Dementia

Cross-Disorder Machine Learning Uncovers Schizophrenia Risk Variants Predictive of Alzheimer’s Disease

Alzheimer’s disease (AD) and Schizophrenia (SCZ) exhibit overlapping clinical features and biological mechanisms, but the extent of their shared genetic etiology and the potential for cross-disorder risk prediction are not fully elucidated, with previous genetic studies yielding mixed results. This study investigated whether integrating statistically-selected SCZ-associated single nucleotide polym...

Targeted Serum Metabolomic Profiling and Machine Learning Approach in Alzheimer’s Disease using the Alzheimer’s Disease Diagnostics Clinical Study (ADDIA) Cohort

Metabolic biomarkers can potentially be used for early diagnosis, prognostic risk stratification and/or early treatment and prevention of individuals at risk to develop Alzheimer’s disease (AD). Our goal is to evaluate changes in metabolite concentration levels associated with AD to identify biomarkers that could support early and accurate diagnosis and therapeutic interventions by using targeted ...

Genotyping TOMM40’523 Poly-T Polymorphisms Using Whole-Genome Sequencing

The TOMM40’523 poly-T repeat polymorphism (rs10524523), located in the TOMM40 gene and in linkage disequilibrium with APOE, has been associated with c...

Silencer variants are key drivers of gene upregulation in Alzheimer’s disease

Alzheimer’s disease (AD), particularly late-onset AD, stands as the most prevalent neurodegenerative disorder globally. Owing to its substantial herit...

Predicting Alzheimer’s Trajectory: A Multi-PRS Machine Learning Approach for Early Diagnosis and Progression Forecasting

Predicting the early onset of dementia due to Alzheimer’s Disease (AD) has major implications for timely clinical management and outcomes. Current dia...

Smartphone-based behavioral profiling for distinguishing Dementia with Lewy bodies from Alzheimer’s Disease

Dementia with Lewy bodies (DLB) is frequently misdiagnosed as Alzheimer’s disease (AD) due to overlapping clinical presentations. In this study, we ev...

Epigenetic signatures of regional tau pathology and cognition in the aging and pathological brain

Primary age-related tauopathy (PART) and Alzheimer’s disease (AD) share hippocampal phospho-tau (p-tau) pathology but differ in ß-amyloid burden and d...

A Task-Optimized Approach for High-Accuracy Alzheimer’s Diagnosis from Handwriting Data

Training complex models on Alzheimer’s Disease (AD) datasets is challenging due to the costly process of extracting features from a wide range of pati...

Leveraging Large Language Models for Identifying Interpretable Linguistic Markers and Enhancing Alzheimer’s Disease Diagnostics

Alzheimer’s Disease (AD) is a progressive irreversible neurodegenerative disorder. Early AD detection is crucial for timely intervention. This study p...

Loneliness, Social Isolation, and Effects on Cognitive Decline in Patients with Dementia: A Retrospective Cohort Study Using Natural Language Processing

The study aimed to compare cognitive trajectories between patients with reports of social isolation and loneliness and those without. Reports of socia...

DeepDrug2: A Germline-focused Graph Neural Network Framework for Alzheimer’s Drug Repurposing Validated by Electronic Health Records

Alzheimer’s disease (AD) is a complex neurodegenerative disorder with limited therapeutic options. The original DeepDrug framework by Li et al. (2025)...

Neuroanatomical-Based Machine Learning Prediction of Alzheimer’s Disease Across Sex and Age

Alzheimer’s Disease (AD) is a progressive neurodegenerative disorder characterized by cognitive decline and memory loss. In 2024, in the US alone, it ...

Interpretable MRI-Based Deep Learning for Alzheimer’s Risk and Progression

Timely intervention for Alzheimer’s disease (AD) requires early detection. The development of immunotherapies targeting amyloid-beta and tau underscor...

ROC Analysis of Biomarker Combinations in Fragile X Syndrome-Specific Clinical Trials: Evaluating Treatment Efficacy via Exploratory Biomarkers

Fragile X Syndrome (FXS) is a rare neurodevelopmental disorder caused by a trinucleotide repeat expansion on the 5’ untranslated region of the FMR1 ge...

Integrative Machine Learning Approach to Risk Prediction for Dementia and Alzheimer’s Disease

Dementia, especially Alzheimer’s disease (AD), is a major global health challenge marked by progressive cognitive impairment, behavioral changes, and ...

Automated Detection of Early-Stage Dementia Using Large Language Models: A Comparative Study on Narrative Speech

The growing global burden of dementia underscores the urgent need for scalable, objective screening tools. While traditional diagnostic methods rely o...

Cross-dataset Evaluation of Dementia Longitudinal Progression Prediction Models

Accurately predicting Alzheimer’s Disease (AD) progression is useful for clinical care. The 2019 TADPOLE (The Alzheimer’s Disease Prediction Of Longit...

Integrating GWAS and Transcriptomic Data Using PrediXcan and Multimodal Deep Learning Reveals Genetic Basis and Drug Repositioning Opportunities for Alzheimer’s Disease

Alzheimer’s disease (AD), the leading cause of dementia, imposes a significant societal and economic burden; however, its complex molecular mechanisms...

Clinically reported covert cerebrovascular disease and risk of neurological disease: a whole-population cohort of 395,273 people using natural language processing

Understanding the relevance of covert cerebrovascular disease (CCD) for later health will allow clinicians to more effectively monitor and target inte...

A systems immunology analysis of Alzheimer’s disease reveals an age- and environmental exposure-independent disturbance in B cell maturation

Alzheimer’s disease is a severe neurodegenerative disorder, with multifactorial mechanisms of disease development and progression. Evidence from genet...