Explainable Brain Age Prediction using coVariance Neural Networks.

Area of Science:

• Computational neuroscience
• Machine learning applications in neuroimaging

Background:

• Brain age prediction from neuroimaging data is gaining traction.
• The brain age gap, a discrepancy between predicted brain age and chronological age, can indicate accelerated aging and increased neurological disease risk.
• Current brain age prediction models lack transparency and methodological justification, hindering clinical adoption.

Approach:

• Leveraging coVariance neural networks (VNNs) for an explanation-driven and anatomically interpretable brain age prediction framework.
• Utilizing cortical thickness features for brain age estimation.
• Extending beyond the standard brain age gap metric in Alzheimer's disease (AD) research.

Key Points:

• VNNs provide anatomical interpretability by identifying specific brain regions contributing to an elevated brain age gap in AD.
• The interpretability of VNNs is dependent on their capacity to utilize specific eigenvectors of the anatomical covariance matrix.
• This framework offers an explainable and anatomically grounded perspective on brain age prediction.

Conclusions:

• The proposed VNN framework enhances transparency and anatomical interpretability in brain age prediction.
• This approach facilitates a deeper understanding of accelerated brain aging and its association with neurological conditions like AD.
• The findings support the potential of explainable AI in clinical decision support for neurodegenerative diseases.