  Optical imaging of the brain has expanded dramatically in the past two
decades. New optics, indicators, and experimental paradigms are now enabling
in-vivo imaging from the synaptic to the cortex-wide scales. To match the
resulting flood of data across scales, computational methods are continuously
being developed to meet the need of extracting biologically relevant
information. In this pursuit, challenges arise in some domains (e.g., SNR and
resolution limits in micron-scale data) that require specialized algorithms.
These algorithms can, for example, make use of state-of-the-art machine
learning to maximally learn the details of a given scale to optimize the
processing pipeline. In contrast, other methods, however, such as graph signal
processing, seek to abstract away from some of the details that are
scale-specific to provide solutions to specific sub-problems common across
scales of neuroimaging. Here we discuss limitations and tradeoffs in
algorithmic design with the goal of identifying how data quality and
variability can hamper algorithm use and dissemination.
