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Clinical Trial Summary

Background:

In the Emilia-Romagna Region approximately 19.000 people are affected by epilepsy. About 25% of epileptic patients are drug-resistant (DRE) and some of them are eligible for resective surgery of the epileptogenic zone (EZ). The precise EZ localization is crucial for a good surgical outcome. Intracranial EEG (icEEG) recordings remain the gold-standard to localise the EZ. New neuroimaging techniques, like simultaneous recording of functional MRI and EEG (EEG-fMRI), with advanced methodological approaches as effective connectivity analysis (i.e. Dynamic Causal Modelling-DCM) might improve the EZ localization.

Objectives:

(1) To develop a non-invasive protocol for the investigation of the epileptic network in patients with surgically remediable epilepsies; (2) To shed light on the patho-physiological mechanisms of drug resistance in DRE; (3) To provide a validation of effective connectivity applied to fMRI data in epilepsy.

Methods:

Two Research Units (RU1, RU2) will identify and characterize a cohort of DRE patients eligible for resective surgery. RU1 will be in charge to perform the EEG/fMRI coregistration and data analysis. RU2 will be responsible for the surgical resection of epileptic foci. IcEEG recordings will be performed at the Claudio Munari Epilepsy Center, Ospedale Niguarda, Milano. RU1&RU2 will evaluate the fMRI data results and compare with icEEG findings or expert's surgical decision. The principle measures of outcome are: (a) percentage of concordance of fMRI results with icEEG findings or electro-clinical features in term epileptic network identification; (b) percentage of concordance between DCM findings and EZ/IZ localization; (c) percentage of concordance of DCM findings with icEEG or electro-clinical features regarding the causal hierarchy within the epileptic network.


Clinical Trial Description

Background:

About 25% of epileptic patients are drug-resistant (DRE) and some of them are eligible for resective surgery of the epileptogenic zone (EZ). The precise EZ localization is crucial for a good surgical outcome. Intracranial EEG (icEEG) recordings remain the gold-standard to localise the EZ. New neuroimaging techniques, like simultaneous recording of functional MRI and EEG (EEG-fMRI), with advanced methodological approaches as effective connectivity analysis (i.e. Dynamic Causal Modelling-DCM) might improve the EZ localization. This innovative tool will have the advantage to be non-invasive and safe with significant decrease of injuries, hospitalization, with a resulting favourable cost/benefit ratio.

Objectives:

(1) to provide a validation of effective connectivity applied to fMRI data in epilepsy. (2)To develop a non-invasive protocol for the investigation of the epileptic network in patients with surgically remediable epilepsies; (3) To shed light on the patho-physiological mechanisms of drug resistance in DRE.

Methods:

Two Research Units (RU1, RU2) will identify and characterize a cohort of DRE patients eligible for resective surgery. RU1 will perform patients' recruitment, presurgical evaluation and EEG/fMRI coregistration and data analysis. RU2 will perform patients' recruitment, presurgical evaluation and surgical resection of epileptic foci. IcEEG recordings will be performed at the Claudio Munari Epilepsy Center, Ospedale Niguarda, Milano . RU1&RU2 will evaluate the fMRI data results and compare with icEEG findings or expert's surgical decision. The principle measures of outcome are: (a) percentage of concordance of fMRI results with icEEG findings or electro-clinical features in term epileptic network identification; (b) percentage of concordance between DCM findings and EZ/IZ localization; (c) percentage of concordance of DCM findings with icEEG or electro-clinical features regarding the causal hierarchy within the epileptic network. ;


Study Design


Related Conditions & MeSH terms


NCT number NCT03582345
Study type Interventional
Source Azienda Unita' Sanitaria Locale Di Modena
Contact
Status Completed
Phase N/A
Start date January 2, 2015
Completion date January 1, 2018

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