Global amyloid burden enhances network efficiency of tau propagation in the brain.

Background: Amyloid-β (Aβ) and hyperphosphorylated tau are crucial biomarkers in Alzheimer's disease (AD) pathogenesis, interacting synergistically to accelerate disease progression. While Aβ initiates cascades leading to tau hyperphosphorylation and neurofibrillary tangles, PET imaging studies suggest a sequential progression from amyloidosis to tauopathy, closely linked with neurocognitive symptoms.

Objective: To analyze the complex interactions between Aβ and tau in AD using probabilistic graphical models, assessing how regional tau accumulation is influenced by Aβ burden.

PodGPT: An audio-augmented large language model for research and education.

The proliferation of scientific podcasts has generated an extensive repository of audio content, rich in specialized terminology, diverse topics, and expert dialogues. Here, we introduce a computational framework designed to enhance large language models (LLMs) by leveraging this informational content from publicly accessible podcast data across science, technology, engineering, mathematics and medical (STEMM) disciplines. This dataset, comprising over 3, 700 hours of audio content, was transcribed to generate over 42 million text tokens.

AI-based differential diagnosis of dementia etiologies on multimodal data.

Differential diagnosis of dementia remains a challenge in neurology due to symptom overlap across etiologies, yet it is crucial for formulating early, personalized management strategies. Here, we present an artificial intelligence (AI) model that harnesses a broad array of data, including demographics, individual and family medical history, medication use, neuropsychological assessments, functional evaluations and multimodal neuroimaging, to identify the etiologies contributing to dementia in individuals. The study, drawing on 51,269 participants across 9 independent, geographically diverse datasets, facilitated the identification of 10 distinct dementia etiologies.

AI-based differential diagnosis of dementia etiologies on multimodal data.

Differential diagnosis of dementia remains a challenge in neurology due to symptom overlap across etiologies, yet it is crucial for formulating early, personalized management strategies. Here, we present an AI model that harnesses a broad array of data, including demographics, individual and family medical history, medication use, neuropsychological assessments, functional evaluations, and multimodal neuroimaging, to identify the etiologies contributing to dementia in individuals. The study, drawing on 51,269 participants across 9 independent, geographically diverse datasets, facilitated the identification of 10 distinct dementia etiologies.