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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., Competing Interests: Declaration of Interests The authors declare no competing interests.

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., (© 2024. The Author(s), under exclusive licence to Springer Nature America, Inc.)

Background: Reactive oxidative stress is a critical player in the amyloid beta (Aβ) toxicity that contributes to neurodegeneration in Alzheimer's disease (AD). Damaged mitochondria are one of the main sources of reactive oxygen species and accumulate in Aβ plaque-associated dystrophic neurites in the AD brain. Although Aβ causes neuronal mitochondria reactive oxidative stress in vitro, this has never been directly observed in vivo in the living mouse brain. Here, we tested for the first time whether Aβ plaques and soluble Aβ oligomers induce mitochondrial oxidative stress in surrounding neurons in vivo, and whether this neurotoxic effect can be abrogated using mitochondrial-targeted antioxidants., Methods: We expressed a genetically encoded fluorescent ratiometric mitochondria-targeted reporter of oxidative stress in mouse models of the disease and performed intravital multiphoton microscopy of neuronal mitochondria and Aβ plaques., Results: For the first time, we demonstrated by direct observation in the living mouse brain exacerbated mitochondrial oxidative stress in neurons after both Aβ plaque deposition and direct application of soluble oligomeric Aβ onto the brain, and determined the most likely pathological sequence of events leading to oxidative stress in vivo. Oxidative stress could be inhibited by both blocking calcium influx into mitochondria and treating with the mitochondria-targeted antioxidant SS31. Remarkably, the latter ameliorated plaque-associated dystrophic neurites without impacting Aβ plaque burden., Conclusions: Considering these results, combination of mitochondria-targeted compounds with other anti-amyloid beta or anti-tau therapies hold promise as neuroprotective drugs for the prevention and/or treatment of AD., (© 2024. The Author(s).)

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., (© 2023 Federation of European Neuroscience Societies and John Wiley & Sons Ltd.)

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., (© 2023 Federation of European Neuroscience Societies and John Wiley & Sons Ltd.)

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., (© The Author(s) 2021. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

Electroencephalography (EEG) has long been used to index brain states, from early studies describing activity in the presence and absence of visual stimulation to modern work employing complex perceptual tasks. These studies have shed light on brain-wide signals but often lack explanatory power at the single neuron level. Similarly, single neuron recordings can suffer from an inability to measure brain-wide signals accessible using EEG. Here, we combined these techniques while monkeys performed a change detection task and discovered a novel link between spontaneous EEG activity and a neural signal embedded in the spiking responses of neuronal populations. This "slow drift" was associated with fluctuations in the subjects' arousal levels over time: decreases in prestimulus α power were accompanied by increases in pupil size and decreases in microsaccade rate. These results show that brain-wide EEG signals can be used to index modes of activity present in single neuron recordings, that in turn reflect global changes in brain state that influence perception and behavior., (Copyright © 2022 Johnston et al.)

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., (Copyright © 2021 the authors.)

Two commonly used approaches to study interactions among neurons are spike count correlation, which describes pairs of neurons, and dimensionality reduction, applied to a population of neurons. Although both approaches have been used to study trial-to-trial neuronal variability correlated among neurons, they are often used in isolation and have not been directly related. We first established concrete mathematical and empirical relationships between pairwise correlation and metrics of population-wide covariability based on dimensionality reduction. Applying these insights to macaque V4 population recordings, we found that the previously reported decrease in mean pairwise correlation associated with attention stemmed from three distinct changes in population-wide covariability. Overall, our work builds the intuition and formalism to bridge between pairwise correlation and population-wide covariability and presents a cautionary tale about the inferences one can make about population activity by using a single statistic, whether it be mean pairwise correlation or dimensionality., Competing Interests: Declaration of interests The authors declare no competing interests., (Copyright © 2021 Elsevier Inc. All rights reserved.)

Climate change mitigation will require substantial investments in renewables. In addition, climate change will affect future renewable supply and hence, power sector investment requirements. We study the implications of climate impacts on renewables for power sector investments under deep decarbonization using a global integrated assessment model. We focus on Latin American and Caribbean, an under-studied region but of great interest due to its strong role in international climate mitigation and vulnerability to climate change. We find that accounting for climate impacts on renewables results in significant additional investments ($12-114 billion by 2100 across Latin American countries) for a region with weak financial infrastructure. We also demonstrate that accounting for climate impacts only on hydropower-a primary focus of previous studies-significantly underestimates cumulative investments, particularly in scenarios with high intermittent renewable deployment. Our study underscores the importance of comprehensive analyses of climate impacts on renewables for improved energy planning.

An animal's decision depends not only on incoming sensory evidence but also on its fluctuating internal state. This state embodies multiple cognitive factors, such as arousal and fatigue, but it is unclear how these factors influence the neural processes that encode sensory stimuli and form a decision. We discovered that, unprompted by task conditions, animals slowly shifted their likelihood of detecting stimulus changes over the timescale of tens of minutes. Neural population activity from visual area V4, as well as from prefrontal cortex, slowly drifted together with these behavioral fluctuations. We found that this slow drift, rather than altering the encoding of the sensory stimulus, acted as an impulsivity signal, overriding sensory evidence to dictate the final decision. Overall, this work uncovers an internal state embedded in population activity across multiple brain areas and sheds further light on how internal states contribute to the decision-making process., Competing Interests: Declaration of Interests The authors declare no competing interests., (Copyright © 2020 Elsevier Inc. All rights reserved.)

Mitochondria contribute to shape intraneuronal Ca 2+ signals. Excessive Ca 2+ taken up by mitochondria could lead to cell death. Amyloid beta (Aβ) causes cytosolic Ca 2+ overload, but the effects of Aβ on mitochondrial Ca 2+ levels in Alzheimer's disease (AD) remain unclear. Using a ratiometric Ca 2+ indicator targeted to neuronal mitochondria and intravital multiphoton microscopy, we find increased mitochondrial Ca 2+ levels associated with plaque deposition and neuronal death in a transgenic mouse model of cerebral β-amyloidosis. Naturally secreted soluble Aβ applied onto the healthy brain increases Ca 2+ concentration in mitochondria, which is prevented by blockage of the mitochondrial calcium uniporter. RNA-sequencing from post-mortem AD human brains shows downregulation in the expression of mitochondrial influx Ca 2+ transporter genes, but upregulation in the genes related to mitochondrial Ca 2+ efflux pathways, suggesting a counteracting effect to avoid Ca 2+ overload. We propose lowering neuronal mitochondrial Ca 2+ by inhibiting the mitochondrial Ca 2+ uniporter as a novel potential therapeutic target against AD.

Paravascular drainage of solutes, including β-amyloid (Aβ), appears to be an important process in brain health and diseases such as Alzheimer's disease (AD) and cerebral amyloid angiopathy (CAA). However, the major driving force for clearance remains largely unknown. Here we used in vivo two-photon microscopy in awake head-fixed mice to assess the role of spontaneous vasomotion in paravascular clearance. Vasomotion correlated with paravascular clearance of fluorescent dextran from the interstitial fluid. Increasing the amplitude of vasomotion by means of visually evoked vascular responses resulted in increased clearance rates in the visual cortex of awake mice. Evoked vascular reactivity was impaired in mice with CAA, which corresponded to slower clearance rates. Our findings suggest that low-frequency arteriolar oscillations drive drainage of solutes. Targeting naturally occurring vasomotion in patients with CAA or AD may be a promising early therapeutic option for prevention of Aβ accumulation in the brain., Competing Interests: Declaration of Interests The authors declare no competing interests., (Copyright © 2019 Elsevier Inc. All rights reserved.)

The detection of amyloid beta deposits and neurofibrillary tangles, both hallmarks of Alzheimer's disease (AD), is key to understanding the mechanisms underlying these pathologies. Luminescent conjugated oligothiophenes (LCOs) enable fluorescence imaging of these protein aggregates. Using LCOs and multiphoton microscopy, individual tangles and amyloid beta deposits were labeled in vivo and imaged longitudinally in a mouse model of tauopathy and cerebral amyloidosis, respectively. Importantly, LCO HS-84, whose emission falls in the green region of the spectrum, allowed for the first time longitudinal imaging of tangle dynamics following a single intravenous injection. In addition, LCO HS-169, whose emission falls in the red region of the spectrum, successfully labeled amyloid beta deposits, allowing multiplexing with other reporters whose emission falls in the green region of the spectrum. In conclusion, this method can provide a new approach for longitudinal in vivo imaging using multiphoton microscopy of AD pathologies as well as other neurodegenerative diseases associated with protein aggregation in mouse models.

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., (Copyright © 2019 the authors.)

Visual neurons respond more vigorously to an attended stimulus than an unattended one. How the brain prepares for response gain in anticipation of that stimulus is not well understood. One prominent proposal is that anticipation is characterized by gain-like modulations of spontaneous activity similar to gains in stimulus responses. Here we test an alternative idea: anticipation is characterized by a mixture of both increases and decreases of spontaneous firing rates. Such a strategy would be adaptive as it supports a simple linear scheme for disentangling internal, modulatory signals from external, sensory inputs. We recorded populations of V4 neurons in monkeys performing an attention task, and found that attention states are signaled by different mixtures of neurons across the population in the presence or absence of a stimulus. Our findings support a move from a stimulation-invariant account of anticipation towards a richer view of attentional modulation in a diverse neuronal population.

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., (© 2018 Federation of European Neuroscience Societies and John Wiley & Sons Ltd.)

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.

The spinal locomotor central pattern generator (CPG) generates rhythmic activity with alternating flexion and extension phases. This rhythmic pattern is likely to result from inhibitory interactions between neural populations representing flexor and extensor half-centers. However, it is unclear whether the flexor-extensor CPG has a quasi-symmetric organization with both half-centers critically involved in rhythm generation, features an asymmetric organization with flexor-driven rhythmogenesis, or comprises a pair of intrinsically rhythmic half-centers. There are experimental data that support each of the above concepts but appear to be inconsistent with the others. In this theoretical/modeling study, we present and analyze a CPG model architecture that can operate in different regimes consistent with the above three concepts depending on conditions, which are defined by external excitatory drives to CPG half-centers. We show that control of frequency and phase durations within each regime depends on network dynamics, defined by the regime-dependent expression of the half-centers' intrinsic rhythmic capabilities and the operating phase transition mechanisms (escape vs. release). Our study suggests state dependency in locomotor CPG operation and proposes explanations for seemingly contradictory experimental data. NEW & NOTEWORTHY Our theoretical/modeling study focuses on the analysis of locomotor central pattern generators (CPGs) composed of conditionally bursting half-centers coupled with reciprocal inhibition and receiving independent external drives. We show that this CPG framework can operate in several regimes consistent with seemingly contradictory experimental data. In each regime, we study how intrinsic dynamics and phase-switching mechanisms control oscillation frequency and phase durations. Our results provide insights into the organization of spinal circuits controlling locomotion.

Many studies use population analysis approaches, such as dimensionality reduction, to characterize the activity of large groups of neurons. To date, these methods have treated each neuron equally, without taking into account whether neurons are excitatory or inhibitory. We studied population activity structure as a function of neuron type by applying factor analysis to spontaneous activity from spiking networks with balanced excitation and inhibition. Throughout the study, we characterized population activity structure by measuring its dimensionality and the percentage of overall activity variance that is shared among neurons. First, by sampling only excitatory or only inhibitory neurons, we found that the activity structures of these two populations in balanced networks are measurably different. We also found that the population activity structure is dependent on the ratio of excitatory to inhibitory neurons sampled. Finally we classified neurons from extracellular recordings in the primary visual cortex of anesthetized macaques as putative excitatory or inhibitory using waveform classification, and found similarities with the neuron type-specific population activity structure of a balanced network with excitatory clustering. These results imply that knowledge of neuron type is important, and allows for stronger statistical tests, when interpreting population activity structure.

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., (Copyright © 2016 the American Physiological Society.)

Rhythmic behaviors such as breathing, walking, and scratching are vital to many species. Such behaviors can emerge from groups of neurons, called central pattern generators, in the absence of rhythmic inputs. In vertebrates, the identification of the cells that constitute the central pattern generator for particular rhythmic behaviors is difficult, and often, its existence has only been inferred. For example, under experimental conditions, intact turtles generate several rhythmic scratch motor patterns corresponding to non-rhythmic stimulation of different body regions. These patterns feature alternating phases of motoneuron activation that occur repeatedly, with different patterns distinguished by the relative timing and duration of activity of hip extensor, hip flexor, and knee extensor motoneurons. While the central pattern generator network responsible for these outputs has not been located, there is hope to use motoneuron recordings to deduce its properties. To this end, this work presents a model of a previously proposed central pattern generator network and analyzes its capability to produce two distinct scratch rhythms from a single neuron pool, selected by different combinations of tonic drive parameters but with fixed strengths of connections within the network. We show through simulation that the proposed network can achieve the desired multi-functionality, even though it relies on hip unit generators to recruit appropriately timed knee extensor motoneuron activity, including a delay relative to hip activation in rostral scratch. Furthermore, we develop a phase space representation, focusing on the inputs to and the intrinsic slow variable of the knee extensor motoneuron, which we use to derive sufficient conditions for the network to realize each rhythm and which illustrates the role of a saddle-node bifurcation in achieving the knee extensor delay. This framework is harnessed to consider bistability and to make predictions about the responses of the scratch rhythms to input changes for future experimental testing.

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.

A recently described fluorescence biosensor platform utilizes single-chain Fv (scFvs) that selectively bind and activate fluorogen molecules. In this report we investigated the display of tandem scFv biosensors at the surface of mammalian cells with the aim of advancing current fluorescence detection strategies. We initially screened different peptide linkers to separate each scFv unit, and discovered that tandem proteins joined by either flexible or α-helical linkers properly fold and display at the surface of mammalian cells. Accordingly, we performed a combinatorial scFv-dimer study and identified that fluorescence activation correlated with the cellular location (membrane distal versus proximal) and selections of the different scFvs. Furthermore, in vitro measurements showed that the stability of each scFv monomer unit influenced the folding and cell surface activities of tandem scFvs. Additionally, we investigated the absence or poor signals from some scFv-dimer combinations and discovered that intramolecular and intermolecular scFv chain mispairings led to protein misfolding and/or secretory-pathway-mediated degradation. Furthermore, when tandem scFvs were utilized as fluorescence reporter tags with surface receptors, the biosensor unit and target protein showed independent activities. Thus, the live cell application of tandem scFvs permitted advanced detection of target proteins via fluorescence signal amplification, Förster resonance energy transfer resulting in the increase of Stokes shift and multi-color vesicular traffic of surface receptors., (© The Author 2015. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.)

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., (Copyright © 2015 the American Physiological Society.)

A central neuroscientific pursuit is understanding neuronal interactions that support computations underlying cognition and behavior. Although neurons interact across disparate scales, from cortical columns to whole-brain networks, research has been restricted to one scale at a time. We measured local interactions through multi-neuronal recordings while accessing global networks using scalp electroencephalography (EEG) in rhesus macaques. We measured spike count correlation, an index of functional connectivity with computational relevance, and EEG oscillations, which have been linked to various cognitive functions. We found a non-monotonic relationship between EEG oscillation amplitude and spike count correlation, contrary to the intuitive expectation of a direct relationship. With a widely used network model, we replicated these findings by incorporating a private signal targeting inhibitory neurons, a common mechanism proposed for gain modulation. Finally, we found that spike count correlation explained nonlinearities in the relationship between EEG oscillations and response time in a spatial selective attention task.

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., (Copyright © 2014 the authors 0270-6474/14/3411222-06$15.00/0.)

Current advancements in biological protein discovery utilize bi-partite methods of fluorescence detection where chromophore and scaffold are uncoupled. One such technology, called fluorogen-activating proteins (FAPs), consists of single-chain-variable-fragments (scFvs) selected against small organic molecules (fluorogens) that are non-fluorescent in solution, but highly fluorescent when bound to the scFv. In unusual circumstances a scFv may activate similar fluorogens from a single chemical family. In this report we identified a scFv biosensor with fluorescence activity against multiple fluorogens from two structurally dissimilar families. In-vitro analysis revealed highly selective scFv-ligand interactions at sub-micromolar ranges. Additionally, each scFv-fluorogen complex possesses unique excitation and emission spectra, which allows broader detection limits from the biosensor. Further analysis indicated that ligand activation, regardless of chemical family, occurs at a common scFv binding region that proves flexible, yet selective for fluorogen binding. As a protein reporter at the surface of mammalian cells, the scFv revealed bright signal detection and minimal background. Additionally, when tagged to a G-protein-coupled receptor, we observed agonist dependent signaling leading to protein traffic from cell surface to endosomes via multi-color fluorescence tracking. In summary, this report unveils a noncanonical scFv biosensor with properties of high ligand affinity and multi-channel fluorescence detection, which consequently offers expanded opportunities for cellular protein discovery.

Correlated variability in the spiking responses of pairs of neurons, also known as spike count correlation, is a key indicator of functional connectivity and a critical factor in population coding. Underscoring the importance of correlation as a measure for cognitive neuroscience research is the observation that spike count correlations are not fixed, but are rather modulated by perceptual and cognitive context. Yet while this context fluctuates from moment to moment, correlation must be calculated over multiple trials. This property undermines its utility as a dependent measure for investigations of cognitive processes which fluctuate on a trial-to-trial basis, such as selective attention. A measure of functional connectivity that can be assayed on a moment-to-moment basis is needed to investigate the single-trial dynamics of populations of spiking neurons. Here, we introduce the measure of population variance in normalized firing rate for this goal. We show using mathematical analysis, computer simulations and in vivo data how population variance in normalized firing rate is inversely related to the latent correlation in the population, and how this measure can be used to reliably classify trials from different typical correlation conditions, even when firing rate is held constant. We discuss the potential advantages for using population variance in normalized firing rate as a dependent measure for both basic and applied neuroscience research.

The Rockport Walk Test (RWT) is a 1-mile walk used to estimate the maximal volume of oxygen uptake (V(O2)max). The purpose of this study was to validate the RWT on a nonmotorized curved treadmill (CT). Twenty-three healthy adults (10 females; 19-44 years old) participated. One trial of the RWT was performed on a measured indoor track (RWTO) and another on the CT (RWTC) on different days in randomized order. Heart rate (HR) and completion time were used to calculate V(O2)max using 6 different general and gender specific equations from previous research. Subjects also performed a treadmill graded exercise test (GXT), which was used as the criterion measure for V(O2)max. Completion times and HR between the 2 RWT were compared using dependent t-tests. Estimated V(O2)max values were compared between the RWTC, RWTO, and GXT through repeated measures analysis of variance, Pearson's correlations (r), and Bland-Altman's plots. There was no difference between completion times for the RWTO and RWTC but HRs were significantly higher with RWTC. When the same equation was applied to the RWTO and RWTC, there were no similar results. All V(O2)max estimations were different from observed V(O2)max except for the estimation from the relative general Kline et al. equation on the RWTO. Despite high correlations (r = 0.75-0.91), the RWTC underestimated V(O2)max. The RWTC underestimates V(O2)max but may be beneficial if a new equation were created specifically for the CT. With appropriate equations for the CT, the RWTC would provide an alternate form of V(O2)max testing.

The thiazide-sensitive NaCl cotransporter (NCC) is the primary mediator of salt reabsorption in the distal convoluted tubule and is a key determinant of the blood pressure set point. Given its complex topology, NCC is inefficiently processed and prone to endoplasmic reticulum (ER)-associated degradation (ERAD), although the mechanisms governing this process remain obscure. Here, we identify factors that impact the ER quality control of NCC. Analyses of NCC immunoprecipitates revealed that the cotransporter formed complexes with the core chaperones Hsp90, Hsp70, and Hsp40. Disruption of Hsp90 function accelerated NCC degradation, suggesting that Hsp90 promotes NCC folding. In addition, two cochaperones, the C terminus of Hsp70-interacting protein (CHIP) and the Hsp70/Hsp90 organizer protein, were associated with NCC. Although CHIP, an E3 ubiquitin ligase, promoted NCC ubiquitination and ERAD, the Hsp70/Hsp90 organizer protein stabilized NCC turnover, indicating that these two proteins differentially remodel the core chaperone systems to favor cotransporter degradation and biogenesis, respectively. Adjusting the folding environment in mammalian cells via reduced temperature enhanced NCC biosynthetic trafficking, increased Hsp90-NCC interaction, and diminished binding to Hsp70. In contrast, cotransporters harboring disease-causing mutations that impair NCC biogenesis failed to escape ERAD as efficiently as the wild type protein when cells were incubated at a lower temperature. Instead, these mutants interacted more strongly with Hsp70, Hsp40, and CHIP, consistent with a role for the Hsp70/Hsp40 system in selecting misfolded NCC for ERAD. Collectively, these observations indicate that Hsp70 and Hsp90 comprise two functionally distinct ER quality control checkpoints that sequentially monitor NCC biogenesis.

Functional networks are comprised of neuronal ensembles bound through synchronization across multiple intrinsic oscillatory frequencies. Various coupled interactions between brain oscillators have been described (e.g., phase-amplitude coupling), but with little evidence that these interactions actually influence perceptual sensitivity. Here, electroencephalographic (EEG) recordings were made during a sustained-attention task to demonstrate that cross-frequency coupling has significant consequences for perceptual outcomes (i.e., whether participants detect a near-threshold visual target). The data reveal that phase-detection relationships at higher frequencies are dependent on the phase of lower frequencies, such that higher frequencies alternate between periods when their phase is either strongly or weakly predictive of visual-target detection. Moreover, the specific higher frequencies and scalp topographies linked to visual-target detection also alternate as a function of lower-frequency phase. Cross-frequency coupling between lower (i.e., delta and theta) and higher frequencies (e.g., low- and high-beta) thus results in dramatic fluctuations of visual-target detection., (Copyright © 2012 Elsevier Inc. All rights reserved.)

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., (Copyright © 2012 Elsevier Inc. All rights reserved.)

To reveal the fundamental processes underlying the different stages of visual object perception, most studies have manipulated relatively complex images, such as photographs, line drawings of natural objects, or perceptual illusions. Here, rather than starting from complex images and working backward to infer simpler processes, we investigated how the visual system parses and integrates information contained in stimuli of the most basic variety. Simple scatterings of a few points of light were manipulated in terms of their numerosity, spatial extent, and organization, and high-density electrophysiological recordings were made from healthy adults engaged in an unrelated task. We reasoned that this approach permitted an uncontaminated view of the spatio-temporal dynamics of the related neural processes. We were guided in our predictions by the "frame-and-fill" model for object perception, whereby fast inputs to the dorsal stream of the visual "where" system first frame the spatial extent of visual objects, which are subsequently "filled-in" by the slower activation of the ventral stream of the visual "what" system. Our findings were consistent with this view, showing a rapidly-onsetting effect of spatial extent in dorsal stream sources, and later-onsetting effects due to dot number and symmetry, which were deemed to be more closely tied to the details of object identity, from ventral stream sources. This collection of observations provides an important baseline from which to understand the spatio-temporal properties of basic visual object perception, and from which to test dysfunction of this system in clinical populations., (Copyright © 2012 IBRO. Published by Elsevier Ltd. All rights reserved.)

Sleep deprivation as a result of long working hours has been associated with an increased risk of adverse events in healthcare professions but not in cardiovascular perfusion. The purpose of this study is to investigate the impact of sleep deprivation on cardiovascular perfusion students. Testing with high-fidelity simulation after 24 hours of sleep deprivation allowed investigators to assess user competency and the effect of fatigue on performance. After informed consent, seven senior perfusion students were enrolled in the study (three declined to participate). The qualitative portion of the study included a focus group session, whereas the quantitative portion included administration of questionnaires, including the Epworth Sleepiness Scale (ESS) and the Stanford Sleepiness Scale (SSS), as well as clinical skills assessment using high-fidelity simulation. Subjects were assessed at three different intervals of sleep deprivation over a 24-hour period: baseline (6:00 AM), 12 hours (6:00 PM), 16 hours (10:00 PM), and 24 hours (6:00 AM) of wakefulness. During each scenario, normally monitored bypass parameters, including mean arterial pressure, activated clotting times, partial pressures of oxygen, partial pressures of carbon dioxide, and venous flow, were manipulated, and the subjects were required to return the parameters to normal levels. In addition, the scenario required calculation of the final protamine dose (using a dose-response curve) and detection of electrocardiography changes. Each task was varied at the different simulation sessions to decrease the effect of learning. Despite any lack of sleep, we hypothesized that, because of repetition, the times to complete the task would decrease at each session. We also hypothesized that the ESS and SSS scores would increase over time. We expected that the students would anticipate which tasks were being evaluated and would react more quickly. The average ESS scores progressively increased at each time period: baseline, 12 hours, 16 hours, and 24 hours. At 24 hours, the ESS and SSS scores were the greatest and the standard deviation was low, suggesting that fatigue affected all participants. During the clinical task evaluations, a "flattening effect" on the learning curve over time was observed. Tasks that required a higher level of cognition had prolonged completion times. Sleep deprivation significantly affects clinical performance as assessed with high-fidelity simulation. To optimize patient and clinician safety, it is important that the question of length of working time be investigated further.

Evidence indicates that when one feature of an object is specifically attended, other task-irrelevant features of that object also receive enhanced processing, presumably as a result of automatic binding processes. On the other hand, evidence also shows that attention can be selectively biased in favor of processing one feature at the expense of processing others. Thus, binding invokes combinatorial processing of related features whereas selective attention emphasizes differential processing. We hypothesized that binding and selective feature-based attention depend on a common resource and therefore might show interference effects. The current study tested this by manipulating binding demands while human participants directed their attention to the color or motion of moving random dot stimuli. Response time measures showed that effects of biased attention were reduced when binding demands were increased. This finding supports the thesis that binding relies, at least in part, upon the same mechanisms invoked by preparatory biasing of selective attention., (Copyright © 2011 Elsevier Srl. All rights reserved.)

Humans have limited cognitive resources to process the nearly limitless information available in the environment. Endogenous, or 'top-down', selective attention to basic visual features such as color or motion is a common strategy for biasing resources in favor of the most relevant information sources in a given context. Opposing this top-down separation of features is a 'bottom-up' tendency to integrate, or bind, the various features that constitute objects. We pitted these two processes against each other in an electrophysiological experiment to test if top-down selective attention can overcome constitutive binding processes. Our results demonstrate that bottom-up binding processes can dominate top-down feature-based attention even when explicitly detrimental to task performance., (© 2012 The Authors. European Journal of Neuroscience © 2012 Federation of European Neuroscience Societies and Blackwell Publishing Ltd.)

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., (Copyright © 2011 Elsevier Inc. All rights reserved.)

The present study investigated the feasibility of acquiring high-density event-related brain potential (ERP) recordings during treadmill walking in human subjects. The work builds upon recent studies testing the applicability of real-world tasks while obtaining electroencephalographic (EEG) recordings. Participants performed a response inhibition GO/NOGO task - designed to evoke a P3 component for correct response inhibitions and an error-related negativity (ERN) for incorrect commission errors - while speed of walking was experimentally manipulated. Robust P3 and ERN components were obtained under all experimental conditions - while participants were sitting, walking at moderate speed (2.4 km/hour), or walking rapidly (5 km/hour). Signal-to-noise ratios were remarkably similar across conditions, pointing to the feasibility of high-fidelity ERP recordings under relatively vigorous activity regimens. There is considerable research and clinical motivation to obtain high quality neurophysiological measures under more naturalistic environmental settings such as these. Strong links between cognitive load and gait abnormalities are seen in a number of clinical populations and these MoBI technologies provide highly promising methods for gaining insights into the underlying pathophysiology.