Investigating the effect of channel pruning on functional near-infrared spectroscopy data collected from children aged 5 to 24 months.

Abstract

SIGNIFICANCE: Infant functional near-infrared spectroscopy (fNIRS) data are particularly vulnerable to noise; participant behavior can result in motion artifacts, and reduced set-up times can cause poor optode coupling. Accurate channel pruning is therefore essential, but approaches vary and often use adult-derived thresholds, risking unnecessary data loss. AIM: We systematically compared pruning approaches and parameter choices to evaluate their effects on data quality and retention in infant fNIRS. APPROACH: Data from 5 to 24-month-old infants were collected across two cohorts, using two paradigms. Channel pruning was performed using the coefficient of variation (CV) and the quality testing of near-infrared scans (QT-NIRS) tool, varying key thresholds. Multilevel models assessed the effects of pruning method, parameter choice, age, motion, and testing site on signal-to-noise ratio (SNR) and channels retained. RESULTS: QT-NIRS produced significantly higher SNR than CV pruning across nearly all age, task, and cohort combinations when matched for data retention. Higher QT-NIRS thresholds improved quality but reduced retention. Motion prevalence strongly reduced both SNR and retention; testing site and age had smaller but notable effects. CONCLUSIONS: QT-NIRS offers a better balance of data quality and retention than CV pruning. Lower QT-NIRS thresholds than adult defaults are recommended for infant data. These findings provide practical guidance for preprocessing pipelines in developmental fNIRS research.