Paper

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

Beaton, Samuel, Lloyd-Fox, Sarah, McCann, Samantha, Bulgarelli, Chiara, Mbye, Ebrima, Blanco, Borja, Elwell, Clare, Blasi Ribera, Anna, Moore, Sophie

2025

Overview

Infant functional near-infrared spectroscopy (fNIRS) data are particularly vulnerable to noise; participant behaviour can result in motion artefacts 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. This work systematically compared pruning approaches and parameter choices to evaluate their effects on data quality and retention in infant fNIRS. Data from 5–24 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 effects of pruning method, parameter choice, age, motion, and testing site on signal-to-noise ratio (SNR) and channels retained. 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. 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.

• Publisher: Cold Spring Harbor Laboratory
• Subject: Neuroscience