Biology subjects

Di Bello, F.

Publications and source records attributed to Di Bello, F..

9 recordsLinked to original sources

bioRxiv2024

Dynamic Attentional Control Through Mixed Prefrontal Cortex Resources

Spatial attention can be either involuntarily drawn to salient, unexpected events (exogenous attention) or voluntarily directed (endogenous attention). Evidence suggests a link of both forms of attention to the (oculo)motor system. We investigated the role of the prefrontal cortex (PFC) in this relationship using a task that spatially dissociated attention from motor planning in two monkeys. PFC units flexibly encoded either the attended location or motor target, with some showing overlapping coding. Endogenous attention promotes an early engagement of these shared resources, producing two key effects: (1) it prevents interference between motor and attention systems, and (2) it regulates the extent of the exogenous shifts for attentional capture, with stronger shifts occurring when endogenous attention is weak. Overall, we show that there are separate attention and motor systems, each of which can flexibly draw on some "overlapping" resources based on environmental and internal demands.

neuroscience↗

bioRxiv2024

The biomechanical state of the effector affects motor control and provides measures of single trial inhibition in a stop signal task

The Stop Signal Task (SST) has been the benchmark for studying the behavioral and physiological basis of movement generation and inhibition. In our study, we extended the scope beyond physiological findings related to muscle activity, focusing our analysis on the initial biomechanical state of the effector. By incorporating a force sensitive resistor (FSR), we continuously monitored the force applied by the effector (here the index finger) during a button release version of the SST. This modified task design allowed us to examine both the baseline force before the relevant Go signal was presented and during the covert state of movement preparation. Notably, variations in force over time in response to the Go signal revealed differences across trials where movement was either generated or successfully inhibited, depending on the amount of force during the baseline period. Specifically, higher baseline force was associated with a delayed movement generation, which simultaneously slowed down the force release, facilitating successful inhibition when requested. Our results highlight the influence of biomechanical variables in movement control, which should be accounted for by the models developed for investigating the physiology of this ability. NEW & NOTEWORTHYMovement involves changing the position of anatomical effectors, like a finger. The initial biomechanical state of an effector impacts movement generation and inhibition. Using the Stop-Signal task, we studied these factors by measuring the force applied to a mouse button before movement onset. Higher initial force delayed movement generation and slowed force release, aiding movement inhibition. This research links behavioral models of action-stopping with movement biomechanics, highlighting the effectors initial state importance.

neuroscience↗

bioRxiv2022

The cost of multiplexing: PFC integrates multiple sources of information in non-orthogonal components accounting for behavioral variability

The frontal eye field (FEF) is a cortical area classically associated with spatial attention, perception, and oculomotor functions. FEF exhibits complex response properties through mixed selectivity neurons, allowing a high dimensional representation of the information. However, recent studies have shown that FEF encodes information in a low-dimensional regime hence limiting the coding capacity of the neural population. How the FEF encodes multiple sources of information with such limited encoding capacity remains elusive. To address this question, we trained two macaques to perform a visual attention task while we recorded FEF neuronal activity using multi-contact electrodes. FEF neurons encoded task- (time in the trial; CTOA) and behaviour- (reaction time, RT; focus of attention, TA) related parameters prior to the target onset. We found a clear modulation of the RT and TA as a function of the CTOA. Using dPCA, we characterized the functional relationship between neural populations associated with each parameter and investigated how this functional relationship predicts behaviour. We found that CTOA variability was associated with two different components the activation of which was correlated with the TA and the RT, respectively. These CTOA-related components were non-orthogonal with the RT and TA-related components, respectively. These results suggest that, when different sources of information are implemented during task performance, they show a very precise geometrical configuration in non-orthogonal components, which allows a high capacity of information coding at a cost of modulating both the capacity of the monkey to use attention information and its responsiveness toward external stimuli.

neuroscience↗

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

Different contribution of the monkey prefrontal and premotor dorsal cortex in decision-making supported by inferential reasoning

Several studies have reported similar neural modulations between brain areas of the frontal cortex, such as the dorsolateral prefrontal (DLPFC) and the premotor dorsal (PMd) cortex, in tasks requiring encoding of the abstract rules for selecting the proper action. Here we compared the neuronal modulation of the DLPFC and PMd of monkeys trained to choose the higher rank from a pair of abstract images (target item), selected from an arbitrarily rank-ordered set (A>B>C>D>E>F) in the context of a transitive inference task. Once acquired by trial-and-error, the ordinal relationship between pairs of adjacent images (i.e., A>B; B>C; C>D; D>E; E>F), monkeys were tested in indicating the ordinal relation between items of the list not paired during learning. During these decisions, we observed that the choice accuracy increased and the reaction time decreased as the rank difference between the compared items enhanced. This result is in line with the hypothesis that after learning, the monkeys built an abstract mental representation of the ranked items, where rank comparisons correspond to the items position comparison on this representation. In both brain areas, we observed higher neuronal activity when the target item appeared in a specific location on the screen with respect to the opposite position and that this difference was particularly enhanced at lower degrees of difficulty. By comparing the time evolution of the activity of the two areas, we observed that the neural encoding of target item spatial position occurred earlier in the DLPFC than in the PMd.

animal behavior and cognition↗

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

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↗