Multivariate fNIRS response patterns to social information are increasingly discriminable from six to sixty months of age
Bibliographic record
Abstract
Improving the geographic and economic inclusiveness of developmental neuroscience is an urgent concern, and functional near-infrared spectroscopy (fNIRS) is one tool extending the reach of neuroimaging beyond the communities closest to university laboratories. Where structural imaging facilities are unavailable, however, comparing fNIRS data across participants remains a challenge, especially as children’s head sizes vary widely with age. In this study, we describe a multivariate pattern analysis approach to describing fNIRS response patterns of infants and children (six- to sixty-months-old) from a low-income neighborhood of Dhaka, Bangladesh while they participated in a social cognition experiment (Perdue et al., 2019, Developmental Science). Instead of comparing the magnitude of hemodynamic responses in anatomical regions of interest, we use all channels simultaneously to compare changes in the discriminability between stimulus classes longitudinally over time (6-24 months, 36-60 months) and between groups (younger vs. older cohorts). From a sample of 53 to 74 children per age group, we find that a correlation-based, channel-space approach (Emberson et al., 2017, PLoS ONE) classifies fNIRS data more accurately with increasing age and is maximized by considering oxygenated and deoxygenated hemoglobin simultaneously. Using the brain response patterns from this sample to classify another, smaller sample of children (36-51 children per age group) from the same neighborhood, we achieve the same accuracy and age effects. These findings complement and extend the published univariate findings with finer-grained quantitative comparisons between ages, illustrating the power of multivariate approaches to understand developmental change without precise anatomical localization and in moderately sized samples.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".