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Related Experiment Video

Updated: Jul 9, 2025

Assessment of Age-related Changes in Cognitive Functions Using EmoCogMeter, a Novel Tablet-computer Based Approach
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BASE: Brain Age Standardized Evaluation.

Lara Dular1, Žiga Špiclin1, 1

  • 1University of Ljubljana, Faculty of Electrical Engineering, Tržaška cesta 25, Ljubljana, 1000, Slovenia.

Neuroimage
|December 8, 2023
PubMed
Summary
This summary is machine-generated.

We developed Brain Age Standardized Evaluation (BASE) for consistent brain age prediction using T1w MRI. BASE offers a standardized dataset and protocol to improve accuracy and reproducibility in deep learning models.

Keywords:
AccuracyBrain ageConsistencyDeep regressionEvaluationReproducibilityRobustnessUK biobank

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Area of Science:

  • Neuroimaging
  • Artificial Intelligence
  • Biomarkers

Background:

  • Brain age, derived from T1-weighted magnetic resonance images (T1w MRI), serves as a key indicator of brain health and neurological conditions.
  • Deep neural networks achieve high accuracy (2-3 year range) in predicting brain age.
  • Current research faces challenges in comparing brain age prediction studies due to variations in datasets and evaluation methods.

Purpose of the Study:

  • To introduce Brain Age Standardized Evaluation (BASE), a novel framework for standardized brain age prediction.
  • To provide a comprehensive dataset and evaluation protocol for assessing brain age models.
  • To enable rigorous comparison and enhance reproducibility in brain age research.

Main Methods:

  • Developed BASE, comprising a standardized multi-site T1w MRI dataset (including unseen, test-retest, and longitudinal data).
  • Established an evaluation protocol with repeated model training and a suite of performance metrics (accuracy, robustness, reproducibility, consistency).
  • Implemented a statistical evaluation framework using linear mixed-effects models for robust performance assessment.

Main Results:

  • BASE was used to comprehensively evaluate four deep learning brain age models.
  • Model performance was assessed across diverse scenarios, including multi-site, test-retest, unseen site, and longitudinal data.
  • The framework facilitates rigorous cross-comparison of model performance.

Conclusions:

  • BASE provides a standardized approach to brain age prediction, addressing current limitations in study comparison.
  • The framework promotes reproducibility and facilitates future research in brain age estimation.
  • Publicly accessible data and code ensure broad application and validation of BASE.