Discover Community Create You

Loading publication...

Noodle always counts identifier-free feature events for service operation. With your permission, Google Analytics can also estimate audience and engagement. Search text, publication identifiers, cookies, IP addresses and browser fingerprints are never included in Noodle's first-party event counters.

Privacy details

Article

NeuropsychBrainAge: A biomarker for conversion from mild cognitive impairment to Alzheimer's disease

1 Oct 2023

Abstract excerpt

INTRODUCTION: BrainAge models based on neuroimaging data have diagnostic classification power but have replicability issues due to site and patient variability. BrainAge models trained on neuropsychological tests could help distinguish stable mild cognitive impairment (sMCI) from progressive MCI (pMCI) to Alzheimer's disease (AD). METHODS: A linear regressor BrainAge model was trained on healthy controls using...

Read the complete abstract on PubMed

Topics

Share this publication in a Topic to start or enrich a Post.

Share to Alzheimer's disease research and treatments
Share to Dementia and Cognitive Impairment Research
Share to Functional Brain Connectivity Studies

Methodology Data sources Editorial policy Privacy About Contact Helena Bioinformatics

Source

Identifiers

PMID: 37908437
DOI: 10.1002/dad2.12493

Provenance

PubMed metadata and Literature Corpus relationships. Corpus snapshot updated 7 Sept 2026.

Related research

Semantic proximity does not establish scientific evidence.

Research trail→

Click a neighbor to travelStep 1 · 12 closest

NeuropsychBrainAge: A biomarker for conversion from mild cognitive impairment to Alzheimer's diseasePMID 37908437Read ↗

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

Machine Learning methods for predicting conversion from mild cognitive impairment to Alzheimer’s disease. A systematic review.Semantic proximity Read ↗

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

Machine Learning Predicts Conversion from Normal Aging to Mild Cognitive Impairment Using Medical History, APOE Genotype, and Neuropsychological Assessment.Semantic proximity Read ↗

Predicting conversion to Alzheimer’s Disease in individuals with Mild Cognitive Impairment using clinically transferable featuresSemantic proximity Read ↗

PRedicting the EVolution of SubjectIvE Cognitive Decline to Alzheimer’s Disease With machine learning: the PREVIEW study protocolSemantic proximity Read ↗

Utility of combinations of biomarkers, cognitive markers, and risk factors to predict conversion from mild cognitive impairment to Alzheimer disease in patients in the Alzheimer's disease neuroimaging initiative.Semantic proximity Read ↗

The Effect of the APOE Genotype on Individual BrainAGE in Normal Aging, Mild Cognitive Impairment, and Alzheimer's Disease.Semantic proximity Read ↗

Six month repeat cognitive testing to identify people with MCI at greatest AD dementia riskSemantic proximity Read ↗

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 ↗

Risk Score Stratification of Alzheimer’s Disease and Mild Cognitive Impairment using Deep LearningSemantic proximity Read ↗

⌁Select a neighboring publication to make it the new centre.