AIMC Topic: Brain Mapping
Clear Filters Showing 121 to 130 of 523 articles
Young children with autism show atypical prefrontal cortical responses to humanoid robots: An fNIRS study.
International journal of psychophysiology : official journal of the International Organization of Psychophysiology
Aug 28, 2022
BACKGROUND: Previous behavioral studies have found that children with autism spectrum disorder (ASD) show greater interest in humanoid robots than in humans. However, the neural mechanism underlying this is not clear. This study compared brain activa...
Predicting social anxiety in young adults with machine learning of resting-state brain functional radiomic features.
Scientific reports
Aug 17, 2022
Social anxiety is a symptom widely prevalent among young adults, and when present in excess, can lead to maladaptive patterns of social behavior. Recent approaches that incorporate brain functional radiomic features and machine learning have shown po...
Uncovering Brain Differences in Preschoolers and Young Adolescents with Autism Spectrum Disorder Using Deep Learning.
International journal of neural systems
Aug 9, 2022
Identifying brain abnormalities in autism spectrum disorder (ASD) is critical for early diagnosis and intervention. To explore brain differences in ASD and typical development (TD) individuals by detecting structural features using T1-weighted magnet...
Towards in vivo ground truth susceptibility for single-orientation deep learning QSM: A multi-orientation gradient-echo MRI dataset.
NeuroImage
Jul 26, 2022
Recently, deep neural networks have shown great potential for solving dipole inversion of quantitative susceptibility mapping (QSM) with improved results. However, these studies utilized their limited dataset for network training and inference, which...
Deep neural networks constrained by neural mass models improve electrophysiological source imaging of spatiotemporal brain dynamics.
Proceedings of the National Academy of Sciences of the United States of America
Jul 26, 2022
Many efforts have been made to image the spatiotemporal electrical activity of the brain with the purpose of mapping its function and dysfunction as well as aiding the management of brain disorders. Here, we propose a non-conventional deep learning-b...
Deep learning-based quantitative susceptibility mapping (QSM) in the presence of fat using synthetically generated multi-echo phase training data.
Magnetic resonance in medicine
Jun 17, 2022
PURPOSE: To enable a fast and automatic deep learning-based QSM reconstruction of tissues with diverse chemical shifts, relevant to most regions outside the brain.
Multi-Level Functional Connectivity Fusion Classification Framework for Brain Disease Diagnosis.
IEEE journal of biomedical and health informatics
Jun 3, 2022
Brain disease diagnosis is a new hotspot in the cross research of artificial intelligence and neuroscience. Quantitative analysis of functional magnetic resonance imaging (fMRI) data can provide valuable biomarkers that contributes to clinical diagno...
The Curative Effect of Pregabalin in the Treatment of Postherpetic Neuralgia Analyzed by Deep Learning-Based Brain Resting-State Functional Magnetic Resonance Images.
Contrast media & molecular imaging
May 10, 2022
This work aimed to investigate the brain resting-state functional magnetic resonance imaging (fMRI) technology based on the depth autoencoders algorithm and to evaluate the clinically curative effect of pregabalin in the treatment of postherpetic neu...
Brain network connectivity feature extraction using deep learning for Alzheimer's disease classification.
Neuroscience letters
May 2, 2022
Early diagnosis and therapeutic intervention for Alzheimer's disease (AD) is currently the only viable option for improving clinical outcomes. Combining structural magnetic resonance imaging (sMRI) and resting-state functional magnetic resonance imag...
Age-Related Changes in Functional Connectivity during the Sensorimotor Integration Detected by Artificial Neural Network.
Sensors (Basel, Switzerland)
Mar 25, 2022
Large-scale functional connectivity is an important indicator of the brain's normal functioning. The abnormalities in the connectivity pattern can be used as a diagnostic tool to detect various neurological disorders. The present paper describes the ...
Don't Miss the Future of Medicine
Join thousands of healthcare professionals staying informed about the latest AI breakthroughs in medicine. Get curated insights delivered to your inbox.
Subscribe NowPopular Topics
- Machine Learning 32021
- Neural Networks, Computer 30231
- Algorithms 27405
- Deep Learning 26912
- Artificial Intelligence 24844
- Robotics 13389
- Image Processing, Computer-Assisted 9738
- Retrospective Studies 9346
- Magnetic Resonance Imaging 6347
- Reproducibility of Results 5670
- Tomography, X-Ray Computed 4866
- Support Vector Machine 4771
- Computational Biology 4333
- Brain 4129
- Computer Simulation 3935
Recent Journals
- Scientific reports 6800
- Sensors (Basel, Switzerland) 4794
- PloS one 4210
- Neural networks : the official journal of the International Neural Network Society 3026
- Computational intelligence and neuroscience 1931
- Computers in biology and medicine 1916
- Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference 1686
- Studies in health technology and informatics 1378
- IEEE journal of biomedical and health informatics 1172
- Journal of chemical information and modeling 1066
- Nature communications 1047
- Computer methods and programs in biomedicine 883
- Bioinformatics (Oxford, England) 874
- Journal of medical Internet research 864
- Briefings in bioinformatics 853