Latest AI and machine learning research in alzheimer's disease for healthcare professionals.
Electroencephalography (EEG) is a promising, non-invasive, and cost-effective modality for Alzheimer's disease (AD) detection, but deep learning methods are limited by small and imbalanced clinical datasets. Generative augmentation offers a solution, yet existing approaches rely on inefficient class-specific models or fail to capture complex spatial and temporal brain dynamics. To address this, we...
Agitation fluctuates over short time horizons in people living with dementia, yet continuous physiological information for anticipating next-day risk is limited. We assessed whether contactless under-mattress signals from the preceding night inform next-day agitation risk and whether preserving minute-level temporal structure improves performance over conventional nightly summaries. We analyzed 42...
Individuals with Down syndrome (DS) display developmental delay, intellectual disability, premature brain ageing, and an increased risk of Alzheimer-l...
BACKGROUND: Accumulations of AD and LATE-NC both contribute to changes in hippocampal volume, possibly via distinct and/or overlapping mechanisms. Mic...
Biological heterogeneity in Alzheimer's Disease (AD) poses a critical diagnostic challenge, particularly for traditional linear methods that fail to c...
Neuropsychiatric symptoms (NPS) are increasingly recognized as critical components of the disease progression in Alzheimer's disease (AD), yet their r...
Early identification of cognitive impairment remains challenging in settings where comprehensive cognitive and clinical assessments are not available....
Spoken-language analysis via prompt-based domain-adaptive models is a promising direction for low-resource, non-invasive dementia screening, but such ...
Aging is a progressive decline in biological function that is proposed to be driven by the accumulation of epigenetic noise and the loss of epigenetic...
Background and Objectives: Word-finding difficulty is common in healthy aging and in neurologic disorders, including temporal lobe epilepsy (TLE) and ...
Material replacement is a common interior-design operation: changing the material of a selected surface while preserving its geometry, surroundings, a...
Background Glucagon-like peptide-1 (GLP-1) receptor agonists and sodium-glucose cotransporter-2 (SGLT2) inhibitors are increasingly used for type 2 di...
In Alzheimer's disease (AD), misfolded proteins emerge across the entire brain in structured, yet not rigid, spatiotemporal patterns. Yet, a systemati...
Abstract EEG foundation models (EEG-FMs) are evaluated almost entirely on disease-discrimination accuracy. A clinical biomarker additionally requires ...
Brain-age models derived from diffusion MRI-based structural connectomes may provide imaging biomarkers of accelerated brain aging, but their biologic...
Anti-amyloid therapies and blood-based biomarkers are changing Alzheimer disease workups into a two-stage measurement workflow: screen broadly with ch...
Background and Objectives: Subjective cognitive decline (SCD), self-reported worsening confusion or memory over the past year, is a common early marke...
Explainability methods applied to deep learning models for Alzheimer's disease neuroimaging produce attribution maps that vary substantially across me...
Alzheimer's disease (AD) progresses as a continuous biological process, whereas most existing neuroimaging-based artificial intelligence methods remai...
Low-cost, scalable screening for dementia remains an open problem. Imaging-based diagnosis is costly and hard to deploy widely. Electroencephalography...