Research Papers

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

[+] Author Affiliations
Ardalan Aarabi

University of Picardie Jules Verne, Faculty of Medicine, Amiens 80036, France

University Research Center (CURS), University Hospital, GRAMFC-Inserm U1105, Amiens 80054, France

Theodore J. Huppert

University of Pittsburgh, Department of Radiology, 4200 Fifth Avenue, Pittsburgh, Pennsylvania 15260, United States

University of Pittsburgh, Department of Bioengineering, 4200 Fifth Avenue, Pittsburgh, Pennsylvania 15260, United States

Neurophoton. 3(2), 025004 (Jun 06, 2016). doi:10.1117/1.NPh.3.2.025004
History: Received January 18, 2016; Accepted May 10, 2016
Text Size: A A A

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.

Figures in this Article
© 2016 Society of Photo-Optical Instrumentation Engineers

Citation

Ardalan Aarabi and Theodore J. Huppert
"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", Neurophoton. 3(2), 025004 (Jun 06, 2016). ; http://dx.doi.org/10.1117/1.NPh.3.2.025004


Access This Article
Sign in or Create a personal account to Buy this article ($20 for members, $25 for non-members).

Some tools below are only available to our subscribers or users with an online account.

Related Content

Customize your page view by dragging & repositioning the boxes below.

Related Book Chapters

Topic Collections

PubMed Articles
Advertisement


 

  • Don't have an account?
  • Subscribe to the SPIE Digital Library
  • Create a FREE account to sign up for Digital Library content alerts and gain access to institutional subscriptions remotely.
Access This Article
Sign in or Create a personal account to Buy this article ($20 for members, $25 for non-members).
Access This Proceeding
Sign in or Create a personal account to Buy this article ($15 for members, $18 for non-members).
Access This Chapter

Access to SPIE eBooks is limited to subscribing institutions and is not available as part of a personal subscription. Print or electronic versions of individual SPIE books may be purchased via SPIE.org.