Alzheimer disease

DS Knopman, H Amieva, RC Petersen… - Nature reviews Disease …, 2021 - nature.com
Alzheimer disease (AD) is biologically defined by the presence of β-amyloid-containing
plaques and tau-containing neurofibrillary tangles. AD is a genetic and sporadic …

Research criteria for the diagnosis of Alzheimer's disease: revising the NINCDS–ADRDA criteria

B Dubois, HH Feldman, C Jacova, ST DeKosky… - The Lancet …, 2007 - thelancet.com
The NINCDS–ADRDA and the DSM-IV-TR criteria for Alzheimer's disease (AD) are the
prevailing diagnostic standards in research; however, they have now fallen behind the …

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 …

Plasma markers predict changes in amyloid, tau, atrophy and cognition in non-demented subjects

JB Pereira, S Janelidze, E Stomrud, S Palmqvist… - Brain, 2021 - academic.oup.com
It is currently unclear whether plasma biomarkers can be used as independent prognostic
tools to predict changes associated with early Alzheimer's disease. In this study, we sought …

Multimodal classification of Alzheimer's disease and mild cognitive impairment

D Zhang, Y Wang, L Zhou, H Yuan, D Shen… - Neuroimage, 2011 - Elsevier
Effective and accurate diagnosis of Alzheimer's disease (AD), as well as its prodromal stage
(ie, mild cognitive impairment (MCI)), has attracted more and more attention recently. So far …

Multi-modal multi-task learning for joint prediction of multiple regression and classification variables in Alzheimer's disease

D Zhang, D Shen… - NeuroImage, 2012 - Elsevier
Many machine learning and pattern classification methods have been applied to the
diagnosis of Alzheimer's disease (AD) and its prodromal stage, ie, mild cognitive impairment …

Integrative network analysis of nineteen brain regions identifies molecular signatures and networks underlying selective regional vulnerability to Alzheimer's disease

M Wang, P Roussos, A McKenzie, X Zhou, Y Kajiwara… - Genome medicine, 2016 - Springer
Background Alzheimer's disease (AD) is the most common form of dementia, characterized
by progressive cognitive impairment and neurodegeneration. However, despite extensive …

Prediction of MCI to AD conversion, via MRI, CSF biomarkers, and pattern classification

C Davatzikos, P Bhatt, LM Shaw, KN Batmanghelich… - Neurobiology of …, 2011 - Elsevier
Magnetic resonance imaging (MRI) patterns were examined together with cerebrospinal
fluid (CSF) biomarkers in serial scans of Alzheimer's Disease Neuroimaging Initiative (ADNI) …

Hippocampal synaptic loss in early Alzheimer's disease and mild cognitive impairment

SW Scheff, DA Price, FA Schmitt, EJ Mufson - Neurobiology of aging, 2006 - Elsevier
One of the major neuropathological findings in the brains of individuals with Alzheimer's
disease (AD) is a loss of synaptic contacts in both the neocortex and hippocampus. Here we …