Fast computational E-field dosimetry for transcranial magnetic stimulation using adaptive cross approximation and auxiliary dipole method (ACA-ADM).

Transcranial Magnetic Stimulation (TMS) is a non-invasive brain stimulation technique that uses a coil to induce an electric field (E-field) in the brain and modulate its activity. Many applications of TMS call for the repeated execution of E-field solvers to determine the E-field induced in the brain for different coil placements. However, the usage of solvers for these applications remains impractical because each coil placement requires the solution of a large linear system of equations. We develop a fast E-field solver that enables the rapid evaluation of the E-field distribution for a brain region of interest (ROI) for a large number of coil placements, which is achieved in two stages. First, during the pre-processing stage, the mapping between coil placement and brain ROI E-field distribution is approximated from E-field results for a few coil placements. Specifically, we discretize the mapping into a matrix with each column having the ROI E-field samples for a fixed coil placement. This matrix is approximated from a few of its rows and columns using adaptive cross approximation (ACA). The accuracy, efficiency, and applicability of the new ACA approach are determined by comparing its E-field predictions with analytical and standard solvers in spherical and MRI-derived head models. During the second stage, the E-field distribution in the brain ROI from a specific coil placement is determined by the obtained rows and columns in milliseconds. For many applications, only the E-field distribution for a comparatively small ROI is required. For example, the solver can complete the pre-processing stage in approximately 4 hours and determine the ROI E-field in approximately 40 ms for a 100 mm diameter ROI with less than 2% error enabling its use for neuro-navigation and other applications. Highlight: We developed a fast solver for TMS computational E-field dosimetry, which can determine the ROI E-field in approximately 40 ms for a 100 mm diameter ROI with less than 2% error.

A Butterfly-Accelerated Volume Integral Equation Solver for Broad Permittivity and Large-Scale Electromagnetic Analysis.

A butterfly-accelerated volume integral equation (VIE) solver is proposed for fast and accurate electromagnetic (EM) analysis of scattering from heterogeneous objects. The proposed solver leverages the hierarchical off-diagonal butterfly (HOD-BF) scheme to construct the system matrix and obtain its approximate inverse, used as a preconditioner. Complexity analysis and numerical experiments validate the O(N log 2 N) construction cost of the HOD-BF-compressed system matrix and O(N 1.5 log N) inversion cost for the preconditioner, where N is the number of unknowns in the high-frequency EM scattering problem. For many practical scenarios, the proposed VIE solver requires less memory and computational time to construct the system matrix and obtain its approximate inverse compared to a H matrix-accelerated VIE solver. The accuracy and efficiency of the proposed solver have been demonstrated via its application to the EM analysis of large-scale canonical and real-world structures comprising of broad permittivity values and involving millions of unknowns.

Fast computational optimization of TMS coil placement for individualized electric field targeting.

BACKGROUND: During transcranial magnetic stimulation (TMS) a coil placed on the scalp is used to non-invasively modulate activity of targeted brain networks via a magnetically induced electric field (E-field). Ideally, the E-field induced during TMS is concentrated on a targeted cortical region of interest (ROI). Determination of the coil position and orientation that best achieve this objective presently requires a large computational effort. OBJECTIVE: To improve the accuracy of TMS we have developed a fast computational auxiliary dipole method (ADM) for determining the optimum coil position and orientation. The optimum coil placement maximizes the E-field along a predetermined direction or, alternatively, the overall E-field magnitude in the targeted ROI. Furthermore, ADM can assess E-field uncertainty resulting from precision limitations of TMS coil placement protocols. METHOD: ADM leverages the electromagnetic reciprocity principle to compute rapidly the TMS induced E-field in the ROI by using the E-field generated by a virtual constant current source residing in the ROI. The framework starts by solving for the conduction currents resulting from this ROI current source. Then, it rapidly determines the average E-field induced in the ROI for each coil position by using the conduction currents and a fast-multipole method. To further speed-up the computations, the coil is approximated using auxiliary dipoles enabling it to represent all coil orientations for a given coil position with less than 600 dipoles. RESULTS: Using ADM, the E-fields generated in an MRI-derived head model when the coil is placed at 5900 different scalp positions and 360 coil orientations per position (over 2.1 million unique configurations) can be determined in under 15 min on a standard laptop computer. This enables rapid extraction of the optimum coil position and orientation as well as the E-field variation resulting from coil positioning uncertainty. ADM is implemented in SimNIBS 3.2. CONCLUSION: ADM enables the rapid determination of coil placement that maximizes E-field delivery to a specific brain target. This method can find the optimum coil placement in under 15 min enabling its routine use for TMS. Furthermore, it enables the fast quantification of uncertainty in the induced E-field due to limited precision of TMS coil placement protocols, enabling minimization and statistical analysis of the E-field dose variability.

Stochastic modeling of geometrical uncertainties on complex domains, with application to additive manufacturing and brain interface geometries.

We present a stochastic modeling framework to represent and simulate spatially-dependent geometrical uncertainties on complex geometries. While the consideration of random geometrical perturbations has long been a subject of interest in computational engineering, most studies proposed so far have addressed the case of regular geometries such as cylinders and plates. Here, standard random field representations, such as Kahrunen-Loève expansions, can readily be used owing, in particular, to the relative simplicity to construct covariance operators on regular shapes. On the contrary, applying such techniques on arbitrary, non-convex domains remains difficult in general. In this work, we formulate a new representation for spatially-correlated geometrical uncertainties that allows complex domains to be efficiently handled. Building on previous contributions by the authors, the approach relies on the combination of a stochastic partial differential equation approach, introduced to capture salient features of the underlying geometry such as local curvature and singularities on the fly, and an information-theoretic model, aimed to enforce non-Gaussianity. More specifically, we propose a methodology where the interface of interest is immersed into a fictitious domain, and define algorithmic procedures to directly sample random perturbations on the manifold. A simple strategy based on statistical conditioning is also presented to update realizations and prevent self-intersections in the perturbed finite element mesh. We finally provide challenging examples to demonstrate the robustness of the framework, including the case of a gyroid structure produced by additive manufacturing and brain interfaces in patient-specific geometries. In both applications, we discuss suitable parameterization for the filtering operator and quantify the impact of the uncertainties through forward propagation.

Comparison of immediate colposcopy, repeat conventional cytology and hrHPV testing for the clinical management of ASC-US cytology in routine health services of Medellin, Colombia: The ASCUS-COL Trial.

In the context of opportunistic cervical cancer screening settings of low-and-middle-income countries, little is known about the benefits of high-risk human papillomavirus (hrHPV) testing on high-grade cervical abnormality detection among women with atypical squamous cells of undetermined significance (ASC-US) cytology in routine clinical practice. We compared the effectiveness of immediate colposcopy (IC), conventional cytology at 6 and 12 months (colposcopy if ≥ASC-US) (RC), and hrHPV testing (colposcopy if hrHPV-positive) (HPV) to detect cervical intraepithelial neoplasia grade 2 or more severe diagnoses (CIN2+) among women aged 20-69 years with ASC-US in routine care. Participants (n=2,661) were evenly randomized into three arms (n=882 IC, n=890 RC, n=889 HPV) to receive services by routine healthcare providers and invited to an exit visit 24 months after recruitment. Histopathology was blindly reviewed by a quality-control external panel (QC). The primary endpoint was the first QC-diagnosed CIN2+ or CIN3+ detected during three periods: enrolment (≤6 months for IC and HPV, ≤12 months for RC), follow-up (between enrolment and exit visit), and exit visit. The trial is completed. Colposcopy was done on 88%, 42%, and 52% of participants in IC, RC, and HPV. Overall, 212 CIN2+ and 52 CIN3+ cases were diagnosed. No differences were observed for CIN2+ detection (p=0.821). However, compared to IC, only HPV significantly reduced CIN3+ cases that providers were unable to detect during the 2-year routine follow-up (relative proportion 0.35, 95% CI 0.09-0.87). In this context, hrHPV testing was the most effective and efficient management strategy for women with ASC-US cytology.

Molecular modulators of store-operated calcium entry.

Three decades ago, store-operated Ca(2+) entry (SOCE) was identified as a unique mechanism for Ca(2+) entry through plasma membrane (PM) Ca(2+)-permeable channels modulated by the intracellular Ca(2+) stores, mainly the endoplasmic reticulum (ER). Extensive analysis of the communication between the ER and the PM leads to the identification of the protein STIM1 as the ER-Ca(2+) sensor that gates the Ca(2+) channels in the PM. Further analysis on the biophysical, electrophysiological and biochemical properties of STIM1-dependent Ca(2+) channels has revealed the presence of a highly Ca(2+)-selective channel termed Ca(2+) release-activated Ca(2+) channel (CRAC), consisting of Orai1 subunits, and non-selective cation channels named store-operated channels (SOC), including both Orai1 and TRPC channel subunits. Since the identification of the key elements of CRAC and SOC channels a number of intracellular modulators have been reported to play essential roles in the stabilization of STIM-Orai interactions, collaboration with STIM1 conformational changes or mediating slow Ca(2+)-dependent inactivation. Here, we review our current understanding of some of the key modulators of STIM1-Orai1 interaction, including the proteins CRACR2A, STIMATE, SARAF, septins, golli and ORMDL3.

SARAF modulates TRPC1, but not TRPC6, channel function in a STIM1-independent manner.

Canonical transient receptor potential-1 (TRPC1) is an almost ubiquitously expressed channel that plays a relevant role in cell function. As other TRPC members, TRPC1 forms receptor-operated cation channels that exhibit both STIM1-dependent and store-independent behaviour. The STIM1 inhibitor SARAF (for store-operated Ca2+ entry (SOCE)-associated regulatory factor) modulates SOCE by interaction with the STIM1 region responsible for Orai1 activation (SOAR). Furthermore, SARAF modulates Ca2+ entry through the arachidonate-regulated Ca2+ (ARC) channels, consisting of Orai1 and Orai3 heteropentamers and plasma membrane-resident STIM1. While a role for STIM1-Orai1-mediated signals has been demonstrated, the possible role of SARAF in TRPC1 function remains unknown. Here, we provide evidence for the interaction of SARAF with TRPC1, independently of STIM1 both in STIM1-deficient NG115-401L cells and SH-SY5Y cells endogenously expressing STIM1. Silencing of SARAF expression in STIM1-deficient cells demonstrated that SARAF plays a negative regulatory role in TRPC1-mediated Ca2+ entry. The interaction of SARAF with TRPC1 in STIM1-deficient cells, as well as with the TRPC1 pool not associated with STIM1 in STIM1-expressing cells was enhanced by stimulation with the physiological agonist ATP. In contrast with TRPC1, we found that the interaction between SARAF and TRPC6 was constitutive rather than inducible by agonist stimulation. Furthermore, we found that SARAF expression silencing was without effect on Ca2+ entry evoked by agonists in TRPC6 overexpressing cells, as well as in Ca2+ influx evoked by the TRPC6 activator Hyp9. These findings provide evidence for a new regulator of TRPC1 channel function and highlight the relevance of SARAF in intracellular Ca2+ homeostasis.

The canonical transient receptor potential 6 (TRPC6) channel is sensitive to extracellular pH in mouse platelets.

The canonical transient receptor potential-6 (TRPC6) is a receptor-activated non-selective Ca(2+) channel regulated by a variety of modulators such as diacylglycerol, Ca(2+)/calmodulin or phosphorylation. The present study is aimed to investigate whether different situations, such as acidic pH, exposure to reactive oxygen species (ROS) or hypoxic-like conditions modulate TRPC6 channel function. Here we show normal aggregation and Ca(2+) mobilization stimulated by thrombin in TRPC6 KO platelets; however, OAG (1-oleoyl-2-acetyl-sn-glycerol)-evoked Ca(2+) entry was attenuated in the absence of TRPC6. Exposure of mouse platelets to acidic pH resulted in abolishment of thrombin-evoked aggregation and attenuated platelet aggregation induced by thapsigargin (TG) or OAG. Both OAG-induced Ca(2+) entry and platelet aggregation were greatly attenuated in cells expressing TRPC6 channels. Exposure of platelets to H2O2 or deferoxamine did not clearly alter thrombin, TG or OAG-induced platelet aggregation. Our results indicate that TRPC6 is sensitive to acidic pH but not to exposure to ROS or hypoxic-like conditions, which might be involved in the pathogenesis of the altered platelet responsiveness to DAG-generating agonists in disorders associated to acidic pH.

A boundary element method of bidomain modeling for predicting cellular responses to electromagnetic fields.

Objective.Commonly used cable equation approaches for simulating the effects of electromagnetic fields on excitable cells make several simplifying assumptions that could limit their predictive power. Bidomain or 'whole' finite element methods have been developed to fully couple cells and electric fields for more realistic neuron modeling. Here, we introduce a novel bidomain integral equation designed for determining the full electromagnetic coupling between stimulation devices and the intracellular, membrane, and extracellular regions of neurons.Approach.Our proposed boundary element formulation offers a solution to an integral equation that connects the device, tissue inhomogeneity, and cell membrane-induced E-fields. We solve this integral equation using first-order nodal elements and an unconditionally stable Crank-Nicholson time-stepping scheme. To validate and demonstrate our approach, we simulated cylindrical Hodgkin-Huxley axons and spherical cells in multiple brain stimulation scenarios.Main Results.Comparison studies show that a boundary element approach produces accurate results for both electric and magnetic stimulation. Unlike bidomain finite element methods, the bidomain boundary element method does not require volume meshes containing features at multiple scales. As a result, modeling cells, or tightly packed populations of cells, with microscale features embedded in a macroscale head model, is simplified, and the relative placement of devices and cells can be varied without the need to generate a new mesh.Significance.Device-induced electromagnetic fields are commonly used to modulate brain activity for research and therapeutic applications. Bidomain solvers allow for the full incorporation of realistic cell geometries, device E-fields, and neuron populations. Thus, multi-cell studies of advanced neuronal mechanisms would greatly benefit from the development of fast-bidomain solvers to ensure scalability and the practical execution of neural network simulations with realistic neuron morphologies.

A semi-automated pipeline for finite element modeling of electric field induced in nonhuman primates by transcranial magnetic stimulation.

BACKGROUND: Transcranial magnetic stimulation (TMS) is used to treat a range of brain disorders by inducing an electric field (E-field) in the brain. However, the precise neural effects of TMS are not well understood. Nonhuman primates (NHPs) are used to model the impact of TMS on neural activity, but a systematic method of quantifying the induced E-field in the cortex of NHPs has not been developed. NEW METHOD: The pipeline uses statistical parametric mapping (SPM) to automatically segment a structural MRI image of a rhesus macaque into five tissue compartments. Manual corrections are necessary around implants. The segmented tissues are tessellated into 3D meshes used in finite element method (FEM) software to compute the TMS induced E-field in the brain. The gray matter can be further segmented into cortical laminae using a volume preserving method for defining layers. RESULTS: Models of three NHPs were generated with TMS coils placed over the precentral gyrus. Two coil configurations, active and sham, were simulated and compared. The results demonstrated a large difference in E-fields at the target. Additionally, the simulations were calculated using two different E-field solvers and were found to not significantly differ. COMPARISON WITH EXISTING METHODS: Current methods segment NHP tissues manually or use automated methods for only the brain tissue. Existing methods also do not stratify the gray matter into layers. CONCLUSION: The pipeline calculates the induced E-field in NHP models by TMS and can be used to plan implant surgeries and determine approximate E-field values around neuron recording sites.

Electric Field Modeling in Personalizing Transcranial Magnetic Stimulation Interventions.

The modeling of transcranial magnetic stimulation (TMS)-induced electric fields (E-fields) is a versatile technique for evaluating and refining brain targeting and dosing strategies, while also providing insights into dose-response relationships in the brain. This review outlines the methodologies employed to derive E-field estimations, covering TMS physics, modeling assumptions, and aspects of subject-specific head tissue and coil modeling. We also summarize various numerical methods for solving the E-field and their suitability for various applications. Modeling methodologies have been optimized to efficiently execute numerous TMS simulations across diverse scalp coil configurations, facilitating the identification of optimal setups or rapid cortical E-field visualization for specific brain targets. These brain targets are extrapolated from neurophysiological measurements and neuroimaging, enabling precise and individualized E-field dosing in experimental and clinical applications. This necessitates the quantification of E-field estimates using metrics that enable the comparison of brain target engagement, functional localization, and TMS intensity adjustments across subjects. The integration of E-field modeling with empirical data has the potential to uncover pivotal insights into the aspects of E-fields responsible for stimulating and modulating brain function and states, enhancing behavioral task performance, and impacting the clinical outcomes of personalized TMS interventions.

Brain tumor detection using proper orthogonal decomposition integrated with deep learning networks.

BACKGROUND AND OBJECTIVE: The central organ of the human nervous system is the brain, which receives and sends stimuli to the various parts of the body to engage in daily activities. Uncontrolled growth of brain cells can result in tumors which affect the normal functions of healthy brain cells. An automatic reliable technique for detecting tumors is imperative to assist medical practitioners in the timely diagnosis of patients. Although machine learning models are being used, with minimal data availability to train, development of low-order based models integrated with machine learning are a tool for reliable detection. METHODS: In this study, we focus on comparing a low-order model such as proper orthogonal decomposition (POD) coupled with convolutional neural network (CNN) on 2D images from magnetic resonance imaging (MRI) scans to effectively identify brain tumors. The explainability of the coupled POD-CNN prediction output as well as the state-of-the-art pre-trained transfer learning models such as MobileNetV2, Inception-v3, ResNet101, and VGG-19 were explored. RESULTS: The results showed that CNN predicted tumors with an accuracy of 99.21% whereas POD-CNN performed better with about 1/3rd of computational time at an accuracy of 95.88%. Explainable AI with SHAP showed MobileNetV2 has better prediction in identifying the tumor boundaries. CONCLUSIONS: Integration of POD with CNN is carried for the first time to detect brain tumor detection with minimal MRI scan data. This study facilitates low-model approaches in machine learning to improve the accuracy and performance of tumor detection.

Capacitative and non-capacitative signaling complexes in human platelets.

Discharge of the intracellular Ca(2+) stores activates Ca(2+) entry through store-operated channels (SOCs). Since the recent identification of STIM1 and STIM2, as well as the Orai1 homologs, Orai2 and Orai3, the protein complexes involved in Ca(2+) signaling needs re-evaluation in native cells. Using real time PCR combined with Western blotting we have found the expression of the three Orai isoforms, STIM1, STIM2 and different TRPCs in human platelets. Depletion of the intracellular Ca(2+) stores with thapsigargin, independently of changes in cytosolic Ca(2+) concentration, enhanced the formation of a signaling complex involving STIM1, STIM2, Orai1, Orai2 and TRPC1. Furthermore, platelet treatment with the dyacylglicerol analog 1-oleoyl-2-acetyl-sn-glycerol (OAG) resulted in specific association of Orai3 with TRPC3. Treatment of platelets with arachidonic acid enhanced the association between Orai1 and Orai3 in human platelets and overexpression of Orai1 and Orai3 in HEK293 cells increased arachidonic acid-induced Ca(2+) entry. These results indicate that Ca(2+) store depletion results in the formation of exclusive signaling complexes involving STIM proteins, as well as Orai1, Orai2 and TRPC1, but not Orai3, which seems to be involved in non-capacitative Ca(2+) influx in human platelets.

Fast And Accurate Population Level Transcranial Magnetic Stimulation via Low-Rank Probabilistic Matrix Decomposition (PMD).

Transcranial magnetic stimulation (TMS) is used to study brain function and treat mental health disorders. During TMS, a coil placed on the scalp induces an E-field in the brain that modulates its activity. TMS is known to stimulate regions that are exposed to a large E-field. Clinical TMS protocols prescribe a coil placement based on scalp landmarks. There are inter-individual variations in brain anatomy that result in variations in the TMS-induced E-field at the targeted region and its outcome. These variations across individuals could in principle be minimized by developing a large database of head subjects and determining scalp landmarks that maximize E-field at the targeted brain region while minimizing its variation using computational methods. However, this approach requires repeated execution of a computational method to determine the E-field induced in the brain for a large number of subjects and coil placements. We developed a probabilistic matrix decomposition-based approach for rapidly evaluating the E-field induced during TMS for a large number of coil placements. Our approach can determine the E-field induced in over 1 Million coil placements in 9.5 hours, in contrast, to over 5 years using a bruteforce approach. After the initial set-up stage, the E-field can be predicted over the whole brain within 2-3 milliseconds and to 2% accuracy. We tested our approach in over 200 subjects and achieved an error of < 2% in most and < 3.5% in all subjects. We will present several examples of bench-marking analysis for our tool in terms of accuracy and speed across and its applicability for population level optimization of coil placement. Highlights: A method for practical E-field informed population-level TMS coil placement strategies is developed.This algorithm enables the determination of E-field informed optimal coil placement in seconds enabling it’s use for close-loop and on-the-fly reconfiguration of TMS.After the initial set-up stage of less than 10 hours, the E-field can be predicted for any coil placement across the whole brain within in 2-3 milliseconds.

Fast and accurate computational E-field dosimetry for group-level transcranial magnetic stimulation targeting.

Transcranial magnetic stimulation (TMS) is used to study brain function and treat mental health disorders. During TMS, a coil placed on the scalp induces an E-field in the brain that modulates its activity. TMS is known to stimulate regions that are exposed to a large E-field. Clinical TMS protocols prescribe a coil placement based on scalp landmarks. There are inter-individual variations in brain anatomy that result in variations in the TMS-induced E-field at the targeted region and its outcome. These variations across individuals could in principle be minimized by developing a large database of head subjects and determining scalp landmarks that maximize E-field at the targeted brain region while minimizing its variation using computational methods. However, this approach requires repeated execution of a computational method to determine the E-field induced in the brain for a large number of subjects and coil placements. We developed a probabilistic matrix decomposition-based approach for rapidly evaluating the E-field induced during TMS for a large number of coil placements due to a pre-defined coil model. Our approach can determine the E-field induced in over 1 Million coil placements in 9.5 h, in contrast, to over 5 years using a brute-force approach. After the initial set-up stage, the E-field can be predicted over the whole brain within 2-3 ms and to 2% accuracy. We tested our approach in over 200 subjects and achieved an error of <2% in most and <3.5% in all subjects. We will present several examples of bench-marking analysis for our tool in terms of accuracy and speed. Furthermore, we will show the methods' applicability for group-level optimization of coil placement for illustration purposes only. The software implementation link is provided in the appendix.

A Boundary Element Method of Bidomain Modeling for Predicting Cellular Responses to Electromagnetic Fields.

Objective: Commonly used cable equation-based approaches for determining the effects of electromagnetic fields on excitable cells make several simplifying assumptions that could limit their predictive power. Bidomain or "whole" finite element methods have been developed to fully couple cells and electric fields for more realistic neuron modeling. Here, we introduce a novel bidomain integral equation designed for determining the full electromagnetic coupling between stimulation devices and the intracellular, membrane, and extracellular regions of neurons. Methods: Our proposed boundary element formulation offers a solution to an integral equation that connects the device, tissue inhomogeneity, and cell membrane-induced E-fields. We solve this integral equation using first-order nodal elements and an unconditionally stable Crank-Nicholson time-stepping scheme. To validate and demonstrate our approach, we simulated cylindrical Hodgkin-Huxley axons and spherical cells in multiple brain stimulation scenarios. Main Results: Comparison studies show that a boundary element approach produces accurate results for both electric and magnetic stimulation. Unlike bidomain finite element methods, the bidomain boundary element method does not require volume meshes containing features at multiple scales. As a result, modeling cells, or tightly packed populations of cells, with microscale features embedded in a macroscale head model, is made computationally tractable, and the relative placement of devices and cells can be varied without the need to generate a new mesh. Significance: Device-induced electromagnetic fields are commonly used to modulate brain activity for research and therapeutic applications. Bidomain solvers allow for the full incorporation of realistic cell geometries, device E-fields, and neuron populations. Thus, multi-cell studies of advanced neuronal mechanisms would greatly benefit from the development of fast-bidomain solvers to ensure scalability and the practical execution of neural network simulations with realistic neuron morphologies.

Real-Time Computation of Brain E-Field for Enhanced Transcranial Magnetic Stimulation Neuronavigation and Optimization.

Transcranial Magnetic Stimulation (TMS) coil placement and pulse waveform current are often chosen to achieve a specified E-field dose on targeted brain regions. TMS neuronavigation could be improved by including real-time accurate distributions of the E-field dose on the cortex. We introduce a method and develop software for computing brain E-field distributions in real-time enabling easy integration into neuronavigation and with the same accuracy as 1st -order finite element method (FEM) solvers. Initially, a spanning basis set (< 400) of E-fields generated by white noise magnetic currents on a surface separating the head and permissible coil placements are orthogonalized to generate the modes. Subsequently, Reciprocity and Huygens' principles are utilized to compute fields induced by the modes on a surface separating the head and coil by FEM, which are used in conjunction with online (real-time) computed primary fields on the separating surface to evaluate the mode expansion. We conducted a comparative analysis of E-fields computed by FEM and in real-time for eight subjects, utilizing two head model types (SimNIBS's 'headreco' and 'mri2mesh' pipeline), three coil types (circular, double-cone, and Figure-8), and 1000 coil placements (48,000 simulations). The real-time computation for any coil placement is within 4 milliseconds (ms), for 400 modes, and requires less than 4 GB of memory on a GPU. Our solver is capable of computing E-fields within 4 ms, making it a practical approach for integrating E-field information into the neuronavigation systems without imposing a significant overhead on frame generation (20 and 50 frames per second within 50 and 20 ms, respectively).

DeeptDCS: Deep Learning-Based Estimation of Currents Induced During Transcranial Direct Current Stimulation.

OBJECTIVE: Transcranial direct current stimulation (tDCS) is a non-invasive brain stimulation technique used to generate conduction currents in the head and disrupt brain functions. To rapidly evaluate the tDCS-induced current density in near real-time, this paper proposes a deep learning-based emulator, named DeeptDCS. METHODS: The emulator leverages Attention U-net taking the volume conductor models (VCMs) of head tissues as inputs and outputting the three-dimensional current density distribution across the entire head. The electrode configurations are also incorporated into VCMs without increasing the number of input channels; this enables the straightforward incorporation of the non-parametric features of electrodes (e.g., thickness, shape, size, and position) in the training and testing of the proposed emulator. RESULTS: Attention U-net outperforms standard U-net and its other three variants (Residual U-net, Attention Residual U-net, and Multi-scale Residual U-net) in terms of accuracy. The generalization ability of DeeptDCS to non-trained electrode configurations can be greatly enhanced through fine-tuning the model. The computational time required by one emulation via DeeptDCS is a fraction of a second. CONCLUSION: DeeptDCS is at least two orders of magnitudes faster than a physics-based open-source simulator, while providing satisfactorily accurate results. SIGNIFICANCE: The high computational efficiency permits the use of DeeptDCS in applications requiring its repetitive execution, such as uncertainty quantification and optimization studies of tDCS.

Evidence of contamination-associated damage in blue sharks (Prionace glauca) from the Northeast Atlantic.

Top predators such as most shark species are extremely vulnerable to amassing high concentrations of contaminants, but not much is known about the effects that the contaminant body burden imparts on these animals. Species like the blue shark (Prionace glauca) are very relevant in this regard, as they have high ecological and socioeconomic value, and have the potential to act as bioindicators of pollution. This work aimed to assess if differences in contaminant body burden found in blue sharks from the Northeast Atlantic would translate into differences in stress responses. Biochemical responses related to detoxification and oxidative stress, and histological alterations were assessed in the liver and gills of 60 blue sharks previously found to have zone-related contamination differences. Similar zone-related differences were found in biomarker responses, with the sharks from the most contaminated zone exhibiting more pronounced responses. Additionally, strong positive correlations were found between contaminants (i.e., As, PCBs, and PBDEs) and relevant biomarkers (e.g., damaged DNA and protective histological alterations). The present results are indicative of the potential that this species and these tools have to be used to monitor pollution in different areas of the Atlantic.

Uncertainty quantification of TMS simulations considering MRI segmentation errors.

Objective.Transcranial magnetic stimulation (TMS) is a non-invasive brain stimulation method that is used to study brain function and conduct neuropsychiatric therapy. Computational methods that are commonly used for electric field (E-field) dosimetry of TMS are limited in accuracy and precision because of possible geometric errors introduced in the generation of head models by segmenting medical images into tissue types. This paper studies E-field prediction fidelity as a function of segmentation accuracy.Approach.The errors in the segmentation of medical images into tissue types are modeled as geometric uncertainty in the shape of the boundary between tissue types. For each tissue boundary realization, we then use an in-house boundary element method to perform a forward propagation analysis and quantify the impact of tissue boundary uncertainties on the induced cortical E-field.Main results.Our results indicate that predictions of E-field induced in the brain are negligibly sensitive to segmentation errors in scalp, skull and white matter (WM), compartments. In contrast, E-field predictions are highly sensitive to possible cerebrospinal fluid (CSF) segmentation errors. Specifically, the segmentation errors on the CSF and gray matter interface lead to higher E-field uncertainties in the gyral crowns, and the segmentation errors on CSF and WM interface lead to higher uncertainties in the sulci. Furthermore, the uncertainty of the average cortical E-fields over a region exhibits lower uncertainty relative to point-wise estimates.Significance.The accuracy of current cortical E-field simulations is limited by the accuracy of CSF segmentation accuracy. Other quantities of interest like the average of the E-field over a cortical region could provide a dose quantity that is robust to possible segmentation errors.