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This book brings together chapters from investigators on the leading edge on this new research area to explore on the leading edge on this new research area to explore common theoretical issues, empirical findings, technical problems, and outstanding questions. This book will serve as a blueprint for work on the interface of vision, language, and action over the next five to ten years.

The aim of the present study is to examine the developmental trajectory of 3D-MOT performance in young soccer players, and to investigate the age-related 3D-MOT performance training effect. First, Experiment 1 assessed 3D-MOT performance of 404 male soccer players aged 12-18 years. Then, 127 athletes in Experiment 1 aged 13, 15, and 17 were selected as participants in Experiment 2, randomly assigned to intervention group and the control group. The intervention group but not the control group received 15 sessions of 3D-MOT training (3 sessions per day for 20 minutes for 5 days). Subsequently, post-test of 3D-MOT performance was conducted on the two groups. Experiment 1 showed that there was a significant main effect of age on the peak speed (p < .001,[eta]² = .041) of 3D-MOT performance, with the performance of participants aged 15 significantly higher than that of those aged 13 (p = .018, d = -0.559). Experiment 2 revealed a significant main effect of the intervention, including peak speed (p < .001), average speed(p < .001),and speed threshold(p < .001), but the interaction between the intervention and the age group was not significant. This research provide evidence that the 3D-MOT performance of young soccer players exhibits an increase between 13 and 15. Moreover, the results showed a significant training improvement in youth soccer players, but there was no significant relationship between the age-related development of 3D-MOT and the training effect in young soccer players. The above results provide new insights into soccer science and practice. Given the lack of data for those players younger than 12, this restricts the inference of whether there are potential sensitive periods. The study suggest that in the future, more evidence on athletes under the age of 12 be added to enhance the understanding of the full developmental trajectory of 3D-MOT performance in soccer players.

Animals readily execute learned behaviors in a consistent manner over long periods of time, and yet no equally stable neural correlate has been demonstrated. How does the cortex achieve this stable control? Using the sensorimotor system as a model of cortical processing, we investigated the hypothesis that the dynamics of neural latent activity, which captures the dominant co-variation patterns within the neural population, must be preserved across time. We recorded from populations of neurons in premotor, primary motor and somatosensory cortices as monkeys performed a reaching task, for up to 2 years. Intriguingly, despite a steady turnover in the recorded neurons, the low-dimensional latent dynamics remained stable. The stability allowed reliable decoding of behavioral features for the entire timespan, while fixed decoders based directly on the recorded neural activity degraded substantially. We posit that stable latent cortical dynamics within the manifold are the fundamental building blocks underlying consistent behavioral execution.Gallego, Perich et al. report that latent dynamics in the neural manifold across three cortical areas are stable throughout years of consistent behavior. The authors posit that these dynamics are fundamental building blocks of learned behavior.

Hintiryan, Foster et al . present an online mouse cortico-striatal projectome describing projections from the entire cortex to dorsal striatum. Computational neuroanatomic analysis of these projections identified 29 distinct striatal domains. This connectomics approach was applied to characterize circuit-specific cortico-striatal connectopathies in a mouse model of Huntington disease and in monoamine oxidase (MAO) A/B knockout mice. Different cortical areas are organized into distinct intracortical subnetworks. The manner in which descending pathways from the entire cortex interact subcortically as a network remains unclear. We developed an open-access comprehensive mesoscale mouse cortico-striatal projectome: a detailed connectivity projection map from the entire cerebral cortex to the dorsal striatum or caudoputamen (CP) in rodents. On the basis of these projections, we used new computational neuroanatomical tools to identify 29 distinct functional striatal domains. Furthermore, we characterized different cortico-striatal networks and how they reconfigure across the rostral–caudal extent of the CP. The workflow was also applied to select cortico-striatal connections in two different mouse models of disconnection syndromes to demonstrate its utility for characterizing circuitry-specific connectopathies. Together, our results provide the structural basis for studying the functional diversity of the dorsal striatum and disruptions of cortico-basal ganglia networks across a broad range of disorders.

How the brain computes accurate estimates of our self-motion relative to the world and our orientation relative to gravity in order to ensure accurate perception and motor control is a fundamental neuroscientific question. Recent experiments have revealed that the vestibular system encodes this information during everyday activities using pathway-specific neural representations. Furthermore, new findings have established that vestibular signals are selectively combined with extravestibular information at the earliest stages of central vestibular processing in a manner that depends on the current behavioural goal. These findings have important implications for our understanding of the brain mechanisms that ensure accurate perception and behaviour during everyday activities and for our understanding of disorders of vestibular processing.In addition to ensuring stable gaze and posture, the vestibular system contributes to the perception of self-motion and orientation. In this Review, Cullen provides a comprehensive overview of recent advances in our understanding of sensory encoding and integration in the vestibular pathways.

The striatum integrates excitatory inputs from the cortex and the thalamus to control diverse functions. Although the striatum is thought to consist of sensorimotor, associative and limbic domains, their precise demarcations and whether additional functional subdivisions exist remain unclear. How striatal inputs are differentially segregated into each domain is also poorly understood. This study presents a comprehensive map of the excitatory inputs to the mouse striatum. The input patterns reveal boundaries between the known striatal domains. The most posterior striatum likely represents the 4th functional subdivision, and the dorsomedial striatum integrates highly heterogeneous, multimodal inputs. The complete thalamo-cortico-striatal loop is also presented, which reveals that the thalamic subregions innervated by the basal ganglia preferentially interconnect with motor-related cortical areas. Optogenetic experiments show the subregion-specific heterogeneity in the synaptic properties of striatal inputs from both the cortex and the thalamus. This projectome will guide functional studies investigating diverse striatal functions. To fully understand how the brain works, we need to understand how different brain structures are organized and how information flows between these structures. For example, the cortex and thalamus communicate with another structure known as the basal ganglia, which is essential for controlling voluntary movement, emotions and reward behaviour in humans and other mammals. Information from the cortex and the thalamus enters the basal ganglia at an area called the striatum. This area is further divided into smaller functional regions known as domains that sort sensorimotor, emotion and executive information into the basal ganglia to control different types of behaviour. Three such domains have been identified in the striatum of mice. However, the boundaries between these domains are vague and it is not clear whether any other domains exist or if the domains can actually be divided into even smaller areas with more precise roles. Information entering the striatum from other parts of the brain can either stimulate activity in the striatum (known as an “excitatory input”) or alter existing excitatory inputs. Now, Hunnicutt et al. present the first comprehensive map of excitatory inputs into the striatum of mice. The experiments show that while many of the excitatory inputs flowing into the striatum from the cortex and thalamus are sorted into the three known domains, a unique combination of the excitatory inputs are sorted into a new domain instead. One of the original three domains of the striatum is known to relay information related to associative learning, for example, linking an emotion to a person or place. Hunnicutt et al. show that this domain has a more complex architecture than the other domains, being made up of many distinct areas. This complexity may help it to process the various types of information required to make such associations. The findings of Hunnicutt et al. provide a framework for understanding how the striatum works in healthy and diseased brains. Since faulty information processing in the striatum is a direct cause of Parkinson’s disease, Huntington’s disease and other neurological disorders in humans, this framework may aid the development of new treatments for these disorders.

Tactile sensory information from facial whiskers provides nocturnal tunnel-dwelling rodents, including mice and rats, with important spatial and textural information about their immediate surroundings. Whiskers are moved back and forth to scan the environment (whisking), and touch signals from each whisker evoke sparse patterns of neuronal activity in whisker-related primary somatosensory cortex (wS1; barrel cortex). Whisking is accompanied by desynchronized brain states and cell-type-specific changes in spontaneous and evoked neuronal activity. Tactile information, including object texture and location, appears to be computed in wS1 through integration of motor and sensory signals. wS1 also directly controls whisker movements and contributes to learned, whisker-dependent, goal-directed behaviours. The cell-type-specific neuronal circuitry in wS1 that contributes to whisker sensory perception is beginning to be defined.