Brain Age Classification from fMRI Data Using Graph Neural Networks and Evolutionary Algorithm

Hasanzadeh, Nastaran; Saniee Abadeh, Mohammad

doi:10.48308/jicse.2025.239720.1070

Brain Age Classification from fMRI Data Using Graph Neural Networks and Evolutionary Algorithm

Document Type : Original Article

Authors

nastaran hasanzadeh ¹

Mohammad Saniee Abadeh ²

¹ Faculty of Electrical and Computer Engineering, Tarbiat Modares University, Tehran, Iran

² Faculty of Electrical and Computer Engineering Tarbiat Modares University Tehran, Iran

https://doi.org/10.48308/jicse.2025.239720.1070

Abstract

Abstract— The brain is a complex organ that undergoes changes with age, and predicting brain age is crucial for monitoring brain health. It provides valuable insights into brain function and helps in the prevention of neurological diseases. This research predicts brain age through age classification based on fMRI data from the HCP dataset, consisting of individuals aged 22 to 36 years. After training a graph convolutional neural network, the model achieved an accuracy of 0.73 on the test data, demonstrating an improvement over previous studies on the same dataset. An evolutionary approach was then applied to optimize the selection of brain regions using a Genetic Algorithm to identify important and informative regions. This selection and optimization process maintained good predictive accuracy while reducing the number of brain regions. The results indicate that, despite using only half the original number of brain regions (8 regions), the model's accuracy remained at 0.65, showing only a slight decline. This highlights the significance of these regions in brain age classification. Identifying these key regions can contribute to the early diagnosis of brain and neurological diseases, enabling experts to better understand and manage the brain aging process.

Keywords

Keywords— brain-age

GNN

fMRI

Subjects