An optical window into brain function in children and adolescents: A systematic review of functional near-infrared spectroscopy studies
Bibliographic record
Abstract
Despite decades of research, our understanding of functional brain development throughout childhood and adolescence remains limited due to the challenges posed by certain neuroimaging modalities. Recently, there has been a growing interest in using functional near-infrared spectroscopy (fNIRS) to elucidate the neural basis of cognitive and socioemotional development and identify the factors shaping these types of development. This article, focusing on the fNIRS methods, presents an up-to-date systematic review of fNIRS studies addressing the effects of age and other factors on brain functions in children and adolescents. Literature searches were conducted using PubMed and PsycINFO. A total of 79 fNIRS studies involving healthy individuals aged 3-17 years that were published in peer-reviewed journals in English before July 2020 were included. Six methodological aspects of these studies were evaluated, including the research design, experimental paradigm, fNIRS measurement, data preprocessing, statistical analysis, and result presentation. The risk of bias, such as selective outcome reporting, was assessed throughout the review. A qualitative synthesis of study findings in terms of the factor effects on changes in oxyhemoglobin concentration was also performed. This unregistered review highlights the strengths and limitations of the existing literature and suggests directions for future research to facilitate the improved use of fNIRS in developmental cognitive neuroscience research.
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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 | 0.005 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.012 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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.
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".