Adaptive filtering to reduce global interference in non-invasive NIRS measures of brain activation: How well and when does it work?

Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA 02129, USA.
NeuroImage (Impact Factor: 6.36). 02/2009; 45(3):788-94. DOI: 10.1016/j.neuroimage.2008.12.048
Source: PubMed


In previous work we introduced a novel method for reducing global interference, based on adaptive filtering, to improve the contrast to noise ratio (CNR) of evoked hemodynamic responses measured non-invasively with near infrared spectroscopy (NIRS). Here, we address the issue of how to generally apply the proposed adaptive filtering method. A total of 156 evoked visual response measurements, collected from 15 individuals, were analyzed. The similarity (correlation) between measurements with far and near source-detector separations collected during the rest period before visual stimulation was used as indicator of global interference dominance. A detailed analysis of CNR improvement in oxy-hemoglobin (O(2)Hb) and deoxy-hemoglobin (HHb), as a function of the rest period correlation coefficient, is presented. Results show that for O(2)Hb measurements, 66% exhibited substantial global interference. For this dataset, dominated by global interference, 71% of the measurements revealed CNR improvements after adaptive filtering, with a mean CNR improvement of 60%. No CNR improvement was observed for HHb. This study corroborates our previous finding that adaptive filtering provides an effective method to increase CNR when there is strong global interference, and also provides a practical way for determining when and where to apply this technique.

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Available from: Giorgio Ganis, Jan 30, 2014
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    • "on this problem employed temporal filtering, estimation of systemic effects from background pixels, or modeling of interference signals with predefined basis functions , with some success (Zhang et al. 2009). However, weak signals are still lost in the interference and other complementary methods are desirable. "
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    • "January 2015 | Volume 9 | Article 3 | 5 et al., 2013). It has been shown that the removal of these artifacts from cerebral signals is possible by employing several different methods: the use of additional short-distance detector(s) (Saager and Berger, 2005; Luu and Chau, 2009; Saager et al., 2011), adaptive filtering (Zhang et al., 2009), statistical parametric mapping (SPM) in which the artifacts are included as regressors into the model (Tachtsidis et al., 2010), and ICA (Kohno et al., 2007; Funane et al., 2014). Kohno et al. (2007) revealed that the spatial distribution of one of the ICs was directly related to the skin blood flow, which was again verified by a laser Doppler tissue blood flow meter. "
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    • "Nevertheless, these approaches still fail in removing physiological noise components originating from the cerebral compartment. Secondly, transient differences of functional signals and physiological noise have been exploited by a variety of methods (Kohno et al., 2007; Zhang et al., 2009, 2012b; Tanaka et al., 2013). These methods typically assume statistical independence between physiological noise and cerebral signal and fail to separate task-evoked responses of physiological parameters. "
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