Accurate Head Models Research Articles

Investigating the effect of template head models on Event-Related Potential source localization: a simulation and real-data study.

Event-Related Potentials (ERPs) are valuable for studying brain activity with millisecond-level temporal resolution. While the temporal resolution of this technique is excellent, the spatial resolution is limited. Source localization aims to identify the brain regions generating the EEG data, thus increasing the spatial resolution, but its accuracy depends heavily on the head model used. This study compares the performance of subject-specific and template-based head models in both simulated and real-world ERP localization tasks. Simulated data mimicking realistic ERPs was created to evaluate the impact of head model choice systematically, after which subject-specific and template-based head models were used for the reconstruction of the data. The different modeling approaches were also applied to a face recognition dataset. The results indicate that the template models capture the simulated activity less accurately, producing more spurious sources and identifying less true sources correctly. Furthermore, the results show that while creating more accurate and detailed head models is beneficial for the localization accuracy when using subject-specific head models, this is less the case for template head models. The main N170 source of the face recognition dataset was correctly localized to the fusiform gyrus, a known face processing area, using the subject-specific models. Apart from the fusiform gyrus, the template models also reconstructed several other sources, illustrating the localization inaccuracies. While template models allow researchers to investigate the neural generators of ERP components when no subject-specific MRIs are available, it could lead to misinterpretations. Therefore, it is important to consider a priori knowledge and hypotheses when interpreting results obtained with template head models, acknowledging potential localization errors.

Characterization and selection of a skull surrogate for the development of a biofidelic head model

This research paper explores the advancement of physical models simulating the human skull-brain complex, focusing on applications in simulating mild Traumatic Brain Injury (mTBI). Existing models, especially head forms, lack biofidelity in accurately representing the native structures of the skull, limiting the understanding of intracranial injury parameters beyond kinematic head accelerations. This study addresses this gap by investigating the use of additive manufacturing (AM) techniques to develop biofidelic skull surrogates. Materials such as Polylactic Acid (PLA), a bone-simulant PLA variant, and Hydroxyapatite-coated Poly(methyl methacrylate) (PMMA) were used to create models tested for their flexural modulus and strength. The trabecular bone regions were simulated by adjusting infill densities (30%, 50%, 80%) and print raster directions, optimizing manufacturing parameters for biofidelic performance. Among the tested materials, PLA and its bone-simulating variant printed at 80% infill density with a side (tangential) print orientation demonstrated the closest approximation to the mechanical properties of cranial bone, yielding a mean flexural modulus of 1337.2 MPa and a mean ultimate strength of 56.9 MPa. Statistical analyses showed that infill density significantly influenced the moduli and strength of the printed simulants. Digital Image Correlation (DIC) corroborated the comparable performance of the simulants, showing similar strain and displacement behaviors to native skull bone. Notably, the performance of the manufactured cortical and trabecular regions underscored their crucial role in achieving biofidelity, with the trabecular structure providing critical dampening effects when the native bone is loaded. This study establishes PLA, particularly its bone-simulant variant, as an optimal candidate for cranial bone simulants, offering significant potential for developing more accurate biofidelic head models in mTBI research.

Evaluation of a pilot regional neurotrauma workshop using 3D printed simulation models

ObjectiveNon-neurosurgeons in regional and rural hospitals may be required to operate on patients presenting with a traumatic brain injury where timely transfer to a tertiary hospital is not possible. Confidence and experience can vary significantly due to limited access to hands-on training. Increasing availability to advanced 3D printed models opens new opportunities to provide accurate head models suitable for this purpose. This study evaluated the experience of regional clinicians and nurses following a neurotrauma workshop where 3D printed head models were used to provide training in burr hole and craniotomy procedures. MethodsA neurotrauma seminar and workshop was hosted at the Sunshine Coast Health Institute, in the state of Queensland, Australia. The workshop component allowed 26 local clinicians and nurses to gain hands-on experience with a 3D printed head model, guided by neurosurgeons from the closest tertiary hospital. Following training, participants completed a short survey. ResultsPrior to this workshop, most participants had never performed a burr hole (58 %, n=15) or interacted with a 3D printed model (69 %, n=18). Overall, most participants indicated that the 3D printed model performed better (58 %, n=15) and much better (15 %, n=4) than their expectations. 81 % (n=21) left the workshop with improved confidence in performing burr hole and craniotomy procedures. Despite some melting of the plastic, 96 % (n=25) of participants would recommend this model to their colleagues.

Perspectives on Optimized Transcranial Electrical Stimulation Based on Spatial Electric Field Modeling in Humans.

Background: Transcranial electrical stimulation (tES) generates an electric field (or current density) in the brain through surface electrodes attached to the scalp. Clinical significance has been demonstrated, although with moderate and heterogeneous results partly due to a lack of control of the delivered electric currents. In the last decade, computational electric field analysis has allowed the estimation and optimization of the electric field using accurate anatomical head models. This review examines recent tES computational studies, providing a comprehensive background on the technical aspects of adopting computational electric field analysis as a standardized procedure in medical applications. Methods: Specific search strategies were designed to retrieve papers from the Web of Science database. The papers were initially screened based on the soundness of the title and abstract and then on their full contents, resulting in a total of 57 studies. Results: Recent trends were identified in individual- and population-level analysis of the electric field, including head models from non-neurotypical individuals. Advanced optimization techniques that allow a high degree of control with the required focality and direction of the electric field were also summarized. There is also growing evidence of a correlation between the computationally estimated electric field and the observed responses in real experiments. Conclusions: Computational pipelines and optimization algorithms have reached a degree of maturity that provides a rationale to improve tES experimental design and a posteriori analysis of the responses for supporting clinical studies.

Novel multi-magnetic material transcranial magnetic stimulation coils for small animals application

Transcranial magnetic stimulation (TMS) is a non-invasive therapeutic approach with proven efficacy in treating certain psychiatric and neurological disorders. To broaden the application of TMS to a wider range of neurological disorders, the induced electric field (e-field) must be able to more focally target specific regions of the brain. Animal coil designs are promising for studying TMS effects and developing new procedures for treating various neurological and psychiatric disorders. However, investigations have mostly used coil designs intended for humans, and, as such, have failed to scale down for small animal sizes. Currently available coil systems have been limited by not adequately incorporating ideal soft magnetic materials as coil cores and not advancing the coil shapes for small animals as is necessary to achieve highly focused e-field concentration. In this study, we used multi-magnetic materials in TMS coils for the first time to our knowledge. We also considered the non-linear, hysteretic behavior of the cores in our FEM simulations of induced e-field and magnetic field in the brain models of the rat. Our study involved a comprehensive assessment of the distribution and intensity of the e-field across various coil configurations using our customized anatomically accurate rat head model. Iron-cobalt-vanadium (Permendur) alloy known for its high relative permeability, high saturation magnetization and low coercivity was used as the core material for all simulations. To enhance coil focality and concentration of the e-field, while avoiding stimulation of adjacent regions, we introduced a shape-conforming, flexible, diamagnetic pyrolytic graphite with susceptibility of -4.5×10-4 for our top performing coil. Simulation results were validated by comparing the simulated magnetic field results with experimental measurements obtained from the v-tip coil with a carbon steel AISI-1010 core at varying tip distances. This thorough study advances our knowledge of TMS coil designs and their optimization, particularly for small animal applications.

The Effect of Coil-to-Cortex Distance and Induced Electric Field Strength on Resting Motor Thresholds in Schizophrenia Patients

Transcranial magnetic stimulation is a noninvasive neuromodulation technique utilized to treat several neuropsychiatric disorders. Schizophrenia patients typically experience positive and negative symptoms, which are commonly treated with antipsychotic medications. Repetitive (r)TMS is explored to alleviate these symptoms by modulating cortical excitability. TMS stimulation strength is determined by the resting motor threshold (RMT), which is determined from single pulse stimulation to the precentral gyrus region of the brain. In this study, 62 schizophrenia and 41 healthy participants’ magnetic resonance imaging (MRI) were used to create anatomically accurate head models. Head models were then used to calculate the coil-to-cortex distance (CCD), gray matter volume (GMV), and finite element analysis was performed to calculate the induced electric field strength (EFS) and depolarized gray matter volume (DGMV). These MRI-derived parameters were then evaluated to establish their relationship with clinically collected RMT in both healthy and schizophrenia populations. In both populations, RMT correlated with CCD, EFS, and DGMV. Our hypothesis that the presence of schizophrenia would have a negative impact on the influence of MRI model-driven parameters was largely supported. Future investigations into RMT variability should incorporate structural connectivity using white matter-based fiber tractography and functional connectivity.

Anatomical details affect electric field predictions for non-invasive brain stimulation in non-human primates

Non-human primates (NHPs) have become key for translational research in noninvasive brain stimulation (NIBS). However, in order to create comparable stimulation conditions for humans it is vital to study the accuracy of current modeling practices across species. Numerical models to simulate electric fields are an important tool for experimental planning in NHPs and translation to human studies. It is thus essential whether and to what extent the anatomical details of NHP models agree with current modeling practices when calculating NIBS electric fields. Here, we create highly accurate head models of two non-human primates (NHP) MR data. We evaluate how muscle tissue and head field of view (depending on MRI parameters) affect simulation results in transcranial electric and magnetic stimulation (TES and TMS). Our findings indicate that the inclusion of anisotropic muscle can affect TES electric field strength up to 22% while TMS is largely unaffected. Additionally, comparing a full head model to a cropped head model illustrates the impact of head field of view on electric fields for both TES and TMS. We find opposing effects between TES and TMS with an increase up to 24.8% for TES and a decrease up to 24.6% for TMS for the cropped head model compared to the full head model. Our results provide important insights into the level of anatomical detail needed for NHP head models and can inform future translational efforts for NIBS studies.

Monte Carlo modeling of Halcyon and Ethos radiotherapy beam using CAD geometry: validation and IAEA-compliant phase space

Objective. A Monte Carlo (MC) model of a Halcyon and Ethos (Varian Medical Systems, a Siemens Healthineers Company) radiotherapy beam was validated and field-independent phase space (PHSP) files were recorded above the dual-layer multileaf collimators (MLC). Approach. The treatment head geometry was modeled according to engineering drawings and the dual-layer MLC was imported from CAD (computer-aided design) files. The information for the incident electron beam was achieved from an iterative electromagnetic solver. The validation of the model was performed by comparing the dose delivered by the square MLC fields as well as complex field measurements. Main results. An electron phase space was generated from linac simulations and achieved improved MC results. The output factors for square fields were within 1% and the largest differences of 5% were found in the build-up region of PDDs and the penumbra region of profiles. With the more complicated MLC-shaped field (Fishbone), the largest differences of up to 8% were found in the MLC leaf tip region due to the uncertainty of the MLC positioning and the mechanical leaf gap value. The impact of the collimator rotation on the PHSP solution has been assessed with both small and large fields, confirming negligible effects on in-field and out-of-field dose distributions. Significance. A computational model of the Halcyon and Ethos radiotherapy beam with a high accuracy implementation of the MLC was shown to be able to reproduce the radiation beam characteristics with square fields and more complex MLC-shaped fields. The field-independent PHSP files that were produced can be used as an accurate treatment head model above the MLC, and reduce the time to simulate particle transport through treatment head components.

The relationship of neuroanatomy on resting motor threshold and induced electric field strength on treatment outcomes in mild to moderate traumatic brain injury patients during transcranial magnetic stimulation

Transcranial magnetic stimulation (TMS) is a non-invasive treatment protocol for treating several psychiatric conditions, including depression, migraine, smoking cessation, and obsessive-compulsive disorder. Past research suggests that TMS treatment outcomes vary based on neuroanatomy, functional connectivity, and tractography-based structural connectivity. In a previous study, 26 mild to moderate traumatic brain injury (mTBI) patients underwent repetitive transcranial magnetic stimulation (rTMS) and showed improvements in depression, post-concussive symptoms, and sleep dysfunction. The present study was a secondary analysis of that data. Anatomically accurate head models were derived from magnetic resonance imaging (MRI), and finite element analysis simulations were performed to mimic empirical data collection. This allowed for examination of the roles that age, brain scalp distance (BSD), gray matter volume (GMV), site-specific electrical field strength (EFS), and depolarized gray matter volume (DGMV) had on resting motor threshold (RMT) at the precentral gyrus (PreCG). We also investigated how EFS simulated at the dorsolateral prefrontal cortex (DLPFC) and RMT influenced rTMS treatment outcomes. Linear regression showed BSD was associated with EFS, RMT, and DGMV supporting efforts to derive accurate parameters from MRI-based modeling. Furthermore, linear mixed effects modeling showed RMT was associated with EFS and DGMV at the PreCG when age and individual neuroanatomy was accounted for suggesting MRI based anatomy and simulated EFS potentially determine TMS dosage. We did not observe any significant relationship between any of the measures from this study on empirically collected rTMS outcomes in mTBI suggesting further investigations into the mechanisms behind these outcomes are needed.

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.

Electroencephalography source localization.

Electroencephalography (EEG) has been and is still widely used in brain function research. EEG has advantages over other neuroimaging modalities. First, it not only directly images the electrical activity of neurons; it has a higher temporal resolution. Furthermore, current advanced technologies enable accurate mathematical calculations and sophisticated localization from EEG data. Several important factors should be considered for EEG analysis using these advanced technologies. First, raw EEG data contain physiological or nonphysiological artifacts. Therefore, preprocessing methods and algorithms to detect and remove these artifacts have been proposed and developed. In the analysis of preprocessed EEG, forward and inverse problems require solving and several proposed models have been applied. To solve the forward problem, the source information and matrix parameters from which the EEG originates are essential. Therefore, an accurate head model is required. In contrast, the possible combinations of the current sources computed inversely from EEG measured at a limited number of electrodes are infinite, referring to the inverse problem. The inverse problem can be solved by setting limits based on assumptions made of the anatomy and physiology on the generation and propagation of the current sources. Thus, methods such as dipole source models and distributed source models have been proposed. Source localization requires the consideration of many factors such as the preprocessing of raw EEG data, artifact removal, accurate head models and forward problems, and inverse computation problems. This review summarizes the methods and considerations applied to the above EEG source localization process. It also introduces the applications of EEG source localization for epilepsy and other diseases as well as brain function studies and discusses future directions.

Effect of neuroanatomy on corticomotor excitability during and after transcranial magnetic stimulation and intermittent theta burst stimulation.

Individual neuroanatomy can influence motor responses to transcranial magnetic stimulation (TMS) and corticomotor excitability after intermittent theta burst stimulation (iTBS). The purpose of this study was to examine the relationship between individual neuroanatomy and both TMS response measured using resting motor threshold (RMT) and iTBS measured using motor evoked potentials (MEPs) targeting the biceps brachii and first dorsal interosseus (FDI). Ten nonimpaired individuals completed sham‐controlled iTBS sessions and underwent MRI, from which anatomically accurate head models were generated. Neuroanatomical parameters established through fiber tractography were fiber tract surface area (FTSA), tract fiber count (TFC), and brain scalp distance (BSD) at the point of stimulation. Cortical magnetic field induced electric field strength (EFS) was obtained using finite element simulations. A linear mixed effects model was used to assess effects of these parameters on RMT and iTBS (post‐iTBS MEPs). FDI RMT was dependent on interactions between EFS and both FTSA and TFC. Biceps RMT was dependent on interactions between EFS and and both FTSA and BSD. There was no groupwide effect of iTBS on the FDI but individual changes in corticomotor excitability scaled with RMT, EFS, BSD, and FTSA. iTBS targeting the biceps was facilitatory, and dependent on FTSA and TFC. MRI‐based measures of neuroanatomy highlight how individual anatomy affects motor system responses to different TMS paradigms and may be useful for selecting appropriate motor targets when designing TMS based therapies.

Determination of the number of components in the PARAFAC model with a nonnegative tensor structure: a simulated EEG data study

Parallel factor analysis (PARAFAC) is a powerful tool for detecting latent components in human electroencephalogram (EEG) in the time-space-frequency domain. As an essential parameter, the number of latent components should be set in advance. However, any component number selection method already proposed in the literature became a rule of thumb. Existing studies have demonstrated the methods’ performance on artificial data with a simplified structure, often not mimicking a real data character. On the other hand, the ground-truth latent structure is not always known for real-world data. With the objective to provide a comprehensive overview of component number selection methods and discuss their applicability to EEG, our study focuses on nontrivial and nonnegative simulated data structures resembling real EEG properties as closely as possible. This is achieved through an accurate head model and well-controlled cortical activation sources. By considering different noise levels and disruptions from the optimal structure, the performance of the twelve component number selection methods is closely inspected. Moreover, we validate a new approach for component number selection, which we recently proposed and applied to EEG tasks. We found that methods based on the eigenvalue analysis, variance explained, or presence of redundant components are inappropriate for component number selection in EEG tensor decomposition. On the other hand, three existing methods and the newly proposed approach produced promising results on nontrivial simulated EEG data. Nevertheless, component number selection for PARAFAC analysis of EEG is a complex yet unresolved problem, and new approaches are needed.

Sensitivity Analysis Highlights the Importance of Accurate Head Models for Electrical Impedance Tomography Monitoring of Intracerebral Hemorrhagic Stroke.

Electrical impedance tomography (EIT) has been proposed as a novel tool for diagnosing stroke. However, so far, the clinical feasibility is unresolved. In this study, we aim to investigate the need for accurate head modeling in EIT and how the inhomogeneities of the head contribute to the EIT measurement and affect its feasibility in monitoring the progression of a hemorrhagic stroke. We compared anatomically detailed six- and three-layer finite element models of a human head and computed the resulting scalp electrode potentials and the lead fields of selected electrode configurations. We visualized the resulting EIT measurement sensitivity distributions, computed the scalp electrode potentials, and examined the inverse imaging with selected cases. The effect of accurate tissue geometry and conductivity values on the EIT measurement is assessed with multiple different hemorrhagic perturbation locations and sizes. Our results show that accurate tissue geometries and conductivity values inside the cranial cavity, especially the highly conductive cerebrospinal fluid, significantly affect EIT measurement sensitivity distribution and measured potentials. We can conclude that the three-layer head models commonly used in EIT literature cannot depict the current paths correctly in the head. Thus, our study highlights the need to consider the detailed geometry of the cerebrospinal fluid (CSF) in EIT. The results clearly show that the CSF should be considered in the head EIT calculations.

Numerical Analysis of Transcranial Magnetic Stimulation Application in Patients With Orofacial Pain.

In this paper, we monitored the accuracy of non-navigated application of repetitive Transcranial Magnetic Stimulation (rTMS) in 10 patients suffering from orofacial pain by using functional magnetic resonance (fMRI), computer modeling and numerical simulation. Through a unique process, each fMRI scan was used to define a Region of Interest (ROI) where the source of the orofacial pain was located, which was to be stimulated using rTMS. For each patient, MRI scans with a spatial resolution of 0.7 mm were converted into an anatomically accurate head model. The head model including the ROI was then co-registered with a model of the stimulation coil in an electromagnetic field numerical simulator. The accuracy of rTMS application was evaluated based on the calculations of electric field intensity distribution in the ROI. The research has yielded unique insight into ROIs (with average volume 904 mm3) in patients with orofacial pain and has also extended further possibilities of human head MRI image semi-automatic segmentation. According to the calculations performed, the average ROI volume that was stimulated by an electric field with an intensity of over 80 V/m was only 4.4%, with the maximum ROI volume being 20.5%. Furthermore, a numerical study of the impact of coil rotation and translation was performed. It demonstrated a) the optimal placement of the stimulation coil can significantly increase the volume of the stimulated ROI up to 60% and b) patients with orofacial pain would need precise coil positioning with a navigation error lower than 10 mm. Due to an acceptable proccessing time of up to 6 hours, described numerical simulation opens up new options for precise rTMS treatment planning. This planning platform together with patient-specific navigated rTMS, could lead to significant increase of treatment outcomes in patients suffering from orofacial pain.

Detection of Resting-State Functional Connectivity from High-Density Electroencephalography Data: Impact of Head Modeling Strategies

Recent technological advances have been permitted to use high-density electroencephalography (hdEEG) for the estimation of functional connectivity and the mapping of resting-state networks (RSNs). The reliable estimate of activity and connectivity from hdEEG data relies on the creation of an accurate head model, defining how neural currents propagate from the cortex to the sensors placed over the scalp. To the best of our knowledge, no study has been conducted yet to systematically test to what extent head modeling accuracy impacts on EEG-RSN reconstruction. To address this question, we used 256-channel hdEEG data collected in a group of young healthy participants at rest. We first estimated functional connectivity in EEG-RSNs by means of band-limited power envelope correlations, using neural activity estimated with an optimized analysis workflow. Then, we defined a series of head models with different levels of complexity, specifically testing the effect of different electrode positioning techniques and head tissue segmentation methods. We observed that robust EEG-RSNs can be obtained using a realistic head model, and that inaccuracies due to head tissue segmentation impact on RSN reconstruction more than those due to electrode positioning. Additionally, we found that EEG-RSN robustness to head model variations had space and frequency specificity. Overall, our results may contribute to defining a benchmark for assessing the reliability of hdEEG functional connectivity measures.

Source localization of epileptic spikes using Multiple Sparse Priors

ObjectiveTo evaluate epileptic source estimation using multiple sparse priors (MSP) inverse method and high-resolution, individual electrical head models. MethodsAccurate source localization is dependent on accurate electrical head models and appropriate inverse solvers. Using high-resolution, individual electrical head models in fifteen epilepsy patients, with surgical resection and clinical outcome as criteria for accuracy, performance of MSP method was compared against standardized low-resolution brain electromagnetic tomography (sLORETA) and coherent maximum entropy on the mean (cMEM) methods. ResultsThe MSP method performed similarly to the sLORETA method and slightly better than the cMEM method in terms of success rate. The MSP and cMEM methods were more focal than sLORETA with the advantage of not requiring an arbitrary selection of a hyperparameter or thresholding of reconstructed current density values to determine focus. MSP and cMEM methods were better than sLORETA in terms of spatial dispersion. ConclusionsResults suggest that the three methods are complementary and could be used together. In practice, the MSP method will be easier to use and interpret compared to sLORETA, and slightly more accurate and faster than the cMEM method. SignificanceSource localization of interictal spikes from dense-array electroencephalography data has been shown to be a reliable marker of epileptic foci and useful for pre-surgical planning. The advantages of MSP make it a useful complement to other inverse solvers in clinical practice.

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.

Training of head and neck segmentation networks with shape prior on small datasets.

Cancer in the head and neck area is commonly treated with radiotherapy. A key step for low-risk treatment is the accurate delineation of organs at risk in the planning imagery. The success of deep learning in image segmentation led to automated algorithms achieving human expert performance on certain datasets. However, such algorithms require large datasets for training and fail to segment previously unseen pathologies, where human experts still succeed. As pathologies are rare and large datasets costly to generate, we investigate the effect of: reduced training data, batch sizes and incorporation of prior knowledge. The small data problem is studied by training a full-volume segmentation network with the reduced amount of data from the MICCAI 2015 head and neck segmentation challenge. To improve the segmentation, we evaluate the batch size as a hyper-parameter and first study and then incorporate a stacked autoencoder as shape prior into the training process. We found that using half of the training data (12 images of 25) results in an accuracy drop of only 3% for the segmentation of organs at risk. Also, the batch size turns out to be relevant for the quality of the segmentation when trained with less than half of the data. By applying PCA on the autoencoder's latent space we achieve a compact and accurate shape model, which is used as a regularizer and significantly improves the segmentation results. Small training data of up to 12 training images is enough to train accurate head and neck segmentation models. By using a shape prior for regularization, the performance of the segmentation can be improved significantly on the full dataset. When training on fewer than 12 images, the batch size is relevant and models have to be trained much longer until convergence.