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Optimizing the general linear model for functional near-infrared spectroscopy: an adaptive hemodynamic response

Minako Uga1, Ippeita Dan1, Toshifumi Sano1

  • 1Jichi Medical University , Center for Development of Advanced Medical Technology, 3311-1 Yakushiji, Shimotsuke, Tochigi 329-0498, Japan ; Chuo University , Applied Cognitive Neuroscience Laboratory, 1-13-27 Kasuga, Bunkyo, Tokyo 112-8551, Japan.

Neurophotonics
|July 10, 2015
PubMed
Summary

This study introduces an adaptive hemodynamic response function (HRF) for functional near-infrared spectroscopy (fNIRS) analysis. This method optimizes temporal parameters, improving the general linear model (GLM) approach for both oxy-hemoglobin and deoxy-hemoglobin signals.

Keywords:
cortical hemodynamicsdiffuse optical imagingoptical topographyregression analysisstatistical analysisstatistical parametric mapping

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Area of Science:

  • Neuroscience
  • Biomedical Engineering
  • Cognitive Science

Background:

  • Functional near-infrared spectroscopy (fNIRS) measures changes in oxy-hemoglobin (oxy-Hb) and deoxy-hemoglobin (deoxy-Hb).
  • The general linear model (GLM) is a standard analysis method for fNIRS, analogous to its use in fMRI.
  • fNIRS offers higher temporal resolution than fMRI, necessitating adjustments to GLM temporal parameters.

Purpose of the Study:

  • To develop a GLM-based method with an adaptive hemodynamic response function (HRF) for fNIRS data.
  • To optimize temporal parameters of the HRF to best explain observed oxy-Hb and deoxy-Hb time series data during cognitive tasks.
  • To enhance the utilization of fNIRS's temporal data structures.

Main Methods:

  • Devised a GLM-based method incorporating an adaptive HRF.
  • Systematically varied HRF peak delay to achieve the best model fit for observed oxy-Hb and deoxy-Hb time series.
  • Evaluated the method during verbal fluency and naming tasks.

Main Results:

  • Optimized peak delay values differed for oxy-Hb, deoxy-Hb, and tasks.
  • Adopting optimized peak delays resulted in comparable activation, statistical power, and spatial patterns between oxy-Hb and deoxy-Hb data.
  • The adaptive HRF method effectively explained Hb parameter behavior across different cognitive loads.

Conclusions:

  • The adaptive HRF method provides an objective approach for fNIRS data analysis.
  • This method fully utilizes the temporal information present in fNIRS signals.
  • It allows for a more comprehensive understanding of brain activity measured by both oxy-Hb and deoxy-Hb.