# The Neural Code and Dynamics of the Reading Network

> **NIH NIH U01** · UNIVERSITY OF TEXAS HLTH SCI CTR HOUSTON · 2022 · $1,254,299

## Abstract

Reading involves complex transformations of word forms, with visual input mapped to lexical, semantic and
phonological systems in less than a second. While much has been learned about the neuroanatomy of reading
from functional imaging and lesion studies, the dynamic and interactive properties of this system remain largely
unknown. We will investigate the rapid computations that allow us to convert from the visual input of a string of
letters to a known word with an associated sound and meaning using our established techniques for precise co-
localization and analysis of a large population intracranial recordings (75 patients), thus circumventing the sparse
sampling problems inherent to human intracranial experiments. In a series of experiments that systematically
vary different properties of written words and modulate what kind of linguistic information participants must attend
to, we will map the brain's global reading network for words. We will evaluate the neurocomputational architecture
across the ventral visual stream that allows us to rapidly identify written words, and probe dynamic interactions
with the broader reading network during a variety of behavioral tasks biasing lexical, phonological and semantic
processes. We will then use autoregressive models to derive metastable brain states during reading and
characterize dynamic network-level interactions during these stages and relate these to observable behavior.
elaborate on the roles of nodes of the reading network in word learning, we will track the modulations in the
distributed reading network that enable successful word learning. This will involve teaching patients new words
and examining the reading network's response changes over a number of days. Critical nodes and transitions in
network states derived from recordings will be validated using unifocal and multifocal direct cortical stimulation.
To accomplish our goals we have set up a large multicenter collaboration. Our team has proven expertise in all
aspects of language, reading, intracranial signal analysis, population level network modeling, and neural
networks. This work will dramatically improve our understanding of written language systems and develop new
ways to model neural computation. It will greatly enhance our understanding of dyslexia and language disorders
following brain injury or degeneration, with our experimental focus on word learning directly informing
neurobiological models of language.

## Key facts

- **NIH application ID:** 10526168
- **Project number:** 1U01NS128921-01
- **Recipient organization:** UNIVERSITY OF TEXAS HLTH SCI CTR HOUSTON
- **Principal Investigator:** NITIN TANDON
- **Activity code:** U01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $1,254,299
- **Award type:** 1
- **Project period:** 2022-08-01 → 2027-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10526168

## Citation

> US National Institutes of Health, RePORTER application 10526168, The Neural Code and Dynamics of the Reading Network (1U01NS128921-01). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10526168. Licensed CC0.

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