Autoregressive model based algorithm for correcting motion and serially correlated errors in fNIRS.
By Jeffrey W. Barker, A. Aarabi, Theodore J. Huppert. Biomedical Optics Express, 2013
317 citations recorded by the source corpus.
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.
Subfields: Biomedical Engineering, Radiology, Nuclear Medicine and Imaging, Surgery
View Paper: https://www.semanticscholar.org/paper/61fc411712ba0e681dce1e2c829cf70b82e95d40
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Citations in the corpus
- Temporal Derivative Distribution Repair (TDDR): A motion correction method for fNIRS (2018, unknown): “, 2014), as well as motion-robust algorithms for estimating activation (Barker et al., 2013) and functional connectivity (Santosa et al.”
- Functional Near-Infrared Spectroscopy and Its Clinical Application in the Field of Neuroscience: Advances and Future Directions (2020, background): “Prewhitening can be used to remove temporally autocorrelated signals, such as heart rate, by decorrelating physiologic signals not associated with the task (Barker et al., 2013).”
- The NIRS Brain AnalyzIR Toolbox (2018, methodology): “In general, ur proposed method of AR-IRLS [16] has higher area under the curve (AUC) than other methods.”