Partial PdAu nanoparticle embedding into TiO support accentuates catalytic contributions from the Au/TiO interface.

Despite the broad catalytic relevance of metal-support interfaces, controlling their chemical nature, the interfacial contact perimeter (exposed to reactants), and consequently, their contributions to overall catalytic reactivity, remains challenging, as the nanoparticle and support characteristics are interdependent when catalysts are prepared by impregnation. Here, we decoupled both characteristics by using a raspberry-colloid-templating strategy that yields partially embedded PdAu nanoparticles within well-defined SiO or TiO supports, thereby increasing the metal-support interfacial contact compared to nonembedded catalysts that we prepared by attaching the same nanoparticles onto support surfaces. Between nonembedded PdAu/SiO and PdAu/TiO, we identified a support effect resulting in a 1.

Motor Cortex Latent Dynamics Encode Spatial and Temporal Arm Movement Parameters Independently.

The fluid movement of an arm requires multiple spatiotemporal parameters to be set independently. Recent studies have argued that arm movements are generated by the collective dynamics of neurons in motor cortex. An untested prediction of this hypothesis is that independent parameters of movement must map to independent components of the neural dynamics.

Expressivity of Neural Networks with Random Weights and Learned Biases.

Landmark universal function approximation results for neural networks with trained weights and biases provided impetus for the ubiquitous use of neural networks as learning models in Artificial Intelligence (AI) and neuroscience. Recent work has pushed the bounds of universal approximation by showing that arbitrary functions can similarly be learned by tuning smaller subsets of parameters, for example the output weights, within randomly initialized networks. Motivated by the fact that biases can be interpreted as biologically plausible mechanisms for adjusting unit outputs in neural networks, such as tonic inputs or activation thresholds, we investigate the expressivity of neural networks with random weights where only biases are optimized.

A Unified, Scalable Framework for Neural Population Decoding.

Our ability to use deep learning approaches to decipher neural activity would likely benefit from greater scale, in terms of both model size and datasets. However, the integration of many neural recordings into one unified model is challenging, as each recording contains the activity of different neurons from different individual animals. In this paper, we introduce a training framework and architecture designed to model the population dynamics of neural activity across diverse, large-scale neural recordings.

Preserved neural dynamics across animals performing similar behaviour.

Animals of the same species exhibit similar behaviours that are advantageously adapted to their body and environment. These behaviours are shaped at the species level by selection pressures over evolutionary timescales. Yet, it remains unclear how these common behavioural adaptations emerge from the idiosyncratic neural circuitry of each individual.

A spinal cord neuroprosthesis for locomotor deficits due to Parkinson's disease.

People with late-stage Parkinson's disease (PD) often suffer from debilitating locomotor deficits that are resistant to currently available therapies. To alleviate these deficits, we developed a neuroprosthesis operating in closed loop that targets the dorsal root entry zones innervating lumbosacral segments to reproduce the natural spatiotemporal activation of the lumbosacral spinal cord during walking. We first developed this neuroprosthesis in a non-human primate model that replicates locomotor deficits due to PD.

Nonlinear manifolds underlie neural population activity during behaviour.

There is rich variety in the activity of single neurons recorded during behaviour. Yet, these diverse single neuron responses can be well described by relatively few patterns of neural co-modulation. The study of such low-dimensional structure of neural population activity has provided important insights into how the brain generates behaviour.

motor learning creates structure in neural activity space that shapes adaptation.

Animals can quickly adapt learned movements in response to external perturbations. Motor adaptation is likely influenced by an animal's existing movement repertoire, but the nature of this influence is unclear. Long-term learning causes lasting changes in neural connectivity which determine the activity patterns that can be produced.

Motor cortex latent dynamics encode spatial and temporal arm movement parameters independently.

The fluid movement of an arm requires multiple spatiotemporal parameters to be set independently. Recent studies have argued that arm movements are generated by the collective dynamics of neurons in motor cortex. An untested prediction of this hypothesis is that independent parameters of movement must map to independent components of the neural dynamics.

Impact of Routines and Rituals on Burden of Treatment, Patient Training, Cognitive Load, and Anxiety in Self-Injected Biologic Therapy.

Background: Self-injection of biologics is a mainstay of chronic disease treatment, yet the process of self-injection often causes persistent apprehension and anxiety, distinct from needle phobia. While literature alludes to the role that routines and rituals play in self-injection, there is no comprehensive study on the routines and rituals self-injectors employ, nor of the process by which they are discovered and ingrained.

Methods: We conducted a mixed-method, observational pilot ethnography study of 27 patients with plaque psoriasis, psoriatic arthritis, or ankylosing spondylitis with and without prior biologic self-injection experience.

Small, correlated changes in synaptic connectivity may facilitate rapid motor learning.

Animals rapidly adapt their movements to external perturbations, a process paralleled by changes in neural activity in the motor cortex. Experimental studies suggest that these changes originate from altered inputs (H) rather than from changes in local connectivity (H), as neural covariance is largely preserved during adaptation. Since measuring synaptic changes in vivo remains very challenging, we used a modular recurrent neural network to qualitatively test this interpretation.

Within-subject biological variation estimates using an indirect data mining strategy. Spanish multicenter pilot study (BiVaBiDa).

Objectives: The estimates of biological variation (BV) have traditionally been determined using direct methods, which present limitations. In response to this issue, two papers have been published addressing these limitations by employing indirect methods. Here, we present a new procedure, based on indirect methods that analyses data collected within a multicenter pilot study.

Local field potentials reflect cortical population dynamics in a region-specific and frequency-dependent manner.

The spiking activity of populations of cortical neurons is well described by the dynamics of a small number of population-wide covariance patterns, whose activation we refer to as 'latent dynamics'. These latent dynamics are largely driven by the same correlated synaptic currents across the circuit that determine the generation of local field potentials (LFPs). Yet, the relationship between latent dynamics and LFPs remains largely unexplored.

Epidural electrical stimulation of the cervical dorsal roots restores voluntary upper limb control in paralyzed monkeys.

Regaining arm control is a top priority for people with paralysis. Unfortunately, the complexity of the neural mechanisms underlying arm control has limited the effectiveness of neurotechnology approaches. Here, we exploited the neural function of surviving spinal circuits to restore voluntary arm and hand control in three monkeys with spinal cord injury, using spinal cord stimulation.

Critical review and meta-analysis of biological variation estimates for tumor markers.

Objectives: Biological variation data (BV) can be used for different applications, but this depends on the availability of robust and relevant BV data. In this study, we aimed to summarize and appraise BV studies for tumor markers, to examine the influence of study population characteristics and concentrations on BV estimates and to discuss the applicability of BV data for tumor markers in clinical practice.

Methods: Studies reporting BV data for tumor markers related to gastrointestinal, prostate, breast, ovarian, haematological, lung, and dermatological cancers were identified by a systematic literature search.

Rapid adaptation of brain-computer interfaces to new neuronal ensembles or participants via generative modelling.

For brain-computer interfaces (BCIs), obtaining sufficient training data for algorithms that map neural signals onto actions can be difficult, expensive or even impossible. Here we report the development and use of a generative model-a model that synthesizes a virtually unlimited number of new data distributions from a learned data distribution-that learns mappings between hand kinematics and the associated neural spike trains. The generative spike-train synthesizer is trained on data from one recording session with a monkey performing a reaching task and can be rapidly adapted to new sessions or monkeys by using limited additional neural data.

Rethinking brain-wide interactions through multi-region 'network of networks' models.

The neural control of behavior is distributed across many functionally and anatomically distinct brain regions even in small nervous systems. While classical neuroscience models treated these regions as a set of hierarchically isolated nodes, the brain comprises a recurrently interconnected network in which each region is intimately modulated by many others. Uncovering these interactions is now possible through experimental techniques that access large neural populations from many brain regions simultaneously.

Machine Learning for Neural Decoding.

Despite rapid advances in machine learning tools, the majority of neural decoding approaches still use traditional methods. Modern machine learning tools, which are versatile and easy to use, have the potential to significantly improve decoding performance. This tutorial describes how to effectively apply these algorithms for typical decoding problems.

Critical appraisal and meta-analysis of biological variation studies on glycosylated albumin, glucose and HbA.

Objectives: Numerous biological variation (BV) studies have been performed over the years, but the quality of these studies vary. The objectives of this study were to perform a systematic review and critical appraisal of BV studies on glycosylated albumin and to deliver updated BV estimates for glucose and HbA, including recently published high-quality studies such as the European Biological Variation study (EuBIVAS).

Methods: Systematic literature searches were performed to identify BV studies.

Long-term stability of cortical population dynamics underlying consistent behavior.

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.

A versatile robotic platform for the design of natural, three-dimensional reaching and grasping tasks in monkeys.

Objective: Translational studies on motor control and neurological disorders require detailed monitoring of sensorimotor components of natural limb movements in relevant animal models. However, available experimental tools do not provide a sufficiently rich repertoire of behavioral signals. Here, we developed a robotic platform that enables the monitoring of kinematics, interaction forces, and neurophysiological signals during user-defined upper limb tasks for monkeys.

A Neural Population Mechanism for Rapid Learning.

Long-term learning of language, mathematics, and motor skills likely requires cortical plasticity, but behavior often requires much faster changes, sometimes even after single errors. Here, we propose one neural mechanism to rapidly develop new motor output without altering the functional connectivity within or between cortical areas. We tested cortico-cortical models relating the activity of hundreds of neurons in the premotor (PMd) and primary motor (M1) cortices throughout adaptation to reaching movement perturbations.

Cortical population activity within a preserved neural manifold underlies multiple motor behaviors.

Populations of cortical neurons flexibly perform different functions; for the primary motor cortex (M1) this means a rich repertoire of motor behaviors. We investigate the flexibility of M1 movement control by analyzing neural population activity during a variety of skilled wrist and reach-to-grasp tasks. We compare across tasks the neural modes that capture dominant neural covariance patterns during each task.

Linear-nonlinear-time-warp-poisson models of neural activity.

Prominent models of spike trains assume only one source of variability - stochastic (Poisson) spiking - when stimuli and behavior are fixed. However, spike trains may also reflect variability due to internal processes such as planning. For example, we can plan a movement at one point in time and execute it at some arbitrary later time.