Alzheimer's Disease

Hyperbolic embedding of brain networks detects regions disrupted by neurodegeneration in Alzheimer's disease

Graph theoretical methods have proven valuable for investigating alterations in both anatomical and functional brain connectivity networks during Alzheimer's disease (AD). Recent studies suggest that representing brain networks in a suitable geometric space can better capture their connectivity structure. This study introduces a novel approach to characterize brain connectivity changes using low...

A discontinuous Galerkin method for the three-dimensional heterodimer model with application to prion-like proteins' dynamics

Neurocognitive disorders, such as Alzheimer's and Parkinson's, have a wide social impact. These proteinopathies involve misfolded proteins accumulating into neurotoxic aggregates. Mathematical and computational models describing the prion-like dynamics offer an analytical basis to study the diseases' evolution and a computational framework for exploring potential therapies. This work focuses on ...

Quantitative Evaluation of the Saliency Map for Alzheimer's Disease Classifier with Anatomical Segmentation

Saliency maps have been widely used to interpret deep learning classifiers for Alzheimer's disease (AD). However, since AD is heterogeneous and has ...

Graph-based deep learning models in the prediction of early-stage Alzheimers.

Alzheimer's disease is the most common age-related problem and progresses in different stages, from cognitively normal to early mild cognitive impairm...

Brain Age Analysis and Dementia Classification using Convolutional Neural Networks trained on Diffusion MRI: Tests in Indian and North American Cohorts.

Deep learning models based on convolutional neural networks (CNNs) have been used to classify Alzheimer's disease or infer dementia severity from T1-w...

Dual Attention Graph Convolutional Network Fusing Imaging and Genetic Data for Early Alzheimer's Disease Diagnosis.

Alzheimer's Disease (AD) poses a significant global neurodegenerative challenge, underscoring the urgency of early clinical intervention. Our paper pr...

TAU-DI Net: A Multi-Scale Convolutional Network Combining Prob-Sparse Attention for EEG-based Depression Identification.

EEG-based detection of major depression disorder (MDD) plays a pivotal role in the subsequent treatment and recovery. With the rapid development of de...

Novel Alzheimer's Disease Stating Based on Comorbidities-Informed Graph Neural Networks.

Alzheimer's Disease (AD), the most prevalent form of dementia, requires early prediction for timely intervention. Leveraging data from the Alzheimer's...

3D Multi-feature fusion convolutional network for Alzheimer's disease diagnosis.

The cognitive decline caused by Alzheimer's disease (AD) is closely related to the structural changes in the hippocampus captured by structural magnet...

Artificial Intelligence Based Hierarchical Classification of Frontotemporal Dementia.

Frontotemporal dementia (FTD) is a typical kind of presenile dementia with three main subtypes: behavioral-variant FTD (bvFTD), non-fluent variant pri...

Multi-class Prediction of Cognitively Normal / Mild Cognitive Impairment / Alzheimer's Disease Status in Dementia Based on Convolutional Neural Networks with Attention Mechanism.

Alzheimer's disease (AD) is a neurodegenerative disease with insidious onset and progressive development. AD is a health issue that is attracting atte...

Machine Learning Approach for Music Familiarity Classification with Single-Channel EEG.

Recognition of familiar music on brainwaves through machine learning (ML) can be instrumental in innovative therapeutic devices that improve memory an...

Computer vision-inspired contrastive learning for self-supervised anomaly detection in sensor-based remote healthcare monitoring.

Sensor-based remote healthcare monitoring is a promising approach for timely detection of adverse health events such as falls or infections in people ...

A Regression Framework for Predicting Cognitive Decline in Frontotemporal Dementia using Recurrent Neural Networks.

Frontotemporal dementia (FTD) is a progressive neurodegenerative disorder with a diverse range of symptoms, including personality changes, behavioral ...

Cross-Modality Translation with Generative Adversarial Networks to Unveil Alzheimer's Disease Biomarkers.

Generative approaches for cross-modality transformation have recently gained significant attention in neuroimaging. While most previous work has focus...

Dementia Detection by In-Text Pause Encoding.

In dementia, particularly Alzheimer's Disease (AD), communication challenges are evident, especially in vocabulary and pragmatic aspects. Affected ind...

Deep Learning Analysis of Retinal Structures and Risk Factors of Alzheimer's Disease.

The importance of early Alzheimer's Disease screening is becoming more apparent, given the fact that there is no way to revert the patient's status af...

[An ensemble model for assisting early Alzheimer's disease diagnosis based on structural magnetic resonance imaging with dual-time-point fusion].

Alzheimer's Disease (AD) is a progressive neurodegenerative disorder. Due to the subtlety of symptoms in the early stages of AD, rapid and accurate cl...

Pervasive Technology-Enabled Care and Support for People with Dementia: The State of Art and Research Issues

Dementia is a mental illness that people live with all across the world. No one is immune. Nothing can predict its onset. The true story of dementia...

An interpretable generative multimodal neuroimaging-genomics framework for decoding Alzheimer's disease

\textbf{Objective:} Alzheimer's disease (AD) is the most prevalent form of dementia worldwide, encompassing a prodromal stage known as Mild Cognitiv...