# Uncovering cell type-specific prefrontal neural mechanisms of visuospatial selective attention in freely behaving mice using a high-throughput touchscreen-based training system

> **NIH NIH R21** · JOHNS HOPKINS UNIVERSITY · 2022 · $245,625

## Abstract

PROJECT SUMMARY
Selective spatial attention, the ability to select and preferentially process information at the most important spatial
location, is essential for adaptive behavior. Although extensive research in primates has established the
necessity of the prefrontal cortex (and specifically, the frontal eye field, FEF) for the control of selective visual
attention, the underlying cell-type and projection-specific neural circuit mechanisms remain elusive. We recently
developed rigorous touchscreen-based tasks for primate-like visuospatial selective attention in freely behaving
mice in order to investigate circuit mechanistic questions in a genetically tractable model and in a naturalistic
(unrestrained) setting. However, investigating the cell-type and projection-specific circuit logic of attention in mice
(using these tasks) is a large-scale effort that critically requires an affordable, high-throughput system for the
parallelized training of large numbers of mice. Specifically, for touchscreen behaviors, which are used extensively
in the behavioral neuroscience community, such a system does not exist either commercially or as open-source.
Here, in Aim 1, we propose to develop and establish a low cost, high-throughput, touchscreen-based hardware
and software platform for parallelized training of 20 mice at a time on complex visually guided behaviors
(including our attention tasks). We hypothesize that this open-source system will cost <1/10th the price, and
occupy <1/3rd the space, of current commercial systems, and offer flexible, easy-to-use software for stimulus
and experimental control. Preliminary data - hardware and software prototypes, establish viability of this aim.
Next, in Aim 2, we will use this high-throughput system to investigate in freely behaving mice, the causal role of
somatostatin-positive (SOM+) inhibitory neurons in the cingulate subdivision (Cg) of the mouse prefrontal cortex
(considered to be an analog of the FEF), in the control of visuospatial selective attention. We will do so with cell-
type specific chemogenetic silencing of SOM+ Cg interneurons in mice trained on our mouse flanker task of
attention, which dissociates the locus of attention from the locus of behavioral report (total of 35 SOM-cre mice).
We will combine behavioral testing with 3-D head-tracking (and eye-tracking). We hypothesize that Cg/SOM+
neurons control stimulus competition and target selection across space, and that their disruption will impair target
selection accuracy without producing purely sensory or motor deficits. Results from this work will have three
major impacts. (a) They will shed new light on the functional role of Cg/SOM+ interneurons in attention control.
(b) They will set the stage for our planned R01 aimed at detailed cell-type and projection-specific dissection of
cingulate sub-circuits (using optogenetics) and cingulate neuronal representations (using endoscopic Ca++
imaging) for visuospatial selective attention in freely beh...

## Key facts

- **NIH application ID:** 10527748
- **Project number:** 1R21MH128601-01A1
- **Recipient organization:** JOHNS HOPKINS UNIVERSITY
- **Principal Investigator:** Shreesh P Mysore
- **Activity code:** R21 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $245,625
- **Award type:** 1
- **Project period:** 2022-07-01 → 2024-04-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10527748

## Citation

> US National Institutes of Health, RePORTER application 10527748, Uncovering cell type-specific prefrontal neural mechanisms of visuospatial selective attention in freely behaving mice using a high-throughput touchscreen-based training system (1R21MH128601-01A1). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10527748. Licensed CC0.

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