Measuring Cerebral Activation From fNIRS Signals: An Approach Based on Compressive Sensing and Taylor-Fourier Model

Functional near-infrared spectroscopy (fNIRS) is a noninvasive and portable neuroimaging technique that uses NIR light to monitor cerebral activity by the so-called haemodynamic responses (HRs). The measurement is challenging because of the presence of severe physiological noise, such as respiratory and vasomotor waves. In this paper, a novel technique for fNIRS signal denoising and HR estimation is described. The method relies on a joint application of compressed sensing theory principles and Taylor-Fourier modeling of nonstationary spectral components. It operates in the frequency domain and models physiological noise as a linear combination of sinusoidal tones, characterized in terms of frequency, amplitude, and initial phase. Algorithm performance is assessed over both synthetic and experimental data sets, and compared with that of two reference techniques from fNIRS literature

Issues in the processing and analysis of functional NIRS imaging and a contrast with fMRI findings in a study of sensorimotor deactivation and connectivity

Includes abstract.~Includes bibliographical references.The first part of this thesis examines issues in the processing and analysis of continuous wave functional linear infrared spectroscopy (fNIRS) of the brain usung the DYNOT system. In the second part, the same sensorimotor experiment is carried out using functional magnetic resonance imaging (fMRI) and near infrared spectroscopy in eleven of the same subjects, to establish whether similar results can be obtained at the group level with each modality. Various techniques for motion artefact removal in fNIRS are compared. Imaging channels with negligible distance between source and detector are used to detect subject motion, and in data sets containing deliberate motion artefacts, independent component analysis and multiple-channel regression are found to improve the signal-to-noise ratio

Developing High-Density Diffuse Optical Tomography for Neuroimaging

Clinicians who care for brain-injured patients and premature infants desire a bedside monitor of brain function. A decade ago, there was hope that optical imaging would be able to fill this role, as it combined fMRI\u27s ability to construct cortical maps with EEG\u27s portable, cap-based systems. However, early optical systems had poor imaging performance, and the momentum for the technique slowed. In our lab, we develop diffuse optical tomography: DOT), which is a more advanced method of performing optical imaging. My research has been to pioneer the in vivo use of DOT for advanced neuroimaging by: 1) quantifying the advantages of DOT through both in silico simulation and in vivo performance metrics,: 2) restoring confidence in the technique with the first retinotopic mapping of the visual cortex: a benchmark for fMRI and PET), and: 3) creating concepts and methods for the clinical translation of DOT. Hospitalized patients are unable to perform complicated neurological tasks, which has motivated us to develop the first DOT methods for resting-state brain mapping with functional connectivity. Finally, in collaboration with neonatologists, I have extended these methods with proof-of-principle imaging of brain-injured premature infants. This work establishes DOT\u27s improvements in imaging performance and readies it for multiple clinical and research roles

High-Density Diffuse Optical Tomography During Passive Movie Viewing: A Platform for Naturalistic Functional Brain Mapping

Human neuroimaging techniques enable researchers and clinicians to non-invasively study brain function across the lifespan in both healthy and clinical populations. However, functional brain imaging methods such as functional magnetic resonance imaging (fMRI) are expensive, resource-intensive, and require dedicated facilities, making these powerful imaging tools generally unavailable for assessing brain function in settings demanding open, unconstrained, and portable neuroimaging assessments. Tools such as functional near-infrared spectroscopy (fNIRS) afford greater portability and wearability, but at the expense of cortical field-of-view and spatial resolution. High-Density Diffuse Optical Tomography (HD-DOT) is an optical neuroimaging modality directly addresses the image quality limitations associated with traditional fNIRS techniques through densely overlapping optical measurements. This thesis aims to establish the feasibility of using HD-DOT in a novel application demanding exceptional portability and flexibility: mapping disrupted cortical activity in chronically malnourished children. I first motivate the need for dense optical measurements of brain tissue to achieve fMRI-comparable localization of brain function (Chapter 2). Then, I present imaging work completed in Cali, Colombia, where a cohort of chronically malnourished children were imaged using a custom HD-DOT instrument to establish feasibility of performing field-based neuroimaging in this population (Chapter 3). Finally, in order to meet the need for age appropriate imaging paradigms in this population, I develop passive movie viewing paradigms for use in optical neuroimaging, a flexible and rich stimulation paradigm that is suitable for both adults and children (Chapter 4)

Quantification of inter-brain coupling: A review of current methods used in haemodynamic and electrophysiological hyperscanning studies

Hyperscanning is a form of neuroimaging experiment where the brains of two or more participants are imaged simultaneously whilst they interact. Within the domain of social neuroscience, hyperscanning is increasingly used to measure inter-brain coupling (IBC) and explore how brain responses change in tandem during social interaction. In addition to cognitive research, some have suggested that quantification of the interplay between interacting participants can be used as a biomarker for a variety of cognitive mechanisms aswell as to investigate mental health and developmental conditions including schizophrenia, social anxiety and autism. However, many different methods have been used to quantify brain coupling and this can lead to questions about comparability across studies and reduce research reproducibility. Here, we review methods for quantifying IBC, and suggest some ways moving forward. Following the PRISMA guidelines, we reviewed 215 hyperscanning studies, across four different brain imaging modalities: functional near-infrared spectroscopy (fNIRS), functional magnetic resonance (fMRI), electroencephalography (EEG) and magnetoencephalography (MEG). Overall, the review identified a total of 27 different methods used to compute IBC. The most common hyperscanning modality is fNIRS, used by 119 studies, 89 of which adopted wavelet coherence. Based on the results of this literature survey, we first report summary statistics of the hyperscanning field, followed by a brief overview of each signal that is obtained from each neuroimaging modality used in hyperscanning. We then discuss the rationale, assumptions and suitability of each method to different modalities which can be used to investigate IBC. Finally, we discuss issues surrounding the interpretation of each method

Computer-Aided, Multi-Modal, and Compression Diffuse Optical Studies of Breast Tissue

Diffuse Optical Tomography and Spectroscopy permit measurement of important physiological parameters non-invasively through ~10 cm of tissue. I have applied these techniques in measurements of human breast and breast cancer. My thesis integrates three loosely connected themes in this context: multi-modal breast cancer imaging, automated data analysis of breast cancer images, and microvascular hemodynamics of breast under compression. As per the first theme, I describe construction, testing, and the initial clinical usage of two generations of imaging systems for simultaneous diffuse optical and magnetic resonance imaging. The second project develops a statistical analysis of optical breast data from many spatial locations in a population of cancers to derive a novel optical signature of malignancy; I then apply this data-derived signature for localization of cancer in additional subjects. Finally, I construct and deploy diffuse optical instrumentation to measure blood content and blood flow during breast compression; besides optics, this research has implications for any method employing breast compression, e.g., mammography

BrainSignals Revisited: Simplifying a Computational Model of Cerebral Physiology

Multimodal monitoring of brain state is important both for the investigation of healthy cerebral physiology and to inform clinical decision making in conditions of injury and disease. Near-infrared spectroscopy is an instrument modality that allows non-invasive measurement of several physiological variables of clinical interest, notably haemoglobin oxygenation and the redox state of the metabolic enzyme cytochrome c oxidase. Interpreting such measurements requires the integration of multiple signals from different sources to try to understand the physiological states giving rise to them. We have previously published several computational models to assist with such interpretation. Like many models in the realm of Systems Biology, these are complex and dependent on many parameters that can be difficult or impossible to measure precisely. Taking one such model, BrainSignals, as a starting point, we have developed several variant models in which specific regions of complexity are substituted with much simpler linear approximations. We demonstrate that model behaviour can be maintained whilst achieving a significant reduction in complexity, provided that the linearity assumptions hold. The simplified models have been tested for applicability with simulated data and experimental data from healthy adults undergoing a hypercapnia challenge, but relevance to different physiological and pathophysiological conditions will require specific testing. In conditions where the simplified models are applicable, their greater efficiency has potential to allow their use at the bedside to help interpret clinical data in near real-time

Functional NIRS to detect covert consciousness in neurocritical patients

Objective This pilot study assesses the feasibility to detect covert consciousness in clinically unresponsive patients by means of functional near infrared spectroscopy (fNIRS) in a real intensive care unit setting. We aimed to verify if the hemodynamic response to familiar music measured with fNIRS varies according to the level consciousness of the patients. Methods 22 neurocritical patients and 6 healthy controls were included. The experiment consisted in 3 subsequent blocks including a first resting state recording, a period of music playback and a second resting state recording. fNIRS measurement were performed on each subject with two optodes on the forehead. Main oscillatory frequencies of oxyhemoglobin signal were analyzed. Spectral changes of low frequency oscillations (LFO) between subsequent experimental blocks were used as a marker of cortical response. Cortical response was compared to the level of consciousness of the patients and their functional outcome, through validated clinical scores. Results Cortical hemodynamic response to music on the left prefrontal brain was associated with the level of consciousness of the patients and with their clinical outcome after three months. Conclusions Variations in LFO spectral power measured with fNIRS may be a new marker of cortical responsiveness to detect covert consciousness in neurocritical patients. Left prefrontal cortex may play an important role in the perception of familiar music. Significance We showed the feasibility of a simple fNIRS approach to detect cortical response in the real setting of an intensive care unit

Direct Estimation of Evoked Hemoglobin Changes by Multimodality Fusion Imaging

In the last two decades, both diffuse optical tomography (DOT) and blood oxygen level dependent (BOLD)-based functional magnetic resonance imaging (fMRI) methods have been developed as noninvasive tools for imaging evoked cerebral hemodynamic changes in studies of brain activity. Although these two technologies measure functional contrast from similar physiological sources, i.e., changes in hemoglobin levels, these two modalities are based on distinct physical and biophysical principles leading to both limitations and strengths to each method. In this work, we describe a unified linear model to combine the complimentary spatial, temporal, and spectroscopic resolutions of concurrently measured optical tomography and fMRI signals. Using numerical simulations, we demonstrate that concurrent optical and BOLD measurements can be used to create cross-calibrated estimates of absolute micromolar deoxyhemoglobin changes. We apply this new analysis tool to experimental data acquired simultaneously with both DOT and BOLD imaging during a motor task, demonstrate the ability to more robustly estimate hemoglobin changes in comparison to DOT alone, and show how this approach can provide cross-calibrated estimates of hemoglobin changes. Using this multimodal method, we estimate the calibration of the 3tesla BOLD signal to be −0.55%±0.40% signal change per micromolar change of deoxyhemoglobin