Analysis of Matrix and Striosomal Cell Activity to Explore and Predict Mouse Behavior in ‘T’ Maze By Meena S. Rajan Submitted to the Department of Electrical Engineering and Computer Science in Partial Fulfillment of the Requirements for the Degree of Master of Engineering in Electrical Engineering and Computer Science at the Massachusetts Institute of Technology February 2020 © 2020 Massachusetts Institute of Technology. All rights reserved. Author: ______________________________________________________________________ Department of Electrical Engineering and Computer Science Jan 20, 2020 Certified by: ______________________________________________________________________ Dr. Ann Graybiel, Institute Professor of Brain and Cognitive Science Thesis Supervisor Jan 20, 2020 Accepted by: ______________________________________________________________________ Katrina LaCurts, Chair, Masters of Engineering Thesis Committee
ABSTRACT
Animals have evolved to allow for decision-making based on rewarding and aversive features of the environment. This ability has been studied in mice and other species as well as the different neuropsychiatric and neurological disorders that undermine this ability. Previous work has shown that some of this decision-making is linked to the striatum, a part of the basal ganglia. There is also previous research that suggests this behavior is partly controlled by a set of distributed striatal microzones known as striosomes. We aim to study the neural activity of striosome and matrix cells in wild type and Huntington disease modeling mice and how they are linked to cost-benefit decision-making. This paper will analyze and model the neural data and train a classifier that can predict the mouse’s behavior as it runs a T-maze. The paper finds some support for the claim that striosomes are correlated to the decision-making process.TABLE OF CONTENTS
1. Introduction ……… 6 2. Background ……….……… 8 a. Huntington’s Disease ……… 8 3. Experiments ……… 9 a. Mice ………..……… 9 b. Tasks ……… 10 c. Setup ……… 11 d. Data Collection ………..……….……….. 13 e. Database ………..……… 13 4. Classification ………... 15 a. Linear Classifier ………..……….. 15 i. Calcium Events ……….. 16 ii. DFF ……….……….. 22 iii. DFF – 2 Standard Deviations Cells ……….... 28 5. Per-Cell Analysis ……….. 34 6. DFF Analysis ……….……….. 45 a. BB Sessions ………. 46 b. CC Sessions ……….. 52 c. CBC Sessions ……….………. 54 d. NCB Sessions ……….. 55 e. 100v100 Sessions ……….……….. 56 7. Conclusion ……….……….. 57 8. Future Work ……….……….. 58 9. Acknowledgements ………..……….. 59 10. References ……….……….. 60LIST OF FIGURES
4.1: Linear Classifier Results for Pure Chocolate Milk vs. Diluted Chocolate Milk …….. 17 4.2: Linear Classifier Results for Left vs. Right Side ………... 19 4.3: Linear Classifier Results for Preferred vs. Non-Preferred Side ………. 20 4.4: Linear Classifier Results for Respective vs. Non-Repetitive Behavior ……….. 21 4.5: Linear Classifier Results for Pure vs. Diluted Milk using DFF Values ……… 24 4.6: Linear Classifier Results for Left vs. Right Side using DFF Values ………... 25 4.7: Linear Classifier Results for Preferred vs. Non-Preferred Side using DFF Values . 26 4.8: Linear Classifier Results for Repetitive vs. Non-Repetitive using DFF Values ……. 27 4.9: Linear Classifier Results for Pure vs. Diluted Milk using Task Responsive Cells ... 30 4.10: Linear Classifier Results for Left vs. Right Side using Task Responsive Cells …... 30 4.11: Linear Classifier Results for Preferred vs. Non-Preferred Side using Task Responsive Cells ……….. 32 4.12: Linear Classifier Results for Repetitive vs. Non-Repetitive Behavior using Task Responsive Cells ……….. 32 5.1: Per-Cell DFF Values for Session 16 ……… 35 5.2: Per-Cell Z-scored DFF Values for Session 16 ……… 36 5.3: Per-Cell Z-scored DFF Values for Session 16 Sorted by Trough Time ……….. 36 5.4: Per-Cell DFF Values for Session 128 ……….. 39 5.5: Per-Cell Z-Scored DFF Values for Session 128 ………. 39 5.6: Per-Cell Z-Scored DFF Values for Session 128 Sorted by Trough Time ………... 40 5.7: Per-Cell Histogram of Peak Times for Session 128 ………... 41 5.8: Per-Cell Z-Scored DFF Values for BB Sessions ………. 42 5.9: Per-Cell Z-Scored DFF Values for CC Sessions ………. 43 6.1: Aggregated DFF Curves for BB Sessions ……… 47 6.2: Aggregated DFF Curves for BB Sessions with only Task Responsive Cells ……….. 49 6.3: Aggregated DFF Curves for BB Sessions with only Event Peak Cells ………... 51 6.4: Aggregated DFF Curves for CC Sessions ………. 52 6.5: Aggregated DFF Curves for CBC Sessions ………. 54 6.6: Aggregated DFF Curves for NCB Sessions ………. 55 6.7: Aggregated DFF Curves for 100v100 Sessions ……….. 561. INTRODUCTION
This paper seeks to study the neural behavior of mice as they run a T-shaped maze with various rewards and costs at each arm. In the experiment, a mouse is made to decide which arm of the maze to choose based on the different rewards and costs in each arm and its ability to remember and evaluate these rewards and costs. We will analyze the neural behavior of the mice during this activity to see if there is anything that can be observed or predicted by the activity of the matrix and striosomal neurons. Prior research has suggested that striosomal cells may be correlated to decision-making and this paper studies this phenomenon further. We will analyze matrix cells as a baseline and to see if there exists a correlation between the matrix neurons and decision-making. In this paper, we only consider the “T” maze and how the mouse performs in this task. We will, however, examine the mouse’s behavior and neural activity with different rewards and costs present. We will begin by using the neural activity of the mouse to train a classifier to predict the mouse’s behavior and choice. We will also model and analyze the neural activity to determine if we can identify what cells are active during various points in the task and what this could suggest about the neural activity as a whole. This analysis will provide insight into the role of various cells during the task and how they affect the decision-making process.2. BACKGROUND
Reward-cost conflicts arise when an individual must decide between various options based on the mixture of the positive and negative attributes of the offer. An interesting phenomenon in nature is an animal’s ability to make decisions based on the rewards and costs present in the environment. Previous work has shown that some of the process of decision-making may occur in the basal ganglia, specifically in the striatum. The striatum is a critical component of both the motor and reward systems of the brain. It receives glutamatergic and dopaminergic inputs from various sources and is the primary input to the rest of the basal ganglia. Functionally, the striatum is know to coordinate multiple aspects of cognition such as action planning, decision-making, motivation, reinforcement, and reward perception. The striatum consists of two complementary chemical compartments: the much larger matrix tissue and striatal patches. The striatal patches, composed of striosome cells, are distinguishable from the larger matrix tissue of the striatum by a few different features such as different expression of many neurotransmitter-related molecules. This paper studies the contributions of matrix and striosomal cells to decision-making in wild type mice as well as in mice that model Huntington’s disease, which research has linked with striosomal abnormalities.2a. Huntington’s Disease
Huntington's disease is an inherited disorder that causes the death of brain cells. The physical symptoms in humans include jerky, random and uncontrollable movementsand later the impairment of psychomotor functions. Cognitive abilities are also progressively impaired. Executive functions are especially affected including planning, cognitive flexibility, and rule acquisition. Preliminary analyses suggest that mice that model Huntington’s disease perform differently in tasks such as the one considered in this paper. They seem to more often get into a “rut” in which they repeat the same choice, whereas wild-type mice are more likely to switch their choice to try the other side. They may not learn as well as wild-type mice and may more frequently have a preference for the side that had the highest reward or lowest cost on the previous day, even though that side is now suboptimal. This paper seeks to analyze the neural activity of these mice as they run the T-shaped maze. The paper will compare matrix and striosome activity to determine if there is a pattern in the activity of these cells that corresponds to the different behavior of the Huntington's mice.
3. EXPERIMENTS
We will first describe the experiments that are run and what data is collected. This consists of the different types of mice used, the different task types, and the data that is recorded from each experiment.3a. Mice
There are a few different categories of mice we will be running through the maze and comparing with each other. The first type is simply our wild type mouse. These are the “normal” mice and will often times be used as a baseline for the “normal” or “expected” behavior and neural activity of a mouse as it runs the maze. We are also interested in modeling Huntington’s disease and the effect it could have on decision-making in mice. Thus, we have Q175 mice, which are mice that model Huntington’s disease. We are interested in comparing these mice to our wild type mice to see if the Q175 mice display any differences in behavior or neural activity. For each of these categories of mice, we are interested in recording and analyzing the neural activity of two different types of neurons: matrix and striosomal cells. Thus, some of the mice will be implanted with a disk to record matrix neural activity while others will be recording striosomal neural activity. At this time, in our new dataset, we have 3 wild type mice (2 recording matrix activity and 1 recording striosomal activity) and 2 Q175 mice (all matrix).3b. Tasks
In our experiment, a mouse is made to run a T-shaped maze and has to decide to go to either the left arm or the right arm of the maze. At the beginning of the experiment, the mouse is placed behind a closed door at the base of the T-maze. When the experimenter presses a button, indicating the start of the trial, the door opens and the mouse is able to enter and run the maze. At each end of the arms is a bowl of milk and possibly a light. These represent the reward and cost respectively of that arm of the maze. There is a light beam (undetectable by the mouse) right in front of the milk which, when broken, serves as an indicator that the mouse drank from the milk at that arm. So, in a single run of the maze, the mouse runs from the base of the maze to one of the arms and licks the milk. The system records each time the light beam (licometer) is broken – each lick the mouse makes. When the mouse is finished drinking the milk, it either runs back or is guided back by the experimenter to the base of the maze. The mouse is made to perform one of five different tasks, which tests the mouse’s decision-making under different reward-cost combinations. Each task is categorized based on the reward-cost combinations in its two arms. In terms of rewards, an arm either has a high reward, pure chocolate milk, or a low reward, a mix of chocolate milk and water. In terms of cost, there is a range of possible light brightness with a brighter light being a higher cost and a dimmer light (or no light) being the lower cost. These are the five different tasks: • Benefit-Benefit (BB): o Arm1: Pure chocolate milk (high reward) paired with no light (no cost)o Arm2: Diluted chocolate milk (low reward) paired with no light (no cost) • Cost-Benefit Conflict (CBC): o Arm1: Pure chocolate milk (high reward) paired with a bright light (high cost) o Arm2: Diluted chocolate milk (low reward) paired with a dimmer light (low cost) • No Conflict Cost-Benefit (NCB) o Arm1: Pure chocolate milk (high reward) paired with a dimmer light (low cost) o Arm2: Diluted chocolate milk (low reward) paired with a bright light (high cost) • Cost-Cost (CC): o Arm1: Pure chocolate milk (equal reward) paired with a bright light (high cost) o Arm2: Pure chocolate milk (equal reward) paired with a dimmer light (low cost) • Equal (100v100): o Arm1: Pure chocolate milk (equal reward) paired with no light (no cost) o Arm2: Pure chocolate milk (equal reward) paired with no light (no cost)
3c. Setup
We have outlined a single run of the maze above and will now describe the full setup of the experiment. Before a mouse is made to run the maze, it is first put in the top part of the maze (the arms of the maze) with the bottom portion of the T-maze blocked off. The mouse then runs back and forth to learn the milk type and brightness (reward and cost) in both arms of the maze. Then the core part of the experiment or "session" begins. Each session consists of 40 "trials.” A “trial” is one run of the maze where the mouse is placed at the base of the maze and runs the maze to choose an arm and drinkfrom the bowl of milk. The type of task (reward/cost in the arms) do not change for the entirety of a session although they could and do change between consecutive sessions. The three most important time points of a trial are “click”, “first lick”, and “last lick”. “Click” refers to when the door to the maze is opened and the mouse starts the trial. “First lick” is when the mouse first licks the milk at either arm of the maze thus making its choice - we are only considering the first choice the mouse makes. As we mentioned before, every lick of the mouse is recorded by the system. So, “last lick” refers to the time of the last lick the mouse makes on the side they chose. Note: it is important to remember that “last lick” is still on the same side as “first lick”. For example, a mouse may choose the right side and lick five times on that side before running to the left side and licking at the milk there. In this case, “first lick” is the time of the first lick on the right side and last lick is the time of the fifth (last) lick on the right side. We do not consider any of the licks on the left side at all. For all intents and purposes, we consider “last lick” to be the end of the trial. This is because there are many things that can happen between last lick and the mouse returning to the base of the maze. The mouse may go to the other side and begin licking, it may run back to the base immediately, or the experimenter may urge or even pick up and place the mouse back at the base of the maze. Due to this inconsistent behavior, we do not want to consider anything that happens between last lick and the next click. Since we do not have any information about the mouse’s behavior in this time, we do not know the cause of any potential interesting neural activity.
3d. Data Collection
During a session, we record a brain-imaging video of the mouse as it runs the maze. As mentioned before, each mouse is implanted with a disk to record neural activity, such as calcium spikes (neurons firing). For some of the mice, we record the cell activity of matrix cells while for others we record striosomal cell activity. Then we perform image processing and alignment in order to extract individual neurons and their neural activity throughout the session. We now have both the raw data – imaging which can be overlaid in order to relate different sessions - as well as a list of cells and when they fired. In addition, we record mouse-tracking information to be able to map the mouse’s movement along the maze as well as metadata about the session type and maze layout.3e. Database
For most of our analysis, we use a central database, which contains most of the neural data from each session. For each session, there are twelve data-structures that represent/record the data for the session: • Date: the data of the session in MM_DD_YY format • Mouse: the mouse number - used to determine if the mouse is a wild type or a Q175 (Huntington’s disease modeling) mouse • Initials: the initials of the experimenter who ran the mouse for this session • Task: the type of the task – always “maze” for our sessions • Compartment: “matrix”/”striosome” – indicates if the mouse is fitted to record matrix or striosomal neural activity • Session Type: BB/CC/NCB/CBC/100v100• Behavior: a list of (time, event code) pairs that were recorded during the session. This is useful to time-align different trials as well as to mark the start and end of each trial. There are events recorded at the beginning of the session, which indicate the rewards and costs at either arm of the maze. This is used to determine the “low cost”, “high reward”, etc. side. Aside from these, the only other events that are used are “click” and “licks”. Each event is paired with a timestamp, which is used to align these events with the DFF and calcium events data-structures. • H5_ts: a list of timestamps, which is used to assign time values to each row in the DFF and calcium event data structures. The timestamp at the i’th row corresponds to the time of the i’th row of the DFF and calcium event data structures. • DFF: 2D matrix representing the DFF values for each cell at each time point. A DFF value (delta F over F) represents the brightness of the cell, which is correlated to the activity of the cell at that point in time. The (i,j) cell corresponds to the DFF value of the j-th cell at the h5_ts[i] time point. • Calcium events: 2D matrix representing the calcium events for each cell at each time point. The (i,j) cell corresponds to the calcium event of the j-th cell at the h5_ts[i] time point. This data structure records very quick calcium spikes so it is very sparsely populated. Most of the cells are 0 with a cell having a non-zero value when that cell experienced a calcium spike (action potential) in which case the value of the cell is the amplitude of the peak of the calcium spike.
4. CLASSIFICATION
To begin, we will analyze neural activity to see if the activity of matrix and striosome cells can be used to predict the mouse’s behavior and choice. Specifically we are trying to train a classifier to use either DFF or calcium event data in order to predict if the mouse will choose left/right, pure chocolate milk/diluted chocolate milk, etc.4a. Linear Classifier
We will first use a linear classifier to try to predict the mouse’s behavior. There are a few different types of input data we will experiment with to determine which has the best predictive power. For now, we will consider neural activity on a sum or average basis rather than on a by cell basis. We do this because we have not linked cells across different sessions and so we cannot determine whether two cells from different sessions are the same or different. Thus, we cannot yet perform analyses that consider the activity of individual cells. For now, we will quantify neural activity as a sum over all the matrix or strio cells during a certain period of time. We start with a linear classifier whose input is an array representing neural activity during 4 different time periods: [click-1.5 secs, click], [click, click+1.5], [firstLick-1.5, firstLick], [firstLick, firstLick+1.5]. These intervals cover the beginning and end of the trial and so we reason contain important information about the mouse’s decision-making process as it moves through the maze. We will analyze this data to see if it can be used to predict the choice the mouse makes as it runs the maze. There are a few different “choices” we would like to try to predict:• High reward vs. low reward side: whether the mouse chooses the side with the pure chocolate milk or the arm with the diluted chocolate milk (for CC sessions: whether the mouse chooses the bright light vs. dim light side). We do not perform this analysis for 100v100 as both arms have the same reward/cost • Left arm vs. right arm: whether the mouse chooses the left or right arm irrespective of the reward/costs associated with the arms • Preferred side vs. non-preferred side: when we talk about preferred side here, we mean which side, over the 40 trials, did the mice choose more often • Repetitive vs. non-repetitive: whether or not the mouse repeats the choice it made in the previous trial We train the liner classifier on 95% of the data and then test it on 5% to see if we could get a predictor with a high test accuracy. We repeated this 20 times and calculated the standard error and mean of test accuracies. We train a classifier for each session type and mouse type pair: BB strio, BB matrix, BB Q175, CC strio, etc. We will analyze these classifiers and compare the performance of matrix vs. strio cells and Q175 vs. wild type mice to see which one is able to more accurately predict the mouse’s behavior. 4ai. Calcium Events The first input array we begin with is an array of summed calcium events. This is a 4-dimensional array, as specified above, where each element is the number of cells that fired – recorded as a calcium spike – in that time bin. Note, that we are only counting the number of cells that spiked not the amplitude of the calcium spike. The reason for
this is that we do not have a high confidence in the amplitudes recorded. As mentioned, we want to compare matrix Q175 (we do not have any 100v100 matrix Q175 sessions), matrix WT, and striosome WT. We train a linear classifier under two different conditions – one regularized with a support vector machine (SVM) and then another using a logistic regression model. We found that SVM performed at least as well as logistic regression and so the results shown below are all using SVM. After training a linear classifier with a 4-dimensional input array, we tried different input arrays with different time-bins. For example, we divided the entire run (click to first lick) into 10 time bins and used this as our input array. However, using the simple 4-dimensional input array performed as well, and sometimes better, than more complicated vectors and so we will perform our analyses using this. Below are the mean test accuracies of the linear classifier with standard error also shown. Figure 4.1: Linear Classifier Results for Pure Chocolate Milk vs. Diluted Chocolate Milk
This graph shows the test accuracies for predicting if the mouse chose the side with the pure chocolate milk or the side with the diluted chocolate milk (or in the case of CC, the side with the bright light or the side with the dim light). As mentioned before, we do not perform this analysis on 100v100 sessions, as these sessions do not have any differences between the two arms. There are a few interesting things to note from this graph. Firstly, the NCB sessions and BB sessions have higher test accuracies than the CBC and CC sessions. This could be because in these two session types, there is no conflict in what choice is “optimal” for the mouse. However, the fact that CC sessions did not achieve as high test accuracy could suggest that perhaps the neural activity used to train the classifier is more indicative/correlated to a choice based on reward and not on cost. Another thing to note is that the matrix Q175 mice seem to have higher test accuracies in all of the session types. This could simply be a product of the data as there are fewer Q175 sessions in our dataset. However, this could also say something about the Q175 mice. Perhaps they have clearer or simpler neural activity that chooses the arm they remember as having the good choice. In terms of matrix vs. strio, there is not a lot that can be determined by this graph. Striosomal cells seem to perform at least as well and sometimes better than matrix cells to predict mouse behavior with respect to what reward and cost are present. Below are results from running a classifier trained to predict if the mouse chose the left arm or the right arm irrespective of the reward/cost associated with each arm.
Figure 4.2: Linear Classifier Results for Left vs. Right Side Interestingly enough, aside from the 100v100 sessions, striosomal cells seem to give us better predictive power than matrix wild type when predicting left vs. right. Once again, Q175 mice seem to have high predictive power. When compared to the previous graph, most of the Q175 test accuracies are higher. This would actually make some sense as Q175 mice seem to often get into a “rut” where they simply make the same choice (left/right) again and again irrespective of if the reward/cost has changed. Thus, the neural activity of the mouse may indicate again and again to make the same left/right choice thus making it easy to train a classifier to predict this. This time, we do not see as much of a difference between the test accuracies of the different sessions. There does not seem to be uniformly higher values for BB and NBC as seen before. Indeed CBC and NCB matrix Q175 seem to have around the same test
accuracy. Perhaps since reward and cost are no longer considered, there is not as much difference between our various session types. Below is the graph for training a classifier to predict preferred or non-preferred arm. For each session, the “preferred” side is calculated as the side that the mouse picked more often over the 40 trials. Then we train a classifier on whether or not the mouse picked that side in the current trial. We get fairly good test accuracies with this metric. Again, here we see that striosomal cells seem to give us better predictive power than matrix cells. This could indicate that striosomal cells are associated with decision-making and they maintain some notion of which side has the better reward (or lower cost) and thus spike to indicate making that preferred choice. They could also spike during an “exploratory” trial where the mouse tries the non-preferred side to sample it and see if it better than the current choice they are making. Figure 4.3: Linear Classifier Results for Preferred vs. Non-Preferred Side
Now we will look at results from training a linear classifier to predict whether the mouse made the same choice (repetitive) as the previous trial or the other choice (non-repetitive). We can see that here that strio cells give us the best test accuracies. This could again suggest that these cells are associated with some sort of decision-making – whether picking the preferred side or, in this case, whether to make the same choice as before or to “explore” the other choice. Surprisingly, the Q175 mice do not perform as well as before – this is interesting because as we noted, these mice are most likely to demonstrate repetitive behavior. However, they do not seem to have neural activity predictive of this behavior. In the below graph, we see that NCB does not perform as well as with previous metrics. This is again surprising as in NCB sessions, there is no conflict – there is a clear “optimal” side that the mouse should choose. Figure 4.4: Linear Classifier Results for Respective vs. Non-Repetitive Behavior
Overall, there are some common features we can see exist in all of these graphs. Surprisingly, the data collected from the Q175 matrix mice seemed to be able to train the best classifier. As mentioned, perhaps this is simply due to the fact that we have fewer data points for Q175 mice although having less data would usually lead to a less accurate classifier. Another possibility is that Q175 mice are very sure of their choice for one reason or another. They are more likely to exhibit repetitive behavior and thus are perhaps more likely to have highly repetitive neural activity which determine their choice. They do not explore or change up their choice as often as wild type mice and so there is less “noise” in the neural activity. Another trend we see is that striosomal cells seem to give us as good and often better test accuracies than matrix wild type mice in most cases. The only notable exception is in 100v100 sessions, where matrix did perform better than strio. This is interesting as this is the case when everything is held equal. In the 100v100 case, the mouse can go wherever it wants because there is no “better” or “worse” choice as the mouse always gets the high reward and no cost. One important thing to note is that there are very few 100v100 sessions. In the database used to train this classifier, there are only five 100v100 sessions. This is probably affecting the results and so we have less confidence in the results from the 100v100 sessions. In the other session types, where there is a difference in rewards and/or costs, the striosomal cells seem to be more correlated to the choice the mouse makes. This supports our original hypothesis that striosomal cells are connected to decision-making.
4aii. DFF Since calcium events are very sparse, they may not be the best data to use to predict mouse behavior. So we also trained a linear classifier on the DFF data that we have in the database. Again we will begin by using a 4-dimensional array of time bins: [click-1.5 secs, click], [click, click+1.5], [firstLick-1.5, firstLick], [firstLick, firstLick+1.5]. Each element in our input array will be the average DFF value over some subset of cells during the corresponding time bin. However, after experimenting with different input vectors, we concluded that this did not give us the best test accuracies. Since each value of our input array is now the average DFF value (instead of a sum as with calcium events), we thought that having more time bins could better represent the neural activity of the mouse as it runs a trial. We, thus, sought to use more fine-grained time bins and see which set of time bins gives us the best predictive power. We began by dividing the trial time – click to first lick – into time bins. We began with 4 bins (similar to before) and increased from there. We found that 20 time bins gives the best predictive power – the test accuracy achievable by a linear classifier plateaued at around 20 time bins. We were also interested to see if adding more information about neural activity after the first lick would be useful and give our classifier more predictive power. So, we added information about the neural activity between the first lick and the last lick. When the mouse makes a choice, it drinks from the milk with a series of licks. We included the neural activity during this drinking time as it could contain information
about the mouse’s choice or possibly its next choice. Again, we trained multiple classifiers to find which/how many time bins best represents this data. We took the time from the first lick to the last lick and split it up into various time bins. We discovered that again splitting this time period into 20 time bins gave us the best test accuracies. So, our final input array is a 40-dimensional array where the first 20 time bins represent the time between click and first lick and the next 20 time bins represent the time between first lick and last lick. We did not include any data from after the last lick because this data is not very reliable or consistent. Figure 4.5: Linear Classifier Results for Pure vs. Diluted Milk using DFF Values Above is the test accuracy for a classifier that predicts whether the mouse chose the pure chocolate milk (bright light) side or the diluted chocolate milk (dim light) side. We see some interesting features here. The first thing to note is that unlike when we
were using calcium events, we do not see as high test accuracies for the NCB sessions. There does not seem to be a lot of variance in how well the classifier predicts between different session types and cell/mouse types. We once again see a slight increase in accuracies for the BB and NCB sessions – which again suggest that perhaps there is more of a correlation between reward and the neural activity we are observing. We do see that BB and CC strio seem to have higher test accuracies. Figure 4.6: Linear Classifier Results for Left vs. Right Side using DFF Values Looking at the graph of test accuracies from predicting whether the mouse goes left or right, we see more variance than before. Here we see that strio cells give us a rather high predictive power. The one exception is CBC sessions, where the mouse is conflicted and has to pick either high reward or low cost. This could suggest that in this case, the mouse has to make a more difficult choice, which is not as easily modeled
with our input vector. In fact, we do see lower test accuracies for CBC sessions in most graphs in this section. Unlike when we were looking at calcium events, Q175 mice tend to give us worse test accuracies. Surprisingly when we look at DFF values, we get fairly high accuracies for 100v100, which we did not get when using calcium events. Looking at DFF values may indeed tell us more about the neural activity of the mice that calcium events did not which allows us to predict whether or not the mice goes left or right. The strio cells of 100v100 do not give us good predictive power but this is probably due to the fact that we do not have many 100v100 strio sessions in our database. Figure 4.7: Linear Classifier Results for Preferred vs. Non-Preferred Side using DFF Values When considering predicting preferred vs. non-preferred, we see that once again, aside from the 100v100 sessions, the strio cells produce better test accuracies than the matrix wild type cells. This could suggest that strio cells do indeed have something to
do with decision-making. We see a lot of the same trends we saw before with BB and NCB sessions giving us slightly higher test accuracies than other session types. CC strio also performs very well in this case as do 100v100 matrix cells. Q175 mice seem to perform around the same as our wild type mice – they actually perform best in the CBC case. This could be an indicator that they make their choice of what side to take based on some sort of repetitive behavior instead of considering the rewards and costs. Figure 4.8: Linear Classifier Results for Repetitive vs. Non-Repetitive using DFF Values Finally, we look at predicting repetitive vs. non-repetitive behavior. As shown above, we do not get good test accuracies using DFF values for this metric. Indeed most of our test accuracies are around 0.5, which is approximately what a random model would produce. This suggests that perhaps looking at the DFF values of matrix and strio cells do not give us information about making the same choice as before. They seem more
correlated to predicting behavior based on left/right or some knowledge of preference and reward/cost. There are a couple of cases where we see high test accuracies in the graph. Specifically BB and NCB strio cells have high test accuracies, again supporting the notion that strio cells may have some correlation to behavior dependent on reward. We also see a high Q175 test accuracy for CBC sessions, which we have seen in previous graphs as well. 4aiii. DFF – 2 Standard Deviations Cells From the graphs above, we see that the 40-dimensional DFF vector gives fairly strong predictive power for most metrics. However, after doing more analysis about different cells and how they fire, we concluded that this is not necessarily the best value to use because different sessions record a different number of cells. Moreover, there are many cells that are task unresponsive meaning that they do not respond to the task. Specifically, these cells never peak or trough during the task. Thus, these cells are not associated with the task and are marked as task unresponsive. We do not want to consider these cells in our analyses, as they will not provide any meaningful information about the correlation between neural activity and mouse behavior. Furthermore, including these cells in our average DFF calculations will serve to pull our values to zero and could be masking some interesting features of our DFF values. Thus, we want to only consider cells that are task responsive in our calculations. In order to do this, we use the metric that a cell is considered task responsive if, during the timespan of the trial, the cell has at least one peak or trough whose amplitude is above two standard deviations from the cell’s mean. This has been shown in previous
research to be a good metric to use for this purpose. There are a few nuances about this calculation that we will discuss. The way this was implemented in our particular analysis was by considering the DFF values for each cell during the timespan of the trial. This array of DFF values was then converted to an array of z-values, on a cell-by-cell basis. Then if there was any z-value above 2 or below -2, the cell was marked task responsive. This means that each cell was only considered with respect to its own mean DFF value. This was to account for any disparities in the neural imaging software. For example, if one neuron was picked up as duller than others in our neural imaging, we do not want its mean value to be influenced by the brightness of other cells. Having done this, we can re-train a linear classifier with only task responsive cells. Just considering task responsive cells (graph below) to predict high reward vs. low reward side gives higher test accuracies than before for the most part. Specifically, we see that striosomal cells perform quite well for all of the session types now. We again see that BB and NCB wild type mice have higher predictive power than the others. BB and CC strio cells perform amongst the best of all session/cell types. When compared to averaging over all cells, we see that some matrix (both wild type and Q175) classifiers perform worse. This could suggest that there is something to be gathered from the matrix cells that do not respond strongly. Perhaps even small deviations in DFF values are significant and can tell us something about the mouse’s decision.
Figure 4.9: Linear Classifier Results for Pure vs. Diluted Milk using Task Responsive Cells Figure 4.10: Linear Classifier Results for Left vs. Right Side using Task Responsive Cells
Looking at only task responsive cells to predict left vs. right, we see that strio cells seem to give us good predictive power. When comparing with the predictor trained from considering all the cells, we see that all of the strio test accuracies improved or stayed around the same. On the other hand, the matrix test accuracies (wild type and Q175) stayed around the same or decreased. This could suggest that for strio cells, the ones that have a significant change in DFF values are indeed task responsive while matrix cells have a lower threshold for being task responsive. This could also be a product of the number of each type of cells that exist. There are fewer striosomal cells while there are many more matrix cells. By only considering cells that deviate by at least two standard deviations, we filter out a lot of matrix cells. This leaves us with fewer cells from which to gather information. The larger number of matrix cells could mean that smaller deviations in DFF values per cell but aggregated over all the matrix cells tell us something about the mouse’s behavior. On the other hand, perhaps striosomal cells are more strongly connected and “reactive” to the mouse’s behavior. When considering preferred vs. non-preferred, we do not see that many changes when we restrict to only considering task responsive cells. The only significant change in test accuracy is that the NCB Q175 accuracy in the graph above is lower than when we considered all of the cells. Other than that, we see much the same pattern as before, where strio cells give high accuracies in all case except for that of 100v100. We do again see high values for BB and NCB (except for the Q175 case). When considering only task responsive cells with this metric, we see higher values for Q175 mice, which we saw when using calcium events but not when using DFF values for all cells.
Figure 4.11: Linear Classifier Results for Preferred vs. Non-Preferred Side using Task Responsive Cells Figure 4.12: Linear Classifier Results for Repetitive vs. Non-Repetitive Behavior using Task Responsive Cells
There is not much to say about repetitive vs. non-repetitive. As before, we do not get significant results when considering this metric. Indeed, the test accuracies are pretty much the same as when we consider all cells. DFF values do not seem to provide enough data to predict if the mouse will repeat its behavior or not. Overall, limiting our data to only include “task responsive” cells was an interesting experiment. We did see some increases in test accuracies but we did also see some decreases. Considering only 2 standard deviation cells resulted in increases in the strio test accuracies values while sometimes decreasing the accuracy when using matrix cells. This could have to do with the number of cells of each type and how much each cell contributes to the overall neural activity and decision made. It is also important to note that we used a very simplistic metric for labeling cells either task responsive or nonresponsive. Although there is some research to suggest that this method is a good metric, it is still important to note that this could cause some of the decreases observed.
5. PER-CELL ANALYSIS
After training a linear classifier, we wanted to do more analysis to dig deeper into the neural activity of each cell during a trial. We want to explore when different cells fire during a trial and if there is some information we are able to extract from that. To this end, we begin to look at each session in isolation – this allows us to look more closely at the raw data instead of some sort of aggregated representation of the data. For each session, we want to represent the neural activity of each cell. As calcium events are sparse and so less interesting, we will look at DFF values for each cell. Once again, we see that there are many cells whose DFF values don’t change throughout the trial. We do not include these cells in our analysis because these cells are considered task nonresponsive. We are using the 2 standard deviation filter to determine task responsiveness – if a cell’s DFF value exceeded 2 standard deviations from its mean value at least once. In order to be able to clearly see cell activity, we order the cells by increasing order of the time of their peak. There are a few specific conditions under which we would like to graph the data. We are particularly interested in neural behavior when the mouse chooses the preferred vs. non-preferred side and also when the mouse chooses the repetitive vs. non-repetitive side. We are interested to see if we can see differences in cell activity under these conditions. To do this, we graph the cell activity for trials that were: non-preferred repetitive, non-preferred non-repetitive, preferred repetitive, and preferred non-repetitive. Each of the sub-graphs shows a cell’s DFF value in a certain time bin averaged over all trials in the given session that met the condition.Each sub-graph shows all the cells we are considering, with each cell being a single row. The cells are numbered on the y-axis. On the x-axis are the time bins – we have split each trial into 30 time bins. We take the time between click and first lick and divide it into 10 time bins – these represent time bins 10 to 20 in our graph. Using that same time step, we extend the graph to consider 10 time steps before click (time bins from 0 to 10) and 10 time steps after first lick (time bins 20 to 30). Figure 5.1: Per-Cell DFF Values for Session 16 Above is the graph showing per-cell activity for session 16. This session was of mouse 221 (a wild type mouse recording matrix neural activity) and was a BB session. We can see the four sub-graphs shown above. While this graph is very helpful and we can roughly see when each cell peaked, it is not very clear. This is because there is one peak that is very high, which masks a lot of interesting behavior. So, in the graph below we normalize each cells DFF value according to its mean value. Note that this does mean that we lose some information about the differences in peak values for different cells
but it allows us to more clearly see smaller deviations in a cell’s DFF value throughout the trial. Figure 5.2: Per-Cell Z-scored DFF Values for Session 16 We have also graphed the same data again but this time, we order the cells according to the time of their trough. This graph is shown below. Figure 5.3: Per-Cell Z-scored DFF Values for Session 16 Sorted by Trough Time
We look at session 16 because there are a few interesting features of this graph which are a good representation for what we see in other sessions. First, we begin by looking at our non-normalized graph. We see that there are a few very bright peaks that can be seen. For the most part, these peaks appear between time bins 15 and 20, which is around when the mouse would be turning to go down either the left or right arm. We will now analyze the normalized graphs to consider more details about the neural activity of each cell. When looking at these graphs we see primarily two places where a lot of cells peaked – these are when the “peak” curve has a steep incline. We see this is primarily around bin 10 (click) and then bins 15-20. This is interesting because these are actually the important events where we would expect to see a lot of cells being active. Looking more closely, it is quite interesting that we see that steep peak around bin 10 mostly in the non-repetitive cases. This could suggest that in trials when the mouse makes a different choice, it exhibits more neural activity right around when it first begins to run the maze. Thus, the mouse requires more neural activity in order to deviate from their previous choice and go down the other arm. It is also interesting to note that this peak in neural activity is as early as the click and even a bit before it. This suggests that the mouse has some idea or inclination of which side to go as early as the click time. Another interesting feature to note is when we compare the peaks and the troughs. We will see this feature more clearly in another session but we can begin to see it here as
well. It seems that cells turn on and off in a complimentary matter. If we look at our sub-graphs we can see that when some cells peak (especially around bins 15-20), a lot of other cells trough (turn off) and vice versa. We see this more clearly when we look at our graph ordered by trough timing. For example, when we look at the non-preferred repetitive sub-graph, we can see that around bin 15 many cells trough. But the cells that do not trough actually peak in this time bin. Similarly, if we look at the preferred non-repetitive sub-graph, we see the same complimentary neural behavior. We actually see this more clearly in this sub-graph. The cells that trough in time bins 5-10 peak around bins 15-20. On the other hand, the cells that trough in bins 15-20 can be seen peaking around bin 5-10. This is a very interesting behavior to note. It suggests that there are perhaps two different types of cells we are seeing here. We have cells that we classify as “click” cells, which peak around click time. We then also have “choice” or “lick” cells, which are the cells that become active (peak) when the mouse is either turning into one of the arms or when the mouse is licking the milk. So the neural activity of click, for example, is composed of the “click” cells turning on and the “lick” cells turning (or staying) off. It is important to note that although we speak of “troughs”, this does not necessarily mean a decrease of the DFF value from the mean. Since the rows are normalized, a “trough” is most probably simple a cell turning off and a “peak” is when a cell turns on (fires). We are very interested in this behavior of cells and the fact that cells turning off are just as important as cells turning on.
Now we will look at another session that also demonstrates this behavior. Below is a graph of session 128, which is another matric wild type BB session. Figure 5.4: Per-Cell DFF Values for Session 128 The graph above shows a few high peaks and troughs in the data but masks a lot of information about individual cell activity. Therefore, we normalize the cells’ DFF values as before and graph it in increasing peak time order and increasing trough order. Figure 5.5: Per-Cell Z-Scored DFF Values for Session 128
Figure 5.6: Per-Cell Z-Scored DFF Values for Session 128 Sorted by Trough Time We are again able to see the phenomenon we discussed earlier, especially in the graph ordered by peak time. We see that many cells peak either early – in this case we see that a lot of cells peak around time bins 0 to 5. We then see a sequence of cells that peak around bins 15 to 20. We can see, specifically in the non-preferred non-repetitive sub-graph, that the cells that do not peak in bins 15-20, trough during these time bins. We see similarly, in the preferred repetitive sub-graph, that cells not peaking in time bins 15-20 have a trough here. On the other hand, we see that the cells that do peak in these time bins seem to trough somewhere between bins 1 to 10. We want to look more closely into this behavior and see if we can find certain time periods when many cells peak and if this gives us more confidence to label cells “lick” cells, “click” cells, etc. To this end, we counted how many cells peaked during a time bin on average across different trials. We represented this data in the histogram below.
Figure 5.7: Per-Cell Histogram of Peak Times for Session 128 In this histogram, we see that there are indeed certain time bins where we see “high peaks” – time bins when many cells are peaking. In this session, we see one such peak sometime between time bin 15 and 20. Each of our sub-graphs has another peak around time bin 0/1 and there is also often a smaller peak bin around bin 10. We can see that in the repetitive cases, we see a peak in our histogram closer to bin 15 where as in our non-repetitive cases; we see this peak a bit later - closer to bin 20. The histograms show that there does seem to be a general bi-modal behavior in our data which gives us some confidence that we can classify cells “click” and “lick” cells and perhaps “start” as well. On the next two pages, we will show results from different BB and CC sessions, which we will then compare and talk about general trends. We will be showing the normalized graphs sorted by peak timing. This is because these graphs are the most informative and the easiest to compare and pick out features.
There are a few interesting features to consider here. The first is the difference we often see between matrix and strio graphs. For the matrix wild type graphs, we see a lot of cells peak around bin 15. This peak tends to be later in the strio graphs (closer to bin 20). This is pretty interesting and it is, in fact, a feature we will see again in the next section. In the matrix case, the cells that peak around bin 15 are more likely to be some sort of “choice” cell, meaning that these cells fire when the mouse is making its choice. Bin 15 represents the time halfway between click and first lick. So this is roughly around when the mouse would turn into one of the arms or a little bit before the turn. Thus the cells that fire during this time are the cells that are active for this decision and turning behavior. On the other hand, if a cell fires closer to bin 20, it probably represents something different. Bin 20 is first lick and so cells firing here are probably cells that are correlated to the action of drinking the milk or considering the reward at the end of the arm. These cells could be “goal-reaching” cells as they fire when the task is over and the mouse gets the reward. It is quite interesting that in the matrix case, we see more cells which are “turn” cells and in the strio case we see a large number of “goal-reaching” cells. Another interesting feature is that we see steeper curves in the matrix Q175 graphs than we see in the matrix wild type graphs. This suggest that perhaps the Q175 mice require more neural activity – more cells firing – to make their choice.
6. DFF ANALYSIS
Now that we have performed per-session analysis, we want to look at aggregated data in order to understand the overall neural activity as the mouse runs the maze. To do this, we will evaluate the average DFF value over all of the cells at a certain time in point. Although the per-cell analysis was helpful in looking at the individual activity of each cell, it was quite hard to compare any differences in neural activity between matrix wild type, strio wild type, and matrix Q175 mice. Doing this sort of aggregated analysis we will be able to look at the average activity for these different type of cells/mice and see if there are any differences in the neural activity. Again, we will be using DFF values because calcium events are sparse and so it is not easy to get a lot of information out of it. At a high level, we will find the average DFF value over all cells for a given point in time. We will then graph this over the entire time of the trial. Each graph has DFF values on the y-axis. On the x-axis are the time bins – we have split each trial into 40 time bins. We take the time between click and first lick and divide it into 10 time bins – these represent time bins 10 to 20 in our graph. Using that same time step, we extend the graph to consider 10 time steps before click (time bins from 0 to 10). We then take the time from first lick to last lick and divide it into 10 bins – which are time bins 20 to 30. Using this time step now, we extend the graph to consider 10 time steps after the last lick – bins 30 to 40. So our events times are the following: bin 10 is click, bin 20 is first lick, and bin 30 is last lick. We again are most interested in analysis concerning preferred and repetitive behavior. Specifically we will be looking at DFF values and comparing values during repetitiveand non-repetitive trials. We will first consider just repetitive vs. non-repetitive trials. We will then consider only trials where the mouse picked the preferred side and then again compare repetitive vs. non-repetitive trials. We then consider only trials where the mouse picked the non-preferred side and compare repetitive vs. non-repetitive trials. We are also interested in looking at only left preferred trials as the disk is in the left hemisphere of the brain and we are interested to see if this has any effect on neural activity. We have also run this analysis to compare pure chocolate milk vs. diluted chocolate milk, left vs. right, etc. However, we will only be looking at preferred and repetitive behavior here as those produce the most interesting graphs. We will show the graphs from all our session types but will only be analyzing the BB and CC graphs, as these are the session types we are most interested in.
6a. BB Sessions
Below are graphs comparing our different mice/cell types for BB sessions. The graphs contain stars, which indicate if there is a significant difference between the repetitive and non-repetitive value at that time point. This was determined using a bootstrap method, which marked a significant difference with confidence 0.005. We sampled from the data 1000 times and found for how many of these samples, curve a was below/above curve b. If this number was less than 5 then the difference was determined to be statistically significant. Standard deviation is also shown as a gray area around each curve – this was again calculated using a bootstrapping method, as we could not assume a normal distribution.Figure 6.1: Aggregated DFF Curves for BB Sessions Looking at these graphs we see a lot of the same trends and features that we talked about earlier in the paper. When we examine the first graph, we see that our curves have one major peak. This peak is much more subtle for the matrix wild type curves than for the matrix Q175 curve. We also see that the matrix peaks tend to be earlier than the peak of the strio curves. Indeed the strio peak is around or a little bit after bin 20 meaning that it correlates more to a “goal-reaching” event than a “turn” event. In addition the higher peak in the Q175 curve may suggest that these mice require more
neural activity to make their decision. Now, looking at the repetitive vs. non-repetitive curves, we see that the non-repetitive case usually has a higher peak than the repetitive curve. This is quite interesting as it suggests that perhaps to make a different choice than before, the mouse requires more neural activity – more cells to fire. Now we will look at the graphs where we are only considering either the preferred trials or the non-preferred trials. We see much the same type of curves when we look at only the preferred trials. One interesting thing to note, however, is that we see a larger dip after the peak in our preferred graph. This is probably just from the cells turning off after firing but it is still interesting to note. Now, looking at the non-preferred graph, we do see some different behavior. To begin with, these curves are a lot less smooth as there are fewer trials where the mouse chose the non-preferred side. The first interesting thing to note is that we now see a higher peak in the matrix wild type curves that we did not see before. This peak seems to be after the peak of the Q175 curves but earlier than the peak of the strio curves. The matrix wild type peak is about as high as the strio and Q175 peak now. This suggests that perhaps in the non-preferred case, more matrix cells have to fire in order for the mouse to pick that side. Another interesting feature we see is that for our strio curves, the non-repetitive curve is higher than the repetitive one but for both our matrix curves, the repetitive curve is higher for most of the trial. This is very interesting and a different behavior than what we were seeing in our previous graphs.
Looking at the left preferred graph, we see that the Q175 mice have a high peak once again whereas the matrix wild type and, to an extent, strio wild type have a less severe curve. We see more separation between repetitive and non-repetitive curves in this graph than we have seen before. The strio curve has more dips than in previous graphs suggesting that there are fewer cells firing during the non-peak time bins, especially in the non-repetitive case. It is quite interesting that we see the non-repetitive curve having a lower DFF value than the repetitive curve in the non-peak time bins. We wanted to again only consider cells that were “task responsive” – so we recreated the graphs using only cells that exceeded 2 standard deviations. We did a few things here but for convenience and space, we will only show our analysis for the repetitive graph and then discuss results from performing the same analysis on the other graphs. Figure 6.2: Aggregated DFF Curves for BB Sessions with only Task Responsive Cells Above we have shown our initial graph as well for comparison. We see that there is not much of a change in the general shape of the curve. However, now we are no longer including the cells that never deviated from their mean – which are usually cells that
did not fire at all. This means that not including these cells should make our curves more extreme as we are not including cells that are pulling the average DFF values towards 0. This is indeed what we see in our graphs. The peaks and even troughs of our curves have become more severe – their amplitudes have increased. An interesting thing to note here is that when we look at our matrix wild type curves, we see that there is no significant difference between the two curves anymore. The repetitive and non-repetitive curves follow each other very closely. This is quite interesting and suggests that perhaps there is some information in the cells that do not fire, as they were cells that did fire in the non-repetitive case and their not firing in the repetitive case is an important difference in neural activity. We want to break this down further and now only consider cells that peak (exceed 2 standard deviations) in a specific time bin to determine how these cells behave in that time bin and in other time bins.
Figure 6.3: Aggregated DFF Curves for BB Sessions with only Event Peak Cells Our results are pretty interesting but also what we expected to see. Note that we have changed the scale of the y-axis from our previous graphs. Previously the range of our axis was [-80,80] and now we have changed it to [-150, 150]. Thus, while the peaks may not appear larger when simply comparing the images, if we take into account the scaling, the peaks we now see have significantly higher amplitudes. We have created 4 different graphs and in each we are only considering the cells that exceeded 2 standard deviations from their mean in a certain time frame. We consider 4 different time frames: bins 8-12 (around click), bins 13-17 (around turn), bins 18-22 (around first lick), and bins 28-32 (around last lick). There are two major features that are interesting to note. The first is the amplitude of our peaks, which are very high. When we consider only the cells that peak in a specific time bin, we see a very high peak in this time bin, as expected. It is interesting to note that these peaks are relatively narrow – meaning that there is a single time bin which has the highest DFF value which is usually the middle of the time period. Another
interesting thing to note is how flat everything other than the peak is. This suggests that these cells are all unimodal – they only have one peak. The cells peak during their specific peak time and then are flat (turned off) during the rest of the trial. This is interesting because it gives us more confidence to label cells as “click” or “lick” cells because we know that the cells fired only once during a trial and thus are associated with only one event.
6b. CC Sessions
Figure 6.4: Aggregated DFF Curves for CC Sessions
We see a lot of the same behavior in the CC graphs as we did in the BB graphs. Once again we see the matrix Q175 curves have a peak around bin 17 or 18 whereas the strio curves peak around bin 20. We also see that matrix wild type don’t have a clearly defined peak – although there is a slight peak around bin 15. Another interesting feature is that for the CC sessions, the Q175 peak is flatter than the peak of the BB sessions. There seems to be a bit of a plateau at the top of the peak, which suggests that there are many cells firing during this entire time period. The plateau stretches from around bin 15 to bin 20 and remains relatively high and flat during this entire period. This suggests that Q175 mice require cells to be firing throughout this entire period. Another thing to consider is that this might be some response to the light that the mouse encounters. We see that for both our matrix mice, for the most part, the non-repetitive curve is above the repetitive curve suggesting that the mouse requires more cells to be active in order to choose differently than the last time. However, for our strio curves, we actually see that in the peak, the repetitive curve has a higher peak value. It is also interesting that we see a switch in these values for the non-preferred graph – here the strio non-repetitive curve is higher than the repetitive curve. This could suggest that when the mouse is repeating the preferred choice, there are more cells firing at the lick point. These cells could be some sort of goal-reaching or “reward” cells. So, when the mouse is not repeating the previous choice and is choosing the non-preferred side, there is also more neural activity. Thus, these cells could be cells that are considering and responding to the reward the mouse encounters from its choice.
