Exploring the complexities of representing brain data in a shared latent space for comprehensive analysis. The chapter discusses the process of merging individual brain data into a single space for efficient mapping and model training. It emphasizes the significance of creating a foundation model from vast fMRI datasets to derive generalizable insights and highlights the computational efficiency despite the model's large parameter count.

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