Konstantin Weise

This paper introduces multichannel TMS (mTMS) for robust voxel-wise brain mapping, showing it outperforms single-coil TMS in accuracy and noise resilience.

This paper develops a real-time TMS motor mapping method using rank correlation and posterior probability maps, improving efficiency and individualization over traditional sigmoid fitting.

This study presents a computational model of the motor pathway that reproduces individual MEP waveforms and reveals the critical role of spinal recurrent inhibition in shaping their temporal structure.

The paper develops methods to optimize TMS coil sequences for motor mapping by reducing redundancy and improving efficiency in robotic TMS platforms.

This paper introduces an adaptive Bayesian method for localizing motor representation areas using TMS, reducing the number of stimuli needed for accurate localization.

This study shows that cortical electric-field strength and connectivity to the subgenual anterior cingulate cortex better predict neuro-cardiac responses to prefrontal TMS than conventional motor-threshold-based dosing.

This study evaluates using impedance spectroscopy with electrode arrays to detect tissue transitions in ex vivo porcine brain tissue, showing promise for intraoperative margin detection.

The paper develops a computational framework to directly reconstruct mesoscale cortical conductivity maps from electron microscopy data, revealing structural heterogeneity in mouse visual cortex.

This study develops a framework to optimize and assess multichannel TMS focality, showing mTMS systems can achieve performance comparable to single-channel TMS for deeper cortical targets.

This study examines how TMS targeting the DLPFC affects heart-brain coupling, finding non-linear effects based on stimulation intensity and target location.

This study evaluates three TMS models for motor cortex mapping, finding that the magnitude and neuron models perform similarly, while the cosine model is less effective and requires more trials.

This paper introduces a computational method for optimizing TTFields electrode placement, showing improved electric field intensity and coverage compared to clinical standards and random placements.

This paper introduces a 4-pole compensation method for impedance spectroscopy of porcine brain tissue, showing it reduces measurement errors and improves conductivity estimates compared to standard values.

This paper introduces a coupling model that translates TMS-induced electric fields into neural state variables by simulating cortical circuits and synaptic interactions.

The paper introduces a leadfield-free optimization framework for determining optimal electrode montages in brain stimulation techniques, demonstrating its effectiveness across multiple applications.

The paper introduces a statistical method to adjust macroscopic electric field predictions by accounting for microscopic field distortions caused by brain microstructure, improving alignment with experimental neuron activation thresholds.

The paper develops a BEM-based method to solve EEG/MEG forward problems for high-resolution head models in about 60 seconds.

This paper models microscopic electric field perturbations in a mouse brain sample to assess their impact on neuronal activation thresholds, finding modest effects compared to macroscopic predictions.

The paper compares dipole source reconstruction accuracy between 3-layer and 5-layer BEM-FMM models, finding that 3-layer models provide good localization but less accurate orientation, with AMR improving 5-layer accuracy.

This paper explores electric field-based dosing for transcranial magnetic stimulation (TMS) to reduce variability in cortical stimulation and discusses challenges and future directions.

This paper introduces an adaptive mesh refinement method to improve accuracy in electromagnetic simulations for brain stimulation and EEG, showing significant error reduction with minimal mesh expansion.

The paper develops computationally efficient average response models of cortical neurons to estimate TMS-induced stimulation thresholds based on electric field orientation.

This study investigates how transcranial magnetic stimulation affects spatial attention using biophysical modeling and fMRI, finding limited correlation between electric fields and behavior but selective attentional inhibition in some subjects.

The paper develops and validates a new EEG source localization algorithm using BEM-FMM, reciprocity principle, and TES forward model matrix, achieving high-resolution source reconstruction with low computational cost.

This study shows that contact pressure variation has minimal effect on porcine postmortem brain tissue impedance, with a coefficient of variation under 3%.

This paper introduces a method to map the human motor cortex with high resolution using nonlinear regression of electric fields and motor evoked potentials.

The paper introduces a four-electrode probe for measuring brain tissue conductivity and permittivity, demonstrating its use on porcine tissue.

The paper investigates the distinction between grey and white matter in a postmortem porcine brain using in situ impedance measurements and replicates conditions for in vivo human brain experiments.

This paper introduces a method to precisely localize cortical areas stimulated by TMS using electric field modeling and MEP measurements, showing that tangential electric field magnitude and orientation are key factors.