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GACT: A Two-Stage Age Prediction Model Combining a Global Attention Block
Summary
This study introduces a new deep learning method using raw fMRI data for more accurate brain age estimation. The approach enhances understanding of brain development and neurological disorders.
Area of Science:
- Neuroscience
- Artificial Intelligence
- Medical Imaging
Background:
- Brain age estimation using neuroimaging is vital for understanding brain development and neurological diseases.
- Current deep learning models using functional MRI (fMRI) often rely on spatial maps or connectivity, potentially losing detailed brain information.
Purpose of the Study:
- To develop a novel deep learning approach for brain age prediction using unsegmented fMRI data.
- To better capture spatiotemporal information from fMRI data for improved age estimation.
Main Methods:
- Utilized raw, unsegmented fMRI data as input features.
- Integrated convolutional neural networks (CNNs) and transformer models to extract spatial and temporal features.
- Employed a Multi-Layer Perceptron (MLP) for the final age prediction.
Main Results:
- The proposed model demonstrated outstanding performance in brain age prediction tasks.
- An explainability method identified key brain regions influencing age regression.
Conclusions:
- Directly using unsegmented fMRI data with CNNs and transformers offers a powerful new methodology for brain age estimation.
- The findings provide valuable insights for future neuroimaging research and understanding brain aging.
