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Comparing different pre-processing routines for infant fNIRS data.

Jessica Gemignani1, Judit Gervain1

  • 1Department of Developmental Psychology and Socialisation, University of Padova, Padova, Italy; Integrative Neuroscience and Cognition Center, CNRS & University of Paris, Paris, France.

Developmental Cognitive Neuroscience
|March 18, 2021
PubMed
Summary
This summary is machine-generated.

Pre-processing functional near-infrared spectroscopy (fNIRS) data in infants requires careful consideration. Artifact rejection preserves hemodynamic response (HRF) characteristics better than correction, though more data is lost.

Keywords:
Cognitive developmental neuroscienceInfantPre-processingfNIRS

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Area of Science:

  • Neuroscience
  • Cognitive Neuroscience
  • Developmental Neuroscience

Background:

  • Functional Near Infrared Spectroscopy (fNIRS) is crucial for cognitive developmental neuroscience.
  • Standardized pre-processing protocols for infant fNIRS are lacking.
  • Infant hemodynamic response (HRF) variability necessitates robust analysis methods.

Purpose of the Study:

  • To systematically compare the performance of five distinct fNIRS pre-processing pipelines.
  • To evaluate pipeline robustness against varying noise levels using synthetic and real infant data.
  • To inform optimal pre-processing strategies for infant neuroimaging studies.

Main Methods:

  • Literature search to identify five relevant infant fNIRS pre-processing pipelines.
  • Experiment 1: Synthetic data analysis to compare recovered HRFs with ground truth and assess noise impact.
  • Experiment 2: Analysis of published infant neuroimaging data (artificial grammar processing).

Main Results:

  • Motion artifact correction retained more trials but reduced HRF amplitude compared to artifact rejection.
  • Artifact rejection excluded more trials but better preserved HRF characteristics.
  • All pipelines showed performance decline with increased noise, yet outperformed no pre-processing.
  • Pre-processing on optical density vs. concentration change data yielded similar results.

Conclusions:

  • Optimal fNIRS pre-processing depends on dataset-specific quality issues, particularly motion artifacts.
  • Artifact rejection is preferable for preserving HRF integrity in infant studies, despite data loss.
  • Further research is needed to refine pre-processing for robust infant neuroimaging.