Recent update on the heterogeneity of the Alzheimer's disease spectrum
KA Jellinger - Journal of Neural Transmission, 2022 - Springer
Abstract Alzheimer's disease (AD), the most common form of dementia worldwide, is a mixed
proteinopathy (β-amyloid, tau and other proteins). Classically defined as a …
proteinopathy (β-amyloid, tau and other proteins). Classically defined as a …
Multi-model and multi-slice ensemble learning architecture based on 2D convolutional neural networks for Alzheimer's disease diagnosis
Alzheimer's Disease (AD) is a chronic neurodegenerative disease without effective
medications or supplemental treatments. Thus, predicting AD progression is crucial for …
medications or supplemental treatments. Thus, predicting AD progression is crucial for …
A review of neuroimaging-based data-driven approach for Alzheimer's disease heterogeneity analysis
Alzheimer's disease (AD) is a complex form of dementia and due to its high phenotypic
variability, its diagnosis and monitoring can be quite challenging. Biomarkers play a crucial …
variability, its diagnosis and monitoring can be quite challenging. Biomarkers play a crucial …
An attention-based 3D CNN with multi-scale integration block for Alzheimer's disease classification
Y Wu, Y Zhou, W Zeng, Q Qian… - IEEE Journal of …, 2022 - ieeexplore.ieee.org
Convolutional Neural Networks (CNNs) have recently been introduced to Alzheimer's
Disease (AD) diagnosis. Despite their encouraging prospects, most of the existing models …
Disease (AD) diagnosis. Despite their encouraging prospects, most of the existing models …
[HTML][HTML] A convolutional neural network and graph convolutional network based framework for AD classification
The neuroscience community has developed many convolutional neural networks (CNNs)
for the early detection of Alzheimer's disease (AD). Population graphs are thought of as non …
for the early detection of Alzheimer's disease (AD). Population graphs are thought of as non …
[HTML][HTML] Effects of patchwise sampling strategy to three-dimensional convolutional neural network-based Alzheimer's disease classification
X Shen, L Lin, X Xu, S Wu - Brain Sciences, 2023 - mdpi.com
In recent years, the rapid development of artificial intelligence has promoted the widespread
application of convolutional neural networks (CNNs) in neuroimaging analysis. Although …
application of convolutional neural networks (CNNs) in neuroimaging analysis. Although …
IDA-Net: inheritable deformable attention network of structural MRI for Alzheimer's disease diagnosis
To precisely diagnose neurological diseases, such as Alzheimer's disease, clinicians need
to observe the microstructural changes of local brain atrophy with the help of structural …
to observe the microstructural changes of local brain atrophy with the help of structural …
[HTML][HTML] Exploring Successful Cognitive Aging: Insights Regarding Brain Structure, Function, and Demographics
X Xu, L Lin, S Wu, S Sun - Brain Sciences, 2023 - mdpi.com
In the realm of cognitive science, the phenomenon of “successful cognitive aging” stands as
a hallmark of individuals who exhibit cognitive abilities surpassing those of their age …
a hallmark of individuals who exhibit cognitive abilities surpassing those of their age …
[HTML][HTML] Imaging markers of cerebral amyloid angiopathy and hypertensive arteriopathy differentiate Alzheimer disease subtypes synergistically
TB Chen, WJ Lee, JP Chen, SY Chang, CF Lin… - Alzheimer's Research & …, 2022 - Springer
Background Both cerebral amyloid angiopathy (CAA) and hypertensive arteriopathy (HA)
are related to cognitive impairment and dementia. This study aimed to clarify CAA-and HA …
are related to cognitive impairment and dementia. This study aimed to clarify CAA-and HA …
[PDF][PDF] 成功认知老化的研究综述
林岚， 金悦， 吴水才 - 中国医疗设备, 2022 - cs.china-cmd.org
人口老龄化的快速发展使得成功认知老化在基础科学和公共卫生的研究工作中越来越受关注.
本文对成功认知老化的相关研究进行了综述, 首先概述了成功认知老化的定义与划分 …
本文对成功认知老化的相关研究进行了综述, 首先概述了成功认知老化的定义与划分 …