Alzheimer's Disease

Home-based eye tracking for early autism screening: a scoping review of approaches, evidence, and implementation challenges.

BACKGROUND: Early identification of autism spectrum disorder (ASD) is essential for improving developmental outcomes but remains challenging due to diagnostic delays, subjectivity, and resource limitations. Eye-tracking offers objective indices of social attention and could improve access to early screening when deployed on consumer devices at home. This scoping review synthesizes evidence on home...

Quantitative sleep EEG identifies CSF core biomarker-related subgroups in Alzheimer's disease.

Early detection and biological characterization of Alzheimer's disease (AD) remain challenging, as current diagnostic approaches rely on invasive cerebrospinal fluid (CSF) sampling or costly neuroimaging, limiting scalability. Sleep quantitative electroencephalography (qEEG) provides a non-invasive measure of brain function and may capture early AD-related neural alterations; however, the high dim...

Cascaded Deep Learning enables multimodal brain PET spatial normalization and quantification for Alzheimer's disease.

Semi-quantitative positron emission tomography (PET) analysis, particularly Centiloid and CenTauRz scaling, is essential for Alzheimer's disease (AD) ...

Machine learning and deep learning for neurological disease analysis: A systematic review across five major disorders.

Artificial Intelligence (AI) has become integral to the research of neurological diseases due to the rapid expansion of neuroimaging, clinical, physio...

Integrated multi-omics and experimental validation reveal CSK and FBLN1 as key targets of stanozolol in accelerating atherosclerosis.

BACKGROUND: Stanozolol, a synthetic anabolic androgenic steroid (AAS) widely abused to enhance performance, has poorly defined toxicological mechanism...

Developing and Testing a Brief Mindfulness Just-in-Time Adaptive Intervention to Reduce Stress Among Caregivers of People With Dementia: Quasi-Experimental Study.

BACKGROUND: Dementia caregiving entails chronic, fluctuating stress with downstream risks to caregivers' mental health and quality of care. Mindfulnes...

Application of Focused Ultrasound in Alzheimer's Disease: A Bibliometric Analysis.

OBJECTIVE: This study uses bibliometric analysis and knowledge mapping methods to systematically explore the emerging research frontiers and developme...

Uncertainty-aware multi-path framework with dynamic arbitration for Alzheimer's disease MRI classification.

As a progressive neurodegenerative disorder, Alzheimer's disease (AD) requires early and accurate diagnosis to delay pathological progression and impr...

Targeting the intrinsically disordered AR-NTD through a machine learning-based enhanced sampling workflow.

Targeting the intrinsically disordered N-terminal domain of the androgen receptor (AR-NTD) represents a promising strategy to overcome resistance in p...

Multimodal neuroimaging-based deep learning framework for pattern analysis and early prediction of neurodegenerative diseases.

Neurodegenerative diseases, such as Mild Cognitive Impairment (MCI) and Alzheimer's, pose significant challenges due to their progressive nature and l...

VAV3 is associated with atherosclerosis and ox-LDL-induced endothelial dysfunctional remodeling.

BACKGROUND: Atherosclerosis is a major cause of ischemic stroke and is characterized by complex immune-metabolic dysregulation. VAV3, a Rho guanine nu...

Results of a new model of videotraining for patients and caregivers for peritoneal dialysis: food for (AI) thought?

BACKGROUND: In an attempt to overcome the space-time limitations of traditional training we used a new telemedicine home-training model (Videotraining...

Profiling cognition and brain metabolism in amyotrophic lateral sclerosis and frontotemporal dementia.

Amyotrophic lateral sclerosis (ALS) and frontotemporal dementia (FTD) are described as a disease continuum, given their shared clinical, genetic and p...

Synthesis of Amyloid Images Using a Generative Adversarial Network from 2-Dimensional 18F-FDG Images and Evaluation for Clinical Use.

The use of amyloid PET to assess patient suitability of disease-modifying drugs for Alzheimer disease is increasing. This study aimed to synthesize am...

Connected-speech digital biomarkers for monitoring transcranial pulse stimulation in Alzheimer's disease: A pilot study.

BackgroundAlzheimer's disease (AD) lacks effective disease-modifying therapies and scalable, ecologically valid biomarkers to monitor treatment respon...

Fluorescence spectroscopy and machine learning methods for detection of Alzheimer's disease from circulating white blood cells.

BackgroundAlzheimer's disease (AD) is the most common cause of dementia whose prevalence is projected to increase significantly in the coming decades....

Speech-based machine learning for detecting neuropsychiatric symptoms in Alzheimer's disease.

BackgroundNeuropsychiatric symptoms (NPS) are common in Alzheimer's disease (AD) and mild cognitive impairment (MCI), yet their detection relies on su...

Machine-Learning-Assisted Triple-Gated Raman Enhancement Platform for Selectively Quantifying Lysophosphatidylcholine (16:0) as a Potential Biomarker for Cognitive Impairments.

Accurate quantification of structurally similar metabolites as biomarkers in biofluids has remained a longstanding challenge. Here, we report a semico...

Integrative Transcriptomic, Network, and Machine Learning Analyses Identify Genistein and Resveratrol-Associated Therapeutic Targets in Alzheimer's Disease.

Alzheimer's disease (AD) is a multifactorial neurodegenerative disorder characterized by complex molecular alterations across multiple brain regions. ...

Evaluating the biological meaning of neural network decisions in EEG-based MCI detection.

OBJECTIVE: High accuracy in medical classification tasks does not ensure that neural networks reason in ways consistent with clinical or neurobiologic...