Physiological System Identification with the Kalman Filter in Diffuse Optical Tomography

Diamond, Solomon; Huppert, Theodore J.; Kolehmainen, Ville; Franceschini, Maria Angela; Kaipio, Jari P.; Arridge, Simon; Boas, David A.

doi:10.1007/11566489_80

Cited by 20 publications

(29 citation statements)

References 10 publications

(11 reference statements)

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“…A further class of approaches is based on state-space modeling using Kalman filtering (Abdelnour and Huppert, 2009;Diamond et al, 2005Diamond et al, , 2006Gagnon et al, 2011Gagnon et al, , 2014Kamrani et al, 2012;Kolehmainen et al, 2003;Prince et al, 2003) or recursive least-squares estimation (Aqil et al, 2012a,b). State-space methods model the data as a system with time-varying parameters that have to be estimated.…”

Section: Multivariate Methods Of Typementioning

confidence: 99%

A review on continuous wave functional near-infrared spectroscopy and imaging instrumentation and methodology

Scholkmann¹,

Kleiser²,

Metz³

et al. 2014

NeuroImage

1,497

1,394

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This year marks the 20th anniversary of functional near-infrared spectroscopy and imaging (fNIRS/fNIRI). As the vast majority of commercial instruments developed until now are based on continuous wave technology, the aim of this publication is to review the current state of instrumentation and methodology of continuous wave fNIRI. For this purpose we provide an overview of the commercially available instruments and address instrumental aspects such as light sources, detectors and sensor arrangements. Methodological aspects, algorithms to calculate the concentrations of oxy-and deoxyhemoglobin and approaches for data analysis are also reviewed. From the single-location measurements of the early years, instrumentation has progressed to imaging initially in two dimensions (topography) and then three (tomography). The methods of analysis have also changed tremendously, from the simple modified Beer-Lambert law to sophisticated image reconstruction and data analysis methods used today. Due to these advances, fNIRI has become a modality that is widely used in neuroscience research and several manufacturers provide commercial instrumentation. It seems likely that fNIRI will become a clinical tool in the foreseeable future, which will enable diagnosis in single subjects. This year marks the 20th anniversary of functional near-infrared spectroscopy and imaging (fNIRS/fNIRI). As the vast majority of commercial instruments developed until now are based on continuous wave technology, the aim of this publication is to review the current state of instrumentation and methodology of continuous wave fNIRI. For this purpose we provide an overview of the commercially available instruments and address instrumental aspects such as light sources, detectors and sensor arrangements. Methodological aspects, algorithms to calculate the concentrations of oxy-and deoxyhemoglobin and approaches for data analysis are also reviewed. From the single-location measurements of the early years, instrumentation has progressed to imaging initially in two dimensions (topography) and then three (tomography). The methods of analysis have also changed tremendously, from the simple modified Beer-Lambert law to sophisticated image reconstruction and data analysis methods used today. Due to these advances, fNIRI has become a modality that is widely used in neuroscience research and several manufacturers provide commercial instrumentation. It seems likely that fNIRI will become a clinical tool in the foreseeable future, which will enable diagnosis in single subjects. Contents

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Section: Multivariate Methods Of Typementioning

confidence: 99%

A review on continuous wave functional near-infrared spectroscopy and imaging instrumentation and methodology

Scholkmann¹,

Kleiser²,

Metz³

et al. 2014

NeuroImage

1,497

1,394

View full text Add to dashboard Cite

show abstract

“…Specifically, because systemic physiology can change in a task-dependent manner, the noise structure of the "baseline" can be different from the "task," which violates assumptions of the T-task and the estimation of brain activity under these conditions. The issue of nonstationary noise in fNIRS has been addressed in a number of publications in the context of dynamic estimation models such as a Kalman filter, [15][16][17][18] but since there is no simple solution to dealing with these noise errors, we will simply note that this is a potential source of error that could cause fNIRS signals to violate the common assumptions in most statistical models used to date in analysis, and very little is known about how much of an effect this has on the sensitivity and specificity of fNIRS methods.…”

Section: Functional Near-infrared Spectroscopy Datamentioning

confidence: 99%

Commentary on the statistical properties of noise and its implication on general linear models in functional near-infrared spectroscopy

2016

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Abstract. Functional near-infrared spectroscopy (fNIRS) is a noninvasive neuroimaging technique that uses low levels of light to measure changes in cerebral blood oxygenation levels. In the majority of NIRS functional brain studies, analysis of this data is based on a statistical comparison of hemodynamic levels between a baseline and task or between multiple task conditions by means of a linear regression model: the so-called general linear model. Although these methods are similar to their implementation in other fields, particularly for functional magnetic resonance imaging, the specific application of these methods in fNIRS research differs in several key ways related to the sources of noise and artifacts unique to fNIRS. In this brief communication, we discuss the application of linear regression models in fNIRS and the modifications needed to generalize these models in order to deal with structured (colored) noise due to systemic physiology and noise heteroscedasticity due to motion artifacts. The objective of this work is to present an overview of these noise properties in the context of the linear model as it applies to fNIRS data. This work is aimed at explaining these mathematical issues to the general fNIRS experimental researcher but is not intended to be a complete mathematical treatment of these concepts. © The Authors. Published by SPIE under a Creative Commons Attribution 3.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.

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“…These methods provide undesirable results when the transfer functions between the sources of activities (here heart and lungs) and the brain are time varying or nonlinear. Diamond et al 44 proposed an adaptive regression method using a Kalman filter to address this issue of stationarity of the transfer function from systemic measurements to the brain signal. However, the assumption of a nonstationary linear transfer function from systemic circulation to the brain is an oversimplification of the system.…”

Section: Activity Component Cluster 1 (C1) Versus Cluster 2 (C2) Deoxmentioning

confidence: 99%

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

Aarabi

Huppert

2016

Neurophoton

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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. 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.

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Physiological System Identification with the Kalman Filter in Diffuse Optical Tomography

Cited by 20 publications

References 10 publications

A review on continuous wave functional near-infrared spectroscopy and imaging instrumentation and methodology

A review on continuous wave functional near-infrared spectroscopy and imaging instrumentation and methodology

Commentary on the statistical properties of noise and its implication on general linear models in functional near-infrared spectroscopy

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

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