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

Abstract

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.

Prefrontal attentional saccades explore space rhythmically

“…14 Converging evidence demonstrate that the prefrontal cortex (PFC) is at the origin of the attentional control 15 signals underlying the behavioral attentional spotlight 7,[14][15][16][17] . Supporting this idea, we recently 16 demonstrated that this attentional spotlight can be reconstructed and tracked from PFC neuronal 17 population activity with a very high spatial and temporal resolution 18,19 . However, recent experimental 18 work provides a completely different perspective onto selective attention, suggesting that spatial 19 attention samples the visual scene rhythmically [20][21][22][23][24][25][26] .…”

“…The above described analyses were performed on a quantification of the accuracy with which attention 234 could be localized in one of the four visual quadrants, based on the observed PFC population neuronal response. In 235 a previous study 18 , we demonstrated that the continuous (x,y) readout of a linear classifier assigning neuronal 236 activities to a spatial location of attention is a relevant proxy for a real-time access to the attentional spotlight 237 represented in the PFC. Importantly, this continuous (x,y) readout of the PFC attentional spotlight is predictive of 238 behavior, both in terms of hit and false alarm rates.…”

“…Previous studies 40 demonstrate that PFC based decoding procedures allow to access in which quadrant [33][34][35] or at which (x,y) location 41 attention is placed 18 . In these studies, neuronal signals were averaged over time intervals ranging from 150 ms -400 42 ms 18,35 . Larger averaging window sizes produce higher decoding accuracies (suppl.…”

“…Importantly, this continuous (x,y) readout of the PFC attentional spotlight is predictive of 238 behavior, both in terms of hit and false alarm rates. In the following, we apply the same approach to extract (x,y) 239 attentional spotlight trajectories in time before and after cue presentation, to the major difference that the readout 240 is obtained at higher temporal resolution, from neuronal responses averaged over 50 ms rather than on 150 ms as 241 presented in the Astrand et al 18 figure 10C, which represents the decoded attentional spotlight trajectory task, transitions between the cued quadrant and the diagonally opposite quadrant, the second most relevant spatial 308 location in the task, become dominant with respect to the other two uncued quadrants ( fig. 10B, red, probability of 309 transition of 22%; for comparison, probability of transition from cued location to position 2: 16%; to position 3: 15%).…”

Decoding of top-down cognitive processing for SSVEP-controlled BMI

“…As our study demonstrates the possibility of seamlessly decoding human top-down processes, if a further refined grid-shaped line array is developed and combined with a more elaborated classification method, a recent study74 provides advances in extending previous studies of decoding visuospatial attention6970717273, which were limited to decoding up to four discrete locations/classes. The authors found a neuronal signature of direct two-dimensional access to the spatial location of covert attention in macaque prefrontal cortex74. Together, these efforts in both externally triggered and internally driven manners may cooperatively open new perspectives to decoding technology in a multi-class and continuous mental representation space.…”