Realistic Computational Model Research Articles

In vivo and dosimetric investigation on electrical vestibular stimulation with frequency- and amplitude-modulated currents

Objective. Normal function of the vestibular system can be disturbed using a noninvasive technique called electrical vestibular stimulation (EVS), which alters a person's sense of balance and causes false sensations of movement. EVS has been widely used to study the function of the vestibular system, and it has recently gained interest as a therapeutic tool to improve postural stability and help those suffering from vestibular dysfunction. Yet, understanding of how EVS stimulates the vestibular system, the current intensity needed to produce an effect and the frequencies at which it occurs have remained unclear.Approach. The effect of EVS on postural sway was examined in five participants using sinusoidal alternating current with time-varying amplitude from 0 to 1.5 mA and frequency from 0.1 to 10 Hz for three electrode configurations. Dosimetry of the current flow inside the head was conducted using anatomically realistic computational models created individually for each subject based on magnetic resonance imaging data. An estimate for the minimal field strength capable of affecting the vestibular system was calculated with the finite element method.Main results. Bipolar EVS at frequencies up to 10 Hz caused harmonic full-body swaying, and the frequency of the sway was the same as that of the stimulation current. The size of the sway was amplified by increasing the current intensity. Dosimetry modeling indicated that, for 0.2 mA current, the average electric field strength in the vestibular system was approximately 10-30 mV m-1, depending on the electrode montage. The size of the measured postural sway was proportional to the montage-specific electric field strength in the vestibular system.Significance. The results provide insight to EVS's working mechanisms and improve its potential as a tool to study the sense of balance.

Atomistic Characterization of Healthy and Damaged Hair Surfaces: A Molecular Dynamics Simulation Study of Fatty Acids on Protein Layer.

Amid the bourgeoning demand for in-silico designed, environmentally sustainable, and highly effective hair care formulations, a growing interest is evident in the exploration of realistic computational model for the hair surface. In this work, we present an atomistic model for the outermost layer of the hair surface derived through molecular dynamics simulations, which comprises 18-Methyleicosanoic acid (18-MEA) fatty acid chains covalently bound onto the keratin-associated protein 10-4 (KAP10-4) at a spacing distance of ~1 nm. Remarkably, this hair surface model facilitates the inclusion of free fatty acids (free 18-MEA) into the gaps between chemically bound 18-MEA chains, up to a maximum number that results in a packing density of 0.22 nm2 per fatty acid molecule, consistent with the optimal spacing identified through free energy analysis. Atomistic insights are provided for the organization of fatty acid chains, structural features, and interaction energies on protein-inclusive hair surface models with varying amounts of free 18-MEA (FMEA) depletion, as well as varying degrees of anionic cysteic acid from damaged bound 18-MEA (BMEA), under both dry and wet conditions. In the presence of FMEA and water, the fatty acid chains in a pristine hair surface prefers to adopt a thermodynamically favored extended chain conformation, forming a thicker protective layer (~3 nm) on the protein surface. Our simulation results reveal that, while the depletion of FMEA can induce a pronounced impact on the thickness, tilt angle, and order parameters of fatty acid chains, the removal of BMEA has a marked effect on water penetration. There is a "sweet spot" spacing between the 18-MEA whereby damaged hair surface properties can be reinstated by replenishing FMEA. Through the incorporation of the protein layer and free fatty acids, the hair surface models presented in this study enables a realistic representation of the intricate details within the hair epicuticle, facilitating a molecular scale assessment of surface properties during the formulation design process.

Modeling circuit mechanisms of opposing cortical responses to visual flow perturbations.

In an ever-changing visual world, animals' survival depends on their ability to perceive and respond to rapidly changing motion cues. The primary visual cortex (V1) is at the forefront of this sensory processing, orchestrating neural responses to perturbations in visual flow. However, the underlying neural mechanisms that lead to distinct cortical responses to such perturbations remain enigmatic. In this study, our objective was to uncover the neural dynamics that govern V1 neurons' responses to visual flow perturbations using a biologically realistic computational model. By subjecting the model to sudden changes in visual input, we observed opposing cortical responses in excitatory layer 2/3 (L2/3) neurons, namely, depolarizing and hyperpolarizing responses. We found that this segregation was primarily driven by the competition between external visual input and recurrent inhibition, particularly within L2/3 and L4. This division was not observed in excitatory L5/6 neurons, suggesting a more prominent role for inhibitory mechanisms in the visual processing of the upper cortical layers. Our findings share similarities with recent experimental studies focusing on the opposing influence of top-down and bottom-up inputs in the mouse primary visual cortex during visual flow perturbations.

Data-Driven and Cell-Specific Determination of Nuclei-Associated Actin Structure.

Quantitative and volumetric assessment of filamentous actin fibers (F-actin) remains challenging due to their interconnected nature, leading researchers to utilize threshold based or qualitative measurement methods with poor reproducibility. Here we introduce a novel machine learning based methodology for accurate quantification and reconstruction of nuclei-associated F-actin. Utilizing a Convolutional Neural Network (CNN), we segment actin filaments and nuclei from 3D confocal microscopy images and then reconstruct each fiber by connecting intersecting contours on cross-sectional slices. This allowed measurement of the total number of actin filaments and individual actin filament length and volume in a reproducible fashion. Focusing on the role of F-actin in supporting nucleocytoskeletal connectivity, we quantified apical F-actin, basal F-actin, and nuclear architecture in mesenchymal stem cells (MSCs) following the disruption of the Linker of Nucleoskeleton and Cytoskeleton (LINC) Complexes. Disabling LINC in mesenchymal stem cells (MSCs) generated F-actin disorganization at the nuclear envelope characterized by shorter length and volume of actin fibers contributing a less elongated nuclear shape. Our findings not only present a new tool for mechanobiology but introduce a novel pipeline for developing realistic computational models based on quantitative measures of F-actin.

Distribution, course, and spatial relationships of the saphenous nerve: A 3D neuroanatomical map for nerve stimulation.

The overall objective of this study was to construct a 3D neuroanatomical map of the saphenous nerve based on cartesian coordinate data to define its course in 3D space relative to bony and soft tissue landmarks. Ten lower limb embalmed specimens were meticulously dissected, digitized, laser scanned, and modelled in 3D. The course of the main branches, number of collateral branches, and relationship of saphenous nerve to the great saphenous vein were defined and quantified using the high-fidelity 3D models. In 60% of specimens, the saphenous nerve was found to have three branches in the leg, infrapatellar, anterior, and posterior. In 40% of specimens, the posterior branch was absent. Three landmarks were found to consistently localize the anterior branch: the medial border of tibia at the level of the tibial tuberosity, the medial border of tibia at the level of the mid-point of leg, and the mid-point of the anterior border of the medial malleolus. The posterior branch, when present, had variable branching patterns but did not extend as far distally as the medial malleolus in any specimen. Anatomically, the anterior and posterior branches at the level of the tibial tuberosity could be most advantageous for nerve stimulation due to their close proximity to the bifurcation of the saphenous nerve where the branches are larger and more readily localizable than distally. Additionally, the tibial tuberosity is a prominent landmark that can be easily identified in most individuals and could be used to localize the anterior and posterior branch using ultrasound or other imaging modalities. These findings will enable implementation of highly realistic computational models that can be used to simulate saphenous nerve stimulation using percutaneous and implanted devices.

Discovering optimal features for neuron-type identification from extracellular recordings.

Advancements in multichannel recordings of single-unit activity (SUA) in vivo present an opportunity to discover novel features of spatially-varying extracellularly-recorded action potentials (EAPs) that are useful for identifying neuron-types. Traditional approaches to classifying neuron-types often rely on computing EAP waveform features based on conventions of single-channel recordings and thus inherit their limitations. However, spatiotemporal EAP waveforms are the product of signals from underlying current sources being mixed within the extracellular space. We introduce a machine learning approach to demix the underlying sources of spatiotemporal EAP waveforms. Using biophysically realistic computational models, we simulate EAP waveforms and characterize them by the relative prevalence of these sources, which we use as features for identifying the neuron-types corresponding to recorded single units. These EAP sources have distinct spatial and multi-resolution temporal patterns that are robust to various sampling biases. EAP sources also are shared across many neuron-types, are predictive of gross morphological features, and expose underlying morphological domains. We then organize known neuron-types into a hierarchy of latent morpho-electrophysiological types based on differences in the source prevalences, which provides a multi-level classification scheme. We validate the robustness, accuracy, and interpretations of our demixing approach by analyzing simulated EAPs from morphologically detailed models with classification and clustering methods. This simulation-based approach provides a machine learning strategy for neuron-type identification.

The influence of geometry on intervertebral disc stiffness

Geometry plays an important role in intervertebral disc (IVD) mechanics. Previous computational studies have found a link between IVD geometry and stiffness. However, few experimental studies have investigated this link, possibly due to difficulties in non-destructively quantifying internal geometric features. Recent advances in ultra-high resolution MRI provides the opportunity to visualise IVD features in unprecedented detail. This study aimed to quantify 3D human IVD geometries using 9.4 T MRIs and to investigate correlations between geometric variations and IVD stiffness. Thirty human lumbar motion segments (fourteen non-degenerate and sixteen degenerate) were scanned using a 9.4 T MRI and geometric parameters were measured. A 1kN compressive load was applied to each motion segment and stiffness was calculated. Degeneration caused a reduction (p < 0.05) in IVD height, a decreased nucleus-annulus area ratio, and a 1.6 ± 3.0 mm inward collapse of the inner annulus. The IVD height, anteroposterior (AP) width, lateral width, cross-sectional area, nucleus-annulus boundary curvature, and nucleus-annulus area ratio had a significant (p < 0.05) influence on IVD stiffness. Linear relationships (p < 0.05, r > 0.47) were observed between these geometric features and IVD compressive stiffness and a multivariate regression model was generated to enable stiffness to be predicted from features observable on clinical imaging (stiffness, N/mm = 6062 – (61.2 × AP width, mm) – (169.2 × IVD height, mm)). This study advances our understanding of disc structure–function relationships and how these change with degeneration, which can be used to both generate and validate more realistic computational models.

A realistic computational model for the formation of a Place Cell

Hippocampal Place Cells (PCs) are pyramidal neurons showing spatially localized firing when an animal gets into a specific area within an environment. Because of their obvious and clear relation with specific cognitive functions, Place Cells operations and modulations are intensely studied experimentally. However, although a lot of data have been gathered since their discovery, the cellular processes that interplay to turn a hippocampal pyramidal neuron into a Place Cell are still not completely understood. Here, we used a morphologically and biophysically detailed computational model of a CA1 pyramidal neuron to show how, and under which conditions, it can turn into a neuron coding for a specific cue location, through the self-organization of its synaptic inputs in response to external signals targeting different dendritic layers. Our results show that the model is consistent with experimental findings demonstrating PCs stability within the same spatial context over different trajectories, environment rotations, and place field remapping to adapt to changes in the environment. To date, this is the only biophysically and morphologically accurate cellular model of PCs formation, which can be directly used in physiologically accurate microcircuits and large-scale model networks to study cognitive functions and dysfunctions at cellular level.

Hydraulic conductivity of human cancer tissue: A hybrid study.

Elevated tumor tissue interstitial fluid pressure (IFP) is an adverse biomechanical biomarker that predicts poor therapy response and an aggressive phenotype. Advances in functional imaging have opened the prospect of measuring IFP non-invasively. Image-based estimation of the IFP requires knowledge of the tissue hydraulic conductivity (K), a measure for the ease of bulk flow through the interstitium. However, data on the magnitude of K in human cancer tissue are not available. We measured the hydraulic conductivity of tumor tissue using modified Ussing chambers in surgical resection specimens. The effect of the tumor microenvironment (TME) on K was investigated by quantifying the collagen content, cell density, and fibroblast density of the tested samples using quantitative immune histochemistry. Also, we developed a computational fluid dynamics (CFD) model to evaluate the role of K on interstitial fluid flow and drug transport in solid tumors. The results show that the hydraulic conductivity of human tumor tissues is very limited, ranging from approximately 10-15 to 10-14 m2/Pa∙s. Moreover, K values varied significantly between tumor types and between different samples from the same tumor. A significant inverse correlation was found between collagen fiber density and hydraulic conductivity values. However, no correlation was detected between K and cancer cell or fibroblast densities. The computational model demonstrated the impact of K on the interstitial fluid flow and the drug concentration profile: higher K values led to a lower IFP and deeper drug penetration. Human tumor tissue is characterized by a very limited hydraulic conductivity, representing a barrier to effective drug transport. The results of this study can inform the development of realistic computational models, facilitate non-invasive IFP estimation, and contribute to stromal targeting anticancer therapies.

Abstract 12194: Clinical Application of Virtual Antiarrhythmic Drug Test Using Digital Twins in Patients Who Recurred Atrial Fibrillation After Catheter Ablation

Introduction: We previously reported that a computational modeling-guided antiarrhythmic drug (AAD) test was feasible for assessing diverse AADs in patients with atrial fibrillation (AF). In this study, we took the virtual AAD test (V-AAD) in patients who took AADs after AF catheter ablation (AFCA). Hypothesis: Patients using an effective drug in the VAAD test will have fewer AF recurrences than patients using an ineffective drug. Methods: This single-center retrospective study included 246 patients (72.8% male, 60.7±10.2 years of age, 38.6% paroxysmal AF) prescribed AADs within 3 months after AFCA. Using realistic computational modeling, we evaluated the effects of five AADs (amiodarone, sotalol, dronedarone, flecainide, and propafenone; 2 doses for each drug). Clinical AADs (C-AAD) were chosen at the discretion of the physicians blinded to the V-AAD test. We defined the effective V-AAD as the V-AAD that terminated AF or converted AF to atrial tachycardia (AT) and the best V-AAD as the V-AAD that ended virtual AF the fastest. We compared AF recurrence rates after the AAD prescription depending on the results of the V-AAD test. Results: The ineffective and best V-AAD were administered in 64 (26.0 %) and 70 patients (28.5%). The recurrence rate within a year after using the ineffective, effective, and best V-AAD were 46.9%, 35.7%, and 25.7%, respectively (log-rank p=0.013). The recurrence rate within a year in patients with at least one V-AAD terminating AF and those without was 33.9% and 50.0%, respectively (log-rank p=0.034). The use of best V-AAD (OR 0.43, 95% CI [0.18-0.96]; p=0.042, vs. ineffective V-AAD) is an independent predictor of AF recurrence within a year of using AAD after AFCA. Conclusions: The digital twins-guided V-AAD test was feasible for evaluating the efficacy of multiple AADs in patients with AF who had a high chance of recurrence after AFCA. We need a prospective randomization study to assess the prediction power of the V-AAD test.

Abstract 14702: Roles of Atrial Mass Reduction and Gaps on the Anti-Atrial Fibrillatory Effects of Pulmonary Vein Isolation: A Computational Modeling Study

Background: Although pulmonary vein isolation (PVI) is the basis of atrial fibrillation (AF) catheter ablation (AFCA), the conditional changes of an anti-AF effect and the wave-dynamic mechanism are poorly understood. Therefore, we compared the effects of PVI based on the ablation circumference, width, and number or locations of gaps with or without amiodarone at the same condition in realistic computational modeling of human AF. Hypothesis: An anatomical level of PVI and characteristics of the PVI gap would affect the anti-AF effects of the AFCA. Methods: We included 50 patients (76.0% persistent AF) who underwent AFCA. Realistic AF modeling reflecting the computed tomography (CT) and the electroanatomical map was performed on each patient. We compared the AF defragmentation rate (DeFR; change to atrial tachycardia (AT) or termination), termination rate (TnR), and changes in the dominant frequency (DF) and the number of phase singularities (PSs) based on the PVI level (antral vs. ostial), PVI width (single vs. 3х width), number and location of 2-mm PVI gaps with or without amiodarone (10mM). We randomly assigned the number and location of the PVI gaps to the AF models. Results: The antral PVI had a significantly higher DeFR (p for trend &lt;0.001) and TnR (p for trend &lt;0.001) than the ostial PVI, PVI gaps, or baseline AF. However, there was no difference depending on PVI width. Among the PVI gaps, the DeFR (p for trend&lt;0.001) decreased as the gap number increased. Additional amiodarone increased DeFR (p=0.003) and TnR (p=0.034) and reduced DF (p&lt;0.001) and the number of PSs (p&lt;0.001) with PVI-gap, especially with a single PVI-gap. Conclusions: In this realistic computational modeling study with integrated patients’ atrial anatomy and electrophysiology, the atrial mass reduction depending on the anatomical PVI level, number of PVI gaps, and additional antiarrhythmic drug significantly affected the AF maintenance mechanisms.

Model-Based Assessment of Elastic Material Parameters in Rheumatic Heart Disease Patients and Healthy Subjects

Non-invasive measurements are important for the development of new treatments for heart failure, which is one of the leading causes of death worldwide. This study aimed to develop realistic subject-specific computational models of human biventricles using clinical data. Three-dimensional finite element models of the human ventricles were created using cardiovascular magnetic resonance images of rheumatic heart disease (RHD) patients and healthy subjects. The material parameter optimization uses inverse modeling based on the finite element method combined with the Levenberg–Marquardt method (LVM) by targeting subject-specific hemodynamics. The study of elastic myocardial parameters between healthy subjects and RHD patients showed an elevated stiffness in diseased hearts. In particular, the anisotropic material behavior of the healthy and diseased cardiac tissue significantly differed from one another. Furthermore, as the LVEF decreased, the stiffness and its orientation-dependent parameters increased. The simulation-derived LV myocardial circumferential and longitudinal stresses were negatively associated with the LVEF. The sensitivity analysis result demonstrated that the observed significant difference between the elastic material parameters of diseased and healthy myocardium was not exclusively attributable to an increased LVEDP in the diseased heart. These results could be applied to future computational studies for developing heart failure treatment.

Simulations of naïve and KLA-activated macrophage plasma membrane models

Macrophages (MAs), which play vital roles in human immune responses and lipid metabolisms, are implicated in the development and progression of atherosclerosis, a major contributor to cardiovascular diseases. Specifically, the abnormal lipid metabolism of oxidized low-density lipids (oxLDLs) in MAs is believed to be a crucial factor. However, the precise mechanism by which the MA membrane contributes to this altered lipid metabolism remains unclear. Lipidomic studies have revealed significant differences in membrane composition between various MA phenotypes. This study serves to provide and characterize complex realistic computational models for naïve (M0) and Kdo2-lipid A-activated (M1) state MA. Analyses of surface area per lipid (SA/lip), area compressibility modulus (KA), carbon‑hydrogen order parameter (SCH), electron density profile (EDP), tilt angles, two-dimension radial distribution functions (2D RDFs), mean squared displacement (MSD), hydrogen bonds (H-bonds), lipid clustering, and lipid wobble were conducted for both models. Results indicate that the M1 state MA membrane is more tightly packed, with increased chain order across lipid species, and forms PSM-DOPG-CHOL and PSM-SLPC-CHOL clusters. Importantly, the bilayer thicknesses reported for the models are in good agreement with experimental data for the thicknesses of transmembrane regions for MA integral proteins. These findings validate the described models as physiologically accurate for future computational studies of MA membranes and their residing proteins.

Optimal, hardware native decomposition of parameterized multi-qubit Pauli gates

We show how to efficiently decompose a parameterized multi-qubit Pauli (PMQP) gate into native parameterized two-qubit Pauli (P2QP) gates minimizing both the circuit depth and the number of P2QP gates. Given a realistic quantum computational model, we argue that the technique is optimal in terms of the number of hardware native gates and the overall depth of the decomposition. Starting from PMQP gate decompositions for the path and star hardware graph, we generalize the procedure to any generic hardware graph and provide exact expressions for the depth and number of P2QP gates of the decomposition. Furthermore, we show how to efficiently combine the decomposition of multiple PMQP gates to further reduce the depth as well as the number of P2QP gates for a combinatorial optimization problem using the Lechner–Hauke–Zoller mapping.

Artificial nanophotonic neuron with internal memory for biologically inspired and reservoir network computing

Neurons with internal memory have been proposed for biological and bio-inspired neural networks, adding important functionality. We introduce an internal time-limited charge-based memory into a III–V nanowire (NW) based optoelectronic neural node circuit designed for handling optical signals in a neural network. The new circuit can receive inhibiting and exciting light signals, store them, perform a non-linear evaluation, and emit a light signal. Using experimental values from the performance of individual III–V NWs we create a realistic computational model of the complete artificial neural node circuit. We then create a flexible neural network simulation that uses these circuits as neuronal nodes and light for communication between the nodes. This model can simulate combinations of nodes with different hardware derived memory properties and variable interconnects. Using the full model, we simulate the hardware implementation for two types of neural networks. First, we show that intentional variations in the memory decay time of the nodes can significantly improve the performance of a reservoir network. Second, we simulate the implementation in an anatomically constrained functioning model of the central complex network of the insect brain and find that it resolves an important functionality of the network even with significant variations in the node performance. Our work demonstrates the advantages of an internal memory in a concrete, nanophotonic neural node. The use of variable memory time constants in neural nodes is a general hardware derived feature and could be used in a broad range of implementations.

Multilayer Model of Gold Nanoparticles (AuNPs) and Its Application in the Classical Molecular Dynamics Simulation of Citrate-Capped AuNPs.

Studies on the interaction between gold nanoparticles (AuNPs) and functional proteins have been useful in developing diagnostic and therapeutic agents. Such studies require a realistic computational model of AuNPs for successful molecular design works. This study offers a new multilayer model of AuNPs to address the inconsistency between its molecular mechanics' interpretation and AuNP's plasmonic nature. We performed partial charge quantum calculation of AuNPs using Au13 and Au55 models. The result showed that it has partial negative charges on the surface and partial positive charges on the inner part, indicating that the AuNP model should be composed of multiatom types. We tested the partial charge parameters of these gold (Au) atoms in classical molecular dynamics simulation (CMD) of AuNPs. The result showed that our parameters performed better in simulating the adsorption of Na+ and dicarboxy acetone in terms of consistency with surface charge density than the zero charges Au in the interface force field (IFF). We proposed that the multiple-charged AuNP model can be developed further into a simpler four-atom type of Au in a larger AuNP size.

Analyzing the SAR in Human Head Tissues under Different Exposure Scenarios

This paper deals with the assessment of induced specific absorption rate (SAR) in various human models under different exposure scenarios, including both laboratory measurements and simulations. Firstly, SAR values were measured in a standardized SAR laboratory using a phantom for two radiofrequency electromagnetic field (RF-EMF) sources at 900 MHz and 1800 MHz. These laboratory measurements served as a reference for SAR calculations conducted on a specific anthropomorphic mannequin (SAM) using a computer simulation technology (CST) program, thus enabling the determination of antenna location and excitation signal levels for further evaluation. Subsequently, simulations were carried out with CST to evaluate average SAR for the head and for specific head tissues such as the brain, muscles, and fat. Realistic computational human models were also used alongside SAM in CST to explore the influence of gender, age, and tissue type on SAR. Various power levels representing low, moderate, and high RF-EMF exposure were applied to the human models to compare against basic restrictions and reference levels. The simulation results indicate significantly higher SAR values calculated for 1800 MHz compared with 900 MHz. The ratio of the highest SAR values at 1800 MHz to 900 MHz is approximately 1.70 for a baby, 2.59 for a child, and 2.84 for both adult female and adult male. While the SAR values for the brain, fat, muscle, and head are comparable at 900 MHz for the baby, the brain’s SAR value at 1800 MHz stands out significantly from the other tissues. In contrast with the baby, the difference in SAR values between 900 MHz and 1800 MHz is more pronounced for the child, adult female and adult male. The lowest SAR values at 900 MHz and 1800 MHz were obtained for brain tissue in all human models, while the head has the highest SAR value. The maximum SAR change ratio between the brain and the head is calculated to be 4.44 for the male at 1800 MHz. The results reveal that, although the applied electromagnetic field levels were below reference levels for general public local exposure, some local SAR values exceeded the International Commission of Non-Ionizing Radiation Protection’s basic restriction for the general public at certain power levels, particularly at 1800 MHz. The SAR analysis derived from this study is significant in understanding the impact of wireless technologies on health, establishing safety standards, guiding technology advancement, conducting risk assessments, and increasing public awareness.

Strain-dependent shear properties of human adipose tissue in vivo

IntroductionHuman adipose tissue (fat) deforms substantially under normal physiological loading and during impact. Thus, accurate data on strain-dependent stiffness of fat is essential for the creation of accurate biomechanical models. Previous studies on ex vivo samples reported human fat to be nonlinear and viscoelastic. When static compression is combined with magnetic resonance (MR) elastography (an imaging technique used to measure viscoelasticity in vivo), the large deformation properties of tissues can be determined. Here, we use magnetic resonance elastography to quantify fat shear modulus in vivo under increasing compressive strain and compare it to the underlying passive gluteal muscle. MethodsThe right buttocks of ten female participants were incrementally compressed at four levels while MR elastography (50 Hz) and mDixon images were acquired. Maps of tissue shear modulus (G*) were reconstructed from the MR elastography phase images. Tissue strain was estimated from registration of deformed and undeformed mDixon images. Linear mixed models were fit to the natural logarithm of the compressive strain and shear modulus data for each tissue. ResultsShear modulus increased in an exponential relationship with compressive strain in fat: Gfat*=748.5*Cyy−1.18Pa, and to a lesser extent in muscle: Gmuscle*=956.4*Cyy−0.36Pa. The baseline (undeformed) stiffness of fat was significantly lower than that of muscle (mean G*fat = 752 Pa, mean G*muscle = 1000 Pa, paired samples t-test, t = −4.24, p = 0.001). However, fat exhibited a significantly higher degree of strain dependence (characterised by the exponent of the curve, t = −6.47, p = 0.0001). ConclusionStatic compression of human adipose tissue results in an increase in apparent viscoelastic shear modulus (stiffness), in an exponentially increasing relationship. The relationships defined here can be used in the development of physiologically realistic computational models for impact, injury and biomechanical modelling.

Design of an adaptable intrafascicular electrode (AIR) for selective nerve stimulation by model-based optimization.

Peripheral nerve stimulation is being investigated as a therapeutic tool in several clinical scenarios. However, the adopted devices have restricted ability to obtain desired outcomes with tolerable off-target effects. Recent promising solutions are not yet employed in clinical practice due to complex required surgeries, lack of long-term stability, and implant invasiveness. Here, we aimed to design a neural interface to address these issues, specifically dimensioned for pudendal and sacral nerves to potentially target sexual, bladder, or bowel dysfunctions. We designed the adaptable intrafascicular radial electrode (AIR) through realistic computational models. They account for detailed human anatomy, inhomogeneous anisotropic conductance, following the trajectories of axons along curving and branching fascicles, and detailed biophysics of axons. The model was validated against available experimental data. Thanks to computationally efficient geometry-based selectivity estimations we informed the electrode design, optimizing its dimensions to obtain the highest selectivity while maintaining low invasiveness. We then compared the AIR with state-of-the-art electrodes, namely InterStim leads, multipolar cuffs and transversal intrafascicular multichannel electrodes (TIME). AIR, comprising a flexible substrate, surface active sites, and radially inserted intrafascicular needles, is designed to be implanted in a few standard steps, potentially enabling fast implants. It holds potential for repeatable stimulation outcomes thanks to its radial structural symmetry. When compared in-silico, AIR consistently outperformed cuff electrodes and InterStim leads in terms of recruitment threshold and stimulation selectivity. AIR performed similarly or better than a TIME, with quantified less invasiveness. Finally, we showed how AIR can adapt to different nerve sizes and varying shapes while maintaining high selectivity. The AIR electrode shows the potential to fill a clinical need for an effective peripheral nerve interface. Its high predicted performance in all the identified requirements was enabled by a model-based approach, readily applicable for the optimization of electrode parameters in any peripheral nerve stimulation scenario.

Modeling Electric Fields in Transcutaneous Spinal Direct Current Stimulation: A Clinical Perspective.

Clinical findings suggest that transcutaneous spinal direct current stimulation (tsDCS) can modulate ascending sensitive, descending corticospinal, and segmental pathways in the spinal cord (SC). However, several aspects of the stimulation have not been completely understood, and realistic computational models based on MRI are the gold standard to predict the interaction between tsDCS-induced electric fields and anatomy. Here, we review the electric fields distribution in the SC during tsDCS as predicted by MRI-based realistic models, compare such knowledge with clinical findings, and define the role of computational knowledge in optimizing tsDCS protocols. tsDCS-induced electric fields are predicted to be safe and induce both transient and neuroplastic changes. This could support the possibility to explore new clinical applications, such as spinal cord injury. For the most applied protocol (2-3 mA for 20-30 min, active electrode over T10-T12 and the reference on the right shoulder), similar electric field intensities are generated in both ventral and dorsal horns of the SC at the same height. This was confirmed by human studies, in which both motor and sensitive effects were found. Lastly, electric fields are strongly dependent on anatomy and electrodes' placement. Regardless of the montage, inter-individual hotspots of higher values of electric fields were predicted, which could change when the subjects move from a position to another (e.g., from the supine to the lateral position). These characteristics underlines the need for individualized and patient-tailored MRI-based computational models to optimize the stimulation protocol. A detailed modeling approach of the electric field distribution might contribute to optimizing stimulation protocols, tailoring electrodes' configuration, intensities, and duration to the clinical outcome.