Thank you for the kind words, Sam! @gabeloewinger.bsky.social deserves most of the credit, both for spotting the need and then doing all the hard work to address it.

We released a package implementing our framework. The methods can be applied to other neural data types too!

code: github.com/gloewing/pho...

Interested in learning more? Gabe Loewinger is giving a talk at SfN Monday 1pm in WCC-201
13/13

FLMM finds effects obscured by standard analyses! For example, FLMM reveals effects that "wash out" when analyzed with AUCs. In published work, Cue Period AUC finds no effects because it averages over time-windows (1) and (2) that have opposing effects. 12/13

FLMM can disentangle components with distinct temporal dynamics. It can also be used to run analogues of standard hypothesis tests (e.g., ANOVAs, correlations) at each trial time-point. Below is an example akin to the FLMM version of a paired t-test. 11/13

Informally, functional random-effects allow one to model variability across animals in the signal "shape." 10/13

Including functional random-effects allow one to model how the dynamics of signal-covariate associations vary across animals. 9/13

FLMM plots can be conceptualized as pooling signal values (dF/F) at a given trial time-point (e.g., 1.7 sec) across animals and trials, correlating it with covariate(s) (e.g., Latency-to-press) and plotting the slope of the correlation. 8/13

FLMM outputs a coefficient estimate plot that shows how the signal–covariate association evolves across trial time-points. 7/13

FLMM exploits autocorrelation to construct *joint* 95% CIs (light grey) that show time windows where effects are statistically significant (any intervals that do not contain 0). All you need to do is visually inspect! 6/13

FLMM combines the benefits of 1) Mixed Models to account for between-animal heterogeneity, and 2) Functional Regression to model covariate effects at each trial-time point. 5/13

Solution: We propose an analysis framework based on Functional Linear Mixed Models (FLMM) that allows one to analyze signal–covariate associations at every trial time point. 4/13

Problem: Photometry is often applied in nested longitudinal experiments with multiple trials per session and sessions per animal. This induces correlation, missing data, etc., that can obscure effects if not accounted for statistically. 3/13

Problem: Common photometry analysis methods reduce detection of effects because, among other things, they average across trials and use summary statistics (e.g., AUC, peak amplitude). 2/13