Alzheimer's Disease

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 affect and cognition. While genome-wide association studies have identified risk-related genes for these diseases, how the expression of these genes compare across species remains unclear. In this study, we examined the spatial and temporal expressio...

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 mechanisms remain difficult to interpret. Explainable artificial intelligence (XAI) methods are typically applied to provide insight into individual model decisions, yet such explanations do not reveal the overall structure of the patterns learned by t...

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 ...

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...

RDoC-informed explainable AI as a paradigm for multilevel Alzheimer's disease diagnosis and progression prediction: a systematic review.

Explainable Artificial Intelligence (XAI) is gaining popularity in early diagnosis and monitoring of dementia. Herein, we recommend the incorporation ...

Semantic memory navigation in mild cognitive impairment: Automated markers with neural and biofluid correlates.

Verbal fluency tasks are ubiquitous in mild cognitive impairment (MCI) screenings. Yet, their assessment is traditionally limited to valid response co...

Radiomics-enhanced 18F-AV45 PET/MRI for integrative assessment and centiloid estimation of amyloid-β burden in Alzheimer's disease.

OBJECTIVE: Reliable assessment of cerebral amyloid-β (Aβ) deposition is essential for the diagnosis and management of Alzheimer's disease (AD). This s...

GENIE: A Two-Stage Interpretable Deep Learning Framework for Revealing the High-Order Genetic Interaction Network of Alzheimer's Disease.

Alzheimer's disease (AD) is a neurodegenerative disorder characterized by progressive cognitive impairment and memory loss. The underlying mechanisms ...