# Laminar Circuit Motifs for Working Memory and Language Combinatorics: From Cells to Systems

> **NIH NIH U01** · UNIVERSITY OF IOWA · 2024 · $1,217,318

## Abstract

PROJECT SUMMARY / ABSTRACT
Language gives meaning to our inner thoughts, interacting with cognitive functions that flexibly manipulate
sensory content in mind. Language combinatorial capacities create meaning by combining symbols (words) and
rely on an extensive neural system, which when affected by neurological impact leads to disorders of language,
memory and thought. Although animal models have deeply informed understanding of neural systems for which
there are correspondences to human cognition, such as those for working memory, the neuronal mechanisms
of language and its entanglement with cognitive function remain poorly understood. The proposed
interdisciplinary team will seek to break through the status quo by leveraging a comprehensive research program
that can provide a well-powered study of human language and working memory interactions with unique scalable
data capable of resolving neural function across neocortical layers from single cells to systems. Aim 1a: Our
language Combinatoric Transformations via Working Memory (CTWM) task will be conducted with up to 100
patients across our neurosurgical partnering sites during high-density laminar array recordings in the operating
room from two key brain areas involved in language combinatorics and working memory. Aim 1b: Insights on
neuronal function across the cortical layers will be deeply informed by unprecedented information from single-
cell genomic and spatial transcriptomic analyses applied to tissue samples after task performance and the
laminar array recordings. Aim 2a is to investigate laminar information flow across the cortical array in local field
potentials, and Aim 2b in interaction with available (non-laminar) subdural intracranial EEG recordings from
other brain areas. Aim 3a is to pre-operatively scale insights to system-wide levels using laminar fMRI in the
same patients with the same task, and Aim 3b will integrate the combined neurophysiological and neuroimaging
data via a next-generation generative biophysical model. The anticipated outcomes are first-in-human insights
on the interplay between language and cognition, unique openly shared multi-modal data and a model that could
be applied world-wide to accelerate the understanding of human laminar circuit motifs in health and disease.

## Key facts

- **NIH application ID:** 10934028
- **Project number:** 1U01NS137991-01
- **Recipient organization:** UNIVERSITY OF IOWA
- **Principal Investigator:** Christopher I Petkov
- **Activity code:** U01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $1,217,318
- **Award type:** 1
- **Project period:** 2024-09-18 → 2029-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10934028

## Citation

> US National Institutes of Health, RePORTER application 10934028, Laminar Circuit Motifs for Working Memory and Language Combinatorics: From Cells to Systems (1U01NS137991-01). Retrieved via AI Analytics 2026-05-26 from https://api.ai-analytics.org/grant/nih/10934028. Licensed CC0.

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