Generic Head Model Research Articles

Inverse kinematics in cervical spine models: Effects of scaling and model degrees of freedom for extension and flexion movements

Intervertebral kinematics can affect model-predicted loads and strains in the spine; therefore knowledge of expected vertebral kinematics error is important for understanding the limitations of model predictions. This study addressed how different kinematic models of the neck affect the prediction of intervertebral kinematics from markers on the head and trunk. Eight subjects executed head and neck extension-flexion motion with simultaneous motion capture and biplanar dynamic stereo-radiography (DSX) of vertebrae C1-C7. A generic head and neck model in OpenSim was scaled by marker data, and three versions of the model were used with an inverse kinematics solver. The models differed according to the number of independent degrees of freedom (DOF) between the head and trunk: 3 rotational DOF with constraints defining intervertebral kinematics as a function of overall head-trunk motion; 24DOF with 3 independent rotational DOF at each level, skull-T1; 48DOF with 3 rotational and 3 translational DOF at each level. Marker tracking error was lower for scaled models compared to generic models and decreased as model DOF increased. The largest mean absolute error (MAE) was found in extension-flexion angle and anterior-posterior translation at C1-C2, and superior-inferior translation at C2-C3. Model scaling and complexity did not have a statistically significant effect on most error metrics when corrected for multiple comparisons, but ranges of motion were significantly different from DSX in some cases. This study evaluated model kinematics in comparison to gold standard radiographic data and provides important information about intervertebral kinematics error that are foundational to model validity.

Polynomial chaos expansion of SAR and temperature increase variability in 3 T MRI due to stochastic input data

Objective. Numerical simulations are largely adopted to estimate dosimetric quantities, e.g. specific absorption rate (SAR) and temperature increase, in tissues to assess the patient exposure to the radiofrequency (RF) field generated during magnetic resonance imaging (MRI). Simulations rely on reference anatomical human models and tabulated data of electromagnetic and thermal properties of biological tissues. However, concerns may arise about the applicability of the computed results to any phenotype, introducing a significant degree of freedom in the simulation input data. In addition, simulation input data can be affected by uncertainty in relative positioning of the anatomical model with respect to the RF coil. The objective of this work is the to estimate the variability of SAR and temperature increase at 3 T head MRI due to different sources of variability in input data, with the final aim to associate a global uncertainty to the dosimetric outcomes. Approach. A stochastic approach based on arbitrary Polynomial Chaos Expansion is used to evaluate the effects of several input variability’s (anatomy, tissue properties, body position) on dosimetric outputs, referring to head imaging with a 3 T MRI scanner. Main results. It is found that head anatomy is the prevailing source of variability for the considered dosimetric quantities, rather than the variability due to tissue properties and head positioning. From knowledge of the variability of the dosimetric quantities, an uncertainty can be attributed to the results obtained using a generic anatomical head model when SAR and temperature increase values are compared with safety exposure limits. Significance. This work associates a global uncertainty to SAR and temperature increase predictions, to be considered when comparing the numerically evaluated dosimetric quantities with reference exposure limits. The adopted methodology can be extended to other exposure scenarios for MRI safety purposes.

Comparison of EEG Source Localization Using Simplified and Anatomically Accurate Head Models in Younger and Older Adults.

Accuracy of electroencephalography (EEG) source localization relies on the volume conduction head model. A previous analysis of young adults has shown that simplified head models have larger source localization errors when compared with head models based on magnetic resonance images (MRIs). As obtaining individual MRIs may not always be feasible, researchers often use generic head models based on template MRIs. It is unclear how much error would be introduced using template MRI head models in older adults that likely have differences in brain structure compared to young adults. The primary goal of this study was to determine the error caused by using simplified head models without individual-specific MRIs in both younger and older adults. We collected high-density EEG during uneven terrain walking and motor imagery for 15 younger (22±3 years) and 21 older adults (74±5 years) and obtained [Formula: see text]-weighted MRI for each individual. We performed equivalent dipole fitting after independent component analysis to obtain brain source locations using four forward modeling pipelines with increasing complexity. These pipelines included: 1) a generic head model with template electrode positions or 2) digitized electrode positions, 3) individual-specific head models with digitized electrode positions using simplified tissue segmentation, or 4) anatomically accurate segmentation. We found that when compared to the anatomically accurate individual-specific head models, performing dipole fitting with generic head models led to similar source localization discrepancies (up to 2 cm) for younger and older adults. Co-registering digitized electrode locations to the generic head models reduced source localization discrepancies by ∼ 6 mm. Additionally, we found that source depths generally increased with skull conductivity for the representative young adult but not as much for the older adult. Our results can help inform a more accurate interpretation of brain areas in EEG studies when individual MRIs are unavailable.

Brainstorm-DUNEuro: An integrated and user-friendly Finite Element Method for modeling electromagnetic brain activity

Human brain activity generates scalp potentials (electroencephalography – EEG), intracranial potentials (iEEG), and external magnetic fields (magnetoencephalography – MEG). These electrophysiology (e-phys) signals can often be measured simultaneously for research and clinical applications. The forward problem involves modeling these signals at their sensors for a given equivalent current dipole configuration within the brain. While earlier researchers modeled the head as a simple set of isotropic spheres, today's magnetic resonance imaging (MRI) data allow for a detailed anatomic description of brain structures and anisotropic characterization of tissue conductivities. We present a complete pipeline, integrated into the Brainstorm software, that allows users to automatically generate an individual and accurate head model based on the subject's MRI and calculate the electromagnetic forward solution using the finite element method (FEM). The head model generation is performed by integrating the latest tools for MRI segmentation and FEM mesh generation. The final head model comprises the five main compartments: white-matter, gray-matter, CSF, skull, and scalp. The anisotropic brain conductivity model is based on the effective medium approach (EMA), which estimates anisotropic conductivity tensors from diffusion-weighted imaging (DWI) data. The FEM electromagnetic forward solution is obtained through the DUNEuro library, integrated into Brainstorm, and accessible with either a user-friendly graphical interface or scripting. With tutorials and example data sets available in an open-source format on the Brainstorm website, this integrated pipeline provides access to advanced FEM tools for electromagnetic modeling to a broader neuroscience community.

Subject-Specific Head Model Generation by Mesh Morphing: A Personalization Framework and Its Applications.

Finite element (FE) head models have become powerful tools in many fields within neuroscience, especially for studying the biomechanics of traumatic brain injury (TBI). Subject-specific head models accounting for geometric variations among subjects are needed for more reliable predictions. However, the generation of such models suitable for studying TBIs remains a significant challenge and has been a bottleneck hindering personalized simulations. This study presents a personalization framework for generating subject-specific models across the lifespan and for pathological brains with significant anatomical changes by morphing a baseline model. The framework consists of hierarchical multiple feature and multimodality imaging registrations, mesh morphing, and mesh grouping, which is shown to be efficient with a heterogeneous dataset including a newborn, 1-year-old (1Y), 2Y, adult, 92Y, and a hydrocephalus brain. The generated models of the six subjects show competitive personalization accuracy, demonstrating the capacity of the framework for generating subject-specific models with significant anatomical differences. The family of the generated head models allows studying age-dependent and groupwise brain injury mechanisms. The framework for efficient generation of subject-specific FE head models helps to facilitate personalized simulations in many fields of neuroscience.

An anatomically detailed and personalizable head injury model: Significance of brain and white matter tract morphological variability on strain

Finite element head (FE) models are important numerical tools to study head injuries and develop protection systems. The generation of anatomically accurate and subject-specific head models with conforming hexahedral meshes remains a significant challenge. The focus of this study is to present two developmental works: first, an anatomically detailed FE head model with conforming hexahedral meshes that has smooth interfaces between the brain and the cerebrospinal fluid, embedded with white matter (WM) fiber tracts; second, a morphing approach for subject-specific head model generation via a new hierarchical image registration pipeline integrating Demons and Dramms deformable registration algorithms. The performance of the head model is evaluated by comparing model predictions with experimental data of brain–skull relative motion, brain strain, and intracranial pressure. To demonstrate the applicability of the head model and the pipeline, six subject-specific head models of largely varying intracranial volume and shape are generated, incorporated with subject-specific WM fiber tracts. DICE similarity coefficients for cranial, brain mask, local brain regions, and lateral ventricles are calculated to evaluate personalization accuracy, demonstrating the efficiency of the pipeline in generating detailed subject-specific head models achieving satisfactory element quality without further mesh repairing. The six head models are then subjected to the same concussive loading to study the sensitivity of brain strain to inter-subject variability of the brain and WM fiber morphology. The simulation results show significant differences in maximum principal strain and axonal strain in local brain regions (one-way ANOVA test, p < 0.001), as well as their locations also vary among the subjects, demonstrating the need to further investigate the significance of subject-specific models. The techniques developed in this study may contribute to better evaluation of individual brain injury and the development of individualized head protection systems in the future. This study also contains general aspects the research community may find useful: on the use of experimental brain strain close to or at injury level for head model validation; the hierarchical image registration pipeline can be used to morph other head models, such as smoothed-voxel models.

Deep Learning-Based Development of Personalized Human Head Model With Non-Uniform Conductivity for Brain Stimulation.

Electromagnetic stimulation of the human brain is a key tool for neurophysiological characterization and the diagnosis of several neurological disorders. Transcranial magnetic stimulation (TMS) is a commonly used clinical procedure. However, personalized TMS requires a pipeline for individual head model generation to provide target-specific stimulation. This process includes intensive segmentation of several head tissues based on magnetic resonance imaging (MRI), which has significant potential for segmentation error, especially for low-contrast tissues. Additionally, a uniform electrical conductivity is assigned to each tissue in the model, which is an unrealistic assumption based on conventional volume conductor modeling. This study proposes a novel approach for fast and automatic estimation of the electric conductivity in the human head for volume conductor models without anatomical segmentation. A convolutional neural network is designed to estimate personalized electrical conductivity values based on anatomical information obtained from T1- and T2-weighted MRI scans. This approach can avoid the time-consuming process of tissue segmentation and maximize the advantages of position-dependent conductivity assignment based on the water content values estimated from MRI intensity values. The computational results of the proposed approach provide similar but smoother electric field distributions of the brain than that provided by conventional approaches.

An improved method to determine neutron fluence in high energy medical accelerators using activation detectors

Scope of the present work was to test the hypothesis that a generic simulation geometry can adequately describe a high energy medical accelerator head for the purpose of estimating the parasitic neutron fluence levels at the position of the isocenter. The experiment was performed using an Elekta Synergy 18 MV linear accelerator. Gold, cobalt, indium and copper activation foils were used. Activation measurements were performed using a calibrated HPGe detector based spectrometry system. Four generic accelerator head models were considered. Neutron spectrum averaged cross-section data for each foil were derived for the examined configurations using the Monte Carlo code MCNP5 in conjuction with cross section data obtained from the International Reactor Dosimetry and Fusion File (IRDFF). It was concluded that the accelerator head can be adequately described either as a solid tungsten sphere of 10 cm radius or a spherical tungsten shell 20 cm in external diameter and 10 cm in thickness. This work contributes towards the development of a simple and computationally cost effective method for the determination of neutron fluence around high energy medical accelerators and therefore the optimization of the radiation protection of the patients and staff in radiation therapy.

Math anxiety: Brain cortical network changes in anticipation of doing mathematics

Following our previous work regarding the involvement of math anxiety (MA) in math-oriented tasks, this study tries to explore the differences in the cerebral networks' topology between self-reported low math-anxious (LMA) and high math-anxious (HMA) individuals, during the anticipation phase prior to a mathematical related experiment. For this reason, multichannel EEG recordings were adopted, while the solution of the inverse problem was applied in a generic head model, in order to obtain the cortical signals. The cortical networks have been computed for each band separately, using the magnitude square coherence metric. The main graph theoretical parameters, showed differences in segregation and integration in almost all EEG bands of the HMAs in comparison to LMAs, indicative of a great influence of the anticipatory anxiety prior to mathematical performance.

Incorporating modern neuroscience findings to improve brain–computer interfaces: tracking auditory attention

Objective. Brain–computer interface (BCI) technology allows users to generate actions based solely on their brain signals. However, current non-invasive BCIs generally classify brain activity recorded from surface electroencephalography (EEG) electrodes, which can hinder the application of findings from modern neuroscience research. Approach. In this study, we use source imaging—a neuroimaging technique that projects EEG signals onto the surface of the brain—in a BCI classification framework. This allowed us to incorporate prior research from functional neuroimaging to target activity from a cortical region involved in auditory attention. Main results. Classifiers trained to detect attention switches performed better with source imaging projections than with EEG sensor signals. Within source imaging, including subject-specific anatomical MRI information (instead of using a generic head model) further improved classification performance. This source-based strategy also reduced accuracy variability across three dimensionality reduction techniques—a major design choice in most BCIs. Significance. Our work shows that source imaging provides clear quantitative and qualitative advantages to BCIs and highlights the value of incorporating modern neuroscience knowledge and methods into BCI systems.

Radiation Pattern Measurement of a Low-Profile Wearable Antenna Using an Optical Fibre and a Solid Anthropomorphic Phantom

This paper presents a study into radiation pattern measurements of an electrically small dielectric resonator antenna (DRA) operating between 2.4 and 2.5 GHz in the industrial, scientific and medical (ISM) radio band for body-centric wireless communication applications. To eliminate the distortion of the radiation pattern associated with the unwanted radiation from a metallic coaxial cable feeding the antenna we have replaced it with a fibre optic feed and an electro-optical (EO) transducer. The optical signal is then converted back to RF using an Opto-Electric Field Sensor (OEFS) system. To ensure traceable measurements of the radiation pattern performance of the wearable antenna a generic head and torso solid anthropomorphic phantom model has been employed. Furthermore, to illustrate the benefits of the method, numerical simulations of the co-polar and cross-polar H-plane radiation patterns at 2.4, 2.45, and 2.5 GHz are compared with the measured results obtained using: (i) an optical fibre; and (ii) a metallic coaxial cable.

Art Critic: Multisignal Vision and Speech Interaction System in a Gaming Context

True immersion of a player within a game can only occur when the world simulated looks and behaves as close to reality as possible. This implies that the game must correctly read and understand, among other things, the player's focus, attitude toward the objects/persons in focus, gestures, and speech. In this paper, we proposed a novel system that integrates eye gaze estimation, head pose estimation, facial expression recognition, speech recognition, and text-to-speech components for use in real-time games. Both the eye gaze and head pose components utilize underlying 3-D models, and our novel head pose estimation algorithm uniquely combines scene flow with a generic head model. The facial expression recognition module uses the local binary patterns with three orthogonal planes approach on the 2-D shape index domain rather than the pixel domain, resulting in improved classification. Our system has also been extended to use a pan-tilt-zoom camera driven by the Kinect, allowing us to track a moving player. A test game, Art Critic, is also presented, which not only demonstrates the utility of our system but also provides a template for player/non-player character (NPC) interaction in a gaming context. The player alters his/her view of the 3-D world using head pose, looks at paintings/NPCs using eye gaze, and makes an evaluation based on the player's expression and speech. The NPC artist will respond with facial expression and synthetic speech based on its personality. Both qualitative and quantitative evaluations of the system are performed to illustrate the system's effectiveness.

Localized High Gamma Motor Oscillations Respond to Perceived Biologic Motion

Power changes in the beta frequency range (17-25 Hz) in the human motor and premotor areas during action observation have been associated with the mirror neuron system and have been studied extensively. These changes mimic motor activity during actual motion execution, albeit reduced in strength. Recent noninvasive (EEG/magnetoencephalography) and invasive studies (electrocorticography) have shown that during actual motion, beta power changes are accompanied by highly localized changes in the high gamma band (70-100 Hz). In this study, we investigate, using 27-channel EEG in combination with a generic head model and a cortical mapping algorithm, whether such high gamma changes are also present during motion observation. Subjects were presented with a 2.7-second video of a moving hand, contrasted with a video of moving scenery of equal length. Our results show nonlateralized beta band decrease in power in response to the moving hand versus the response to the moving scenery. We also find significant increase in high gamma power. However, unlike the beta band response, increases in this band are lateralized, with a preference for the hemisphere of the dominant hand.

A comprehensive system for facial animation of generic 3D head models driven by speech

A comprehensive system for facial animation of generic 3D head models driven by speech is presented in this article. In the training stage, audio-visual information is extracted from audio-visual training data, and then used to compute the parameters of a single joint audio-visual hidden Markov model (AV-HMM). In contrast to most of the methods in the literature, the proposed approach does not require segmentation/classification processing stages of the audio-visual data, avoiding the error propagation related to these procedures. The trained AV-HMM provides a compact representation of the audio-visual data, without the need of phoneme (word) segmentation, which makes it adaptable to different languages. Visual features are estimated from the speech signal based on the inversion of the AV-HMM. The estimated visual speech features are used to animate a simple face model. The animation of a more complex head model is then obtained by automatically mapping the deformation of the simple model to it, using a small number of control points for the interpolation. The proposed algorithm allows the animation of 3D head models of arbitrary complexity through a simple setup procedure. The resulting animation is evaluated in terms of intelligibility of visual speech through perceptual tests, showing a promising performance. The computational complexity of the proposed system is analyzed, showing the feasibility of its real-time implementation.

Effect of realistic human head modelling on brain source distribution

The accuracy of the source localisation using EEG measurements depends on the noise as well as the computational model, so the improvement of localisation performance by using a very realistic model is limited. In this reported work, the source localisation effects of various human head models with different geometrical deformation for low signal-to-noise ratio (SNR) measurement are investigated. Assuming the most dense mesh as a model without model mismatch, it was found that some error due to the model mismatch was tolerably small in the range of low SNRs less than 0 dB. This provides a cost-effective strategy of head model generation, depending on a given tolerable level of localisation error.

Specific absorption rate intersubject variability in 7T parallel transmit MRI of the head

Patient-specific radiofrequency shimming in high-field MRI strengthens the need for online, patient-specific specific absorption rate (SAR) monitoring. Numerical simulation is currently most effective for this purpose but may require a patient-specific dielectric model. To investigate whether a generic model may be combined with a safety factor to account for variation within the population, generic SAR behavior is studied for 7T MRI of the head. For six detailed head models, radiofrequency fields were simulated for an eight-channel parallel transmit array. SAR behavior is studied through comparison of the eigenvalues/eigenvectors of the local Q-matrices. Furthermore, numerical radiofrequency shimming experiments without and with SAR constraints were performed where SAR during optimization was evaluated on a generic model. In both cases, the ability of different generic models to predict actual SAR levels was evaluated. The largest eigenvalue distribution is comparable between models. Radiofrequency shimming without constraints improves the |B +1| homogeneity while the SAR increases substantially. Imposing constraints on SAR during optimization, estimating SAR on a generic model, was effective. A safety factor of 1.4 was found to be sufficient. Generic SAR behavior makes a generic head model a practical alternative to patient-specific models and allows effective |B +1| shimming with SAR constraints.

High gamma mapping using EEG

High gamma (HG) power changes during motor activity, especially at frequencies above 70 Hz, play an important role in functional cortical mapping and as control signals for BCI (brain–computer interface) applications. Most studies of HG activity have used ECoG (electrocorticography) which provides high-quality spatially localized signals, but is an invasive method. Recent studies have shown that non-invasive modalities such as EEG and MEG can also detect task-related HG power changes. We show here that a 27 channel EEG (electroencephalography) montage provides high-quality spatially localized signals non-invasively for HG frequencies ranging from 83 to 101 Hz. We used a generic head model, a weighted minimum norm least squares (MNLS) inverse method, and a self-paced finger movement paradigm. The use of an inverse method enables us to map the EEG onto a generic cortex model. We find the HG activity during the task to be well localized in the contralateral motor area. We find HG power increases prior to finger movement, with average latencies of 462 ms and 82 ms before EMG (electromyogram) onset. We also find significant phase-locking between contra- and ipsilateral motor areas over a similar HG frequency range; here the synchronization onset precedes the EMG by 400 ms. We also compare our results to ECoG data from a similar paradigm and find EEG mapping and ECoG in good agreement. Our findings demonstrate that mapped EEG provides information on two important parameters for functional mapping and BCI which are usually only found in HG of ECoG signals: spatially localized power increases and bihemispheric phase-locking.

Automatic Head Model Generation Based on Optimized Local Affine Transform Using Facial Range Scan Data

Automatic Head Model Generation Based on Optimized Local Affine Transform Using Facial Range Scan Data

3D head model generation using full‐face head shape measurement system

AbstractRecently, the application of human facial shapes has been attracting interest in such fields as authentication, amusement, and medical care. However, the systems now used in practice heavily burden the person undergoing measurement, because a long time is required for measurement and the user must maintain his or her facial expression. The paper considers the measurement of the human face and proposes a fast method for head shape measurement in which the number of projected patterns is decreased by utilizing the continuity of the facial shape and the uniformity of its reflectivity. Specifically, based on the assumption of continuity of the facial shape, the pattern projection of the upper bits is omitted in the spatial coding, and the spatial code is restored by utilizing the continuity of the facial shape. By applying this method, shape measurement with 8‐bit spatial resolution in the depth direction is achieved by five pattern light projection operations. Based on the fact that the reflectivity of the face is almost uniform, binarization is performed without complementary pattern projection, by using average image comparison and local threshold setting. With these measures, the head model can be acquired by the proposed method in 14 pattern imaging operations, compared with 36 pattern imaging operations required in the conventional measurement system based on spatial coding using complementary pattern light projection. © 2007 Wiley Periodicals, Inc. Syst Comp Jpn, 38(13): 82–91, 2007; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/scj.20377

Image-Based 3D Face Modeling System

This paper describes an automatic system for 3D face modeling using frontal and profile images taken by an ordinary digital camera. The system consists of four subsystems including frontal feature detection, profile feature detection, shape deformation, and texture generation modules. The frontal and profile feature detection modules automatically extract the facial parts such as the eye, nose, mouth, and ear. The shape deformation module utilizes the detected features to deform the generic head mesh model such that the deformed model coincides with the detected features. A texture is created by combining the facial textures augmented from the input images and the synthesized texture and mapped onto the deformed generic head model. This paper provides a practical system for 3D face modeling, which is highly automated by aggregating, customizing, and optimizing a bunch of individual computer vision algorithms. The experimental results show a highly automated process of modeling, which is sufficiently robust to various imaging conditions. The whole model creation including all the optional manual corrections takes only 2 3 minutes.