Neuroimaging in pediatric language development and disorders: a scoping review protocol
Bibliographic record
Abstract
BACKGROUND: Infancy and early childhood represent critical periods for language development, as well as for the diagnosis and intervention of language disorders. Neuroimaging techniques, such as functional magnetic resonance imaging (fMRI), diffusion tensor imaging (DTI) and magnetoencephalography (MEG), have revolutionized our understanding of the brain's role in language development and disorders. These techniques provide detailed insights into the neural mechanisms underlying language acquisition and the deviations associated with language disorders. However, there is a notable lack of comprehensive literature reviews on the application of neuroimaging techniques in studying pediatric language development and disorders, particularly in children under eight years old in the field of speech-language pathology. This gap in the literature hinders the ability to form a cohesive understanding of the current state of research and its clinical implications, i.e., garnering an understanding of the contributions of these technological tools in understanding language development and disorders. METHODS: To address this gap, we will conduct a scoping review to synthesize literature involving neuroimaging techniques that examines language development and disorders in typically developing children and those diagnosed with language disorders under the age of eight. This review will be guided by the methodological framework proposed by Arksey and O'Malley [18]. A comprehensive and systematic search will be performed across multiple databases (MEDLINE, Embase, EBSCO CINAHL, PsycINFO, SCOPUS, and The Cochrane Library) to identify relevant peer-reviewed publications as well as grey literature. Studies will be screened according to predefined inclusion criteria. Key data from eligible studies will be extracted, synthesized and presented using both quantitative (numerical) and qualitative (narrative) approaches to address the research questions. DISCUSSION: This scoping review aims to provide a comprehensive overview and summary of the methodologies and key findings in neuroimaging studies related to pediatric language development and disorders. The results will identify critical gaps in the current research, highlight the strengths and limitations of various neuroimaging techniques, and suggest future research directions in the field of pediatric neuroimaging for language development. SYSTEMATIC REVIEW REGISTRATION: Open Science Framework (osf.io/5jhk6).
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.068 | 0.065 |
| Meta-epidemiology (narrow) | 0.005 | 0.005 |
| Meta-epidemiology (broad) | 0.015 | 0.012 |
| Bibliometrics | 0.029 | 0.020 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.007 | 0.007 |
| Research integrity | 0.008 | 0.005 |
| Insufficient payload (model declined to judge) | 0.054 | 0.008 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".