The proposed framework can be extended to address complex tasks such as population receptive field (pRF) mapping and brain disease diagnosis by incorporating specialized neural network architectures tailored to these tasks. For pRF mapping, the framework can integrate models that focus on spatial receptive fields and their organization in the visual cortex. By training the framework on datasets specifically designed for pRF mapping tasks, it can learn to reconstruct visual stimuli based on the unique neural responses associated with different regions of the visual field. This would involve adapting the GAN structure to capture the spatial relationships between neural activity patterns and corresponding visual inputs.
For brain disease diagnosis, the framework can be enhanced by incorporating additional layers in the neural network that are trained to recognize patterns indicative of specific neurological conditions. By leveraging datasets that include fMRI scans from individuals with known neurological disorders, the framework can learn to identify subtle differences in brain activity patterns that are characteristic of different diseases. This extension would require the integration of expert knowledge in neuroimaging and neurology to guide the training process and ensure accurate diagnosis based on fMRI data.