Pushing the Boundaries of Background Functional Connectivity for Infant fNIRS Data: Evaluating Alternative Analytical Approaches
Bibliographic record
Abstract
There is increasing interest in task-based functional connectivity analyses to examine the emergence of functional networks during specific cognitive states starting early in development (e.g., infancy). However, studying functional connectivity in infants presents unique methodological challenges. Task-based neuroimaging studies must be carefully designed to collect sufficient high-quality data, while remaining sensitive to infants’ developing motor and cognitive abilities. This limits the feasibility of certain analysis techniques, particularly those requiring a high number of trials or strict adherence to contiguous trial structures. This study aims to expand established analysis approaches for infant functional connectivity studies by reanalysing an existing fNIRS dataset with a recognised signature of task-based functional connectivity. We assess the feasibility of moving away from conventional analyses that require several contiguous trials per condition - a requirement that is often impractical in infant studies. Instead, we explore whether background functional connectivity (BGFC) analyses, which utilises the residual neural response (i.e., what remains after removing task-evoked responses), can offer a more flexible and scalable approach. To do so, we evaluate three analytic strategies: (1) downsampling, which systematically reduces the number of trials included to assess how many are necessary for well-powered analyses; (2) shuffling, which randomly reorders trial-level residuals within conditions to test whether connectivity patterns are driven by trial sequence; and (3) averaging single-trial residuals, which examines whether connectivity measures can be meaningfully extracted from individual trial residuals. Our findings indicate that downsampling was informative about the number of trials needed for well-powered analyses and the timing of the functional connectivity effect. Removing trials from the start of the block was not viable, but this approach was viable when trials were removed from the end. The shuffling approach was not viable, suggesting that trial order may be important for capturing meaningful connectivity patterns. Single-trial residual analyses revealed that while connectivity can be examined toward the level of individual trials, averaging trial-level residuals before performing connectivity analyses produced more robust and interpretable results. These findings provide new insights into the methodological feasibility of background functional connectivity analyses for infant fNIRS data. By demonstrating that downsampling and averaged single-trial connectivity approaches are viable, our study highlights ways to enhance the accessibility and flexibility of task-based functional connectivity studies in infancy. These results have important implications for task design, as they suggest that strict adherence to contiguous trial structures may not be necessary, allowing researchers to design more adaptable and infant-friendly paradigms. Overall, this work contributes to advancing the methodological toolkit for studying functional brain networks in early development.
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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 | 0.020 | 0.102 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".