# Predictive models of brain dynamics during decision making and their validation using distributed optogenetic stimulation > **NIH NIH R01** · NEW YORK UNIVERSITY · 2021 · $667,164 ## Abstract Project Summary During behavior, the oculomotor system is tasked with selecting objects from an ever-changing visual field and guiding eye movements to these locations. The attentional priority given to sensory targets during selection can be strongly influenced by external stimulus properties (“bottom-up”) or internal goals based on previous experience (“top-down”). Although these exogenous and endogenous drivers of selection are known to operate across partially overlapping time scales, how neural circuits mechanistically support top-down and bottom-up processing has been difficult to disentangle. This is because the neural circuits for spatial attention and selection are distributed across the frontal and parietal cortices and operate across multiple spatial scales spanning the activity of individual neurons and neuronal populations. In this Targeted Brain Circuit R01 Project proposal, an experimental group (Pesaran/NYU) and a theory group (Shanechi/USC) will use cutting-edge techniques developed under the NIH BRAIN Initiative support to validate predictive models of neuronal dynamics and test hypotheses about how frontal-parietal cortices perform attentional selection. A behavioral task that dissociates bottom up and top-down processing will let us define bottom-up and top-down target states. We will then build predictive models of neuronal dynamics within and between frontal and parietal cortex and empirically validate the models by stimulating neural activity to achieve the desired neural state. Aim 1 validates predictive models of local circuit dynamics. We will stimulate within PFC to achieve target states in PFC. Aim 2 validates predictive models of long-range circuit dynamics. We will stimulate sites in PPC that functionally connect to PFC in order to achieve target states in PFC. Aim 3 validates predictive models of distributed circuit dynamics. We will simultaneously stimulate both PFC and PPC to achieve the target states. In each case, successfully directing activity toward the target state will indicate the model is valid. If the target state reflects a causal role in attention, as opposed to correlating with attentional processes, we predict that behavioral choices will be biased. This proposal tackles several of the major topic areas of the BRAIN 2025 report. We will identify fundamental principles about circuit dynamics and functional connectivity for understanding the biological basis of mental processes through development of new theoretical and data analysis tools (Topic 5). We will produce a dynamic picture of the functioning brain by developing and applying improved methods for large-scale monitoring of neural activity (Topic 3). We will demonstrate causality by linking brain activity to behavior with precise interventional tools that change neural circuit dynamics (Topic 4). Recent years have seen dramatic advances in our ability to experimentally interface with the primate brain with increasing precision scale. A fruitful ... ## Key facts - **NIH application ID:** 10240643 - **Project number:** 5R01NS104923-05 - **Recipient organization:** NEW YORK UNIVERSITY - **Principal Investigator:** Roozbeh Kiani - **Activity code:** R01 (R01, R21, SBIR, etc.) - **Funding institute:** NIH - **Fiscal year:** 2021 - **Award amount:** $667,164 - **Award type:** 5 - **Project period:** 2017-09-25 → 2023-08-31 ## Primary source NIH RePORTER: https://reporter.nih.gov/project-details/10240643 ## Citation > US National Institutes of Health, RePORTER application 10240643, Predictive models of brain dynamics during decision making and their validation using distributed optogenetic stimulation (5R01NS104923-05). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/10240643. Licensed CC0. --- *[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*