Neural networks : the official journal of the International Neural Network Society
37030279
Learning to navigate a complex environment is not a difficult task for a mammal. For example, finding the correct way to exit a maze following a sequence of cues, does not need a long training session. Just a single or a few runs through a new enviro...
BACKGROUND: Dementia's diagnostic protocols are mostly based on standardised neuroimaging data collected in the Global North from homogeneous samples. In other non-stereotypical samples (participants with diverse admixture, genetics, demographics, MR...
A robust cascaded deep learning framework with integrated hippocampal gray matter (HGM) probability map was developed to improve the hippocampus segmentation (called HGM-cNet) due to its significance in various neuropsychiatric disorders such as Alzh...
The predictive nature of the hippocampus is thought to be useful for memory-guided cognitive behaviors. Inspired by the reinforcement learning literature, this notion has been formalized as a predictive map called the successor representation (SR). T...
OBJECTIVES: Hippocampal characterization is one of the most significant hallmarks of Alzheimer's disease (AD); rather, the single-level feature is insufficient. A comprehensive hippocampal characterization is pivotal for developing a well-performing ...
Hippocampal subregions differ in specialization and vulnerability to cell death. Neuron death and hippocampal atrophy have been a marker for the progression of Alzheimer's disease. Relatively few studies have examined neuronal loss in the human brain...
Experiential richness creates tissue-level changes and synaptic plasticity as patterns emerge from rhythmic spatiotemporal activity of large interconnected neuronal assemblies. Despite numerous experimental and computational approaches at different s...
BACKGROUND: Alzheimer's disease (AD), a major dementia cause, lacks effective treatment. MRI-based hippocampal volume measurement using artificial intelligence offers new insights into early diagnosis and intervention in AD progression.
The purpose of this study was to demonstrate the performance of a fully automated, deep learning-based brain segmentation (DLS) method in healthy controls and in patients with neurodevelopmental disorders, SCN1A mutation, under eleven. The whole, cor...