Using the force: STEM knowledge and experience construct shared neural representations of engineering concepts

How does STEM knowledge learned in school change students' brains? Using fMRI, we presented photographs of real-world structures to engineering students with classroom-based knowledge and hands-on lab experience, examining how their brain activity differentiated them from their "novice" peers not pursuing engineering degrees. A data-driven MVPA and machine-learning approach revealed that neural response patterns of engineering students were convergent with each other and distinct from novices' when considering physical forces acting on the structures. Furthermore, informational network analysis demonstrated that the distinct neural response patterns of engineering students reflected relevant concept knowledge: learned categories of mechanical structures. Information about mechanical categories was predominantly represented in bilateral anterior ventral occipitotemporal regions. Importantly, mechanical categories were not explicitly referenced in the experiment, nor does visual similarity between stimuli account for mechanical category distinctions. The results demonstrate how learning abstract STEM concepts in the classroom influences neural representations of objects in the world.

Assessing Feedback Response With a Wearable Electroencephalography System

Background: Event related potential (ERP) components, such as P3, N2, and FRN, are potential metrics for assessing feedback response as a form of performance monitoring. Most research studies investigate these ERP components using clinical or research-grade electroencephalography (EEG) systems. Wearable EEGs, which are an affordable alternative, have the potential to assess feedback response using ERPs but have not been sufficiently evaluated. Feedback-related ERPs also have not been scientifically evaluated in interactive settings that are similar to daily computer use. In this study, a consumer-grade wearable EEG system was assessed for its feasibility to collect feedback-related ERPs through an interactive software module that provided an environment in which users were permitted to navigate freely within the program to make decisions. Methods: The recording hardware, which costs < $1,500 in total, incorporated the OpenBCI Cyton Board with Daisy chain, a consumer-grade EEG system that costs $949 USD. Seventeen participants interacted with an oddball paradigm and an interactive module designed to elicit feedback-related ERPs. The features of interests for the oddball paradigm were the P3 and N2 components. The features of interests for the interactive module were the P3, N2, and FRN components elicited in response to positive, neutral, and two types of negative feedback. The FRN was calculated by subtracting the positive feedback response from the negative feedback responses. Results: The P3 and N2 components of the oddball paradigm indicated statistically significant differences between infrequent targets and frequent targets which is in line with current literature. The P3 and N2 components elicited in the interactive module indicated statistically significant differences between positive, neutral, and negative feedback responses. There were no significant differences between the FRN types and significant interactions with channel group and FRN type. Conclusion: The OpenBCI Cyton, after some modifications, shows potential for eliciting and assessing P3, N2, and FRN components, which are important indicators for performance monitoring, in an interactive setting.

Simultaneous EEG, eye-tracking, behavioral, and screen-capture data during online German language learning

This article presents concurrent multimodal data, including EEG, eye-tracking, and behavioral data (cursor movements and clicks), acquired from individuals (N = 22) while engaging in several German language lessons using the web-based Duolingo interface. Lessons were restricted to visual learning only (excluding audio and speech components), including reading and writing vocabulary words and sentences, and matching vocabulary to images. EEG data was collected using the open-source OpenBCI device utilizing dry Ag-AgCl electrodes, while eye-tracking data was recorded using the Gazepoint GP3 system. Timestamped screen captures associated with mouse click and keypress events and user behavior (cursor movements) were acquired using AutoHotKey macro scripts. These data provide neural (EEG), gaze (eye-tracking), and behavioral (mouse movements, clicks, and keypresses) data, with respect to presented language-learning media (Duolingo screen captures) for a wide range of possible scientific analyses and methods development.

A Feasibility Study of Nonlinear Spectroscopic Measurement of Magnetic Nanoparticles Targeted to Cancer Cells

OBJECTIVE

Magnetic nanoparticles (MNPs) are an emerging platform for targeted diagnostics in cancer. An important component needed for translation of MNPs is the detection and quantification of targeted MNPs bound to tumor cells.

METHOD

This study explores the feasibility of a multifrequency nonlinear magnetic spectroscopic method that uses excitation and pickup coils and is capable of discriminating between quantities of bound and unbound MNPs in 0.5 ml samples of KB and Igrov human cancer cell lines. The method is tested over a range of five concentrations of MNPs from 0 to 80 μg/ml and five concentrations of cells from 50 to 400 000 count per ml.

RESULTS

A linear model applied to the magnetic spectroscopy data was able to simultaneously measure bound and unbound MNPs with agreement between the model-fit and lab assay measurements (p < 0.001). The detectable iron of the presented method to bound and unbound MNPs was < 2 μg in a 0.5 ml sample. The linear model parameters used to determine the quantities of bound and unbound nanoparticles in KB cells were also used to measure the bound and unbound MNP in the Igrov cell line and vice versa.

CONCLUSION

Nonlinear spectroscopic measurement of MNPs may be a useful method for studying targeted MNPs in oncology.

SIGNIFICANCE

Determining the quantity of bound and unbound MNP in an unknown sample using a linear model represents an exciting opportunity to translate multifrequency nonlinear spectroscopy methods to in vivo applications where MNPs could be targeted to cancer cells.

The role of visualization and 3-D printing in biological data mining

Background

Biological data mining is a powerful tool that can provide a wealth of information about patterns of genetic and genomic biomarkers of health and disease. A potential disadvantage of data mining is volume and complexity of the results that can often be overwhelming. It is our working hypothesis that visualization methods can greatly enhance our ability to make sense of data mining results. More specifically, we propose that 3-D printing has an important role to play as a visualization technology in biological data mining. We provide here a brief review of 3-D printing along with a case study to illustrate how it might be used in a research setting.

Results

We present as a case study a genetic interaction network associated with grey matter density, an endophenotype for late onset Alzheimer’s disease, as a physical model constructed with a 3-D printer. The synergy or interaction effects of multiple genetic variants were represented through a color gradient of the physical connections between nodes. The digital gene-gene interaction network was then 3-D printed to generate a physical network model.

Conclusions

The physical 3-D gene-gene interaction network provided an easily manipulated, intuitive and creative way to visualize the synergistic relationships between the genetic variants and grey matter density in patients with late onset Alzheimer’s disease. We discuss the advantages and disadvantages of this novel method of biological data mining visualization.

Extended arrays for nonlinear susceptibility magnitude imaging

This study implements nonlinear susceptibility magnitude imaging (SMI) with multifrequency intermodulation and phase encoding. An imaging grid was constructed of cylindrical wells of 3.5-mm diameter and 4.2-mm height on a hexagonal two-dimensional 61-voxel pattern with 5-mm spacing. Patterns of sample wells were filled with 40-μl volumes of Fe3O4 starch-coated magnetic nanoparticles (mNPs) with a hydrodynamic diameter of 100 nm and a concentration of 25 mg/ml. The imaging hardware was configured with three excitation coils and three detection coils in anticipation that a larger imaging system will have arrays of excitation and detection coils. Hexagonal and bar patterns of mNP were successfully imaged (R2>0.9) at several orientations. This SMI demonstration extends our prior work to feature a larger coil array, enlarged field-of-view, effective phase encoding scheme, reduced mNP sample size, and more complex imaging patterns to test the feasibility of extending the method beyond the pilot scale. The results presented in this study show that nonlinear SMI holds promise for further development into a practical imaging system for medical applications.

Nonlinear Susceptibility Magnitude Imaging of Magnetic Nanoparticles

This study demonstrates a method for improving the resolution of susceptibility magnitude imaging (SMI) using spatial information that arises from the nonlinear magnetization characteristics of magnetic nanoparticles (mNPs). In this proof-of-concept study of nonlinear SMI, a pair of drive coils and several permanent magnets generate applied magnetic fields and a coil is used as a magnetic field sensor. Sinusoidal alternating current (AC) in the drive coils results in linear mNP magnetization responses at primary frequencies, and nonlinear responses at harmonic frequencies and intermodulation frequencies. The spatial information content of the nonlinear responses is evaluated by reconstructing tomographic images with sequentially increasing voxel counts using the combined linear and nonlinear data. Using the linear data alone it is not possible to accurately reconstruct more than 2 voxels with a pair of drive coils and a single sensor. However, nonlinear SMI is found to accurately reconstruct 12 voxels (R² = 0.99, CNR = 84.9) using the same physical configuration. Several time-multiplexing methods are then explored to determine if additional spatial information can be obtained by varying the amplitude, phase and frequency of the applied magnetic fields from the two drive coils. Asynchronous phase modulation, amplitude modulation, intermodulation phase modulation, and frequency modulation all resulted in accurate reconstruction of 6 voxels (R² > 0.9) indicating that time multiplexing is a valid approach to further increase the resolution of nonlinear SMI. The spatial information content of nonlinear mNP responses and the potential for resolution enhancement with time multiplexing demonstrate the concept and advantages of nonlinear SMI.

Spectroscopic AC Susceptibility Imaging (sASI) of Magnetic Nanoparticles

This study demonstrates a method for alternating current (AC) susceptibility imaging (ASI) of magnetic nanoparticles (mNPs) using low cost instrumentation. The ASI method uses AC magnetic susceptibility measurement to create tomographic images using an array of drive coils, compensation coils and fluxgate magnetometers. Using a spectroscopic approach in conjunction with ASI, a series of tomographic images can be created for each frequency measurement and is termed sASI. The advantage of sASI is that mNPs can be simultaneously characterized and imaged in a biological medium. System calibration was performed by fitting the in-phase and out-of-phase susceptibility measurements of an mNP sample with a hydrodynamic diameter of 100 nm to a Brownian relaxation model (R² = 0.96). Samples of mNPs with core diameters of 10 and 40 nm and a sample of 100 nm hydrodynamic diameter were prepared in 0.5 ml tubes. Three mNP samples were arranged in a randomized array and then scanned using sASI with six frequencies between 425 and 925 Hz. The sASI scans showed the location and quantity of the mNP samples (R² = 0.97). Biological compatibility of the sASI method was demonstrated by scanning mNPs that were injected into a pork sausage. The mNP response in the biological medium was found to correlate with a calibration sample (R² = 0.97, p <0.001). These results demonstrate the concept of ASI and advantages of sASI.

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.

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.

Development of a magnetic nanoparticle susceptibility magnitude imaging array

There are several emerging diagnostic and therapeutic applications of magnetic nanoparticles (mNPs) in medicine. This study examines the potential for developing an mNP imager that meets these emerging clinical needs with a low cost imaging solution that uses arrays of digitally controlled drive coils in a multiple-frequency, continuous-wave operating mode and compensated fluxgate magnetometers. The design approach is described and a mathematical model is developed to support measurement and imaging. A prototype is used to demonstrate active compensation of up to 185 times the primary applied magnetic field, depth sensitivity up to 2.5 cm (p < 0.01), and linearity over five dilutions (R(2) > 0.98, p < 0.001). System frequency responses show distinguishable readouts for iron oxide mNPs with single magnetic domain core diameters of 10 and 40 nm, and multi-domain mNPs with a hydrodynamic diameter of 100 nm. Tomographic images show a contrast-to-noise ratio of 23 for 0.5 ml of 12.5 mg Fe ml(-1) mNPs at 1 cm depth. A demonstration involving the injection of mNPs into pork sausage shows the potential for use in biological systems. These results indicate that the proposed mNP imaging approach can potentially be extended to a larger array system with higher-resolution.

T1 magnetic resonance imaging head segmentation for diffuse optical tomography and electroencephalography

Accurate segmentation of structural magnetic resonance images is critical for creating subject-specific forward models for functional neuroimaging source localization. In this work, we present an innovative segmentation algorithm that generates accurate head tissue layer thicknesses that are needed for diffuse optical tomography (DOT) data analysis. The presented algorithm is compared against other publicly available head segmentation methods. The proposed algorithm has a root mean square scalp thickness error of 1.60 mm, skull thickness error of 1.96 mm, and summed scalp and skull error of 1.49 mm. We also introduce a segmentation evaluation metric that evaluates the accuracy of tissue layer thicknesses in regions of the head where optodes are typically placed. The presented segmentation algorithm and evaluation metric are tools for improving the localization accuracy of neuroimaging with DOT, and also multimodal neuroimaging such as combined electroencephalography and DOT.

Continuous correction of differential path length factor in near-infrared spectroscopy

In continuous-wave near-infrared spectroscopy (CW-NIRS), changes in the concentration of oxyhemoglobin and deoxyhemoglobin can be calculated by solving a set of linear equations from the modified Beer-Lambert Law. Cross-talk error in the calculated hemodynamics can arise from inaccurate knowledge of the wavelength-dependent differential path length factor (DPF). We apply the extended Kalman filter (EKF) with a dynamical systems model to calculate relative concentration changes in oxy- and deoxyhemoglobin while simultaneously estimating relative changes in DPF. Results from simulated and experimental CW-NIRS data are compared with results from a weighted least squares (WLSQ) method. The EKF method was found to effectively correct for artificially introduced errors in DPF and to reduce the cross-talk error in simulation. With experimental CW-NIRS data, the hemodynamic estimates from EKF differ significantly from the WLSQ (p < 0.001). The cross-correlations among residuals at different wavelengths were found to be significantly reduced by the EKF method compared to WLSQ in three physiologically relevant spectral bands 0.04 to 0.15 Hz, 0.15 to 0.4 Hz and 0.4 to 2.0 Hz (p < 0.001). This observed reduction in residual cross-correlation is consistent with reduced cross-talk error in the hemodynamic estimates from the proposed EKF method.

Compliant head probe for positioning electroencephalography electrodes and near-infrared spectroscopy optodes

A noninvasive head probe that combines near-infrared spectroscopy (NIRS) and electroencephalography (EEG) for simultaneous measurement of neural dynamics and hemodynamics in the brain is presented. It is composed of a compliant expandable mechanism that accommodates a wide range of head size variation and an elastomeric web that maintains uniform sensor contact pressure on the scalp as the mechanism expands and contracts. The design is intended to help maximize optical and electrical coupling and to maintain stability during head movement. Positioning electrodes at the inion, nasion, central, and preauricular fiducial locations mechanically shapes the probe to place 64 NIRS optodes and 65 EEG electrodes following the 10-5 scalp coordinates. The placement accuracy, precision, and scalp pressure uniformity of the sensors are evaluated. A root-mean-squared (RMS) positional precision of 0.89 ± 0.23 mm, percent arc subdivision RMS accuracy of 0.19 ± 0.15%, and mean normal force on the scalp of 2.28 ± 0.88 N at 5 mm displacement were found. Geometric measurements indicate that the probe will accommodate the full range of adult head sizes. The placement accuracy, precision, and uniformity of sensor contact pressure of the proposed head probe are important determinants of data quality in noninvasive brain monitoring with simultaneous NIRS-EEG.

Measuring cerebral hemodynamics with a modified magnetoencephalography system

Magnetoencephalography (MEG) systems are designed to noninvasively measure magnetic fields produced by neural electrical currents. This project examines the possibility of measuring hemodynamics with an MEG system that has been modified with dc electromagnets to measure magnetic susceptibility while maintaining the capability of measuring neural dynamics. A forward model is presented that simulates the interaction of an applied magnetic field with changes in magnetic susceptibility in the brain associated with hemodynamics. Model predictions are compared with an experiment where deionized water was pumped into an inverted flask under the MEG sensor array of superconducting quantum interference device (SQUID) gradiometers (R(2) = 0.98, p < 0.001). The forward model was used to simulate the SQUID readouts from hemodynamics in the scalp and brain induced by performing the Valsalva maneuver. Experimental human subject recordings (N = 10) were made from the prefrontal region during Valsalva using concurrent measurement with the modified MEG system and near-infrared spectroscopy (NIRS). The NIRS deoxyhemoglobin signal was found to correlate significantly with the SQUID readouts (R(2) = 0.84, p < 0.01). SQUID noise was found to increase with the applied field, which will need to be mitigated in future work. These results demonstrate the potential and technical challenges of measuring cerebral hemodynamics with a modified MEG system.

Effect of deep breathing on extracted oxygen and cerebral hemoglobin levels

This study examines the relationship between oxygen expired and functional near infrared spectroscopy (fNIRS) measured hemoglobin levels in the brain. Analysis of these two signals during normal versus deep breathing provides insight into the dynamics of cerebral physiology. Intersubject variation suggests the existence of two distinct groups with respect to oxygen extraction and hemoglobin levels.

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.

Modeling habituation in rat EEG-evoked responses via a neural mass model with feedback

Habituation is a generic property of the neural response to repeated stimuli. Its strength often increases as inter-stimuli relaxation periods decrease. We propose a simple, broadly applicable control structure that enables a neural mass model of the evoked EEG response to exhibit habituated behavior. A key motivation for this investigation is the ongoing effort to develop model-based reconstruction of multi-modal functional neuroimaging data. The control structure proposed here is illustrated and validated in the context of a biophysical neural mass model, developed by Riera et al. (Hum Brain Mapp 27(11):896-914, 2006; 28(4):335-354, 2007), and of simplifications thereof, using data from rat EEG response to medial nerve stimuli presented at frequencies from 1 to 8 Hz. Performance was tested by predictions of both the response to the next stimulus based on the current one, and also of continued stimuli trains over 4-s time intervals based on the first stimulus in the interval, with similar success statistics. These tests demonstrate the ability of simple generative models to capture key features of the evoked response, including habituation.

An investigation of the NOCSAE linear impactor test method based on in vivo measures of head impact acceleration in American football

The performance characteristics of football helmets are currently evaluated by simulating head impacts in the laboratory using a linear drop test method. To encourage development of helmets designed to protect against concussion, the National Operating Committee for Standards in Athletic Equipment recently proposed a new headgear testing methodology with the goal of more closely simulating in vivo head impacts. This proposed test methodology involves an impactor striking a helmeted headform, which is attached to a nonrigid neck. The purpose of the present study was to compare headform accelerations recorded according to the current (n=30) and proposed (n=54) laboratory test methodologies to head accelerations recorded in the field during play. In-helmet systems of six single-axis accelerometers were worn by the Dartmouth College men's football team during the 2005 and 2006 seasons (n=20,733 impacts; 40 players). The impulse response characteristics of a subset of laboratory test impacts (n=27) were compared with the impulse response characteristics of a matched sample of in vivo head accelerations (n=24). Second- and third-order underdamped, conventional, continuous-time process models were developed for each impact. These models were used to characterize the linear head/headform accelerations for each impact based on frequency domain parameters. Headform linear accelerations generated according to the proposed test method were less similar to in vivo head accelerations than headform accelerations generated by the current linear drop test method. The nonrigid neck currently utilized was not developed to simulate sport-related direct head impacts and appears to be a source of the discrepancy between frequency characteristics of in vivo and laboratory head/headform accelerations. In vivo impacts occurred 37% more frequently on helmet regions, which are tested in the proposed standard than on helmet regions tested currently. This increase was largely due to the addition of the facemask test location. For the proposed standard, impactor velocities as high as 10.5 m/s were needed to simulate the highest energy impacts recorded in vivo. The knowledge gained from this study may provide the basis for improving sports headgear test apparatuses with regard to mimicking in vivo linear head accelerations. Specifically, increasing the stiffness of the neck is recommended. In addition, this study may provide a basis for selecting appropriate test impact energies for the standard performance specification to accompany the proposed standard linear impactor test method.

Feasibility of NIRS in the neurointensive care unit: a pilot study in stroke using physiological oscillations

INTRODUCTION

Near-infrared spectroscopy (NIRS) is a non-invasive, real-time bedside modality sensitive to changes in cerebral perfusion and oxygenation and is highly sensitive to physiological oscillations at different frequencies. However, the clinical feasibility of NIRS remains limited, partly due to concerns regarding NIRS signal quantification, which relies on mostly arbitrary assumptions on hemoglobin concentrations and tissue layers. In this pilot study comparing stroke patients to healthy controls, we explored the utility of the interhemispheric correlation coefficient (IHCC) during physiological oscillations in detecting asymmetry in hemispheric microvascular hemodynamics.

METHODS

Using bi-hemispheric continuous-wave NIRS, 12 patients with hemispheric strokes and 9 controls were measured prospectively. NIRS signal was band-pass filtered to isolate cardiac (0.7-3 Hz) and respiratory (0.15-0.7 Hz) oscillations. IHCCs were calculated in both oscillation frequency bands. Using Fisher's Z-transform for non-Gaussian distributions, the IHCC during cardiac and respiratory oscillations were compared between both groups.

RESULTS

Nine patients and nine controls had data of sufficient quality to be included in the analysis. The IHCCs during cardiac and respiratory oscillations were significantly different between patients versus controls (cardiac 0.79 +/- 0.18 vs. 0.94 +/- 0.07, P = 0.025; respiratory 0.24 +/- 0.28 vs. 0.59 +/- 0.3; P = 0.016).

CONCLUSIONS

Computing the IHCC during physiological cardiac and respiratory oscillations may be a new NIRS analysis technique to quantify asymmetric microvascular hemodynamics in stroke patients in the neurocritical care unit. It allows each subject to serve as their own control obviating the need for arbitrary assumptions on absolute hemoglobin concentration. Future clinical applications may include rapid identification of patients with ischemic brain injury in the pre-hospital setting. This promising new analysis technique warrants further validation.

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 (CMRO(2)) 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 CMRO(2).

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.