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Exploring Cognitive Functions in Babies, Children & Adults with Near Infrared Spectroscopy
Published on: July 28, 2009
Investigating the effect of channel pruning on functional near-infrared spectroscopy data collected from children
Samuel Beaton1, Borja Blanco2, Chiara Bulgarelli3
1King's College London, Department of Women & Children's Health, London, United Kingdom.
Insights
The QT-NIRS tool provides better data quality and retention for infant functional near-infrared spectroscopy (fNIRS) than CV pruning. Lower thresholds are recommended for infant fNIRS data processing.
Area of Science:
- Developmental neuroscience
- Neuroimaging techniques
Background:
- Infant functional near-infrared spectroscopy (fNIRS) data are susceptible to noise from motion artifacts and poor optode coupling.
- Current channel pruning methods often use adult-derived thresholds, potentially leading to excessive data loss in infant studies.
Purpose of the Study:
- To systematically compare different channel pruning approaches and parameter settings for infant fNIRS data.
- To evaluate the impact of these methods on data quality (signal-to-noise ratio) and data retention.
Main Methods:
- Collected infant fNIRS data from 5- to 24-month-olds across two cohorts and two paradigms.
- Applied channel pruning using the coefficient of variation (CV) and the quality testing of near-infrared scans (QT-NIRS) tool, varying key thresholds.
- Utilized multilevel models to assess the influence of pruning method, parameters, age, motion, and site on SNR and retained channels.
Main Results:
- QT-NIRS significantly outperformed CV pruning in SNR across most conditions when data retention was comparable.
- Increasing QT-NIRS thresholds enhanced data quality but decreased the amount of retained data.
- Motion prevalence substantially decreased both SNR and data retention; age and testing site also had smaller effects.
Conclusions:
- QT-NIRS demonstrates a superior balance between data quality and retention for infant fNIRS compared to CV pruning.
- Recommends utilizing lower QT-NIRS thresholds for infant data than those typically used for adults.
- Provides essential practical guidance for optimizing preprocessing pipelines in developmental fNIRS research.
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.
