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Brain age estimation using multi-feature-based networks.

Xia Liu1, Iman Beheshti2, Weihao Zheng3

  • 1School of Computer Science, Qinghai Normal University, Xining, Qinghai Province, China.

Computers in Biology and Medicine
|February 14, 2022
PubMed
Summary

A new multi-feature-based network (MFN) improves brain age estimation by analyzing structural similarities. This method is more accurate than traditional morphological features for predicting brain age in healthy controls.

Keywords:
Brain ageMulti-feature-based networksSupport vector regressionsMRI

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Area of Science:

  • Neuroscience
  • Radiology
  • Biomedical Engineering

Background:

  • Understanding brain aging is crucial for distinguishing typical from atypical cognitive decline.
  • Traditional morphological features alone have shown limited performance in estimating brain age, potentially overlooking inter-regional structural relationships.

Purpose of the Study:

  • To investigate the efficacy of a novel multi-feature-based network (MFN) for brain age estimation.
  • To test the hypothesis that MFN is more efficient and robust than traditional morphological features in predicting brain age.

Main Methods:

  • Utilized six morphological features (cortical volume, thickness, curvature index, folding index, local gyrification index, surface area) to construct individual MFNs.
  • Employed T1-weighted structural magnetic resonance imaging (sMRI) data from 2501 healthy controls (HCs).
  • Compared the performance of MFN against traditional morphological features for brain age estimation.

Main Results:

  • The MFN achieved a mean absolute error (MAE) of 3.73 years on an independent test set.
  • Traditional morphological features resulted in a higher MAE of 5.30 years.
  • MFN demonstrated superior efficiency and robustness in brain age estimation.

Conclusions:

  • The multi-feature-based network (MFN) is an effective and reliable metric for estimating brain age.
  • MFN captures crucial information regarding structural similarities among cortical regions, outperforming traditional methods.
  • This approach enhances our understanding of brain aging processes.