Prefrontal Control of Proactive and Reactive Mechanisms of Visual Suppression.

In everyday life, we are continuously struggling at focusing on our current goals while at the same time avoiding distractions. Attention is the neuro-cognitive process devoted to the selection of behaviorally relevant sensory information while at the same time preventing distraction by irrelevant information. Distraction can be prevented proactively, by strategically prioritizing task-relevant information at the expense of irrelevant information, or reactively, by suppressing the ongoing processing of distractors. The distinctive neuronal signature of these suppressive mechanisms is still largely unknown. Thanks to machine-learning decoding methods applied to prefrontal cortical activity, we monitor the dynamic spatial attention with an unprecedented spatial and temporal resolution. We first identify independent behavioral and neuronal signatures for long-term (learning-based spatial prioritization) and short-term (dynamic spatial attention) mechanisms. We then identify distinct behavioral and neuronal signatures for proactive and reactive suppression mechanisms. We find that while distracting task-relevant information is suppressed proactively, task-irrelevant information is suppressed reactively. Critically, we show that distractor suppression, whether proactive or reactive, strongly depends on the implementation of both long-term and short-term mechanisms of selection. Overall, we provide a unified neuro-cognitive framework describing how the prefrontal cortex deals with distractors in order to flexibly optimize behavior in dynamic environments.

Time-resolved estimation of strength of motor imagery representation by multivariate EEG decoding.

Objective. Multivariate decoding enables access to information encoded in multiple brain activity features with high temporal resolution. However, whether the strength, of which this information is represented in the brain, can be extracted across time within single trials remains largely unexplored.Approach.In this study, we addressed this question by applying a support vector machine (SVM) to extract motor imagery (MI) representations, from electroencephalogram (EEG) data, and by performing time-resolved single-trial analyses of the multivariate decoding. EEG was recorded from a group of healthy participants during MI of opening and closing of the same hand.Main results.Cross-temporal decoding revealed both dynamic and stationary MI-relevant features during the task. Specifically, features representing MI evolved dynamically early in the trial and later stabilized into a stationary network of MI features. Using a hierarchical genetic algorithm for selection of MI-relevant features, we identified primarily contralateral alpha and beta frequency features over the sensorimotor and parieto-occipital cortices as stationary which extended into a bilateral pattern in the later part of the trial. During the stationary encoding of MI, by extracting the SVM prediction scores, we analyzed MI-relevant EEG activity patterns with respect to the temporal dynamics within single trials. We show that the SVM prediction score correlates to the amplitude of univariate MI-relevant features (as documented from an extensive repertoire of previous MI studies) within single trials, strongly suggesting that these are functional variations of MI strength hidden in trial averages.Significance.Our work demonstrates a powerful approach for estimating MI strength continually within single trials, having far-reaching impact for single-trial analyses. In terms of MI neurofeedback for motor rehabilitation, these results set the ground for more refined neurofeedback reflecting the strength of MI that can be provided to patients continually in time.

Neuronal population correlates of target selection and distractor filtering.

Frontal Eye Field (FEF) neurons discriminate between relevant and irrelevant visual stimuli and their response magnitude predicts conscious perception. How this is reflected in the spatial representation of a visual stimulus at the neuronal population level is unknown. We recorded neuronal population activity in the FEF while monkeys were performing a forced choice cued detection task with identical target and distractor stimuli. We quantified, using machine learning techniques, estimates of target and distractor location from FEF population multiunit activities. We found that the FEF population activity provides a precise single trial estimate of reported stimuli locations. Importantly, the closer this prefrontal population single trial estimate is to the veridical stimulus location, the higher the probability that the target or the distractor is reported as perceived. We show that stimulus perception is rescued by the estimate of attention allocation specifically when the latter is close enough to the actual stimulus location, thus indicating a partial independence between attention and perception. Overall, we thus show that how and what we perceive of our environment depends on the spatial precision with which this environment is coded by prefrontal neuronal populations.

A continuous time-resolved measure decoded from EEG oscillatory activity predicts working memory task performance.

OBJECTIVE: Working memory (WM), crucial for successful behavioral performance in most of our everyday activities, holds a central role in goal-directed behavior. As task demands increase, inducing higher WM load, maintaining successful behavioral performance requires the brain to work at the higher end of its capacity. Because it is depending on both external and internal factors, individual WM load likely varies in a continuous fashion. The feasibility to extract such a continuous measure in time that correlates to behavioral performance during a working memory task remains unsolved. APPROACH: Multivariate pattern decoding was used to test whether a decoder constructed from two discrete levels of WM load can generalize to produce a continuous measure that predicts task performance. Specifically, a linear regression with L2-regularization was chosen with input features from EEG oscillatory activity recorded from healthy participants while performing the n-back task, [Formula: see text]. MAIN RESULTS: The feasibility to extract a continuous time-resolved measure that correlates positively to trial-by-trial working memory task performance is demonstrated (r = 0.47, p < 0.05). It is furthermore shown that this measure allows to predict task performance before action (r = 0.49, p < 0.05). We show that the extracted continuous measure enables to study the temporal dynamics of the complex activation pattern of WM encoding during the n-back task. Specifically, temporally precise contributions of different spectral features are observed which extends previous findings of traditional univariate approaches. SIGNIFICANCE: These results constitute an important contribution towards a wide range of applications in the field of cognitive brain-machine interfaces. Monitoring mental processes related to attention and WM load to reduce the risk of committing errors in high-risk environments could potentially prevent many devastating consequences or using the continuous measure as neurofeedback opens up new possibilities to develop novel rehabilitation techniques for individuals with degraded WM capacity.

Direct Two-Dimensional Access to the Spatial Location of Covert Attention in Macaque Prefrontal Cortex.

Direct access to motor cortical information now enables tetraplegic patients to precisely control neuroprostheses and recover some autonomy. In contrast, explicit access to higher cortical cognitive functions, such as covert attention, has been missing. Indeed, this cognitive information, known only to the subject, can solely be inferred by an observer from the subject's overt behavior. Here, we present direct two-dimensional real-time access to where monkeys are covertly paying attention, using machine-learning decoding methods applied to their ongoing prefrontal cortical activity. Decoded attention was highly predictive of overt behavior in a cued target-detection task. Indeed, monkeys had a higher probability of detecting a visual stimulus as the distance between decoded attention and stimulus location decreased. This was true whether the visual stimulus was presented at the cued target location or at another distractor location. In error trials, in which the animals failed to detect the cued target stimulus, both the locations of attention and visual cue were misencoded. This misencoding coincided with a specific state of the prefrontal cortical population in which the shared variability between its different neurons (or noise correlations) was high, even before trial onset. This observation strongly suggests a functional link between high noise-correlation states and attentional failure. Overall, this real-time access to the attentional spotlight, as well as the identification of a neural signature of attentional lapses, open new perspectives both to the study of the neural bases of attention and to the remediation or enhancement of the attentional function using neurofeedback.

Differential dynamics of spatial attention, position, and color coding within the parietofrontal network.

Despite an ever growing knowledge on how parietal and prefrontal neurons encode low-level spatial and color information or higher-level information, such as spatial attention, an understanding of how these cortical regions process neuronal information at the population level is still missing. A simple assumption would be that the function and temporal response profiles of these neuronal populations match that of its constituting individual cells. However, several recent studies suggest that this is not necessarily the case and that the single-cell approach overlooks dynamic changes in how information is distributed over the neuronal population. Here, we use a time-resolved population pattern analysis to explore how spatial position, spatial attention and color information are differentially encoded and maintained in the macaque monkey prefrontal (frontal eye fields) and parietal cortex (lateral intraparietal area). Overall, our work brings about three novel observations. First, we show that parietal and prefrontal populations operate in two distinct population regimens for the encoding of sensory and cognitive information: a stationary mode and a dynamic mode. Second, we show that the temporal dynamics of a heterogeneous neuronal population brings about complementary information to that of its functional subpopulations. Thus, both need to be investigated in parallel. Last, we show that identifying the neuronal configuration in which a neuronal population encodes given information can serve to reveal this same information in a different context. All together, this work challenges common views on neural coding in the parietofrontal network.

Altered expression of myelin-associated inhibitors and their receptors after traumatic brain injury in the mouse.

PURPOSE: When central nervous system axons are injured, regeneration is partly inhibited by myelin-associated inhibitors (MAIs). Following traumatic brain injury (TBI) in the rat, pharmacological neutralisation of the MAIs Nogo-A and myelin-associated glycoprotein (MAG) resulted in improved functional outcome. In contrast, genetic or pharmacological neutralization of the MAI receptors Nogo-66 receptor 1 (NgR1) or paired-immunoglobulin like receptor-B (PirB) showed an unaltered or impaired outcome following TBI in mice. The aim of the present study was thus to evaluate the MAI expression levels following TBI in mice. METHODS: Quantitative reverse transcriptase PCR (qRT-PCR) was used to measure total RNA isolated from brains of young adult male C57BL/6 mice at one, three or seven days following controlled cortical impact TBI or sham injury. Hippocampal and neocortical tissue ipsi- and contralateral to the injury was analyzed for Nogo-A, oligodendrocyte-myelin glycoprotein (OMgp), MAG, and the MAI receptors PirB and NgR1, including its co-receptor Lingo1. RESULTS: Compared to sham-injured controls, PirB neocortical expression was significantly upregulated at one day and NgR1 expression downregulated at seven days post-TBI. In the hippocampus, transcriptional upregulation was observed in Nogo-A (one day post-injury), MAG and PirB at seven days post-injury. In contrast, the hippocampal transcripts of NgR1 and Lingo1 were decreased at seven days post-injury. The expression of OMgp was unaltered at all time points post-injury. CONCLUSION: These results suggest that early dynamic changes in MAI gene expression occur following TBI in the mouse, particularly in the hippocampus, which may play an inhibitory role for post-injury regeneration and plasticity.

Selective visual attention to drive cognitive brain-machine interfaces: from concepts to neurofeedback and rehabilitation applications.

Brain-machine interfaces (BMIs) using motor cortical activity to drive an external effector like a screen cursor or a robotic arm have seen enormous success and proven their great rehabilitation potential. An emerging parallel effort is now directed to BMIs controlled by endogenous cognitive activity, also called cognitive BMIs. While more challenging, this approach opens new dimensions to the rehabilitation of cognitive disorders. In the present work, we focus on BMIs driven by visuospatial attention signals and we provide a critical review of these studies in the light of the accumulated knowledge about the psychophysics, anatomy, and neurophysiology of visual spatial attention. Importantly, we provide a unique comparative overview of the several studies, ranging from non-invasive to invasive human and non-human primates studies, that decode attention-related information from ongoing neuronal activity. We discuss these studies in the light of the challenges attention-driven cognitive BMIs have to face. In a second part of the review, we discuss past and current attention-based neurofeedback studies, describing both the covert effects of neurofeedback onto neuronal activity and its overt behavioral effects. Importantly, we compare neurofeedback studies based on the amplitude of cortical activity to studies based on the enhancement of cortical information content. Last, we discuss several lines of future research and applications for attention-driven cognitive brain-computer interfaces (BCIs), including the rehabilitation of cognitive deficits, restored communication in locked-in patients, and open-field applications for enhanced cognition in normal subjects. The core motivation of this work is the key idea that the improvement of current cognitive BMIs for therapeutic and open field applications needs to be grounded in a proper interdisciplinary understanding of the physiology of the cognitive function of interest, be it spatial attention, working memory or any other cognitive signal.

Comparison of classifiers for decoding sensory and cognitive information from prefrontal neuronal populations.

Decoding neuronal information is important in neuroscience, both as a basic means to understand how neuronal activity is related to cerebral function and as a processing stage in driving neuroprosthetic effectors. Here, we compare the readout performance of six commonly used classifiers at decoding two different variables encoded by the spiking activity of the non-human primate frontal eye fields (FEF): the spatial position of a visual cue, and the instructed orientation of the animal's attention. While the first variable is exogenously driven by the environment, the second variable corresponds to the interpretation of the instruction conveyed by the cue; it is endogenously driven and corresponds to the output of internal cognitive operations performed on the visual attributes of the cue. These two variables were decoded using either a regularized optimal linear estimator in its explicit formulation, an optimal linear artificial neural network estimator, a non-linear artificial neural network estimator, a non-linear naïve Bayesian estimator, a non-linear Reservoir recurrent network classifier or a non-linear Support Vector Machine classifier. Our results suggest that endogenous information such as the orientation of attention can be decoded from the FEF with the same accuracy as exogenous visual information. All classifiers did not behave equally in the face of population size and heterogeneity, the available training and testing trials, the subject's behavior and the temporal structure of the variable of interest. In most situations, the regularized optimal linear estimator and the non-linear Support Vector Machine classifiers outperformed the other tested decoders.

Readout of the intrinsic and extrinsic properties of a stimulus from un-experienced neuronal activities: towards cognitive neuroprostheses.

While sensory and motor systems have attracted most of the research effort in the field neuroprosthetics, little attention has been devoted to higher order cortical processes. Here, we propose a first step in the direction of applying neural decoding to the study and manipulation of visuospatial attention, an endogenous process at the interface between sensory and motor functions. To this aim, we investigate whether the offline activity of a population of non-human primate frontal eye field neurons (FEF) in response to an endogenous cue can be readout on a trial by trial basis to provide a precise description of the cue's attributes, namely, its location and identity, but also the allocation of attention following its interpretation. Using a linear decoder, we reach up to 86% correct predictions for the different decoded variables, including the spatial allocation of endogenous attention. We show that the decoding performance drops on incorrect trials, indicating that cue encoding participates to the animal's behavioral performance. Last, we show that the temporal resolution of the decoding influences readout performance. These results are a strong indication of the feasibility of the readout of endogenous variables by standard decoding algorithms, on a suboptimal dataset. However, its validity remains to be proved in a real-time situation.

Mapping retinal degeneration and loss-of-function in Rd-FTL mice.

PURPOSE: Retinitis pigmentosa (RP) is a blinding disease caused by the degeneration of photoreceptors. To further understand the process of degeneration in RP, the authors have traced activation patterns associated with rod and cone photoreceptor degeneration in a mouse model of RP METHODS: The authors used a double-mutant mouse, Rd-FTL, which contains a natural mutation, rd1, affecting the rod photoreceptors and an axon-targeted beta-galactosidase reporter system, which is under the regulation of the promoter of the c-fos gene. These mice allowed the authors to trace degeneration-related activity that corresponded to rod and cone death. RESULTS: The authors traced cell death-associated activation in both rods and cones, allowing them to accurately determine the time course of cone degeneration in these mice. In the analysis of downstream activation patterns in the inner retina, they found that amacrine and ganglion cells maintain their photopic light-related activation until at least the initiation of cone degeneration. These cell populations then show increased activity during the peak time of cone cell degeneration. The authors also examined light-regulated functional activation of a subclass of amacrine cells, the dopaminergic amacrine cells. These cells showed light-induced functional activation after rod photoreceptor death and until the period of cone photoreceptor death, suggesting that they can be regulated by cone photoreceptors alone. CONCLUSIONS: These findings have helped to accurately trace the periods of photoreceptor degeneration in this model of RP and show that correct light-regulated inner retinal activation is maintained until the time of cone degeneration.