Article

A machine learning-based prediction of tau load and distribution in Alzheimer’s disease using plasma, MRI and clinical variables

2024-05-31

Abstract excerpt

Tau positron emission tomography (PET) is a reliable neuroimaging technique for assessing regional load of tau pathology in the brain, commonly used in Alzheimer’s disease (AD) research and clinical trials. However, its routine clinical use is limited by cost and accessibility barriers. Here we explore using machine learning (ML) models to predict clinically useful tau-PET composites from low-cost and non-invasive...

Topics

Open a Topic to create a Post that cites this publication.

Identifiers and source

Literature Corpus work: 6bbe9308-40d4-5dbe-801d-5620ac8fc519
DOI: 10.1101/2024.05.31.24308264

Open publication

Related research

Semantic proximity does not establish scientific evidence.

Click a neighbor to travelStep 1 · 12 closest

A machine learning-based prediction of tau load and distribution in Alzheimer’s disease using plasma, MRI and clinical variablesDOI 10.1101/2024.05.31.24308264Read

Multimodal Integration of Alzheimer's Plasma Biomarkers, MRI, and Genetic Risk for Individual Prediction of Cerebral Amyloid BurdenSemantic proximity Read

A unified model for staging amyloid and tau pathology in Alzheimer’s diseaseSemantic proximity Read

Generating synthetic tau-PET scans in Alzheimer’s disease from MRI, blood biomarkers and demographics with deep learningSemantic proximity Read

Interpretable MRI-Based Deep Learning for Alzheimer’s Risk and ProgressionSemantic proximity Read

Comprehensive evaluation of AT(N) imaging biomarkers for predicting cognitionSemantic proximity Read

Clinical application of plasma P-tau217 to assess eligibility for amyloid-lowering immunotherapy in memory clinic patients with early Alzheimer’s diseaseSemantic proximity Read

Multimodal Imaging and Logistic Weighted Cognitive Scores for Classification of MCI, AD, and FTD SubtypesSemantic proximity Read

Detection of tau-PET positivity in clinically diagnosed mild cognitive impairment with multidimensional features.Semantic proximity Read

Integrating Machine Learning Pipelines for Multimodal Biomarker Prediction in Alzheimer’s and Parkinson’s Disease: A Component of the Neurodiagnoses FrameworkSemantic proximity Read

Diagnosis and Prognosis Using Machine Learning Trained on Brain Morphometry and White Matter ConnectomesSemantic proximity Read

Promising clinical tools for specific Alzheimer disease diagnosis from plasma pTau217 and ApoE genotype in a cognitive disorder unitSemantic proximity Read

Identification of a simple and novel cut-point based CSF and MRI signature for predicting Alzheimer’s disease progression that reinforces the 2018 NIA-AA research frameworkSemantic proximity Read