Dementia

Regional variability in the associations between social and health-related risk factors and memory across Europe.

Interventions targeting social and health-related risk factors are thought to reduce the risk of cognitive decline and dementia in older age. Despite well-known social, economic, and cultural differences across European countries, little is known about how these factors influence associations with memory function in different geographical contexts. This study examined the relationship between five...

Measurement fidelity, not model complexity, governs individual cognitive trajectory prediction in Parkinson's disease.

Whether individual-level cognitive trajectories in Parkinson's disease are predictable remains unresolved. Here, we provide convergent evidence that measurement fidelity, rather than model complexity, governs the prediction ceiling. We evaluated ten machine learning paradigm families across 26 configurations in two independent cohorts-the Parkinson's Progression Markers Initiative (PPMI, N = 1018)...

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 di...

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 cere...

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...

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...

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...

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...

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....

Sign language narrative reveals universal and modality-specific features of cortical timescale hierarchy.

Human languages are unique in their capacity to tell stories. Prior neuroimaging studies show that spoken language narrative comprehension engages a n...

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...

A Cross-Disease Microglial Transcriptional Program Characterizes Neurodegeneration and Highlights SPP1 as a Biomarker.

Microglial cells are key players in maintaining brain homeostasis and responding to pathological conditions. Their multifaceted roles in health and di...