Tuesday, November 29, 2022
11/29/2022
Prefrontal cortex (PFC) is critical for a range of high-level cognitive functions, such as attention and decision- making. Studying these processes is difficult, since they are covert, dynamic and under the control of the subject, rather than the experimenter. Their measurement traditionally relies on inferring their presence via behavior. Recent technical advances, particularly the increase in the number of neurons that can be simultaneously recorded, has raised the possibility of decoding cognitive processes directly from neural activity. In the proposed project, we will capitalize on the recent development of high-density, high-channel count, silicon probes (Neuropixels) that can produce a more than 20-fold increase in neuronal-recording yield over conventional methods. We will use these new probes and decoding algorithms to identify the cellular and circuit- level mechanisms of attentional control and decision-making in primate PFC. We will perform this decoding in real-time and use the output to control the application of stimulus perturbations or electrical microstimulation, which will ‘close the loop’ and determine the necessity of the decoded signals for cognition. Finally, we will study the contribution of specific subpopulations of neurons by restricting the decoder to those populations. The first aim focuses on understanding how attentional control is achieved by lateral prefrontal cortex (LPFC). We will record simultaneously from large populations of neurons within the frontal eye field and area 46 of monkeys performing a selective attention task. Neurons will be characterized by the laminar location, by their sensory, motor and memory-related properties, and as putative pyramidal or interneurons. The contribution of specific subpopulations to attentional control will be determined by building a decoder to report the animal’s current attentional locus using neural activity from the subpopulation of interest. In turn, the decoder will be used to control stimulus perturbations to assess the behavioral effect of the decoded signals on attention. In the second aim, we will examine the contribution of orbitofrontal cortex (OFC) neuronal subpopulations to value- based decision-making, including the contribution of different cortical layers, cell types, and OFC subregions. In addition, the decoder output will be used to control the application of electrical microstimulation to examine whether choice behavior can be biased towards a specific choice alternative. In the last Aim, we will build on the work from the first two aims to determine how value and attention interact. While some evidence suggests that attention is prioritized to stimuli with high value, other evidence suggests that the opposite is true. Results from Aims 1 and 2 will be used to optimize a decoder in OFC that will output the value of objects currently under consideration, and a decoder in LPFC that will output the current location of covert spatial attention. We will then cross-correlate both decoder output signals to determine whether one reliably lags or leads the other. The proposed work leverages the expertise of the three PIs: Moore is an expert in the role of LPFC in attention, Wallis in the role of OFC in decision-making, and Shenoy in real-time decoding and closed-loop control.
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