# Example fastFMM application to variable trial lengths

    suppressMessages(library(fastFMM))
    suppressMessages(library(dplyr))

## Introduction

Version 1.0+ of `fastFMM` allows users to fit concurrent functional mixed models, which are particularly useful for inference on experiments with variable trial lengths. In Xin et al. (2025), we described an example analysis on a dataset where mice receive rewards at different times for each trial, with data sourced from Machen et al. (2025). In this vignette, we provide a detailed walkthrough for this portion, with the goal of demonstrating how to fit these models and providing more in-depth interpretation and clarification. For a more general introduction to the fast univariate inference method and `fastFMM`, please refer to the main vignette, “Introduction to fastFMM model fitting” (Cui, Loewinger, and Xin 2025).

## Experimental design

For this vignette, we sourced data from “The encoding of interoceptive-based predictions by the paraventricular nucleus of the thalamus D2+ neurons”, which includes photometry signals recorded from mice in a foraging-like task (Machen et al. 2025). Experimenters placed mice in a corridor and provided a food reward when the mice arrived in a specified reward zone at the end of the corridor. Though the original paper included different types of rewards and whether mice were hungry or satiated, we will only focus on comparing the signal associated with receiving a strawberry milkshake reward (SMS) versus a water reward. Observations from these trials are available within `fastFMM` as the dataset `fastFMM::d2pvt`.

    d2pvt <- fastFMM::d2pvt

### Variable trial lengths

For this experiment, we want trials to include both the approaching phase and reward phase of the experiment. However, defining trials based on reward latency introduces variation in the trial lengths. Standard methods of summarizing over the trials (such as AUC, averaging, or summing) can complicate statistical analyses and the interpretation of results. Thus, a non-concurrent model can produce coefficients that can be difficult to interpret, which we described in detail in Xin et al. (2025).

In this vignette, we will demonstrate how variable trial lengths can be modeled as an interaction between a term of interest and an indicator of whether the trial is ongoing. Specifically, we incorporate a functional covariate corresponding to whether the mouse has received the reward in a trial and include an interaction between the reward type and the functional covariate. We then compare the results to a non-concurrent model with latency as a covariate. Within Xin et al. (2025), we also compare this approach to two different non-functional linear mixed models, which we skip here for brevity.

### Data processing

We converted the scalar covariate of `latency` (i.e., the time, in seconds, for a mouse to receive the reward in a given trial) to the functional covariate `rewarded` (also referred to as RZ, or reward zone in later models). We set `rewarded = 0` at trial timepoints earlier than `latency` and `= 1` for timepoints later than `latency`. We also translate and scale trial to the interval `[0, 1]` where 0 is Trial 0 and 1 is the maximum trial number across all mice.

The functional outcome of `photometry` (the measured signal) and the functional covariate of `rewarded` are encoded as \(N \times L\) matrix columns within `d2pvt`, where \(N\) is the total number of observations and \(L\) is the size of the functional domain. It is sometimes convenient to unravel these matrices into separate columns, especially for visualization purposes. `fastFMM::fui` can handle functional data in either form. However, the prefixes of all included variables need to be unique, as the function relies on string matching to detect the proper functional outcome and covariates.

    class(d2pvt$photometry)
    dim(d2pvt$photometry)

    d2pvt <- cbind(
      d2pvt %>% select(-photometry, -rewarded),
      # this unravels the matrices into data frames
      as.data.frame(d2pvt$photometry),
      as.data.frame(d2pvt$rewarded)
    )

For the concurrent model, it is also necessary to remove functional covariates with zero variance. In practice, the cutoff for the column variance may need to be higher than zero based on the other predictors and the total number of observations. Here, we applied a cutoff of 0.01. An example of how `rewarded` is encoded is displayed below.

    # Remove columns of all zeros or all ones
    zero_var <- sapply(
      select(d2pvt, rewarded_1:rewarded_121),
      function(x)
        var(x) <= 0.01
    )

    zero_var_cols <- c(
      paste0("photometry_", which(zero_var)),
      paste0("rewarded_", which(zero_var))
    )

    d2pvt_nonzero <- d2pvt[, !colnames(d2pvt) %in% zero_var_cols]

## Concurrent model with an interaction term

Within this section, let \(i\) index the mouse ID and \(j\) index the trial. Furthermore, assume random effects and noise are Normally distributed. We specified the concurrent model as

\[ Y_{i, j}(s) = \beta_0 (s) + \gamma_{i, 0} (s) + [\beta_1 (s) + \gamma_{i, 1} (s)] \texttt{SMS}_{i, j} + \beta_2 (s) \texttt{RZ}_{i, j} (s) + \beta_3 (s) \texttt{SMS}_{i, j} \texttt{RZ}_{i, j} (s) + \epsilon_{i, j} (s), \]

where we define the covariates

- \(\texttt{RZ}_{i, j} (s)\): whether the mouse has received the reward , with 1 meaning rewarded and 0 meaning not yet rewarded by timepoint \(s\), and
- \(\texttt{SMS}_{i, j}\): the trial outcome, where 1 indicates SMS reward and 0 indicates water reward.

Within the data frame, the covariate \(\texttt{RZ} (s_l)\) corresponds to the column `rewarded_l`, where \(l\) is some integer indexing the functional domain.

In this model, the fixed effects coefficients are interpreted as:

- \(\beta_0(s)\): the mean signal on a water trial at a timepoint \(s\) when the reward has not yet been received, i.e., when \(\texttt{RZ}_{i, j} (s) = \texttt{SMS}_{i, j} = 0\),
- \(\beta_1 (s)\): the mean difference in signal between SMS and water trials at a timepoint \(s\) when the reward has not yet been received,
- \(\beta_2 (s)\): the average difference between approach and reward in water trials at time \(s\), and
- \(\beta_3 (s)\): the difference in differences \(A - B\) between A) the average signal difference between approach and reward on SMS trials, and B) the average difference in signal values between approach and reward on water trials (i.e., \(\beta_2\)), at trial timepoint \(s\),

and the random effects are interpreted as:

- \(\gamma_{i, 0} (s)\): the random intercept for mouse \(i\), or the subject-specific average perturbation of \(\beta_0 (s)\) such that \(\beta_0 (s) + \gamma_{i, 0} (s)\) is the average signal for mouse \(i\) on water trials at time \(s\) when the reward has not yet been received, and
- \(\gamma_{i, 1} (s)\): the random slope for mouse \(i\), or the subject-specific average perturbation of \(\beta_1 (s)\) such that \(\beta_1 (s) + \gamma_{i, 1} (s)\) is the average photometry signal change associated with being in an SMS trial for mouse \(i\) at time \(s\) when the reward has not yet been received.

The concurrent model can be fit as follows:

    concurrent <- fastFMM::fui(
      photometry ~ SMS * rewarded + (SMS | id),
      d2pvt_nonzero,
      concurrent = TRUE,
      silent = TRUE
    )

As previously mentioned, the concurrent model cannot fit timepoints when the functional covariates do not vary across animals or trials (i.e., have zero or near-zero variance), so we fit to the data `d2pvt_nonzero`, a subset of `d2pvt`. In this case, the functional covariate `rewarded` is the same across all mice at the beginning and end of the trial.

## Interpretation and discussion

### Plotting the models

The raw outputs from `fui` reflect the indices of the functional domain. When visualizing the results with `plot_fui`, we can rescale the x-axis to the sampling rate (8 Hz) and shift it to reflect the trial start time (3 seconds after the start of recording). In the concurrent case, we also need to adjust the trial start time to reflect the discarded data points at the beginning of the trial.

    sampling_Hz <- 8

    dummy <- fastFMM::plot_fui(
      concurrent,
      x_rescale = sampling_Hz,
      # 30 / sampling_Hz corresponds to the discarded columns
      align_x = 3 - 30 / sampling_Hz
    )

At first, the concurrent model’s coefficients might be surprising, as it seems that `SMS` has no contribution to the signal change within a trial. However, \(\beta_1 (s)\) (the fixed coefficient corresponding to `SMS`) is the average effect associated with being in an SMS trial while the mouse has not yet received the reward. Thus, because a mouse should not be affected by the reward before receiving it, then \(\beta_1 (s) \approx 0\) for all values of \(s\).

As an aside, the term `SMS * rewarded` implicitly includes the non-interaction terms `SMS + rewarded`. In this case, because the effect of the strawberry milkshake should be zero unless the value of `rewarded` is 1, it would also be valid to recode the formula as `reward + SMS:rewarded`, removing the `SMS` term from the model. However, for visualization and clarity purposes, we do not make this modification.

### Concurrent model coefficients

For convenience, we can restate the model in its expectation form to avoid writing out the noise. Furthermore, we can consider the induced marginal mean of the outcome, i.e., the expression after marginalizing over the random effects such that their expectation is zero. For each mouse \(i\) and trial \(j\), we have the covariate vector \(\mathbf{X}_{i, j} (s) = [\texttt{SMS}_{i, j}, \texttt{RZ}_{i,j} (s)]^{\intercal}\). The expectation of the response given the covariates is:

\[ \mathbb{E} [Y_{i, j}(s) | \mathbf{X}_{i, j} (s) ] = \beta_0 (s) + \beta_1 (s) \texttt{SMS}_{i, j} + \beta_2 (s) \texttt{RZ}_{i, j} (s) + \beta_3 (s) \texttt{SMS}_{i, j} \texttt{RZ}_{i, j} (s), \]

which can be rearranged

\[ \mathbb{E} [Y_{i, j}(s) | \mathbf{X}_{i, j} (s) ] = \beta_0 (s) + \beta_1 (s) \texttt{SMS}_{i, j} + \texttt{RZ}_{i, j} (s) \left[ \beta_2 (s) + \beta_3 (s) \texttt{SMS}_{i, j} \right] . \]

The second form helps illustrate why \(\beta_1 (s)\) should be approximately zero for all values of \(s\). The fixed coefficient \(\beta_1 (s)\) captures the contribution of the type of reward, \(\texttt{SMS}_{i, j}\), only when mouse \(i\) on trial \(j\) has not yet entered the reward zone, i.e., \(\texttt{RZ}_{i, j} (s) = 0\).

The fixed coefficient \(\beta_3 (s)\), or the fixed coefficient corresponding to the interaction term `SMS:RZ`, is more relevant to interpreting the effect associated with the SMS reward, though this is not its precise definition. Defined explicitly, \(\beta_3 (s)\) quantifies the contribution of receiving a SMS reward at \(s\) that cannot already be explained by the total expected signal from receiving a water reward and being in a trial with SMS. In this case, because we do not see an average signal change from being in an SMS trial without receiving the reward, \(\beta_3 (s)\) roughly corresponds to the expected signal change from delivering the SMS reward compared to the water reward at time \(s\). Here, we have that \(\beta_3 (s)\) is significantly greater than zero for trial timepoints between approximately 2 seconds to 7 seconds, suggesting that mice experience greater average signal change when receiving SMS compared to water.

To see this, consider the expected signal for receiving an SMS reward (top) versus receiving a water reward (bottom):

\[ \mathbb{E} [Y_{i, j}(s) \mid \texttt{SMS}_{i, j} (s) = 1, \texttt{RZ}_{i, j} = 1] = \beta_0 (s) + \beta_1 (s) + \beta_2 (s) + \beta_3 (s) , \\ \mathbb{E} [Y_{i, j}(s) \mid \texttt{SMS}_{i, j} (s) = 0, \texttt{RZ}_{i, j} = 1] = \beta_0 (s) + \beta_2 (s). \]

The difference between these two expectations is \(\beta_1 (s) + \beta_3 (s)\), which is approximately \(\beta_3 (s)\) if we consider that \(\beta_1 (s) \approx 0\) across the domain.

### Limitations of the concurrent model

In most cases with variable trial lengths, we anticipate that researchers will find the concurrent approach with an interaction term a useful tool for interpreting the instantaneous relationships between covariates. However, the concurrent model also has its own set of drawbacks.

As mentioned previously, the concurrent model cannot fit time points when the functional covariate has zero variance. Because all mice are approaching the reward at the start of trial and all mice are rewarded by the end of the trial, the concurrent FLMM cannot fit the extreme ends of the trial time frame. Here, this limitation is not relevant because there are likely no meaningful differences in signal values between SMS and water before reward delivery because mice are unaware of the reward they will receive.

Furthermore, within this model, `RZ` or \(\texttt{RZ}_{i, j} (s)\) treats all mice in the reward zone the same. Coefficient estimates later in the trial may be unreliable, as the model fits a coefficient estimate to mice that have recently received a reward and mice that have been waiting in the reward zone for some time.

## Comparing concurrent and non-concurrent models

Although we demonstrate fitting a concurrent interaction model, it is also possible fit a non-concurrent model on this dataset. Rather than convert the scalar covariate of `latency` to the functional covariate `RZ_1, ..., RZ_L`, `latency` itself can be incorporated into the model. Generally, this is an option for any experiment with varying trial lengths. However, we would advise against this approach because the resulting coefficients may be difficult to interpret.
Thus, this section exists to illustrate a shortcoming of a non-concurrent approach, and users looking for a practical demonstration of fitting data may skip it.

### Specification of the non-concurrent model

To model the problem with the trial latency, we can specify the non-concurrent model

\[ Y_{i, j}(s) = \beta_0 (s) + \gamma_{i, 0} (s) + [\beta_1 (s) + \gamma_{i, 1} (s)] \texttt{SMS}_{i, j} + \beta_2 (s) \texttt{Lat}_{i, j} + \beta_3 (s) \texttt{SMS}_{i, j} \texttt{Lat}_{i, j} + \epsilon_{i, j} (s), \]

where \(Y_{i, j} (s)\) is the recorded photometry signal of mouse \(i\), trial \(j\), at trial timepoint \(s\).

We define the scalar covariates

- \(\texttt{SMS}_{i, j}\): the trial outcome, where \(\texttt{SMS}_{i, j} = 1\) for a trial with strawberry milkshake (SMS) and \(\texttt{SMS}_{i, j} = 0\) for water, and
- \(\texttt{Lat}_{i, j}\): the latency, in seconds, until mouse \(i\) in trial \(j\) received the food reward. To aid interpretation, we centered the latency values around the mean latency across all mice and trials. For example, \(\texttt{Lat}_{i, j} < 0\) when mouse \(i\) in trial \(j\) ran the corridor faster than average and \(\texttt{Lat}_{i, j} > 0\) when mouse \(i\) in trial \(j\) ran slower than average.

The fixed effect coefficients are

- \(\beta_0 (s)\): the mean signal at \(s\) on water trials when a mouse responds with an average latency, i.e., when \(\texttt{SMS}_{i, j} = \texttt{Lat}_{i, j} = 0\),
- \(\beta_1 (s)\): the mean difference in signals between SMS and water trials when a mouse responds with an average latency at timepoint \(s\),
- \(\beta_2 (s)\): the change in mean signal associated with a one second increase in latency on water trials at timepoint \(s\), and
- \(\beta_3 (s)\): the difference \(A - B\) between A) the change in mean signal associated with a one second increase in latency on SMS trials at timepoint \(s\) and B) the change in the mean signal associated with a one second increase in latency on water trials at timepoint \(s\) (i.e., \(\beta_2 (s)\)),

and the random effects are

- \(\gamma_{i, 0} (s)\): the random intercept for mouse \(i\), interpreted as the subject-specific perturbation of \(\beta_0 (s)\) such that \(\beta_0 (s) + \gamma_{i, 0} (s)\) is the average signal at point \(s\) for mouse \(i\) on trials with water reward and average latency, and
- \(\gamma_{i, 1} (s)\): the random slope for mouse \(i\), interpreted as the subject-specific perturbation to \(\beta_1 (s)\) such that \(\beta_1 (s) + \gamma_{i, 1} (s)\) is the average signal change associated with SMS reward at time \(s\) on mouse \(i\) with average latency.

We can fit the non-concurrent model as follows:

    nonconcurrent <- fastFMM::fui(
      photometry ~ SMS * latency + (SMS | id),
      d2pvt,
      concurrent = FALSE,
      silent = TRUE
    )

Fitting a non-concurrent model with `fastFMM::fui(..., concurrent = FALSE)` is the same FLMM fitting procedure implemented in the previous `fastFMM` package versions, described further in Cui et al. (2022).

### Non-concurrent coefficients

The coefficient plot is below:

    sampling_Hz <- 8
    dummy <- fastFMM::plot_fui(
      nonconcurrent,
      x_rescale = sampling_Hz,
      align_x = 3
    )

Although the non-concurrent model shows strong trends in the coefficients, they can be hard to interpret. For example, the dip in the estimated coefficients for \(\beta_2(s)\) and \(\beta_3 (s)\) at around 3 seconds does not necessarily mean that mice with a slower-than-average latency will have a lower photometry response when they reach the reward. It could reflect that, in general, rewards increase the photometry signal magnitude and the slow mice have not yet reached the reward zone.

We can better illustrate this by referring back to the non-concurrent model, copied below in expectation form for convenience. Let \(\mathbf{X}_{i, j} = [\texttt{SMS}_{i, j}, \texttt{Lat}_{i, j}]^T\) be the covariate matrix for mouse \(i\) and trial \(j\).

\[ \mathbb{E} [Y_{i, j}(s) \mid \mathbf{X}_{i, j}] = \beta_0 (s) + \beta_1 (s) \texttt{SMS}_{i, j} + \beta_2 (s) \texttt{Lat}_{i, j} + \beta_3 (s) \texttt{SMS}_{i, j} \texttt{Lat}_{i, j}, \]

To explain the shape of \(\beta_2 (s)\) (i.e., the fixed coefficient corresponding to `latency`), let’s contrast the expected signal for average latency (\(\texttt{Lat}_{i, j} = 0\)) versus slower-than-average latency (\(\texttt{Lat}_{i, j} = c > 0\) where \(c\) is some arbitrary positive real value) on a water trial:

\[ \mathbb{E} [Y_{i, j}(s) \mid \texttt{SMS}_{i, j} = 0, \texttt{Lat}_{i, j} = c > 0] = \beta_0 (s) + c \beta_2 (s) \\ \mathbb{E} [Y_{i, j}(s) \mid \texttt{SMS}_{i, j} = 0, \texttt{Lat}_{i, j} = 0] = \beta_0 (s). \]

The curve of \(\beta_0 (s)\) reflects the expected signal for a mouse running an average latency. For some timepoints after the average latency, the first equation (slow mouse) reflects the expected photometry signal of a mouse that has not yet reached the reward while the second equation reflects a mouse that has just received the reward. Both equations contain \(\beta_0 (s)\) (the intercept), but we assume that the slow mouse will have a lower expected signal. Thus, the negative contribution of \(c \beta_2 (3)\) could be interpreted as offsetting the intercept for slow mice and may not be meaningfully related to the reward.

The shape of the coefficient curve for the interaction `SMS:latency` also shows a dip. However, it is not the case that this implies that slow mice exhibit decreased average signal association with the SMS reward. Again, let’s write out the expressions for different latencies in a SMS trial:

\[ \mathbb{E} [Y_{i, j}(s) \mid \texttt{SMS}_{i, j} = 1, \texttt{Lat}_{i, j} = c > 0] = \beta_0 (s) + \beta_1 (s) + c \beta_2 (s) + c \beta_3 (s) \\ \mathbb{E} [Y_{i, j}(s) \mid \texttt{SMS}_{i, j} = 1, \texttt{Lat}_{i, j} = 0] = \beta_0 (s) + \beta_1 (s). \]

We encounter a similar problem, where for some \(s\) after the reward is received, the expression when conditioning on slow latencies contains the expression capturing the signal on average latencies. Thus, the shape of \(\beta_3 (s)\) (corresponding to `SMS:latency` in the plot) may be explained as a compensation for the strongly positive \(\beta_2 (s)\) (`SMS`) main effect functional coefficient for slower-than-average mice.

### Comparing the non-concurrent and concurrent model

Unlike the non-concurrent model, the concurrent model does not contain potentially misleading interpretations between a time-related summary statistic and the functional outcome. Transforming the scalar latency value to the functional covariate \(\texttt{RZ}_{i, j} (s)\) appropriately separates mice that have reached the reward zone and mice that are still in the corridor, allowing the experimenter to better visualize the association between the time-varying behavior and photometry signal. Within the non-concurrent FLMM, the instantaneous interpretation of the coefficients is less pertinent to the question of identifying the association between the average signal and behavior. In experiments with variable trial lengths, we anticipate that researchers will find the concurrent approach more applicable.

## References

Cui, Erjia, Andrew Leroux, Ekaterina Smirnova, and Ciprian M. Crainiceanu. 2022. “Fast Univariate Inference for Longitudinal Functional Models.” Journal of Computational and Graphical Statistics 31 (1): 219–30. https://doi.org/10.1080/10618600.2021.1950006.
Cui, Erjia, Gabriel Loewinger, and Al Xin. 2025. “fastFMM: Fast Functional Mixed Models Using Fast Univariate Inference.” https://cran.csail.mit.edu/web/packages/fastFMM/index.html.
Machen, Briana, Sierra N. Miller, Al Xin, Carine Lampert, Lauren Assaf, Julia Tucker, Francisco Pereira, Gabriel Loewinger, and Sofia Beas. 2025. “The Encoding of Interoceptive-Based Predictions by the Paraventricular Nucleus of the Thalamus D2+ Neurons.” https://doi.org/10.1101/2025.03.10.642469.
Xin, Al W., Erjia Cui, Francisco Pereira, and Gabriel Loewinger. 2025. “Capturing Instantaneous Neural Signal-Behavior Relationships with Concurrent Functional Mixed Models.” bioRxiv. https://doi.org/10.1101/2025.09.08.671383.