Cerebral calcification is a common incidental finding upon brain imaging and its epileptogenicity is often underestimated. Here, we report a case of intractable epilepsy arising in conjunction with a solitary cerebral calcification. A 42-year-old male with intractable epilepsy was admitted to the epilepsy clinic for invasive epilepsy surgery. Brain magnetic resonance imaging revealed a slight high-intensity signal change in the right amygdala and a small, calcified lesion in the right lateral temporal region. The patient underwent invasive monitoring with subdural electrodes. He had five habitual seizures with automatisms and fast activity. These seizures initiated in the right lateral temporal area just above the solitary calcified lesion. Neuropathology of the calcified lesion showed no specific findings apart from a fibrocalcific nodule. Thus, although solitary cerebral calcifications may be an asymptomatic or coincidental finding in some patients, they may also have a highly epileptogenic focus.
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http://dx.doi.org/10.14581/jer.17021 | DOI Listing |
Int J Neurosci
January 2025
Department of Functional Neurosurgery, Beijing Institute of Functional Neurosurgery, Xuanwu Hospital, Capital Medical University, Beijing, 100053, China.
Purpose: To investigate the activity of default mode network (DMN), frontoparietal network (FPN) and cerebellar network (CN) in drug-resistant epilepsy (DRE) patients undergoing vagus nerve stimulation (VNS).
Methods: Fifteen patients were recruited and underwent resting-state fMRI scans. Independent component analysis and paired sample t-tests were used to examine activity changes of DMN, FPN and CN before and after VNS.
Front Neurol
December 2024
Department of Diagnostic Radiology, Fuzong Clinical Medical College of Fujian Medical University, Fuzhou, Fujian, China.
Objective: This investigation aimed to elucidate alterations in metabolic brain network connectivity in drug-resistant mesial temporal lobe epilepsy (DR-MTLE) patients, relating these changes to varying surgical outcomes.
Methods: A retrospective cohort of 87 DR-MTLE patients who underwent selective amygdalohippocampectomy was analyzed. Patients were categorized based on Engel surgical outcome classification into seizure-free (SF) or non-seizure-free (NSF) groups.
Clin Neurophysiol
December 2024
Unidad Ejecutora para el Estudio de las Neurociencias y Sistemas Complejos (ENyS), Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Buenos Aires, Argentina; Laboratorio de Anatomía Viviente, 3ra Cátedra de Anatomía Normal, Facultad de Medicina, Universidad de Buenos Aires, Buenos Aires, Argentina.
Objective: To investigate the neural networks involved in idiomatic expressions (IE) comprehension in healthy controls and patients with drug-resistant temporal lobe epilepsy (TLE), with a functional magnetic resonance imaging (fMRI) task.
Methods: Thirty-two patients with TLE (left or right) and seventeen healthy controls were evaluated. Activated nodes in the fMRI task were defined as Regions of Interest (ROIs) for a posterior functional connectivity analysis.
Front Neurol
December 2024
Department of Neurosurgery, Xuanwu Hospital, Capital Medical University, Beijing, China.
Background: Stereoelectroencephalography (SEEG), as a minimally invasive method that can stably collect intracranial electroencephalographic information over long periods, has increasingly been applied in the diagnosis and treatment of intractable epilepsy in recent years. Over the past 20 years, with the advancement of materials science and computer science, the application scenarios of SEEG have greatly expanded. Bibliometrics, as a method of scientifically analyzing published literature, can summarize the evolutionary process in the SEEG field and offer insights into its future development prospects.
View Article and Find Full Text PDFEpilepsia
December 2024
Department of Neuroradiology, University Hospital Bonn, Bonn, Germany.
Objective: Focal cortical dysplasia (FCD) is a common cause of drug-resistant focal epilepsy but can be challenging to detect visually on magnetic resonance imaging. Three artificial intelligence models for automated FCD detection are publicly available (MAP18, deepFCD, MELD) but have only been compared on single-center data. Our first objective is to compare them on independent multicenter test data.
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