nwb =
NwbFile with properties:
+.S10 { margin: 15px 10px 5px 4px; padding: 0px; line-height: 18px; min-height: 0px; white-space: pre-wrap; color: rgb(33, 33, 33); font-family: Helvetica, Arial, sans-serif, Helvetica, Arial, sans-serif; font-style: normal; font-size: 17px; font-weight: 700; text-align: left; }
+
Behavior Data
This tutorial will guide you in writing behavioral data to NWB.
Creating an NWB File
Create an NWBFile object with the required fields (session_description, identifier, and session_start_time) and additional metadata.
nwb = NwbFile( ...
'session_description', 'mouse in open exploration',...
'identifier', 'Mouse5_Day3', ...
'session_start_time', datetime(2018, 4, 25, 2, 30, 3, 'TimeZone', 'local'), ...
'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
SpatialSeries: Storing continuous spatial data
SpatialSeries is a subclass of TimeSeries that represents data in space, such as the spatial direction e.g., of gaze or travel or position of an animal over time.
Create data that corresponds to x, y position over time.
position_data = [linspace(0, 10, 50); linspace(0, 8, 50)]; % 2 x nT array
In SpatialSeries data, the first dimension is always time (in seconds), the second dimension represents the x, y position. However, as described in the dimensionMapNoDataPipes tutorial, when a MATLAB array is exported to HDF5, the array is transposed. Therefore, in order to correctly export the data, in MATLAB the last dimension of an array should be time. SpatialSeries data should be stored as one continuous stream as it is acquired, not by trials as is often reshaped for analysis. Data can be trial-aligned on-the-fly using the trials table. See the trials tutorial for further information.
For position data reference_frame indicates the zero-position, e.g. the 0,0 point might be the bottom-left corner of an enclosure, as viewed from the tracking camera.
timestamps = linspace(0, 50, 50)/ 200;
position_spatial_series = types.core.SpatialSeries( ...
'description', 'Postion (x, y) in an open field.', ...
'data', position_data, ...
'timestamps', timestamps, ...
'reference_frame', '(0,0) is the bottom left corner.' ...
)
SpatialSeries: Storing continuous spatial data
SpatialSeries is a subclass of TimeSeries that represents data in space, such as the spatial direction e.g., of gaze or travel or position of an animal over time.
Create data that corresponds to x, y position over time.
position_data = [linspace(0, 10, 50); linspace(0, 8, 50)]; % 2 x nT array
In SpatialSeries data, the first dimension is always time (in seconds), the second dimension represents the x, y position. However, as described in the dimensionMapNoDataPipes tutorial, when a MATLAB array is exported to HDF5, the array is transposed. Therefore, in order to correctly export the data, in MATLAB the last dimension of an array should be time. SpatialSeries data should be stored as one continuous stream as it is acquired, not by trials as is often reshaped for analysis. Data can be trial-aligned on-the-fly using the trials table. See the trials tutorial for further information.
For position data reference_frame indicates the zero-position, e.g. the 0,0 point might be the bottom-left corner of an enclosure, as viewed from the tracking camera.
timestamps = linspace(0, 50, 50)/ 200;
position_spatial_series = types.core.SpatialSeries( ...
'description', 'Postion (x, y) in an open field.', ...
'data', position_data, ...
'timestamps', timestamps, ...
'reference_frame', '(0,0) is the bottom left corner.' ...
)
Position: Storing position measured over time
To help data analysis and visualization tools know that this SpatialSeries object represents the position of the subject, store the SpatialSeries object inside a Position object, which can hold one or more SpatialSeries objects.
position = types.core.Position();
position.spatialseries.set('SpatialSeries', position_spatial_series);
Create a Behavior Processing Module
Create a processing module called "behavior" for storing behavioral data in the NWBFile, then add the Position object to the processing module.
behavior_processing_module = types.core.ProcessingModule('description', 'stores behavioral data.');
behavior_processing_module.nwbdatainterface.set("Position", position);
nwb.processing.set("behavior", behavior_processing_module);
CompassDirection: Storing view angle measured over time
Analogous to how position can be stored, we can create a SpatialSeries object for representing the view angle of the subject.
For direction data reference_frame indicates the zero direction, for instance in this case "straight ahead" is 0 radians.
view_angle_data = linspace(0, 4, 50);
direction_spatial_series = types.core.SpatialSeries( ...
'description', 'View angle of the subject measured in radians.', ...
'data', view_angle_data, ...
'timestamps', timestamps, ...
'reference_frame', 'straight ahead', ...
'data_unit', 'radians' ...
);
direction = types.core.CompassDirection();
direction.spatialseries.set('spatial_series', direction_spatial_series);
We can add a CompassDirection object to the behavior processing module the same way we have added the position data.
behavior_processing_module.nwbdatainterface.set('CompassDirection', direction);
BehaviorTimeSeries: Storing continuous behavior data
BehavioralTimeSeries is an interface for storing continuous behavior data, such as the speed of a subject.
speed_data = linspace(0, 0.4, 50);
speed_time_series = types.core.TimeSeries( ...
'data', speed_data, ...
'starting_time', 1.0, ... % NB: Important to set starting_time when using starting_time_rate
'starting_time_rate', 10.0, ... % Hz
'description', 'he speed of the subject measured over time.', ...
'data_unit', 'm/s' ...
);
behavioral_time_series = types.core.BehavioralTimeSeries();
behavioral_time_series.timeseries.set('speed', speed_time_series);
% Add behavioral_time_series to the processing module
behavior_processing_module.nwbdatainterface.set('BehavioralTimeSeries', behavioral_time_series);
BehavioralEvents: Storing behavioral events
BehavioralEvents is an interface for storing behavioral events. We can use it for storing the timing and amount of rewards (e.g. water amount) or lever press times.
reward_amount = [1.0, 1.5, 1.0, 1.5];
event_timestamps = [1.0, 2.0, 5.0, 6.0];
time_series = types.core.TimeSeries( ...
'data', reward_amount, ...
'timestamps', event_timestamps, ...
'description', 'The water amount the subject received as a reward.', ...
'data_unit', 'ml' ...
);
behavioral_events = types.core.BehavioralEvents();
behavioral_events.timeseries.set('lever_presses', time_series);
% Add behavioral_events to the processing module
behavior_processing_module.nwbdatainterface.set('BehavioralEvents', behavioral_events);
Storing only the timestamps of the events is possible with the ndx-events NWB extension. You can also add labels associated with the events with this extension. You can find information about installation and example usage here.
BehavioralEpochs: Storing intervals of behavior data
BehavioralEpochs is for storing intervals of behavior data. BehavioralEpochs uses IntervalSeries to represent the time intervals. Create an IntervalSeries object that represents the time intervals when the animal was running. IntervalSeries uses 1 to indicate the beginning of an interval and -1 to indicate the end.
run_intervals = types.core.IntervalSeries( ...
'description', 'Intervals when the animal was running.', ...
'data', [1, -1, 1, -1, 1, -1], ...
'timestamps', [0.5, 1.5, 3.5, 4.0, 7.0, 7.3] ...
);
behavioral_epochs = types.core.BehavioralEpochs();
behavioral_epochs.intervalseries.set('running', run_intervals);
sleep_intervals = types.core.IntervalSeries( ...
'description', 'Intervals when the animal was sleeping', ...
'data', [1, -1, 1, -1], ...
'timestamps', [15.0, 30.0, 60.0, 95.0] ...
);
behavioral_epochs.intervalseries.set('sleeping', sleep_intervals);
% Add behavioral_epochs to the processing module
behavior_processing_module.nwbdatainterface.set('BehavioralEpochs', behavioral_epochs);
Another approach: TimeIntervals
Using TimeIntervals to represent time intervals is often preferred over BehavioralEpochs and IntervalSeries. TimeIntervals is a subclass of DynamicTable, which offers flexibility for tabular data by allowing the addition of optional columns which are not defined in the standard DynamicTable class.
sleep_intervals = types.core.TimeIntervals( ...
'description', 'Intervals when the animal was sleeping.', ...
'colnames', {'start_time', 'stop_time', 'stage'} ...
);
sleep_intervals.addRow('start_time', 0.3, 'stop_time', 0.35, 'stage', 1);
sleep_intervals.addRow('start_time', 0.7, 'stop_time', 0.9, 'stage', 2);
sleep_intervals.addRow('start_time', 1.3, 'stop_time', 3.0, 'stage', 3);
nwb.intervals.set('sleep_intervals', sleep_intervals);
EyeTracking: Storing continuous eye-tracking data of gaze direction
EyeTracking is for storing eye-tracking data which represents direction of gaze as measured by an eye tracking algorithm. An EyeTracking object holds one or more SpatialSeries objects that represent the gaze direction over time extracted from a video.
eye_position_data = [linspace(-20, 30, 50); linspace(30, -20, 50)];
right_eye_position = types.core.SpatialSeries( ...
'description', 'The position of the right eye measured in degrees.', ...
'data', eye_position_data, ...
'starting_time', 1.0, ... % NB: Important to set starting_time when using starting_time_rate
'starting_time_rate', 50.0, ... % Hz
'reference_frame', '(0,0) is middle', ...
'data_unit', 'degrees' ...
);
left_eye_position = types.core.SpatialSeries( ...
'description', 'The position of the right eye measured in degrees.', ...
'data', eye_position_data, ...
'starting_time', 1.0, ... % NB: Important to set starting_time when using starting_time_rate
'starting_time_rate', 50.0, ... % Hz
'reference_frame', '(0,0) is middle', ...
'data_unit', 'degrees' ...
);
eye_tracking = types.core.EyeTracking();
eye_tracking.spatialseries.set('right_eye_position', right_eye_position);
eye_tracking.spatialseries.set('left_eye_position', left_eye_position);
behavior_processing_module.nwbdatainterface.set('EyeTracking', eye_tracking);
PupilTracking: Storing continuous eye-tracking data of pupil size
PupilTracking is for storing eye-tracking data which represents pupil size. PupilTracking holds one or more TimeSeries objects that can represent different features such as the dilation of the pupil measured over time by a pupil tracking algorithm.
pupil_diameter = types.core.TimeSeries( ...
'description', 'Pupil diameter extracted from the video of the right eye.', ...
'data', linspace(0.001, 0.002, 50), ...
'starting_time', 1.0, ... % NB: Important to set starting_time when using starting_time_rate
'starting_time_rate', 20.0, ... % Hz
'data_unit', 'meters' ...
);
pupil_tracking = types.core.PupilTracking();
pupil_tracking.timeseries.set('pupil_diameter', pupil_diameter);
behavior_processing_module.nwbdatainterface.set('PupilTracking', pupil_tracking);
Writing the behavior data to an NWB file
% Save to tutorials/tutorial_nwb_files folder
nwbFilePath = misc.getTutorialNwbFilePath('behavior_tutorial.nwb');
nwbExport(nwb, nwbFilePath);
fprintf('Exported NWB file to "%s"\n', 'behavior_tutorial.nwb')