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Gabe Loewinger @gabeloewinger.bsky.social · 26/01/2026

In fact, we propose analyses to probe the causal effect of treatment on expectancy/belief/blinding integrity in the MSM/sequentially randomized design manuscript section (e.g., testing how expectancy changes over time in response to different treatment sequences).

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Gabe Loewinger @gabeloewinger.bsky.social · 26/01/2026

Thanks for your question! Our warning against conditioning on post-treatment belief does *not* extend to cautioning against testing blinding success. Testing for causal effects of experimentally manipulated variables (e.g., treatment) on outcomes like blinding/belief/expectancy is totally valid.

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Gabe Loewinger @gabeloewinger.bsky.social · 09/12/2025

Why This Matters: • For advocates & those skeptical of psychedelics' benefits, this provides a framework to quantify drug effects + expectancy contributions • Brings rigorous analytical tools to complement existing study design solutions • Applicable to other functionally unmasked interventions

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Gabe Loewinger @gabeloewinger.bsky.social · 09/12/2025

• CDEs can be estimated from existing trial data if the right variables are measured • We use modern semiparametric estimation methods that can incorporate flexible machine learning • We propose sequentially randomized designs to probe the durability of effects of the treatment and expectancy

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Gabe Loewinger @gabeloewinger.bsky.social · 09/12/2025

We propose to address functional unmasking by quantifying treatment effects at fixed expectancy levels • We specifically target controlled direct effect (CDE) causal mediation quantities • We propose both experimental and observational causal inference approaches

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Gabe Loewinger @gabeloewinger.bsky.social · 09/12/2025

It is natural to try to statistically adjust for unmasking w/regression or stratifying But post-treatment variables require careful analytical handling: intuitive approaches like stratifying results on perceived treatment can make even beneficial interventions appear harmful due to collider bias!

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Gabe Loewinger @gabeloewinger.bsky.social · 09/12/2025

Key insight: Unmasking isn't about confounding, it's about mediation through expectancy 🧠 Even "successfully masked" studies can yield misleading results if post-treatment expectancy levels differ across arms So how do we address unmasking?

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Gabe Loewinger @gabeloewinger.bsky.social · 09/12/2025

The FDA's MDMA decision exposed a major challenge in psychedelic research: participants know when they got the real drug (hard to miss the "trip"). So how do we interpret results from psychedelic RCTs? Do benefits come from the compound itself or heightened expectancies about symptom improvement?

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Gabe Loewinger @gabeloewinger.bsky.social · 20/03/2025

Thank you! Great question-nesting neurons in subjects does seem natural (esp if you want interpretation of animal population as opposed to neuron population). I've found FLMM is way better for finding subpopulations than e.g. clustering raw traces. I've done methods work on this. Happy to discuss!

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Gabe Loewinger @gabeloewinger.bsky.social · 18/03/2025

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

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Gabe Loewinger @gabeloewinger.bsky.social · 18/03/2025

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 timepoint. Below is an example akin to the FLMM version of a paired t-test. 11/13

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Gabe Loewinger @gabeloewinger.bsky.social · 18/03/2025

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

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Gabe Loewinger @gabeloewinger.bsky.social · 18/03/2025

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

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Gabe Loewinger @gabeloewinger.bsky.social · 18/03/2025

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

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Gabe Loewinger @gabeloewinger.bsky.social · 18/03/2025

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

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Gabe Loewinger @gabeloewinger.bsky.social · 18/03/2025

FLMMs exploit 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

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Gabe Loewinger @gabeloewinger.bsky.social · 18/03/2025

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

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Gabe Loewinger @gabeloewinger.bsky.social · 18/03/2025

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

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Gabe Loewinger @gabeloewinger.bsky.social · 18/03/2025

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

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Gabe Loewinger @gabeloewinger.bsky.social · 18/03/2025

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

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Gabe Loewinger @gabeloewinger.bsky.social · 18/03/2025

Test effects of behavior/events at every trial timepoint in photometry analyses! Paper with Erjia Cui, Dave Lovinger, Francisco Pereira. “A Statistical Framework for Analysis of Trial-Level Temporal Dynamics in Fiber Photometry Experiments.” Python+R packages! elifesciences.org/articles/95802. 1/13

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