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

Overview

Journal Neurophotonics

Date 2016 Jun 24

PMID 27335886

Citations 8

Authors

Ardalan Aarabi

Theodore J Huppert

Affiliations

Soon will be listed here.

Abstract

Functional near-infrared spectroscopy (fNIRS) is a noninvasive neuroimaging technique used to measure changes in oxygenated hemoglobin (oxy-Hb) and deoxygenated hemoglobin (deoxy-Hb) in the brain. In this study, we present a decomposition approach based on single-channel independent component analysis (scICA) to investigate the contribution of physiological noise to fNIRS signals during rest. Single-channel ICA is an underdetermined decomposition method, which separates a single time series into components containing nonredundant spectral information. Using scICA, fNIRS signals from a total of 17 subjects were decomposed into the constituent physiological components. The percentage contribution of the classes of physiology to the fNIRS signals including low-frequency (LF) fluctuations, respiration, and cardiac oscillations was estimated using spectral domain classification methods. Our results show that LF oscillations accounted for 40% to 55% of total power of both the oxy-Hb and deoxy-Hb signals. Respiration and its harmonics accounted for 10% to 30% of the power, and cardiac pulsations and cardio-respiratory components accounted for 10% to 30%. We describe this scICA method for decomposing fNIRS signals, which unlike other approaches to spatial covariance reduction is applicable to both single- or multiple-channel fNIRS signals and discuss how this approach allows functionally distinct sources of noise with disjoint spectral support to be separated from obscuring systemic physiology.

Citing Articles

Parallel factor analysis for multidimensional decomposition of functional near-infrared spectroscopy data.

Husser A, Caron-Desrochers L, Tremblay J, Vannasing P, Martinez-Montes E, Gallagher A Neurophotonics. 2022; 9(4):045004.

PMID: 36405999 PMC: 9665873. DOI: 10.1117/1.NPh.9.4.045004.

Deep-learning informed Kalman filtering for priori-free and real-time hemodynamics extraction in functional near-infrared spectroscopy.

Liu D, Zhang Y, Zhang P, Li T, Li Z, Zhang L Biomed Opt Express. 2022; 13(9):4787-4801.

PMID: 36187239 PMC: 9484432. DOI: 10.1364/BOE.467943.

Frequency-domain analysis of fNIRS fluctuations induced by rhythmic mental arithmetic.

Molina-Rodriguez S, Mirete-Fructuoso M, M Martinez L, Ibanez-Ballesteros J Psychophysiology. 2022; 59(10):e14063.

PMID: 35394075 PMC: 9540762. DOI: 10.1111/psyp.14063.

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

Wyser D, Mattille M, Wolf M, Lambercy O, Scholkmann F, Gassert R Neurophotonics. 2020; 7(3):035011.

PMID: 33029548 PMC: 7523733. DOI: 10.1117/1.NPh.7.3.035011.

Combining the intersubject correlation analysis and the multivariate distance matrix regression to evaluate associations between fNIRS signals and behavioral data from ecological experiments.

Barreto C, Zimeo Morais G, Vanzella P, Sato J Exp Brain Res. 2020; 238(10):2399-2408.

PMID: 32770351 DOI: 10.1007/s00221-020-05895-8.

References

Strangman G, Franceschini M, Boas D . Factors affecting the accuracy of near-infrared spectroscopy concentration calculations for focal changes in oxygenation parameters. Neuroimage. 2003; 18(4):865-79. DOI: 10.1016/s1053-8119(03)00021-1. View

Cope M, Delpy D, Reynolds E, Wray S, Wyatt J, van der Zee P . Methods of quantitating cerebral near infrared spectroscopy data. Adv Exp Med Biol. 1988; 222:183-9. DOI: 10.1007/978-1-4615-9510-6_21. View

Chiarelli A, Maclin E, Fabiani M, Gratton G . A kurtosis-based wavelet algorithm for motion artifact correction of fNIRS data. Neuroimage. 2015; 112:128-137. PMC: 4408240. DOI: 10.1016/j.neuroimage.2015.02.057. View

Smielewski P, Czosnyka M, Pickard J, Kirkpatrick P . Clinical evaluation of near-infrared spectroscopy for testing cerebrovascular reactivity in patients with carotid artery disease. Stroke. 1997; 28(2):331-8. DOI: 10.1161/01.str.28.2.331. View

Zhang R, Zuckerman J, Iwasaki K, Wilson T, Crandall C, Levine B . Autonomic neural control of dynamic cerebral autoregulation in humans. Circulation. 2002; 106(14):1814-20. DOI: 10.1161/01.cir.0000031798.07790.fe. View

Diamond S, Huppert T, Kolehmainen V, Franceschini M, Kaipio J, Arridge S . Physiological system identification with the Kalman filter in diffuse optical tomography. Med Image Comput Comput Assist Interv. 2006; 8(Pt 2):649-56. DOI: 10.1007/11566489_80. View

Jimenez-Gonzalez A, James C . Extracting sources from noisy abdominal phonograms: a single-channel blind source separation method. Med Biol Eng Comput. 2009; 47(6):655-64. DOI: 10.1007/s11517-009-0474-8. View

Selvaraj N, Mendelson Y, Shelley K, Silverman D, Chon K . Statistical approach for the detection of motion/noise artifacts in Photoplethysmogram. Annu Int Conf IEEE Eng Med Biol Soc. 2012; 2011:4972-5. DOI: 10.1109/IEMBS.2011.6091232. View

Duan L, Zhang Y, Zhu C . Quantitative comparison of resting-state functional connectivity derived from fNIRS and fMRI: a simultaneous recording study. Neuroimage. 2012; 60(4):2008-18. DOI: 10.1016/j.neuroimage.2012.02.014. View

10.

Santosa H, Hong M, Kim S, Hong K . Noise reduction in functional near-infrared spectroscopy signals by independent component analysis. Rev Sci Instrum. 2013; 84(7):073106. DOI: 10.1063/1.4812785. View

11.

Cui X, Bray S, Bryant D, Glover G, Reiss A . A quantitative comparison of NIRS and fMRI across multiple cognitive tasks. Neuroimage. 2010; 54(4):2808-21. PMC: 3021967. DOI: 10.1016/j.neuroimage.2010.10.069. View

12.

Hoshi Y, Tamura M . Fluctuations in the cerebral oxygenation state during the resting period in functional mapping studies of the human brain. Med Biol Eng Comput. 1997; 35(4):328-30. DOI: 10.1007/BF02534085. View

13.

Akgul C, Akin A, Sankur B . Extraction of cognitive activity-related waveforms from functional near-infrared spectroscopy signals. Med Biol Eng Comput. 2006; 44(11):945-58. DOI: 10.1007/s11517-006-0116-3. View

14.

Ferrari M, Quaresima V . A brief review on the history of human functional near-infrared spectroscopy (fNIRS) development and fields of application. Neuroimage. 2012; 63(2):921-35. DOI: 10.1016/j.neuroimage.2012.03.049. View

15.

Villringer A, Planck J, Hock C, Schleinkofer L, Dirnagl U . Near infrared spectroscopy (NIRS): a new tool to study hemodynamic changes during activation of brain function in human adults. Neurosci Lett. 1993; 154(1-2):101-4. DOI: 10.1016/0304-3940(93)90181-j. View

16.

Sato H, Tanaka N, Uchida M, Hirabayashi Y, Kanai M, Ashida T . Wavelet analysis for detecting body-movement artifacts in optical topography signals. Neuroimage. 2006; 33(2):580-7. DOI: 10.1016/j.neuroimage.2006.06.028. View

17.

Zhang Y, Brooks D, Franceschini M, Boas D . Eigenvector-based spatial filtering for reduction of physiological interference in diffuse optical imaging. J Biomed Opt. 2005; 10(1):11014. DOI: 10.1117/1.1852552. View

18.

Saad Z, Ropella K, Cox R, Deyoe E . Analysis and use of FMRI response delays. Hum Brain Mapp. 2001; 13(2):74-93. PMC: 6872085. DOI: 10.1002/hbm.1026. View

19.

Izzetoglu M, Devaraj A, Bunce S, Onaral B . Motion artifact cancellation in NIR spectroscopy using Wiener filtering. IEEE Trans Biomed Eng. 2005; 52(5):934-8. DOI: 10.1109/TBME.2005.845243. View

20.

White B, Snyder A, Cohen A, Petersen S, Raichle M, Schlaggar B . Resting-state functional connectivity in the human brain revealed with diffuse optical tomography. Neuroimage. 2009; 47(1):148-56. PMC: 2699418. DOI: 10.1016/j.neuroimage.2009.03.058. View