  Combining Functional MRI (fMRI) data across different subjects and datasets
is crucial for many neuroscience tasks. Relying solely on shared anatomy for
brain-to-brain mapping is inadequate. Existing functional transformation
methods thus depend on shared stimuli across subjects and fMRI datasets, which
are often unavailable. In this paper, we propose an approach for computing
functional brain-to-brain transformations without any shared data, a feat not
previously achieved in functional transformations. This presents exciting
research prospects for merging and enriching diverse datasets, even when they
involve distinct stimuli that were collected using different fMRI machines of
varying resolutions (e.g., 3-Tesla and 7-Tesla). Our approach combines
brain-to-brain transformation with image-to-fMRI encoders, thus enabling to
learn functional transformations on stimuli to which subjects were never
exposed. Furthermore, we demonstrate the applicability of our method for
improving image-to-fMRI encoding of subjects scanned on older low-resolution 3T
fMRI datasets, by using a new high-resolution 7T fMRI dataset (scanned on
different subjects and different stimuli).
