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Weise, K.

Publications and source records attributed to Weise, K..

At least 19 recordsLinked to original sources

One placement, many fields: robust voxel-wise mapping with multichannel TMS

Understanding the human brain at the individual level is central to understanding its function and a prerequisite for personalized therapy. Transcranial magnetic stimulation (TMS) can map structure-function relationships at high resolution by regressing trial-wise variation in the TMS-induced electric field (E-field) against a response such as motor evoked potentials (MEPs). This requires distinct E-fields across pulses at different cortical locations, which single-coil TMS (sTMS) achieves by physically repositioning the coil (typically supported by neuronavigation software such as InVesalius Navigator; Souza et al., 2018)--increasingly reliant on robotic guidance and capped by the coil geometry. Multichannel TMS (mTMS) instead steers the E-field electronically via independent currents in several coils, opening a design space of high variability without physical repositioning. We adapt determinant-based sequence optimization from discrete sTMS coil-placement sets to the continuous mTMS current space (D-optimal design), identifying pulse sequences whose induced cortical E-fields are as mutually dissimilar as biophysical constraints allow. We benchmarked optimized mTMS sequences against random baselines and optimized sTMS placements in virtual motor mapping across multiple head models, assessing three mTMS array geometries (5-channel planar, 6- and 12-channel spherical), two figure-of-eight sTMS coils, and varying pulse counts, response noise, and cortical depth. D-optimal mTMS currents substantially outperformed random currents and optimized sTMS at matched pulse counts. The gain was modest without noise but increased significantly with it: at the highest noise level, optimized sTMS failed to converge after 100 pulses (~20-22 mm error), whereas all mTMS arrays reached median errors at or below 6 mm. mTMS also recovered deep targets better than sTMS (up to 35 mm skin-cortex distance, versus 25 mm) and performed uniformly across the region of interest. A coil-space scheme based only on the magnetic vector potential, precomputable once per array without per-subject MRI, captured most of the benefit, matching subject-specific optimization at 100 pulses. mTMS thus makes voxel-wise mapping robust throughout a region of interest without coil repositioning.

neuroscience↗

Real-time posterior probabilistic TMS motor mapping with robust individualized stopping criteria

Background. Electric-field-informed regression provides high-resolution transcranial magnetic stimulation (TMS) motor maps, but conventional nonlinear fitting is too slow to update the map during acquisition, so sessions rely on predefined stimulation budgets, despite substantial inter-individual variability in the data required for stable localization. Objective. To develop a real-time analysis updating a posterior probability map between pulses and compare its hotspots with finger-tapping functional MRI (fMRI). Methods. We analyzed two independent cohorts (12 and 13 participants, one newly collected), analyzing each hand muscle independently. We replaced the conventional per-element sigmoid fit with Spearman's rank correlation coefficient and transformed the resulting score map into a posterior probability map using marginal-likelihood evidence. Acquisition stopped once posterior mass within 5 mm of the hotspot passed a preset confidence level. Results. The Spearman and linear scores updated the region of interest within milliseconds, versus tens of seconds for sigmoid fitting. Spearman more closely matched the full-data sigmoid hotspot in recorded sessions and converged faster and more precisely to the true source in virtual subjects. Across both fast scores, at a confidence level of 0.85, the stopping criterion was met after a median of 74-159 pulses, with 26-454 across individual problems. FDI hotspots lay 8.3 {+/-} 4.3 mm from the fMRI center of gravity, in the precentral hand region. Conclusions. Rank correlation reduces computation time without loss of accuracy relative to sigmoid fitting. The resulting posterior probability map provides a session-specific stopping criterion in place of a fixed pulse budget.

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Spinal Recurrent Inhibition Shapes the Dynamics of TMS-induced Motor-Evoked Potentials: A Computational Modeling Study

Motor-evoked potentials (MEPs) recorded via surface electromyography (EMG) from peripheral muscles following transcranial magnetic stimulation (TMS) of the motor cortex reflect the integrity of the entire corticospinal pathway and are widely used in both basic neuroscience and clinical practice. However, the relative contributions of spinal and peripheral mechanisms to the observed MEP waveform remain poorly understood, partly because computational models that capture individual MEP characteristics are lacking. Here, we present a biologically plausible and computationally efficient model of the descending motor pathway, spanning the spinal cord and hand muscles, that can be fitted to individual MEP waveforms across a range of TMS intensities. The model successfully reproduces individual MEP waveforms, accounting for approximately 90% of the observed variance in waveforms across 10 healthy participants. Crucially, we demonstrate that recurrent inhibition of Renshaw cells in the spinal cord is indispensable for reproducing the fine temporal structure of MEP waveforms, even when input-output curve fitting appears adequate without it. Beyond waveform reproduction, the fitted model provides interpretable estimates of latent neural dynamics and subject-specific pathway parameters, including motor neuron size distribution, synaptic receptor balance, axonal conduction delay, and hand muscle refractoriness, that are consistent with known biological ranges. These results suggest that individual MEP waveforms, when analyzed using a biologically grounded model, carry substantially more information about spinal and peripheral motor pathway integrity than conventional amplitude-based measures alone.

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An Approach to Measure Conductivity Gradients of Brain Tissue Transitions using Electrode Arrays

Accurate determination of surgical margins is critical in tumor resection to ensure complete removal of tumor-infiltrated tissue while preserving healthy tissue. Pathological assessment provides reliable information but is time-consuming. This study investigates the feasibility of using impedance spectroscopy to detect tissue transitions at a macroscopic level. Two electrode arrays--one-dimensional and two-dimensional--were applied to ex vivo porcine brain tissue. Measurements were performed using both two- and four-electrode configurations, and data were corrected using the multiple-load compensation method. Results demonstrate that the one-dimensional array provides continuous conductivity profiles corresponding to tissue transitions, while the two-dimensional array showed less consistent results. These findings suggest that impedance spectroscopy is a promising tool for intraoperative margin detection, but further optimization of electrode geometry and measurement data processing is required.

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Adaptive Bayesian localization of motor representation areas

Transcranial magnetic stimulation (TMS) enables non-invasive localization of cortical motor representations, with important clinical applications in presurgical planning. Existing methods either disregard spatial information about the TMS-induced electric field (E-field) or use acquisition schemes that do not leverage previously elicited motor responses to guide subsequent stimulation. We present an adaptive Bayesian localization method that combines real-time E-field optimization with per-trial probabilistic inference. The cortical origin of the motor responses is represented as a spatial probability distribution that is updated after each stimulus. Subsequent stimuli are then optimized to maximize the expected localization improvement given previous responses. We validated the method experimentally, using multi-locus TMS for adaptive localization and single-coil TMS as a non-adaptive reference with randomized coil placements. Across eight subjects, the adaptive protocol at least halved the number of stimuli required for stable localization compared to the randomized protocol, converging in 60 stimuli on average, with 95% highest-density regions often below 10 mm2 by 150 stimuli.

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Optimal Pre-Experimental Coil Sequence Selection for TMS Motor Mapping

Transcranial magnetic stimulation (TMS) motor mapping increasingly relies on electric-field (E-field) modeling to localize cortical targets, but many candidate coil placements induce highly redundant cortical patterns. We frame prospective coil-sequence design as a subset-selection problem and compare farthest-point sampling, determinant-based objectives, and related controls in virtual mapping experiments across 12 realistic head models. Across convergence, matrix-diagnostic, and manifold-coverage analyses, the best-performing objectives combined low between-stimulation redundancy with preserved inter-element separability on the cortex; objectives that maximized one of these at the expense of the other fell below random sampling. An RBF-kernelized D-optimal objective matched FPS in mapping accuracy while using [~]15 fewer unique scalp positions per 100 pulses, suggesting reduced arm-reconfiguration cost on robotic TMS platforms. A singular-spectrum analysis of the candidate library provides a low-cost a-priori indicator that predicts when discretization choices fall below the diversity floor required for stable mapping. These results recast prospective TMS mapping as a limited manifold-coverage problem and offer concrete design rules for sample-efficient, robotically efficient sequence planning. Algorithm implementations are available as part of the open-source pynibs tool-box at https://gitlab.gwdg.de/tms-localization/pynibs/-/tree/dev/pynibs/optimization.

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Cortical Stimulation Strength and sgACC Connectivity Shape Neuro-Cardiac Responses to Prefrontal TMS

Autonomic dysregulation is a core feature of major depressive disorder and a promising physiological readout for early signs of clinical effectiveness of therapeutic TMS targeting prefrontal regions. Yet the stimulation dose and network properties required to optimally engage autonomic circuits remain elusive. Here, we applied a cortex- and circuit-centered dosing framework combining resting-state fMRI, simulations of the TMS-induced electric field, and electrocardiography acquired during repetitive TMS of six left dorsolateral prefrontal cortex (DLPFC) targets in healthy adults. Heart-brain coupling (HBC), defined as spectral power in the cardiac response locked to the TMS onset frequency, was modeled as a function of either conventional motor-threshold-based dose or cortical electric-field strength, with and without DLPFC connectivity to the subgenual anterior cingulate cortex (sgACC). Across linear and nonlinear models, cortical electric-field dose explained substantially more variance in HBC, indicating that prefrontal TMS dosing is best assessed at the cortical level. Incorporating sgACC connectivity further improved model fit and revealed site-specific electric-field-by-connectivity interactions, with the strongest and most reliable HBC modulation at DLPFC regions anticorrelated with sgACC. Voxel-wise TMS mapping identified individualized cortical hotspots spatially overlapping with sgACC-anticorrelated tissue. Notably, whereas negative connectivity showed the most robust and monotonic effects, nonlinear dose-response analyses suggested a confined stimulation range over which positively connected sites also exhibited HBC modulation. Together, these findings position HBC as a network-sensitive physiological readout of DLPFC-sgACC-vagal engagement and offer a principled answer to where and how much to stimulate, supporting an electric-field- and connectivity-informed dosing framework for refining prefrontal TMS in depression-related autonomic dysregulation.

neuroscience↗

Direct Reconstruction of DC Cortical Conductivity from Large-Scale Electron Microscopy Data

Electrical conductivity of cortical gray matter governs the magnitude and spatial distribution of electric fields generated by brain stimulation and intrinsic neuronal activity measured with M/EEG and intracortical recordings. However, reported macroscopic conductivity values vary by more than threefold, limiting the fidelity of bioelectromagnetic models and leaving unresolved whether this variability reflects measurement uncertainty or genuine structural heterogeneity of cortical tissue. Here, we present a multiscale computational framework that, for the first time, attempts to derive mesoscale conductivity maps of mouse visual cortex at 50-{micro}m resolution directly from large-volume, segmented nanometer-scale electron microscopy data. The Minnie 65 subvolume of the MICrONS dataset is accurately subdivided into 1,224 50-{micro}m cubic blocks. Each block contains, on average, 40-50 million membrane facets of a highly convoluted and dense cellular structure. Three orthogonal electrode pairs are applied to each isolated block to estimate the three principal components of the conductivity tensor. Quasistatic electric modeling is enabled by an iterative boundary-element fast multipole method (BEM-FMM) under the approximation of non-conducting membranes (DC conductivity). Spatially averaged conductivity values predicted by our framework agree well with prior low-resolution measurements in rats, validating the approach. At the same time, the resulting mesoscale maps reveal pronounced conductivity granularity at 50-100 {micro}m scales as well as significant variations in both radial and tangential directions. These results indicate that mesoscale conductivity heterogeneity could be an intrinsic structural property of the cortex. Limitations and extensions of this study are discussed in detail.

neuroscience↗

A 4-pole Theory-Based Compensation Method using Multiple Loads for Impedance Spectroscopy of Porcine Brain Tissue

Accurate determination of the electrical properties of brain tissue--conductivity and relative permittivity--is essential for modeling and optimizing neurostimulation and neuroimaging and modulation techniques such as EEG, MEG, TMS, and TES. However, large discrepancies in reported conductivity values highlight the methodological challenges associated with impedance measurements. In this study, we present a systematic evaluation of compensation methods in impedance spectroscopy to improve measurement accuracy using a self-developed four-point platinum electrode designed for biological tissue applications. Three compensation algorithms--open-short, open-short-load, and multiple-load--were implemented and compared using an RC parallel test circuit that emulated typical measurement conditions, with load resistances ranging from 250 {Omega} to 5000 {Omega}. Quantitative error analysis demonstrated that the multiple-load method achieved the lowest normalized root mean square error (3.4%) and superior stability across frequencies from 100 Hz to 1 MHz. The calibrated probe was subsequently applied to post mortem porcine brain tissue, yielding conductivity values of grey matter of 0.109 S/m and white matter of 0.074 S/m. These results are significantly lower than standard simulation parameters of 0.275 S/m, and 0.126 S/m for grey and white matter, respectively. Finite-element simulations revealed that using the measured conductivities increased electric field predictions by 2.1% for TMS and 46% for TES in the maximum electric field magnitude, respectively. Our findings demonstrate that accurate impedance compensation is critical for reliable characterization of brain tissue and for enhancing the precision of neurostimulation modeling in the future.

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A New Method for Optimal Placement of Tumor Treating Fields Electrodes

OverviewTumor Treating Fields (TTFields) provide a non-invasive treatment option for newly diagnosed glioblastoma. While optimization of electrode placement is important to increase treatment efficacy, clinical therapy planning is done using an undisclosed and proprietary software (NovoTAL(R)), which is clinically unvalidated. This study investigates a new computational approach for optimizing TTFields electrode placement and is compared to the current clinical standard. MethodsWe developed a new computational pipeline integrating patient-specific anatomical data to optimize electrode configurations in five representative glioblastoma cases with diverse tumor locations and sizes. Two optimization strategies were employed: one maximizing electric field intensity at the tumor, and another enhancing coverage of the adjacent brain while maintaining sufficient tumor intensity. Results were compared to electrode placements generated by NovoTAL(R). Additional simulations with artificial tumors assessed the effects of tumor size and location. ResultsOptimized electrode placements improved electric field intensity in tumors by 18%-34% compared to the clinical standard. Coverage-weighted optimizations provided broader field coverage without significantly compromising tumor intensity. Smaller or surface-adjacent tumors benefited most from optimization, achieving precise targeting and enhanced coverage. Extensive randomized placement analyses highlighted the superior performance of the optimized configurations. Analysis of artificial models showed consistent improvements across varying tumor locations and sizes. ConclusionPersonalized optimization of TTFields electrode placement significantly improves electric field targeting of tumors and adjacent brain regions. This approach outperforms standardized planning software and clinical practices and supports future development of adaptive, automated strategies for individualized TTFields therapy in glioblastoma. Keypoints- Optimized TTFields array placement enhanced field intensity by 18-34% vs. clinical standard. - Optimized TTFields planning improved field coverage in tumor-adjacent regions. - Optimized TTFields planning outperformed standard methods and random placement. Importance of the studyTTFields are increasingly used as adjuvant therapy for glioblastoma. However, current individualized treatment planning relies on proprietary, undisclosed, and clinically unvalidated software, limiting transparency, optimization, and innovation in the field. This study introduces an individualized, semi-automated, and open-source method for optimal electrode placement based on standard MRI data, addressing a critical need for validated and adaptable planning tools. Our approach increased field intensity in tumors by 18-34% and achieved broader coverage compared to the standard clinical tool (NovoTAL(R)), without compromising therapeutic strength. Notably, the method also consistently outperformed extensive random electrode placements across diverse tumor types and sizes, highlighting its robustness and ability to achieve true optimal configurations. Open-source availability enhances reproducibility and clinical translation, representing a significant step toward more effective, individualized TTFields therapy. This advancement has the potential to improve outcomes for glioblastoma patients and underscores the importance of technology validation in neuro-oncology.

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Optimizing and assessing multichannel TMS focality

BackgroundMultichannel transcranial magnetic stimulation (mTMS) enables electronic steering of induced electric fields across multiple cortical targets without physical coil repositioning, addressing key limitations of conventional single-channel TMS (sTMS). However, determining optimal input currents for focal stimulation remains challenging, and different mTMS systems have not been systematically compared under realistic hardware constraints. ObjectiveTo develop an user-centric framework for optimizing and assessing mTMS focality by introducing a generic optimization algorithm, establishing meaningful quality metrics, and comparing mTMS coil arrays with traditional single-channel TMS across cortical targets. MethodsWe developed a fast optimization framework incorporating target E-field constraints via parametrization of degenerated hyperellipsoids, explicitly integrating current-rate limits, for example from stimulator electronics and coil heating. Using high-resolution finite-element models of nine individual brains, we compared two mTMS designs (5-channel planar and 6/12-channel spherical systems) with standard sTMS figure-of-eight coils through E-field simulations. Three complementary metrics quantified stimulation quality: Focality, Target2Max, and OverstimulatedArea. ResultsDespite using a single optimized placement for all region-of-interest targets, mTMS achieved focality comparable to repositioned single-channel TMS in this in-silico study. For superficial targets, single-channel TMS showed slightly better stimulation, but for deeper cortical targets (>25 mm skin-cortex distance), mTMS performed similarly. More mTMS channels improved focality but required stronger current-rate constraints. The planar design performed better for deeper targets, while spherical designs improved with additional channels. ConclusionmTMS systems demonstrate remarkable performance comparable to standard TMS, enabling efficient multi-target stimulation without repositioning. Our open-source framework provides practical tools for designing and evaluating mTMS systems, supporting goal-directed mTMS development and effective application.

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A Coupling Model of Transcranial Magnetic Stimulation Induced Electric Fields to Neural State Variables

Transcranial magnetic stimulation (TMS) is a non-invasive brain stimulation technique used to modulate neural activity, with applications in clinical treatment, diagnostics, and neuroscientific research. TMS targeting the human primary motor cortex (M1) is well studied, aided by experimental readouts from the cerebral cortex, the spinal cord, and activated muscle targets. One key readout is a series of pulses that descend the spinal cord following TMS, called DI-waves. These reflect the output of M1 to the spinal cord and are influenced by TMS parameters such as orientation, strength, and waveform. Previous modeling studies have deployed numerous strategies to explain DI-wave generation, but generally approximate TMS inputs as semi-arbitrary current or synaptic inputs. A consistent missing piece to these models is a biophysically motivated coupling between TMS and the neural states of cells and cell populations. This study aims to leverage cable simulations of realistic neuron morphologies to couple TMS induced electric fields to average state variables of cortical cell populations. This coupling model quantifies the spatial-temporal activation function of directly stimulated axonal fibers and the average input current that downstream cells receive due to synaptic inputs from directly stimulated cells. An example M1 cortical circuit is studied, in which TMS stimulates layer 2/3 excitatory and inhibitory neurons that project synapses onto layer 5 corticospinal neurons. Results indicate that TMS induces unique directionally sensitive distributions of synaptic outputs in time and space for each cell type. Directional and dosage sensitivity carries forward to the dendritic current flowing into layer 5 cells. Ultimately, the coupling model provides a novel architecture to translate electric fields from TMS into activation functions that alter neural states and serve as inputs to cortical circuit modeling. The study of other brain regions is achievable through an alternate choice of cell morphologies, cell locations, and circuit design. Author summaryTranscranial magnetic stimulation is a powerful technique to interfere with the brains function in a non-invasive way. It does so by magnetically inducing an electric field in the brain that influences the state of neurons. It has widespread medical application for diagnostics and treatment of diseases, such as depression. Moreover, it is valuable for fundamental brain research as it allows stimulating the brain and then observing its response. In order to optimize its utility in medicine and drawing more well-founded conclusions from scientific experiments, it is important to precisely describe its working mechanisms. In this work, we introduce a computational technique to simulate a crucial ingredient of these mechanisms, namely the precise way the induced electric field excites populations of interconnected neurons. We exemplify the technique for the case of stimulating the motor areas of the cortex, causing muscle activity. However, it can be generalized to a wide variety of use cases, thus forming an important tool that helps researchers to devise accurate mechanistic links between transcranial magnetic stimulation and its observed effects. In consequence, it supports future research aiming at the development of better diagnostics and treatment of brain diseases, as well as better understanding of brain function.

neuroscience↗

Target-Specificity and Repeatability in Neuro-Cardiac-Guided TMS for Heart-Brain Coupling

The dorsolateral prefrontal cortex (DLPFC) is a principal target for transcranial magnetic stimulation (TMS) in treating major depressive disorder, with therapeutic effects thought to be mediated by its connectivity with the subgenual anterior cingulate cortex. As both regions are involved in autonomic regulation, short-term heart rate changes following DLPFC stimulation may serve as physiological markers to identify stimulation targets. We employed neuro-cardiac guided TMS in a cohort of healthy participants to examine the effects of stimulation intensity and DLPFC target specificity on heart-brain coupling (HBC). We used generalized additive models to assess nonlinear effects of stimulation intensity and target location on HBC, while accounting for pain ratings and other side effects. Intra-subject repeatability across three sessions was evaluated using intraclass correlation coefficients. We observed a non-linear modulation of HBC depending on stimulation intensity and target location, with greater effects at the F3 lateral and F3 posterior targets compared to sham. By evaluating these effects across sessions within participants, we demonstrate the robustness of our results beyond the influence of pain and other side effects on HBC modulation. Exploratory analyses of the directionality show a consistent decrease in HR only at the F3 lateral target with suprathreshold stimulation. These results demonstrate that HBC is modulated in a target- and intensity-specific manner, with particularly consistent effects at F3 lateral sites within the DLPFC. The findings enhance the understanding of TMS-modulated heart-brain interactions, offering a potential framework for optimizing individualized rTMS treatment protocols for depression.

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A Leadfield-Free Optimization Framework for Transcranially Applied Electric Currents

BackgroundTranscranial Electrical Stimulation (TES), Temporal Interference Stimulation (TIS), Electroconvulsive Therapy (ECT) and Tumor Treating Fields (TTFields) are based on the application of electric current patterns to the brain. ObjectiveThe optimal electrode positions, shapes and alignments for generating a desired current pattern in the brain vary between persons due to anatomical variability. The aim is to develop a flexible and efficient computational approach to determine individually optimal montages based on electric field simulations. MethodsWe propose a leadfield-free optimization framework that allows the electrodes to be placed freely on the head surface. It is designed for the optimization of montages with a low to moderate number of spatially extended electrodes or electrode arrays. Spatial overlaps are systematically prevented during optimization, enabling arbitrary electrode shapes and configurations. The approach supports maximizing the field intensity in target region-of-interests (ROI) and optimizing for a desired focality-intensity tradeoff. ResultsWe demonstrate montage optimization for standard two-electrode TES, focal center-surround TES, TIS, ECT and TTFields. Comparisons against reference simulations are used to validate the performance of the algorithm. The system requirements are kept moderate, allowing the optimization to run on regular notebooks and promoting its use in basic and clinical research. Conclusion(s)The new framework complements existing optimization methods that require small electrodes, a predetermined discretization of the electrode positions on the scalp and work best for multi-channel systems. It strongly extends the possibilities to optimize electrode montages towards application-specific aims and supports researchers in discovering innovative stimulation schemes. The framework is available in SimNIBS.

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Effects of Electric Field Direction on TMS-based Motor Cortex Mapping

BackgroundTranscranial magnetic stimulation (TMS) modulates brain activity by inducing electric fields (E-fields) that can elicit action potentials in cortical neurons. Neuronal responses to TMS depend not only on the magnitude of the induced E-field but also on various physiological factors. In this study, we incorporated a novel average response model that efficiently estimates the firing threshold of neurons based on their orientation relative to the applied E-field, thereby advancing TMS mapping for motor function. MethodsWe conducted a regression-based TMS mapping experiment with fourteen subjects to localize cortical origins of motor evoked potential (MEP) on the first dorsal interosseous (FDI) muscle. Firing thresholds were estimated for excitatory neurons in cortical layers 2/3 and 5 via an average response model. Regression was performed between MEPs and three E-field quantities: the magnitude (magnitude model), the normal component (cosine model), and the effective E-field, which scales the E-field magnitude based on the firing thresholds specific to the neuronal orientation (neuron model). To validate, we applied TMS to ten subjects with optimized coil placements based on these three models to determine which model could yield the highest MEPs. ResultsThe magnitude and neuron models performed similarly, while the cosine model showed lower explained variance in regression results, required more TMS trials for stable mapping, and yielded the lowest MEP in the validation. ConclusionThis study is the first to advance TMS modeling by incorporating neuron-specific factors at the individual level. Results show that on the motor cortex, the magnitude model is-as expected-a good approximation of cortical TMS effects as it shows similar results as the neuron model. In contrast, the classic cosine model exhibited lower performance and required more TMS trials for stable results, and is not recommended for future studies.

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Statistical method accounts for microscopic electric field distortions around neurons when simulating activation thresholds

IntroductionNotwithstanding advances in computational models of neuromodulation, there are mismatches between simulated and experimental activation thresholds. Transcranial Magnetic Stimulation (TMS) of the primary motor cortex generates motor evoked potentials (MEPs). At the threshold of MEP generation, whole-head models predict macroscopic (at millimeter scale) electric fields (50-70 V/m) which are considerably below conventionally simulated cortical neuron thresholds (175-350 V/m). MethodsWe hypothesize that this apparent contradiction is in part a consequence of electrical field warping by brain microstructure. Classical neuronal models ignore the physical presence of neighboring neurons and microstructure and assume that the macroscopic field directly acts on the neurons. In previous work, we performed advanced numerical calculations considering realistic microscopic compartments (e.g., cells, blood vessels), resulting in locally inhomogeneous (micrometer scale) electric field and altered neuronal activation thresholds. Here we combine detailed neural threshold simulations under homogeneous field assumptions with microscopic field calculations, leveraging a novel statistical approach. ResultsWe show that, provided brain-region specific microstructure metrics, a single statistically derived scaling factor between microscopic and macroscopic electric fields can be applied in predicting neuronal thresholds. For the cortical sample considered, the statistical methods match TMS experimental thresholds. ConclusionsOur approach can be broadly applied to neuromodulation models, where fully coupled microstructure scale simulations may not be computationally tractable.

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Fast and Accurate EEG/MEG BEM-Based Forward Problem Solution for High-Resolution Head Models

A BEM (boundary element method) based approach is developed to accurately solve an EEG/MEG forward problem for a modern high-resolution head model in approximately 60 seconds using a common workstation. The method utilizes a charge-based BEM with fast multipole acceleration (BEM-FMM) and a "smart" mesh pre-refinement (called b-refinement) close to the singular source(s). No costly matrix-filling or direct solution steps typical for the standard BEM are required; the method generates on-skin voltages as well as MEG magnetic fields for high-resolution head models in approximately 60 seconds after initial model assembly. The method is verified both theoretically and experimentally.

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Accuracy of dipole source reconstruction in the 3-layerBEM model against the 5-layer BEM-FMM model

ObjectiveTo compare cortical dipole fitting spatial accuracy between the widely used yet highly simplified 3-layer and modern more realistic 5-layer BEM-FMM models with and without adaptive mesh refinement (AMR) methods. MethodsWe generate simulated noiseless 256-channel EEG data from 5-layer (7-compartment) meshes of 15 subjects from the Connectome Young Adult dataset. For each subject, we test four dipole positions, three sets of conductivity values, and two types of head segmentation. We use the boundary element method (BEM) with fast multipole method (FMM) acceleration, with or without (AMR), for forward modeling. Dipole fitting is carried out with the FieldTrip MATLAB toolbox. ResultsThe average position error (across all tested dipoles, subjects, and models) is [~]4 mm, with a standard deviation of [~]2 mm. The orientation error is [~]20{degrees} on average, with a standard deviation of [~]15{degrees}. Without AMR, the numerical inaccuracies produce a larger disagreement between the 3- and 5-layer models, with an average position error of [~]8 mm (6 mm standard deviation), and an orientation error of 28{degrees} (28{degrees} standard deviation). ConclusionsThe low-resolution 3-layer models provide excellent accuracy in dipole localization. On the other hand, dipole orientation is retrieved less accurately. Therefore, certain applications may require more realistic models for practical source reconstruction. AMR is a critical component for improving the accuracy of forward EEG computations using a high-resolution 5-layer volume conduction model. SignificanceImproving EEG source reconstruction accuracy is important for several clinical applications, including epilepsy and other seizure-inducing conditions.

bioinformatics↗