tank-lab-to-nwb 0.2.0

NWB conversion scripts and tutorials.

tank-lab-to-nwb

NWB conversion scripts and tutorials. A collaboration with the Tank lab, funded by the Simons Foundation.

Install

$ pip install tank-lab-to-nwb

Usage

There are two ways to go about converting Neuropixel and Virmen behavior data.

(1) The primary processing pipeline synchronizes the task data with the electrophysiology data through TTL pulse and writes the spiking output to the same NWB file.

The required arguments for the use of the relevant functions are denoted in the comments of their respective sections of the conversion script. These include the file or folder locations of the data to be converted to NWB format, as well as several optional fields such as Subject information (species/age/weight).

After editing the conversion script convert_towers.py with the proper path for Neuropixel and behavior data, the conversion can be executed from the terminal:

$ cd tank-lab-to-nwb
$ python tank_lab_to_nwb/convert_towers_task/convert_towers.py

Alternatively, the conversion can be done using a custom tailored jupyter notebook spikeinterface_pipeline.ipynb that can be launched from the terminal:

$ jupyter notebook notebooks/spikeinterface_pipeline.ipynb 

(2) The NWBFile can be inspected by reading it from a python script:

from pynwb import NWBHDF5IO

file_path = 'TowersTask_stub.nwb'
io = NWBHDF5IO(file_path, 'r')
nwb = io.read()
print(nwb)

Alternatively, the NWB data can be visualized with nwb-jupyter-widgets in a jupyter notebook:

$ jupyter notebook notebooks/towers_task_custom_widget.ipynb

from pynwb import NWBHDF5IO
from nwbwidgets import nwb2widget
from tank_lab_to_nwb.nwbwidgets import custom_timeseries_widget_for_behavior
from nwbwidgets.view import default_neurodata_vis_spec
import pynwb


file_path = 'TowersTask_stub.nwb'
io = NWBHDF5IO(file_path, 'r')
nwb = io.read()

default_neurodata_vis_spec[pynwb.TimeSeries] = custom_timeseries_widget_for_behavior
nwb2widget(nwb)

Background

Behavioral data mapping

The behavioral data is contained in .matfiles similar to the form: PoissonBlocksReboot4_cohort4_Bezos3_E65_T_20180202.mat This matlab file contains a struct (log) which contains several fields relevant for conversion. The list of fields that are extracted from this struct can be found below.

NWBFile

Location in Virmen (.mat) file	Location in NWB file	Description
log.session.start	nwb.session_start_time	datetime when session started
[not in file]	nwb.session_description	additional information about session (optional)
[name of file]	nwb.session_id	unique identifier of the session

Subject

Location in Virmen (.mat) file	Location in NWB file	Description
log.session.start	nwb.subject.age	age (days) in isoformat (optional)
[not in file]	nwb.species	information about the species (optional)
log.animal.name	nwb.subject_id	identifier of the subject
[not in file]	nwb.genotype	information about the genotype (optional)
[not in file]	nwb.sex	information about the sex of the subject (optional)

LabMetaData

The lab specific metadata is populated in tank_lab_to_nwb/convert_towers_task/virmenbehaviordatainterface.py using the custom extension ndx-tank-metadata built for extending the NWB LabMetaData schema with the required fields:

Location in Virmen (.mat) file	Location in NWB file	Description
log.version.code	nwb.lab_meta_data['LabMetaData'].experiment_name	name of experiment run
log.version.name	nwb.lab_meta_data['LabMetaData'].world_file_name	name of world run
log.animal.protocol	nwb.lab_meta_data['LabMetaData'].protocol_name	name of protocol run
log.animal.stimulusBank	nwb.lab_meta_data['LabMetaData'].stimulus_bank_path	path of stimulus bank file
log.version.repository	nwb.lab_meta_data['LabMetaData'].commit_id	commit id for session run
log.session.end	nwb.lab_meta_data['LabMetaData'].session_end_time	datetime when session ended
log.version.rig.rig	nwb.lab_meta_data['LabMetaData'].location	name of rig where session was run
[not in file]	nwb.lab_meta_data['LabMetaData'].num_trials	number of trials in the session
[not in file]	nwb.lab_meta_data['LabMetaData'].session_performance	performance of correct responses in % (optional)*
log.version.rig	nwb.lab_meta_data['LabMetaData'].rig.fields	rig information
log.version.mazes	nwb.lab_meta_data['LabMetaData'].mazes.to_dataframe()	maze information

• session_performance can be edited from virmenbehaviordatainterface.py.

Rig

Rig information is converted from a log.version.rig struct object to a dictionary like as in this example:

{'rig': 'NPX',
 'simulationMode': 1,
 'hasDAQ': 1,
 'hasSyncComm': 0,
 'minIterationDT': 0.01,
 'arduinoPort': 'COM18',
 'sensorDotsPerRev': array([2469.2, 2469.2, 2469.2, 2469.2]),
 'ballCircumference': 63.8,
 'toroidXFormP1': 0.3879,
 'toroidXFormP2': 0.392,
 'colorAdjustment': array([0. , 0.4, 0.5]),
 'soundAdjustment': 0.2,
 'nidaqDevice': 1,
 'nidaqPort': 1,
 'nidaqLines': array([ 0, 11], dtype=int32),
 'syncClockChannel': 5,
 'syncDataChannel': 6,
 'rewardChannel': 0,
 'rewardSize': 0.004,
 'rewardDuration': 0.05,
 'laserChannel': 1,
 'rightPuffChannel': 2,
 'leftPuffChannel': 3}

Mazes

Maze information is converted to a DynamicTable object that can be converted to a pandas dataframe by calling .to_dataframe() as in this example:

id	world	lStart	lCue	lMemory	cueDuration	cueVisibleAt	cueProbability	...	blockPerform
0	1	5	45	10	nan	inf	inf	...	0.7
1	1	30	120	20	nan	inf	inf	...	0.7
2	1	30	220	20	nan	inf	inf	...	0.7
3	1	30	300	20	nan	inf	inf	...	0.7
4	1	30	380	20	nan	inf	inf	...	0.7

Epochs

Location in Virmen (.mat) file	Location in NWB file	Description
index of log.block structure	nwb.intervals['epochs'].id	number of epoch in session
log.block.mazeID	nwb.intervals['epochs'].maze_id	number of maze in epoch
log.block.mainMazeID	nwb.intervals['epochs'].main_maze_id	number of maze of "highest" level for subject
log.block.easyBlockFlag	nwb.intervals['epochs'].easy_epoch	1 if block was flagged as easy (maze_id < main_maze_id)
log.block.firstTrial	nwb.intervals['epochs'].first_trial	first trial run in an epoch
[not in file]	nwb.intervals['epochs'].num_trials	number of trials for each epoch
log.block.start	nwb.intervals['epochs'].start_time	datetime when epoch started with respect to the start time of the session
log.block.duration	nwb.intervals['epochs'].duration	epoch duration in seconds
log.block.rewardMiL	nwb.intervals['epochs'].reward_ml	ml of reward in an epoch

Trials

Location in Virmen (.mat) file	Location in NWB file	Description
[not in file]	nwb.intervals['trials'].id	unique identifier of trial for all epochs
index of log.block.trial	nwb.intervals['trials'].trial_id	identifier of trial within an epoch
log.block.trial.trialType	nwb.intervals['trials'].trial_type	type of trial (L=Left, R=Right)
log.block.trial.choice	nwb.intervals['trials'].choice	(L=Left, R=Right, nil=Trial violation)
log.block.trial.start	nwb.intervals['trials'].start_time	start time of trial with respect to the start time of the epoch
log.block.trial.duration	nwb.intervals['trials'].duration	duration of trial in seconds
log.block.trial.iterations	nwb.intervals['trials'].iterations	number of frames in a trial
log.block.trial.iCueEntry	nwb.intervals['trials'].iCueEntry	iteration # when subject entered cue region
log.block.trial.iMemEntry	nwb.intervals['trials'].iMemEntry	iteration # when subject entered memory region
log.block.trial.iTurnEntry	nwb.intervals['trials'].iTurnEntry	iteration # when subject entered turn region
log.block.trial.iArmEntry	nwb.intervals['trials'].iArmEntry	iteration # when subject entered arm region
log.block.trial.iBlank	nwb.intervals['trials'].iBlank	iteration # when screen if turned off
log.block.trial.cueCombo	nwb.intervals['trials'].left_cue_presence, nwb.intervals['trials'].right_cue_presence	indicates if nth cue appeared on left or right
log.block.trial.cuePosition	nwb.intervals['trials'].left_cue_position, nwb.intervals['trials'].right_cue_position	position in maze for each cue
log.block.trial.cueOnset	nwb.intervals['trials'].left_cue_onset, nwb.intervals['trials'].right_cue_onset	iteration number when cues appeared in trial
log.block.trial.cueOffset	nwb.intervals['trials'].left_cue_offset, nwb.intervals['trials'].right_cue_offset	iteration number when cues disappeared in trial
log.block.trial.excessTravel	nwb.intervals['trials'].excessTravel	parameter that measures extra distance run by subject
log.block.trial.rewardScale	nwb.intervals['trials'].rewardScale	Multiplier of reward for each correct trial

Behavior

Position, ViewAngle, Velocity, Collision

Location in Virmen (.mat) file	Location in NWB file	Description
log.block.trial.time	nwb.processing['behavior'].data_interfaces['Time']	time vector for each frame measured by Virmen
log.block.trial.position	nwb.processing['behavior'].data_interfaces['Position'].spatial_series	position matrix for each frame (X(cm), Y(cm))
log.block.trial.position	nwb.processing['behavior'].data_interfaces['ViewAngle'].spatial_series	viewAngle for each frame (degrees)
log.block.trial.velocity	nwb.processing['behavior'].data_interfaces['Velocity']	velocity matrix for each frame (X(cm/s), Y(cm/s))
log.block.trial.collision	nwb.processing['behavior'].data_interfaces['Collision']	for each frame 1= collision detected