Analysis of task-evoked systemic interference in fNIRS measurements: Insights from fMRI

Functional near infrared spectroscopy (fNIRS) is a promising method for monitoring cerebral hemodynamics with a wide range of clinical applications. fNIRS signals are contaminated with systemic physiological interferences from both the brain and superficial tissues, resulting in a poor estimation of...

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Bibliographic Details
Published in:NeuroImage (Orlando, Fla.) Fla.), 2014-02, Vol.87, p.490-504
Main Authors: ERDOGAN, Sinem B, YÜCEL, Meryem A, AKIN, Ata
Format: Article
Language:English
Subjects:
Biological and medical sciences
Brain - physiology
Brain Mapping - methods
Cognitive task
Functional near infrared spectroscopy
Fundamental and applied biological sciences. Psychology
Hemodynamic response
Hemodynamics - physiology
Humans
Magnetic Resonance Imaging
Methods
Migration
NMR
Noise
Nuclear magnetic resonance
Physiological artifact removal
Principal components analysis
Signal Processing, Computer-Assisted
Spectroscopy, Near-Infrared - methods
Spectrum analysis
Studies
Systemic interference
Vertebrates: nervous system and sense organs
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Summary:Functional near infrared spectroscopy (fNIRS) is a promising method for monitoring cerebral hemodynamics with a wide range of clinical applications. fNIRS signals are contaminated with systemic physiological interferences from both the brain and superficial tissues, resulting in a poor estimation of the task related neuronal activation. In this study, we use the anatomical resolution of functional magnetic resonance imaging (fMRI) to extract scalp and brain vascular signals separately and construct an optically weighted spatial average of the fMRI blood oxygen level-dependent (BOLD) signal for characterizing the scalp signal contribution to fNIRS measurements. We introduce an extended superficial signal regression (ESSR) method for canceling physiology-based systemic interference where the effects of cerebral and superficial systemic interference are treated separately. We apply and validate our method on the optically weighted BOLD signals, which are obtained by projecting the fMRI image onto optical measurement space by use of the optical forward problem. The performance of ESSR method in removing physiological artifacts is compared to i) a global signal regression (GSR) method and ii) a superficial signal regression (SSR) method. The retrieved signals from each method are compared with the neural signals that represent the ‘ground truth’ brain activation cleaned from cerebral systemic fluctuations. We report significant improvements in the recovery of task induced neural activation with the ESSR method when compared to the other two methods as reflected in the Pearson R2 coefficient and mean square error (MSE) metrics (two tailed paired t-tests, p
ISSN:1053-8119
1095-9572
DOI:10.1016/j.neuroimage.2013.10.024