# Neural Processing of Speech Signals in Children Who Stutter

> **NIH NIH R01** · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · 2022 · $500,618

## Abstract

PROJECT SUMMARY/ABSTRACT
Developmental stuttering is a dynamic, multifactorial neurodevelopmental disorder characterized by unintended
disruptions in fluent speech production. Speech planning and production rely on intact speech sound processing,
which helps develop and maintain internal speech sound models. Unstable internal speech sound models, which
regulate motor signals in the speech motor articulatory network (SMAN), may contribute to disfluent speech in
children who stutter (CWS). In concert with frontoparietal attention network, SMAN also modulates attention to
phonetic/syllabic information in speech, particularly in difficult listening conditions. CWS often perform worse on
speech processing tasks than fluent peers, especially on more challenging tasks, potentially due to inefficiencies
in these auxiliary networks. However, the underlying causes of speech processing deficits in CWS remain
unclear. A mechanistic understanding of speech sound processing will facilitate future development of
neurobiologically informed stuttering interventions that target the specific neural deficits in CWS. The current
proposal extends previous findings of atypical speech sound processing in CWS. Combining the complementary
expertise of a cross-disciplinary team of investigators, the current project will evaluate the integrity of neural
processes underlying speech sound encoding and the ways in which these processes are modulated by task
demands using multimodal neuroimaging and systems-level computational modeling approaches. Aim 1 will
measure electroencephalography (EEG) in 150 CWS and 150 fluent peers, aged 7-15 years, while children
complete four tasks of varying difficulty: A) a syllable identification task (/ba/ vs /da/) in quiet; B) a continuous
speech narrative comprehension task in quiet; and C & D) complex speech encoding tasks with syllables and
continuous speech presented simultaneously, with attention directed either toward syllables (C) or toward the
narrative (D). Directly comparing neural responses elicited in simpler and more complex listening conditions
(A/C, B/D) and responses to the same stimuli when attended vs. ignored (C/D) is critical for characterizing effects
of task demands on speech sound processing. State-of-the-art machine-learning approaches for EEG will enable
simultaneous extraction of temporally precise neural representations of fast and slow temporal fluctuations in
speech in the transformation from acoustic to syllable representations. Aim 2 will leverage functional MRI (fMRI)
to assess multiple neural systems underlying speech sound processing in CWS. Employing the same tasks in
the same participants as Aim 1 will allow for quantifying neural activations and representations in auditory,
SMAN, and attention networks during simple and complex speech tasks. Aim 3 will develop a systems-level
computational model of speech sound processing in CWS. The model, based on combined EEG and fMRI data,
will simulate how interactions...

## Key facts

- **NIH application ID:** 10337369
- **Project number:** 1R01DC019904-01
- **Recipient organization:** UNIVERSITY OF PITTSBURGH AT PITTSBURGH
- **Principal Investigator:** Amanda M Hampton Wray
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $500,618
- **Award type:** 1
- **Project period:** 2022-03-09 → 2027-02-28

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10337369

## Citation

> US National Institutes of Health, RePORTER application 10337369, Neural Processing of Speech Signals in Children Who Stutter (1R01DC019904-01). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10337369. Licensed CC0.

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