Using the Alzheimer's Disease Neuroimaging Initiative to improve early detection, diagnosis, and treatment of Alzheimer's disease
DP Veitch, MW Weiner, PS Aisen… - Alzheimer's & …, 2022 - Wiley Online Library
Abstract Introduction The Alzheimer's Disease Neuroimaging Initiative (ADNI) has
accumulated 15 years of clinical, neuroimaging, cognitive, biofluid biomarker and genetic …
accumulated 15 years of clinical, neuroimaging, cognitive, biofluid biomarker and genetic …
Neuroimaging modalities in Alzheimer's disease: diagnosis and clinical features
Alzheimer's disease (AD) is a neurodegenerative disease causing progressive cognitive
decline until eventual death. AD affects millions of individuals worldwide in the absence of …
decline until eventual death. AD affects millions of individuals worldwide in the absence of …
Generalizable deep learning model for early Alzheimer's disease detection from structural MRIs
Early diagnosis of Alzheimer's disease plays a pivotal role in patient care and clinical trials.
In this study, we have developed a new approach based on 3D deep convolutional neural …
In this study, we have developed a new approach based on 3D deep convolutional neural …
[HTML][HTML] Gamma frequency sensory stimulation in mild probable Alzheimer's dementia patients: Results of feasibility and pilot studies
Non-invasive Gamma ENtrainment Using Sensory stimulation (GENUS) at 40Hz reduces
Alzheimer's disease (AD) pathology such as amyloid and tau levels, prevents cerebral …
Alzheimer's disease (AD) pathology such as amyloid and tau levels, prevents cerebral …
MADGAN: Unsupervised medical anomaly detection GAN using multiple adjacent brain MRI slice reconstruction
Background Unsupervised learning can discover various unseen abnormalities, relying on
large-scale unannotated medical images of healthy subjects. Towards this, unsupervised …
large-scale unannotated medical images of healthy subjects. Towards this, unsupervised …
[HTML][HTML] Layer-wise relevance propagation for explaining deep neural network decisions in MRI-based Alzheimer's disease classification
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 …
including Alzheimer's disease (AD) detection based on structural magnetic resonance …
Automatic detection of Alzheimer's disease progression: An efficient information fusion approach with heterogeneous ensemble classifiers
Predicting Alzheimer's disease (AD) progression is crucial for improving the management of
this chronic disease. Usually, data from AD patients are multimodal and time series in …
this chronic disease. Usually, data from AD patients are multimodal and time series in …
Two-stage deep learning model for Alzheimer's disease detection and prediction of the mild cognitive impairment time
Alzheimer's disease (AD) is an irreversible neurodegenerative disease characterized by
thinking, behavioral and memory impairments. Early prediction of conversion from mild …
thinking, behavioral and memory impairments. Early prediction of conversion from mild …
Structural magnetic resonance imaging for the early diagnosis of dementia due to Alzheimer's disease in people with mild cognitive impairment
G Lombardi, G Crescioli, E Cavedo… - Cochrane Database …, 2020 - cochranelibrary.com
Background Mild cognitive impairment (MCI) due to Alzheimer's disease is the symptomatic
predementia phase of Alzheimer's disease dementia, characterised by cognitive and …
predementia phase of Alzheimer's disease dementia, characterised by cognitive and …
Microbiota modulation as preventative and therapeutic approach in Alzheimer's disease
The gut microbiota coevolves with its host, and numerous factors like diet, lifestyle, drug
intake and geographical location continuously modify its composition, deeply influencing …
intake and geographical location continuously modify its composition, deeply influencing …