Evidence map›Paper›PMID 38435340›Full record

ArticleApplied sciences (Basel, Switzerland)2023

DIVA Meets EEG: Model Validation Using Formant-Shift Reflex.

Jhosmary Cuadros, Lucía Z-Rivera, Christian Castro, Grace Whitaker, Mónica Otero, Alejandro Weinstein, Eduardo Martínez-Montes, Pavel Prado, Matías Zañartu

Open access · goldAbstract read
In one paragraph

Article in Applied sciences (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
1.4field-weighted citation impact, top 20% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed, 8 citations in OpenAlex.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors at 4 institutions in 3 countries.

Jhosmary CuadrosDepartment of Electronic Engineering, Universidad Técnica Federico Santa María, Valparaíso 2390123, Chile.ORCID 0000-0001-7665-0171
Lucía Z-RiveraAdvanced Center for Electrical and Electronic Engineering, Universidad Técnica Federico Santa María, Valparaíso 2390123, Chile.ORCID 0000-0001-9243-193X
Christian CastroAdvanced Center for Electrical and Electronic Engineering, Universidad Técnica Federico Santa María, Valparaíso 2390123, Chile.
Grace WhitakerAdvanced Center for Electrical and Electronic Engineering, Universidad Técnica Federico Santa María, Valparaíso 2390123, Chile.
Mónica OteroFacultad de Ingeniería, Arquitectura y Diseño, Universidad San Sebastián, Santiago 8420524, Chile.ORCID 0000-0001-7254-9244
Alejandro WeinsteinAdvanced Center for Electrical and Electronic Engineering, Universidad Técnica Federico Santa María, Valparaíso 2390123, Chile.ORCID 0000-0003-1486-7003
Eduardo Martínez-MontesBrain Mapping Division, Cuban Neuroscience Center, Habana 11300, Cuba.ORCID 0000-0002-5852-964X
Pavel PradoEscuela de Fonoaudiología, Facultad de Odontología y Ciencias de la Rehabilitación, Universidad San Sebastián, Santiago 7510602, Chile.ORCID 0000-0002-1324-6400
Matías ZañartuDepartment of Electronic Engineering, Universidad Técnica Federico Santa María, Valparaíso 2390123, Chile.ORCID 0000-0001-5581-4392
Federico Santa María Technical University · CLSan Sebastián University · CLCuban Neuroscience Center · CUUniversidad Nacional Experimental del Táchira · VE

Funding

Use of ambulatory biofeedback to improve behavioral treatment of vocal hyperfunctionP50DC015446 · NIDCD · MASSACHUSETTS GENERAL HOSPITAL · PI Matias Zanartu · 2017 to 2026
$25.5M
NIDCD NIH HHS P50 DC015446
6 · The paper itself

Abstract

The neurocomputational model 'Directions into Velocities of Articulators' (DIVA) was developed to account for various aspects of normal and disordered speech production and acquisition. The neural substrates of DIVA were established through functional magnetic resonance imaging (fMRI), providing physiological validation of the model. This study introduces DIVA_EEG an extension of DIVA that utilizes electroencephalography (EEG) to leverage the high temporal resolution and broad availability of EEG over fMRI. For the development of DIVA_EEG, EEG-like signals were derived from original equations describing the activity of the different DIVA maps. Synthetic EEG associated with the utterance of syllables was generated when both unperturbed and perturbed auditory feedback (first formant perturbations) were simulated. The cortical activation maps derived from synthetic EEG closely resembled those of the original DIVA model. To validate DIVA_EEG, the EEG of individuals with typical voices (N = 30) was acquired during an altered auditory feedback paradigm. The resulting empirical brain activity maps significantly overlapped with those predicted by DIVA_EEG. In conjunction with other recent model extensions, DIVA_EEG lays the foundations for constructing a complete neurocomputational framework to tackle vocal and speech disorders, which can guide model-driven personalized interventions.

Indexed as

auditory feedbackDIVA modelEEGfeedback perturbationvocal compensation

Identifiers

PMID38435340
PMCPMC10906992
OpenAlexW4382134230

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.