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Depth-selective data analysis for time-domain fNIRS: moments vs. time windows.

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Time-domain functional near-infrared spectroscopy (fNIRS) methods are improved by analyzing photon time-of-flight distributions. Ratios of late time window photon counts offer superior depth selectivity for brain imaging.

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Area of Science:

  • Neuroscience
  • Biomedical Engineering
  • Optics

Background:

  • Functional near-infrared spectroscopy (fNIRS) is challenged by extracerebral and systemic effects.
  • Time-domain measurements offer a solution by analyzing photon time-of-flight distributions.
  • Quantitative characterization of analysis methods for time-domain fNIRS is needed.

Purpose of the Study:

  • To systematically compare the spatial sensitivity and depth selectivity of different time-domain analysis methods in fNIRS.
  • To evaluate the performance of moments (integral, mean, variance) and photon count ratios.
  • To investigate the impact of the instrument response function (IRF) on these measurands.

Main Methods:

  • Perturbation simulations of small, localized absorption changes in the adult human brain.
  • Comparison of spatial sensitivity profiles and depth selectivity for various measurands.
  • Analysis of moments (integral, mean time of flight, variance) and photon counts in time windows.
  • Assessment of the influence of the instrument response function (IRF) and source-detector separation.

Main Results:

  • Variance among moments showed the highest depth selectivity.
  • Ratios of photon counts in late time windows demonstrated even greater depth selectivity.
  • Moments proved robust against variations in the IRF shape and instrumental drifts.

Conclusions:

  • Photon count ratios in late time windows provide superior depth selectivity for time-domain fNIRS.
  • Moments offer a robust alternative for analyzing time-domain fNIRS data, particularly when dealing with instrumental variations.
  • These findings enhance the quantitative analysis of fNIRS brain imaging data.