Alzheimer's Disease

Speech Acoustic Markers Detect APOE-ε4 Carrier Status in Cognitively Healthy Individuals

APOE-ε4, the strongest genetic risk factor for Alzheimer’s disease (AD), is linked to early motor vulnerability, including subtle speech control changes. Because speech integrates fine neuromotor processes, acoustic analysis could offer a sensitive, noninvasive marker of preclinical effects. To determine if speech acoustics distinguish cognitively healthy APOE-ε4 carriers from non-carriers, and to...

Neuro: Machine Learning Optimized to Detect Neurodegenerative Diseases Pilot Study

Alzheimer’s Disease (AD) is a progressive neurodegenerative disorder that primarily affects cognitive function. Early detection is a crucial factor in slowing the disease’s progression, improving quality of life, and retaining cognitive function for years beyond diagnosis. Traditional detection methods have included a mix of mental tests, Magnetic Resonance Imaging (MRI) scans, and Positron Emissi...

Predicting future cognitive impairment in preclinical Alzheimer’s disease using multimodal imaging: a multisite machine learning study

Predicting the likelihood of developing Alzheimer’s disease (AD) dementia in at-risk individuals is important for the design of and optimal recruitmen...

Alzheimer’s Disease Stage Classification via Multimodal CNN on EEG Spectrograms and Cube-Drawing Images

Clinicians currently lack reliable tools to determine, at the point of mild cognitive impairment (MCI), which individuals will progress to Alzheimer’s...

A new ANMerge-based blood transcriptomic resource to support Alzheimer’s disease research

Alzheimer’s disease (AD) has greater prevalence in women and lacks effective treatments. Integrating multimodal data using machine learning (ML) may h...

Quantitative pathology and APOE genotype reveal dementia risk and progression in Lewy body disease

Dementia in Lewy body diseases (LBD) is common and arises through heterogeneous and incompletely understood pathways. Evidence suggests contributions ...

Predicting Conversion from Mild Cognitive Impairment to Alzheimer’s Disease Using a Vision Transformer and Hippocampal MRI Slices

Convolutional neural networks (CNNs) have been the standard for computer vision tasks including applications in Alzheimer’s disease (AD). Recently, Vi...

A unified deep learning framework for cross-platform harmonization of multi-tracer PET quantification

Quantitative PET underpins diagnosis and treatment monitoring in neurodegenerative disease, yet systematic biases between PET-MRI and PET-CT preclude ...

Automating Evaluation of LLM-generated Responses to Patient Questions about Rare Diseases

Patients with rare diseases often struggle to find accurate medical information, and large language model (LLM)-based chatbots may help meet this need...

Agentic Generative Artificial Intelligence System for Classification of Pathology-Confirmed Primary Progressive Aphasia Variants

Accurate clinical and pathological diagnoses are essential in neurodegenerative diseases, especially given the emergence of pathology-specific disease...

Predicting Amyloid Positivity Through Proteomic and Machine Learning Approaches

Alzheimer’s disease is a progressive neurodegenerative disorder where early detection remains difficult. To address this challenge, we analysed a larg...

Regional brain aging patterns reveal disease-specific pathways of neurodegeneration

The heterogeneity of brain aging is a hallmark of neurological and psychiatric disorders, yet machine-learning tools used to characterize this process...

Comparative Mortality Risk of Aripiprazole, Olanzapine, Quetiapine and Risperidone in Alzheimer’s Disease: A Real□World Cohort Study with Treatment Effect Heterogeneity Analysis

Second-generation antipsychotics (SGAs) are frequently used off-label to manage behavioral symptoms in Alzheimer’s disease (AD), despite ongoing conce...

Characterizing Dementia Phenotypes from Unstructured EHR Notes with Generative AI and Interpretable Machine Learning

Dementia encompasses diverse clinical syndromes where diseases of the brain can manifest as impaired cognitive abilities, such as in Alzheimer’s disea...

CSF Proteomics and Machine Learning Reveal Distinct Stages Across the Alzheimer’s Disease Continuum

Alzheimer’s disease (AD) is a neurodegenerative disorder characterized by heterogeneous pathophysiological changes that begin years before symptoms em...

Examining public acceptance of AI versus human-centric dementia care across NHS England’s dementia pathway stages

With dementia diagnoses in the UK projected to exceed one million in 2025, there is an urgent need for scalable and effective care solutions to ease p...

Circulating Metabolites are Linked to Dementia and Brain Imaging Phenotypes, and Mediate Modifiable Risk Pathways

Dementia poses an escalating global health burden, yet its underlying mechanisms remain incompletely understood. In this large-scale, targeted metabol...

Sleep Staging Foundation Models Encode Neural Disorder-Related EEG Representations that Generalize to Wakefulness

To leverage sleep foundation models trained on large datasets of polysomnography for neurological disorder detection during an awake state. Three publ...

Neural networks for inhibitory function and error detection in younger and older adults for early detection of cognitive decline: a comparative study

Cognitive function can decline irreversibly with age, potentially progressing to dementia. Intervention during the preclinical stage is considered eff...

A Novel Method to Disentangle Tightly Linked Risk and Resilience Genes for Brain Disorders: Application to Alzheimer’s Disease

Genetic risk factors for neuropsychiatric disorders are well documented. However, some individuals with high genetic risk remain unaffected, and the m...