PubMed Health⌕ Search

PubMed · 12044255

Neurostimulation therapy for epilepsy.

Abstract

Neurostimulation therapy for epilepsy is growing in popularity. By appropriate targeting of applied electrical activation at selected nervous system sites, antiseizure effects may be achieved without the common sedative side effects of antiepileptic medications. Risks of neurostimulation therapy are those associated with the device implantation surgical procedures. Vagus nerve stimulation (VNS) reduces seizures by 45% and has been employed in over 13,000 patients worldwide. New reports suggest VNS is particularly beneficial for patients with Lennox-Gastuat syndome. VNS also reduces sudden unexpected death in epilepsy. New publications describing small, uncontrolled case series also suggest deep brain stimulation and transcranial magnetic stimulation may develop into effective antiepileptic therapies in the future.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Douglas Labar, Andy Dean. 2002. Neurostimulation therapy for epilepsy.. https://doi.org/10.1007/s11910-002-0011-8

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related citations

A penalized likelihood approach to magnetic resonance image reconstruction.

Currently, images acquired via magnetic resonance imaging (MRI) and functional magnetic resonance imaging (fMRI) technology are reconstructed using the discrete inverse Fourier transform. While computationally convenient, this approach is not able to filter out noise. This is a serious limitation because the amount of noise in MRI and fMRI can be substantial. In this paper, we propose an alternative approach to reconstruction, based on penalized likelihood methodology. In particular, we focus on non-linear shrinkage estimators and show that this approach achieves a great reduction in integrated mean squared error (IMSE) of the estimated image with respect to the currently used estimator. This approach is extremely fast and easy to implement computationally. In addition, it can be combined with various alternative approaches to MR image reconstruction and can be easily adapted to other, non-MRI contexts, in which the observed data and the quantities of interest are related via a linear transform.

Brain Diseases↗