in stress-induced anxiety, defining a broadly relevant new top-down behavioural regulation pathway.

Aquatic ecosystems are threatened by eutrophication from nutrient pollution. In lakes, eutrophication causes a plethora of deleterious effects, such as harmful algal blooms, fish kills and increased methane emissions. However, lake-specific responses to nutrient changes are highly variable, complicating eutrophication management. These lake-specific responses could result from short-term stochastic drivers overshadowing lake-independent, long-term relationships between phytoplankton and nutrients. Here, we show that strong stoichiometric long-term relationships exist between nutrients and chlorophyll a (Chla) for 5-year simple moving averages (SMA, median R² = 0.87) along a gradient of total nitrogen to total phosphorus (TN:TP) ratios. These stoichiometric relationships are consistent across 159 shallow lakes (defined as average depth < 6 m) from a cross-continental, open-access database. We calculate 5-year SMA residuals to assess short-term variability and find substantial short-term Chla variation which is weakly related to nutrient concentrations (median R² = 0.12). With shallow lakes representing 89% of the world's lakes, the identified stoichiometric long-term relationships can globally improve quantitative nutrient management in both lakes and their catchments through a nutrient-ratio-based strategy. Nutrient limitation is a well-known control of phytoplankton growth, but predicting specific responses in individual lakes based on nutrient data alone has proven challenging. Here, the authors show that long-term signals of chlorophyll-a dynamics in shallow lakes can be captured based on stoichiometric effects of N and P concentrations along a continuum of total N:total P ratios. [ABSTRACT FROM AUTHOR]