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Drug Resistant Epilepsy

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Optimizing detection and deep learning-based classification of pathological high-frequency oscillations in epilepsy.

Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology
OBJECTIVE: This study aimed to explore sensitive detection methods for pathological high-frequency oscillations (HFOs) to improve seizure outcomes in epilepsy surgery.

Deep-learning predicted PET can be subtracted from the true clinical fluorodeoxyglucose PET co-registered to MRI to identify the epileptogenic zone in focal epilepsy.

Epilepsia open
OBJECTIVE: Normal interictal [ F]FDG-PET can be predicted from the corresponding T1w MRI with Generative Adversarial Networks (GANs). A technique we call SIPCOM (Subtraction Interictal PET Co-registered to MRI) can then be used to compare epilepsy pa...

Deep learning based source imaging provides strong sublobar localization of epileptogenic zone from MEG interictal spikes.

NeuroImage
Electromagnetic source imaging (ESI) offers unique capability of imaging brain dynamics for studying brain functions and aiding the clinical management of brain disorders. Challenges exist in ESI due to the ill-posedness of the inverse problem and th...

Learning Curve in Robotic Stereoelectroencephalography: Single Platform Experience.

World neurosurgery
BACKGROUND: Learning curve, training, and cost impede widespread implementation of new technology. Neurosurgical robotic technology introduces challenges to visuospatial reasoning and requires the acquisition of new fine motor skills. Studies detaili...

Electrophysiological brain imaging based on simulation-driven deep learning in the context of epilepsy.

NeuroImage
Identifying the location, the spatial extent and the electrical activity of distributed brain sources in the context of epilepsy through ElectroEncephaloGraphy (EEG) recordings is a challenging task because of the highly ill-posed nature of the under...

[Safety of robot-assisted implantation of deep electrodes for invasive stereo-EEG monitoring].

Zhurnal voprosy neirokhirurgii imeni N. N. Burdenko
UNLABELLED: Robot-assisted implantation of deep electrodes for stereo-EEG monitoring has become popular in recent years in patients with drug-resistant epilepsy. However, there are still few data on safety of this technique.

Frame-based versus robot-assisted stereo-electro-encephalography for drug-resistant epilepsy.

Acta neurochirurgica
BACKGROUND: Stereoelectroencephalography (SEEG) is an effective presurgical invasive evaluation for drug-resistant epilepsies. The introduction of robotic devices provides a simplified, accurate, and safe alternative to the conventional SEEG techniqu...

Microstate-based brain network dynamics distinguishing temporal lobe epilepsy patients: A machine learning approach.

NeuroImage
Temporal lobe epilepsy (TLE) stands as the predominant adult focal epilepsy syndrome, characterized by dysfunctional intrinsic brain dynamics. However, the precise mechanisms underlying seizures in these patients remain elusive. Our study encompassed...

Deep Learning-Based Tract Classification of Preoperative DWI Tractography Advances the Prediction of Short-Term Postoperative Language Improvement in Children With Drug-Resistant Epilepsy.

IEEE transactions on bio-medical engineering
OBJECTIVE: To develop an innovative deep convolutional neural network (DCNN)-based tract classification to enhance the prediction of short-term postoperative language improvement using axonal connectivity markers derived from specific language modula...