Results of the T-maze screen analysis, both individual and combined.

Yellow 1 (Positive Control): Gr28bd-G4, TrpA1-G4

Parameters: Light: intensity (500 Lux side, 1000 Lux bottom); frequency = 20Hz; Delay = 1 ms; Duration = 9.9 ms; volts = 6.4

The mean ratio of the flies that stay in the middle during the experiments.

Correlation plot between the mean ratio of the flies that stay in the middle versus the Weighted PIs

Slope = 0.0053
Intercept = 0.240
R square value = -0.03834

contrary to the expectations, there seems to be no correlation .

Below is a plot of all the flies of 11 genotypes for the wiggle difference. This is calculated by taking the sum of the difference of the tracepoint for each step. Thus, wiggle = sum(difference in tracepoint at each step). This is done for the entire 20 minutes time.

Now we wanted to measure the difference in on wiggle and off wiggle. On wiggle is the wiggle for when the fly was in the part which is supposed to have light on and similarly off wiggle is the wiggle when light is supposed to be off(that is in the portion in which we want to train it to be in). So below is the difference of on wiggle and off wiggle i.e – on wiggle – off wiggle:-

Now means of the difference in wiggle:-

Below is given the plot of effect-sizes of reinforcement of 11 genotypes. On the y-axis are the PI values for learning effect sizes.  These scores are calculated by taking the average of PI values of training periods and then subtracting pretest PI value from it. 

Yellow 1 (Positive Control): Gr28bd-G4, TrpA1-G4

Parameters:
Light: intensity (500 Lux side, 1000 Lux bottom)

frequency = 20Hz

Delay = 1 ms

Duration = 9.9 ms

volts = 6.4

plotting the mean ratio of flies which stay in the middle during experiment.

Yellow 1 (Positive Control):  Gr28bd-G4, TrpA1-G4

Parameters:
Light: intensity (500 Lux side, 1000 Lux bottom)

frequency = 20Hz

Delay = 1 ms

Duration = 9.9 ms

volts = 6.4

This is the linear model with its statistics

This is the linear model adding interactions. It is perfectly possible to have interactions between neurons, kind of what occurs with olfactory processing where ORNs activated alone or in different combinations have completely different meanings

I uploaded in slack the bayesian linear model with interactions. For any reason, it does not let me upload it now to the website

I am trying one of the ways of nonlinear models: GAM (Generalized additive models). Here one fit splines to the effects to certain degrees of freedom.

This is the kind of bar graphs I thought I could use for all the plots.

We wanted to measure learning and reinforcement in the flies for eight genotypes that we have tested as of now. For calculating the reinforcement scores we took the average of the two training periods combined PI scores and then subtract the PI value of the pretest period.

Reinforcement = average (combined training PI) – (combined pretest PI)

Below is the boxplot of the effect size for each fly for all these genotypes:-

We then calculated the mean of these reinforcement values for each of the screens and below is a bargraph for these means:-

Our experiments are performed after blinding ie we do not know the genotype of the flies tested. Code for this line is Orange1. Intensity was approximately 400 Lux. The frequency of yellow light was 20Hz with 50:50 duty cycle.

Dopaminergic clusters are differently targeted by the different Gal4s. Some of the express faintly, others stronger. Here I try to see if the dose-response curve (or expression-PI curve) seems to be linear or not. Here I put two examples from the 17 clusters, where the first two seem to have nonlinear curves, with and optimal expression level, and the last two seem to have a linear response curve.

This will be important for the modelling in order to decide to make a linear/nonlinear model. Down below I show the results from a linear model and it´s statistics. From Aso et al. 2012, one could see that activating the lines with TrpA1 shows a linear response curve. But in this case it does not necessarily seem to be the case. Therefore, light intensities might have an effect, as well as the expression level, and conclusion needs to be taken carefully.

In addition it is difficult to calculate this for all the clusters with just one single light intensity test, because not all clusters are expressed in several Gal4s to different level, so that we can estimate from there. So for the interesting lines we might need to make several experiments at different intensities, and see the dose response curve.

The G4s I have used for the modelling are the ones shown here.