Alzheimer's Disease

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 patient tasks. Developing high-performance AD detection models that rely on a small number of tasks can help reduce dataset acquisition costs and improve the interpretability of the AD detection model. To address this, we propose a two-stage forward-back...

Probabilistic Mapping and Automated Segmentation of Human Brainstem White Matter Bundles

Brainstem white matter bundles are essential conduits for neural signaling involved in modulation of vital functions ranging from homeostasis to human consciousness. Their architecture forms the anatomic basis for brainstem connectomics, subcortical mesoscale circuit models, and deep brain navigation tools. However, their small size and complex morphology compared to cerebral white matter structur...

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...

Evaluating the performance and potential bias of predictive models for detection of transthyretin cardiac amyloidosis

Delays in the diagnosis of transthyretin amyloid cardiomyopathy (ATTR-CM) contribute to the significant morbidity of the condition, especially in the ...

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...

Multi-Orientation Hippocampus-Centered 3D CNN with Attention Mechanism for Alzheimer’s Disease Classification from MRI Scans

Alzheimer’s disease detection faces challenges in capturing hippocampal atrophy across multiple anatomical orientations. This study presents a multi-o...

Cascaded Multimodal Deep Learning in the Differential Diagnosis, Progression Prediction, and Staging of Alzheimer’s and Frontotemporal Dementia

Dementia is a complex condition whose multifaceted nature poses significant challenges in the diagnosis, prognosis, and treatment of patients. Despite...

CharMark: A Markov Approach to Linguistic Biomarkers in Dementia

Dementia, one of the most prevalent neurodegenerative diseases, affects millions worldwide. Understanding linguistic markers of dementia is crucial fo...

A 60-Second Interpretable Voice Model for Early Dementia Screening

Early detection of cognitive impairment in assisted living is hindered by time-intensive tools like MMSE and MoCA. We present a 60-second voice-based ...

Multimodal Integration of Alzheimer’s Plasma Biomarkers, MRI, and Genetic Risk for Individual Prediction of Cerebral Amyloid Burden

Alzheimer’s disease (AD), the most prevalent neurodegenerative disorder, is marked by the accumulation of amyloid-β (Aβ) plaques. Although cerebral Aβ...