Effects of Systemic Physiology on Mapping Resting-State Networks Using Functional Near-Infrared Spectroscopy.

Resting-state functional connectivity (rsFC) has gained popularity mainly due to its simplicity and potential for providing insights into various brain disorders. In this vein, functional near-infrared spectroscopy (fNIRS) is an attractive choice due to its portability, flexibility, and low cost, allowing for bedside imaging of brain function. While promising, fNIRS suffers from non-neural signal contaminations (i.e., systemic physiological noise), which can increase correlation across fNIRS channels, leading to spurious rsFC networks. In the present work, we hypothesized that additional measurements with short channels, heart rate, mean arterial pressure, and end-tidal CO2 could provide a better understanding of the effects of systemic physiology on fNIRS-based resting-state networks. To test our hypothesis, we acquired 12 min of resting-state data from 10 healthy participants. Unlike previous studies, we investigated the efficacy of different pre-processing approaches in extracting resting-state networks. Our results are in agreement with previous studies and reinforce the fact that systemic physiology can overestimate rsFC. We expanded on previous work by showing that removal of systemic physiology decreases intra- and inter-subject variability, increasing the ability to detect neural changes in rsFC across groups and over longitudinal studies. Our results show that by removing systemic physiology, fNIRS can reproduce resting-state networks often reported with functional magnetic resonance imaging (fMRI). Finally, the present work details the effects of systemic physiology and outlines how to remove (or at least ameliorate) their contributions to fNIRS signals acquired at rest.

Improving Diagnosis and Prognosis in Acute Severe Brain Injury: A Multimodal Imaging Protocol.

Multi-modal neuroimaging techniques have the potential to dramatically improve the diagnosis of the level consciousness and prognostication of neurological outcome for patients with severe brain injury in the intensive care unit (ICU). This protocol describes a study that will utilize functional Magnetic Resonance Imaging (fMRI), electroencephalography (EEG), and functional Near Infrared Spectroscopy (fNIRS) to measure and map the brain activity of acute critically ill patients. Our goal is to investigate whether these modalities can provide objective and quantifiable indicators of good neurological outcome and reliably detect conscious awareness. To this end, we will conduct a prospective longitudinal cohort study to validate the prognostic and diagnostic utility of neuroimaging techniques in the ICU. We will recruit 350 individuals from two ICUs over the course of 7 years. Participants will undergo fMRI, EEG, and fNIRS testing several times over the first 10 days of care to assess for residual cognitive function and evidence of covert awareness. Patients who regain behavioral awareness will be asked to complete web-based neurocognitive tests for 1 year, as well as return for follow up neuroimaging to determine which acute imaging features are most predictive of cognitive and functional recovery. Ultimately, multi-modal neuroimaging techniques may improve the clinical assessments of patients' level of consciousness, aid in the prediction of outcome, and facilitate efforts to find interventional methods that improve recovery and quality of life.

The Potential Role of fNIRS in Evaluating Levels of Consciousness.

Over the last few decades, neuroimaging techniques have transformed our understanding of the brain and the effect of neurological conditions on brain function. More recently, light-based modalities such as functional near-infrared spectroscopy have gained popularity as tools to study brain function at the bedside. A recent application is to assess residual awareness in patients with disorders of consciousness, as some patients retain awareness albeit lacking all behavioural response to commands. Functional near-infrared spectroscopy can play a vital role in identifying these patients by assessing command-driven brain activity. The goal of this review is to summarise the studies reported on this topic, to discuss the technical and ethical challenges of working with patients with disorders of consciousness, and to outline promising future directions in this field.

Direct assessment of extracerebral signal contamination on optical measurements of cerebral blood flow, oxygenation, and metabolism.

Significance: Near-infrared spectroscopy (NIRS) combined with diffuse correlation spectroscopy (DCS) provides a noninvasive approach for monitoring cerebral blood flow (CBF), oxygenation, and oxygen metabolism. However, these methods are vulnerable to signal contamination from the scalp. Our work evaluated methods of reducing the impact of this contamination using time-resolved (TR) NIRS and multidistance (MD) DCS. Aim: The magnitude of scalp contamination was evaluated by measuring the flow, oxygenation, and metabolic responses to a global hemodynamic challenge. Contamination was assessed by collecting data with and without impeding scalp blood flow. Approach: Experiments involved healthy participants. A pneumatic tourniquet was used to cause scalp ischemia, as confirmed by contrast-enhanced NIRS, and a computerized gas system to generate a hypercapnic challenge. Results: Comparing responses acquired with and without the tourniquet demonstrated that the TR-NIRS technique could reduce scalp contributions in hemodynamic signals up to 4 times ( r SD = 3 cm ) and 6 times ( r SD = 4 cm ). Similarly, blood flow responses from the scalp and brain could be separated by analyzing MD DCS data with a multilayer model. Using these techniques, there was no change in metabolism during hypercapnia, as expected, despite large increases in CBF and oxygenation. Conclusion: NIRS/DCS can accurately monitor CBF and metabolism with the appropriate enhancement to depth sensitivity, highlighting the potential of these techniques for neuromonitoring.

Characterizing dynamic cerebral vascular reactivity using a hybrid system combining time-resolved near-infrared and diffuse correlation spectroscopy.

This study presents the characterization of dynamic cerebrovascular reactivity (CVR) in healthy adults by a hybrid optical system combining time-resolved (TR) near-infrared spectroscopy (NIRS) and diffuse correlation spectroscopy (DCS). Blood flow and oxygenation (oxy- and deoxy-hemoglobin) responses to a step hypercapnic challenge were recorded to characterize dynamic and static components of CVR. Data were acquired at short and long source-detector separations (r SD) to assess the impact of scalp hemodynamics, and moment analysis applied to the TR-NIRS to further enhance the sensitivity to the brain. Comparing blood flow and oxygenation responses acquired at short and long r SD demonstrated that scalp contamination distorted the CVR time courses, particularly for oxyhemoglobin. This effect was significantly diminished by the greater depth sensitivity of TR NIRS and less evident in the DCS data due to the higher blood flow in the brain compared to the scalp. The reactivity speed was similar for blood flow and oxygenation in the healthy brain. Given the ease-of-use, portability, and non-invasiveness of this hybrid approach, it is well suited to investigate if the temporal relationship between CBF and oxygenation is altered by factors such as age and cerebrovascular disease.

Assessing Time-Resolved fNIRS for Brain-Computer Interface Applications of Mental Communication.

Brain-computer interfaces (BCIs) are becoming increasingly popular as a tool to improve the quality of life of patients with disabilities. Recently, time-resolved functional near-infrared spectroscopy (TR-fNIRS) based BCIs are gaining traction because of their enhanced depth sensitivity leading to lower signal contamination from the extracerebral layers. This study presents the first account of TR-fNIRS based BCI for "mental communication" on healthy participants. Twenty-one (21) participants were recruited and were repeatedly asked a series of questions where they were instructed to imagine playing tennis for "yes" and to stay relaxed for "no." The change in the mean time-of-flight of photons was used to calculate the change in concentrations of oxy- and deoxyhemoglobin since it provides a good compromise between depth sensitivity and signal-to-noise ratio. Features were extracted from the average oxyhemoglobin signals to classify them as "yes" or "no" responses. Linear-discriminant analysis (LDA) and support vector machine (SVM) classifiers were used to classify the responses using the leave-one-out cross-validation method. The overall accuracies achieved for all participants were 75% and 76%, using LDA and SVM, respectively. The results also reveal that there is no significant difference in accuracy between questions. In addition, physiological parameters [heart rate (HR) and mean arterial pressure (MAP)] were recorded on seven of the 21 participants during motor imagery (MI) and rest to investigate changes in these parameters between conditions. No significant difference in these parameters was found between conditions. These findings suggest that TR-fNIRS could be suitable as a BCI for patients with brain injuries.

Quantification of cerebral blood flow in adults by contrast-enhanced near-infrared spectroscopy: Validation against MRI.

The purpose of this study was to assess the accuracy of absolute cerebral blood flow (CBF) measurements obtained by dynamic contrast-enhanced (DCE) near-infrared spectroscopy (NIRS) using indocyanine green as a perfusion contrast agent. For validation, CBF was measured independently using the MRI perfusion method arterial spin labeling (ASL). Data were acquired at two sites and under two flow conditions (normocapnia and hypercapnia). Depth sensitivity was enhanced using time-resolved detection, which was demonstrated in a separate set of experiments using a tourniquet to temporally impede scalp blood flow. A strong correlation between CBF measurements from ASL and DCE-NIRS was observed (slope = 0.99 ± 0.08, y-intercept = -1.7 ± 7.4 mL/100 g/min, and R2 = 0.88). Mean difference between the two techniques was 1.9 mL/100 g/min (95% confidence interval ranged from -15 to 19 mL/100g/min and the mean ASL CBF was 75.4 mL/100 g/min). Error analysis showed that structural information and baseline absorption coefficient were needed for optimal CBF reconstruction with DCE-NIRS. This study demonstrated that DCE-NIRS is sensitive to blood flow in the adult brain and can provide accurate CBF measurements with the appropriate modeling techniques.

Using fMRI to investigate the potential cause of inverse oxygenation reported in fNIRS studies of motor imagery.

Motor imagery (MI) is a commonly used cognitive task in brain-computer interface (BCI) applications because it produces reliable activity in motor-planning regions. However, a number of functional near-infrared spectroscopy (fNIRS) studies have reported the unexpected finding of inverse oxygenation: increased deoxyhemoglobin and decreased oxyhemoglobin during task periods. This finding questions the reliability of fNIRS for BCI applications given that MI activation should result in a focal increase in blood oxygenation. In an attempt to elucidate this phenomenon, fMRI and fNIRS data were acquired on 15 healthy participants performing a MI task. The fMRI data provided global coverage of brain activity, thus allowing visualization of all potential brain regions activated and deactivated during task periods. Indeed, fMRI results from seven subjects included activation in the primary motor cortex and/or the pre-supplementary motor area during the rest periods in addition to the expected activation in the supplementary motor and premotor areas. Of these seven subjects, two showed inverse oxygenation with fNIRS. The proximity of the regions showing inverse oxygenation to the motor planning regions suggests that inverse activation detected by fNIRS may likely be a consequence of partial volume errors due to the sensitivity of the optodes to both primary motor and motor planning regions.

Development of a stand-alone DCS system for monitoring absolute cerebral blood flow.

Diffuse correlation spectroscopy (DCS) is a noninvasive optical technique for monitoring cerebral blood flow (CBF). This work presents a stand-alone DCS system capable of monitoring absolute CBF by incorporating a quantitative dynamic contrast-enhanced (DCE) technique. Multi-distance data were acquired to measure the tissue optical properties and to perform DCE experiments. Feasibility of the technique was assessed in piglets in which the optical properties were measured independently by time-resolved near-infrared spectroscopy. A strong linear correlation was observed between CBF values derived using the two sets of optical properties, demonstrating that this hybrid DCS approach can provide real-time monitoring of absolute CBF.

Evaluation of hyperspectral NIRS for quantitative measurements of tissue oxygen saturation by comparison to time-resolved NIRS.

Near-infrared spectroscopy (NIRS) is considered ideal for brain monitoring during preterm infancy because it is non-invasive and provides a continuous measure of tissue oxygen saturation (StO2). Hyperspectral NIRS (HS NIRS) is an inexpensive, quantitative modality that can measure tissue optical properties and oxygen saturation (StO2) by differential spectroscopy. In this study, experiments were conducted using newborn piglets to measure StO2 across a range of oxygenation levels from hyperoxia to hypoxia by HS and time-resolved (TR) NIRS for validation. A strong correlation between StO2 measurements from the two techniques was observed (R2 = 0.98, average slope of 1.02 ± 0.28); however, the HS-NIRS estimates were significantly higher than the corresponding TR-NIRS values. These regression results indicate that HS NIRS could become a clinically feasible method for monitoring StO2 in preterm infants.

Can time-resolved NIRS provide the sensitivity to detect brain activity during motor imagery consistently?

Previous functional magnetic resonance imaging (fMRI) studies have shown that a subgroup of patients diagnosed as being in a vegetative state are aware and able to communicate by performing a motor imagery task in response to commands. Due to the fMRI's cost and accessibility, there is a need for exploring different imaging modalities that can be used at the bedside. A promising technique is functional near infrared spectroscopy (fNIRS) that has been successfully applied to measure brain oxygenation in humans. Due to the limited depth sensitivity of continuous-wave NIRS, time-resolved (TR) detection has been proposed as a way of enhancing the sensitivity to the brain, since late arriving photons have a higher probability of reaching the brain. The goal of this study was to assess the feasibility and sensitivity of TR fNIRS in detecting brain activity during motor imagery. Fifteen healthy subjects were recruited in this study, and the fNIRS results were validated using fMRI. The change in the statistical moments of the distribution of times of flight (number of photons, mean time of flight and variance) were calculated for each channel to determine the presence of brain activity. The results indicate up to an 86% agreement between fMRI and TR-fNIRS and the sensitivity ranging from 64 to 93% with the highest value determined for the mean time of flight. These promising results highlight the potential of TR-fNIRS as a portable brain computer interface for patients with disorder of consciousness.

Quantification of blood-brain barrier permeability by dynamic contrast-enhanced NIRS.

The blood-brain barrier (BBB) is integral to maintaining a suitable microenvironment for neurons to function properly. Despite its importance, there are no bedside methods of assessing BBB disruption to help guide management of critical-care patients. The aim of this study was to demonstrate that dynamic contrast-enhanced (DCE) near-infrared spectroscopy (NIRS) can quantify the permeability surface-area product (PS) of the BBB. Experiments were conducted in rats in which the BBB was opened by image-guided focused ultrasound. DCE-NIRS data were acquired with two dyes of different molecular weight, indocyanine green (ICG, 67 kDa) and 800CW carboxylate (IRDye, 1166 Da), and PS maps were generated by DCE computer tomography (CT) for comparison. Both dyes showed a strong correlation between measured PS values and sonication power (R2 = 0.95 and 0.92 for ICG and IRDye respectively), and the PS values for IRDye were in good agreement with CT values obtained with a contrast agent of similar molecular weight. These proof-of-principle experiments demonstrate that DCE NIRS can quantify BBB permeability. The next step in translating this method to critical care practice will be to adapt depth sensitive methods to minimize the effects of scalp contamination on NIRS PS values.

Single-session communication with a locked-in patient by functional near-infrared spectroscopy.

There is a growing interest in the possibility of using functional neuroimaging techniques to aid in detecting covert awareness in patients who are thought to be suffering from a disorder of consciousness. Immerging optical techniques such as time-resolved functional near-infrared spectroscopy (TR-fNIRS) are ideal for such applications due to their low-cost, portability, and enhanced sensitivity to brain activity. The aim of this case study was to investigate for the first time the ability of TR-fNIRS to detect command driven motor imagery (MI) activity in a functionally locked-in patient suffering from Guillain-Barré syndrome. In addition, the utility of using TR-fNIRS as a brain-computer interface (BCI) was also assessed by instructing the patient to perform an MI task as affirmation to three questions: (1) confirming his last name, (2) if he was in pain, and (3) if he felt safe. At the time of this study, the patient had regained limited eye movement, which provided an opportunity to accurately validate a BCI after the fNIRS study was completed. Comparing the two sets of responses showed that fNIRS provided the correct answers to all of the questions. These promising results demonstrate for the first time the potential of using an MI paradigm in combination with fNIRS to communicate with functionally locked-in patients without the need for prior training.

Subtraction-based approach for enhancing the depth sensitivity of time-resolved NIRS.

The aim of this study was to evaluate enhancing of the depth sensitivity of time-resolved near-infrared spectroscopy with a subtraction-based approach. Due to the complexity of light propagation in a heterogeneous media, and to prove the validity of the proposed method in a heterogeneous turbid media we conducted a broad analysis taking into account a number of parameters related to the method as well as various parameters of this media. The results of these experiments confirm that the depth sensitivity of the subtraction-based approach is better than classical approaches using continuous-wave or time-resolved methods. Furthermore, the results showed that the subtraction-based approach has a unique, selective sensitivity to a layer at a specific depth. In vivo application of the proposed method resulted in a greater magnitude of the hemodynamic changes during functional activation than with the standard approach.

Time-resolved subtraction method for measuring optical properties of turbid media.

Near-infrared spectroscopy is a noninvasive optical method used primarily to monitor tissue oxygenation due to the absorption properties of hemoglobin. Accurate estimation of hemoglobin concentrations and other light absorbers requires techniques that can separate the effect of absorption from the much greater effect of light scattering. One of the most advanced methods is time-resolved near-infrared spectroscopy (TR-NIRS), which measures the absorption and scattering coefficients of a turbid medium by modeling the recorded distribution time of flight of photons. A challenge with TR-NIRS is that it requires accurate characterization of the dispersion caused by the system. In this study, we present a method for circumventing this problem by applying statistical moment analysis to two time-of-flight distributions measured at separated source-detector distances. Simulations based on analytical models and Monte Carlo code, and tissue-mimicking phantoms, were used to demonstrate its accuracy for source-detector distances typically used in neuroimaging applications. The simplicity of the approach is well suited to real-time applications requiring accurate quantification of the optical properties of a turbid medium.