OSCR

Neural encoding of auditory processing in schizophrenia

Overview

Authors: Lakshma Reddy Vuyyuru1, Anitha Raju1
  1. Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Vaddeswaram, Andhra Pradesh, India
Journal: Frontiers in neuroscience, volume 20, article 1893300
Dates: received 28 May 2026; accepted 19 August 2026; published online 15 September 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI · PMCID PMC13619899
Categories: fMRI (modality), human (organism), schizophrenia / psychosis (population)
Methods: Connectivity, Spectral & time-frequency, Statistics, Smoothing, state filtering, decompositions, Machine learning, Preprocessing, fMRI & imaging
Keywords: auditory cortex, brain encoding, fMRI, language, schizophrenia, speech models, voxel-wise models
Citations: not cited yet (Europe PMC); 45 references in the paper

Abstract

Introduction: Auditory processing abnormalities are a core feature of schizophrenia and are linked to deficits in speech perception, cognitive function, and everyday communication. It remains unclear which stages of auditory processing are most disrupted and how these disruptions relate to symptoms at the level of population-wide neural representations.

Methods: We used fMRI recordings acquired while individuals with schizophrenia and matched healthy controls completed a controlled block-design speech-perception task. Voxel-wise encoding models mapped self-supervised speech-model representations, together with complementary acoustic and linguistic features, to neural responses.

Results: Across cross-validated evaluations, individuals with schizophrenia showed numerically higher encoding alignment than matched controls across all evaluated regions of interest, although no effect survived false discovery rate correction in this small sample. The largest numerical group differences occurred in frontal language regions, including the middle frontal gyrus and dorsomedial prefrontal cortex, and in early auditory cortex. Subgroup analyses suggested that patients without auditory verbal hallucinations drove much of this numerical separation.

Discussion: These descriptive, hypothesis-generating findings suggest that model-based encoding can reveal a frontal-auditory signature in schizophrenia, but they do not establish a clinically actionable biomarker or preserved or enhanced speech encoding. Larger, adequately powered studies with richer feature spaces are needed for confirmatory inference and to test whether encoding-based measures can serve as clinically meaningful biomarkers.

Reproduced under the paper's license (CC BY), from the paper cited above.

Code

The paper links to its data and to no code of its authors: see the Data section.

Tracing map

A tracing map links a paper to the code its authors published: this paper has none, so it has no map.

Data

Datasets cited

Data availability statement

The data analyzed in this study are publicly available in OpenNeuro under accession ds004302 at https://doi.org/10.18112/openneuro.ds004302.v1.0.0. The original contributions presented in the study are included in the article; further inquiries can be directed to the corresponding author.

Reproduced under the paper's license (CC BY), from the paper cited above.

Versions

The history of this record: each version stored by the harvester or made by a correction of its authors or of the maintainers of its code, and what changed in its facts. The texts of the paper (its abstract, its availability statements) are not part of it; versions that changed only those are not listed.

Version 1, 1 October 2026: the first record

Recorded: type, language, journal, volume, pages, dates, 2 authors, 7 keywords, 42 references.

Cite

This paper

Vuyyuru, L. R., & Raju, A. (2026). Neural encoding of auditory processing in schizophrenia. Frontiers in neuroscience, 20, 1893300.

BibTeX

@article{vuyyuru2026neural,
author = {Vuyyuru, Lakshma Reddy and Raju, Anitha},
title = {{Neural encoding of auditory processing in schizophrenia}},
journal = {Frontiers in neuroscience},
year = {2026},
month = sep,
volume = {20},
pages = {1893300},
publisher = {Frontiers Media SA},
issn = {1662-4548},
pmcid = {PMC13619899}
}

RIS

TY - JOUR
AU - Vuyyuru, Lakshma Reddy
AU - Raju, Anitha
TI - Neural encoding of auditory processing in schizophrenia
T2 - Frontiers in neuroscience
J2 - Front Neurosci
PY - 2026
DA - 2026/09/30
VL - 20
SP - 1893300
SN - 1662-4548
PB - Frontiers Media SA
LA - en
ER -

CSL-JSON

{
"id": "pmcid:PMC13619899",
"type": "article-journal",
"title": "Neural encoding of auditory processing in schizophrenia",
"container-title": "Frontiers in neuroscience",
"author": [
{
"family": "Vuyyuru",
"given": "Lakshma Reddy"
},
{
"family": "Raju",
"given": "Anitha"
}
],
"container-title-short": "Front Neurosci",
"volume": "20",
"page": "1893300",
"PMCID": "PMC13619899",
"ISSN": "1662-4548",
"publisher": "Frontiers Media SA",
"language": "en",
"issued": {
"date-parts": [
[
2026,
9,
30
]
]
}
}

Similar papers

The papers with a page that share the most with this one: the tools found in their code, their categories, datasets, cited references and authors, the rarest counting most.

[1] doi:10.1162/imag_a_00575 [code]
The Voxelwise Encoding Model framework: A tutorial introduction to fitting encoding models to fMRI data.
Journal: Imaging neuroscience (Cambridge, Mass.)
In common: fMRI, 12 references
[2] doi:10.1038/s41467-024-49173-5 [code]
Shared functional specialization in transformer-based language models and the human brain.
Journal: Nature communications
In common: 12 references
[3] doi:10.1371/journal.pcbi.1014906 [code]
Interpretable embeddings of speech explain and enhance the brain encoding performance of audio models.
Journal: PLoS computational biology
In common: 9 references
[4] doi:10.1016/j.neuroimage.2022.119728 [code]
Feature-space selection with banded ridge regression.
Journal: NeuroImage
In common: 9 references
[5] doi:10.1162/imag.a.1349 [code]
Rapid cortical mapping with cross-participant encoding models.
Journal: Imaging neuroscience (Cambridge, Mass.)
In common: fMRI, 8 references
[6] doi:10.1038/s41597-023-02437-z [code]
A natural language fMRI dataset for voxelwise encoding models.
Journal: Scientific data
In common: fMRI, 8 references
[7] doi:10.1016/j.neuroimage.2024.120772 [code]
Stacked regressions and structured variance partitioning for interpretable brain maps.
Journal: NeuroImage
In common: 8 references
[8] doi:10.1038/s42003-024-05909-z [code]
The cortical representation of language timescales is shared between reading and listening.
Journal: Communications biology
In common: 8 references
[9] doi:10.1162/imag_a_00155 [code]
Exploring an EM-algorithm for banded regression in computational neuroscience.
Journal: Imaging neuroscience (Cambridge, Mass.)
In common: fMRI, 7 references
[10] doi:10.1038/s41597-025-05462-2 [code]
The "Podcast" ECoG dataset for modeling neural activity during natural language comprehension.
Journal: Scientific data
In common: 7 references

Contribute

The authors of this paper can claim it, correct its record and validate its tracing map, and the maintainers of its code (its owner, or a public member of its organization) correct what it says of their repository; anyone signed in can ask for its removal. Every request goes to OSCR's own machine, which answers it; your account page follows them.

Sign in with ORCID to claim this paper as one of its authors, correct its record or validate its tracing map: when the paper's metadata lists your ORCID iD, you are recognized at once. Maintainers of its code: sign in with GitHub, then claim the repository on your account page.

Request its removal

To ask OSCR to remove this record, the copies of its authors' scripts or its tracing map, use the removal request page: signed in, you say who you are, what to remove and why, then review and confirm the request. Published rules decide every request (how).

Discussion, reproductions, activity

Discussion: code errors and mismatches between the code and the paper about this paper and its code, from readers and its authors, beside the paper and its tracing map.

Reproductions: reports from readers who ran the authors' code and did not get the paper's result, what they ran, with which environment, commit and data.

Activity: what happens around this paper: new versions of its record, its map's validation, discussions and reproductions. It opens with sign-in.