# NWB Tutorial¶

Welcome to the Neurodata Without Borders (NWB) format official tutorial! NWB is a format for publishing and exchanging neuroscience data. The purpose of this tutorial is to demonstrate how to read and write data to NWB files. We will cover:

• the layout of an NWB file
• the primary NWB abstractions: Module, Interface, and TimeSeries
• NWB schemas and extensions
• the official NWB API

We will explore these concepts by analyzing some rodent hippocampal data and appending the result to an existing NWB file.

## Software Dependencies¶

This tutorial requires the following software:

• NWB API
• HDFView, a graphical interface for browsing HDF5 files
• Python, version 2 or 3
• Python libraries:

## What is NWB for?¶

Neurophysiological data analysts are faced with many kinds of data. These may be divided into major types: (1) metadata, including subject type, device settings, experimental protocol and more; and (2) primary data, consisting of mainly numerical arrays. Metadata is sometimes descriptive of an entire experiment (e.g. subject characteristics), and sometimes descriptive of one or more specific arrays (e.g. stimulus descriptions for an array that numerically codes stimuli).

The aim of NWB is to place these diverse data within a single coherent structure. The established HDF5 format provides a solid, well-tested foundation; NWB thus defines a structure within the constraints of the familiar, filesystem-like hierarchy already provided by HDF5. The design principles informing this structure include:

• human-readability: Names of individual data elements should be comprehensible rather than cryptic. Deep hierarchies should be avoided where possible.
• machine-readability: the structure should be sufficiently constrained that APIs can be written against broad sets of NWB files
• extensibility: users can easily add new kinds of data to the format
• one file per session: individual NWB files should represent a single experimental "session", defined as a contiguous span of time.

To satisfy these principles, NWB strikes a balance between standardization and flexiblity. High and low-level file structures are fixed, but the medium level is free to vary. As we will see below, all NWB files contain the same top-level directories. You can always expect to find session-level metadata in the same location. Complex metadata (e.g. electrode maps) have a standard structure. And because most primary data are time series, NWB also provides a standard structure for time series. The choice of how to arrange the primary data is largely left up to individual users. This flexibility permits a single file to represent many possible data processing graphs.

## Tutorial Overview¶

In this tutorial, we will demonstrate one recommended way of using NWB. This method entails adding successive modules to a single file, with each module encapsulating a coherent set of data. We will work with data from one of the founding NWB datasets (Buzsaki hc-3). The data are extracellular recordings from a rat running back and forth on a linear track to obtain a water reward. Embedded in the hippocampal formation are two different multielectrode arrays, each having 4 shanks, with each shank having 8 recording sites ("channels"). We only use a single session here, but the data from many similar sessions is hosted on CRCNS.

Working through the tutorial will give you experience working with the NWB API and reading, writing, and browsing an NWB file. Specifically, we are going to project the LFP into a new space (identified with independent component analysis) and segment the temporal dimension of the data into distinct trials (passes of the linear track). We will plot the projected LFP against animal position to reveal LFP features that selectively activate in specific locations across trials (behavior analogous to that of hippocampal place cells). Along the way, we will write our results and useful intermediates to the NWB file we start with. Because some of the data we will generate does not fit into pre-defined NWB structures, we will need to define an extension using the specification language.

Figure \ref{fig:flow_chart} shows a data processing graph. Each node represents a numerical array, and is colored according to the module to which it belongs. Blue nodes belong to the "published" module, which contains data elements published on CRCNS. Red nodes belong to "filtered", and contain further processed forms of the data. Green nodes belong to "ica", and contain results from independent component analysis. In this tutorial, we will begin with a special file containing both "published" and "filtered", and add "ica".

The specific steps we will take are:

• review the contents of the NWB file
• load the LFP and position data (with h5py)
• perform independent component analysis on the downsampled, filtered LFP
• plot the energy of basis functions learned through ICA against trial and position
• create a new Module to hold our analysis results
• write the projected representation of the LFP to an Interface within the new Module
• write the segmented trial information to an Interface within the Module
• describe the new Interfaces with a schema written in the [NWB Specification Language]{T:link}
• validate our modified file

Because the focus of this tutorial is on the mechanics of LFP files, rather than analysis, most of the code has been placed into an external module buzsaki_hc.

### (1) Review the Contents of the NWB File¶

First, let's orient ourselves to the structure of NWB. Open ec014.468.nwb with HDFView. You should see this:

Notice that there are 5 top-level datasets and 6 top-level groups. While most metadata is stored in the /general group, there are a few items that were judged important enough to include at the top level. These are the datasets:

• file_create_date: timestamp for the creation of this NWB file
• identifier: globally unique identifier for this NWB file
• nwb_version: version number for this NWB file
• session_description: a summary of the experiment that yielded the data stored in this file
• session_start_time: the absolute time at which the session began

The 6 groups contain the following kinds of data:

• /acquisition: Directly recorded data streams
• /analysis: free-form, unspecified data analysis products; intended for internal use, not publication. Published analysis products should be placed in a processing Module in /processing, with an accompanying schema.
• /epochs: Time slices of the experiment (e.g. subexperiments, trials)
• /general: Experimental metadata (e.g. protocol, notes, description of hardware)
• /processing: Data analysis products. /processing has a special structure that we cover in more detail below.
• /stimulus: Data pushed into the system (e.g. video stimulus, sound, voltage, etc) and/or secondary representations of that data (e.g. measurements of something used as a stimulus)

More details about these groups can be found in the official documentation. In this tutorial, we are interested only in the contents of /processing. /acquisition, /epochs, and /stimulus, are empty for our session (the raw data would normally be present in /acquisition, but we do not provide it in order to keep the file size small).

#### /processing¶

The contents of /processing have the most complex structure in NWB. This is because /processing is intended to contain machine-readable representations of arbitrary analysis products. The contents of /processing are always a three-level hierarchy. A particular NWB abstraction corresponds to each level of the hierarchy.

• Modules are stored at the top-level of /processing. A Module is just an HDF5 group that declares its contents in an attribute interfaces. Because a Module's internal structure is fully described by the listed interfaces, Modules are not declared in a specification and may be named arbitrarily.
• Interfaces are the top-level contents of Modules. Interfaces are groups that contain a specific set of subgroups and/or datasets. Most interfaces contain one or more TimeSeries and related metadata. The contents of the Interface must be declared in the file's schema. Thus the name of the Interface group must match:
• an entry in the containing Module's interfaces attribute
• an Interface declaration in the file's schema
• TimeSeries are found inside Interfaces (and other places in an NWB file). A TimeSeries is a group that contains a time series and associated metadata. All TimeSeries are instances of an associated TimeSeries class, which is part of a hierarchy analogous to that of object-oriented programming languages. The core TimeSeries hierarchy is defined in the core NWB schema. It provides a set of TimeSeries intended to cover a broad range of data types. All TimeSeries must provide two key datasets:
• data: contains the time-aligned data. Time must be on the first axis.
• timestamps or starting_time: timestamps is a 1-dimensional dataset containing a timestamp for each row of data. These timestamps are relative to the experiment start time. Alternatively, starting_time is a single timestamp (also relative to experiment start time) that stores the start of the TimeSeries. starting_time must have an attribute rate which gives the sample rate. This allows the full list of timestamps to be derived from starting_time.

Now that we've reviewed the general structure of /processing, let's have a look at our file's /processing folder. Click on /processing in HDFView to examine its contents. You should see 2 groups inside: published and filtered. These are Modules. Click on published. Notice that its interfaces attribute (visible in the bottom pane of HDFView) has 6 entries: Clustering, EventWaveform, FeatureExtraction, LFP, Position, and UnitTimes. There is a subgroup corresponding to each entry. Clustering, EventWaveform, FeatureExtraction, and UnitTimes contain spike-related information. You can learn more about them in the NWB docs. In this tutorial, we are concerned only with LFP and Position.

Click on LFP to examine it. LFP is a built-in interface type-- it is defined in NWB's core schema. It must contain one or more instances of the TimeSeries class ElectricalSeries. inside LFP,you should see 13 groups: all, shank_1, shank_2, ..., shank_12. all contains the LFP recorded from all 64 electrodes. The shank_<N> groups contain HDF5 region references into the all dataset (corresponding to the channels on each shank). They are provided for convenience. Note that the LFP interface definition does not require this particular internal structure; it just requires one or more ElectricalSeries.

Now let's find the position. The position representation we will use is a product of processing overhead video of the experiment. The subject had two LEDs mounted on its head; the X and Y coordinates of each LED were extracted. Like LFP, Position interface is defined in the NWB core schema. It must contain one or more TimeSeries of class SpatialSeries. Also like LFP, our Position interface includes an all timeseries, containing the coordinates of both LEDs, and led_1 and led_2, which are region references to the coordinates of each separate LED.

Because we are concerned with theta-band (5-10 Hz) activity in this tutorial, we need a filtered form of the LFP. We also need a one-dimensional representation of the position. These data (the red nodes in Figure \ref{fig:flow_chart}) have been included with this tutorial's NWB file in /processing/filtered. This module contains BehavioralSegments, FilteredEphys and Position interfaces. BehavioralSegments contains an IntervalSeries containing temporal boundaries for each traversal of the track. FilteredEphys contains a downsampled, bandpass-filtered version of the LFP. Position contains a SpatialSeries with a one-dimensional version of the position, obtained through principal component analysis.

### (2) load the LFP and position data¶

Now that you've explored the structure of our file, we're ready to work with some data. Let's load the LFP into memory. At present, NWB does not have a read API. Therefore we will use Python's mature HDF5 interface library h5py. First we need to create an h5py File object. Because we will be adding to the source file, you should open it in 'r+' mode:

In [ ]:
import h5py
data = h5py.File('ec014.468.nwb', 'r')


For the remainder of this tutorial, we will read our NWB file through the File object data. Now let's load the LFP time series. h5py provides it as a Dataset object that behaves similarly to a numpy array.

In [ ]:
lfp = data['/processing/filtered/FilteredEphys/theta/data']


The matrix now stored in lfp is the primary data field of a TimeSeries. The shape is (4947125,99). The first dimension is time, and the second is channel number. Time is always the first dimension for NWB TimeSeries. Notice that there is also a starting_time dataset inside all. Its rate attribute tells us that the sample rate is 1250 Hz. Thus the temporal extent of our LFP data is 4947125/1250 = 3,965.6 seconds, or approximately 66 minutes.

Now let's load position:

In [ ]:
pos = data['/processing/filtered/Position/pca_1d/data']


The shape of pos is (152246,4). Like lfp, the first dimension of pos represents time. The second dimension contains two pairs of X and Y coordinates that capture the position of two LEDs mounted on the head of the subject. Though pos and lfp are aligned to the same timebase, notice that the temporal dimension of pos is much smaller than that of of lfp. This is because pos is sampled at a much lower rate than lfp; 39.0625 Hz, according to Position/all/starting_time.rate.

## (3) Perform independent component analysis on the LFP¶

Independent Component Analysis is a technique for separating a multivariate signal into a set of additive components. Beginning with a set of observed signals, we model our signal at each time point as a linear combination of fixed basis functions. The contributions of the individual basis functions are assumed to be statistically independent. You can learn more about ICA here. The model can be summarized as:

$$X = AS$$

Where $X$ is the "observed signal", $A$ is the "mixing matrix" (whose columns are the aforementioned basis functions), and $S$ is the "source signal" (with each row correpsonding to the coefficients for one basis function).

Here, our observed signal is the all-channel filtered LFP that we have loaded into lfp. The basis functions extracted from applying ICA to LFP signals have no straightforward physiological interpretation. However, when the LFPs are taken from the hippocampus of navigating rats, some of the basis functions behave like place cells; that is, their activities (given by the learned coefficients) are correlated to particular locations in the environment.

We call the bhc module to perform ICA for us. This yields two matrices: sources and components. Sources is $S$ in the model above, and components is $A^{-1}$.

In [ ]:
sources, components = bhc.lfp_ica(filt_lfp)


## (4) Plot Component Activation vs Position¶

We now have our ICA-derived representation of LFP and trial-separated position. We can visualize the relationship of feature activation to position using heat maps of the feature coefficients. In the grid of plots below, each row corresponds to one basis function. The left column corresponds to leftward trials, and the right column to rightward trials. Within each plot, the x-axis represents position and the y-axis represents trial number. The pixel value represents the average coefficient value at the corresponding position and trial. Thus, light-colored columns (as visible in BFs 2 and 3) represent consisent selectivity of a basis function for a particular location. This is the same behavior we see with place cells.

In [ ]:
bhc.plot_source_heatmaps(sources, trials)