Sergey N Makaroff

The paper evaluates numerical accuracy and convergence of hippocampal electric field modeling across three methods in ECT patients, finding significant discrepancies at low resolutions.

The paper presents a multichannel LI-TMS array capable of electronically steering electric fields for network-level neuromodulation, achieving cortical fields comparable to tES without scalp contact.

The paper introduces a disposable wearable electrotherapy patch that accelerates wound healing in rats by delivering electrical stimulation comparable to a conventional device, with reduced closure time and enhanced tissue remodeling.

The paper introduces a residual-driven adaptive mesh refinement method for charge-based boundary element modeling to improve accuracy in electrical stimulation and EEG simulations.

This study investigates how transcranial magnetic stimulation (TMS) activates Purkinje cells and climbing fibers in turtle cerebellum, showing direct and synaptic activation mechanisms within the first 1.2 ms.

This study validates finite element models for directional DBS by comparing experimental measurements with different boundary condition configurations, finding Dirichlet conditions provide higher accuracy in predicting VTA volume.

This paper improves the localization of auditory evoked fields using reciprocal BEM-FMM, achieving higher accuracy and focality compared to standard BEM methods.

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

The paper analyzes synaptic cleft orientations in human and mouse brain data, finding directional biases that suggest mesoscale anisotropy in cortical layers.

This paper introduces a fast MEG source estimation method using reciprocal relationships and BEM-FMM, achieving high-resolution gain matrices for up to 1 million dipoles.

The paper constructs accurate turtle cerebellum models using microCT data and simulates neuromodulation with BEM-FMM for computational electromagnetics verification.

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 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 introduces a MATLAB software package for TMS coil analysis and design, using FMM to compute fields and inductances with high accuracy.

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.