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.

20Functional neuronal correlations between pairs of neurons are thought to play an important role 21 in neuronal information processing and optimal neuronal computations during attention, 22 perception, decision-making and learning. These noise correlations are often assumed to be 23 stable in time. However, recent studies suggest that cognitive processes are rhythmic, this 24 rhythmicity accounting for variations in overt behavioral performance. Whether this 25 rhythmicity coincides with variations in shared noise variability is unknown. Here, we perform 26 simultaneous recordings from the macaque frontal eye fields, while animals are engaged in a 27 spatial memory task. We report that noise correlations in prefrontal cortex fluctuate 28 rhythmically in the high alpha (10-16Hz) and beta (20-30Hz) frequency ranges. Importantly, 29 these rhythmic modulations in shared neuronal variability account for dynamic changes in overt 30 behavioral performance. They also coincide with increased spike-LFP phase coupling in these 31 specific frequency ranges, the spatial profile of which vary between superficial and deep 32 cortical layers. Finally, we demonstrate, using an artificial neuronal model, that rhythmic 33 variations in noise correlation oscillations parsimoniously arise from long range (LFP) and local 34 spike-LFP phase coupling mechanisms. Thus a significant portion of noise correlation 35 fluctuations can be attributed to long-range global network rhythmicity. 36 37 65 rhythmic modulations in noise correlations account both for overt behavioral performance and 66 for layer specific modulations in spike-field phase coupling. Based on an artificial model, we 67 demonstrate that rhythmic variations in noise correlation oscillations parsimoniously arise from 68 long range (LFP) and local spike-LFP phase coupling mechanisms. 69 70 4 Results 71Neuronal recordings were performed in the prefrontal cortex, specifically in the frontal 72 eye field (FEF, figure 1A), a structure known to play a key role in covert spatial attention 28,33-73 35 . In each session, multi-unit activity (MUA) and local field potential (LFP) were recorded 74 bilaterally, while monkeys performed a memory guided saccade task ( figure 1B). Specifically, 75 monkeys were required to hold the position of a spatial cue in memory for 700 to 1900ms and 76 to perform a saccade towards the memorized spatial location on the extinction of the fixation 77 point that served as a go signal. In the following, noise correlations between the different 78 prefrontal signals of the same hemisphere were computed during the time interval running from 79 300ms to 1500ms following cue offset, on neuronal activities averaged over 200ms sliding 80 windows (step of 10ms). As shown by previous studies, noise correlations decrease as a 81 function of cortical distance ( Figure S1A, 1-way ANOVA, p<0.001, Wilcoxon rank sum test, 82 p<0.001 for 750 µm, p<0.001 for 1000 µm, 23,36,37 and are significantly lower among neuronal 83 pairs with different spatial selectivity than neu...

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.