Brain Status Transferring Generative Adversarial Network for Decoding Individualized Atrophy in Alzheimer's Disease

Area of Science:

• Neuroimaging
• Computational Neuroscience
• Artificial Intelligence in Medicine

Background:

• Deep learning is crucial for brain disease diagnosis using computational analysis of brain images.
• Current group-wise analysis methods lack the ability to detect individual pathological changes, hindering personalized medicine.
• Individualized interpretation of disease variance is essential for precision medicine and effective treatment strategies.

Purpose of the Study:

• To propose a novel generative adversarial network (BrainStatTrans-GAN) for generating healthy brain images from diseased ones.
• To enable the decoding of individualized brain atrophy for enhanced disease diagnosis and interpretation.
• To develop a residual-based multi-level fusion network (RMFN) for more accurate disease diagnosis.

Main Methods:

• Developed a BrainStatTrans-GAN comprising a generator, discriminator, and status discriminator for generating healthy counterparts of patient brain images.
• Implemented adversarial learning with a status discriminator to overcome the lack of paired healthy and diseased brain image data.
• Quantified pathological brain changes by computing the residual between generated healthy images and original patient images.
• Utilized a residual-based multi-level fusion network (RMFN) for final disease diagnosis.

Main Results:

• The proposed BrainStatTrans-GAN successfully generates healthy brain images from patient scans, enabling the quantification of individualized brain atrophy.
• The residual-based approach effectively models pathological changes at the individual subject level.
• Experimental results on T1-weighted MRI data from 1,739 subjects across three datasets demonstrate the method's effectiveness.
• The approach facilitates more accurate disease diagnosis and interpretation compared to existing group-wise methods.

Conclusions:

• The BrainStatTrans-GAN method enables individualized brain atrophy modeling, crucial for precision medicine in neurological disorders.
• This approach enhances the diagnostic accuracy and interpretability of brain diseases like Alzheimer's disease.
• The study highlights the potential of generative adversarial networks for personalized computational neuroimaging analysis.