Machine learning methods for predicting progression from mild cognitive impairment to Alzheimer's disease dementia: a systematic review
S Grueso, R Viejo-Sobera - Alzheimer's research & therapy, 2021 - Springer
Background An increase in lifespan in our society is a double-edged sword that entails a
growing number of patients with neurocognitive disorders, Alzheimer's disease being the …
growing number of patients with neurocognitive disorders, Alzheimer's disease being the …
Neuroimaging advances regarding subjective cognitive decline in preclinical Alzheimer's disease
Subjective cognitive decline (SCD) is regarded as the first clinical manifestation in the
Alzheimer's disease (AD) continuum. Investigating populations with SCD is important for …
Alzheimer's disease (AD) continuum. Investigating populations with SCD is important for …
Single subject prediction of brain disorders in neuroimaging: Promises and pitfalls
Neuroimaging-based single subject prediction of brain disorders has gained increasing
attention in recent years. Using a variety of neuroimaging modalities such as structural …
attention in recent years. Using a variety of neuroimaging modalities such as structural …
Hybrid high-order functional connectivity networks using resting-state functional MRI for mild cognitive impairment diagnosis
Conventional functional connectivity (FC), referred to as low-order FC, estimates temporal
correlation of the resting-state functional magnetic resonance imaging (rs-fMRI) time series …
correlation of the resting-state functional magnetic resonance imaging (rs-fMRI) time series …
Hierarchical feature representation and multimodal fusion with deep learning for AD/MCI diagnosis
For the last decade, it has been shown that neuroimaging can be a potential tool for the
diagnosis of Alzheimer's Disease (AD) and its prodromal stage, Mild Cognitive Impairment …
diagnosis of Alzheimer's Disease (AD) and its prodromal stage, Mild Cognitive Impairment …
Latent feature representation with stacked auto-encoder for AD/MCI diagnosis
Recently, there have been great interests for computer-aided diagnosis of Alzheimer's
disease (AD) and its prodromal stage, mild cognitive impairment (MCI). Unlike the previous …
disease (AD) and its prodromal stage, mild cognitive impairment (MCI). Unlike the previous …
Scalable high-performance image registration framework by unsupervised deep feature representations learning
Feature selection is a critical step in deformable image registration. In particular, selecting
the most discriminative features that accurately and concisely describe complex …
the most discriminative features that accurately and concisely describe complex …
RNN-based longitudinal analysis for diagnosis of Alzheimer's disease
R Cui, M Liu… - … Medical Imaging and …, 2019 - Elsevier
Alzheimer's disease (AD) is an irreversible neurodegenerative disorder with progressive
impairment of memory and other mental functions. Magnetic resonance images (MRI) have …
impairment of memory and other mental functions. Magnetic resonance images (MRI) have …
State-space model with deep learning for functional dynamics estimation in resting-state fMRI
Studies on resting-state functional Magnetic Resonance Imaging (rs-fMRI) have shown that
different brain regions still actively interact with each other while a subject is at rest, and …
different brain regions still actively interact with each other while a subject is at rest, and …
Alzheimer's disease: connecting findings from graph theoretical studies of brain networks
The interrelationships between pathological processes and emerging clinical phenotypes in
Alzheimer's disease (AD) are important yet complicated to study, because the brain is a …
Alzheimer's disease (AD) are important yet complicated to study, because the brain is a …