OSA's Digital Library

Biomedical Optics Express

Biomedical Optics Express

  • Editor: Joseph A. Izatt
  • Vol. 4, Iss. 8 — Aug. 1, 2013
  • pp: 1366–1379

Autoregressive model based algorithm for correcting motion and serially correlated errors in fNIRS

Jeffrey W. Barker, Ardalan Aarabi, and Theodore J. Huppert  »View Author Affiliations


Biomedical Optics Express, Vol. 4, Issue 8, pp. 1366-1379 (2013)
http://dx.doi.org/10.1364/BOE.4.001366


View Full Text Article

Enhanced HTML    Acrobat PDF (1792 KB)





Browse Journals / Lookup Meetings

Browse by Journal and Year


   


Lookup Conference Papers

Close Browse Journals / Lookup Meetings

Article Tools

Share
Citations

Abstract

Systemic physiology and motion-induced artifacts represent two major sources of confounding noise in functional near infrared spectroscopy (fNIRS) imaging that can reduce the performance of analyses and inflate false positive rates (i.e., type I errors) of detecting evoked hemodynamic responses. In this work, we demonstrated a general algorithm for solving the general linear model (GLM) for both deconvolution (finite impulse response) and canonical regression models based on designing optimal pre-whitening filters using autoregressive models and employing iteratively reweighted least squares. We evaluated the performance of the new method by performing receiver operating characteristic (ROC) analyses using synthetic data, in which serial correlations, motion artifacts, and evoked responses were controlled via simulations, as well as using experimental data from children (3–5 years old) as a source baseline physiological noise and motion artifacts. The new method outperformed ordinary least squares (OLS) with no motion correction, wavelet based motion correction, or spline interpolation based motion correction in the presence of physiological and motion related noise. In the experimental data, false positive rates were as high as 37% when the estimated p-value was 0.05 for the OLS methods. The false positive rate was reduced to 5–9% with the proposed method. Overall, the method improves control of type I errors and increases performance when motion artifacts are present.

© 2013 osa

OCIS Codes
(100.2960) Image processing : Image analysis
(170.5380) Medical optics and biotechnology : Physiology
(300.0300) Spectroscopy : Spectroscopy
(170.2655) Medical optics and biotechnology : Functional monitoring and imaging

ToC Category:
Image Processing

History
Original Manuscript: April 25, 2013
Revised Manuscript: June 7, 2013
Manuscript Accepted: June 8, 2013
Published: July 17, 2013

Citation
Jeffrey W. Barker, Ardalan Aarabi, and Theodore J. Huppert, "Autoregressive model based algorithm for correcting motion and serially correlated errors in fNIRS," Biomed. Opt. Express 4, 1366-1379 (2013)
http://www.opticsinfobase.org/boe/abstract.cfm?URI=boe-4-8-1366


Sort:  Author  |  Year  |  Journal  |  Reset  

References

  1. F. F. Jöbsis, “Noninvasive, infrared monitoring of cerebral and myocardial oxygen sufficiency and circulatory parameters,” Science198, 1264–1267 (1977). [CrossRef] [PubMed]
  2. M. Cope, D. T. Delpy, E. O. Reynolds, S. Wray, J. Wyatt, and P. van der Zee, “Methods of quantitating cerebral near infrared spectroscopy data,” Adv. Exp. Med. Biol.222, 183–189 (1988). [CrossRef] [PubMed]
  3. I. Miyai, H. C. Tanabe, I. Sase, H. Eda, I. Oda, I. Konishi, Y. Tsunazawa, T. Suzuki, T. Yanagida, and K. Kubota, “Cortical mapping of gait in humans: a near-infrared spectroscopic topography study,” Neuroimage14, 1186–1192 (2001). [CrossRef] [PubMed]
  4. M. Suzuki, I. Miyai, T. Ono, and K. Kubota, “Activities in the frontal cortex and gait performance are modulated by preparation: an fNIRS study,” Neuroimage39, 600–607 (2008). [CrossRef]
  5. H. Karim, S. I. Fuhrman, P. Sparto, J. Furman, and T. Huppert, “Functional brain imaging of multi-sensory vestibular processing during computerized dynamic posturography using near-infrared spectroscopy,” Neuroimage74, 318–325 (2013). [CrossRef] [PubMed]
  6. H. Karim, B. Schmidt, D. Dart, N. Beluk, and T. Huppert, “Functional near-infrared spectroscopy (fNIRS) of brain function during active balancing using a video game system,” Gait Posture35, 367–372 (2012). [CrossRef]
  7. H. T. Karim, S. I. Fuhrman, J. M. Furman, and T. J. Huppert, “Neuroimaging to detect cortical projection of vestibular response to caloric stimulation in young and older adults using functional near-infrared spectroscopy (fNIRS),” Neuroimage76, 1–10 (2013). [CrossRef] [PubMed]
  8. X. Cui, D. M. Bryant, and A. L. Reiss, “NIRS-based hyperscanning reveals increased interpersonal coherence in superior frontal cortex during cooperation,” Neuroimage59, 2430–2437 (2012). [CrossRef]
  9. T. J. Huppert, S. G. Diamond, M. A. Franceschini, and D. A. Boas, “Homer: a review of time-series analysis methods for near-infrared spectroscopy of the brain,” Appl. Opt.48, 280–298 (2009). [CrossRef]
  10. F. Scholkmann, S. Spichtig, T. Muehlemann, and M. Wolf, “How to detect and reduce movement artifacts in near-infrared imaging using moving standard deviation and spline interpolation,” Physiol. Meas.31, 649–662 (2010). [CrossRef] [PubMed]
  11. B. Molavi and G. A. Dumont, “Wavelet-based motion artifact removal for functional near-infrared spectroscopy,” Physiol. Meas.33, 259–270 (2012). [CrossRef] [PubMed]
  12. M. Izzetoglu, P. Chitrapu, S. Bunce, and B. Onaral, “Motion artifact cancellation in NIR spectroscopy using discrete Kalman filtering,” Biomed. Eng. Online9, 16 (2010). [CrossRef] [PubMed]
  13. R. J. Cooper, J. Selb, L. Gagnon, D. Phillip, H. W. Schytz, H. K. Iversen, M. Ashina, and D. A. Boas, “A systematic comparison of motion artifact correction techniques for functional near-infrared spectroscopy,” Front. Neurosci.6, 147 (2012). [CrossRef] [PubMed]
  14. J. C. Ye, S. Tak, K. E. Jang, J. Jung, and J. Jang, “NIRS-SPM: statistical parametric mapping for near-infrared spectroscopy,” Neuroimage44, 428–447 (2009). [CrossRef]
  15. G. H. Orcutt and D. Cochrane, “A sampling study of the merits of autoregressive and reduced form transformation in regression analysis,” J. Am. Stat. Assoc.44, 356–372 (1949). [CrossRef] [PubMed]
  16. M. J. Hofmann, M. J. Herrmann, I. Dan, H. Obrig, M. Conrad, L. Kuchinke, A. M. Jacobs, and A. J. Fallgatter, “Differential activation of frontal and parietal regions during visual word recognition: an optical topography study,” Neuroimage40, 1340–1349 (2008). [CrossRef] [PubMed]
  17. M. M. Plichta, M. J. Herrmann, C. G. Baehne, A. C. Ehlis, M. M. Richter, P. Pauli, and A. J. Fallgatter, “Event-related functional near-infrared spectroscopy (fNIRS): are the measurements reliable?” Neuroimage31, 116–124 (2006). [CrossRef] [PubMed]
  18. M. M. Plichta, S. Heinzel, A. C. Ehlis, P. Pauli, and A. J. Fallgatter, “Model-based analysis of rapid event-related functional near-infrared spectroscopy (NIRS) data: a parametric validation study,” Neuroimage35, 625–634 (2007). [CrossRef] [PubMed]
  19. A. C. Harvey, The Econometric Analysis of Time Series(MIT Press, 1990).
  20. T. Speed and B. Yu, “Model selection and prediction: normal regression,” Ann. Inst. Stat. Math.45, 35–54 (1993). [CrossRef]
  21. K. J. Friston, J. T. Ashburner, S. J. Kiebel, T. E. Nichols, and W. D. Penny, Statistical Parametric Mapping: The Analysis of Functional Brain Images: The Analysis of Functional Brain Images(Academic Press, 2011).
  22. A. M. Dale, “Optimal experimental design for event-related fmri,” Hum. Brain Mapp.8, 109–114 (1999). [CrossRef] [PubMed]
  23. S. G. Diamond, T. J. Huppert, V. Kolehmainen, M. A. Franceschini, J. P. Kaipio, S. R. Arridge, and D. A. Boas, “Dynamic physiological modeling for functional diffuse optical tomography,” Neuroimage30, 88–101 (2006). [CrossRef]
  24. P. W. Holland and R. E. Welsch, “Robust regression using iteratively reweighted least-squares,” Commun. Stat.-Theory Methods6, 813–827 (1977). [CrossRef]
  25. A. E. Beaton and J. W. Tukey, “The fitting of power series, meaning polynomials, illustrated on band-spectroscopic data,” Technometrics16, 147–185 (1974). [CrossRef]
  26. P. J. Huber, “Robust regression: asymptotics, conjectures and monte carlo,” Ann. Stat.1, 799–821 (1973). [CrossRef]
  27. S. B. Perlman, B. Luna, T. Hein, and T. J. Huppert, “fNIRS evidence of prefrontal regulation of frustration in early childhood,” Neuroimage (to be published). [PubMed]

Cited By

Alert me when this paper is cited

OSA is able to provide readers links to articles that cite this paper by participating in CrossRef's Cited-By Linking service. CrossRef includes content from more than 3000 publishers and societies. In addition to listing OSA journal articles that cite this paper, citing articles from other participating publishers will also be listed.


« Previous Article  |  Next Article »

OSA is a member of CrossRef.

CrossCheck Deposited