Object: The goal of this study was to evaluate the definition of treatment-resistant depression (TRD), review the literature regarding deep brain stimulation (DBS) for TRD, and identify potential anatomical and functional targets for future widespread clinical application.
Methods: A comprehensive literature review was performed to determine the current status of DBS for TRD, with an emphasis on the scientific support for various implantation sites.
Results: The definition of TRD is presented, as is its management scheme. The rationale behind using DBS for depression is reviewed. Five potential targets have been identified in the literature: ventral striatum/nucleus accumbens, subgenual cingulate cortex (area 25), inferior thalamic peduncle, rostral cingulate cortex (area 24a), and lateral habenula. Deep brain stimulation electrodes thus far have been implanted and activated in only the first 3 of these structures in humans. These targets have proven to be safe and effective, albeit in a small number of cases.
Conclusions: Surgical intervention for TRD in the form of DBS is emerging as a viable treatment alternative to existing modalities. Although the studies reported thus far have small sample sizes, the results appear to be promising. Various surgical targets, such as the subgenual cingulate cortex, inferior thalamic peduncle, and nucleus accumbens, have been shown to be safe and to lead to beneficial effects with various stimulation parameters. Further studies with larger patient groups are required to adequately assess the safety and efficacy of these targets, as well as the optimal stimulation parameters and long-term effects.
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http://dx.doi.org/10.3171/FOC/2008/25/7/E3 | 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.
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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.
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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.
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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.
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