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Effect of confounding variables on hemodynamic response function estimation using averaging and deconvolution

Ardalan Aarabi1, Victoria Osharina2, Fabrice Wallois3

  • 1Faculty of Medicine, University of Picardie Jules Verne, Amiens 80036, France; GRAMFC-Inserm U1105, University Research Center (CURS), University Hospital, Amiens, 80054 France.

Neuroimage
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Summary

Deconvolution methods (DM) are more robust than conventional averaging (CA) for estimating brain hemodynamic response functions in rapid event-related designs, especially with low signal-to-noise ratios. Careful consideration of event timing and noise is crucial for accurate results.

Keywords:
AveragingConfounding variablesDeconvolution methodEvent-related designFinite impulse responseGamma basis setHemodynamic response functionNear-infrared spectroscopy

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Area of Science:

  • Neuroimaging techniques
  • Brain activity analysis
  • Hemodynamic response modeling

Background:

  • Functional near-infrared spectroscopy (fNIRS) and fMRI utilize event-related designs to study brain responses.
  • Conventional averaging (CA) and deconvolution methods (DM) are primary techniques for estimating the hemodynamic response function (HRF).
  • Understanding the performance of these methods under various experimental conditions is critical for accurate neuroimaging data interpretation.

Purpose of the Study:

  • To evaluate the performance of CA and DM in slow and rapid event-related designs.
  • To investigate the impact of confounding factors like event timing, noise, SNR, temporal autocorrelation, and filtering on HRF estimation.
  • To compare systematic errors in HRF amplitude, latency, and duration estimates between CA and DM.

Main Methods:

  • Simulations using synthetic and real NIRS data were performed.
  • The study examined slow and rapid event-related designs.
  • Performance was assessed based on HRF parameter accuracy and sensitivity to confounding factors, including different deconvolution basis sets (FIR and gamma).

Main Results:

  • DM demonstrated superior robustness against confounding factors compared to CA.
  • Event timing significantly impacted CA accuracy, while DM performed well in rapid designs across various SNRs (especially > -5 dB).
  • Low-frequency fluctuations, phase-locked noise, temporal autocorrelation, and high-pass filtering were identified as significant challenges affecting both methods, particularly at low SNRs.

Conclusions:

  • Deconvolution methods are recommended for rapid event-related designs due to their enhanced sensitivity and robustness.
  • Accurate characterization of event timing, background noise, and signal-to-noise ratio is essential for reliable HRF estimation using both CA and DM.
  • Researchers should be mindful of potential distortions introduced by temporal filtering and autocorrelation when interpreting HRF data.