Functional stereotaxy was introduced in the late 1940s to reduce the morbidity of lobotomy in psychiatric disease by using more focal lesions. The advent of neuroleptics led to a drastic decline in psychosurgery for several decades. Functional stereotactic neurosurgery has recently been revitalized, starting with treatment of Parkinson's disease, in which deep brain stimulation (DBS) facilitates reversible focal neuromodulation of altered basal ganglia circuits. DBS is now being extended to treatment of neuropsychiatric conditions such as Gilles de la Tourette syndrome, obsessive-compulsive disorder, depression and addiction. In this review, we discuss the concept that dysfunction of motor, limbic and associative cortico-basal ganglia-thalamocortical loops underlies these various disorders, which might now be amenable to DBS treatment.
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http://dx.doi.org/10.1016/j.tins.2010.07.002 | DOI Listing |
Aim: Successful deep brain stimulation (DBS) requires precise electrode placement. However, brain shift from loss of cerebrospinal fluid or pneumocephalus still affects aim accuracy. Multidetector computed tomography (MDCT) provides absolute spatial sensitivity, and intraoperative cone-beam computed tomography (iCBCT) has become increasingly used in DBS procedures.
View Article and Find Full Text PDFObjective: To assist in the rapid clinical identification of brain tumor types while achieving segmentation detection, this study investigates the feasibility of applying the deep learning YOLOv5s algorithm model to the segmentation of brain tumor magnetic resonance images and optimizes and upgrades it on this basis.
Methods: The research institute utilized two public datasets of meningioma and glioma magnetic resonance imaging from Kaggle. Dataset 1 contains a total of 3,223 images, and Dataset 2 contains 216 images.
Quant Imaging Med Surg
January 2025
Department of Radiology, Beijing Friendship Hospital, Capital Medical University, Beijing, China.
Background: There are currently no deep learning models applying resting-state functional magnetic resonance imaging (rs-fMRI) data to distinguish patients with Parkinson's disease (PD) and healthy controls (HCs). Moreover, no study has correlated objective gait parameters with brain network alterations in patients with PD. We propose BrainNetCNN + CL, applying a convolutional neural network (CNN) and joint contrastive learning (CL) method to brain network analysis to classify patients with PD and HCs, and compare their performance with classical classification methods.
View Article and Find Full Text PDFQuant Imaging Med Surg
January 2025
Radiology and Nuclear Medicine Department, Erasmus MC, Rotterdam, The Netherlands.
Background: Gadolinium-based contrast agents (GBCAs) are usually employed for glioma diagnosis. However, GBCAs raise safety concerns, lead to patient discomfort and increase costs. Parametric maps offer a potential solution by enabling quantification of subtle tissue changes without GBCAs, but they are not commonly used in clinical practice due to the need for specifically targeted sequences.
View Article and Find Full Text PDFCancer Imaging
January 2025
Department of Imaging, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA.
Background: Radiomic analysis of quantitative features extracted from segmented medical images can be used for predictive modeling of prognosis in brain tumor patients. Manual segmentation of the tumor components is time-consuming and poses significant reproducibility issues. We compare the prediction of overall survival (OS) in recurrent high-grade glioma(HGG) patients undergoing immunotherapy, using deep learning (DL) classification networks along with radiomic signatures derived from manual and convolutional neural networks (CNN) automated segmentation.
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