The NIRS Analysis Package: Noise Reduction and Statistical Inference

Near infrared spectroscopy (NIRS) is a non-invasive optical imaging technique that can be used to measure cortical hemodynamic responses to specific stimuli or tasks. While analyses of NIRS data are normally adapted from established fMRI techniques, there are nevertheless substantial differences between the two modalities. Here, we investigate the impact of NIRS-specific noise; e.g., systemic (physiological), motion-related artifacts, and serial autocorrelations, upon the validity of statistical inference within the framework of the general linear model. We present a comprehensive framework for noise reduction and statistical inference, which is custom-tailored to the noise characteristics of NIRS. These methods have been implemented in a public domain Matlab toolbox, the NIRS Analysis Package (NAP). Finally, we validate NAP using both simulated and actual data, showing marked improvement in the detection power and reliability of NIRS

Monitoring Cellular Metabolism with Near-infrared Spectroscopy

Aims: Although NIRS oximetry has been widely used in clinical and research settings to monitor oxygen consumption in muscles (1), it is not necessarily able to obtain cellular metabolic levels directly. Oxygen saturation will only change with aerobic metabolism (2); however, anaerobic metabolism cannot be assessed by monitoring oxygen saturation. This work aims to measure cell oxygenation and blood oxygenation, simultaneously, by applying oximetry to hemoglobin/deoxyhemoglobin and to cytochrome c oxidase (CCOX). A custom build NIRS device called the cytoximeter was constructed to achieve this goal. This work could have important impacts on monitoring neurological diseases, Postural Tachycardia Syndrome (POTS), and epilepsy. Introduction: CCOX is the terminal enzyme in the electron transport chain (ETC) and as such will be responsive in changes in either aerobic or anaerobic metabolism (3). CCOX transports a number of electrons over a single cycle of the ETC. As the enzyme accepts electrons, it enters a reduced state. Monitoring the redox state of CCOX in a given tissue will reflect the amount of metabolic activity in that tissue (9). We developed a novel device that monitors the changes in optical densities of a tissue. Using Beer’s law and the difference in absorption spectra of reduced and oxidized CCOX, it is possible to measure the relative changes in concentration of these chemicals (4). Methods: In order to observe and separate the CCOX signals from absorption changes due to whole blood changes, a six wavelength absorption spectroscopy device is constructed. This device uses the optimal source detector separations, obtained by numerical photon migration simulations, for monitoring superficial muscles and cortical brain tissue. In order to validate the device performance, several phantom studies are conducted followed by clinical investigations. In the clinical setting, the device was applied to the gastrocnemius muscles of patients undergoing a tilt table test during a standard of care neurological examination. Results: The phantom studies showed that the device was able to obtain changes in the concentration and the redox state of CCOX in a medium with optical properties similar to the tissues found in the calf and skull. When applied in a clinical setting, the device produced reproducible and predictable results. The clinical results are partially verified by the use of a commercially available oximeter, which validates the changes in hemoglobin and oxy hemoglobin obtained by our custom-made cytoximeter. Conclusion: This work is a novel approach to the non-invasive monitoring of CCOX simultaneously with blood oxygen saturation by use of NIR spectroscopy. While there is currently no gold standard with which to compare these results to, the ability to separate cellular metabolism in the presence of large changes in blood volume during a clinical procedure is a promising first step toward clinically monitoring the energy expenditure of tissues. Further work is underway to correlate changes in CCOX redox state to the level muscular exertion. This will allow the researchers to quantitatively monitor CCOX redox changes during different levels of exercise

Development of A Versatile Multichannel CWNIRS Instrument for Optical Brain-Computer Interface Applications

This thesis describes the design, development, and implementation of a versatile multichannel continuous-wave near-infrared spectroscopy (CWNIRS) instrument for brain-computer interface (BCI) applications. Specifically, it was of interest to assess what gains could be achieved by using a multichannel device compared to the single channel device implemented by Coyle in 2004. Moreover, the multichannel approach allows for the assessment of localisation of functional tasks in the cerebral cortex, and can identify lateralisation of haemodynamic responses to motor events. The approach taken to extend single channel to multichannel was based on a software-controlled interface. This interface allowed flexibility in the control of individual optodes including their synchronisation and modulation (AM, TDM, CDMA). Furthermore, an LED driver was developed for custom-made triple-wavelength LEDs. The system was commissioned using a series of experiments to verify the performance of individual components in the system. The system was then used to carry out a set of functional studies including motor imagery and cognitive tasks. The experimental protocols based on motor imagery and overt motor tasks were verified by comparison with fMRI. The multichannel approach identified stroke rehabilitation as a new application area for optical BCI. In addition, concentration changes in deoxyhaemoglobin were identified as being a more localised indicator of functional activity, which is important for effective BCI design. An assessment was made on the effect of the duration of the stimulus period on the haemodynamic signals. This demonstrated the possible benefits of using a shorter stimulus period to reduce the adverse affects of low blood pressure oscillations. i

Development of A Versatile Multichannel CWNIRS Instrument for Optical Brain-Computer Interface Applications

This thesis describes the design, development, and implementation of a versatile multichannel continuous-wave near-infrared spectroscopy (CWNIRS) instrument for brain-computer interface (BCI) applications. Specifically, it was of interest to assess what gains could be achieved by using a multichannel device compared to the single channel device implemented by Coyle in 2004. Moreover, the multichannel approach allows for the assessment of localisation of functional tasks in the cerebral cortex, and can identify lateralisation of haemodynamic responses to motor events. The approach taken to extend single channel to multichannel was based on a software-controlled interface. This interface allowed flexibility in the control of individual optodes including their synchronisation and modulation (AM, TDM, CDMA). Furthermore, an LED driver was developed for custom-made triple-wavelength LEDs. The system was commissioned using a series of experiments to verify the performance of individual components in the system. The system was then used to carry out a set of functional studies including motor imagery and cognitive tasks. The experimental protocols based on motor imagery and overt motor tasks were verified by comparison with fMRI. The multichannel approach identified stroke rehabilitation as a new application area for optical BCI. In addition, concentration changes in deoxyhaemoglobin were identified as being a more localised indicator of functional activity, which is important for effective BCI design. An assessment was made on the effect of the duration of the stimulus period on the haemodynamic signals. This demonstrated the possible benefits of using a shorter stimulus period to reduce the adverse affects of low blood pressure oscillations. i

Reduction of global interference in functional multidistance near-infrared spectroscopy using empirical mode decomposition and recursive least squares: a Monte Carlo study

Functional near-infrared spectroscopy (fNIRS) is a sensitive technique that has the potential to detect haemodynamic changes during the performance of specific activation tasks. However, in real situations, fNIRS recordings are often corrupted by physiological phenomena, especially by cardiac contraction, breathing and blood pressure fluctuations, and these forms of interference can severely limit the utility of fNIRS. We present a novel fNIRS enhancement based on the multidistance fNIRS method with short-distance and long-distance optode pairs. With this method empirical mode decomposition (EMD) is applied to decompose the superficial haemodynamic changes, derived from the short-distance fNIRS measurements, into a series of intrinsic mode functions (IMFs). By utilizing the weighting parameters for the IMFs, we perform an estimation for global interference in the desired haemodynamic changes, derived from the long-distance fNIRS measurements. We recover the evoked brain activity by minimizing least squares between the desired haemodynamic changes and the estimated global interference. To accelerate the computation, we adopt the recursive least squares (RLS) to decrease the computation complexity due to the matrix inversion. Monte Carlo simulations based on a five-layered slab model of a human adult head was implemented to evaluate our methodology. The results demonstrate that the EMD-RLS method can effectively remove contamination from the evoked brain activity

Using the General Linear Model to Improve Performance in fNIRS Single Trial Analysis and Classification: A Perspective

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

Transient increase in systemic interferences in the superficial layer and its influence on event-related motor tasks: a functional near-infrared spectroscopy study

Abstract. Functional near-infrared spectroscopy (fNIRS) is a widely utilized neuroimaging tool in fundamental neuroscience research and clinical investigation. Previous research has revealed that task-evoked systemic artifacts mainly originating from the superficial-tissue may preclude the identification of cerebral activation during a given task. We examined the influence of such artifacts on event-related brain activity during a brisk squeezing movement. We estimated task-evoked superficial-tissue hemodynamics from short source–detector distance channels (15 mm) by applying principal component analysis. The estimated superficial-tissue hemodynamics exhibited temporal profiles similar to the canonical cerebral hemodynamic model. Importantly, this task-evoked profile was also observed in data from a block design motor experiment, suggesting a transient increase in superficial-tissue hemodynamics occurs following motor behavior, irrespective of task design. We also confirmed that estimation of event-related cerebral hemodynamics was improved by a simple superficial-tissue hemodynamic artifact removal process using 15-mm short distance channels, compared to the results when no artifact removal was applied. Thus, our results elucidate task design-independent characteristics of superficial-tissue hemodynamics and highlight the need for the application of superficial-tissue hemodynamic artifact removal methods when analyzing fNIRS data obtained during event-related motor tasks

Biophysical modeling of hemodynamic-based neuroimaging techniques

Thesis (Ph. D. in Medical Engineering and Medical Physics)--Harvard-MIT Program in Health Sciences and Technology, 2013.Cataloged from PDF version of thesis.Includes bibliographical references (pages 163-182).Two different hemodynamic-based neuroimaging techniques were studied in this work. Near-Infrared Spectroscopy (NIRS) is a promising technique to measure cerebral hemodynamics in a clinical setting due to its potential for continuous monitoring. However, the presence of strong systemic interference in the signal significantly limits our ability to recover the hemodynamic response without averaging tens of trials. Developing a new methodology to clean the NIRS signal from systemic interference and isolate the cortical signal would therefore significantly increase our ability to recover the hemodynamic response opening the door for clinical NIRS studies such as epilepsy. Toward this goal, a new method based on multi-distance measurements and state-space modeling was developed and further optimized to remove systemic physiological oscillations contaminating the NIRS signal. Furthermore, the cortical and pial contributions to the NIRS signal were quantified using a new multimodal regression analysis. Functional Magnetic Resonance Imaging (fMRI) based on the Blood Oxygenation Level Dependent (BOLD) response has become the method of choice for exploring brain function, and yet the physiological basis of this technique is still poorly understood. Despite the effort, a detailed and validated model relating the signal measured to the physiological changes occurring in the cortical tissue is still lacking. Modeling the BOLD signal is challenging because of the difficulty to take into account the complex morphology of the cortical microvasculature, the distribution of oxygen in those microvessels and its dynamics during neuronal activation. Here, we overcome this difficulty by performing Monte Carlo simulations over real microvascular networks and oxygen distributions measured in vivo on rodents, at rest and during forepaw stimulation, using two-photon microscopy. Our model reveals for the first time the specific contribution of individual vascular compartment to the BOLD signal, for different field strengths and different cortical orientations. Our model makes a new prediction: the amplitude of the BOLD signal produced by a given physiological change during neuronal activation depends on the spatial orientation of the cortical region in the MRI scanner. This occurs because veins are preferentially oriented either perpendicular or parallel to the cortical surface in the gray matter.by Louis Gagnon.Ph.D.in Medical Engineering and Medical Physic