Short-channel regression in functional near-infrared spectroscopy is more effective when considering heterogeneous scalp hemodynamics

Abstract. Significance: The reliability of functional near-infrared spectroscopy (fNIRS) measurements is reduced by systemic physiology. Short-channel regression algorithms aim at removing systemic “noise” by subtracting the signal measured at a short source–detector separation (mainly scalp hemodynamics) from the one of a long separation (brain and scalp hemodynamics). In literature, incongruent approaches on the selection of the optimal regressor signal are reported based on different assumptions on scalp hemodynamics properties. Aim: We investigated the spatial and temporal distribution of scalp hemodynamics over the sensorimotor cortex and evaluated its influence on the effectiveness of short-channel regressions. Approach: We performed hand-grasping and resting-state experiments with five subjects, measuring with 16 optodes over sensorimotor areas, including eight 8-mm channels. We performed detailed correlation analyses of scalp hemodynamics and evaluated 180 hand-grasping and 270 simulated (overlaid on resting-state measurements) trials. Five short-channel regressor combinations were implemented with general linear models. Three were chosen according to literature, and two were proposed based on additional physiological assumptions [considering multiple short channels and their Mayer wave (MW) oscillations]. Results: We found heterogeneous hemodynamics in the scalp, coming on top of a global close-to-homogeneous behavior (correlation 0.69 to 0.92). The results further demonstrate that short-channel regression always improves brain activity estimates but that better results are obtained when heterogeneity is assumed. In particular, we highlight that short-channel regression is more effective when combining multiple scalp regressors and when MWs are additionally included. Conclusion: We shed light on the selection of optimal regressor signals for improving the removal of systemic physiological artifacts in fNIRS. We conclude that short-channel regression is most effective when assuming heterogeneous hemodynamics, in particular when combining spatial- and frequency-specific information. A better understanding of scalp hemodynamics and more effective short-channel regression will promote more accurate assessments of functional brain activity in clinical and research settings.

[1]  Hanli Liu,et al.  A Location-Adaptive, Frequency-Specific Cancellation Algorithm to Improve Optical Brain Functional Imaging , 2008 .

[2]  Ata Akin,et al.  Analysis of task-evoked systemic interference in fNIRS measurements: Insights from fMRI , 2014, NeuroImage.

[3]  R. Saager,et al.  Direct characterization and removal of interfering absorption trends in two-layer turbid media. , 2005, Journal of the Optical Society of America. A, Optics, image science, and vision.

[4]  R. Gassert,et al.  Silicon photomultipliers for improved detection of low light levels in miniature near-infrared spectroscopy instruments , 2013, Biomedical optics express.

[5]  David A Boas,et al.  Direct estimation of evoked hemoglobin changes by multimodality fusion imaging. , 2008, Journal of biomedical optics.

[6]  Martin Wolf,et al.  A review on continuous wave functional near-infrared spectroscopy and imaging instrumentation and methodology , 2014, NeuroImage.

[7]  Andrew J Berger,et al.  Short-channel functional near-infrared spectroscopy regressions improve when source-detector separation is reduced , 2014, Neurophotonics.

[8]  C. Aalkjær,et al.  Vasomotion – what is currently thought? , 2011, Acta physiologica.

[9]  Rolf B. Saager,et al.  Two-detector Corrected Near Infrared Spectroscopy (C-NIRS) detects hemodynamic activation responses more robustly than single-detector NIRS , 2011, NeuroImage.

[10]  Martin Wolf,et al.  Wearable and modular functional near-infrared spectroscopy instrument with multidistance measurements at four wavelengths , 2017, Neurophotonics.

[11]  G. Strangman,et al.  Depth Sensitivity and Source-Detector Separations for Near Infrared Spectroscopy Based on the Colin27 Brain Template , 2013, PLoS ONE.

[12]  D. Boas,et al.  HomER: a review of time-series analysis methods for near-infrared spectroscopy of the brain. , 2009, Applied optics.

[13]  D. Delpy,et al.  Optical pathlength measurements on adult head, calf and forearm and the head of the newborn infant using phase resolved optical spectroscopy. , 1995, Physics in medicine and biology.

[14]  David A. Boas,et al.  Further improvement in reducing superficial contamination in NIRS using double short separation measurements , 2014, NeuroImage.

[15]  Bülent Sankur,et al.  Constraining the general linear model for sensible hemodynamic response function waveforms , 2008, Medical & Biological Engineering & Computing.

[16]  David A. Boas,et al.  A Systematic Comparison of Motion Artifact Correction Techniques for Functional Near-Infrared Spectroscopy , 2012, Front. Neurosci..

[17]  Ilias Tachtsidis,et al.  Current Status and Issues Regarding Pre-processing of fNIRS Neuroimaging Data: An Investigation of Diverse Signal Filtering Methods Within a General Linear Model Framework , 2019, Front. Hum. Neurosci..

[18]  M. Ferrari,et al.  A Mini-Review on Functional Near-Infrared Spectroscopy (fNIRS): Where Do We Stand, and Where Should We Go? , 2019, Photonics.

[19]  Charles L. Lawson,et al.  Solving least squares problems , 1976, Classics in applied mathematics.

[20]  Donghui Chen,et al.  Nonnegativity constraints in numerical analysis , 2009, The Birth of Numerical Analysis.

[21]  Masa-aki Sato,et al.  Reduction of global interference of scalp-hemodynamics in functional near-infrared spectroscopy using short distance probes , 2016, NeuroImage.

[22]  Robert Riener,et al.  Motor execution detection based on autonomic nervous system responses , 2013, Physiological measurement.

[23]  David A. Boas,et al.  Improved physiological noise regression in fNIRS: A multimodal extension of the General Linear Model using temporally embedded Canonical Correlation Analysis , 2019, NeuroImage.

[24]  Keum-Shik Hong,et al.  Cortical brain imaging by adaptive filtering of NIRS signals , 2012, Neuroscience Letters.

[25]  Catie Chang,et al.  Sympathetic activity contributes to the fMRI signal , 2019, Communications Biology.

[26]  Klaus-Robert Müller,et al.  A new blind source separation framework for signal analysis and artifact rejection in functional Near-Infrared Spectroscopy , 2019, NeuroImage.

[27]  Takanori Sato,et al.  Real-Time Reduction of Task-Related Scalp-Hemodynamics Artifact in Functional Near-Infrared Spectroscopy with Sliding-Window Analysis , 2018 .

[28]  David A Boas,et al.  Diffuse optical imaging of the whole head. , 2006, Journal of biomedical optics.

[29]  Ilias Tachtsidis,et al.  False positives and false negatives in functional near-infrared spectroscopy: issues, challenges, and the way forward , 2016, Neurophotonics.

[30]  R. Turner,et al.  Event-Related fMRI: Characterizing Differential Responses , 1998, NeuroImage.

[31]  H. Nilsson,et al.  Vasomotion: mechanisms and physiological importance. , 2003, Molecular interventions.

[32]  Jie Chen,et al.  Nonnegative Least-Mean-Square Algorithm , 2011, IEEE Transactions on Signal Processing.

[33]  Martin Wolf,et al.  Advances in near-infrared spectroscopy to study the brain of the preterm and term neonate. , 2009, Clinics in perinatology.

[34]  Xu Xu,et al.  Multiregional functional near-infrared spectroscopy reveals globally symmetrical and frequency-specific patterns of superficial interference. , 2015, Biomedical optics express.

[35]  Heidrun Wabnitz,et al.  The physiological origin of task-evoked systemic artefacts in functional near infrared spectroscopy , 2012, NeuroImage.

[36]  Robert J Cooper,et al.  Review of recent progress toward a fiberless, whole-scalp diffuse optical tomography system , 2017, Neurophotonics.

[37]  Heidrun Wabnitz,et al.  Identifying and quantifying main components of physiological noise in functional near infrared spectroscopy on the prefrontal cortex , 2013, Front. Hum. Neurosci..

[38]  Katherine L. Perdue,et al.  Extraction of heart rate from functional near-infrared spectroscopy in infants , 2014, Journal of biomedical optics.

[39]  Heidrun Wabnitz,et al.  Special Section Guest Editorial:Clinical near-infrared spectroscopy and imaging of the brain , 2016, Neurophotonics.

[40]  J. Culver,et al.  Brain Specificity of Diffuse Optical Imaging: Improvements from Superficial Signal Regression and Tomography , 2010, Front. Neuroenerg..

[41]  A. Villringer,et al.  Spontaneous Low Frequency Oscillations of Cerebral Hemodynamics and Metabolism in Human Adults , 2000, NeuroImage.

[42]  Giovanni Sparacino,et al.  A reference-channel based methodology to improve estimation of event-related hemodynamic response from fNIRS measurements , 2013, NeuroImage.

[43]  M Wolf,et al.  How to detect and reduce movement artifacts in near-infrared imaging using moving standard deviation and spline interpolation , 2010, Physiological measurement.

[44]  Y. Hoshi,et al.  Fluctuations in the cerebral oxygenation state during the resting period in functional mapping studies of the human brain , 1997, Medical and Biological Engineering and Computing.

[45]  David A. Boas,et al.  Short separation channel location impacts the performance of short channel regression in NIRS , 2012, NeuroImage.

[46]  Michele L. Pierro,et al.  Low-Frequency Spontaneous Oscillations of Cerebral Hemodynamics Investigated With Near-Infrared Spectroscopy: A Review , 2012, IEEE Journal of Selected Topics in Quantum Electronics.

[47]  Sungho Tak,et al.  NIRS-SPM: Statistical parametric mapping for near-infrared spectroscopy , 2009, NeuroImage.

[48]  Meryem A Yücel,et al.  Mayer waves reduce the accuracy of estimated hemodynamic response functions in functional near-infrared spectroscopy. , 2016, Biomedical optics express.

[49]  Robert Riener,et al.  What’s Your Next Move? Detecting Movement Intention for Stroke Rehabilitation , 2011 .

[50]  Ardalan Aarabi,et al.  Characterization of the relative contributions from systemic physiological noise to whole-brain resting-state functional near-infrared spectroscopy data using single-channel independent component analysis , 2016, Neurophotonics.

[51]  I. Miyai,et al.  Removal of the skin blood flow artifact in functional near-infrared spectroscopic imaging data through independent component analysis. , 2007, Journal of biomedical optics.

[52]  David A. Boas,et al.  Quantification of the cortical contribution to the NIRS signal over the motor cortex using concurrent NIRS-fMRI measurements , 2012, NeuroImage.

[53]  Robert Riener,et al.  Detection of motor execution using a hybrid fNIRS-biosignal BCI: a feasibility study , 2013, Journal of NeuroEngineering and Rehabilitation.

[54]  Sabrina Brigadoi,et al.  How short is short? Optimum source–detector distance for short-separation channels in functional near-infrared spectroscopy , 2015, Neurophotonics.

[55]  Ludovico Minati,et al.  Intra- and extra-cranial effects of transient blood pressure changes on brain near-infrared spectroscopy (NIRS) measurements , 2011, Journal of Neuroscience Methods.

[56]  R. Saager,et al.  Measurement of layer-like hemodynamic trends in scalp and cortex: implications for physiological baseline suppression in functional near-infrared spectroscopy. , 2008, Journal of biomedical optics.

[57]  Yunjie Tong,et al.  Denoising of neuronal signal from mixed systemic low-frequency oscillation using peripheral measurement as noise regressor in near-infrared imaging , 2019, Neurophotonics.

[58]  Myung Yung Jeong,et al.  Optimal hemodynamic response model for functional near-infrared spectroscopy , 2015, Front. Behav. Neurosci..

[59]  E. Gratton,et al.  Near-infrared study of fluctuations in cerebral hemodynamics during rest and motor stimulation: temporal analysis and spatial mapping. , 2000, Medical physics.

[60]  Rieko Osu,et al.  Transient increase in systemic interferences in the superficial layer and its influence on event-related motor tasks: a functional near-infrared spectroscopy study , 2017, Journal of biomedical optics.

[61]  Ann-Christine Ehlis,et al.  Model-based analysis of rapid event-related functional near-infrared spectroscopy (NIRS) data: A parametric validation study , 2007, NeuroImage.

[62]  Ilias Tachtsidis,et al.  Publisher’s note: False positives and false negatives in functional near-infrared spectroscopy: issues, challenges, and the way forward , 2016, Neurophotonics.

[63]  Sergio Fantini,et al.  Optical measurements of absorption changes in two-layered diffusive media. , 2004, Physics in medicine and biology.

[64]  Matthias Hein,et al.  Sparse recovery by thresholded non-negative least squares , 2011, NIPS.

[65]  Sungho Tak,et al.  Statistical analysis of fNIRS data: A comprehensive review , 2014, NeuroImage.

[66]  Hanli Liu,et al.  Enhanced Functional Brain Imaging by Using Adaptive Filtering and a Depth Compensation Algorithm in Diffuse Optical Tomography , 2011, IEEE Transactions on Medical Imaging.

[67]  David A. Boas,et al.  Noninvasive Imaging of Cerebral Activation with Diffuse Optical Tomography , 2009 .

[68]  Meryem A Yücel,et al.  Short separation regression improves statistical significance and better localizes the hemodynamic response obtained by near-infrared spectroscopy for tasks with differing autonomic responses , 2015, Neurophotonics.

[69]  A. Dalla Mora,et al.  Non-contact time-domain imaging of functional brain activation and heterogeneity of superficial signals , 2017, European Conference on Biomedical Optics.

[70]  David A. Boas,et al.  Improved recovery of the hemodynamic response in diffuse optical imaging using short optode separations and state-space modeling , 2011, NeuroImage.

[71]  Matthew Caldwell,et al.  Modelling confounding effects from extracerebral contamination and systemic factors on functional near-infrared spectroscopy , 2016, NeuroImage.

[72]  M. Ferrari,et al.  Principles, techniques, and limitations of near infrared spectroscopy. , 2004, Canadian journal of applied physiology = Revue canadienne de physiologie appliquee.

[73]  D. Delpy,et al.  Performance comparison of several published tissue near-infrared spectroscopy algorithms. , 1995, Analytical biochemistry.

[74]  G R Müller-Putz,et al.  Separating heart and brain: on the reduction of physiological noise from multichannel functional near-infrared spectroscopy (fNIRS) signals , 2014, Journal of neural engineering.

[75]  Emery N Brown,et al.  Adaptive filtering for global interference cancellation and real-time recovery of evoked brain activity: a Monte Carlo simulation study. , 2007, Journal of biomedical optics.

[76]  J-M Lina,et al.  Complex wavelets applied to diffuse optical spectroscopy for brain activity detection. , 2008, Optics express.

[77]  C. Julien The enigma of Mayer waves: Facts and models. , 2006, Cardiovascular research.

[78]  Quan Zhang,et al.  Adaptive filtering to reduce global interference in non-invasive NIRS measures of brain activation: How well and when does it work? , 2009, NeuroImage.

[79]  Thomas E. Nichols,et al.  Accelerated estimation and permutation inference for ACE modeling , 2019, Human brain mapping.