MétaCan
Menu
Back to cohort
Record W2810903279 · doi:10.4236/jbbs.2018.87026

Impact of Motor Speech Intervention on Neural Activity in Children with Speech Sound Disorders: Use of Magnetoencephalography

2018· article· en· W2810903279 on OpenAlexafffund
Vickie Y. Yu, Darren S. Kadis, Debra Goshulak, Aravind Kumar Namasivayam, Margit Pukonen, Robert M. Kroll, Luc F. De Nil, Elizabeth W. Pang

Bibliographic record

VenueJournal of Behavioral and Brain Science · 2018
Typearticle
Languageen
FieldPsychology
TopicLanguage Development and Disorders
Canadian institutionsMental Health Research CanadaHospital for Sick ChildrenUniversity of TorontoThe Speech and Stuttering Institute
FundersCanadian Institutes of Health Research
KeywordsMagnetoencephalographySpeech productionArticulation (sociology)Speech soundPsychologyNeuroimagingSyllableNeurocomputational speech processingAudiologyBrain activity and meditationSpeech disorderIntervention (counseling)Task (project management)Speech recognitionSpeech perceptionNeuroscienceMedicinePerceptionElectroencephalographyComputer science

Abstract

fetched live from OpenAlex

We present the novel use of a neuroimaging technique, magnetoenceph-alography (MEG), for examining therapy-related changes in neural activity during a speech and a non-speech motor task in children with speech sound disorders (SSD). Nine children (mean age = 4.2 years) with SSD were scanned in the MEG before and after an eight-week course of intensive motor speech therapy. MEG tasks involved an oromotor and a syllable production task. MEG analyses identified significant post-therapy changes in brain regions related to oromotor control and speech production. Behavioral assessments showed significant improvements on measures of motor speech skills and articulation following intervention. This is the first demonstration of the ability of MEG to: 1) capture brain activations resulting from oromotor movements and simple syllable production in young children, and 2) capture brain changes related to speech therapy. As the findings from this study are promising, we discuss directions for the design of future studies to further examine specific neural dysfunctions in speech sound disorders.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.082
Threshold uncertainty score0.303

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.030
GPT teacher head0.356
Teacher spread0.326 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations5
Published2018
Admission routes2
Has abstractyes

Explore more

Same venueJournal of Behavioral and Brain ScienceSame topicLanguage Development and DisordersFrench-language works237,207