Dementia

Digital Conversation Companions.

Advances in voice recognition and artificial intelligence (AI) could soon yield digital conversation companions-software that combines conversational AI with health monitoring and promotion. We describe how this technology, once developed and deployed, could transform the way older adults age and receive care. By obviating the need to read or type, the software could be used by individuals with re...

Facial phenotypes in Alzheimer's disease: from neurobiology to artificial intelligence.

Facial analysis is increasingly being explored as a source of scalable behavioral signals relevant to Alzheimer's disease (AD) and AD-related cognitive impairment. In this narrative review, informed by a structured literature search, we summarize current evidence on the biological and behavioral basis of facial alterations in AD, with particular emphasis on affective expressivity, neuropsychiatric...

Deep contrastive learning framework identifies cell-type-specific drug targets in Alzheimer's disease.

INTRODUCTION: Identifying disease-modifying drug targets is crucial for developing effective Alzheimer's disease (AD) treatments. METHODS: We present ...

Advances in 3D cell culture models for alzheimer's disease drug discovery.

INTRODUCTION: Alzheimer's Disease (AD) affects more than 57 million people, yet drug development faces high failure rates due to the limited translati...

Integrated machine learning, molecular docking, and molecular dynamics simulations for in silico identification of GSK3β inhibitors for Alzheimer's disease.

Glycogen Synthase Kinase-3 Beta is a multifunctional serine/threonine kinase, involved in regulating multiple cellular processes. Its dysregulation pl...

Machine learning classification and regional differentiation of neuropathologically-confirmed Alzheimer's disease and comorbid Lewy body disease.

BACKGROUND: Alzheimer's disease (AD) and dementia with Lewy bodies (DLB) co-occur frequently, and growing evidence, including neuropathology, supports...

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 t...

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 wit...

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...

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....