Deep brain stimulation (DBS) has become increasingly important for the treatment and relief of neurological disorders such as Parkinson's disease, tremor, dystonia and psychiatric illness. As DBS implantations and any other stereotactic and functional surgical procedure require accurate, precise and safe targeting of the brain structure, the technical aids for preoperative planning, intervention and postoperative follow-up have become increasingly important. The aim of this paper was to give an overview, from a biomedical engineering perspective, of a typical implantation procedure and current supporting techniques. Furthermore, emerging technical aids not yet clinically established are presented. This includes the state-of-the-art of neuroimaging and navigation, patient-specific simulation of DBS electric field, optical methods for intracerebral guidance, movement pattern analysis, intraoperative data visualisation and trends related to new stimulation devices. As DBS surgery already today is an information technology intensive domain, an "intuitive visualisation" interface for improving management of these data in relation to surgery is suggested.
Download full-text PDF |
Source |
---|---|
http://dx.doi.org/10.1007/s11517-010-0633-y | DOI Listing |
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.
View Article and Find Full Text PDFBrief Bioinform
November 2024
Department of Neurosurgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100730, China.
This study aimed to investigate the genetic association between glioblastoma (GBM) and unsupervised deep learning-derived imaging phenotypes (UDIPs). We employed a combination of genome-wide association study (GWAS) data, single-nucleus RNA sequencing (snRNA-seq), and scPagwas (pathway-based polygenic regression framework) methods to explore the genetic links between UDIPs and GBM. Two-sample Mendelian randomization analyses were conducted to identify causal relationships between UDIPs and GBM.
View Article and Find Full Text PDFMed Phys
January 2025
Deparment of Radiation Oncology, Duke University, Durham, North Carolina, USA.
Background: Stereotactic radiosurgery (SRS) is widely used for managing brain metastases (BMs), but an adverse effect, radionecrosis, complicates post-SRS management. Differentiating radionecrosis from tumor recurrence non-invasively remains a major clinical challenge, as conventional imaging techniques often necessitate surgical biopsy for accurate diagnosis. Machine learning and deep learning models have shown potential in distinguishing radionecrosis from tumor recurrence.
View Article and Find Full Text PDFAnn Indian Acad Neurol
January 2025
Department of Neurology, All India Institute of Medical Sciences, New Delhi, India.
"Tardive syndrome" is an umbrella term for a group of drug-induced movement disorders associated with the prolonged use of mainly dopamine receptor blockers and also other medications. Early recognition followed by gradual withdrawal of the incriminating drug may lead to reversal, although not in all patients. Tardive syndromes are usually mixed movement disorders, with specific phenotypes, which may lead to severe disability.
View Article and Find Full Text PDFiScience
February 2025
Division of Newborn Medicine, Department of Pediatrics, Boston Children's Hospital, Boston, MA, USA.
Neurodevelopmental impairments associated with congenital heart disease (CHD) may arise from perturbations in brain developmental pathways, including the formation of sulcal patterns. While genetic factors contribute to sulcal features, the association of noncoding variants (ncDNVs) with sulcal patterns in people with CHD remains poorly understood. Leveraging deep learning models, we examined the predicted impact of ncDNVs on gene regulatory signals.
View Article and Find Full Text PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!