Effects of Processing Methods on fNIRS Signals Assessed During Active Walking Tasks in Older Adults

Functional near infrared spectroscopy (fNIRS) is a noninvasive optics-based neuroimaging modality successfully applied to real-life settings. The technology uses light in the near infrared range (650-950nm) to track changes in oxygenated (HbO2) and deoxygenated hemoglobin (Hb) obtained from measured light intensity using light-tissue interaction principles. fNIRS data processing involves artifact removal and hemodynamic signal conversion using modified Beer-Lambert law (MBLL) to obtain Hb and HbO2, reliably. fNIRS signals can get contaminated by various noise sources of physiological and non-physiological origins. Various algorithms have been proposed for the elimination of artifacts from frequency selective filters to blind source separation methods. Hemodynamic signal extraction using raw intensity measurements at multiple wavelengths based on MBLL usually involves apriori knowledge of certain conversion parameters such as molar extinction coefficients ( $\varepsilon$ ) and differential path length factor (DPF). Different sets of conversion parameters dependent upon wavelength, chromophores, and age have been reported. Variation in processing algorithms and parameters can cause differences in Hb and HbO2 extraction which can in turn change study outcomes. Using fNIRS, we have previously shown significant increases in oxygenation in the prefrontal cortex from Single-Task-Walking (STW) to Dual-task-Walking (DTW) conditions in older adults due to greater cognitive demands inherent in the latter condition. In the current study, we re-analyzed our data and determined that although using different conversion parameters i.e. $\varepsilon $ and age dependent DPF and filter cut-off frequencies at 0.14 and 0.08Hz including those designed to remove confounding effects of Mayer waves had caused some linear increases or decreases on the extracted Hb and HbO2 signals, those effects were minimal in task related comparisons and hence, the overall study outcomes.

[1]  Xu Cui,et al.  Functional near infrared spectroscopy (NIRS) signal improvement based on negative correlation between oxygenated and deoxygenated hemoglobin dynamics , 2010, NeuroImage.

[2]  S. Bunce,et al.  Functional near-infrared neuroimaging , 2005, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[3]  A. Villringer,et al.  Determination of the wavelength dependence of the differential pathlength factor from near-infrared pulse signals , 1998, Physics in medicine and biology.

[4]  Jeffrey M. Hausdorff,et al.  Effects of aging on prefrontal brain activation during challenging walking conditions , 2017, Brain and Cognition.

[5]  R. Motl,et al.  Brain activation changes during locomotion in middle-aged to older adults with multiple sclerosis , 2016, Journal of the Neurological Sciences.

[6]  Marco Ferrari,et al.  Functional Near-Infrared Spectroscopy (fNIRS) for Assessing Cerebral Cortex Function During Human Behavior in Natural/Social Situations: A Concise Review , 2019 .

[7]  Turgay Batbat,et al.  Combining functional near-infrared spectroscopy and EEG measurements for the diagnosis of attention-deficit hyperactivity disorder , 2019, Neural Computing and Applications.

[8]  S. Arridge,et al.  Spectral Dependence of Temporal Point Spread Functions in Human Tissues , 2022 .

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

[10]  A. Maki,et al.  Quantification of systemic interference in optical topography data during frontal lobe and motor cortex activation: an independent component analysis , 2011, Advances in experimental medicine and biology.

[11]  D. Delpy,et al.  System for long-term measurement of cerebral blood and tissue oxygenation on newborn infants by near infra-red transillumination , 1988, Medical and Biological Engineering and Computing.

[12]  E. Okada,et al.  Monte Carlo prediction of near-infrared light propagation in realistic adult and neonatal head models. , 2003, Applied optics.

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

[14]  Daniel Hamacher,et al.  Brain activity during walking: A systematic review , 2015, Neuroscience & Biobehavioral Reviews.

[15]  David A. Boas,et al.  Motion artifacts in functional near-infrared spectroscopy: A comparison of motion correction techniques applied to real cognitive data , 2014, NeuroImage.

[16]  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..

[17]  Terry K Koo,et al.  A Guideline of Selecting and Reporting Intraclass Correlation Coefficients for Reliability Research. , 2016, Journal Chiropractic Medicine.

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

[19]  A. Seiyama,et al.  Regulation of cerebral blood flow during stimulus-induced brain activation: Instructions for the correct interpretation of fNIRS signals , 2014 .

[20]  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.

[21]  David A. Boas,et al.  Factors affecting the accuracy of near-infrared spectroscopy concentration calculations for focal changes in oxygenation parameters , 2003, NeuroImage.

[22]  Anders M. Dale,et al.  Diffuse optical imaging of brain activation: approaches to optimizing image sensitivity, resolution, and accuracy , 2004, NeuroImage.

[23]  Jeannette R. Mahoney,et al.  Neurological Gait Abnormalities Moderate the Functional Brain Signature of the Posture First Hypothesis , 2015, Brain Topography.

[24]  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.

[25]  D. Delpy,et al.  Characterization of the near infrared absorption spectra of cytochrome aa3 and haemoglobin for the non-invasive monitoring of cerebral oxygenation. , 1988, Biochimica et biophysica acta.

[26]  Martin Wolf,et al.  General equation for the differential pathlength factor of the frontal human head depending on wavelength and age , 2013, Journal of biomedical optics.

[27]  W. Zijlstra,et al.  Visible and Near Infrared Absorption Spectra of Human and Animal Haemoglobin : Determination and Application , 2000 .

[28]  Hiroki Sato,et al.  Quantitative evaluation of deep and shallow tissue layers' contribution to fNIRS signal using multi-distance optodes and independent component analysis , 2014, NeuroImage.

[29]  K. Hong,et al.  Detection of primary RGB colors projected on a screen using fNIRS , 2017 .

[30]  D. Delpy,et al.  Investigation of Cerebral Haemodynamics by Near-infrared Spectroscopy in Young Healthy Volunteers Reveals Posture-dependent Spontaneous Oscillations , 2004 .

[31]  M. Copet,et al.  A Monte Carlo investigation of optical pathlength in inhomogeneous tissue and its application to near-infrared spectroscopy , 1993 .

[32]  Keum-Shik Hong,et al.  fNIRS-based brain-computer interfaces: a review , 2015, Front. Hum. Neurosci..

[33]  Ting Li,et al.  Optimal hemoglobin extinction coefficient data set for near-infrared spectroscopy. , 2017, Biomedical optics express.

[34]  S. Bunce,et al.  Functional near-infrared spectroscopy , 2006, IEEE Engineering in Medicine and Biology Magazine.

[35]  R. Holtzer,et al.  The effect of diabetes on prefrontal cortex activation patterns during active walking in older adults , 2018, Brain and Cognition.

[36]  J. Kim,et al.  Variation of haemoglobin extinction coefficients can cause errors in the determination of haemoglobin concentration measured by near-infrared spectroscopy , 2007, Physics in medicine and biology.

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

[38]  R. Holtzer,et al.  Distinct fNIRS-Derived HbO2 Trajectories During the Course and Over Repeated Walking Trials Under Single- and Dual-Task Conditions: Implications for Within Session Learning and Prefrontal Cortex Efficiency in Older Adults. , 2018, The journals of gerontology. Series A, Biological sciences and medical sciences.

[39]  Guang-Zhong Yang,et al.  Assessment of the cerebral cortex during motor task behaviours in adults: A systematic review of functional near infrared spectroscopy (fNIRS) studies , 2011, NeuroImage.

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

[41]  Meltem Izzetoglu,et al.  Online fronto-cortical control of simple and attention-demanding locomotion in humans , 2015, NeuroImage.

[42]  Meltem Izzetoglu,et al.  Neural correlates of obstacle negotiation in older adults: An fNIRS study. , 2017, Gait & posture.

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

[44]  Meltem Izzetoglu,et al.  Interactions of Subjective and Objective Measures of Fatigue Defined in the Context of Brain Control of Locomotion , 2016, The journals of gerontology. Series A, Biological sciences and medical sciences.

[45]  Jasmine Menant,et al.  Prefrontal cortical activation measured by fNIRS during walking: effects of age, disease and secondary task , 2019, PeerJ.

[46]  Dennis Hamacher,et al.  Functional near-infrared spectroscopy in movement science: a systematic review on cortical activity in postural and walking tasks , 2017, Neurophotonics.

[47]  A. Villringer,et al.  Cross talk in the Lambert-Beer calculation for near-infrared wavelengths estimated by Monte Carlo simulations. , 2002, Journal of biomedical optics.

[48]  R. Holtzer,et al.  The effect of polypharmacy on prefrontal cortex activation during single and dual task walking in community dwelling older adults , 2019, Pharmacological research.

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

[50]  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.

[51]  Simone Cutini,et al.  Functional near Infrared Optical Imaging in Cognitive Neuroscience: An Introductory Review , 2012 .

[52]  Fantao Meng,et al.  Determination of extinction coefficients of human hemoglobin in various redox states. , 2017, Analytical biochemistry.

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

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

[55]  D. Delpy,et al.  Measurement of Cranial Optical Path Length as a Function of Age Using Phase Resolved Near Infrared Spectroscopy , 1994 .

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

[57]  S. Umeyama,et al.  Multidistance probe arrangement to eliminate artifacts in functional near-infrared spectroscopy. , 2009, Journal of biomedical optics.

[58]  Joy Hirsch,et al.  Separation of the global and local components in functional near-infrared spectroscopy signals using principal component spatial filtering , 2016, Neurophotonics.

[59]  Marco Ferrari,et al.  A semi-immersive virtual reality incremental swing balance task activates prefrontal cortex: A functional near-infrared spectroscopy study , 2014, NeuroImage.

[60]  Jeannette R. Mahoney,et al.  Neuroimaging of mobility in aging: a targeted review. , 2014, The journals of gerontology. Series A, Biological sciences and medical sciences.

[61]  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.

[62]  S. Stuart,et al.  fNIRS response during walking — Artefact or cortical activity? A systematic review , 2017, Neuroscience & Biobehavioral Reviews.

[63]  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.

[64]  S. Arridge,et al.  Estimation of optical pathlength through tissue from direct time of flight measurement , 1988 .

[65]  G. Dumont,et al.  Wavelet based motion artifact removal for Functional Near Infrared Spectroscopy , 2010, 2010 Annual International Conference of the IEEE Engineering in Medicine and Biology.

[66]  Jeannette R. Mahoney,et al.  Stress and gender effects on prefrontal cortex oxygenation levels assessed during single and dual‐task walking conditions , 2017, The European journal of neuroscience.

[67]  J. Mayhew,et al.  Cerebral Vasomotion: A 0.1-Hz Oscillation in Reflected Light Imaging of Neural Activity , 1996, NeuroImage.

[68]  Hellmuth Obrig,et al.  A wearable multi-channel fNIRS system for brain imaging in freely moving subjects , 2014, NeuroImage.

[69]  Hanli Liu,et al.  Extinction coefficients of hemoglobin for near-infrared spectroscopy of tissue , 2005, IEEE Engineering in Medicine and Biology Magazine.

[70]  Yunfa Fu,et al.  A hybrid BCI based on EEG and fNIRS signals improves the performance of decoding motor imagery of both force and speed of hand clenching , 2015, Journal of neural engineering.