Deep brain stimulation has demonstrated efficacy in reducing seizure frequency in patients with drug-resistant epilepsy who may otherwise not be candidates for other surgical procedures. Recently, a clinical device that can monitor neural activity in the form of local field potentials around the deep brain stimulator lead implant site has been introduced. While this technology has been clinically adopted in other disorders treated with deep brain stimulation, such as Parkinson's disease, its application in epilepsy remains unclear. Previous research using investigational devices has suggested that specific frequency bands may correlate with clinical response to deep brain stimulation in epilepsy, but features of the clinical device may prevent its use. The authors present their experience with using this technology in epilepsy patients and describe some of its limitations. Ultimately, novel biomarkers will need to be identified to elucidate how neural activity at deep brain stimulation sites may change with clinical response.
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http://dx.doi.org/10.1016/j.nec.2023.08.005 | DOI Listing |
Neurosurg Rev
January 2025
Department of Neurosurgery, King's College Hospital Foundation Trust, London, UK.
Minimally invasive parafascicular surgery (MIPS) with the use of tubular retractors achieve a safe resection in deep seated tumours. Diffusion changes noted on postoperative imaging; the significance and clinical correlation of this remains poorly understood. Single centre retrospective cohort study of neuro-oncology patients undergoing MIPS.
View Article and Find Full Text PDFBrain Struct Funct
January 2025
Department of Biomedical Engineering, College of Chemistry and Life Sciences, Beijing University of Technology, Beijing, 100124, China.
The brain undergoes atrophy and cognitive decline with advancing age. The utilization of brain age prediction represents a pioneering methodology in the examination of brain aging. This study aims to develop a deep learning model with high predictive accuracy and interpretability for brain age prediction tasks.
View Article and Find Full Text PDFNeuroradiology
January 2025
Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, 400016, China.
Introduction: Bipolar disorder (BD) and major depressive disorder (MDD) have overlapping clinical presentations which may make it difficult for clinicians to distinguish them potentially resulting in misdiagnosis. This study combined structural MRI and machine learning techniques to determine whether regional morphological differences could distinguish patients with BD and MDD.
Methods: A total of 123 participants, including BD (n = 31), MDD (n = 48), and healthy controls (HC, n = 44), underwent high-resolution 3D T1-weighted imaging.
Microsc Res Tech
January 2025
AIDA Lab. College of Computer and Information Sciences (CCIS), Prince Sultan University, Riyadh, Saudi Arabia.
The development of deep learning algorithms has transformed medical image analysis, especially in brain tumor recognition. This research introduces a robust automatic microbrain tumor identification method utilizing the VGG16 deep learning model. Microscopy magnetic resonance imaging (MMRI) scans extract detailed features, providing multi-modal insights.
View Article and Find Full Text PDFBMC Neurol
January 2025
Department of Neurology, University Hospital Bern, Inselspital, University of Bern, Bern, Switzerland.
Background: Effects of subthalamic nucleus deep brain stimulation (STN-DBS) on neuropsychiatric symptoms of Parkinson's disease (PD) remain debated. Sensor technology might help to objectively assess behavioural changes after STN-DBS.
Case Presentation: 5 PD patients were assessed 1 before and 5 months after STN-DBS with the Movement Disorders Society Unified Parkinson's Disease Rating Scale part III in the medication ON (plus postoperatively stimulation ON) condition, the Montreal Cognitive Assessment, the Questionnaire for Impulsive-Compulsive Behaviors in Parkinson's Disease Rating Scale present version, the Hospital Anxiety and Depression Scale and the Starkstein Apathy Scale.
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