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] Multimodal deep learning models for early detection of Alzheimer's disease stage
Most current Alzheimer's disease (AD) and mild cognitive disorders (MCI) studies use single
data modality to make predictions such as AD stages. The fusion of multiple data modalities …
data modality to make predictions such as AD stages. The fusion of multiple data modalities …
Braingb: a benchmark for brain network analysis with graph neural networks
Mapping the connectome of the human brain using structural or functional connectivity has
become one of the most pervasive paradigms for neuroimaging analysis. Recently, Graph …
become one of the most pervasive paradigms for neuroimaging analysis. Recently, Graph …
A new switching-delayed-PSO-based optimized SVM algorithm for diagnosis of Alzheimer's disease
In healthcare sector, it is of crucial importance to accurately diagnose Alzheimer's disease
(AD) and its prophase called mild cognitive impairment (MCI) so as to prevent degeneration …
(AD) and its prophase called mild cognitive impairment (MCI) so as to prevent degeneration …
[HTML][HTML] Classification and prediction of brain disorders using functional connectivity: promising but challenging
Brain functional imaging data, especially functional magnetic resonance imaging (fMRI)
data, have been employed to reflect functional integration of the brain. Alteration in brain …
data, have been employed to reflect functional integration of the brain. Alteration in brain …
Multimodal neuroimaging feature learning with multimodal stacked deep polynomial networks for diagnosis of Alzheimer's disease
The accurate diagnosis of Alzheimer's disease (AD) and its early stage, ie, mild cognitive
impairment, is essential for timely treatment and possible delay of AD. Fusion of multimodal …
impairment, is essential for timely treatment and possible delay of AD. Fusion of multimodal …
Interpretable graph neural networks for connectome-based brain disorder analysis
Human brains lie at the core of complex neurobiological systems, where the neurons,
circuits, and subsystems interact in enigmatic ways. Understanding the structural and …
circuits, and subsystems interact in enigmatic ways. Understanding the structural and …
Artificial intelligence in brain MRI analysis of Alzheimer's disease over the past 12 years: A systematic review
Introduction Multiple structural brain changes in Alzheimer's disease (AD) and mild cognitive
impairment (MCI) have been revealed on magnetic resonance imaging (MRI). There is a fast …
impairment (MCI) have been revealed on magnetic resonance imaging (MRI). There is a fast …
Diagnostic power of resting‐state fMRI for detection of network connectivity in Alzheimer's disease and mild cognitive impairment: A systematic review
Resting‐state fMRI (rs‐fMRI) detects functional connectivity (FC) abnormalities that occur in
the brains of patients with Alzheimer's disease (AD) and mild cognitive impairment (MCI). FC …
the brains of patients with Alzheimer's disease (AD) and mild cognitive impairment (MCI). FC …