Dementia

DeepDrug as an expert guided and AI driven drug repurposing methodology for selecting the lead combination of drugs for Alzheimer's disease.

Alzheimer's Disease (AD) significantly aggravates human dignity and quality of life. While newly approved amyloid immunotherapy has been reported, effective AD drugs remain to be identified. Here, we propose a novel AI-driven drug-repurposing method, DeepDrug, to identify a lead combination of approved drugs to treat AD patients. DeepDrug advances drug-repurposing methodology in four aspects. Firs...

The global research of magnetic resonance imaging in Alzheimer's disease: a bibliometric analysis from 2004 to 2023.

BACKGROUND: Alzheimer's disease (AD) is a common neurodegenerative disorder worldwide and the using of magnetic resonance imaging (MRI) in the management of AD is increasing. The present study aims to summarize MRI in AD researches via bibliometric analysis and predict future research hotspots.

Drug repurposing using artificial intelligence, molecular docking, and hybrid approaches: A comprehensive review in general diseases vs Alzheimer's disease.

BACKGROUND: Alzheimer's disease (AD), the most prevalent form of dementia, remains enigmatic in its origins despite the widely accepted "amyloid hypot...

DML-MFCM: A multimodal fine-grained classification model based on deep metric learning for Alzheimer's disease diagnosis.

BACKGROUND: Alzheimer's disease (AD) is a neurodegenerative disorder. There are no drugs and methods for the treatment of AD, but early intervention c...

Predicting cognitive decline from neuropsychiatric symptoms and Alzheimer's disease biomarkers: A machine learning approach to a population-based data.

BACKGROUND: The aim of this study was to examine the potential added value of including neuropsychiatric symptoms (NPS) in machine learning (ML) model...

Unveiling neural activity changes in mild cognitive impairment using microstate analysis and machine learning.

BACKGROUND: Mild cognitive impairment (MCI) is recognized as a condition that may increase the risk of developing Alzheimer's disease (AD). Understand...

Designing a Multimodal and Culturally Relevant Alzheimer Disease and Related Dementia Generative Artificial Intelligence Tool for Black American Informal Caregivers: Cognitive Walk-Through Usability Study.

BACKGROUND: Many members of Black American communities, faced with the high prevalence of Alzheimer disease and related dementias (ADRD) within their ...

Uncovering the epigenetic regulatory clues of PRRT1 in Alzheimer's disease: a strategy integrating multi-omics analysis with explainable machine learning.

BACKGROUND: Alzheimer's disease (AD) is a complex neurodegenerative disorder with a largely unexplored epigenetic landscape.

Harmonic Wavelet Neural Network for Discovering Neuropathological Propagation Patterns in Alzheimer's Disease.

Emerging researchindicates that the degenerative biomarkers associated with Alzheimer's disease (AD) exhibit a non-random distribution within the cere...

Attention-Guided 3D CNN With Lesion Feature Selection for Early Alzheimer's Disease Prediction Using Longitudinal sMRI.

Predicting the progression from mild cognitive impairment (MCI) to Alzheimer's disease (AD) is critical for early intervention. Towards this end, vari...

From Genes to Metabolites: HSP90B1's Role in Alzheimer's Disease and Potential for Therapeutic Intervention.

Alzheimer's disease (AD) is a prototypical neurodegenerative disorder, predominantly affecting individuals in the presenile and elderly populations, w...

Alzheimer's disease recognition using graph neural network by leveraging image-text similarity from vision language model.

Alzheimer's disease (AD), a progressive neurodegenerative condition, notably impacts cognitive functions and daily activity. One method of detecting d...

An EEG-based framework for automated discrimination of conversion to Alzheimer's disease in patients with amnestic mild cognitive impairment: an 18-month longitudinal study.

BACKGROUND: As a clinical precursor to Alzheimer's disease (AD), amnestic mild cognitive impairment (aMCI) bears a considerably heightened risk of tra...

Xixin Decoction's novel mechanism for alleviating Alzheimer's disease cognitive dysfunction by modulating amyloid-β transport across the blood-brain barrier to reduce neuroinflammation.

PURPOSE: Xixin Decoction (XXD) is a classical formula that has been used to effectively treat dementia for over 300 years. Modern clinical studies hav...

ReIU: an efficient preliminary framework for Alzheimer patients based on multi-model data.

The rising incidence of Alzheimer's disease (AD) poses significant challenges to traditional diagnostic methods, which primarily rely on neuropsycholo...

Convolutional Neural Networks for the segmentation of hippocampal structures in postmortem MRI scans.

BACKGROUND: The hippocampus plays a crucial role in memory and is one of the first structures affected by Alzheimer's disease. Postmortem MRI offers a...

Selective diagnostics of Amyotrophic Lateral Sclerosis, Alzheimer's and Parkinson's Diseases with machine learning and miRNA.

The diagnosis of neurological diseases can be expensive, invasive, and inaccurate, as it is often difficult to distinguish between different types of ...

Multi-Modal Diagnosis of Alzheimer's Disease Using Interpretable Graph Convolutional Networks.

The interconnection between brain regions in neurological disease encodes vital information for the advancement of biomarkers and diagnostics. Althoug...

Orthogonal Mixed-Effects Modeling for High-Dimensional Longitudinal Data: An Unsupervised Learning Approach.

The linear mixed-effects model is commonly utilized to interpret longitudinal data, characterizing both the global longitudinal trajectory across all ...

Towards the automatic detection of activities of daily living using eye-movement and accelerometer data with neural networks.

Early diagnosis of neurodegenerative diseases, such as Alzheimer's disease, improves treatment and care outcomes for patients. Early signs of cognitiv...