  Functional connectomes derived from functional magnetic resonance imaging
have long been used to understand the functional organization of the brain.
Nevertheless, a connectome is intrinsically linked to the atlas used to create
it. In other words, a connectome generated from one atlas is different in scale
and resolution compared to a connectome generated from another atlas. Being
able to map connectomes and derived results between different atlases without
additional pre-processing is a crucial step in improving interpretation and
generalization between studies that use different atlases. Here, we use optimal
transport, a powerful mathematical technique, to find an optimum mapping
between two atlases. This mapping is then used to transform time series from
one atlas to another in order to reconstruct a connectome. We validate our
approach by comparing transformed connectomes against their "gold-standard"
counterparts (i.e., connectomes generated directly from an atlas) and
demonstrate the utility of transformed connectomes by applying these
connectomes to predictive models based on a different atlas. We show that these
transformed connectomes are significantly similar to their "gold-standard"
counterparts and maintain individual differences in brain-behavior
associations, demonstrating both the validity of our approach and its utility
in downstream analyses. Overall, our approach is a promising avenue to increase
the generalization of connectome-based results across different atlases.
