Publications by authors named "C Perez-Enriquez"

Objective: Stereo-electroencephalography (SEEG)-guided radiofrequency thermocoagulation (RFTC) is being used incrementally in the invasive diagnosis of epilepsy. There is currently a lack of information regarding the potential cognitive consequences of the extended use of this technique. This work describes, for the first time, the cognitive outcomes after RFTC in patients with temporal lobe epilepsy (TLE), evaluated longitudinally and using a control group.

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Introduction: Complex attention and non-verbal fluency tasks are used in neuropsychological assessments with the aim of exploring subdomains of executive function. The purpose of this study is to provide norms and age-, education-, and sex-adjusted data for the Delis Kaplan-Design Fluency Test (DK-DFT), Color Trails Test (CTT), and Dual Task (DT) as part of the NEURONORMA-Plus project.

Methods: The sample included 308 cognitively unimpaired individuals aged between 18 and 92 years.

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Objective: The present study aims to provide norms and age-, education-, and sex-adjusted data for the Wisconsin Card Sorting Test (WCST), the Modified Taylor Complex Figure (MTCF), and the Ruff-Light Trail Learning Test (RULIT) as part of the NEURONORMA-Plus project.

Methods: We recruited 308 cognitively healthy individuals aged between 18 and 92 years. Tables are provided to convert raw scores to age-adjusted scaled scores, as well as adjustments for education and sex after applying independent regression models in 2 age groups (< 50 and ≥ 50 years).

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Objective: Coupled with stereo-electroencephalography (SEEG), radiofrequency thermocoagulation (RFTC) has emerged as a therapeutic alternative for patients with refractory focal epilepsy, with proven safe but highly variable results across studies. The authors aimed to describe the outcomes and safety of SEEG-RFTC, focusing on patients with MRI-negative epilepsy.

Methods: A retrospective observational study was conducted on patients evaluated by SEEG in the authors' center.

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One outstanding challenge for machine learning in diagnostic biomedical imaging is algorithm interpretability. A key application is the identification of subtle epileptogenic focal cortical dysplasias (FCDs) from structural MRI. FCDs are difficult to visualize on structural MRI but are often amenable to surgical resection.

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