Attention-induced variance and noise correlation reduction in macaque V1 is mediated by NMDA receptors

Arousal and locomotion make distinct contributions to cortical activity patterns and visual encoding

Spontaneous and sensory-evoked cortical activity is highly state-dependent, yet relatively little is known about transitions between distinct waking states. Patterns of activity in mouse V1 differ dramatically between quiescence and locomotion, but this difference could be explained by either motor feedback or a change in arousal levels. We recorded single cells and local field potentials from area V1 in mice head-fixed on a running wheel and monitored pupil diameter to assay arousal. Using naturally occurring and induced state transitions, we dissociated arousal and locomotion effects in V1. Arousal suppressed spontaneous firing and strongly altered the temporal patterning of population activity. Moreover, heightened arousal increased the signal-to-noise ratio of visual responses and reduced noise correlations. In contrast, increased firing in anticipation of and during movement was attributable to locomotion effects. Our findings suggest complementary roles of arousal and locomotion in promoting functional flexibility in cortical circuits.

Visual attention mitigates information loss in small- and large-scale neural codes

The visual system transforms complex inputs into robust and parsimonious neural codes that efficiently guide behavior. Because neural communication is stochastic, the amount of encoded visual information necessarily decreases with each synapse. This constraint requires that sensory signals are processed in a manner that protects information about relevant stimuli from degradation. Such selective processing--or selective attention--is implemented via several mechanisms, including neural gain and changes in tuning properties. However, examining each of these effects in isolation obscures their joint impact on the fidelity of stimulus feature representations by large-scale population codes. Instead, large-scale activity patterns can be used to reconstruct representations of relevant and irrelevant stimuli, thereby providing a holistic understanding about how neuron-level modulations collectively impact stimulus encoding.

Attention and normalization circuits in macaque V1

Attention affects neuronal processing and improves behavioural performance. In extrastriate visual cortex these effects have been explained by normalization models, which assume that attention influences the circuit that mediates surround suppression. While normalization models have been able to explain attentional effects, their validity has rarely been tested against alternative models. Here we investigate how attention and surround/mask stimuli affect neuronal firing rates and orientation tuning in macaque V1. Surround/mask stimuli provide an estimate to what extent V1 neurons are affected by normalization, which was compared against effects of spatial top down attention. For some attention/surround effect comparisons, the strength of attentional modulation was correlated with the strength of surround modulation, suggesting that attention and surround/mask stimulation (i.e. normalization) might use a common mechanism. To explore this in detail, we fitted multiplicative and additive models of attention to our data. In one class of models, attention contributed to normalization mechanisms, whereas in a different class of models it did not. Model selection based on Akaike's and on Bayesian information criteria demonstrated that in most cells the effects of attention were best described by models where attention did not contribute to normalization mechanisms. This demonstrates that attentional influences on neuronal responses in primary visual cortex often bypass normalization mechanisms.

Combined contributions of feedforward and feedback inputs to bottom-up attention

In order to deal with a large amount of information carried by visual inputs entering the brain at any given point in time, the brain swiftly uses the same inputs to enhance processing in one part of visual field at the expense of the others. These processes, collectively called bottom-up attentional selection, are assumed to solely rely on feedforward processing of the external inputs, as it is implied by the nomenclature. Nevertheless, evidence from recent experimental and modeling studies points to the role of feedback in bottom-up attention. Here, we review behavioral and neural evidence that feedback inputs are important for the formation of signals that could guide attentional selection based on exogenous inputs. Moreover, we review results from a modeling study elucidating mechanisms underlying the emergence of these signals in successive layers of neural populations and how they depend on feedback from higher visual areas. We use these results to interpret and discuss more recent findings that can further unravel feedforward and feedback neural mechanisms underlying bottom-up attention. We argue that while it is descriptively useful to separate feedforward and feedback processes underlying bottom-up attention, these processes cannot be mechanistically separated into two successive stages as they occur at almost the same time and affect neural activity within the same brain areas using similar neural mechanisms. Therefore, understanding the interaction and integration of feedforward and feedback inputs is crucial for better understanding of bottom-up attention.

Global cognitive factors modulate correlated response variability between V4 neurons

Recent studies have shown that cognitive factors such as spatial and feature-based attention, learning, and task-switching can change the extent to which the trial-to-trial variability in the responses of neurons in sensory cortex is shared between pairs of neurons (for review, see Cohen and Kohn, 2011). Global cognitive factors related to concentration, motivation, effort, arousal, or alertness also affect performance on perceptual tasks and the responses of individual neurons in many cortical areas (Spitzer et al., 1988; Spitzer and Richmond, 1991; Motter, 1993; Bichot et al., 2001; Hasegawa et al., 2004; Boudreau et al., 2006; Niwa et al., 2012). The question of how global cognitive factors affect correlated response variability is important because these factors likely vary both across and within all psychophysical and physiological studies. Furthermore, global cognitive factors might provide a convenient platform for studying the neuronal mechanisms underlying how cognitive factors affect correlated variability because they can be manipulated easily without training complex perceptual tasks. We recorded simultaneously from groups of neurons in visual area V4 while rhesus monkeys performed a contrast discrimination task whose difficulty changed in blocks of trials. We found that correlated variability decreased when the task was more difficult, even when the visual stimuli were far outside the receptive fields of the recorded neurons. Our results suggest that studying global cognitive factors might provide a general framework for studying how cognitive factors affect the responses of neurons throughout sensory cortex.

NMDA Receptor Antagonist Ketamine Distorts Object Recognition by Reducing Feedback to Early Visual Cortex

It is a well-established fact that top-down processes influence neural representations in lower-level visual areas. Electrophysiological recordings in monkeys as well as theoretical models suggest that these top-down processes depend on NMDA receptor functioning. However, this underlying neural mechanism has not been tested in humans. We used fMRI multivoxel pattern analysis to compare the neural representations of ambiguous Mooney images before and after they were recognized with their unambiguous grayscale version. Additionally, we administered ketamine, an NMDA receptor antagonist, to interfere with this process. Our results demonstrate that after recognition, the pattern of brain activation elicited by a Mooney image is more similar to that of its easily recognizable grayscale version than to the pattern evoked by the identical Mooney image before recognition. Moreover, recognition of Mooney images decreased mean response; however, neural representations of separate images became more dissimilar. So from the neural perspective, unrecognizable Mooney images all "look the same", whereas recognized Mooneys look different. We observed these effects in posterior fusiform part of lateral occipital cortex and in early visual cortex. Ketamine distorted these effects of recognition, but in early visual cortex only. This suggests that top-down processes from higher- to lower-level visual areas might operate via an NMDA pathway.

Sensory gain outperforms efficient readout mechanisms in predicting attention-related improvements in behavior

Spatial attention has been postulated to facilitate perceptual processing via several different mechanisms. For instance, attention can amplify neural responses in sensory areas (sensory gain), mediate neural variability (noise modulation), or alter the manner in which sensory signals are selectively read out by postsensory decision mechanisms (efficient readout). Even in the context of simple behavioral tasks, it is unclear how well each of these mechanisms can account for the relationship between attention-modulated changes in behavior and neural activity because few studies have systematically mapped changes between stimulus intensity, attentional focus, neural activity, and behavioral performance. Here, we used a combination of psychophysics, event-related potentials (ERPs), and quantitative modeling to explicitly link attention-related changes in perceptual sensitivity with changes in the ERP amplitudes recorded from human observers. Spatial attention led to a multiplicative increase in the amplitude of an early sensory ERP component (the P1, peaking ∼80-130 ms poststimulus) and in the amplitude of the late positive deflection component (peaking ∼230-330 ms poststimulus). A simple model based on signal detection theory demonstrates that these multiplicative gain changes were sufficient to account for attention-related improvements in perceptual sensitivity, without a need to invoke noise modulation. Moreover, combining the observed multiplicative gain with a postsensory readout mechanism resulted in a significantly poorer description of the observed behavioral data. We conclude that, at least in the context of relatively simple visual discrimination tasks, spatial attention modulates perceptual sensitivity primarily by modulating the gain of neural responses during early sensory processing.

Explaining attention-related changes in behavior and electroencephalography data through computational modeling

In a recent article, Itthipuripat and colleagues combined psychophysics, neurophysiology, and mathematical modeling to investigate the neural mechanism underlying behavioral benefits of spatial attention (Itthipuripat S, Ester EF, Deering S, Serences JT. J Neurosci 34: 13384-13398, 2014). They found that attention-related effects on behavior as well as neural signals could be better explained by a response gain model than by a noise reduction model or an efficient read-out model. In this Neuro Forum we discuss these results and raise several interesting questions and potential interpretations.

A role beyond learning for NMDA receptors in reward-based decision-making-a pharmacological study using d-cycloserine

N-methyl-D-aspartate (NMDA) receptors are known to fulfill crucial functions in many forms of learning and plasticity. More recently, biophysical models, however, have suggested an additional role of NMDA receptors in evidence integration for decision-making, going beyond their role in learning. We designed a task to study the role of NMDA receptors in human reward-guided learning and decision-making. Human participants were assigned to receive either 250 mg of the partial NMDA agonist d-cycloserine (n=20) or matching placebo capsules (n=27). Reward-guided learning and decision-making were assessed using a task in which participants had to integrate learnt and explicitly shown value information to maximize their monetary wins and minimize their losses. To tease apart the effects of NMDA on learning and decision-making we used simple learning models. D-cycloserine shifted decision-making towards a more optimal integration of the learnt and the explicitly shown information, in the absence of a direct learning effect. In conclusion, our results reveal a distinct role for NMDA receptors in reward-guided decision-making. We discuss these findings in the context of NMDA's roles in neuronal super-additivity and as crucial for evidence integration for decisions.

Attention can either increase or decrease spike count correlations in visual cortex

Visual attention enhances the responses of visual neurons that encode the attended location. Several recent studies have shown that attention also decreases correlations between fluctuations in the responses of pairs of neurons (termed spike count correlation or r(SC)). These results are consistent with two hypotheses. First, attention-related changes in rate and r(SC) might be linked (perhaps through a common mechanism), with attention always decreasing r(SC). Second, attention might either increase or decrease r(SC), possibly depending on the role of the neurons in the behavioral task. We recorded simultaneously from dozens of neurons in area V4 while monkeys performed a discrimination task. We found strong evidence in favor of the second hypothesis, showing that attention can flexibly increase or decrease correlations depending on whether the neurons provide evidence for the same or opposite choices. These results place important constraints on models of the neuronal mechanisms underlying cognitive factors.

Alpha and gamma oscillations characterize feedback and feedforward processing in monkey visual cortex

Cognitive functions rely on the coordinated activity of neurons in many brain regions, but the interactions between cortical areas are not yet well understood. Here we investigated whether low-frequency (α) and high-frequency (γ) oscillations characterize different directions of information flow in monkey visual cortex. We recorded from all layers of the primary visual cortex (V1) and found that γ-waves are initiated in input layer 4 and propagate to the deep and superficial layers of cortex, whereas α-waves propagate in the opposite direction. Simultaneous recordings from V1 and downstream area V4 confirmed that γ- and α-waves propagate in the feedforward and feedback direction, respectively. Microstimulation in V1 elicited γ-oscillations in V4, whereas microstimulation in V4 elicited α-oscillations in V1, thus providing causal evidence for the opposite propagation of these rhythms. Furthermore, blocking NMDA receptors, thought to be involved in feedback processing, suppressed α while boosting γ. These results provide new insights into the relation between brain rhythms and cognition.

Goal-directed control with cortical units that are gated by both top-down feedback and oscillatory coherence

The brain is able to flexibly select behaviors that adapt to both its environment and its present goals. This cognitive control is understood to occur within the hierarchy of the cortex and relies strongly on the prefrontal and premotor cortices, which sit at the top of this hierarchy. Pyramidal neurons, the principal neurons in the cortex, have been observed to exhibit much stronger responses when they receive inputs at their soma/basal dendrites that are coincident with inputs at their apical dendrites. This corresponds to inputs from both lower-order regions (feedforward) and higher-order regions (feedback), respectively. In addition to this, coherence between oscillations, such as gamma oscillations, in different neuronal groups has been proposed to modulate and route communication in the brain. In this paper, we develop a simple, but novel, neural mass model in which cortical units (or ensembles) exhibit gamma oscillations when they receive coherent oscillatory inputs from both feedforward and feedback connections. By forming these units into circuits that can perform logic operations, we identify the different ways in which operations can be initiated and manipulated by top-down feedback. We demonstrate that more sophisticated and flexible top-down control is possible when the gain of units is modulated by not only top-down feedback but by coherence between the activities of the oscillating units. With these types of units, it is possible to not only add units to, or remove units from, a higher-level unit's logic operation using top-down feedback, but also to modify the type of role that a unit plays in the operation. Finally, we explore how different network properties affect top-down control and processing in large networks. Based on this, we make predictions about the likely connectivities between certain brain regions that have been experimentally observed to be involved in goal-directed behavior and top-down attention.

Serotoninergic and dopaminergic modulation of cortico-striatal circuit in executive and attention deficits induced by NMDA receptor hypofunction in the 5-choice serial reaction time task

Executive functions are an emerging propriety of neuronal processing in circuits encompassing frontal cortex and other cortical and subcortical brain regions such as basal ganglia and thalamus. Glutamate serves as the major neurotrasmitter in these circuits where glutamate receptors of NMDA type play key role. Serotonin and dopamine afferents are in position to modulate intrinsic glutamate neurotransmission along these circuits and in turn to optimize circuit performance for specific aspects of executive control over behavior. In this review, we focus on the 5-choice serial reaction time task which is able to provide various measures of attention and executive control over performance in rodents and the ability of prefrontocortical and striatal serotonin 5-HT_1A, 5-HT_2A, and 5-HT_2C as well as dopamine D₁- and D₂-like receptors to modulate different aspects of executive and attention disturbances induced by NMDA receptor hypofunction in the prefrontal cortex. These behavioral studies are integrated with findings from microdialysis studies. These studies illustrate the control of attention selectivity by serotonin 5-HT_1A, 5-HT_2A, 5-HT_2C, and dopamine D₁- but not D₂-like receptors and a distinct contribution of these cortical and striatal serotonin and dopamine receptors to the control of different aspects of executive control over performance such as impulsivity and compulsivity. An association between NMDA antagonist-induced increase in glutamate release in the prefrontal cortex and attention is suggested. Collectively, this review highlights the functional interaction of serotonin and dopamine with NMDA dependent glutamate neurotransmission in the cortico-striatal circuitry for specific cognitive demands and may shed some light on how dysregulation of neuronal processing in these circuits may be implicated in specific neuropsychiatric disorders.

State dependence of noise correlations in macaque primary visual cortex

Shared, trial-to-trial variability in neuronal populations has a strong impact on the accuracy of information processing in the brain. Estimates of the level of such noise correlations are diverse, ranging from 0.01 to 0.4, with little consensus on which factors account for these differences. Here we addressed one important factor that varied across studies, asking how anesthesia affects the population activity structure in macaque primary visual cortex. We found that under opioid anesthesia, activity was dominated by strong coordinated fluctuations on a timescale of 1-2 Hz, which were mostly absent in awake, fixating monkeys. Accounting for these global fluctuations markedly reduced correlations under anesthesia, matching those observed during wakefulness and reconciling earlier studies conducted under anesthesia and in awake animals. Our results show that internal signals, such as brain state transitions under anesthesia, can induce noise correlations but can also be estimated and accounted for based on neuronal population activity.

Mismatch negativity (MMN) as an index of cognitive dysfunction

Cognition is often affected in a variety of neuropsychiatric, neurological, and neurodevelopmental disorders. The neural discriminative response, reflected in mismatch negativity (MMN) and its magnetoencephalographic equivalent (MMNm), has been used as a tool to study a variety of disorders involving auditory cognition. MMN/MMNm is an involuntary brain response to auditory change or, more generally, to pattern regularity violation. For a number of disorders, MMN/MMNm amplitude to sound deviance has been shown to be attenuated or the peak-latency of the component prolonged compared to controls. This general finding suggests that while not serving as a specific marker to any particular disorder, MMN may be useful for understanding factors of cognition in various disorders, and has potential to serve as an indicator of risk. This review presents a brief history of the MMN, followed by a description of how MMN has been used to index auditory processing capability in a range of neuropsychiatric, neurological, and neurodevelopmental disorders. Finally, we suggest future directions for research to further enhance our understanding of the neural substrate of deviance detection that could lead to improvements in the use of MMN as a clinical tool.

Simultaneous selection by object-based attention in visual and frontal cortex

Models of visual attention hold that top-down signals from frontal cortex influence information processing in visual cortex. It is unknown whether situations exist in which visual cortex actively participates in attentional selection. To investigate this question, we simultaneously recorded neuronal activity in the frontal eye fields (FEF) and primary visual cortex (V1) during a curve-tracing task in which attention shifts are object-based. We found that accurate performance was associated with similar latencies of attentional selection in both areas and that the latency in both areas increased if the task was made more difficult. The amplitude of the attentional signals in V1 saturated early during a trial, whereas these selection signals kept increasing for a longer time in FEF, until the moment of an eye movement, as if FEF integrated attentional signals present in early visual cortex. In erroneous trials, we observed an interareal latency difference because FEF selected the wrong curve before V1 and imposed its erroneous decision onto visual cortex. The neuronal activity in visual and frontal cortices was correlated across trials, and this trial-to-trial coupling was strongest for the attended curve. These results imply that selective attention relies on reciprocal interactions within a large network of areas that includes V1 and FEF.

Subjective visual perception: from local processing to emergent phenomena of brain activity

The combination of electrophysiological recordings with ambiguous visual stimulation made possible the detection of neurons that represent the content of subjective visual perception and perceptual suppression in multiple cortical and subcortical brain regions. These neuronal populations, commonly referred to as the neural correlates of consciousness, are more likely to be found in the temporal and prefrontal cortices as well as the pulvinar, indicating that the content of perceptual awareness is represented with higher fidelity in higher-order association areas of the cortical and thalamic hierarchy, reflecting the outcome of competitive interactions between conflicting sensory information resolved in earlier stages. However, despite the significant insights into conscious perception gained through monitoring the activities of single neurons and small, local populations, the immense functional complexity of the brain arising from correlations in the activity of its constituent parts suggests that local, microscopic activity could only partially reveal the mechanisms involved in perceptual awareness. Rather, the dynamics of functional connectivity patterns on a mesoscopic and macroscopic level could be critical for conscious perception. Understanding these emergent spatio-temporal patterns could be informative not only for the stability of subjective perception but also for spontaneous perceptual transitions suggested to depend either on the dynamics of antagonistic ensembles or on global intrinsic activity fluctuations that may act upon explicit neural representations of sensory stimuli and induce perceptual reorganization. Here, we review the most recent results from local activity recordings and discuss the potential role of effective, correlated interactions during perceptual awareness.

Mechanisms underlying the basal forebrain enhancement of top-down and bottom-up attention

Both attentional signals from frontal cortex and neuromodulatory signals from basal forebrain (BF) have been shown to influence information processing in the primary visual cortex (V1). These two systems exert complementary effects on their targets, including increasing firing rates and decreasing interneuronal correlations. Interestingly, experimental research suggests that the cholinergic system is important for increasing V1's sensitivity to both sensory and attentional information. To see how the BF and top-down attention act together to modulate sensory input, we developed a spiking neural network model of V1 and thalamus that incorporated cholinergic neuromodulation and top-down attention. In our model, activation of the BF had a broad effect that decreases the efficacy of top-down projections and increased the reliance of bottom-up sensory input. In contrast, we demonstrated how local release of acetylcholine in the visual cortex, which was triggered through top-down gluatmatergic projections, could enhance top-down attention with high spatial specificity. Our model matched experimental data showing that the BF and top-down attention decrease interneuronal correlations and increase between-trial reliability. We found that decreases in correlations were primarily between excitatory-inhibitory pairs rather than excitatory-excitatory pairs and suggest that excitatory-inhibitory decorrelation is necessary for maintaining low levels of excitatory-excitatory correlations. Increased inhibitory drive via release of acetylcholine in V1 may then act as a buffer, absorbing increases in excitatory-excitatory correlations that occur with attention and BF stimulation. These findings will lead to a better understanding of the mechanisms underyling the BF's interactions with attention signals and influences on correlations.