Evaluating the data quality of the Gazepoint GP3 low-cost eye tracker when used independently by study participants.

The portability of low-cost eye trackers makes them attractive for research outside of the laboratory. Such research may require independent eye-tracker use. The present work compared the data quality of the Gazepoint GP3 when used independently by research participants with expert eye-tracking users. Twenty participants completed a training and a testing session 1 week apart. At training visits, participants were taught how to set up and use eye-tracking hardware and software and how to complete two tasks: a calibration task to measure accuracy and precision, as well as a visual search task to assess target fixations. At the testing session, participants set up the Gazepoint eye tracker and completed the two tasks without assistance. Participant accuracy and precision and visual search performance were compared to values obtained from two expert eye-tracking users. Additionally, the eye-tracker sampling rate, which is sensitive to factors such as head motion, was assessed in both participants and the expert users. Participant accuracy and precision closely approximated expert user values. Participant target fixations were detected with a 92.5% sensitivity and 76.8% specificity, closely mirroring expert user sensitivity and specificity. The sampling rate distribution was also similar between the participants and expert user (the means of those distributions were 16.99 ± 3.0 ms and 16.43 ± 2.3 ms, respectively). When used independently, data quality obtained from a low-cost, portable eye-tracking setup closely approximated values obtained from an expert user and was adequate enough to be a feasible option for some studies that require independent use by study participants.

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.

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.

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.

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.

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.

Decoding individual differences in STEM learning from functional MRI data.

Traditional tests of concept knowledge generate scores to assess how well a learner understands a concept. Here, we investigated whether patterns of brain activity collected during a concept knowledge task could be used to compute a neural 'score' to complement traditional scores of an individual's conceptual understanding. Using a novel data-driven multivariate neuroimaging approach-informational network analysis-we successfully derived a neural score from patterns of activity across the brain that predicted individual differences in multiple concept knowledge tasks in the physics and engineering domain. These tasks include an fMRI paradigm, as well as two other previously validated concept inventories. The informational network score outperformed alternative neural scores computed using data-driven neuroimaging methods, including multivariate representational similarity analysis. This technique could be applied to quantify concept knowledge in a wide range of domains, including classroom-based education research, machine learning, and other areas of cognitive science.

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.

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.

Near-infrared spectroscopy measurement of the pulsatile component of cerebral blood flow and volume from arterial oscillations.

We describe a near-infrared spectroscopy (NIRS) method to noninvasively measure relative changes in the pulsate components of cerebral blood flow (pCBF) and volume (pCBV) from the shape of heartbeat oscillations. We present a model that is used and data to show the feasibility of the method. We use a continuous-wave NIRS system to measure the arterial oscillations originating in the brains of piglets. Changes in the animals' CBF are induced by adding CO(2) to the breathing gas. To study the influence of scalp on our measurements, comparative, invasive measurements are performed on one side of the head simultaneously with noninvasive measurements on the other side. We also did comparative measurements of CBF using a laser Doppler system to validate the results of our method. The results indicate that for sufficient source-detector separation, the signal contribution of the scalp is minimal and the measurements are representative of the cerebral hemodynamics. Moreover, good correlation between the results of the laser Doppler system and the NIRS system indicate that the presented method is capable of measuring relative changes in CBF. Preliminary results show the potential of this NIRS method to measure pCBF and pCBV relative changes in neonatal pigs.

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.

Diffuse optical imaging of the whole head.

Near-Infrared Spectroscopy (NIRS) and diffuse optical imaging (DOI) are increasingly used to detect hemodynamic changes in the cerebral cortex induced by brain activity. Until recently, the small number of optodes in NIRS instruments has hampered measurement of optical signals from diverse brain regions. Our new DOI system has 32 detectors and 32 sources; by arranging them in a specific pattern, we can cover most of the adult head. With the increased number of optodes, we can collect optical data from prefrontal, sensorimotor, and visual cortices in both hemispheres simultaneously. We describe the system and report system characterization measurements on phantoms as well as on human subjects at rest and during visual, motor, and cognitive stimulation. Taking advantage of the system's larger number of sources and detectors, we explored the spatiotemporal patterns of physiological signals during rest. These physiological signals, arising from cardiac, respiratory, and blood-pressure modulations, interfere with measurement of the hemodynamic response to brain stimulation. Whole-head optical measurements, in addition to providing maps of multiple brain regions' responses to brain activation, will enable better understandings of the physiological signals, ultimately leading to better signal processing algorithms to distinguish physiological signal clutter from brain activation signals.

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.

Modeling Neurovascular Coupling from Clustered Parameter Sets for Multimodal EEG-NIRS.

Despite significant improvements in neuroimaging technologies and analysis methods, the fundamental relationship between local changes in cerebral hemodynamics and the underlying neural activity remains largely unknown. In this study, a data driven approach is proposed for modeling this neurovascular coupling relationship from simultaneously acquired electroencephalographic (EEG) and near-infrared spectroscopic (NIRS) data. The approach uses gamma transfer functions to map EEG spectral envelopes that reflect time-varying power variations in neural rhythms to hemodynamics measured with NIRS during median nerve stimulation. The approach is evaluated first with simulated EEG-NIRS data and then by applying the method to experimental EEG-NIRS data measured from 3 human subjects. Results from the experimental data indicate that the neurovascular coupling relationship can be modeled using multiple sets of gamma transfer functions. By applying cluster analysis, statistically significant parameter sets were found to predict NIRS hemodynamics from EEG spectral envelopes. All subjects were found to have significant clustered parameters (P < 0.05) for EEG-NIRS data fitted using gamma transfer functions. These results suggest that the use of gamma transfer functions followed by cluster analysis of the resulting parameter sets may provide insights into neurovascular coupling in human neuroimaging data.

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.

Near-infrared spiroximetry: noninvasive measurements of venous saturation in piglets and human subjects.

We present a noninvasive method to measure the venous oxygen saturation (Sv(O(2))) in tissues using near-infrared spectroscopy (NIRS). This method is based on the respiration-induced oscillations of the near-infrared absorption in tissues, and we call it spiroximetry (the prefix spiro means respiration). We have tested this method in three piglets (hind leg) and in eight human subjects (vastus medialis and vastus lateralis muscles). In the piglet study, we compared our NIRS measurements of the Sv(O(2)) (Sv(O(2))-NIRS(resp)) with the Sv(O(2)) of blood samples. Sv(O(2))-NIRS(resp) and Sv(O(2)) of blood samples agreed well over the whole range of Sv(O(2)) considered (20-95%). The two measurements showed an average difference of 1.0% and a standard deviation of the difference of 5.8%. In the human study, we found a good agreement between Sv(O(2))-NIRS(resp) and the Sv(O(2)) values measured with the NIRS venous occlusion method. Finally, in a preliminary test involving muscle exercise, Sv(O(2))-NIRS(resp) showed an expected postexercise decrease from the initial baseline value and a subsequent recovery to baseline.

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.