Redesigning existing transcranial magnetic stimulation coils to reduce energy: application to low field magnetic stimulation.

Objective: To present a systematic framework and exemplar for the development of a compact and energy-efficient coil that replicates the electric field (E-field) distribution induced by an existing transcranial magnetic stimulation coil.

Approach: The E-field generated by a conventional low field magnetic stimulation (LFMS) coil was measured for a spherical head model and simulated in both spherical and realistic head models. Then, using a spherical head model and spatial harmonic decomposition, a spherical-shaped cap coil was synthesized such that its windings conformed to a spherical surface and replicated the E-field on the cortical surface while requiring less energy.

Field Distribution of Transcranial Static Magnetic Stimulation in Realistic Human Head Model.

Objective: The objective of this work was to characterize the magnetic field (B-field) that arises in a human brain model from the application of transcranial static magnetic field stimulation (tSMS).

Materials And Methods: The spatial distribution of the B-field magnitude and gradient of a cylindrical, 5.08 cm × 2.

Simulating Clinical Trials With and Without Intracranial EEG Data.

Objective: It is currently unknown if knowledge of clinically silent (electrographic) seizures improves the statistical efficiency of clinical trials.

Methods: Using data obtained from 10 patients with chronically implanted subdural electrodes over an average of 1 year, a Monte Carlo bootstrapping simulation study was performed to estimate the statistical power of running a clinical trial based on A) patient reported seizures with intracranial EEG (icEEG) confirmation, B) all patient reported events, or C) all icEEG confirmed seizures. A "drug" was modeled as having 10%, 20%, 30%, 40% and 50% efficacy in 1000 simulated trials each.

A big data approach to the development of mixed-effects models for seizure count data.

Objective: Our objective was to develop a generalized linear mixed model for predicting seizure count that is useful in the design and analysis of clinical trials. This model also may benefit the design and interpretation of seizure-recording paradigms. Most existing seizure count models do not include children, and there is currently no consensus regarding the most suitable model that can be applied to children and adults.