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 …
Recent publications from the Alzheimer's Disease Neuroimaging Initiative: Reviewing progress toward improved AD clinical trials
MW Weiner, DP Veitch, PS Aisen, LA Beckett… - Alzheimer's & …, 2017 - Elsevier
Abstract Introduction The Alzheimer's Disease Neuroimaging Initiative (ADNI) has continued
development and standardization of methodologies for biomarkers and has provided an …
development and standardization of methodologies for biomarkers and has provided an …
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 …
A novel grading biomarker for the prediction of conversion from mild cognitive impairment to Alzheimer's disease
Objective: Identifying mild cognitive impairment (MCI) subjects who will progress to
Alzheimer's disease (AD) is not only crucial in clinical practice, but also has a significant …
Alzheimer's disease (AD) is not only crucial in clinical practice, but also has a significant …
Iterative sparse and deep learning for accurate diagnosis of Alzheimer's disease
Deep learning techniques have been increasingly applied to the diagnosis of Alzheimer's
disease (AD) and the conversion from mild cognitive impairment (MCI) to AD. Despite their …
disease (AD) and the conversion from mild cognitive impairment (MCI) to AD. Despite their …
Conversion and time-to-conversion predictions of mild cognitive impairment using low-rank affinity pursuit denoising and matrix completion
In this paper, we aim to predict conversion and time-to-conversion of mild cognitive
impairment (MCI) patients using multi-modal neuroimaging data and clinical data, via cross …
impairment (MCI) patients using multi-modal neuroimaging data and clinical data, via cross …
Integrating spatial-anatomical regularization and structure sparsity into SVM: Improving interpretation of Alzheimer's disease classification
In recent years, machine learning approaches have been successfully applied to the field of
neuroimaging for classification and regression tasks. However, many approaches do not …
neuroimaging for classification and regression tasks. However, many approaches do not …
Multi-stage diagnosis of Alzheimer's disease with incomplete multimodal data via multi-task deep learning
Utilization of biomedical data from multiple modalities improves the diagnostic accuracy of
neurodegenerative diseases. However, multi-modality data are often incomplete because …
neurodegenerative diseases. However, multi-modality data are often incomplete because …
A graph Gaussian embedding method for predicting Alzheimer's disease progression with MEG brain networks
Characterizing the subtle changes of functional brain networks associated with the
pathological cascade of Alzheimer's disease (AD) is important for early diagnosis and …
pathological cascade of Alzheimer's disease (AD) is important for early diagnosis and …
Graph-guided joint prediction of class label and clinical scores for the Alzheimer's disease
Accurate diagnosis of Alzheimer's disease and its prodromal stage, ie, mild cognitive
impairment, is very important for early treatment. Over the last decade, various machine …
impairment, is very important for early treatment. Over the last decade, various machine …