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Significance: Short-separation (SS) regression and diffuse optical tomography (DOT) image reconstruction, two widely adopted methods in functional near-infrared spectroscopy (fNIRS), were demonstrated to individually facilitate the separation of brain activation and physiological signals, with further improvement using both sequentially. We hypothesized that doing both simultaneously would further improve the performance. Aim: Motivated by the success of these two approaches, we propose a method, SS-DOT, which applies SS and DOT simultaneously. Approach: The method, which employs spatial and temporal basis functions to represent the hemoglobin concentration changes, enables us to incorporate SS regressors into the time series DOT model. To benchmark the performance of the SS-DOT model against conventional sequential models, we use fNIRS resting state data augmented with synthetic brain response as well as data acquired during a ball squeezing task. The conventional sequential models comprise performing SS regression and DOT. Results: The results show that the SS-DOT model improves the image quality by increasing the contrast-to-background ratio by a threefold improvement. The benefits are marginal at small brain activation. Conclusions: The SS-DOT model improves the fNIRS image reconstruction quality.

RESUMEN

BACKGROUND: Functional near-infrared spectroscopy (fNIRS) has been increasingly employed to monitor cerebral hemodynamics in normal and diseased conditions. However, fNIRS suffers from its susceptibility to superficial activity and systemic physiological noise. The objective of the study was to establish a noise reduction method for fNIRS in a whole-head montage. NEW METHOD: We have developed an automated denoising method for whole-head fNIRS. A high-density montage consisting of 109 long-separation channels and 8 short-separation channels was used for recording. Auxiliary sensors were also used to measure motion, respiration and pulse simultaneously. The method incorporates principal component analysis and general linear model to identify and remove a globally uniform superficial component. Our denoising method was evaluated in experimental data acquired from a group of healthy human subjects during a visually cued motor task and further compared with a minimal preprocessing method and three established denoising methods in the literature. Quantitative metrics including contrast-to-noise ratio, within-subject standard deviation and adjusted coefficient of determination were evaluated. RESULTS: After denoising, whole-head topography of fNIRS revealed focal activations concurrently in the primary motor and visual areas. COMPARISON WITH EXISTING METHODS: Analysis showed that our method improves upon the four established preprocessing methods in the literature. CONCLUSIONS: An automatic, effective and robust preprocessing pipeline was established for removing physiological noise in whole-head fNIRS recordings. Our method can enable fNIRS as a reliable tool in monitoring large-scale, network-level brain activities for clinical uses.

Asunto(s)

Encéfalo , Espectroscopía Infrarroja Corta , Encéfalo/diagnóstico por imagen , Mapeo Encefálico , Hemodinámica , Humanos , Modelos Lineales

RESUMEN

Significance: High-density diffuse optical tomography (HD-DOT) has been shown to approach the resolution and localization accuracy of blood oxygen level dependent-functional magnetic resonance imaging in the adult brain by exploiting densely spaced, overlapping samples of the probed tissue volume, but the technique has to date required large and cumbersome optical fiber arrays. Aim: To evaluate a wearable HD-DOT system that provides a comparable sampling density to large, fiber-based HD-DOT systems, but with vastly improved ergonomics. Approach: We investigated the performance of this system by replicating a series of classic visual stimulation paradigms, carried out in one highly sampled participant during 15 sessions to assess imaging performance and repeatability. Results: Hemodynamic response functions and cortical activation maps replicate the results obtained with larger fiber-based systems. Our results demonstrate focal activations in both oxyhemoglobin and deoxyhemoglobin with a high degree of repeatability observed across all sessions. A comparison with a simulated low-density array explicitly demonstrates the improvements in spatial localization, resolution, repeatability, and image contrast that can be obtained with this high-density technology. Conclusions: The system offers the possibility for minimally constrained, spatially resolved functional imaging of the human brain in almost any environment and holds particular promise in enabling neuroscience applications outside of the laboratory setting. It also opens up new opportunities to investigate populations unsuited to traditional imaging technologies.

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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.

RESUMEN

Within a decade, single trial analysis of functional Near Infrared Spectroscopy (fNIRS) signals has gained significant momentum, and fNIRS joined the set of modalities frequently used for active and passive Brain Computer Interfaces (BCI). A great variety of methods for feature extraction and classification have been explored using state-of-the-art Machine Learning methods. In contrast, signal preprocessing and cleaning pipelines for fNIRS often follow simple recipes and so far rarely incorporate the available state-of-the-art in adjacent fields. In neuroscience, where fMRI and fNIRS are established neuroimaging tools, evoked hemodynamic brain activity is typically estimated across multiple trials using a General Linear Model (GLM). With the help of the GLM, subject, channel, and task specific evoked hemodynamic responses are estimated, and the evoked brain activity is more robustly separated from systemic physiological interference using independent measures of nuisance regressors, such as short-separation fNIRS measurements. When correctly applied in single trial analysis, e.g., in BCI, this approach can significantly enhance contrast to noise ratio of the brain signal, improve feature separability and ultimately lead to better classification accuracy. In this manuscript, we provide a brief introduction into the GLM and show how to incorporate it into a typical BCI preprocessing pipeline and cross-validation. Using a resting state fNIRS data set augmented with synthetic hemodynamic responses that provide ground truth brain activity, we compare the quality of commonly used fNIRS features for BCI that are extracted from (1) conventionally preprocessed signals, and (2) signals preprocessed with the GLM and physiological nuisance regressors. We show that the GLM-based approach can provide better single trial estimates of brain activity as well as a new feature type, i.e., the weight of the individual and channel-specific hemodynamic response function (HRF) regressor. The improved estimates yield features with higher separability, that significantly enhance accuracy in a binary classification task when compared to conventional preprocessing-on average +7.4% across subjects and feature types. We propose to adapt this well-established approach from neuroscience to the domain of single-trial analysis and preprocessing wherever the classification of evoked brain activity is of concern, for instance in BCI.

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Functional near-infrared spectroscopy (fNIRS) is an increasingly common neuromonitoring technique used to observe evoked haemodynamic changes in the brain in response to a stimulus. The measurement is typically in terms of concentration changes of oxy- (∆HbO2) and deoxy- (∆HHb) haemoglobin. However, noise from systemic fluctuations in the concentration of these chromophores can contaminate stimulus-evoked haemodynamic responses, leading to misinterpretation of results. Short-separation channels can be used to regress out extracerebral haemodynamics to better reveal cerebral changes, significantly improving the reliability of fNIRS. Broadband NIRS can be used to additionally monitor concentration changes of the oxidation state of cytochrome-c-oxidase (∆oxCCO). Recent studies have shown ∆oxCCO to be a depth-dependent and hence brain-specific signal. This study aims to investigate whether ∆oxCCO can produce a more robust marker of functional activation. Continuous frontal lobe NIRS measurements were collected from 17 healthy adult volunteers. Short 1 cm source-detector separation channels were regressed from longer separation channels in order to minimise the extracerebral contribution to standard fNIRS channels. Significant changes in ∆HbO2 and ∆HHb were seen at 1 cm channels but were not observed in ∆oxCCO. An improvement in the haemodynamic signals was achieved with regression of the 1 cm channel. Broadband NIRS-measured concentration changes of the oxidation state of cytochrome-c-oxidase has the potential to be an alternative and more brain-specific marker of functional activation.

Asunto(s)

Biomarcadores/metabolismo , Encéfalo/metabolismo , Complejo IV de Transporte de Electrones/análisis , Lóbulo Frontal/metabolismo , Hemoglobinas/metabolismo , Espectroscopía Infrarroja Corta/métodos , Adulto , Complejo IV de Transporte de Electrones/metabolismo , Femenino , Lóbulo Frontal/química , Hemodinámica , Humanos , Masculino , Memoria a Corto Plazo/fisiología , Especificidad de Órganos , Consumo de Oxígeno/fisiología , Adulto Joven

RESUMEN

In recent years, it has been demonstrated that using functional near-infrared spectroscopy (fNIRS) channels with short separations to explicitly sample extra-cerebral tissues can provide a significant improvement in the accuracy and reliability of fNIRS measurements. The aim of these short-separation channels is to measure the same superficial hemodynamics observed by standard fNIRS channels while also being insensitive to the brain. We use Monte Carlo simulations of photon transport in anatomically informed multilayer models to determine the optimum source-detector distance for short-separation channels in adult and newborn populations. We present a look-up plot that provides (for an acceptable value of short-separation channel brain sensitivity relative to standard channel brain sensitivity) the optimum short-separation distance. Though values vary across the scalp, when the acceptable ratio of the short-separation channel brain sensitivity to standard channel brain sensitivity is set at 5%, the optimum short-separation distance is 8.4 mm in the typical adult and 2.15 mm in the term-age infant.