Data source for each figure.

(DOCX)</p

Motion artifact correction for GCaMP and GFP expressing animals.

A) Decoding accuracy when decoding whole-body curvature from GFP recordings with different motion correction methods applied. These animals do not express an activity-dependent fluorophore so all decoding comes from motion artifacts. The mean and median of decoding of each method is listed. B) Decoding accuracy when decoding whole-body curvature from GCaMP expressing animals. The ratio of B to the median for each method in A is the value reported in Fig 2C. C) As in Fig 3C, but each decoding value has been divided by the mean (rather than median) decoding values from each metric in A. (PDF)</p

TMAC inferred neural activity from an immobilized animal.

A) GCaMP and RFP fluorescence from a neuron that TMAC estimates to have a high ratio of activity variance (σ2a) to motion and noise variances (σ2m,r,g), recorded from an immobilized worm. B) GCaMP and RFP fluorescence from a different neuron in that same recording that TMAC estimates to have a high ratio of motion variance (σ2m) to activity and noise variances (σ2a,r,g). Because the worm is immobilized, the motion artifacts are still small even for the highest motion variance neurons. When there is low motion artifact, TMAC estimates the activity is similar to a smoothed version of the green channel. (PDF)</p

Behavior induces motion artifacts in optical recordings.

A) Diagram of a moving C. elegans undergoing motion and deformation during simultaneous measurements of GCaMP and RFP fluorescence. Deformations of the cell can conspire with imperfect segmentation to create motion artifacts. RFP intensity fluctuations reflect motion or noise. GCaMP intensity fluctuations reflect motion, calcium activity, and noise. B) Top, Animal body curvature. Bottom fluorescence of a neuron from a whole-brain recording of a moving worm expressing both GFP and RFP. Both fluorophores are activity-independent, yet we observe large highly correlated fluctuations in the two-channel fluorescence. C) Top, Body curvature. Bottom, fluorescence of a single neuron taken from a whole-brain recording in a moving worm expressing both GCaMP and RFP. The two channels still show correlated fluctuations despite the activity dependence of GCaMP. D) Histogram of the Pearson correlation coefficient squared between the red and the green channel for a dataset of whole-brain recordings from 10 moving GFP, RFP control worms. E) Same as D but in 9 GCaMP, RFP worms.</p

TMAC reduces decodable motion artifacts in experimental data.

A) Top, animal body curvature over time. Middle, GCaMP and RFP fluorescence from a neuron that TMAC estimates to have high signal to noise, recorded from a behaving worm. Bottom, GCaMP and RFP fluorescence from a different neuron in that same recording that TMAC estimates to have large motion artifacts. B) Time trace of animal curvature and predicted behavior, decoded from activity inferred by TMAC in a GCaMP worm. Gray shaded regions were used to train the decoder, white region was held out and used to evaluate decoding performance. C) Ratio of decoding accuracy (ρ2) when decoding GCaMP divided by the median accuracy for a GFP worm across different models (S2 Table). D) Histogram over all neurons of correlation squared between RFP and activity inferred by TMAC from a GFP worm. RFP vs GFP data the same as in Fig 1D and 1E. E) Same as F but in a GCaMP worm.</p

Accuracy of motion correction methods on synthetic data.

Each of the 5 methods for motion correction were tested on the synthetic dataset from Fig 2. The reported value is the distribution of correlation squared between inferred activity and true activity over instantiations of neurons. This synthetic data was generated from TMAC itself so it is unsurprising that it outperforms other methods on this dataset. The linear regression method also performs well because, like TMAC, it assumes an additive interaction between motion and activity. (PDF)</p

TMAC infers activity and hyperparameters from synthetic data.

A) Diagram of the structure of TMAC. The green channel is modeled as the sum of the calcium activity, motion artifact, and independent gaussian noise. The red channel is modeled as the sum of the motion artifact and independent gaussian noise. The motion artifact is shared between the two channels. B) Top: fluorescence from a synthetic green and red channel. Middle and bottom: inferred activity and motion compared with the true activity and motion. C) Correlation squared between estimated activity from TMAC and true activity over many synthetic datasets. D) Violin plot of inferred and true parameter values when fitting TMAC.</p

Models for motion artifact correction.

(DOCX)</p