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Robust Brain Age Estimation via Regression Models and MRI-Derived Features

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Advances in Computational Collective Intelligence (ICCCI 2023)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1864))

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Abstract

The determination of biological brain age is a crucial biomarker in the assessment of neurological disorders and understanding of the morphological changes that occur during aging. Various machine learning models have been proposed for estimating brain age through Magnetic Resonance Imaging (MRI) of healthy controls. However, developing a robust brain age estimation (BAE) framework has been challenging due to the selection of appropriate MRI-derived features and the high cost of MRI acquisition. In this study, we present a novel BAE framework using the Open Big Healthy Brain (OpenBHB) dataset, which is a new multi-site and publicly available benchmark dataset that includes region-wise feature metrics derived from T1-weighted (T1-w) brain MRI scans of 3965 healthy controls aged between 6 to 86 years. Our approach integrates three different MRI-derived region-wise features and different regression models, resulting in a highly accurate brain age estimation with a Mean Absolute Error (MAE) of 3.25 years, demonstrating the framework’s robustness. We also analyze our model’s regression-based performance on gender-wise (male and female) healthy test groups. The proposed BAE framework provides a new approach for estimating brain age, which has important implications for the understanding of neurological disorders and age-related brain changes.

M. Patterson and I. U. Khan—Equal contribution.

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Notes

  1. 1.

    The dataset was provided (in part) by Neurospin, CEA, France.

  2. 2.

    http://fcon_1000.projects.nitrc.org/indi/abide/.

  3. 3.

    https://www.nitrc.org/projects/gspdata.

  4. 4.

    http://fcon_1000.projects.nitrc.org/indi/CoRR/html/.

  5. 5.

    http://brain-development.org/ixi-dataset/.

  6. 6.

    https://osf.io/vhtf6/wiki/Localizer/.

  7. 7.

    https://openneuro.org/datasets/ds000221/.

  8. 8.

    https://openneuro.org/datasets/ds002345/.

  9. 9.

    https://openneuro.org/datasets/ds002330/.

  10. 10.

    https://openneuro.org/datasets/ds003974/.

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Author information

Authors and Affiliations

  1. Lahore University of Management Sciences, Lahore, Pakistan

    Mansoor Ahmed, Usama Sardar & Imdad Ullah Khan

  2. Georgia State University, Atlanta, GA, USA

    Sarwan Ali & Murray Patterson

  3. Massey University, Auckland, New Zealand

    Shafiq Alam

Authors
  1. Mansoor Ahmed
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  2. Usama Sardar
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  3. Sarwan Ali
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  4. Shafiq Alam
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  5. Murray Patterson
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  6. Imdad Ullah Khan
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Corresponding authors

Correspondence to Murray Patterson or Imdad Ullah Khan .

Editor information

Editors and Affiliations

  1. Wrocław University of Science and Technology, Wrocław, Poland

    Ngoc Thanh Nguyen

  2. Eötvös Loránd University, Budapest, Hungary

    János Botzheim

  3. Eötvös Loránd University, Budapest, Hungary

    László Gulyás

  4. Universidad Complutense de Madrid, Madrid, Spain

    Manuel Nunez

  5. Vrije Universiteit Amsterdam, Amsterdam, The Netherlands

    Jan Treur

  6. University of Münster, Münster, Germany

    Gottfried Vossen

  7. Wrocław University of Science and Technology, Wrocław, Poland

    Adrianna Kozierkiewicz

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Ahmed, M., Sardar, U., Ali, S., Alam, S., Patterson, M., Khan, I.U. (2023). Robust Brain Age Estimation via Regression Models and MRI-Derived Features. In: Nguyen, N.T., et al. Advances in Computational Collective Intelligence. ICCCI 2023. Communications in Computer and Information Science, vol 1864. Springer, Cham. https://doi.org/10.1007/978-3-031-41774-0_52

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  • DOI: https://doi.org/10.1007/978-3-031-41774-0_52

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-41773-3

  • Online ISBN: 978-3-031-41774-0

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