Correcting motion induced fluorescence artifacts in two-channel neural imaging

TL;DR

This paper introduces TMAC, a novel two-channel motion artifact correction method for neural imaging that improves decoding accuracy of neural signals during animal behavior by systematically comparing it with existing approaches.

Contribution

The paper presents TMAC, a new generative model-based approach for correcting motion artifacts in two-channel neural imaging data, and introduces a ground-truth evaluation metric based on behavior decodability.

Findings

TMAC outperforms existing methods in decoding locomotion from neural data.

Decoding accuracy improves 15-fold with TMAC compared to control.

Systematic comparison of five motion correction methods demonstrates TMAC's superior performance.

Abstract

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.