Evidence map›Paper›PMID 42764116›Full record

ArticleNeuroscience2026

Structural connectivity of auditory-linguistic brain networks predicts success in speech categorization and listening in noise.

Rose Rizzi, Jack R Stirn, Zara Eisenhut, Gavin M Bidelman

Abstract read
In one paragraph

Article in Neuroscience, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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1 · What the graph read from it

What it found

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2 · The registry

The trial behind it

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3 · Its place in the literature

Who cites it

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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

4 authors.

Rose RizziDepartment of Speech, Language and Hearing Sciences, Indiana University, Bloomington, IN, USA; Program in Neuroscience, Indiana University, Bloomington, IN, USA.
Jack R StirnDepartment of Speech, Language and Hearing Sciences, Indiana University, Bloomington, IN, USA.
Zara EisenhutDepartment of Speech, Language and Hearing Sciences, Indiana University, Bloomington, IN, USA.
Gavin M BidelmanDepartment of Speech, Language and Hearing Sciences, Indiana University, Bloomington, IN, USA; Program in Neuroscience, Indiana University, Bloomington, IN, USA; Cognitive Science Program, Indiana University, Bloomington, IN, USA. Electronic address: gbidel@iu.edu.

Funding

Quality Assurance and Quality Control Project Management: Improving Submissions and Study Conduct in the Human Subjects Research Prior Approval ProcessUL1TR002529 · NCATS · INDIANA UNIVERSITY INDIANAPOLIS · PI MOE, SHARON M, WIEHE, SARAH ELIZABETH · 2018 to 2022
$27.2M
Neuroimaging biomarkers of speech processing deficits in MCIR01DC016267 · NIDCD · UNIVERSITY OF MEMPHIS · PI BIDELMAN, GAVIN M. · 2018 to 2022
$2.1M
Neural correlates and functional benefits of continuous vs. categorical listeningF31DC023124 · NIDCD · TRUSTEES OF INDIANA UNIVERSITY · PI Rose Rizzi · 2026 to 2026
$28k
NCATS NIH HHS UL1 TR002529NIDCD NIH HHS F31 DC023124NIDCD NIH HHS R01 DC016267
6 · The paper itself

Abstract

Successful speech perception requires listeners to bin continuous acoustic information into discrete phonetic categories. However, some people maintain within-category acoustic information (gradient listening) while others discard category-irrelevant information (discrete listening) during perception. Listeners also vary in how consistently they label speech sounds and more gradient/consistent labeling has been linked with better speech-in-noise (SIN) perception. Here, we test how neuroanatomical properties of the brain's major speech-language and auditory pathways relate to individual differences in speech categorization and SIN processing. We measured phonetic categorization and SIN comprehension via phoneme labeling and QuickSIN tasks. Diffusion-weighted imaging (DWI) with probabilistic tractography estimated axonal density within the bilateral arcuate fasciculi and brainstem-cortical auditory projections. Anatomical morphology (surface area, gray matter volume, thickness) was also quantified in the adjacent frontotemporal cortical areas and midbrain. Behaviorally, we found more perceptually consistent categorizers had better performance on the QuickSIN. DWI showed that more gradient listeners had greater white matter density in the left arcuate fasciculus and brainstem-cortical auditory pathways, while better SIN performance was predicted by denser white matter in the brainstem-cortical auditory pathways. Morphometric results revealed more consistent listening was associated with greater cortical thickness in right superior temporal gyrus and more gradient listening was associated with greater surface area in right pars opercularis. We infer that individual differences in phonetic categorization relate to SIN comprehension and are at least partially explained by neuroanatomical properties of the auditory-linguistic brain.

Indexed as

CategorizationConsistencyDiffusion-weighted imaging (DWI)GradienceMagnetic resonance imaging (MRI)Speech-in-noise

Identifiers

PMID42764116
PMCPMC13633569

What OpenQuestion holds

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.