Biology subjects

Ben Hadj Hassen, S.

Publications and source records attributed to Ben Hadj Hassen, S..

8 recordsLinked to original sources

bioRxiv2021

Distractibility and impulsivity neural states are distinct from attention states and modulate the implementation of attention

In the context of visual attention, it has been classically assumed that missing the response to a target or erroneously selecting a distractor occurs as a consequence of the (miss)allocation of attention in space. In the present paper, we challenge this view and provide evidence that, in addition to encoding spatial attention, prefrontal neurons also encode a distractibility-to-impulsivity state. Using supervised dimensionality reduction techniques, we identify two partially overlapped neuronal subpopulations associated either with attention or overt behaviour. The degree of overlap accounts for the behavioural gain associated with the good allocation of attention. We further describe the neural variability accounting for distractibility-to-impulsivity behaviour by a two dimensional state associated with optimality in task and responsiveness. Overall, we thus show that behavioural performance arises from the integration of task-specific neuronal processes and pre-existing neuronal states describing task-independent behavioural states, shedding new light on attention disorders such as ADHD.

neuroscience↗

bioRxiv2021

Attentional brain rhythms during prolonged cognitive activity

As routine and lower demand cognitive tasks are taken over by automated assistive systems, human operators are increasingly required to sustain cognitive demand over long periods of time. This has been reported to have long term adverse effects on cardiovascular and mental health. However, it remains unclear whether prolonged cognitive activity results in a monotonic decrease in the efficiency of the recruited brain processes, or whether the brain is able to sustain functions over time spans of one hour and more. Here, we show that during working sessions of one hour or more, contrary to the prediction of a monotonic decline, behavioral performance in both humans and non-human primates consistently fluctuates between periods of optimal and suboptimal performance at a very slow rhythm of circa 5 cycles per hour. These fluctuations are observed in both high attentional (in non-human primates) and low attentional (in humans) demand conditions. They coincide with fluctuations in pupil diameter, indicating underlying changes in arousal and information-processing load. Accordingly, we show that these rhythmic behavioral fluctuations correlate, at the neurophysiological level, with fluctuations in the informational attention orientation and perception processing capacity of prefrontal neuronal populations. We further identify specific markers of these fluctuations in LFP power, LFP coherence and spike-field coherence, pointing towards long-range rhythmic modulatory inputs to the prefrontal cortex rather than a local prefrontal origin. These results shed light on the resilience of brain mechanisms to sustained effort and have direct implications on how to optimize high cognitive demand working and learning environments.

neuroscience↗

bioRxiv2020

Information-based signal selection improves decoding of attention spotlight from multi-units & local field potentials and enhances correlation with behavior.

The ability to access brain information in real-time is crucial both for a better understanding of cognitive functions and for the development of therapeutic applications based on brain-machine interfaces. Great success has been achieved in the field of neural motor prosthesis. Progress is still needed in the real-time decoding of higher-order cognitive processes such as covert attention. Recently, we showed that we can track the location of the attentional spotlight using classification methods applied to prefrontal multi-unit activity (MUA) in the non-human primate (Astrand et al., 2016). Importantly, we demonstrated that the decoded (x,y) attentional spotlight parametrically correlates with the behavior of the monkeys thus validating our decoding of attention. We also demonstrate that this spotlight is extremely dynamic (Gaillard et al., 2020). Here, in order to get closer to non-invasive decoding applications, we extend our previous work to local field potential signals (LFP). Specifically, we achieve, for the first time, high decoding accuracy of the (x,y) location of the attentional spotlight from prefrontal LFP signals, to a degree comparable to that achieved from MUA signals, and we show that this LFP content is predictive of behavior. This LFP attention-related information is maximal in the gamma band. In addition, we introduce a novel two-step decoding procedure based on the labelling of maximally attention-informative trials during the decoding procedure. This procedure strongly improves the correlation between our real-time MUA and LFP based decoding and behavioral performance, thus further refining the functional relevance of this real-time decoding of the (x,y) locus of attention. This improvement is more marked for LFP signals than for MUA signals, suggesting that LFP signals may contain other sources of task-related variability than spatial attention information. Overall, this study demonstrates that the attentional spotlight can be accessed from LFP frequency content, in real-time, and can be used to drive high-information content cognitive brain machine interfaces for the development of new therapeutic strategies. HighlightsWe use machine learning to decode attention spotlight from prefrontal MUA & LFP. We achieve high decoding accuracy of (x,y) spatial attention spotlight. (x,y) attention spotlight position accuracy is maximal from LFP gamma frequency range. MUA and LFP decoded attention position predicts behavioral performances. Selecting high information signals improves decoding and behavioral correlates.

neuroscience↗

bioRxiv2020

Selection and suppression of visual information in the macaque prefrontal cortex

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. Visual selection can be implemented by both long-term (learning-based spatial prioritization) and short term (dynamic spatial attention) mechanisms. On the other hand, distraction can be prevented proactively, by strategically prioritizing task-relevant information at the expense of irrelevant information, or reactively, by actively suppressing the processing of distractors. The distinctive neuronal signature of each of these four processes is largely unknown. Likewise, how selection and suppression mechanisms interact to drive perception has never been explored neither at the behavioral nor at the neuronal level. Here, we apply machine-learning decoding methods to prefrontal cortical (PFC) activity to monitor dynamic spatial attention with an unprecedented spatial and temporal resolution. This leads to several novel observations. We first identify independent behavioral and neuronal signatures for learning-based attention prioritization and dynamic attentional selection. Second, we 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 both learning-based attention prioritization and dynamic attentional selection. Overall, we thus provide a unified neuro-cognitive framework describing how the prefrontal cortex implements spatial selection and distractor suppression in order to flexibly optimize behavior in dynamic environments.

neuroscience↗

bioRxiv2019

Rhythmic variations in prefrontal inter-neuronal correlations, their underlying mechanisms and their behavioral correlates

Functional neuronal correlations between pairs of neurons are thought to play an important role in neuronal information processing and optimal neuronal computations during attention, perception, decision-making and learning. These noise correlations are often assumed to be stable in time. However, recent studies suggest that cognitive processes are rhythmic, this rhythmicity accounting for variations in overt behavioral performance. Whether this rhythmicity coincides with variations in shared noise variability is unknown. Here, we perform simultaneous recordings from the macaque frontal eye fields, while animals are engaged in a spatial memory task. We report that noise correlations in prefrontal cortex fluctuate rhythmically in the high alpha (10-16Hz) and beta (20-30Hz) frequency ranges. Importantly, these rhythmic modulations in shared neuronal variability account for dynamic changes in overt behavioral performance. They also coincide with increased spike-LFP phase coupling in these specific frequency ranges, the spatial profile of which vary between superficial and deep cortical layers. Finally, we demonstrate, using an artificial neuronal model, that rhythmic variations in noise correlation oscillations parsimoniously arise from long range (LFP) and local spike-LFP phase coupling mechanisms. Thus a significant portion of noise correlation fluctuations can be attributed to long-range global network rhythmicity.

neuroscience↗

bioRxiv2019

Prefrontal attentional saccades explore space at an alpha rhythm

Recent studies suggest that attention samples space rhythmically through oscillatory interactions in the frontoparietal network. However, the precise mechanism through which the prefrontal cortex, at the source of attention control signals, organizes this rhythmic exploration of space remains unknown. We show that, when decoded at a high spatial (0.1{degrees}) and temporal resolution (50ms), the prefrontal covert attentional spotlight, aka the minds eye, continuously explores space at an alpha 7-12 Hz rhythm. When sensory events are presented at a specific optimal phase (resp. anti-phase) with respect to this rhythm, sensory encoding and behavioral report are accurate (resp. poor). We propose that this rhythmic prefrontal attentional spotlight dynamics corresponds to a continuous overt exploration of space via alpha-clocked attentional saccades. These attentional saccades are highly flexible, their pattern of space exploration depending both on within-trial and across-task contingencies. These results are discussed in the context of exploration and exploitation strategies and prefrontal top-down attentional control.\n\nHighlightsO_LIThe decoded prefrontal attentional spotlight samples visual space in rhythmic cycles\nC_LIO_LIThis rhythmic attentional exploration predicts neuronal sensory processing accuracy\nC_LIO_LIThis rhythmic attentional exploration predicts overt behavioral accuracy\nC_LIO_LIThese rhythmic cycles define alpha-clocked attentional saccades\nC_LIO_LISpace exploration by attentional saccades is highly flexible and under top-down control\nC_LI

neuroscience↗

bioRxiv2019

Interneuronal correlations dynamically adjust to task demands at multiple time-scales

Functional neuronal correlations between pairs of neurons are thought to play an important role in neuronal information processing and optimal neuronal computations during attention, perception, decision-making and learning. Here, we report dynamic changes in prefrontal neuronal noise correlations at multiple time-scales, as a function of task contingencies. Specifically, we record neuronal activity from the macaque frontal eye fields, a cortical region at the source of spatial attention top-down control, while the animals are engaged in tasks of varying cognitive demands. First, we show that noise correlations decrease as cognitive engagement and task demands increase, both across tasks and within-trials. Second, we demonstrate, for the first time, rhythmic modulations of noise correlations in the alpha and beta frequency ranges that account both for overt behavioral performance and for layer specific modulations in spike-field coherence. All this taken together demonstrates a strong functional role of noise correlations in cognitive flexibility.

neuroscience↗

bioRxiv2018

Norepinephrine improves attentional orienting in a predictive context

The role of norepinephrine (NE) in visuo-spatial attention remains poorly understood. Our goal was to identify the attentional processes under the influence of NE and to characterize these influences. We tested the effects of atomoxetine injections (ATX), a NE-reuptake inhibitor that boosts the level of NE in the brain, on seven monkeys performing a saccadic cued task in which cues and distractors were used to manipulate spatial attention. We found that when the cue accurately predicted the location of the upcoming cue in 80% of the trials, ATX consistently improved attentional orienting, as measured from reaction times (RTs). These effects were best accounted for by a faster accumulation rate in the valid trials, rather than by a change in the decision threshold. By contrast, the effect of ATX on alerting and distractor interference was more mitigated. Finally, we also found that, under ATX, RTs to non-cued targets were longer when these were presented separately from cued targets. This suggests that the impact of NE on visuo-spatial attention depends on the context, such that the adaptive changes elicited by the highly informative value of the cues in the most frequent trials were accompanied by a cost in the less frequent trials.

neuroscience↗