AIMC Topic: Drug Resistant Epilepsy

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Probabilistic machine learning for the evaluation of presurgical language dominance.

Journal of neurosurgery
OBJECTIVE Providing a reliable assessment of language lateralization is an important task to be performed prior to neurosurgery in patients with epilepsy. Over the last decade, functional MRI (fMRI) has emerged as a useful noninvasive tool for langua...

Decoding intracranial EEG data with multiple kernel learning method.

Journal of neuroscience methods
BACKGROUND: Machine learning models have been successfully applied to neuroimaging data to make predictions about behavioral and cognitive states of interest. While these multivariate methods have greatly advanced the field of neuroimaging, their app...

[Localizing target for transcranial electrical stimulation in epilepsy patients combining scalp electroencephalogram and neural computational model].

Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi
For patients with MRI-negative drug-resistant epilepsy, noninvasive localization of targets for transcranial electrical stimulation (tES) remains a clinical challenge. This study proposes a novel target localization approach that integrates electroen...

Recent advances in sMRI and artificial intelligence for presurgical planning in focal cortical dysplasia: A systematic review.

Journal of neuroradiology = Journal de neuroradiologie
BACKGROUND: Focal Cortical Dysplasia (FCD) is a leading cause of drug-resistant epilepsy, particularly in children and young adults, necessitating precise presurgical planning. Traditional structural MRI often fails to detect subtle FCD lesions, espe...

Dynamic alterations of SEEG characteristics during peri-ictal period and localization of seizure onset zone.

Neurobiology of disease
BACKGROUND: The evolution in peri-ictal period (from pre-ictal to ictal phase) of seizures contains abundant epileptogenic information, which aids in exploring the mechanism of seizures and localizing the epileptogenic zone (EZ). This study aims to i...

The interaction of UBR4, LRP1, and OPHN1 in refractory epilepsy: Drosophila model to investigate the oligogenic effect on epilepsy.

Neurobiology of disease
Refractory epilepsy is an intractable neurological disorder that can be associated with oligogenic/polygenic etiologies. Through trio-based whole-exome sequencing analysis, we identified a clinical case of refractory epilepsy with three candidate gen...

Automated Whole-Brain Focal Cortical Dysplasia Detection Using MR Fingerprinting With Deep Learning.

Neurology
BACKGROUND AND OBJECTIVES: Focal cortical dysplasia (FCD) is a common pathology for pharmacoresistant focal epilepsy, yet detection of FCD on clinical MRI is challenging. Magnetic resonance fingerprinting (MRF) is a novel quantitative imaging techniq...

Predicting seizure onset zones from interictal intracranial EEG using functional connectivity and machine learning.

Scientific reports
Functional connectivity (FC) analyses of intracranial EEG (iEEG) signals can potentially improve the mapping of epileptic networks in drug-resistant focal epilepsy. However, it remains unclear whether FC-based metrics provide additional value beyond ...

[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.