Article

Predicting future cognitive impairment in preclinical Alzheimer’s disease using multimodal imaging: a multisite machine learning study

2025-10-17

Abstract excerpt

Predicting the likelihood of developing Alzheimer’s disease (AD) dementia in at-risk individuals is important for the design of and optimal recruitment for clinical trials of disease-modifying therapies. Machine learning (ML) has been shown to excel in this task; however, there remains a lack of models developed specifically for the preclinical AD population, who display early signs of abnormal brain amyloidosis b...

Topics

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

Identifiers and source

Literature Corpus work: 9306aeb0-0e90-55d6-afe1-701d9c3782be
DOI: 10.1101/2025.10.15.25337507

Open publication

Related research

Semantic proximity does not establish scientific evidence.

Click a neighbor to travelStep 1 · 12 closest

Predicting future cognitive impairment in preclinical Alzheimer’s disease using multimodal imaging: a multisite machine learning studyDOI 10.1101/2025.10.15.25337507Read

Standardized Comparison of Clinical, Cognitive, Genetic, Neuroimaging, and Fluid Biomarkers for Predicting 24-Month Progression from Mild Cognitive Impairment to Alzheimer’s DiseaseSemantic proximity Read

A Novel Ensemble-Based Machine Learning Algorithm To Predict The Conversion From Mild Cognitive Impairment To Alzheimer’s Disease Using Socio-demographic Characteristics, Clinical Information And Neuropsychological MeasuresSemantic proximity Read

A Machine Learning Approach to Predict Cognitive Decline in Alzheimer’s Disease Clinical TrialsSemantic proximity Read

Screening for early-stage Alzheimer’s disease using optimized feature sets and machine learningSemantic proximity Read

Predicting Continuous Cognitive Decline: The Generalizability of a Multimodal Machine Learning Approach Including Structural MRI and Non-Brain DataSemantic proximity Read

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

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

Detecting prodromal Alzheimer’s disease with MRI through deep learningSemantic proximity Read

A machine learning approach to screen for preclinical Alzheimer's disease.Semantic proximity Read

Predicting 24-Month MCI-to-Alzheimer’s Conversion Using Routine Clinical Assessments Without Neuroimaging or Genetic TestingSemantic proximity Read

Prediction, prognosis and monitoring of neurodegeneration at biobank-scale via machine learning and imagingSemantic proximity Read

Aging as an active player in Alzheimer’s Disease Classification: Insights from feature selection in BrainAge ModelsSemantic proximity Read