In medical image analysis, the utilization of biophysical models for signal analysis offers valuable insights into the underlying tissue types and microstructural processes. In diffusion-weighted magnetic resonance imaging (DWI), a major challenge li...
Neural networks : the official journal of the International Neural Network Society
Nov 26, 2024
The application of deep learning techniques to analyze brain functional magnetic resonance imaging (fMRI) data has led to significant advancements in identifying prospective biomarkers associated with various clinical phenotypes and neurological cond...
Translational magnetic resonance imaging of the rodent brain provides invaluable information for preclinical drug development. However, the automated segmentation of such images for quantitative analyses is limited compared to human brain imaging mai...
Along with the computer application technology progress, machine learning, and block-chain techniques have been applied comprehensively in various fields. The application of machine learning, and block-chain techniques into medical image retrieval, c...
Alzheimer's disease (AD), a prevalent neurodegenerative disorder, leads to progressive dementia, which impairs decision-making, problem-solving, and communication. While there is no cure, early detection can facilitate treatments to slow its progress...
INTRODUCTION: Major depressive disorder (MDD) is a common, relapse-prone psychiatric disorder with unknown pathogenesis. Previous studies on resting-state functional magnetic resonance imaging of MDD have mostly focused on the spontaneous activity of...
Signal decomposition techniques utilizing multi-channel spatial features are critical for analyzing, denoising, and classifying electroencephalography (EEG) signals. To facilitate the decomposition of signals recorded with limited channels, this pape...
Empathy is central to individual and societal well-being. Numerous studies have examined how trait of empathy affects prosocial behavior. However, little studies explored the psychological and neural mechanisms by which different dimensions of trait ...
Using single-task deep learning methods to reconstruct Magnetic Resonance Imaging (MRI) data acquired with different imaging sequences is inherently challenging. The trained deep learning model typically lacks generalizability, and the dissimilarity ...
Multimodal neuroimaging data, including magnetic resonance imaging (MRI) and positron emission tomography (PET), provides complementary information about the brain that can aid in Alzheimer's disease (AD) diagnosis. However, most existing deep learni...
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