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Recent publications from the Alzheimer's Disease Neuroimaging Initiative: Reviewing progress toward improved AD clinical trials

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TLDR
The Alzheimer's Disease Neuroimaging Initiative (ADNI) has continued development and standardization of methodologies for biomarkers and has provided an increased depth and breadth of data available to qualified researchers.
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The article was published on 2017-04-01 and is currently open access. It has received 169 citations till now. The article focuses on the topics: Alzheimer's Disease Neuroimaging Initiative & Biomarker (medicine).

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Citations
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Automated classification of Alzheimer's disease and mild cognitive impairment using a single MRI and deep neural networks

TL;DR: A deep learning algorithm is built and validated predicting the individual diagnosis of Alzheimer's disease and mild cognitive impairment who will convert to AD (c-MCI) based on a single cross-sectional brain structural MRI scan, demonstrating that it is exploitable by not-trained operators and likely to be generalizable to unseen patient data.
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Multimodal and Multiscale Deep Neural Networks for the Early Diagnosis of Alzheimer’s Disease using structural MR and FDG-PET images

TL;DR: This paper proposes a novel deep-learning-based framework to discriminate individuals with AD utilizing a multimodal and multiscale deep neural network and delivers 82.4% accuracy and 94.23% sensitivity in classifying individuals with clinical diagnosis of probable AD.
References
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Hierarchical Interactions Model for Predicting Mild Cognitive Impairment (MCI) to Alzheimer's Disease (AD) Conversion

TL;DR: This paper proposes to fit the prediction models using pairwise biosignature interactions, thus capturing higher-order relationship among biosignatures, and discovers several significant interactions predictive of MCI-to-AD conversion.
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ApoE4 effects on automated diagnostic classifiers for mild cognitive impairment and Alzheimer's disease.

TL;DR: The proposed biomarker trajectory in AD, which postulates that amyloid markers become abnormal early in the disease course while markers of neurodegeneration become abnormal later in the Disease course, is supported and suggests that ApoE4 could be at least partially responsible for some of the observed disease heterogeneity.
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Ontology driven decision support for the diagnosis of mild cognitive impairment

TL;DR: An ontology driven decision support method which is an automated procedure for diagnosing MCI through magnetic resonance imaging (MRI) and construct a rule set using machine learning algorithms to automatically distinguish MCI patients from normal controls.
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Brain structure and function as mediators of the effects of amyloid on memory

TL;DR: Testing whether effects of β-amyloid pathology on episodic memory were mediated by metabolism and gray matter volume in the early stages of Alzheimer disease found changes in brain structure and function appear to be, in part, downstream events from Aβ pathology, ultimately resulting in episodicMemory deficits.
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White matter signal abnormality quality differentiates mild cognitive impairment that converts to Alzheimer's disease from nonconverters.

TL;DR: Data demonstrate changes in white matter tissue properties that occur within WMSA in individuals with MCI that will subsequently obtain a clinical diagnosis of AD within 18 months and suggest that WMSAs are a critical component for this conversion and are acritical component of this clinical syndrome.
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