Object: Some patients are not seizure free even after epileptogenic cortical resection. The authors recently described a case of frontal lobe epilepsy cured after the resection of periventricular white matter and striatum, in which dysplastic neurons were revealed. The authors attempted to confirm similar cases.
Methods: They reviewed the records of 8 children with frontal lobe epilepsy who had daily (7) or monthly (1) seizures and underwent resections including deep brain structures.
Results: Five patients underwent multiple resections. Neuroimaging of the deep structures showed the transmantle sign in 3 patients, ictal hyperperfusion in 6, reduced iomazenil uptake in 2, and spike dipole clustering in 6. All patients became seizure free postoperatively. Focal cortical dysplasia of various types was diagnosed in all patients. Dysmorphic neurons were found in the cortex and subcortical white matter of 5 patients. The striatum was verified in 3 patients in whom dysmorphic neurons were scattered. In the periventricular white matter, prominent astrocytosis was evident in all cases.
Conclusions: Pathological abnormalities such as dysmorphic neurons and astrocytosis in deep brain structures would play a key role in epileptogenesis.
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http://dx.doi.org/10.3171/2012.6.PEDS11325 | DOI Listing |
Neurol Res Pract
January 2025
Institute of Clinical Epidemiology and Biometry, Julius-Maximilians-Universität Würzburg (JMU), Haus D7, Josef-Schneider-Straße 2, 97080, Würzburg, Germany.
Background: Comprehensive clinical data regarding factors influencing the individual disease course of patients with movement disorders treated with deep brain stimulation might help to better understand disease progression and to develop individualized treatment approaches.
Methods: The clinical core data set was developed by a multidisciplinary working group within the German transregional collaborative research network ReTune. The development followed standardized methodology comprising review of available evidence, a consensus process and performance of the first phase of the study.
Nat Cancer
January 2025
Dept. of Neuropathology, University Hospital Heidelberg, Heidelberg, Germany.
The diagnostic landscape of brain tumors integrates comprehensive molecular markers alongside traditional histopathological evaluation. DNA methylation and next-generation sequencing (NGS) have become a cornerstone in central nervous system (CNS) tumor classification. A limiting requirement for NGS and methylation profiling is sufficient DNA quality and quantity, which restrict its feasibility.
View Article and Find Full Text PDFProg Neuropsychopharmacol Biol Psychiatry
January 2025
Department of Physiology, Albert Szent-Györgyi Medical School, University of Szeged, Szeged, Hungary. Electronic address:
Comorbidities between gastrointestinal diseases and psychiatric disorders have been widely reported, with the gut-brain axis implicated as a potential biological basis. Thus, dysbiosis may play an important role in the etiology of schizophrenia, which is barely detected. Triple-hit Wisket model rats exhibit various schizophrenia-like behavioral phenotypes.
View Article and Find Full Text PDFJ Clin Neurosci
January 2025
Macquarie Medical School, Faculty of Medicine, Health and Human Sciences, Macquarie University, NSW, Australia; Computational NeuroSurgery (CNS) Lab, Macquarie University, NSW, Australia.
Purpose: This literature review aims to synthesise current research on the application of artificial intelligence (AI) for the segmentation of brain neuroanatomical structures in magnetic resonance imaging (MRI).
Methods: A literature search was conducted using the databases Embase, Medline, Scopus, and Web of Science, and captured articles were assessed for inclusion in the review. Data extraction was performed for the summary of the AI model used, and key findings of each article, advantages and disadvantages were identified.
J Med Internet Res
January 2025
Department of Computer Science and Software Engineering, United Arab Emirates University, Al Ain, United Arab Emirates.
Background: Neuroimaging segmentation is increasingly important for diagnosing and planning treatments for neurological diseases. Manual segmentation is time-consuming, apart from being prone to human error and variability. Transformers are a promising deep learning approach for automated medical image segmentation.
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