Availability of Hand Sanitizers in Food Establishments of New York City: An Observational Study.

Hand hygiene is a crucial tool to limit the transmission of common respiratory and gastrointestinal infections. While hand sanitizers were ubiquitous early in the COVID-19 pandemic, the number of food establishments that have adequately maintained them remains unknown. Through systematic observations in 89 New York City food establishments, we found that hand sanitizer dispensers were present in only 40% of the stores, and only 23% had functional ones. This scarcity highlights the necessity of providing ongoing support to small business owners nationwide to promote and maintain primary prevention measures at all times, extending beyond periods of public health crises.

Dynamic attention signalling in V4: Relation to fast-spiking/non-fast-spiking cell class and population coupling.

The computational role of a neuron during attention depends on its firing properties, neurotransmitter expression and functional connectivity. Neurons in the visual cortical area V4 are reliably engaged by selective attention but exhibit diversity in the effect of attention on firing rates and correlated variability. It remains unclear what specific neuronal properties shape these attention effects. In this study, we quantitatively characterised the distribution of attention modulation of firing rates across populations of V4 neurons. Neurons exhibited a continuum of time-varying attention effects. At one end of the continuum, neurons' spontaneous firing rates were slightly depressed with attention (compared to when unattended), whereas their stimulus responses were enhanced with attention. The other end of the continuum showed the converse pattern: attention depressed stimulus responses but increased spontaneous activity. We tested whether the particular pattern of time-varying attention effects that a neuron exhibited was related to the shape of their actions potentials (so-called 'fast-spiking' [FS] neurons have been linked to inhibition) and the strength of their coupling to the overall population. We found an interdependence among neural attention effects, neuron type and population coupling. In particular, we found neurons for which attention enhanced spontaneous activity but suppressed stimulus responses were less likely to be fast-spiking (more likely to be non-fast-spiking) and tended to have stronger population coupling, compared to neurons with other types of attention effects. These results add important information to our understanding of visual attention circuits at the cellular level.

Altered task demands lead to a division of labour for sensory and cognitive processing in the middle temporal area.

Neurons in the primate middle temporal (MT) area signal information about visual motion and work together with the lateral prefrontal cortex (LPFC) to support memory-guided comparisons of visual motion direction. These areas are reciprocally connected, and both contain neurons that signal visual motion direction in the strength of their responses. Previously, LPFC was shown to display marked changes in stimulus coding with altered task demands, including changes in selectivity for motion direction, trial-to-trial variability in responses and comparison effects. Since MT and LPFC are directly interconnected, we sought to determine if MT neurons display similar dependence on task demands. We found that active participation in a motion direction comparison task affected both sensory and nonsensory activity in MT neurons. In fact, neurons that became less selective for motion direction during the active task showed increased signalling for cognitive aspects of the task. This heterogeneity in neural modification with heightened task demands suggests a division of labour in MT, whereby sensory and cognitive signals are both heightened in different subpopulations of neurons.

The eyes reflect an internal cognitive state hidden in the population activity of cortical neurons.

Decades of research have shown that global brain states such as arousal can be indexed by measuring the properties of the eyes. The spiking responses of neurons throughout the brain have been associated with the pupil, small fixational saccades, and vigor in eye movements, but it has been difficult to isolate how internal states affect the eyes, and vice versa. While recording from populations of neurons in the visual and prefrontal cortex (PFC), we recently identified a latent dimension of neural activity called "slow drift," which appears to reflect a shift in a global brain state. Here, we asked if slow drift is correlated with the action of the eyes in distinct behavioral tasks. We recorded from visual cortex (V4) while monkeys performed a change detection task, and PFC, while they performed a memory-guided saccade task. In both tasks, slow drift was associated with the size of the pupil and the microsaccade rate, two external indicators of the internal state of the animal. These results show that metrics related to the action of the eyes are associated with a dominant and task-independent mode of neural activity that can be accessed in the population activity of neurons across the cortex.

A Stable Population Code for Attention in Prefrontal Cortex Leads a Dynamic Attention Code in Visual Cortex.

Attention often requires maintaining a stable mental state over time while simultaneously improving perceptual sensitivity. These requirements place conflicting demands on neural populations, as sensitivity implies a robust response to perturbation by incoming stimuli, which is antithetical to stability. Functional specialization of cortical areas provides one potential mechanism to resolve this conflict. We reasoned that attention signals in executive control areas might be highly stable over time, reflecting maintenance of the cognitive state, thereby freeing up sensory areas to be more sensitive to sensory input (i.e., unstable), which would be reflected by more dynamic attention signals in those areas. To test these predictions, we simultaneously recorded neural populations in prefrontal cortex (PFC) and visual cortical area V4 in rhesus macaque monkeys performing an endogenous spatial selective attention task. Using a decoding approach, we found that the neural code for attention states in PFC was substantially more stable over time compared with the attention code in V4 on a moment-by-moment basis, in line with our guiding thesis. Moreover, attention signals in PFC predicted the future attention state of V4 better than vice versa, consistent with a top-down role for PFC in attention. These results suggest a functional specialization of attention mechanisms across cortical areas with a division of labor. PFC signals the cognitive state and maintains this state stably over time, whereas V4 responds to sensory input in a manner dynamically modulated by that cognitive state.SIGNIFICANCE STATEMENT Attention requires maintaining a stable mental state while simultaneously improving perceptual sensitivity. We hypothesized that these two demands (stability and sensitivity) are distributed between prefrontal and visual cortical areas, respectively. Specifically, we predicted attention signals in visual cortex would be less stable than in prefrontal cortex, and furthermore prefrontal cortical signals would predict attention signals in visual cortex in line with the hypothesized role of prefrontal cortex in top-down executive control. Our results are consistent with suggestions deriving from previous work using separate recordings in the two brain areas in different animals performing different tasks and represent the first direct evidence in support of this hypothesis with simultaneous multiarea recordings within individual animals.

Distinct Sources of Variability Affect Eye Movement Preparation.

The sequence of events leading to an eye movement to a target begins the moment visual information has reached the brain, well in advance of the eye movement itself. The process by which visual information is encoded and used to generate a motor plan has been the focus of substantial interest partly because of the rapid and reproducible nature of saccadic eye movements, and the key role that they play in primate behavior. Signals related to eye movements are present in much of the primate brain, yet most neurophysiological studies of the transition from vision to eye movements have measured the activity of one neuron at a time. Less is known about how the coordinated action of populations of neurons contribute to the initiation of eye movements. One cortical area of particular interest in this process is the frontal eye fields, a region of prefrontal cortex that has descending projections to oculomotor control centers. We recorded from populations of frontal eye field neurons in macaque monkeys engaged in a memory-guided saccade task. We found a variety of neurons with visually evoked responses, saccade-aligned responses, and mixtures of both. We took advantage of the simultaneous nature of the recordings to measure variability in individual neurons and pairs of neurons from trial-to-trial, as well as the moment-to-moment population activity structure. We found that these measures were related to saccadic reaction times, suggesting that the population-level organization of frontal eye field activity is important for the transition from perception to movement.SIGNIFICANCE STATEMENT The transition from perception to action involves coordination among neurons across the brain. In the case of eye movements, visual and motor signals coexist in individual neurons as well as in neighboring neurons. We used a task designed to compartmentalize the visual and motor aspects of this transition and studied populations of neurons in the frontal eye fields, a key cortical area containing neurons that are implicated in the transition from vision to eye movements. We found that the time required for subjects to produce an eye movement could be predicted from the statistics of the neuronal response of populations of frontal eye field neurons, suggesting that these neurons coordinate their activity to optimize the transition from perception to action.

What does scalp electroencephalogram coherence tell us about long-range cortical networks?

Long-range interactions between cortical areas are undoubtedly a key to the computational power of the brain. For healthy human subjects, the premier method for measuring brain activity on fast timescales is electroencephalography (EEG), and coherence between EEG signals is often used to assay functional connectivity between different brain regions. However, the nature of the underlying brain activity that is reflected in EEG coherence is currently the realm of speculation, because seldom have EEG signals been recorded simultaneously with intracranial recordings near cell bodies in multiple brain areas. Here, we take the early steps towards narrowing this gap in our understanding of EEG coherence by measuring local field potentials with microelectrode arrays in two brain areas (extrastriate visual area V4 and dorsolateral prefrontal cortex) simultaneously with EEG at the nearby scalp in rhesus macaque monkeys. Although we found inter-area coherence at both scales of measurement, we did not find that scalp-level coherence was reliably related to coherence between brain areas measured intracranially on a trial-to-trial basis, despite that scalp-level EEG was related to other important features of neural oscillations, such as trial-to-trial variability in overall amplitudes. This suggests that caution must be exercised when interpreting EEG coherence effects, and new theories devised about what aspects of neural activity long-range coherence in the EEG reflects.

Early steps toward understanding neuronal communication.

PURPOSE OF REVIEW: The computational power of the brain arises from the complex interactions between neurons. One straightforward method to quantify the strength of neuronal interactions is by measuring correlation and coherence. Efforts to measure correlation have been advancing rapidly of late, spurred by the development of advanced recording technologies enabling recording from many neurons and brain areas simultaneously. This review highlights recent results that provide clues into the principles of neural coordination, connections to cognitive and neurological phenomena, and key directions for future research. RECENT FINDINGS: The correlation structure of neural activity in the brain has important consequences for the encoding properties of neural populations. Recent studies have shown that this correlation structure is not fixed, but adapts in a variety of contexts in ways that appear beneficial to task performance. By studying these changes in biological neural networks and computational models, researchers have improved our understanding of the principles guiding neural communication. SUMMARY: Correlation and coherence are highly informative metrics for studying coding and communication in the brain. Recent findings have emphasized how the brain modifies correlation structure dynamically in order to improve information-processing in a goal-directed fashion. One key direction for future research concerns how to leverage these dynamic changes for therapeutic purposes.

Dynamics of excitatory and inhibitory networks are differentially altered by selective attention.

Inhibition and excitation form two fundamental modes of neuronal interaction, yet we understand relatively little about their distinct roles in service of perceptual and cognitive processes. We developed a multidimensional waveform analysis to identify fast-spiking (putative inhibitory) and regular-spiking (putative excitatory) neurons in vivo and used this method to analyze how attention affects these two cell classes in visual area V4 of the extrastriate cortex of rhesus macaques. We found that putative inhibitory neurons had both greater increases in firing rate and decreases in correlated variability with attention compared with putative excitatory neurons. Moreover, the time course of attention effects for putative inhibitory neurons more closely tracked the temporal statistics of target probability in our task. Finally, the session-to-session variability in a behavioral measure of attention covaried with the magnitude of this effect. Together, these results suggest that selective targeting of inhibitory neurons and networks is a critical mechanism for attentional modulation.

Establishing a Statistical Link between Network Oscillations and Neural Synchrony.

Pairs of active neurons frequently fire action potentials or "spikes" nearly synchronously (i.e., within 5 ms of each other). This spike synchrony may occur by chance, based solely on the neurons' fluctuating firing patterns, or it may occur too frequently to be explicable by chance alone. When spike synchrony above chances levels is present, it may subserve computation for a specific cognitive process, or it could be an irrelevant byproduct of such computation. Either way, spike synchrony is a feature of neural data that should be explained. A point process regression framework has been developed previously for this purpose, using generalized linear models (GLMs). In this framework, the observed number of synchronous spikes is compared to the number predicted by chance under varying assumptions about the factors that affect each of the individual neuron's firing-rate functions. An important possible source of spike synchrony is network-wide oscillations, which may provide an essential mechanism of network information flow. To establish the statistical link between spike synchrony and network-wide oscillations, we have integrated oscillatory field potentials into our point process regression framework. We first extended a previously-published model of spike-field association and showed that we could recover phase relationships between oscillatory field potentials and firing rates. We then used this new framework to demonstrate the statistical relationship between oscillatory field potentials and spike synchrony in: 1) simulated neurons, 2) in vitro recordings of hippocampal CA1 pyramidal cells, and 3) in vivo recordings of neocortical V4 neurons. Our results provide a rigorous method for establishing a statistical link between network oscillations and neural synchrony.

Correlations in V1 are reduced by stimulation outside the receptive field.

The trial-to-trial response variability of nearby cortical neurons is correlated. These correlations may strongly influence population coding performance. Numerous studies have shown that correlations can be dynamically modified by attention, adaptation, learning, and potent stimulus drive. However, the mechanisms that influence correlation strength remain poorly understood. Here we test whether correlations are influenced by presenting stimuli outside the classical receptive field (RF) of visual neurons, where they recruit a normalization signal termed surround suppression. We recorded simultaneously the activity of dozens of cells using microelectrode arrays implanted in the superficial layers of V1 in anesthetized, paralyzed macaque monkeys. We presented annular stimuli that encircled--but did not impinge upon--the RFs of the recorded cells. We found that these "extra-classical" stimuli reduced correlations in the absence of stimulation of the RF, closely resembling the decorrelating effects of stimulating the RFs directly. Our results suggest that normalization signals may be an important mechanism for modulating correlations.

Stimulus-dependent spiking relationships with the EEG.

The development and refinement of noninvasive techniques for imaging neural activity is of paramount importance for human neuroscience. Currently, the most accessible and popular technique is electroencephalography (EEG). However, nearly all of what we know about the neural events that underlie EEG signals is based on inference, because of the dearth of studies that have simultaneously paired EEG recordings with direct recordings of single neurons. From the perspective of electrophysiologists there is growing interest in understanding how spiking activity coordinates with large-scale cortical networks. Evidence from recordings at both scales highlights that sensory neurons operate in very distinct states during spontaneous and visually evoked activity, which appear to form extremes in a continuum of coordination in neural networks. We hypothesized that individual neurons have idiosyncratic relationships to large-scale network activity indexed by EEG signals, owing to the neurons' distinct computational roles within the local circuitry. We tested this by recording neuronal populations in visual area V4 of rhesus macaques while we simultaneously recorded EEG. We found substantial heterogeneity in the timing and strength of spike-EEG relationships and that these relationships became more diverse during visual stimulation compared with the spontaneous state. The visual stimulus apparently shifts V4 neurons from a state in which they are relatively uniformly embedded in large-scale network activity to a state in which their distinct roles within the local population are more prominent, suggesting that the specific way in which individual neurons relate to EEG signals may hold clues regarding their computational roles.

Neural Dynamics Underlying False Alarms in Extrastriate Cortex.

The unfolding of neural population activity can be approximated as a dynamical system. Stability in the latent dynamics that characterize neural population activity has been linked with consistency in animal behavior, such as motor control or value-based decision-making. However, whether similar dynamics characterize perceptual activity and decision-making in the visual cortex is not well understood. To test this, we recorded V4 populations in monkeys engaged in a non-match-to-sample visual change-detection task that required sustained engagement. We measured how the stability in the latent dynamics in V4 might affect monkeys' perceptual behavior. Specifically, we reasoned that unstable sensory neural activity around dynamic attractor boundaries may make animals susceptible to taking incorrect actions when withholding action would have been correct ("false alarms"). We made three key discoveries: 1) greater stability was associated with longer trial sequences; 2) false alarm rate decreased (and reaction times slowed) when neural dynamics were more stable; and, 3) low stability predicted false alarms on a single-trial level, and this relationship depended on the elapsed time during the trial, consistent with the latent neural state approaching an attractor boundary. Our results suggest the same outward false alarm behavior can be attributed to two different potential strategies that can be disambiguated by examining neural stability: 1) premeditated false alarms that might lead to greater stability in population dynamics and faster reaction time and 2) false alarms due to unstable sensory activity consistent with misperception.

Automated customization of large-scale spiking network models to neuronal population activity.

Understanding brain function is facilitated by constructing computational models that accurately reproduce aspects of brain activity. Networks of spiking neurons capture the underlying biophysics of neuronal circuits, yet their activity's dependence on model parameters is notoriously complex. As a result, heuristic methods have been used to configure spiking network models, which can lead to an inability to discover activity regimes complex enough to match large-scale neuronal recordings. Here we propose an automatic procedure, Spiking Network Optimization using Population Statistics (SNOPS), to customize spiking network models that reproduce the population-wide covariability of large-scale neuronal recordings. We first confirmed that SNOPS accurately recovers simulated neural activity statistics. Then, we applied SNOPS to recordings in macaque visual and prefrontal cortices and discovered previously unknown limitations of spiking network models. Taken together, SNOPS can guide the development of network models, thereby enabling deeper insight into how networks of neurons give rise to brain function.

Oscillatory alpha-band suppression mechanisms during the rapid attentional shifts required to perform an anti-saccade task.

Neuroimaging has demonstrated anatomical overlap between covert and overt attention systems, although behavioral and electrophysiological studies have suggested that the two systems do not rely on entirely identical circuits or mechanisms. In a parallel line of research, topographically-specific modulations of alpha-band power (~8-14 Hz) have been consistently correlated with anticipatory states during tasks requiring covert attention shifts. These tasks, however, typically employ cue-target-interval paradigms where attentional processes are examined across relatively protracted periods of time and not at the rapid timescales implicated during overt attention tasks. The anti-saccade task, where one must first covertly attend for a peripheral target, before executing a rapid overt attention shift (i.e. a saccade) to the opposite side of space, is particularly well-suited for examining the rapid dynamics of overt attentional deployments. Here, we asked whether alpha-band oscillatory mechanisms would also be associated with these very rapid overt shifts, potentially representing a common neural mechanism across overt and covert attention systems. High-density electroencephalography in conjunction with infra-red eye-tracking was recorded while participants engaged in both pro- and anti-saccade task blocks. Alpha power, time-locked to saccade onset, showed three distinct phases of significantly lateralized topographic shifts, all occurring within a period of less than 1s, closely reflecting the temporal dynamics of anti-saccade performance. Only two such phases were observed during the pro-saccade task. These data point to substantially more rapid temporal dynamics of alpha-band suppressive mechanisms than previously established, and implicate oscillatory alpha-band activity as a common mechanism across both overt and covert attentional deployments.

Automated customization of large-scale spiking network models to neuronal population activity.

Understanding brain function is facilitated by constructing computational models that accurately reproduce aspects of brain activity. Networks of spiking neurons capture the underlying biophysics of neuronal circuits, yet the dependence of their activity on model parameters is notoriously complex. As a result, heuristic methods have been used to configure spiking network models, which can lead to an inability to discover activity regimes complex enough to match large-scale neuronal recordings. Here we propose an automatic procedure, Spiking Network Optimization using Population Statistics (SNOPS), to customize spiking network models that reproduce the population-wide covariability of large-scale neuronal recordings. We first confirmed that SNOPS accurately recovers simulated neural activity statistics. Then, we applied SNOPS to recordings in macaque visual and prefrontal cortices and discovered previously unknown limitations of spiking network models. Taken together, SNOPS can guide the development of network models and thereby enable deeper insight into how networks of neurons give rise to brain function.

Oscillatory alpha-band mechanisms and the deployment of spatial attention to anticipated auditory and visual target locations: supramodal or sensory-specific control mechanisms?

Oscillatory alpha-band activity (8-15 Hz) over parieto-occipital cortex in humans plays an important role in suppression of processing for inputs at to-be-ignored regions of space, with increased alpha-band power observed over cortex contralateral to locations expected to contain distractors. It is unclear whether similar processes operate during deployment of spatial attention in other sensory modalities. Evidence from lesion patients suggests that parietal regions house supramodal representations of space. The parietal lobes are prominent generators of alpha oscillations, raising the possibility that alpha is a neural signature of supramodal spatial attention. Furthermore, when spatial attention is deployed within vision, processing of task-irrelevant auditory inputs at attended locations is also enhanced, pointing to automatic links between spatial deployments across senses. Here, we asked whether lateralized alpha-band activity is also evident in a purely auditory spatial-cueing task and whether it had the same underlying generator configuration as in a purely visuospatial task. If common to both sensory systems, this would provide strong support for "supramodal" attention theory. Alternately, alpha-band differences between auditory and visual tasks would support a sensory-specific account. Lateralized shifts in alpha-band activity were indeed observed during a purely auditory spatial task. Crucially, there were clear differences in scalp topographies of this alpha activity depending on the sensory system within which spatial attention was deployed. Findings suggest that parietally generated alpha-band mechanisms are central to attentional deployments across modalities but that they are invoked in a sensory-specific manner. The data support an "interactivity account," whereby a supramodal system interacts with sensory-specific control systems during deployment of spatial attention.

Ready, set, reset: stimulus-locked periodicity in behavioral performance demonstrates the consequences of cross-sensory phase reset.

The simultaneous presentation of a stimulus in one sensory modality often enhances target detection in another sensory modality, but the neural mechanisms that govern these effects are still under investigation. Here, we test a hypothesis proposed in the neurophysiological literature: that auditory facilitation of visual-target detection operates through cross-sensory phase reset of ongoing neural oscillations (Lakatos et al., 2009). To date, measurement limitations have prevented this potentially powerful neural mechanism from being directly linked with its predicted behavioral consequences. The present experiment uses a psychophysical approach in humans to demonstrate, for the first time, stimulus-locked periodicity in visual-target detection, following a temporally informative sound. Our data further demonstrate that periodicity in behavioral performance is strongly influenced by the probability of audiovisual co-occurrence. We argue that fluctuations in visual-target detection result from cross-sensory phase reset, both at the moment it occurs and persisting for seconds thereafter. The precise frequency at which this periodicity operates remains to be determined through a method that allows for a higher sampling rate.

Early electrophysiological indices of illusory contour processing within the lateral occipital complex are virtually impervious to manipulations of illusion strength.

The visual system can automatically interpolate or "fill-in" the boundaries of objects when inputs are fragmented or incomplete. A canonical class of visual stimuli known as illusory-contour (IC) stimuli has been extensively used to study this contour interpolation process. Visual evoked potential (VEP) studies have identified a neural signature of these boundary completion processes, the so-called IC-effect, which typically onsets at 90-110 ms and is generated within the lateral occipital complex (LOC). Here we set out to determine the delimiting factors of automatic boundary completion with the use of illusory contour stimuli and high-density scalp recordings of brain activity. Retinal eccentricity, ratio of real to illusory contours (i.e. support ratio), and inducer diameter were each varied parametrically, and any resulting effects on the amplitude and latency of the IC-effect were examined. Somewhat surprisingly, the amplitude of the IC-effect was found to be impervious to all changes in these stimulus parameters, manipulations that are known to impact perceived illusion strength. Thus, this automatic stage of object processing appears to be a binary process in which, so-long as minimal conditions are met, contours are automatically completed. At the same time, the latency of the IC-effect was found to vary inversely with support ratio, likely reflecting the additional time necessary to interpolate across the relatively longer induced boundaries of the implied object. These data are interpreted in the context of a two stage object-recognition model that parses processing into an early automatic perceptual stage that is followed by a more effortful conceptual processing stage.