MatNWB Optical Physiology Tutorial

Table of Contents

Introduction

In this tutorial, we will create fake data for a hypothetical optical physiology experiment with a freely moving animal. The types of data we will convert are:

Installing MatNWB

Use the code below within the brackets to install MatNWB from source. MatNWB works by automatically creating API classes based on the schema. Use generateCore() to generate these classes.
%{
!git clone https://github.com/NeurodataWithoutBorders/matnwb.git
cd matnwb
addpath(genpath(pwd));
generateCore();
%}

Set up the NWB file

An NWB file represents a single session of an experiment. Each file must have a session_description, identifier, and session start time. Create a new NWBFile object with those and additional metadata. For all MatNWB functions, we use the Matlab method of entering keyword argument pairs, where arguments are entered as name followed by value.
nwb = NwbFile( ...
'session_description', 'mouse in open exploration',...
'identifier', 'Mouse5_Day3', ...
'session_start_time', datetime(2018, 4, 25, 2, 30, 3), ...
'general_experimenter', 'My Name', ... % optional
'general_session_id', 'session_1234', ... % optional
'general_institution', 'University of My Institution', ... % optional
'general_related_publications', 'DOI:10.1016/j.neuron.2016.12.011'); % optional
nwb
nwb =
NwbFile with properties: nwb_version: '2.4.0' acquisition: [0×1 types.untyped.Set] analysis: [0×1 types.untyped.Set] file_create_date: [] general: [0×1 types.untyped.Set] general_data_collection: [] general_devices: [0×1 types.untyped.Set] general_experiment_description: [] general_experimenter: 'My Name' general_extracellular_ephys: [0×1 types.untyped.Set] general_extracellular_ephys_electrodes: [] general_institution: 'University of My Institution' general_intracellular_ephys: [0×1 types.untyped.Set] general_intracellular_ephys_experimental_conditions: [] general_intracellular_ephys_filtering: [] general_intracellular_ephys_intracellular_recordings: [] general_intracellular_ephys_repetitions: [] general_intracellular_ephys_sequential_recordings: [] general_intracellular_ephys_simultaneous_recordings: [] general_intracellular_ephys_sweep_table: [] general_keywords: [] general_lab: [] general_notes: [] general_optogenetics: [0×1 types.untyped.Set] general_optophysiology: [0×1 types.untyped.Set] general_pharmacology: [] general_protocol: [] general_related_publications: 'DOI:10.1016/j.neuron.2016.12.011' general_session_id: 'session_1234' general_slices: [] general_source_script: [] general_source_script_file_name: [] general_stimulus: [] general_subject: [] general_surgery: [] general_virus: [] identifier: 'Mouse5_Day3' intervals: [0×1 types.untyped.Set] intervals_epochs: [] intervals_invalid_times: [] intervals_trials: [] processing: [0×1 types.untyped.Set] scratch: [0×1 types.untyped.Set] session_description: 'mouse in open exploration' session_start_time: 2018-04-25T02:30:03.000000-04:00 stimulus_presentation: [0×1 types.untyped.Set] stimulus_templates: [0×1 types.untyped.Set] timestamps_reference_time: [] units: []

Subject information

Create a Subject object to store information about the experimental subject, such as age, species, genotype, sex, and a freeform description. Then set nwb.general_subject to the Subject object.
Each of these fields is free-form, so any values will be valid, but here are our recommendations:
subject = types.core.Subject( ...
'subject_id', '001', ...
'age', 'P90D', ...
'description', 'mouse 5', ...
'species', 'Mus musculus', ...
'sex', 'M')
subject =
Subject with properties: age: 'P90D' date_of_birth: [] description: 'mouse 5' genotype: [] sex: 'M' species: 'Mus musculus' strain: [] subject_id: '001' weight: []
nwb.general_subject = subject;

Behavior

SpatialSeries and Position

Many types of data have special data types in NWB. To store the spatial position of a subject, we will use the SpatialSeries and Position classes.
SpatialSeries is a subclass of TimeSeries. TimeSeries is a common base class for measurements sampled over time, and provides fields for data and time (regularly or irregularly sampled).
Here, we put a SpatialSeries object called 'SpatialSeries' in a Position object, and put that in a ProcessingModule named 'behavior'.
position_data = [linspace(0,10,100); linspace(0,8,100)]';
spatial_series_ts = types.core.SpatialSeries( ...
'data', position_data, ...
'reference_frame', '(0,0) is bottom left corner', ...
'timestamps', linspace(0, 100)/200)
spatial_series_ts =
SpatialSeries with properties: reference_frame: '(0,0) is bottom left corner' starting_time_unit: 'seconds' timestamps_interval: 1 timestamps_unit: 'seconds' comments: 'no comments' control: [] control_description: [] data: [100×2 double] data_continuity: [] data_conversion: 1 data_resolution: -1 data_unit: 'meters' description: 'no description' starting_time: [] starting_time_rate: [] timestamps: [1×100 double]
To help data analysis and visualization tools know that this SpatialSeries object represents the position of the animal, store the SpatialSeries object inside of a Position object.
Position = types.core.Position('SpatialSeries', spatial_series_ts);
NWB differentiates between raw, acquired data, which should never change, and processed data, which are the results of preprocessing algorithms and could change. Let's assume that the animal's position was computed from a video tracking algorithm, so it would be classified as processed data. Since processed data can be very diverse, NWB allows us to create processing modules, which are like folders, to store related processed data or data that comes from a single algorithm.
Create a processing module called "behavior" for storing behavioral data in the NWBFile and add the Position object to the module.
% create processing module
behavior_mod = types.core.ProcessingModule( ...
'description', 'contains behavioral data')
behavior_mod =
ProcessingModule with properties: description: 'contains behavioral data' dynamictable: [0×1 types.untyped.Set] nwbdatainterface: [0×1 types.untyped.Set]
% add the Position object (that holds the SpatialSeries object)
behavior_mod.nwbdatainterface.set(...
'Position', Position);
% add the processing module to the NWBFile object, and name it "behavior"
nwb.processing.set('behavior', behavior_mod);

Test write

Now, write the NWB file that we have built so far.
nwbExport(nwb, 'ophys_tutorial1.nwb')
We can then read the file and print it to inspect its contents. We can also print the SpatialSeries data that we created by referencing the names of the objects in the hierarchy that contain it. The processing module called 'behavior' contains our Position object. By default, the Position object is named 'Position'. The Position object contains our SpatialSeries object named 'SpatialSeries'.
read_nwbfile = nwbRead('ophys_tutorial1.nwb')
read_nwbfile =
NwbFile with properties: nwb_version: '2.4.0' acquisition: [0×1 types.untyped.Set] analysis: [0×1 types.untyped.Set] file_create_date: [1×1 types.untyped.DataStub] general: [0×1 types.untyped.Set] general_data_collection: [] general_devices: [0×1 types.untyped.Set] general_experiment_description: [] general_experimenter: [1×1 types.untyped.DataStub] general_extracellular_ephys: [0×1 types.untyped.Set] general_extracellular_ephys_electrodes: [] general_institution: 'University of My Institution' general_intracellular_ephys: [0×1 types.untyped.Set] general_intracellular_ephys_experimental_conditions: [] general_intracellular_ephys_filtering: [] general_intracellular_ephys_intracellular_recordings: [] general_intracellular_ephys_repetitions: [] general_intracellular_ephys_sequential_recordings: [] general_intracellular_ephys_simultaneous_recordings: [] general_intracellular_ephys_sweep_table: [] general_keywords: [] general_lab: [] general_notes: [] general_optogenetics: [0×1 types.untyped.Set] general_optophysiology: [0×1 types.untyped.Set] general_pharmacology: [] general_protocol: [] general_related_publications: [1×1 types.untyped.DataStub] general_session_id: 'session_1234' general_slices: [] general_source_script: [] general_source_script_file_name: [] general_stimulus: [] general_subject: [1×1 types.core.Subject] general_surgery: [] general_virus: [] identifier: 'Mouse5_Day3' intervals: [0×1 types.untyped.Set] intervals_epochs: [] intervals_invalid_times: [] intervals_trials: [] processing: [1×1 types.untyped.Set] scratch: [0×1 types.untyped.Set] session_description: 'mouse in open exploration' session_start_time: 2018-04-25T02:30:03.000000-04:00 stimulus_presentation: [0×1 types.untyped.Set] stimulus_templates: [0×1 types.untyped.Set] timestamps_reference_time: 2018-04-25T02:30:03.000000-04:00 units: []
read_nwbfile.processing.get('behavior').nwbdatainterface.get('Position').spatialseries.get('SpatialSeries')
ans =
SpatialSeries with properties: reference_frame: '(0,0) is bottom left corner' starting_time_unit: 'seconds' timestamps_interval: 1 timestamps_unit: 'seconds' comments: 'no comments' control: [] control_description: [] data: [1×1 types.untyped.DataStub] data_continuity: [] data_conversion: 1 data_resolution: -1 data_unit: 'meters' description: 'no description' starting_time: [] starting_time_rate: [] timestamps: [1×1 types.untyped.DataStub]
We can also use the HDFView tool to inspect the resulting NWB file.

Trials

Trials are stored in a TimeIntervals object which is a subclass of DynamicTable. DynamicTable objects are used to store tabular metadata throughout NWB, including for trials, electrodes, and sorted units. They offer flexibility for tabular data by allowing required columns, optional columns, and custom columns.
The trials DynamicTable can be thought of as a table with this structure:
Trials are stored in a TimeInterval object which subclasses DynamicTable. Here, we are adding 'correct', which will be a boolean array.
trials = types.core.TimeIntervals( ...
'colnames', {'start_time', 'stop_time', 'correct'}, ...
'description', 'trial data and properties', ...
'id', types.hdmf_common.ElementIdentifiers('data', 0:2), ...
'start_time', types.hdmf_common.VectorData('data', [.1, 1.5, 2.5], ...
'description','start time of trial'), ...
'stop_time', types.hdmf_common.VectorData('data', [1., 2., 3.], ...
'description','end of each trial'), ...
'correct', types.hdmf_common.VectorData('data', [false, true, false], ...
'description', 'whether the trial was correct'))
trials =
TimeIntervals with properties: start_time: [1×1 types.hdmf_common.VectorData] stop_time: [1×1 types.hdmf_common.VectorData] tags: [] tags_index: [] timeseries: [] timeseries_index: [] colnames: {'start_time' 'stop_time' 'correct'} description: 'trial data and properties' id: [1×1 types.hdmf_common.ElementIdentifiers] vectordata: [1×1 types.untyped.Set]
nwb.intervals_trials = trials;

Optical Physiology

Optical physiology results are written in four steps:
  1. Create imaging plane
  2. Acquired two-photon images
  3. Image segmentation
  4. Fluorescence and dF/F responses

Imaging Plane

First, you must create an ImagingPlane object, which will hold information about the area and method used to collect the optical imaging data. This requires creation of a Device object for the microscope and an OpticalChannel object. Then you can create an ImagingPlane.
optical_channel = types.core.OpticalChannel( ...
'description', 'description', ...
'emission_lambda', 500.);
device = types.core.Device();
nwb.general_devices.set('Device', device);
imaging_plane_name = 'imaging_plane';
imaging_plane = types.core.ImagingPlane( ...
'optical_channel', optical_channel, ...
'description', 'a very interesting part of the brain', ...
'device', types.untyped.SoftLink(device), ...
'excitation_lambda', 600., ...
'imaging_rate', 5., ...
'indicator', 'GFP', ...
'location', 'my favorite brain location');
nwb.general_optophysiology.set(imaging_plane_name, imaging_plane);

Storing raw optical physiology data

Now that you have your ImagingPlane, you can create a TwoPhotonSeries the class representing two photon imaging data. TwoPhotonSeries, like SpatialSeries, inherits from TimeSeries.
From here you have two options. The first option is to supply the image data using the data argument. The other option is the provide a path the images. These two options have trade-offs, so it is worth spending time considering how you want to store this data.

Adding the data directly to the NWBFile

image_series = types.core.TwoPhotonSeries( ...
'imaging_plane', types.untyped.SoftLink(imaging_plane), ...
'starting_time', 0.0, ...
'starting_time_rate', 3.0, ...
'data', ones(200, 100, 1000), ...
'data_unit', 'lumens');
nwb.acquisition.set('TwoPhotonSeries', image_series);

Linking externally to the data

image_series = types.core.TwoPhotonSeries( ...
'external_file', 'images.tiff', ...
'imaging_plane', types.untyped.SoftLink(imaging_plane), ...
'external_file_starting_frame', 0, ...
'format', 'tiff', ...
'starting_time_rate', 3.0, ...
'starting_time', 0.0, ...
'data', NaN(2, 2, 2), ...
'data_unit', 'lumens');
nwb.acquisition.set('TwoPhotonSeries2', image_series);

Plane Segmentation

Image segmentation stores the detected regions of interest in the TwoPhotonSeries data. ImageSegmentation allows you to have more than one segmentation by creating more PlaneSegmentation objects.

Regions of interest (ROIs)

Add ROIs using an image mask. An image mask is an array that is the same size as a single frame of the TwoPhotonSeries, and indicates where a single region of interest is. This image mask may be boolean or continuous between 0 and 1.
% generate fake image_mask data
imaging_shape = [100, 100];
x = imaging_shape(1);
y = imaging_shape(2);
n_rois = 20;
image_mask = zeros(y, x, n_rois);
for i = 1:n_rois
start = randi(90,2,1);
image_mask(start(1):start(1)+10, start(2):start(2)+10, 1) = 1;
end
% add data to NWB structures
plane_segmentation = types.core.PlaneSegmentation( ...
'colnames', {'image_mask'}, ...
'description', 'output from segmenting my favorite imaging plane', ...
'id', types.hdmf_common.ElementIdentifiers('data', int64(0:19)), ...
'imaging_plane', types.untyped.SoftLink(imaging_plane));
plane_segmentation.image_mask = types.hdmf_common.VectorData( ...
'data', image_mask, 'description', 'image masks');
Now create an ImageSegmentation object and put the plane_segmentation object inside of it, naming it PlaneSegmentation.
img_seg = types.core.ImageSegmentation();
img_seg.planesegmentation.set('PlaneSegmentation', plane_segmentation)
ans =
Set with properties: PlaneSegmentation: [types.core.PlaneSegmentation]
Now create a ProcessingModule called "ophys" and put our img_seg object in it, calling it ImageSegmentation, and add the ProcessingModule to nwb.
ophys_module = types.core.ProcessingModule( ...
'description', 'contains optical physiology data')
ophys_module =
ProcessingModule with properties: description: 'contains optical physiology data' dynamictable: [0×1 types.untyped.Set] nwbdatainterface: [0×1 types.untyped.Set]
ophys_module.nwbdatainterface.set('ImageSegmentation', img_seg);
nwb.processing.set('ophys', ophys_module);

Storing fluorescence of ROIs over time

Now that ROIs are stored, you can store fluorescence dF/F data for these regions of interest. This type of data is stored using the RoiResponseSeries class. You will not need to instantiate this class directly to create objects of this type, but it is worth noting that this is the class you will work with after you read data back in.
First, create a data interface to store this data in
roi_table_region = types.hdmf_common.DynamicTableRegion( ...
'table', types.untyped.ObjectView(plane_segmentation), ...
'description', 'all_rois', ...
'data', [0 n_rois-1]');
roi_response_series = types.core.RoiResponseSeries( ...
'rois', roi_table_region, ...
'data', NaN(n_rois, 100), ...
'data_unit', 'lumens', ...
'starting_time_rate', 3.0, ...
'starting_time', 0.0);
fluorescence = types.core.Fluorescence();
fluorescence.roiresponseseries.set('RoiResponseSeries', roi_response_series);
ophys_module.nwbdatainterface.set('Fluorescence', fluorescence);
Finally, the ophys ProcessingModule is added to the NwbFile.
nwb.processing.set('ophys', ophys_module);
Write the NWB file
nwbExport(nwb, 'ophys_tutorial.nwb');

Reading the NWB file

read_nwb = nwbRead('ophys_tutorial.nwb');
Data arrays are read passively from the file. Calling TimeSeries.data does not read the data values, but presents an HDF5 object that can be indexed to read data.
read_nwb.processing.get('ophys').nwbdatainterface.get('Fluorescence')...
.roiresponseseries.get('RoiResponseSeries').data
ans =
DataStub with properties: filename: 'ophys_tutorial.nwb' path: '/processing/ophys/Fluorescence/RoiResponseSeries/data' dims: [20 100] ndims: 2
This allows you to conveniently work with datasets that are too large to fit in RAM all at once. Access the data in the matrix using the load method.
load with no input arguments reads the entire dataset:
read_nwb.processing.get('ophys').nwbdatainterface.get('Fluorescence'). ...
roiresponseseries.get('RoiResponseSeries').data.load
ans = 20×100
NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
If all you need is a section of the data, you can read only that section by indexing the DataStub object like a normal array in MATLAB. This will just read the selected region from disk into RAM. This technique is particularly useful if you are dealing with a large dataset that is too big to fit entirely into your available RAM.
read_nwb.processing.get('ophys'). ...
nwbdatainterface.get('Fluorescence'). ...
roiresponseseries.get('RoiResponseSeries'). ...
data(1:5, 1:10)
ans = 5×10
NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
% read back the image masks
read_nwb.processing.get('ophys'). ...
nwbdatainterface.get('ImageSegmentation'). ...
planesegmentation.get('PlaneSegmentation'). ...
image_mask.data.load
ans =
ans(:,:,1) = 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 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