A review on neuroimaging-based classification studies and associated feature extraction methods for Alzheimer's disease and its prodromal stages
Neuroimaging has made it possible to measure pathological brain changes associated with
Alzheimer's disease (AD) in vivo. Over the past decade, these measures have been …
Alzheimer's disease (AD) in vivo. Over the past decade, these measures have been …
Single subject prediction of brain disorders in neuroimaging: Promises and pitfalls
Neuroimaging-based single subject prediction of brain disorders has gained increasing
attention in recent years. Using a variety of neuroimaging modalities such as structural …
attention in recent years. Using a variety of neuroimaging modalities such as structural …
[HTML][HTML] Brain MRI analysis for Alzheimer's disease diagnosis using an ensemble system of deep convolutional neural networks
Alzheimer's disease is an incurable, progressive neurological brain disorder. Earlier
detection of Alzheimer's disease can help with proper treatment and prevent brain tissue …
detection of Alzheimer's disease can help with proper treatment and prevent brain tissue …
Hierarchical feature representation and multimodal fusion with deep learning for AD/MCI diagnosis
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 …
diagnosis of Alzheimer's Disease (AD) and its prodromal stage, Mild Cognitive Impairment …
Latent feature representation with stacked auto-encoder for AD/MCI diagnosis
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 …
disease (AD) and its prodromal stage, mild cognitive impairment (MCI). Unlike the previous …
Using support vector machine to identify imaging biomarkers of neurological and psychiatric disease: a critical review
G Orru, W Pettersson-Yeo, AF Marquand… - Neuroscience & …, 2012 - Elsevier
Standard univariate analysis of neuroimaging data has revealed a host of neuroanatomical
and functional differences between healthy individuals and patients suffering a wide range …
and functional differences between healthy individuals and patients suffering a wide range …
[HTML][HTML] Linguistic markers predict onset of Alzheimer's disease
Background The aim of this study is to use classification methods to predict future onset of
Alzheimer's disease in cognitively normal subjects through automated linguistic analysis …
Alzheimer's disease in cognitively normal subjects through automated linguistic analysis …
[HTML][HTML] The Decoding Toolbox (TDT): a versatile software package for multivariate analyses of functional imaging data
The multivariate analysis of brain signals has recently sparked a great amount of interest, yet
accessible and versatile tools to carry out decoding analyses are scarce. Here we introduce …
accessible and versatile tools to carry out decoding analyses are scarce. Here we introduce …
Performance of machine learning algorithms for predicting progression to dementia in memory clinic patients
Importance Machine learning algorithms could be used as the basis for clinical decision-
making aids to enhance clinical practice. Objective To assess the ability of machine learning …
making aids to enhance clinical practice. Objective To assess the ability of machine learning …
[HTML][HTML] Prediction and classification of Alzheimer's disease based on combined features from apolipoprotein-E genotype, cerebrospinal fluid, MR, and FDG-PET …
Alzheimer's disease (AD), including its mild cognitive impairment (MCI) phase that may or
may not progress into the AD, is the most ordinary form of dementia. It is extremely important …
may not progress into the AD, is the most ordinary form of dementia. It is extremely important …