Calibrating the BOLD signal during a motor task using an extended fusion model incorporating DOT, BOLD and ASL data

Continuous Wave-Diffuse Optical Tomography (CW-DOT) in Human Brain Mapping: A Review

Continuous wave-diffuse optical tomography (CW-DOT) has emerged as a promising non-invasive neuroimaging technique for assessing brain function. Its ability to provide brain mapping with high spatial resolution over traditional functional near-infrared spectroscopy (fNIRS) has garnered significant interest in clinical and cognitive neuroscience. In this review, we critically summarized the hardware, reconstruction algorithms, and applications of CW-DOT for human brain mapping, providing an up-to-date overview and guidelines for future studies to conduct CW-DOT studies. ScienceDirect, PubMed, Web of Science, and IEEE Xplore databases were searched from their inception up to 1 July 2024. A total of 83 articles were included in the final systematic review. The review focused on existing hardware systems, reconstruction algorithms for CW-DOT, and the applications of CW-DOT in both clinical settings and cognitive neuroscience. Finally, we highlighted current challenges and potential directions of CW-DOT in future research, including the absence of standardized protocols and a pressing need for enhanced quantitative precision. This review underscores the sophisticated capabilities of CW-DOT systems, particularly in the realm of human brain imaging. Extensive clinical and neuroscience research has attested to the technique's anatomical precision and reliability, establishing it as a potent instrument in research and clinical practice.

First implementation of dynamic oxygen-17 (17O) magnetic resonance imaging at 7 Tesla during neuronal stimulation in the human brain

OBJECTIVE

First implementation of dynamic oxygen-17 (17O) MRI at 7 Tesla (T) during neuronal stimulation in the human brain.

METHODS

Five healthy volunteers underwent a three-phase 17O gas (17O2) inhalation experiment. Combined right-side visual stimulus and right-hand finger tapping were used to achieve neuronal stimulation in the left cerebral hemisphere. Data analysis included the evaluation of the relative partial volume (PV)-corrected time evolution of absolute 17O water (H217O) concentration and of the relative signal evolution without PV correction. Statistical analysis was performed using a one-tailed paired t test. Blood oxygen level-dependent (BOLD) experiments were performed to validate the stimulation paradigm.

RESULTS

The BOLD maps showed significant activity in the stimulated left visual and sensorimotor cortex compared to the non-stimulated right side. PV correction of 17O MR data resulted in high signal fluctuations with a noise level of 10% due to small regions of interest (ROI), impeding further quantitative analysis. Statistical evaluation of the relative H217O signal with PV correction (p = 0.168) and without (p = 0.382) did not show significant difference between the stimulated left and non-stimulated right sensorimotor ROI.

DISCUSSION

The change of cerebral oxygen metabolism induced by sensorimotor and visual stimulation is not large enough to be reliably detected with the current setup and methodology of dynamic 17O MRI at 7 T.

Time-Lagged Functional Ultrasound for Multi-Parametric Cerebral Hemodynamic Imaging

We introduce an ultrasound speckle decorrelation-based time-lagged functional ultrasound technique (tl-fUS) for the quantification of the relative changes in cerebral blood flow speed (rCBF [Formula: see text]), cerebral blood volume (rCBV) and cerebral blood flow (rCBF) during functional stimulations. Numerical simulations, phantom validations, and in vivo mouse brain experiments were performed to test the capability of tl-fUS to parse out and quantify the ratio change of these hemodynamic parameters. The blood volume change was found to be more prominent in arterioles compared to venules and the peak blood flow changes were around 2.5 times the peak blood volume change during brain activation, agreeing with previous observations in the literature. The tl-fUS shows the ability of distinguishing the relative changes of rCBFspeed, rCBV, and rCBF, which can inform specific physiological interpretations of the fUS measurements.

Short-separation regression incorporated diffuse optical tomography image reconstruction modeling for high-density functional near-infrared spectroscopy

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.

Diffuse optical tomography in the human brain: A briefly review from the neurophysiology to its applications

The present work describes the use of noninvasive diffuse optical tomography (DOT) technology to measure hemodynamic changes, providing relevant information which helps to understand the basis of neurophysiology in the human brain. Advantages such as portability, direct measurements of hemoglobin state, temporal resolution, non‐restricted movements as occurs in magnetic resonance imaging (MRI) devices mean that DOT technology can be used in research and clinical fields. In this review we covered the neurophysiology, physical principles underlying optical imaging during tissue‐light interactions, and technology commonly used during the construction of a DOT device including the source‐detector requirements to improve the image quality. DOT provides 3D cerebral activation images due to complex mathematical models which describe the light propagation inside the tissue head. Moreover, we describe briefly the use of Bayesian methods for raw DOT data filtering as an alternative to linear filters widely used in signal processing, avoiding common problems such as the filter selection or a false interpretation of the results which is sometimes due to the interference of background physiological noise with neural activity.

The potential of calibrated fMRI in the understanding of stress in eating disorders

Eating disorders (ED), including Anorexia Nervosa (AN), Bulimia Nervosa (BN), and Binge Eating Disorder (BED), are medically dangerous psychiatric disorders of unknown etiology. Accumulating evidence supports a biopsychosocial model that includes genetic heritability, neurobiological vulnerability, and psychosocial factors, such as stress, in the development and maintenance of ED. Notably, stress hormones influence appetite and eating, and dysfunction of the physiological stress response has been implicated in ED pathophysiology. Stress signals also appear associated with food reward neurocircuitry response in ED, providing a possible mechanism for the role of stress in appetite dysregulation. This paper provides a review of some of the interacting psychological, behavioral, physiological, and neurobiological mechanisms involved in the stress response among individuals with ED, and discusses novel neuroimaging techniques to address potential physiological confounds of studying neural correlates of stress in ED, such as calibrated fMRI.

Validation and optimization of hypercapnic-calibrated fMRI from oxygen-sensitive two-photon microscopy

Hypercapnic-calibrated fMRI allows the estimation of the relative changes in the cerebral metabolic rate of oxygen (rCMRO2) from combined BOLD and arterial spin labelling measurements during a functional task, and promises to permit more quantitative analyses of brain activity patterns. The estimation relies on a macroscopic model of the BOLD effect that balances oxygen delivery and consumption to predict haemoglobin oxygenation and the BOLD signal. The accuracy of calibrated fMRI approaches has not been firmly established, which is limiting their broader adoption. We use our recently developed microscopic vascular anatomical network model in mice as a ground truth simulator to test the accuracy of macroscopic, lumped-parameter BOLD models. In particular, we investigate the original Davis model and a more recent heuristic simplification. We find that these macroscopic models are inaccurate using the originally defined parameters, but that the accuracy can be significantly improved by redefining the model parameters to take on new values. In particular, we find that the parameter α that relates cerebral blood-volume changes to cerebral blood-flow changes is significantly smaller than typically assumed and that the optimal value changes with magnetic field strength. The results are encouraging in that they support the use of simple BOLD models to quantify BOLD signals, but further work is needed to understand the physiological interpretation of the redefined model parameters.This article is part of the themed issue 'Interpreting BOLD: a dialogue between cognitive and cellular neuroscience'.

Dynamic causal modelling for functional near-infrared spectroscopy

Functional near-infrared spectroscopy (fNIRS) is an emerging technique for measuring changes in cerebral hemoglobin concentration via optical absorption changes. Although there is great interest in using fNIRS to study brain connectivity, current methods are unable to infer the directionality of neuronal connections. In this paper, we apply Dynamic Causal Modelling (DCM) to fNIRS data. Specifically, we present a generative model of how observed fNIRS data are caused by interactions among hidden neuronal states. Inversion of this generative model, using an established Bayesian framework (variational Laplace), then enables inference about changes in directed connectivity at the neuronal level. Using experimental data acquired during motor imagery and motor execution tasks, we show that directed (i.e., effective) connectivity from the supplementary motor area to the primary motor cortex is negatively modulated by motor imagery, and this suppressive influence causes reduced activity in the primary motor cortex during motor imagery. These results are consistent with findings of previous functional magnetic resonance imaging (fMRI) studies, suggesting that the proposed method enables one to infer directed interactions in the brain mediated by neuronal dynamics from measurements of optical density changes.

Correspondence of electroencephalography and near-infrared spectroscopy sensitivities to the cerebral cortex using a high-density layout

This study investigates the correspondence of the cortical sensitivity of electroencephalography (EEG) and near-infrared spectroscopy (NIRS). EEG forward model sensitivity to the cerebral cortex was calculated for 329 EEG electrodes following the 10-5 EEG positioning system using a segmented structural magnetic resonance imaging scan of a human subject. NIRS forward model sensitivity was calculated for the same subject using 156 NIRS source-detector pairs selected from 32 source and 32 detector optodes positioned on the scalp using a subset of the 10-5 EEG positioning system. Sensitivity correlations between colocalized NIRS source-detector pair groups and EEG channels yielded R = 0.46 ± 0.08. Groups of NIRS source-detector pairs with maximum correlations to EEG electrode sensitivities are tabulated. The mean correlation between the point spread functions for EEG and NIRS regions of interest (ROI) was R = 0.43 ± 0.07. Spherical ROIs with radii of 26 mm yielded the maximum correlation between EEG and NIRS averaged across all cortical mesh nodes. These sensitivity correlations between EEG and NIRS should be taken into account when designing multimodal studies of neurovascular coupling and when using NIRS as a statistical prior for EEG source localization.

Validation of the hypercapnic calibrated fMRI method using DOT-fMRI fusion imaging

Calibrated functional magnetic resonance imaging (fMRI) is a widely used method to investigate brain function in terms of physiological quantities such as the cerebral metabolic rate of oxygen (CMRO2). The first and one of the most common methods of fMRI calibration is hypercapnic calibration. This is achieved via simultaneous measures of the blood-oxygenation-level dependent (BOLD) and the arterial spin labeling (ASL) signals during a functional task that evokes regional changes in CMRO2. A subsequent acquisition is then required during which the subject inhales carbon dioxide for short periods of time. A calibration constant, typically labeled M, is then estimated from the hypercapnic data and is subsequently used together with the BOLD-ASL recordings to compute evoked changes in CMRO2 during the functional task. The computation of M assumes a constant CMRO2 during the CO2 inhalation, an assumption that has been questioned since the origin of calibrated fMRI. In this study we used diffuse optical tomography (DOT) together with BOLD and ASL--an alternative calibration method that does not require any gas manipulation and therefore no constant CMRO2 assumption--to cross-validate the estimation of M obtained from a traditional hypercapnic calibration. We found a high correlation between the M values (R=0.87, p<0.01) estimated using these two approaches. The findings serve to validate the hypercapnic fMRI calibration technique and suggest that the inter-subject variability routinely obtained for M is reproducible with an alternative method and might therefore reflect inter-subject physiological variability.

Sensitivity of near-infrared spectroscopy and diffuse correlation spectroscopy to brain hemodynamics: simulations and experimental findings during hypercapnia

Near-infrared spectroscopy (NIRS) and diffuse correlation spectroscopy (DCS) are two diffuse optical technologies for brain imaging that are sensitive to changes in hemoglobin concentrations and blood flow, respectively. Measurements for both modalities are acquired on the scalp, and therefore hemodynamic processes in the extracerebral vasculature confound the interpretation of cortical hemodynamic signals. The sensitivity of NIRS to the brain versus the extracerebral tissue and the contrast-to-noise ratio (CNR) of NIRS to cerebral hemodynamic responses have been well characterized, but the same has not been evaluated for DCS. This is important to assess in order to understand their relative capabilities in measuring cerebral physiological changes. We present Monte Carlo simulations on a head model that demonstrate that the relative brain-to-scalp sensitivity is about three times higher for DCS (0.3 at 3 cm) than for NIRS (0.1 at 3 cm). However, because DCS has higher levels of noise due to photon-counting detection, the CNR is similar for both modalities in response to a physiologically realistic simulation of brain activation. Even so, we also observed higher CNR of the hemodynamic response during graded hypercapnia in adult subjects with DCS than with NIRS.

Practical steps for applying a new dynamic model to near-infrared spectroscopy measurements of hemodynamic oscillations and transient changes: implications for cerebrovascular and functional brain studies

RATIONALE AND OBJECTIVES

Perturbations in cerebral blood volume (CBV), blood flow (CBF), and metabolic rate of oxygen (CMRO2) lead to associated changes in tissue concentrations of oxy- and deoxy-hemoglobin (ΔO and ΔD), which can be measured by near-infrared spectroscopy (NIRS). A novel hemodynamic model has been introduced to relate physiological perturbations and measured quantities. We seek to use this model to determine functional traces of cbv(t) and cbf(t) - cmro2(t) from time-varying NIRS data, and cerebrovascular physiological parameters from oscillatory NIRS data (lowercase letters denote the relative changes in CBV, CBF, and CMRO2 with respect to baseline). Such a practical implementation of a quantitative hemodynamic model is an important step toward the clinical translation of NIRS.

MATERIALS AND METHODS

In the time domain, we have simulated O(t) and D(t) traces induced by cerebral activation. In the frequency domain, we have performed a new analysis of frequency-resolved measurements of cerebral hemodynamic oscillations during a paced breathing paradigm.

RESULTS

We have demonstrated that cbv(t) and cbf(t) - cmro2(t) can be reliably obtained from O(t) and D(t) using the model, and that the functional NIRS signals are delayed with respect to cbf(t) - cmro2(t) as a result of the blood transit time in the microvasculature. In the frequency domain, we have identified physiological parameters (e.g., blood transit time, cutoff frequency of autoregulation) that can be measured by frequency-resolved measurements of hemodynamic oscillations.

CONCLUSIONS

The ability to perform noninvasive measurements of cerebrovascular parameters has far-reaching clinical implications. Functional brain studies rely on measurements of CBV, CBF, and CMRO2, whereas the diagnosis and assessment of neurovascular disorders, traumatic brain injury, and stroke would benefit from measurements of local cerebral hemodynamics and autoregulation.

Further improvement in reducing superficial contamination in NIRS using double short separation measurements

Near-Infrared Spectroscopy (NIRS) allows the recovery of the evoked hemodynamic response to brain activation. In adult human populations, the NIRS signal is strongly contaminated by systemic interference occurring in the superficial layers of the head. An approach to overcome this difficulty is to use additional NIRS measurements with short optode separations to measure the systemic hemodynamic fluctuations occurring in the superficial layers. These measurements can then be used as regressors in the post-experiment analysis to remove the systemic contamination and isolate the brain signal. In our previous work, we showed that the systemic interference measured in NIRS is heterogeneous across the surface of the scalp. As a consequence, the short separation measurement used in the regression procedure must be located close to the standard NIRS channel from which the evoked hemodynamic response of the brain is to be recovered. Here, we demonstrate that using two short separation measurements, one at the source optode and one at the detector optode, further increases the performance of the short separation regression method compared to using a single short separation measurement. While a single short separation channel produces an average reduction in noise of 33% for HbO, using a short separation channel at both source and detector reduces noise by 59% compared to the standard method using a general linear model (GLM) without short separation. For HbR, noise reduction of 3% is achieved using a single short separation and this number goes to 47% when two short separations are used. Our work emphasizes the importance of integrating short separation measurements both at the source and at the detector optode of the standard channels from which the hemodynamic response is to be recovered. While the implementation of short separation sources presents some difficulties experimentally, the improvement in noise reduction is significant enough to justify the practical challenges.

The physics of functional magnetic resonance imaging (fMRI)

Functional magnetic resonance imaging (fMRI) is a methodology for detecting dynamic patterns of activity in the working human brain. Although the initial discoveries that led to fMRI are only about 20 years old, this new field has revolutionized the study of brain function. The ability to detect changes in brain activity has a biophysical basis in the magnetic properties of deoxyhemoglobin, and a physiological basis in the way blood flow increases more than oxygen metabolism when local neural activity increases. These effects translate to a subtle increase in the local magnetic resonance signal, the blood oxygenation level dependent (BOLD) effect, when neural activity increases. With current techniques, this pattern of activation can be measured with resolution approaching 1 mm(3) spatially and 1 s temporally. This review focuses on the physical basis of the BOLD effect, the imaging methods used to measure it, the possible origins of the physiological effects that produce a mismatch of blood flow and oxygen metabolism during neural activation, and the mathematical models that have been developed to understand the measured signals. An overarching theme is the growing field of quantitative fMRI, in which other MRI methods are combined with BOLD methods and analyzed within a theoretical modeling framework to derive quantitative estimates of oxygen metabolism and other physiological variables. That goal is the current challenge for fMRI: to move fMRI from a mapping tool to a quantitative probe of brain physiology.

Further improvement in reducing superficial contamination in NIRS using double short separation measurements

Near-Infrared Spectroscopy (NIRS) allows the recovery of the evoked hemodynamic response to brain activation. In adult human populations, the NIRS signal is strongly contaminated by systemic interference occurring in the superficial layers of the head. An approach to overcome this difficulty is to use additional NIRS measurements with short optode separations to measure the systemic hemodynamic fluctuations occurring in the superficial layers. These measurements can then be used as regressors in the post-experiment analysis to remove the systemic contamination and isolate the brain signal. In our previous work, we showed that the systemic interference measured in NIRS is heterogeneous across the surface of the scalp. As a consequence, the short separation measurement used in the regression procedure must be located close to the standard NIRS channel from which the evoked hemodynamic response of the brain is to be recovered. Here, we demonstrate that using two short separation measurements, one at the source optode and one at the detector optode, further increases the performance of the short separation regression method compared to using a single short separation measurement. While a single short separation channel produces an average reduction in noise of 33% for HbO, using a short separation channel at both source and detector reduces noise by 59% compared to the standard method using a general linear model (GLM) without short separation. For HbR, noise reduction of 3% is achieved using a single short separation and this number goes to 47% when two short separations are used. Our work emphasizes the importance of integrating short separation measurements both at the source and at the detector optode of the standard channels from which the hemodynamic response is to be recovered. While the implementation of short separation sources presents some difficulties experimentally, the improvement in noise reduction is significant enough to justify the practical challenges.

Somatosensory evoked changes in cerebral oxygen consumption measured non-invasively in premature neonates

The hemodynamic functional response is used as a reliable marker of neuronal activity in countless studies of brain function and cognition. In newborns and infants, however, conflicting results have appeared in the literature concerning the typical response, and there is little information on brain metabolism and functional activation. Measurement of all hemodynamic components and oxygen metabolism is critical for understanding neurovascular coupling in the developing brain. To this end, we combined multiple near infrared spectroscopy techniques to measure oxy- and deoxy-hemoglobin concentrations, cerebral blood volume (CBV), and relative cerebral blood flow (CBF) in the somatosensory cortex of 6 preterm neonates during passive tactile stimulation of the hand. By combining these measures we estimated relative changes in the cerebral metabolic rate of oxygen consumption (rCMRO2). CBF starts increasing immediately after stimulus onset, and returns to baseline before blood volume. This is consistent with the model of pre-capillary arteriole active dilation driving the CBF response, with a subsequent CBV increase influenced by capillaries and veins dilating passively to accommodate the extra blood. rCMRO2 estimated using the steady-state formulation shows a biphasic pattern: an increase immediately after stimulus onset, followed by a post-stimulus undershoot due to blood flow returning faster to baseline than oxygenation. However, assuming a longer mean transit time from the arterial to the venous compartment, due to the immature vascular system of premature infants, reduces the post-stimulus undershoot and increases the flow/consumption ratio to values closer to adult values reported in the literature. We are the first to report changes in local rCBF and rCMRO2 during functional activation in preterm infants. The ability to measure these variables in addition to hemoglobin concentration changes is critical for understanding neurovascular coupling in the developing brain, and for using this coupling as a reliable functional imaging marker in neonates.