Solomon G. Diamond

HomER: a review of time-series analysis methods for near-infrared spectroscopy of the brain

Near-infrared spectroscopy (NIRS) is a noninvasive neuroimaging tool for studying evoked hemodynamic changes within the brain. By this technique, changes in the optical absorption of light are recorded over time and are used to estimate the functionally evoked changes in cerebral oxyhemoglobin and deoxyhemoglobin concentrations that result from local cerebral vascular and oxygen metabolic effects during brain activity. Over the past three decades this technology has continued to grow, and today NIRS studies have found many niche applications in the fields of psychology, physiology, and cerebral pathology. The growing popularity of this technique is in part associated with a lower cost and increased portability of NIRS equipment when compared with other imaging modalities, such as functional magnetic resonance imaging and positron emission tomography. With this increasing number of applications, new techniques for the processing, analysis, and interpretation of NIRS data are continually being developed. We review some of the time-series and functional analysis techniques that are currently used in NIRS studies, we describe the practical implementation of various signal processing techniques for removing physiological, instrumental, and motion-artifact noise from optical data, and we discuss the unique aspects of NIRS analysis in comparison with other brain imaging modalities. These methods are described within the context of the MATLAB-based graphical user interface program, HomER, which we have developed and distributed to facilitate the processing of optical functional brain data.

Assessment of Infant Brain Development With Frequency-Domain Near-Infrared Spectroscopy

This is the first report to demonstrate quantitative monitoring of infant brain development with frequency-domain near-infrared spectroscopy (FD-NIRS). Regionally specific increases in blood volume and oxygen consumption were measured in healthy infants during their first year. The results agree with prior PET and SPECT reports; but, unlike these methods, FD-NIRS is portable and uses nonionizing radiation. Further, new information includes the relatively constant tissue oxygenation with age and location, suggesting a tight control between local oxygen delivery and consumption in healthy infants during brain development. FD-NIRS could become the preferred clinical tool for quantitatively assessing infant brain development.

Dynamic physiological modeling for functional diffuse optical tomography

Diffuse optical tomography (DOT) is a noninvasive imaging technology that is sensitive to local concentration changes in oxy- and deoxyhemoglobin. When applied to functional neuroimaging, DOT measures hemodynamics in the scalp and brain that reflect competing metabolic demands and cardiovascular dynamics. The diffuse nature of near-infrared photon migration in tissue and the multitude of physiological systems that affect hemodynamics motivate the use of anatomical and physiological models to improve estimates of the functional hemodynamic response. In this paper, we present a linear state-space model for DOT analysis that models the physiological fluctuations present in the data with either static or dynamic estimation. We demonstrate the approach by using auxiliary measurements of blood pressure variability and heart rate variability as inputs to model the background physiology in DOT data. We evaluate the improvements accorded by modeling this physiology on ten human subjects with simulated functional hemodynamic responses added to the baseline physiology. Adding physiological modeling with a static estimator significantly improved estimates of the simulated functional response, and further significant improvements were achieved with a dynamic Kalman filter estimator (paired t tests, n=10, P<0.05). These results suggest that physiological modeling can improve DOT analysis. The further improvement with the Kalman filter encourages continued research into dynamic linear modeling of the physiology present in DOT. Cardiovascular dynamics also affect the blood-oxygen-dependent (BOLD) signal in functional magnetic resonance imaging (fMRI). This state-space approach to DOT analysis could be extended to BOLD fMRI analysis, multimodal studies and real-time analysis.

Coupling between somatosensory evoked potentials and hemodynamic response in the rat

We studied the relationship between somatosensory evoked potentials (SEP) recorded with scalp electroencephalography (EEG) and hemoglobin responses recorded non-invasively with diffuse optical imaging (DOI) during parametrically varied electrical forepaw stimulation in rats. Using these macroscopic techniques we verified that the hemodynamic response is not linearly coupled to the somatosensory evoked potentials, and that a power or threshold law best describes the coupling between SEP and the hemoglobin response, in agreement with the results of most invasive studies. We decompose the SEP response in three components (P1, N1, and P2) to determine which best predicts the hemoglobin response. We found that N1 and P2 predict the hemoglobin response significantly better than P1 and the input stimuli (S). Previous electrophysiology studies reported in the literature show that P1 originates in layer IV directly from thalamocortical afferents, while N1 and P2 originate in layers I and II and reflect the majority of local cortico-cortical interactions. Our results suggest that the evoked hemoglobin response is driven by the cortical synaptic activity and not by direct thalamic input. The N1 and P2 components, and not P1, need to be considered to correctly interpret neurovascular coupling.

Estimating cerebral oxygen metabolism from fMRI with a dynamic multicompartment Windkessel model.

Stimulus evoked changes in cerebral blood flow, volume, and oxygenation arise from responses to underlying neuronally mediated changes in vascular tone and cerebral oxygen metabolism. There is increasing evidence that the magnitude and temporal characteristics of these evoked hemodynamic changes are additionally influenced by the local properties of the vasculature including the levels of baseline cerebral blood flow, volume, and blood oxygenation. In this work, we utilize a physiologically motivated vascular model to describe the temporal characteristics of evoked hemodynamic responses and their expected relationships to the structural and biomechanical properties of the underlying vasculature. We use this model in a temporal curve‐fitting analysis of the high‐temporal resolution functional MRI data to estimate the underlying cerebral vascular and metabolic responses in the brain. We present evidence for the feasibility of our model‐based analysis to estimate transient changes in the cerebral metabolic rate of oxygen (CMRO2) in the human motor cortex from combined pulsed arterial spin labeling (ASL) and blood oxygen level dependent (BOLD) MRI. We examine both the numerical characteristics of this model and present experimental evidence to support this model by examining concurrently measured ASL, BOLD, and near‐infrared spectroscopy to validate the calculated changes in underlying CMRO2. Hum Brain Mapp 2009.

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 3 tesla BOLD signal to be -0.55%+/-0.40% signal change per micromolar change of deoxyhemoglobin.

A cerebrovascular response model for functional neuroimaging including dynamic cerebral autoregulation

Functional neuroimaging techniques such as functional magnetic resonance imaging (fMRI) and near-infrared spectroscopy (NIRS) can be used to isolate an evoked response to a stimulus from significant background physiological fluctuations. Data analysis approaches typically use averaging or linear regression to remove this physiological baseline with varying degrees of success. Biophysical model-based analysis of the functional hemodynamic response has also been advanced previously with the Balloon and Windkessel models. In the present work, a biophysical model of systemic and cerebral circulation and gas exchange is applied to resting state NIRS neuroimaging data from 10 human subjects. The model further includes dynamic cerebral autoregulation, which modulates the cerebral arteriole compliance to control cerebral blood flow. This biophysical model allows for prediction, from noninvasive blood pressure measurements, of the background hemodynamic fluctuations in the systemic and cerebral circulations. Significantly higher correlations with the NIRS data were found using the biophysical model predictions compared to blood pressure regression and compared to transfer function analysis (multifactor ANOVA, p<0.0001). This finding supports the further development and use of biophysical models for removing baseline activity in functional neuroimaging analysis. Future extensions of this work could model changes in cerebrovascular physiology that occur during development, aging, and disease.

Algorithm to find high density EEG scalp coordinates and analysis of their correspondence to structural and functional regions of the brain.

BACKGROUND Interpretation and analysis of electroencephalography (EEG) measurements relies on the correspondence of electrode scalp coordinates to structural and functional regions of the brain. NEW METHOD An algorithm is introduced for automatic calculation of the International 10-20, 10-10, and 10-5 scalp coordinates of EEG electrodes on a boundary element mesh of a human head. The EEG electrode positions are then used to generate parcellation regions of the cerebral cortex based on proximity to the EEG electrodes. RESULTS The scalp electrode calculation method presented in this study effectively and efficiently identifies EEG locations without prior digitization of coordinates. The average of electrode proximity parcellations of the cortex were tabulated with respect to structural and functional regions of the brain in a population of 20 adult subjects. COMPARISON WITH EXISTING METHODS Parcellations based on electrode proximity and EEG sensitivity were compared. The parcellation regions based on sensitivity and proximity were found to have 44.0 ± 11.3% agreement when demarcated by the International 10-20, 32.4 ± 12.6% by the 10-10, and 24.7 ± 16.3% by the 10-5 electrode positioning system. CONCLUSIONS The EEG positioning algorithm is a fast and easy method of locating EEG scalp coordinates without the need for digitized electrode positions. The parcellation method presented summarizes the EEG scalp locations with respect to brain regions without computation of a full EEG forward model solution. The reference table of electrode proximity versus cortical regions may be used by experimenters to select electrodes that correspond to anatomical and functional regions of interest.

Quantitative assessment of diffuse optical tomography sensitivity to the cerebral cortex using a whole-head probe.

We quantify the variability in diffuse optical tomography (DOT) sensitivity over the cortical surface in eight young adult subjects. We use the 10/5 electroencephalography system as a basis for our whole-head optical high-density probe design. The contrast-to-noise ratio (CNR) is calculated along with the percentage of the cortex that is above a CNR = 0 dB threshold. We also quantify the effect of including vasculature on the forward model and list our assumptions that allow us to estimate light penetration depth in the head. We show that using the 10/5 system for the optical probe design allows for the measurement of 37% of the cortical surface on average, with a mean CNR in the visible region of 5.5 dB. Certain anatomical regions, such as the lateral occipital cortex, had a very high percentage above the CNR threshold, while other regions such as the cingulate cortex were not measurable. Vasculature blocked optical sensitivity over 1% of the cortex. Cortical coverage was positively correlated with intracranial volume and relative cerebrospinal fluid volume, and negatively correlated with relative scalp volume and skull volume. These contributions allow experimenters to understand how anatomical variation in a subject population may impact DOT or functional near-infrared spectroscopy measurements.

Heart-Rate Variability as a Quantitative Measure of Hypnotic Depth

Abstract The authors investigated whether heart-rate variability can serve as a device for real-time quantitative measurement of hypnotic depth. This study compared the continuous self-rated hypnotic depth (SRHD) of 10 volunteers with heart rate, amplitude, and frequency changes from a time-frequency analysis of heart-rate variability (HRV). The authors found significant linear relationships between SRHD and the high-frequency (HF) component of HRV. Specifically, SRHD was correlated negatively with the frequency of the HF component and positively with the amplitude of the HF component. Unexpectedly, the average temporal trend in SRHD fit well (R 2 = .99) to the step response of a first-order system with a 4-minute time constant. The findings suggest that the reactivity of the parasympathetic branch of the autonomic nervous system reflected in HRV could become part of a real-time, quantitative measure of hypnotic depth.