Dementia

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 help improve diagnostics and prognostics. We produced a large and updatable blood transcriptomic dataset (n=1021, with n=317 replicates). Technical robustness was assessed using sampling-at-random, batch adjustment and classification metrics. Transcri...

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 from genetic factors, including APOE ε4 genotype, co-pathology including concomitant Alzheimer’s disease pathology and hypoperfusion related to orthostatic hypotension. However, the relative impact of these factors remains unclear. To address this, w...

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...

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...

Development of Alzheimer’s Disease Risk Score for Future Primary Care: A White-Box Approach

Interpretable scoring system can contribute to bridge the gap between the timeliness and complexity of diagnosing Alzheimer’s disease (AD) and promote...

Machine learning-based prediction of future dementia using routine clinical MRI brain scans and healthcare data

Early identification of dementia risk is essential for preventive care and timely enrolment into disease-modifying interventions. Current approaches r...

Predicting Alzheimer’s Disease Diagnosis, a Decade or more Years before Onset using the Electronic Health Record and Random Forest Machine Learning Models

There is need to detect and intervene in pre-clinical phases of Alzheimer’s disease (AD). Electronic health records (EHRs) may help predict AD using m...

Integrating Infection Burden and Multimodal Biomarkers for Early Detection of Alzheimers Disease: A Sheaf-ML Framework

Alzheimers disease (AD) remains a major global health challenge, with growing evidence linking chronic infections, immune aging, and neurodegeneration...

Neuroimaging-derived brain endophenotypes link molecular mechanisms to Alzheimer’s disease and aging

Alzheimer’s disease (AD) genome-wide association studies (GWAS), typically based on clinical phenotypes, have identified numerous risk loci, yet linki...

A Large-Scale Serum Metabolite Panel for Baseline Detection of Alzheimer’s Disease

Blood-based metabolomic signatures offer promising, non-invasive avenues for Alzheimer’s disease (AD) detection. We aimed to identify a serum metaboli...