# Spatial Calibration for Multiple Objectives

Multiple users of the scope may be running projects that require different types of objectives.  How to keep a spatial calibration for each and switch between them when necessary?

Scanbox now includes an “objective” configuration variable — a cell array of strings each with the name of a different objective.  Right now I have:


% objective list
sbconfig.objectives = {'Nikon 16x','Nikon 25x'};



Now, within the Knobby panel you will see a pull down list containing the objective names:

Select the desired objective before performing a calibration.  Once the calibration is finished it will be saved.  If a calibration is not present for a given objective the calibration button will read “No Calibration” and the mouse control will be disabled. If you change the objective, simply select the corresponding entry in the pull down list to apply the new calibration (no need to restart Scanbox).

When collecting data the info structure will include the objective name and calibration data:


info =

struct with fields:

resfreq: 7930
postTriggerSamples: 5000
recordsPerBuffer: 512
bytesPerBuffer: 10240000
channels: 2
ballmotion: []
abort_bit: 0
scanbox_version: 2
scanmode: 1
config: [1×1 struct]
sz: [512 796]
otwave: []
otwave_um: []
otparam: []
otwavestyle: 1
volscan: 0
opto2pow: []
area_line: 1
calibration: [1×13 struct]
objective: 'Nikon 25x'
messages: {}
usernotes: ''



# Visualizing Individual Slices during Volumetic Imaging

During volumetric imaging, Scanbox displays all images as they are acquired. This can be inconvenient if we are trying to visually assess the activity of neurons within any one optical slice.  One solution is to display the images separately in a montage by means of a Scanbox plug-in.  However, it would be better to have such an option integrated into Scanbox.  A new feature offers this possibility.

Turning on a  “Slice” checkbox within the Optotune panel will make a pull-down menu within the display appear.  This menu allows selection of the optical slice you want to visualize. Unchecking the slice checkbox allows Scanbox to go back to its normal operation of showing all images in the incoming stream.

Here is a brief demo showing this feature.  Enjoy…

# Automatic Control of Laser Power

A new checkbox within the Laser panel (labeled AGC) allows you to turn an automatic control of laser power on and off.

When AGC is on, Scanbox checks the distribution of pixel values on the image every T seconds, and increases or decreases the laser power by a certain factor if the fraction of pixels above a threshold is outside the desired range.

The values of these parameters are found in a new section of the sbconfig.m file:

% Laser AGC
sbconfig.agc_period = 1;            % adjust power every T seconds
sbconfig.agc_factor = [0.93 1.08];  % factor to change laser power down or up if outside prctile bounds
sbconfig.agc_prctile = [1e-5 1e-3]; % bounds on percent pixels saturated wanted
sbconfig.agc_threshold = 250;       % threshold above which is considered saturated (255 is max value)


Below is a video showing AGC in action.

At the beginning of the video,  I focus on pollen grains using low power. When the AGC is turned on, it brings the power up. Then, if I increase the PMT gain, the laser power is decreased in response.  If the laser power is changed manually, AGC will re-adjust it to bring the pixel distribution within the desired limits.

AGC of laser power is useful when running a z-stack with a range that is larger than 100um or so.  In that case, engaging AGC will make Scanbox adjust laser power as a function of depth. Another situation where AGC may be useful is while running very long experiments/sessions where water may evaporate slowly leading to a reduction of the signal. In that case, turning AGC would compensate and could render the data usable.

# Automatic Optotune Calibration

We previously explained how to calibrate the optotune manually.  With the introduction of Knobby 2, we are able to make this process automatic.  You will now find a ‘Calibration’ button in the Optotune panel.  To use it, do the following:

1. Set the optotune slider to its lowest value (slide all the way down)
2. Bring some pollen grains into focus
3. Stop focusing
4. Make sure the data directory has a directory named xx0
5. Click the ‘Calibrate’ button
6. Sit back and relax.  Wait for the process to complete.

Knobby will run some z-stack acquisitions for different values of the optotune current setting.  The volumetric data will be used to calculate the shift in z at various values of the current. A panel will display the progress in processing the images (it takes about 2 min). Scanbox will then plot the raw data and a fit by a quadratic polynomial, which may look something like this:

Scanbox will write a calibration file which will take effect next time you start Scanbox.

After restarting Scanbox, you can check the calibration as follows:

1. Set the optotune slider to its lowest value (slide all the way down)
2. Set Knobby to super-fine mode
3. Focus on some pollen grains
4. Zero Knobby (XYZ)
5. Move out-of-focus by moving the optotune slider up to some value
6. Now bring sample back into focus using the z-axis knob
7. Compare the reading of the z-axis in Knobby’s screen with the depth noted in the optotune panel. These two numbers should match very closely.

# Click-and-Center with Knobby Mouse Control

If you have been making use of Knobby’s spatial calibration button, you can now move on to interacting with Knobby remotely.

The new Knobby scheduler panel looks a bit expanded.  The speed (coarse/fine/super-fine), the mode (normal/rotate), and zero (XYZ/XYZA) buttons should be self explanatory. They do exactly the same as if you were to be using them on Knobby.  The action you select on the panel will be reflected on Knobby’s screen as well.

The range, steps and frames entries provide a quick way to edit the scheduler’s table if you intend to do a simple z-stack.  Range is the extent of the distance in z you want to span, steps is the the size of the step (in um), and frames is the number of frames you want to scan at each position.

Finally, there is new checkbox labeled “Mouse control”. When this is active and you are scanning you can easily move around the sample by clicking the cell you want to bring to the center of the screen, as shown by the video example above.  Once you click on a cell, Knobby does the rest. We call this feature click-and-center. This works at all magnifications (even if you change it on the fly). At the same time, scrolling the mouse wheel allows you to focus up and down.  The speed of the movement will be controlled by the selected speed. You may want to start at the slowest speed (Super-fine).

We added two new features to the knobby scheduler panel, as shown below by the arrows.

One (left arrow) allows knobby to perform an automatic spatial calibration of the system, measuring the (x,y) size of pixels at all magnifications.  To perform this calculation do the following:

1. Focus on some pollen grains.  Make sure one of the pollen grains is well centered on the screen at the highest magnification (x8).
2. Make you there is an empty folder named xx0 in the data directory you selected.
3. Hit the calibration button (pointed by the left arrow).
4. The system will ask you if you want to proceed.
5. Scanbox will then loop over all magnifications, collecting data for 8 sec and moving the sample by a known distance.
6. When it finishes, Scanbox will display the optimal value of a resonant gain magnification variable that will make your pixels square.  Note that value.

Now, when you restart Scanbox the system will read the new spatial calibration in the calibration button.  The format is [xsize ysize] [xfov yfov].  The left pair shows the width and height of a pixel (in micrometers), and the right pair shows the width and height of the field-of-view at the current magnification setting (also in micrometers).  In the example above, the (x,y) size is [0.72 0.70] um, meaning the aspect ratio is close to one.

If the aspect ratio is far from one, and you prefer square pixels, do the following:

1. Change the value of the “gain_resonant_mult” variable in the configuration file to the one suggested by the Scanbox spatial calibration, restart Scanbox.
2. Re-run the spatial calibration.  Thi will create a new calibration file.
3. Restart Scanbox to read the newly created calibration file.  Now the aspect ratio should be close to one.

The second feature is one is a checkbox labeled Return. When you run a knobby schedule you have click this box to make knobby return to the initial position it started from. So, for example, upon completion of a z-stack the system will go back to where it started. Note: this feature uses the Store/Recall C function, so if you have anything stored there it will be erased.

# A Plug-in Server

Scanbox has a memory mapped mechanism to share the incoming data stream with other processes that intend to consume the data in real-time.

Examples of simple uses of such data sharing include the calculation of rolling averages, displaying a real-time histogram, or generating a montage display for volumetric data.

Of course, different experiments may need to be processed by different plug-ins.  To simplify this process we now provide a single mechanism that allows the selection of a plug-in via a pull-down menu in the Scanbox GUI.

The configuration variable “plugin” can be used to list a number of different options to call.  In the example below, two possible options are listed.

sbconfig.plugin = {'rolling','montage'}; %plugin options


These must be names of Matlab scripts that will be called to process the data.

When Scanbox starts it will list these options in a pull down menu within the real-time processing panel, along with two check-boxes, one labeled “plugin” and another labeled “focus”.

The pull down menu allows the user to select one plugin among the available selections.

The plug-in checkbox indicates you want the data to be shared via the memory mapped mechanism, and the “focus” checkbox indicates whether you want the data to be shared not only during a grabbing operation, but also when you are focusing.

For example, adjusting the laser intensity by analyzing the histogram of values in the incoming data may be something you want to do ahead of an actual experiment, and something you will want to run while focusing.

Now, when you start Scanbox a new Matlab instance is opened and calls the sbxplugin_server.m script (assuming mmap=true in the config file).  This is a simple script that waits for new data to arrive and calls the selected plug-in.

% Scanbox plug in server script
% This code will run upon startup of Scanbox on a separate matlab instance if mmap=true
% in the scanbox_config file

% Open memory mapped file -- define just the header first

scanbox_config; % read the scanbox config structure

mmfile = memmapfile('scanbox.mmap','Writable',true, ...
'Format', { 'int16' [1 16] 'header' } , 'Repeat', 1);
flag = 1;

% Process all incoming frames until Scanbox stops

running = false;

while(true)

disp('Waiting for imaging data stream');

while ~running
end

plugin_id = mmfile.Data.header(7); % get the plug in to be used

fprintf('Plugin id=%d\n',plugin_id);

while running

running = (mmfile.Data.header(1) ~= -2); % no stop signal present

if running && mmfile.Data.header(1)>=0 % if not stopped and frame present

switch plugin_id

case 1
% replace the line below with your own script for
% plugin with id #1

case 2
% replace the line below with your own script for
% plugin with id #2

otherwise
disp('Invalid plugin id number');
end

mmfile.Data.header(1) = -1; % signal Scanbox that frame has been consumed!

end
end
end

clear(mmfile); % close the memory mapped file
close all; % close all figures


The default server code simply lists number of of the incoming frame and the selected plug-in.  Just add an eval() call to the script name at the indicated locations.

The output of the script, as is, will look like:


Waiting for imaging data stream
Plugin id=2
ROI=0 Expt=0
Frame: 00000 Plugin: montage
Frame: 00001 Plugin: montage
Frame: 00002 Plugin: montage
Frame: 00003 Plugin: montage
Frame: 00004 Plugin: montage
Frame: 00005 Plugin: montage
Frame: 00006 Plugin: montage
Frame: 00007 Plugin: montage
Frame: 00008 Plugin: montage
Frame: 00009 Plugin: montage
Frame: 00010 Plugin: montage
Waiting for imaging data stream



The user can now easily switch between plugins from the Scanbox window by changing the selection in the pull down menu.

# New alignment and segmentation tools

Improved alignment and segmentation tools have now been released in the latest version of Scanbox, while retaining much of the functionality of the last version.

sbxaligntool. The new alignment tool, shown below, adds batch processing of files, including the processing of eye and ball motion if those data are present.  A region-of-interest (ROI) can optionally be selected manually or automatically.  For file entries where manual selection was specified, the program will stop and present a rectangle on the screen for the user to specify the ROI.  Typically, automatic ROI works fine, and it does not require the user to stand by the computer to specify the ROI each time a new file starts to process.

As the files are aligned, the Status column and Status message will display the progress. The alignment procedure can also be visualized by clicking the Live update checkbox, which will display the mean of the entire image stack as the process moves along.  Pan and Zoom buttons allow the user to inspect details in the live image, such as fine branches, as the system is carrying out the alignment. This tool performs rigid alignment and the result is stored in a *_rigid.sbx file.  The original data is left untouched. The tool can align images relatively fast (about 65 frames/sec in my computer), but it will take a few minutes to compute the reference image if the sequence is 15 min or more (please be patient). Alignment improves with the number of passes requested.  Usually one pass is very good, but you can try two or more passes by changing the appropriate entry in the column. The alignment algorithm has been improved.

sbxsegmenttool. The segmentation tool works in a similar way as before. After loading the aligned *_rigid.sbx file, it will display the correlation map.  Segmentation then proceeds as in the previous version.

Once a number of cells are selected, you must save the segmentation and then extract the signals by pressing the corresponding buttons. After the signals are extracted you can select a cell with the pull down menu on the bottom left and the traces corresponding to that cell (now highlighted in green) will be displayed.  The blue trace represents the average signal within the cell, the gray trace is the neuropil, and the trace is the estimated spike rate using the Vanilla algorithm with parameters optimized for GCaMP6f.

Improvements include an Undo button, which will remove the last cell segmented. The ability to load a previous segmentation (it will load automatically after you select the *_rigid.sbx file), to continue adding cells to it.  The ability to define a ROI in the correlation map to automatically increase the contrast of the correlation map as the most salient cells are selected. A zoomed version of the mean image on the right to go along with the correlation map.  And the tool now saves the neuropil and deconvolved signal as well.

Give these tools a try. Report back any suggestions for improvements or problems you encounter.

# Spikefinder: the Vanilla algorithm

Over the last few months, the Spikefinder challenge has provided a playing ground for colleagues to offer ideas about how to estimate the spiking of individual neurons based on the measured activity of fluorescent calcium indicators.

The challenge was for people to come up with strategies that beat the performance of state-of-the-art algorithms, STM & oopsi. A good number of algorithms were able to achieve this in short time, including one I submitted, termed Vanilla.

The best performing algorithms seem to have relied on modern machine learning methods. Vanilla is nothing more than a linear filter followed by a static-nonlinearity $y(t) = \phi ( h(t) * x(t) )$ — thus the name.

The filter $h(t)$ is a linear combination of an even filter, estimating the mean of the signal at time $t$, and an odd filter, estimating the derivative of the signal at time $t$.

The even filter is a Gaussian, $h_{even} = A \exp ( -t^2 / 2 \sigma^2 )$, and the odd filter is the derivative of a Gaussian $h_{odd} = B t \exp (-t^2 / 2 \sigma^2)$.  The constants $A$ and $B$ are such that the norm of the filters is normalized to one, $\| A \| = \| B \| = 1$.  These two filters are linearly combined while keeping the norm of resulting filter equal to one, $h(t) = \cos \alpha \: h_{even}(t) + \sin \alpha \: h_{odd}(t)$.

The output nonlinearity is a rectifier to a power, $\phi ( x ) = (x- \theta)^\beta$ if $x>\theta$, and zero otherwise.

The model has only 4 parameters, $\{\sigma, \alpha, \theta, \beta \}$. The amount of smoothing of the signal is controlled by $\sigma$, the shape of the filter is controlled by $\alpha$, and the threshold $\theta$ and power $\beta$ determine the shape of the nonlinearity.

The model is fit by finding the optimal values of $\{\sigma, \alpha, \theta, \beta \}$ that maximize the correlation between its output $y(t)$ and the recorded spiking of the neuron.  I used Matlab’s fminsearch() to perform this optimization, which was typically finished in about 60 sec or less for most datasets.

The only pre-processing done was a z-scoring of the raw signals.  In one dataset (dataset #5, GCaMP6s in V1), we allowed for an extra-delay parameter between the signal and the prediction.

I was surprised this entry did relatively well, as the algorithm it is basically a version of STM. I think the particular shape of the output nonlinearity (rectifier+power vs exponential), the constrain imposed on the shape of the filters, and the resulting small number of parameters, paid a role in Vanilla doing better overall.

The top algorithms reached an absolute performance of about 0.47 and it seems unlikely this performance can be improved by a lot. This seems to highlight the limitations of the current families of calcium indicators in yielding precise spiking information — so there is plenty of opportunity to improve them.

It is interesting that despite its simplicity, the relative performance of Vanilla, with a correlation coefficient of 0.428 was not dramatically inferior to that of the top performing, deep network models, with all its bells and whistles, which landed at 0.464.  So, one must pay due respects to deep networks, but I was honestly expecting Vanilla to be completely blown out of the water in terms of performance, and I don’t think it was.

Finally, Vanilla is a non-casual algorithm, as both past and future samples are used to predict the response at time $t$. In some situations, however, when we are trying close the loop as fast as possible by controlling a stimulus based on neural activity itself, we need algorithms that are casual and can provide a fast, online estimate of spiking activity. I wonder if any of the submissions are causal algorithms and, if not, what would be the best performance the methods can attain if we allow them to provide estimates of spiking based only on past samples.