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Peripheral nerve injury induces dystonia-like movements and dysregulation in the energy metabolism: A multi-omics descriptive study in Thap1 mice.
Neurobiol Dis
December 2024
Department of Neurology, University Hospital of Wuerzburg, Germany. Electronic address:
DYT-THAP1 dystonia is a monogenetic form of dystonia, a movement disorder characterized by the involuntary co-contraction of agonistic and antagonistic muscles. The disease is caused by mutations in the THAP1 gene, although the precise mechanisms by which these mutations contribute to the pathophysiology of dystonia remain unclear. The incomplete penetrance of DYT-THAP1 dystonia, estimated at 40 to 60 %, suggests that an environmental trigger may be required for the manifestation of the disease in genetically predisposed individuals.
View Article and Find Full Text PDFIntrinsic plasticity coding improved spiking actor network for reinforcement learning.
Neural Netw
December 2024
School of Artificial Intelligence, Anhui University, Hefei, 230601, Anhui, China; Engineering Research Center of Autonomous Unmanned System Technology, Ministry of Education, Hefei, 230601, Anhui, China; Anhui Provincial Engineering Research Center for Unmanned Systems and Intelligent Technology, Hefei, 230601, Anhui, China; School of Automation, Southeast University, Nanjing, 211189, Jiangsu, China. Electronic address:
Deep reinforcement learning (DRL) exploits the powerful representational capabilities of deep neural networks (DNNs) and has achieved significant success. However, compared to DNNs, spiking neural networks (SNNs), which operate on binary signals, more closely resemble the biological characteristics of efficient learning observed in the brain. In SNNs, spiking neurons exhibit complex dynamic characteristics and learn based on principles of biological plasticity.
View Article and Find Full Text PDFIdentifying influential nodes in brain networks via self-supervised graph-transformer.
Comput Biol Med
December 2024
Center for Brain and Brain-Inspired Computing Research, School of Computer Science, Northwestern Polytechnical University, Xi'an, China. Electronic address:
Background: Studying influential nodes (I-nodes) in brain networks is of great significance in the field of brain imaging. Most existing studies consider brain connectivity hubs as I-nodes such as the regions of high centrality or rich-club organization. However, this approach relies heavily on prior knowledge from graph theory, which may overlook the intrinsic characteristics of the brain network, especially when its architecture is not fully understood.
View Article and Find Full Text PDFSurvival prediction of glioblastoma patients using machine learning and deep learning: a systematic review.
BMC Cancer
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Department of Data Science, Faculty of Interdisciplinary Science and Technology, Tarbiat Modares University, Tehran, Iran.
Glioblastoma Multiforme (GBM), classified as a grade IV glioma by the World Health Organization (WHO), is a prevalent and notably aggressive form of brain tumor derived from glial cells. It stands as one of the most severe forms of primary brain cancer in humans. The median survival time of GBM patients is only 12-15 months, making it the most lethal type of brain tumor.
View Article and Find Full Text PDFChallenges and opportunities of acquiring cortical recordings for chronic adaptive deep brain stimulation.
Nat Biomed Eng
December 2024
Department of Psychiatry and Behavioral Sciences, University of Minnesota, Minneapolis, MN, USA.
Deep brain stimulation (DBS), a proven treatment for movement disorders, also holds promise for the treatment of psychiatric and cognitive conditions. However, for DBS to be clinically effective, it may require DBS technology that can alter or trigger stimulation in response to changes in biomarkers sensed from the patient's brain. A growing body of evidence suggests that such adaptive DBS is feasible, it might achieve clinical effects that are not possible with standard continuous DBS and that some of the best biomarkers are signals from the cerebral cortex.
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