Reading NWB Files with MatNWB on DandiHub

Authors: Ryan Ly, with modification by Lawrence Niu

DandiHub edition* authors: Thomas Kuenzel & Vijay Iyer

Last Updated: 2023-09-05

(*) minimally modified to utilize dandi package for download and to skip the MatNWB installation

Introduction

In this tutorial, we will read single neuron spiking data that is in the NWB standard format and do a basic visualization of the data. More thorough documentation regarding reading files as well as the NwbFile class, can be found in the NWB Overview Documentation

Download the Dataset

Use the dandi Python package to download the dataset to the user-local dandisets folder. If you are running this livescript on DandiHub, the dandi package is already pre-installed. On a local environment, you need to install the dandi package in the python environment returned by running pyenv() in MATLAB.

environment = "DandiHub";
switch environment
    case "DandiHub"
        targetFolder = "/home/jovyan/dandisets/000004";
    case "Local"
        targetFolder = fullfile(userpath(), "dandisets", "000004");
end
py.dandi.download.download("dandi://dandi/000004/sub-P11HMH/", targetFolder, existing='overwrite')

No module or function named 'dandi'.

Read the NWB file

You can read any NWB file using nwbRead. You will find that the print out for this shows a summary of the data within.

nwb = nwbRead(fullfile(targetFolder, "sub-P11HMH", "sub-P11HMH_ses-20061101_ecephys+image.nwb"))

Stimulus

Now lets take a look at the visual stimuli presented to the subject. They will be in nwb.stimulus_presentation

nwb.stimulus_presentation

This results shows us that nwb.stimulus_presentation is a Set object that contains a single data object called StimulusPresentation, which is an OpticalSeries neurodata type. Use the get method to return this OpticalSeries. Set objects store a collection of other NWB objects.

nwb.stimulus_presentation.get('StimulusPresentation')

OpticalSeries is a neurodata type that stores information about visual stimuli presented to subjects. This print out shows all of the attributes in the OpticalSeries object named StimulusPresentation. The images are stored in StimulusPresentation.data

StimulusImageData = nwb.stimulus_presentation.get('StimulusPresentation').data

When calling a data object directly, the data is not read but instead a DataStub is returned. This is because data is read “lazily” in MatNWB. Instead of reading the entire dataset into memory, this provides a “window” into the data stored on disk that allows you to read only a section of the data. In this case, the last dimension indexes over images. You can index into any DataStub as you would any MATLAB matrix.

% get the image and display it
% the dimension order is provided as follows:
% [rgb, y, x, image index]
img = StimulusImageData(1:3, 1:300, 1:400, 32);

A bit of manipulation allows us to display the image using MATLAB’s imshow.

img = permute(img,[3, 2, 1]);  % fix orientation
img = flip(img, 3); % reverse color order
F = figure();
imshow(img, 'InitialMagnification', 'fit');
daspect([3, 5, 5]);

To read an entire dataset, use the DataStub.load method without any input arguments. We will use this approach to read all of the image display timestamps into memory.

stimulus_times = nwb.stimulus_presentation.get('StimulusPresentation').timestamps.load();

Quick PSTH and raster

Here, I will pull out spike times of a particular unit, align them to the image display times, and finally display the results.

First, let us show the first row of the NWB Units table representing the first unit.

nwb.units.getRow(1)

Let us specify some parameters for creating a cell array of spike times aligned to each stimulus time.

%% Align spikes by stimulus presentations

unit_ind =8;
before =1;
after =3;

getRow provides a convenient method for reading this data out.

unit_spikes = nwb.units.getRow(unit_ind, 'columns', {'spike_times'}).spike_times{1}

Spike times from this unit are aligned to each stimulus time and compiled in a cell array

results = cell(1, length(stimulus_times));
for itime = 1:length(stimulus_times)
    stimulus_time = stimulus_times(itime);
    spikes = unit_spikes - stimulus_time;
    spikes = spikes(spikes > -before);
    spikes = spikes(spikes < after);
    results{itime} = spikes;
end

Plot results

Finally, here is a (slightly sloppy) peri-stimulus time histogram

figure();
hold on
for i = 1:length(results)
    spikes = results{i};
    yy = ones(length(spikes)) * i;

    plot(spikes, yy, 'k.');
end
hold off
ylabel('trial');
xlabel('time (s)');
axis('tight')

figure();
all_spikes = cat(1, results{:});
histogram(all_spikes, 30);
ylabel('count')
xlabel('time (s)');
axis('tight')

Conclusion

This is an example of how to get started with understanding and analyzing public NWB datasets. This particular dataset was published with an extensive open analysis conducted in both MATLAB and Python, which you can find here. For more datasets, or to publish your own NWB data for free, check out the DANDI archive here.