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Background: Programming deep brain stimulation in dystonia is difficult because of the delayed benefits and absence of evidence-based guidelines. Therefore, we evaluated the efficacy of a programming algorithm applied in a double-blind, sham-controlled multicenter study of pallidal deep brain stimulation in dystonia.
Methods: A standardized monopolar review to identify the contact with the best acute antidystonic effect was applied in 40 patients, who were then programmed 0.5 V below the adverse effect threshold and maintained on these settings for at least 3 months, if tolerated. If no acute effects were observed, contact selection was based on adverse effects or anatomical criteria. Three-year follow-up data was available for 31 patients, and five-year data for 32 patients. The efficacy of the algorithm was based on changes in motor scores, adverse events, and the need for reprogramming.
Results: The mean (±standard deviation) dystonia motor score decreased by 73 ± 24% at 3 years and 63 ± 38% at 5 years for contacts that exhibited acute improvement of dystonia ( = 17) during the monopolar review. Contacts without acute benefit improved by 58 ± 30% at 3 years ( = 63) and 53 ± 31% at 5 years ( = 59). Interestingly, acute worsening or induction of dystonia/dyskinesia ( = 9) correlated significantly with improvement after 3 years, but not 5 years.
Conclusions: Monopolar review helped to detect the best therapeutic contact in approximately 30% of patients exhibiting acute modulation of dystonic symptoms. Acute improvement, as well as worsening of dystonia, predicted a good long-term outcome, while induction of phosphenes did not correlate with outcome.
Trial Registration: ClinicalTrials.gov NCT00142259.
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http://dx.doi.org/10.1186/s42466-019-0032-2 | DOI Listing |
Hum Brain Mapp
September 2025
Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, and Emory University, Atlanta, Georgia, USA.
Investigating neuroimaging data to identify brain-based markers of mental illnesses has gained significant attention. Nevertheless, these endeavors encounter challenges arising from a reliance on symptoms and self-report assessments in making an initial diagnosis. The absence of biological data to delineate nosological categories hinders the provision of additional neurobiological insights into these disorders.
View Article and Find Full Text PDFLancet Reg Health West Pac
September 2025
Department of Neurosurgery, West China Hospital, Sichuan University, Chengdu, China.
Background: There is ongoing controversy as to whether surgical intervention to haematoma evacuation benefits patients with acute intracerebral haemorrhage (ICH). This study aimed to evaluate the association of surgical intervention to evacuate the haematoma and 6-month functional outcome in participants of the third Intensive Care Bundle with Blood Pressure Reduction in Acute Cerebral Haemorrhage Trial (INTERACT3).
Methods: This was a secondary analysis of INTERACT3, which enrolled adults (age ≥18 years) spontaneous ICH patients within 6 h after onset.
Neuropsychiatr Dis Treat
September 2025
Department of Radiology, No. 926 Hospital, Joint Logistics Support Force of PLA, Kaiyuan, Yunnan, 661699, People's Republic of China.
Parkinson's disease (PD) represents a progressive neurodegenerative disorder with escalating global burden, with mechanistic studies revealing α-synuclein propagation through gut-brain axis, mitochondrial defects, and neuroinflammatory cascades driven by genetic-environmental interplay. Recent advancements in diagnostic paradigms have successfully combined α-synuclein seed amplification assays with multimodal neuroimaging techniques, achieving an impressive diagnostic accuracy of 92% during the prodromal stages of disease. Phase II trials highlight disease-modifying potential of α-synuclein-targeting immunotherapies (40% reduction in motor decline) and LRRK2 kinase inhibitors showing blood-brain barrier penetration.
View Article and Find Full Text PDFMed Phys
September 2025
School of Computer, Electronics and Information, Guangxi University, Nanning, China.
Background: Deformable medical image registration is a critical task in medical imaging-assisted diagnosis and treatment. In recent years, medical image registration methods based on deep learning have made significant success by leveraging prior knowledge, and the registration accuracy and computational efficiency have been greatly improved. Models based on Transformers have achieved better performance than convolutional neural network methods (ConvNet) in image registration.
View Article and Find Full Text PDFComput Biol Med
September 2025
Postgraduate Program in Computing, Center for Technological Development, Federal University of Pelotas, Pelotas, 96010-610, Rio Grande do Sul, Brazil.
In the task of image classification for emotion recognition, facial expression data is commonly used. However, electrical brain signals generated by neural activity provide data with greater integrity. We can capture these signals non-invasively using electroencephalogram (EEG) recording devices.
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