Alzheimer's Disease

Encoding Discordance in the Alzheimer's Disease A/T/N Framework

INTRODUCTION: The biomarker-based amyloid/ tau/ neurodegeneration (A/T/N) framework has become a popular staging method for Alzheimer's disease (AD) research. Previous studies use the framework either as a rule-based or data-driven approach but typically sacrifice either adaptivity or interpretability. METHODS: We present an interpretable, hybrid method, called Neurosymodal Data Fusion, for predic...

Multi-model Segmentation and Morphometric Quantification of Cerebral Amyloid Angiopathy in Alzheimer's Disease Whole Slide Histopathology Images

Introduction: Cerebral amyloid angiopathy (CAA) is characterized by amyloid-beta deposition in cortical and leptomeningeal vessels and associated with cognitive impairment and hemorrhage. Current neuropathological assessments rely on semiquantitative grading and lack vessel-level resolution and scalability. Existing computational pathology approaches also fail to capture individual vessel morpholo...

When does more data help? Spectral Geometry and Scaling Laws in MRI Transformers

Scaling laws describe how model performance improves as the amount of training data increases, and recent theories such as the zeta law suggest that s...

Death in People with Down syndrome: Mortality statistics and novel predictors in US Medicaid and Medicare enrolled adults.

People with Down syndrome have higher age-specific mortality rates compared to the general population as well as peers with other intellectual and dev...

What Do Persistent Misclassifications Tell Us About Alzheimer's Disease Detection using Structural MRI?

Deep learning classifiers applied to structural MRI (sMRI) have achieved high performance in detecting Alzheimer's Disease (AD), yet systematic invest...

Calibrated Alzheimer's Conversion Risk in Mild Cognitive Impairment: Persistent Homology of Clinical Trajectories with Conformal Guarantees

Background. Predicting conversion from mild cognitive impairment (MCI) to Alzheimer's disease (AD) is central to trial enrichment and care planning, y...

Differentiable Logic Gate Networks for Low-Latency EEG Classification on Edge Devices

Real-time EEG classification on edge devices is bottlenecked by the floating-point arithmetic of conventional neural networks. We investigated Differe...

CORAL: Learning Amyloid Fibril Ligand Docking with Cooperative Binding Rewards

A hallmark of neurodegenerative diseases such as Alzheimer's and Parkinson's is the aberrant aggregation of proteins into amyloid fibrils, and small m...

Sex Differences in the Alzheimer's Brain Age Gap: APOE ε4 Plays a Major Role

INTRODUCTION: Brain age gap (BAG) is the difference between a person's chronological age and the age predicted from the structural appearance of their...

Parameter-efficient Prompt Tuning of Vision Foundation Model With Adaptive Focal Loss for Interpretable MCI Screening

Mild Cognitive Impairment is a critical early stage of cognitive decline that frequently precedes Alzheimer's disease, yet its automated detection fro...

A Multimodal Benchmark for Evaluating Cause-of-Death Inference Using Child Health and Mortality Data

Accurately attributing causes of death is vital for global health, yet fewer than 5% of deaths in resource-constrained regions are medically certified...

The Swiss Integrated Care (INCA) Study: Description of a Novel Prospective Cohort of Patients and Caregivers in Reimbursed Informal Care

Methods INCA is a prospective, single-center cohort study with nationwide recruitment. Participation is open to adult patients and informal caregivers...

The Prognostic Value of Genetic Architectures in Cognitive Decline

Background & Methods: The multifaceted physical nature of heritable cognitive impairment in dementia presents significant challenges for traditional l...

Explainable Longitudinal Machine Learning for Dementia Progression Using Cognitive and MRI Biomarkers

Dementia is a progressive neurological condition characterized by cognitive decline and structural brain changes that evolve. Longitudinal modeling of...

Potent substoichiometric inhibition of alpha-synuclein aggregation by de novo oligomer-binding proteins

The non-amyloid {beta} component (NAC) domain of alpha synuclein (Syn) drives Syn aggregation in Parkinson disease, yet as an intrinsically disordered...

Imputation-free transformer learning enables robust Alzheimer's disease prediction and calibrated uncertainty quantification across heterogeneous clinical cohorts

Accurate diagnostic classification and disease-severity prediction for Alzheimer's disease are hampered by the incompleteness and heterogeneity of rea...

General-Purpose vs. Domain-Specific Large Language Models in Antibiotic Clinical Decision-Making: A Double-Blind Evaluation with a 2X2 Factorial Design

Background: Antimicrobial resistance poses a major threat to global public health. Large language models (LLMs) offer new possibilities for optimizing...

Explainable Machine Learning Models for Alzheimer's Diagnosis Using Routine and Low-Cost Clinical Data

Emerging as a significant global health challenge, Alzheimer's Disease (AD) is a progressive neurodegenerative disorder that causes memory loss and co...

Exploring Attitudes of Primary Caregivers Towards Pediatric Tissue-Based Research using Large Language Models: Insights from Rural and Urban Community Calls and Surveys

Objectives: Explore the perspectives of primary caregivers towards pediatric tissue-based research participation. Design: Cross-sectional. Setting: Tw...

Imputation-free transformer learning enables robust Alzheimer's disease prediction and calibrated uncertainty quantification across heterogeneous clinical cohorts

Accurate diagnostic classification and disease-severity prediction for Alzheimer's disease are hampered by the incompleteness and heterogeneity of rea...