Over the last two decades there has been an exponential rise in the number of patients receiving deep brain stimulation (DBS) to manage debilitating neurological symptoms in conditions such as Parkinson's disease, essential tremor, and dystonia. Novel applications of DBS continue to emerge including treatment of various psychiatric conditions (e.g. obsessive-compulsive disorder, major depression) and cognitive disorders such as Alzheimer's disease. Despite widening therapeutic applications, our understanding of the mechanisms underlying DBS remains limited. In addition to modulation of local and network-wide neuronal activity, growing evidence suggests that DBS may also have important neuroprotective effects in the brain by limiting synaptic dysfunction and neuronal loss in neurodegenerative disorders. In this review, we consider evidence from preclinical and clinical studies of DBS in Parkinson's disease, Alzheimer's disease, and epilepsy that suggest chronic stimulation has the potential to mitigate neuronal loss and disease progression.
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http://dx.doi.org/10.1002/acn3.682 | DOI Listing |
J Imaging Inform Med
January 2025
Department of Chinese and Bilingual Studies, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong Special Administrative Region, China.
Deep learning models have shown promise in diagnosing neurodevelopmental disorders (NDD) like ASD and ADHD. However, many models either use graph neural networks (GNN) to construct single-level brain functional networks (BFNs) or employ spatial convolution filtering for local information extraction from rs-fMRI data, often neglecting high-order features crucial for NDD classification. We introduce a Multi-view High-order Network (MHNet) to capture hierarchical and high-order features from multi-view BFNs derived from rs-fMRI data for NDD prediction.
View Article and Find Full Text PDFSci Rep
January 2025
Shanghai Key Laboratory of Magnetic Resonance, School of Physics and Electronic Science, East China Normal University, Shanghai, China.
Prediction of isocitrate dehydrogenase (IDH) mutation status and epilepsy occurrence are important to glioma patients. Although machine learning models have been constructed for both issues, the correlation between them has not been explored. Our study aimed to exploit this correlation to improve the performance of both of the IDH mutation status identification and epilepsy diagnosis models in patients with glioma II-IV.
View Article and Find Full Text PDFNeural Netw
January 2025
Image Processing Lab., Universitat de València, 46980 Paterna, Spain. Electronic address:
There is an open debate on the role of artificial networks to understand the visual brain. Internal representations of images in artificial networks develop human-like properties. In particular, evaluating distortions using differences between internal features is correlated to human perception of distortion.
View Article and Find Full Text PDFJ Magn Reson Imaging
January 2025
Department of Radiology, The Fourth Clinical Medical College of Guangzhou University of Chinese Medicine (Shenzhen Traditional Chinese Medicine Hospital), Shenzhen, China.
Background: Multifrequency MR elastography (mMRE) enables noninvasive quantification of renal stiffness in patients with chronic kidney disease (CKD). Manual segmentation of the kidneys on mMRE is time-consuming and prone to increased interobserver variability.
Purpose: To evaluate the performance of mMRE combined with automatic segmentation in assessing CKD severity.
Brain Topogr
January 2025
Department of Electrical Engineering, Indian Institute of Technology Roorkee, Roorkee, Uttarakhand, 247667, India.
EEG involves recording electrical activity generated by the brain through electrodes placed on the scalp. Imagined speech classification has emerged as an essential area of research in brain-computer interfaces (BCIs). Despite significant advances, accurately classifying imagined speech signals remains challenging due to their complex and non-stationary nature.
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