AIMC Topic: Brain

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Machine learning using brain activity can capture the state of mind when a person is preoccupied by the unpleasant sensory experience of itch.

Journal of the European Academy of Dermatology and Venereology : JEADV Feb 5, 2024
Machine Learning Brain Female Humans Pruritus Electroencephalography Adult Male
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NPB-REC: A non-parametric Bayesian deep-learning approach for undersampled MRI reconstruction with uncertainty estimation.

Artificial intelligence in medicine Feb 5, 2024
The ability to reconstruct high-quality images from undersampled MRI data is vital in improving MRI temporal resolution and reducing acquisition times. Deep learning methods have been proposed for this task, but the lack of verified methods to quanti...
Magnetic Resonance Imaging Bayes Theorem Deep Learning Image Processing, Computer-Assisted Uncertainty Brain
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Phase Aberration Correction for In Vivo Ultrasound Localization Microscopy Using a Spatiotemporal Complex-Valued Neural Network.

IEEE transactions on medical imaging Feb 2, 2024
Ultrasound Localization Microscopy (ULM) can map microvessels at a resolution of a few micrometers ( [Formula: see text]). Transcranial ULM remains challenging in presence of aberrations caused by the skull, which lead to localization errors. Herein,...
Brain Animals Neural Networks, Computer Microscopy Microbubbles Skull Ultrasonography Mice
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Two is better than one: longitudinal detection and volumetric evaluation of brain metastases after Stereotactic Radiosurgery with a deep learning pipeline.

Journal of neuro-oncology Feb 1, 2024
PURPOSE: Close MRI surveillance of patients with brain metastases following Stereotactic Radiosurgery (SRS) treatment is essential for assessing treatment response and the current disease status in the brain. This follow-up necessitates the compariso...
Radiosurgery Humans Brain Brain Neoplasms Deep Learning Retrospective Studies
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Assessment of brain tumor detection techniques and recommendation of neural network.

Biomedizinische Technik. Biomedical engineering Jan 30, 2024
OBJECTIVES: Brain tumor classification is amongst the most complex and challenging jobs in the computer domain. The latest advances in brain tumor detection systems (BTDS) are presented as they can inspire new researchers to deliver new architectures...
Humans Neural Networks, Computer Brain Neoplasms Algorithms Brain
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Unraveling motor imagery brain patterns using explainable artificial intelligence based on Shapley values.

Computer methods and programs in biomedicine Jan 30, 2024
BACKGROUND AND OBJECTIVE: Motor imagery (MI) based brain-computer interfaces (BCIs) are widely used in rehabilitation due to the close relationship that exists between MI and motor execution (ME). However, the underlying brain mechanisms of MI remain...
Movement Imagination Algorithms Brain Electroencephalography Humans Artificial Intelligence Brain-Computer Interfaces
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Mapping Extracellular Space Features of Viral Encephalitis to Evaluate the Proficiency of Anti-Viral Drugs.

Advanced materials (Deerfield Beach, Fla.) Jan 29, 2024
The extracellular space (ECS) is an important barrier against viral attack on brain cells, and dynamic changes in ECS microstructure characteristics are closely related to the progression of viral encephalitis in the brain and the efficacy of antivir...
Humans Mice Animals Quantum Dots Machine Learning Antiviral Agents Extracellular Space Rheology Brain Encephalitis, Viral
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Special Issue-Biosensors and Neuroscience: Is Biosensors Engineering Ready to Embrace Design Principles from Neuroscience?

Biosensors Jan 29, 2024
In partnership with the Air Force Office of Scientific Research (AFOSR), the National Science Foundation's (NSF) Emerging Frontiers and Multidisciplinary Activities (EFMA) office of the Directorate for Engineering (ENG) launched an Emerging Frontiers...
Artificial Intelligence Brain
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Navigation Learning Assessment Using EEG-Based Multi-Time Scale Spatiotemporal Compound Model.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society Jan 29, 2024
This study presents a novel method to assess the learning effectiveness using Electroencephalography (EEG)-based deep learning model. It is difficult to assess the learning effectiveness of professional courses in cultivating students' ability object...
Brain Cognition Electroencephalography Machine Learning Algorithms Humans Neural Networks, Computer
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Unsupervised motion artifact correction of turbo spin-echo MRI using deep image prior.

Magnetic resonance in medicine Jan 28, 2024
PURPOSE: In MRI, motion artifacts can significantly degrade image quality. Motion artifact correction methods using deep neural networks usually required extensive training on large datasets, making them time-consuming and resource-intensive. In this...
Image Processing, Computer-Assisted Magnetic Resonance Imaging Artifacts Computer Simulation Motion Algorithms Deep Learning Brain Humans Neural Networks, Computer
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