Alzheimer's Disease

[Magnetic resonance imaging in dementia].

Magnetic resonance imaging (MRI) plays a pivotal role in the diagnostic work-up of dementia. In addition to excluding secondary and potentially treatable causes, MRI enables the identification of characteristic neuroanatomical patterns that support the differentiation between major dementia entities. In daily clinical practice, the diagnostic value of MRI relies primarily on systematic visual asse...

Claustrum and Hippocampus Segmentation-Based Alzheimer's Disease Identification Model Using RoT-Kmeans and CoLU-CNN.

Alzheimer's disease (AD) is a dementia disease that causes loss of cognitive functions. Also, it is a noncurable disease. However, early diagnosis and proper medication reduce AD's progression time. Yet, the prevailing AD diagnosis models did not concentrate on the claustrum in the brain, reducing the efficiency of the AD diagnosis. Thus, this framework proposes an effective AD identification mode...

Salivary metabolomics for early detection of vascular contributions to cognitive impairment and dementia: Exploring microbiome dysbiosis and sex differences.

BackgroundVascular factors contribute to dementia in approximately 20 million individuals, notably in vascular contributions to cognitive impairment a...

Gait and movement analysis for discrimination between people with dementia and healthy control persons based on pose estimation and machine learning.

BackgroundDementia disorders are affecting millions of people globally, characterized by memory loss, communication difficulties, and motor function d...

Robotic Pets in Hospitals: Fostering Connection.

Acute care hospitals often fail to meet the relational needs of older adults with dementia, for whom many environmental stressors can exacerbate behav...

Chronological Diagnostic Algorithm Predicting Neuropathology in Parkinsonism.

OBJECTIVE: Pre-mortem diagnosis of parkinsonism is often challenging due to atypical presentations, overlapping syndromes, and co-pathologies. This st...

Autophagy-exosome crosstalk in neurodegeneration: Mechanisms and therapeutic opportunities.

Neurodegenerative diseases (NDs), including Alzheimer's, Parkinson's, Huntington's, amyotrophic lateral sclerosis, and multiple sclerosis, share a com...

Estimating cognitive function score from mild cognitive impairment to moderate dementia using a hybrid model combining plasma biomarkers with electroencephalogram signal.

BackgroundPredicting cognitive function across dementia stages remains challenging. Plasma biomarkers and electroencephalogram (EEG) features may prov...

Integrated network toxicology and computational profiling of acetyl tributyl citrate-related mechanisms in Alzheimer's disease.

BackgroundAcetyl tributyl citrate (ATBC), an eco-friendly plasticizer, exhibits poorly characterized neurotoxic effects.ObjectiveWe integrated network...

Causal Discovery in Observational Medical Research: Scoping Review.

BACKGROUND: Observational data are fundamental to medical research but present formidable challenges for causal inference. Machine learning-based caus...

Predictive modeling for early diagnosis of dementia using sequential data analysis and data mining.

Dementia, a progressively debilitating neurodegenerative disorder, poses a growing challenge to global healthcare due to its subtle early onset and la...

Machine-learning prediction and risk stratification of 12-month cognitive decline in Alzheimer's disease using routine clinical and MRI data.

Early identification of patients with Alzheimer's disease (AD) who will experience near-term cognitive decline can support trial enrichment and risk-s...

Identifying serum amino acid as biomarkers of gestational diabetes mellitus in second-trimester among Chinese pregnant women: a machine learning-based cross-sectional study.

BACKGROUND: Levels of plasma branched-chain and aromatic amino acids in pregnancy have been associated with gestational diabetes mellitus (GDM), but t...

Improved polygenic risk prediction for alzheimer's disease and related dementias using deep learning: age and APOE-stratified analysis.

Early detection of Alzheimer's disease based on leveraging multimodal features of the clock drawing test.

BackgroundAlthough multi-task handwriting analysis has the potential to improve early detection of Alzheimer's disease (AD), the educational bias inhe...

Detecting multimorbidity patterns in Alzheimer's disease using unsupervised machine learning: A nationwide emergency department study (2007-2022).

BackgroundAlzheimer's disease (AD) patients frequently present to emergency departments (EDs) with complex comorbidities that complicate triage and ma...

Exploratory analysis of the associations of the brain age gap with cognitive function and amyloid-β accumulation: participants selection based on metabolic and physiological blood markers.

The brain age gap (BAG) is defined as the difference between brain age estimated from MRI using artificial intelligence and chronological age, and has...

Amyloid PET quantification with deep learning segmentation models without MRI.

Establishing a relationship between iron-based blood measures and structural brain changes using neural networks in UK Biobank.

BACKGROUND: Disturbance of iron homeostasis in both the brain and blood is linked to cognitive impairment and neurodegenerative diseases. Investigatio...

Dual modal data-driven virtual reality-based mild cognitive Impairment assessment using MCIformer.

BACKGROUND: Mild Cognitive Impairment (MCI) assessment is critical for identifying cognitive decline and enabling early intervention to reduce the ris...