Quantitative comparison of correction techniques for removing systemic physiological signal in functional near-infrared spectroscopy studies
| Autor: | Xuetong Zhai, Patrick J. Sparto, Hendrik Santosa, Theodore J. Huppert, Frank A. Fishburn |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
Paper
Computer science Neuroscience (miscellaneous) 01 natural sciences 010309 optics 03 medical and health sciences 0302 clinical medicine sensitivity–specificity Component analysis 0103 physical sciences Linear regression functional near-infrared spectroscopy Radiology Nuclear Medicine and imaging systemic physiological noise General linear model Radiological and Ultrasound Technology business.industry Statistical model Pattern recognition short-separation measurements noise removal Research Papers Communication noise Principal component analysis Functional near-infrared spectroscopy Artificial intelligence business 030217 neurology & neurosurgery Linear filter |
| Zdroj: | Neurophotonics |
| ISSN: | 2329-423X |
| Popis: | Significance: Isolating task-evoked brain signals from background physiological noise (e.g., cardiac, respiratory, and blood pressure fluctuations) poses a major challenge for the analysis of functional near-infrared spectroscopy (fNIRS) data. Aim: The performance of several analytic methods to separate background physiological noise from brain activity including spatial and temporal filtering, regression, component analysis, and the use of short-separation (SS) measurements were quantitatively compared. Approach: Using experimentally recorded background signals (breath-hold task), receiver operating characteristics simulations were performed by adding various levels of additive synthetic “brain” responses in order to examine the sensitivity and specificity of several previously proposed analytic approaches. Results: We found that the use of SS fNIRS channels as regressors of no-interest within a linear regression model was the best performing approach examined. Furthermore, we found that the addition of all available SS data, including all recorded channels and both hemoglobin species, improved the method performance despite the additional degrees-of-freedom of the models. When SS data were not available, we found that principal component filtering using a separate baseline scan was the best alternative. Conclusions: The use of multiple SS measurements as regressors of no interest implemented in a robust, iteratively prewhitened, general linear model has the best performance of the tested existing methods. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|