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A novel patch-based procedure for estimating brain age across adulthood.

Iman Beheshti1, Pierre Gravel1, Olivier Potvin1

  • 1Centre de recherche CERVO, 2601 de la Canardière, Québec, G1J 2G3, Canada.

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|May 16, 2019
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Summary
This summary is machine-generated.

This study introduces a new machine learning method to estimate brain age using brain imaging. The novel patch-based approach accurately predicts cortical age, aiding in understanding brain aging trajectories.

Keywords:
Anatomical MRIBrain ageGradingPatch-based segmentation

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Area of Science:

  • Neuroimaging
  • Computational Neuroscience
  • Gerontology

Background:

  • Brain aging involves structural changes detectable via neuroimaging.
  • Monitoring brain alterations across the lifespan is crucial for understanding neurological health.
  • Identifying deviations from typical aging patterns can inform early detection of cognitive changes.

Purpose of the Study:

  • To develop and validate a novel patch-based grading procedure for estimating cortical brain age.
  • To assess the reliability and robustness of the proposed method in cognitively intact individuals.
  • To provide a tool for understanding brain aging trajectories and potential departures from normal aging.

Main Methods:

  • A machine learning algorithm was employed for a patch-based grading procedure.
  • The method computed cortical age based on image similarity between test and training cortical labels.
  • The framework was trained on 100 cognitively intact individuals (aged 19-61) and tested on 78 others.

Main Results:

  • The patch-based framework achieved a high R-squared value of 0.94.
  • A low mean absolute error of 1.66 years was reported, indicating high accuracy.
  • The method demonstrated reliability and robustness even with a limited training dataset.

Conclusions:

  • The proposed patch-based grading framework is a reliable and robust method for estimating brain age from neuroimaging data.
  • This approach offers a valuable tool for assessing cortical aging in research and clinical settings.
  • The method's accuracy supports its utility in identifying deviations from typical brain aging patterns.