Alzheimer's disease: key insights from two decades of clinical trial failures

CK Kim, YR Lee, L Ong, M Gold… - Journal of Alzheimer's …, 2022 - content.iospress.com
Given the acknowledged lack of success in Alzheimer's disease (AD) drug development
over the past two decades, the objective of this review was to derive key insights from the …

Biological subtypes of Alzheimer disease: a systematic review and meta-analysis

D Ferreira, A Nordberg, E Westman - Neurology, 2020 - AAN Enterprises
Objective To test the hypothesis that distinct subtypes of Alzheimer disease (AD) exist and
underlie the heterogeneity within AD, we conducted a systematic review and meta-analysis …

A global view of the genetic basis of Alzheimer disease

C Reitz, MA Pericak-Vance, T Foroud… - Nature Reviews …, 2023 - nature.com
The risk of Alzheimer disease (AD) increases with age, family history and informative genetic
variants. Sadly, there is still no cure or means of prevention. As in other complex diseases …

[HTML][HTML] Layer-wise relevance propagation for explaining deep neural network decisions in MRI-based Alzheimer's disease classification

M Böhle, F Eitel, M Weygandt, K Ritter - Frontiers in aging …, 2019 - frontiersin.org
Deep neural networks have led to state-of-the-art results in many medical imaging tasks
including Alzheimer's disease (AD) detection based on structural magnetic resonance …

Associations between vascular risk factors and brain MRI indices in UK Biobank

SR Cox, DM Lyall, SJ Ritchie, ME Bastin… - European heart …, 2019 - academic.oup.com
Aims Several factors are known to increase risk for cerebrovascular disease and dementia,
but there is limited evidence on associations between multiple vascular risk factors (VRFs) …

[HTML][HTML] Predicting Alzheimer's disease progression using deep recurrent neural networks

M Nguyen, T He, L An, DC Alexander, J Feng, BTT Yeo… - NeuroImage, 2020 - Elsevier
Early identification of individuals at risk of develo** Alzheimer's disease (AD) dementia is
important for develo** disease-modifying therapies. In this study, given multimodal AD …

Large‐scale plasma proteomic profiling identifies a high‐performance biomarker panel for Alzheimer's disease screening and staging

Y Jiang, X Zhou, FC Ip, P Chan, Y Chen… - Alzheimer's & …, 2022 - Wiley Online Library
Introduction Blood proteins are emerging as candidate biomarkers for Alzheimer's disease
(AD). We systematically profiled the plasma proteome to identify novel AD blood biomarkers …

Prevalence and risk of progression of preclinical Alzheimer's disease stages: a systematic review and meta-analysis

L Parnetti, E Chipi, N Salvadori, K D'Andrea… - Alzheimer's research & …, 2019 - Springer
Background Alzheimer's disease (AD) pathology begins several years before the clinical
onset. The long preclinical phase is composed of three stages according to the …

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 …

[HTML][HTML] Modeling and prediction of clinical symptom trajectories in Alzheimer's disease using longitudinal data

N Bhagwat, JD Viviano, AN Voineskos… - PLoS computational …, 2018 - journals.plos.org
Computational models predicting symptomatic progression at the individual level can be
highly beneficial for early intervention and treatment planning for Alzheimer's disease (AD) …