  Imaging neural activity in a behaving animal presents unique challenges in
part because motion from an animal's movement creates artifacts in fluorescence
intensity time-series that are difficult to distinguish from neural signals of
interest. One approach to mitigating these artifacts is to image two channels;
one that captures an activity-dependent fluorophore, such as GCaMP, and another
that captures an activity-independent fluorophore such as RFP. Because the
activity-independent channel contains the same motion artifacts as the
activity-dependent channel, but no neural signals, the two together can be used
to remove the artifacts. Existing approaches for this correction, such as
taking the ratio of the two channels, do not account for channel independent
noise in the measured fluorescence. Moreover, no systematic comparison has been
made of existing approaches that use two-channel signals. Here, we present
Two-channel Motion Artifact Correction (TMAC), a method which seeks to remove
artifacts by specifying a generative model of the fluorescence of the two
channels as a function of motion artifact, neural activity, and noise. We
further present a novel method for evaluating ground-truth performance of
motion correction algorithms by comparing the decodability of behavior from two
types of neural recordings; a recording that had both an activity-dependent
fluorophore (GCaMP and RFP) and a recording where both fluorophores were
activity-independent (GFP and RFP). A successful motion-correction method
should decode behavior from the first type of recording, but not the second. We
use this metric to systematically compare five methods for removing motion
artifacts from fluorescent time traces. We decode locomotion from a GCaMP
expressing animal 15x more accurately on average than from control when using
TMAC inferred activity and outperform all other methods of motion correction
tested.
