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 …

Neuroimaging advances regarding subjective cognitive decline in preclinical Alzheimer's disease

X Wang, W Huang, L Su, Y **ng, F Jessen… - Molecular …, 2020 - Springer
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 …

Single subject prediction of brain disorders in neuroimaging: Promises and pitfalls

MR Arbabshirani, S Plis, J Sui, VD Calhoun - Neuroimage, 2017 - Elsevier
Neuroimaging-based single subject prediction of brain disorders has gained increasing
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

Y Zhang, H Zhang, X Chen, SW Lee, D Shen - Scientific reports, 2017 - nature.com
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 …

Hierarchical feature representation and multimodal fusion with deep learning for AD/MCI diagnosis

HI Suk, SW Lee, D Shen… - NeuroImage, 2014 - Elsevier
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 …

Latent feature representation with stacked auto-encoder for AD/MCI diagnosis

HI Suk, SW Lee, D Shen… - Brain Structure and …, 2015 - Springer
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 …

Scalable high-performance image registration framework by unsupervised deep feature representations learning

G Wu, M Kim, Q Wang, BC Munsell… - IEEE transactions on …, 2015 - ieeexplore.ieee.org
Feature selection is a critical step in deformable image registration. In particular, selecting
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 …

State-space model with deep learning for functional dynamics estimation in resting-state fMRI

HI Suk, CY Wee, SW Lee, D Shen - NeuroImage, 2016 - Elsevier
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 …

Alzheimer's disease: connecting findings from graph theoretical studies of brain networks

BM Tijms, AM Wink, W De Haan, WM van der Flier… - Neurobiology of …, 2013 - Elsevier
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 …