BIFROST: A method for registering diverse imaging datasets

The heterogeneity of brain imaging methods in neuroscience provides rich
data that cannot be captured by a single technique, and our
interpretations benefit from approaches that enable easy comparison both
within and across different data types. For example, comparing brain-wide
neural dynamics across experiments and aligning such data to anatomical
resources, such as gene expression patterns or connectomes, requires
precise alignment to a common set of anatomical coordinates. However, this
is challenging because registering in vivo functional imaging data to ex
vivo reference atlases requires accommodating differences in imaging
modality, microscope specification, and sample preparation. We overcome
these challenges in Drosophila by building an in vivo reference atlas from
multiphoton-imaged brains, called the Functional Drosophila Atlas (FDA).
We then develop a two-step pipeline, BrIdge For Registering Over
Statistical Templates (BIFROST), for transforming neural imaging data into
this common space and for importing ex vivo resources such as connectomes.
Using genetically labeled cell types as ground truth, we demonstrate
registration with a precision of less than 10 microns. Overall, BIFROST
provides a pipeline for registering functional imaging datasets in the
fly, both within and across experiments.