Alzheimer's Disease

Advancing fair and explainable machine learning for neuroimaging dementia pattern classification in multi-racial and multi-ethnic populations.

Dementia, a degenerative disease affecting millions globally, is projected to triple by 2050. Early and precise diagnosis is essential for effective treatment and improved quality of life. However, current diagnostic approaches often show inconsistent performance across multi-racial and multi-ethnic groups, raising concerns about fairness and clinical reliability. This study investigates performan...

Novel Alzheimer's disease-associated variants and genetic interactions identified from UK biobank whole-exome sequencing data using IBI-DT.

Alzheimer's disease (AD) is a highly heritable neurodegenerative disorder whose genetic architecture remains incompletely understood, particularly with respect to rare variants and higher-order interactions. We applied Individualized Bayesian Inference-Decision Tree (IBI-DT) to 790,000 whole-exome sequencing variants from 8,292 unrelated White British individuals in the UK Biobank, using genome-wi...

Reinforcement learning-driven adaptive game therapy for cognitive impairment patients with improved vision transformer based detection model.

BACKGROUND: Cognitive impairment is the growing challenge that requires early diagnosis and personalized management of neurodegenerative conditions li...

Expression patterns of risk genes associated with three evolutionarily relevant syndromes in rhesus macaque and human brains.

Depressive disorder (DD), Alzheimer's disease (AD), and schizophrenia (SZ) are evolutionarily relevant traits that disrupt neural networks supporting ...

Aggregating XAI-based explanations to identify spectral-spatial patterns in CNN-based resting-state EEG classification.

Convolutional neural networks (CNNs) achieve high performance in electroencephalographic (EEG) classification tasks; however, their decision-making me...

Machine learning for predicting institutionalization and mortality risks among older home care recipients with routinely collected need assessment data: explainable AI for long-term care.

BACKGROUND: Identifying older home care recipients at risk of institutionalization in advance is crucial for providing preventive services. Supporting...

Design of an artificial intelligence model to screen spontaneous speech to detect Alzheimer's Disease.

There is a shortage of physicians trained in the specialized care of Alzheimer's disease (AD). One possible solution is to use machine learning (ML)/a...

Real-World Generalizability of Alzheimer's Volumetric MRI Machine-Learning Models: External Validation with British Data.

PURPOSE: Assessing generalizability and performance of machine learning models in clinical settings is crucial. In this study, we aimed to test our mo...

Enhancing Cognitive Functions of Older Adults With Software Robot: Longitudinal Exploratory Field Study.

BACKGROUND: The global prevalence of dementia continues to rise and demands scalable, nonpharmacological interventions. Digital cognitive training has...

Metabolomic signatures of brain aging: A multimodal and genetic study.

Accelerated brain aging is increasingly recognized as a transdiagnostic risk factor for neuropsychiatric and neurodegenerative disorders, yet its meta...

6PPD-quinone exposure and Alzheimer's disease: insights from integrative network pharmacology, transcriptomics, machine learning, and molecular docking.

OBJECTIVES: To systematically investigate the molecular associations between 6PPD-quinone (6PPD-Q), an environmental transformation product of the tir...

FEDI-CODE: A federated and causally-informed framework for dementia risk prediction using multi-site patient data.

Early detection of dementia is critical for timely intervention and disease management, yet it remains a challenging task due to the fragmented nature...

Outer nuclear layer thinning as an in vivo biomarker for discriminating probable FTLD-tau from probable FTLD-TDP with PET-supported subtyping.

BACKGROUND AND OBJECTIVES: Outer nuclear layer (ONL) thinning has been identified in frontotemporal lobar degeneration (FTLD); however, its utility fo...

CTE has multiple pathologic variants that might relate to different clinical symptom presentation.

Chronic traumatic encephalopathy (CTE) is a progressive neurodegenerative disease found in individuals with a history of repetitive head injury (RHI) ...

Noninvasive Electrophysiological Biomarkers of Olfactory Responses Across Cognitive States in Alzheimer Dementia: Cross-Sectional Study.

BACKGROUND: Alzheimer disease (AD) is characterized by progressive cognitive decline, with olfactory dysfunction emerging among its earliest symptoms....

Applying ensemble machine learning techniques to MRI scans to predict Alzheimer's disease.

Alzheimer's disease (AD) is a progressive neurodegenerative disorder and the leading cause of dementia globally. Early prediction, prior to the onset ...

Bobcat-optimized hybrid quantum-classical spike-driven network for MRI-based Alzheimer's stage prediction.

Alzheimer's disease (AD) is a progressive neurodegenerative disorder that severely affects memory, cognition, and behavioral functions, making early a...

Alzheimer's disease risk prediction from clinical and social determinants of health: a machine learning cohort study in UK Biobank.

OBJECTIVES: Social determinants of health (SDOH) may improve Alzheimer's disease (AD) risk prediction by capturing upstream contextual risk beyond rou...

Clinical pathways matter for multimodal deep learning in early Alzheimer's disease detection.

Identifying individuals at risk of Alzheimer's disease (AD), particularly in the preclinical and early stages, remains challenging. Although deep lear...

Structure-aware acoustic scene classification: a feature decoupling framework using HPSS and asymmetric convolutions.

Addressing the computational efficiency and cross-device generalization challenges faced by acoustic scene classification in resource-constrained envi...