Objectives: Guanine nucleotide-binding protein alpha-activating activity polypeptide O (GNAO1) syndrome, a rare congenital monogenetic disorder, is characterized by a neurodevelopmental syndrome and the presence of dystonia. Dystonia can be very pronounced and even lead to a life-threatening status dystonicus. In a small number of pharmaco-refractory cases, deep brain stimulation (DBS) has been attempted to reduce dystonia. In this study, we summarize the current literature on outcome, safety, and outcome predictors of DBS for GNAO1-associated dystonia.
Materials And Methods: We conducted a systematic review and meta-analysis on individual patient data. We included 18 studies describing 28 unique patients.
Results: The mean age of onset of symptoms was 2.4 years (SD 3.8); 16 of 28 patients were male, and dystonia was nearly always generalized (20/22 patients). Symptoms were present before DBS for a median duration of 19.5 months, although highly variable, occurring between 3 and 168 months. The exact phenotype, genotype, and radiologic abnormalities varied and seemed to be of little importance in terms of DBS outcome. All studies described an improvement in dystonia. Our meta-analysis focused on pallidal DBS and found an absolute and relative improvement in Burke-Fahn-Marsden Dystonia Rating Scale (BFMDRS) of 32.5 points (37.9%; motor part; p = 0.001) and 5.8 points (21.5%; disability part; p = 0.043) at last follow-up compared with preoperative state; 80% of patients were considered responders (BFMDRS-M reduction by ≥25%). Although worsening over time does occur, an improvement was still observed in patients after >10 years. All reported cases of status dystonicus resolved after DBS surgery. Skin erosion and infection were observed in 18% of patients.
Conclusion: Pallidal DBS can be efficacious and safe in GNAO1-associated dystonia.
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http://dx.doi.org/10.1016/j.neurom.2023.10.187 | 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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