Vu 2024 demo¶

This tutorial demonstrates how to access an NWB file from the Vu 2024 dataset using pynwb.

This dataset contains fiber photometry recordings from multi-fiber arrays implanted in the striatum of head-fixed mice running on a treadmill.

Streaming an NWB file¶

This section demonstrates how to access the files on the DANDI Archive without downloading them. Based on the Streaming NWB files tutorial from PyNWB.

The dandi.dandiapi.DandiAPIClient can be used to get the S3 URL of the NWB file stored in the DANDI Archive.

In [3]:

from dandi.dandiapi import DandiAPIClient

client = DandiAPIClient.for_dandi_instance("dandi")

dandiset_id = "001084"
file_path = "sub-DL18/sub-DL18_ses-211110_image+ophys.nwb"

with DandiAPIClient() as client:
    asset = client.get_dandiset(dandiset_id, 'draft').get_asset_by_path(file_path)
    s3_url = asset.get_content_url(follow_redirects=1, strip_query=True)

from dandi.dandiapi import DandiAPIClient client = DandiAPIClient.for_dandi_instance("dandi") dandiset_id = "001084" file_path = "sub-DL18/sub-DL18_ses-211110_image+ophys.nwb" with DandiAPIClient() as client: asset = client.get_dandiset(dandiset_id, 'draft').get_asset_by_path(file_path) s3_url = asset.get_content_url(follow_redirects=1, strip_query=True)

We will use remfile for streaming the file. You can read more about remfile at this tutorial section.

In [5]:

import h5py
from pynwb import NWBHDF5IO
import remfile

# We stream the file using remfile and open it with h5py and pynwb
file = remfile.File(s3_url)
h5_file = h5py.File(file, "r")
io = NWBHDF5IO(file=h5_file, load_namespaces=True)

nwbfile = io.read()

import h5py from pynwb import NWBHDF5IO import remfile # We stream the file using remfile and open it with h5py and pynwb file = remfile.File(s3_url) h5_file = h5py.File(file, "r") io = NWBHDF5IO(file=h5_file, load_namespaces=True) nwbfile = io.read()

Access subject metadata¶

This section demonstrates how to access the Subject field in an NWBFile.

The Subject field can be accessed as nwbfile.subject.

In [6]:

nwbfile.subject

nwbfile.subject

Out[6]:

subject (Subject)

age__reference: birth

genotype: wildtype

sex: F

species: Mus musculus

subject_id: DL18

date_of_birth

2021-04-21 00:00:00-04:00

strain: C57BL/6J - Jackson Labs 000664

Access raw imaging¶

This section demonstrates how to access the imaging data in the NWBFile.

NWB organizes data into different groups depending on the type of data. Groups can be thought of as folders within the file. Here are some of the groups within an NWBFile and the types of data they are intended to store:

• acquisition: raw, acquired data that should never change
• processing: processed data, typically the results of preprocessing algorithms and could change

Fiber array imaging¶

The fiber bundle imaging data was acquired using HCImage Live (Hamamatsu) at 30 Hz. It is stored in a pynwb.ophys.OnePhotonSeries object which is added to nwbfile.acquisition.

In [7]:

one_photon_series = nwbfile.acquisition["OnePhotonSeriesGreen"]
one_photon_series

one_photon_series = nwbfile.acquisition["OnePhotonSeriesGreen"] one_photon_series

Out[7]:

OnePhotonSeriesGreen (OnePhotonSeries)

starting_time: 0.7785000000000001

rate: 29.99490086685263

resolution: -1.0

comments: no comments

description: Fiber bundle imaging acquired using HCImage Live (Hamamatsu). Single wavelength excitation and emission was performed with continuous, internally triggered imaging at 30Hz.

conversion: 1.0

offset: 0.0

unit: n.a.

data

starting_time_unit: seconds

dimension

imaging_plane

optical_channel

0

description: An optical channel of the microscope.

emission_lambda: 511.0

description: Imaging plane for the one-photon microscope.

device

description: The microscope (Hamamatsu Orca Fusion BT Gen III) used to acquire the image of the fiber bundle.

manufacturer: Hamamatsu Photonics

excitation_lambda: 470.0

indicator: dLight1.3b

location: STR

conversion: 1.0

unit: meters

The information about the imaging plane can accessed as nwbfile.acquisition["OnePhotonSeries"].imaging_plane or nwbfile.imaging_planes["ImagingPlane"].

In [8]:

nwbfile.imaging_planes["ImagingPlaneGreen"]

nwbfile.imaging_planes["ImagingPlaneGreen"]

Out[8]:

ImagingPlaneGreen (ImagingPlane)

optical_channel

0

description: An optical channel of the microscope.

emission_lambda: 511.0

description: Imaging plane for the one-photon microscope.

device

description: The microscope (Hamamatsu Orca Fusion BT Gen III) used to acquire the image of the fiber bundle.

manufacturer: Hamamatsu Photonics

excitation_lambda: 470.0

indicator: dLight1.3b

location: STR

conversion: 1.0

unit: meters

In [9]:

# Visualize the imaging data.

from matplotlib import pyplot as plt

frame = one_photon_series.data[50]
plt.imshow(frame)
plt.title("Raw Fiber Array Imaging")
plt.show()

# Visualize the imaging data. from matplotlib import pyplot as plt frame = one_photon_series.data[50] plt.imshow(frame) plt.title("Raw Fiber Array Imaging") plt.show()

Fiber photometry traces¶

The raw fluorescence traces from the multi-fiber array are added to nwbfile.acquisition and are stored in a FiberPhotometryResponseSeries object using ndx-fiber-photometry. The fluorescence data during 470 nm excitation can be accessed as nwbfile.acquisition["FiberPhotometryResponseSeriesGreen"].

In [10]:

fiber_photometry_response_series = nwbfile.acquisition["FiberPhotometryResponseSeriesGreen"]

fiber_photometry_response_series = nwbfile.acquisition["FiberPhotometryResponseSeriesGreen"]

In [11]:

import pandas as pd
from matplotlib import pyplot as plt

# Prepare data for plotting
fiber_indices = [0, 4]
data = fiber_photometry_response_series.data[100:500, fiber_indices]
timestamps = fiber_photometry_response_series.get_timestamps()[100:500]

fig, axes = plt.subplots(nrows=data.shape[1], ncols=1, figsize=(8, 3), sharey=True, sharex=True, dpi=300)

for i, ax in enumerate(axes):
    ax.plot(timestamps, data[:, i], linewidth=0.5, color="green")

    ax.tick_params(axis='y', labelsize=8)
    ax.tick_params(axis='x', labelsize=8)

    ax.legend([f"Fiber {i+1}"], frameon=False, bbox_to_anchor=(.95, 1), loc='upper left', prop={'size': 8})

    ax.spines['top'].set_visible(False)
    ax.spines['right'].set_visible(False)

axes[0].spines['bottom'].set_visible(False)
axes[0].set_title("Raw fluorescence traces", fontsize=8)
plt.xlabel('Time (s)', fontsize=8)
plt.tick_params(axis='x', labelsize=8)

plt.tight_layout()
plt.show()

import pandas as pd from matplotlib import pyplot as plt # Prepare data for plotting fiber_indices = [0, 4] data = fiber_photometry_response_series.data[100:500, fiber_indices] timestamps = fiber_photometry_response_series.get_timestamps()[100:500] fig, axes = plt.subplots(nrows=data.shape[1], ncols=1, figsize=(8, 3), sharey=True, sharex=True, dpi=300) for i, ax in enumerate(axes): ax.plot(timestamps, data[:, i], linewidth=0.5, color="green") ax.tick_params(axis='y', labelsize=8) ax.tick_params(axis='x', labelsize=8) ax.legend([f"Fiber {i+1}"], frameon=False, bbox_to_anchor=(.95, 1), loc='upper left', prop={'size': 8}) ax.spines['top'].set_visible(False) ax.spines['right'].set_visible(False) axes[0].spines['bottom'].set_visible(False) axes[0].set_title("Raw fluorescence traces", fontsize=8) plt.xlabel('Time (s)', fontsize=8) plt.tick_params(axis='x', labelsize=8) plt.tight_layout() plt.show()

Fiber photometry metadata¶

The fiber photometry metadata includes the type of indicator(s), optical fiber(s), excitation source(s), photodector(s), dichroic mirror(s), and optical filter(s) that were used to construct a single fluorescence signal.

The metadata is stored in a FiberPhotometryTable object using ndx-fiber-photometry and is added to nwbfile.lab_meta_data. It can be accessed as nwbfile.lab_meta_data["FiberPhotometry"].fiber_photometry_table.

In [12]:

nwbfile.lab_meta_data["FiberPhotometry"].fiber_photometry_table[:]

nwbfile.lab_meta_data["FiberPhotometry"].fiber_photometry_table[:]

Out[12]:

	location	indicator	optical_fiber	excitation_source	photodetector	dichroic_mirror	allen_atlas_coordinates	included	coordinates	emission_filter	excitation_filter
id
0	Caudoputamen	dLight1.3b abc.Indicator at 0x5312211472\nFiel...	FiberArray abc.OpticalFiber at 0x5312205648\nF...	ExcitationSource470 abc.ExcitationSource at 0x...	CMOSCamera pynwb.device.Device at 0x5312316432...	DichroicMirror1 abc.DichroicMirror at 0x531227...	[442, 644, 229]	False	[0.83, 0.74, 1.78]	OpticalFilter525 abc.BandOpticalFilter at 0x53...	OpticalFilter470 abc.BandOpticalFilter at 0x53...
1	Caudoputamen	dLight1.3b abc.Indicator at 0x5312211472\nFiel...	FiberArray abc.OpticalFiber at 0x5312205648\nF...	ExcitationSource470 abc.ExcitationSource at 0x...	CMOSCamera pynwb.device.Device at 0x5312316432...	DichroicMirror1 abc.DichroicMirror at 0x531227...	[454, 642, 304]	False	[0.71, 0.72, 2.44]	OpticalFilter525 abc.BandOpticalFilter at 0x53...	OpticalFilter470 abc.BandOpticalFilter at 0x53...
2	Primary motor area Layer 6a	dLight1.3b abc.Indicator at 0x5312211472\nFiel...	FiberArray abc.OpticalFiber at 0x5312205648\nF...	ExcitationSource470 abc.ExcitationSource at 0x...	CMOSCamera pynwb.device.Device at 0x5312316432...	DichroicMirror1 abc.DichroicMirror at 0x531227...	[460, 674, 180]	False	[0.65, 1.04, 1.35]	OpticalFilter525 abc.BandOpticalFilter at 0x53...	OpticalFilter470 abc.BandOpticalFilter at 0x53...
3	Caudoputamen	dLight1.3b abc.Indicator at 0x5312211472\nFiel...	FiberArray abc.OpticalFiber at 0x5312205648\nF...	ExcitationSource470 abc.ExcitationSource at 0x...	CMOSCamera pynwb.device.Device at 0x5312316432...	DichroicMirror1 abc.DichroicMirror at 0x531227...	[495, 685, 260]	False	[0.3, 1.15, 2.05]	OpticalFilter525 abc.BandOpticalFilter at 0x53...	OpticalFilter470 abc.BandOpticalFilter at 0x53...
4	Primary motor area Layer 6a	dLight1.3b abc.Indicator at 0x5312211472\nFiel...	FiberArray abc.OpticalFiber at 0x5312205648\nF...	ExcitationSource470 abc.ExcitationSource at 0x...	CMOSCamera pynwb.device.Device at 0x5312316432...	DichroicMirror1 abc.DichroicMirror at 0x531227...	[400, 686, 160]	False	[1.25, 1.16, 1.17]	OpticalFilter525 abc.BandOpticalFilter at 0x53...	OpticalFilter470 abc.BandOpticalFilter at 0x53...
...	...	...	...	...	...	...	...	...	...	...	...
98	Caudoputamen	dLight1.3b abc.Indicator at 0x5312211472\nFiel...	FiberArray abc.OpticalFiber at 0x5312205648\nF...	ExcitationSource470 abc.ExcitationSource at 0x...	CMOSCamera pynwb.device.Device at 0x5312316432...	DichroicMirror1 abc.DichroicMirror at 0x531227...	[433, 775, 249]	False	[0.92, 2.05, 1.96]	OpticalFilter525 abc.BandOpticalFilter at 0x53...	OpticalFilter470 abc.BandOpticalFilter at 0x53...
99	Caudoputamen	dLight1.3b abc.Indicator at 0x5312211472\nFiel...	FiberArray abc.OpticalFiber at 0x5312205648\nF...	ExcitationSource470 abc.ExcitationSource at 0x...	CMOSCamera pynwb.device.Device at 0x5312316432...	DichroicMirror1 abc.DichroicMirror at 0x531227...	[444, 815, 381]	True	[0.81, 2.45, 3.12]	OpticalFilter525 abc.BandOpticalFilter at 0x53...	OpticalFilter470 abc.BandOpticalFilter at 0x53...
100	Caudoputamen	dLight1.3b abc.Indicator at 0x5312211472\nFiel...	FiberArray abc.OpticalFiber at 0x5312205648\nF...	ExcitationSource470 abc.ExcitationSource at 0x...	CMOSCamera pynwb.device.Device at 0x5312316432...	DichroicMirror1 abc.DichroicMirror at 0x531227...	[457, 833, 280]	False	[0.68, 2.63, 2.23]	OpticalFilter525 abc.BandOpticalFilter at 0x53...	OpticalFilter470 abc.BandOpticalFilter at 0x53...
101	Primary somatosensory area mouth layer 6a	dLight1.3b abc.Indicator at 0x5312211472\nFiel...	FiberArray abc.OpticalFiber at 0x5312205648\nF...	ExcitationSource470 abc.ExcitationSource at 0x...	CMOSCamera pynwb.device.Device at 0x5312316432...	DichroicMirror1 abc.DichroicMirror at 0x531227...	[482, 821, 396]	False	[0.43, 2.51, 3.25]	OpticalFilter525 abc.BandOpticalFilter at 0x53...	OpticalFilter470 abc.BandOpticalFilter at 0x53...
102	Primary motor area Layer 2/3	dLight1.3b abc.Indicator at 0x5312211472\nFiel...	FiberArray abc.OpticalFiber at 0x5312205648\nF...	ExcitationSource470 abc.ExcitationSource at 0x...	CMOSCamera pynwb.device.Device at 0x5312316432...	DichroicMirror1 abc.DichroicMirror at 0x531227...	[396, 791, 209]	False	[1.29, 2.21, 1.6]	OpticalFilter525 abc.BandOpticalFilter at 0x53...	OpticalFilter470 abc.BandOpticalFilter at 0x53...

103 rows × 11 columns

The fiber_photometry_table_region attribute in the FiberPhotometryResponseSeries object references row(s) of this table:

In [14]:

fiber_photometry_table_region = nwbfile.acquisition["FiberPhotometryResponseSeriesGreen"].fiber_photometry_table_region[:]
fiber_photometry_table_region.head()

fiber_photometry_table_region = nwbfile.acquisition["FiberPhotometryResponseSeriesGreen"].fiber_photometry_table_region[:] fiber_photometry_table_region.head()

Out[14]:

	location	indicator	optical_fiber	excitation_source	photodetector	dichroic_mirror	allen_atlas_coordinates	included	coordinates	emission_filter	excitation_filter
id
0	Caudoputamen	dLight1.3b abc.Indicator at 0x5312211472\nFiel...	FiberArray abc.OpticalFiber at 0x5312205648\nF...	ExcitationSource470 abc.ExcitationSource at 0x...	CMOSCamera pynwb.device.Device at 0x5312316432...	DichroicMirror1 abc.DichroicMirror at 0x531227...	[442, 644, 229]	False	[0.83, 0.74, 1.78]	OpticalFilter525 abc.BandOpticalFilter at 0x53...	OpticalFilter470 abc.BandOpticalFilter at 0x53...
1	Caudoputamen	dLight1.3b abc.Indicator at 0x5312211472\nFiel...	FiberArray abc.OpticalFiber at 0x5312205648\nF...	ExcitationSource470 abc.ExcitationSource at 0x...	CMOSCamera pynwb.device.Device at 0x5312316432...	DichroicMirror1 abc.DichroicMirror at 0x531227...	[454, 642, 304]	False	[0.71, 0.72, 2.44]	OpticalFilter525 abc.BandOpticalFilter at 0x53...	OpticalFilter470 abc.BandOpticalFilter at 0x53...
2	Primary motor area Layer 6a	dLight1.3b abc.Indicator at 0x5312211472\nFiel...	FiberArray abc.OpticalFiber at 0x5312205648\nF...	ExcitationSource470 abc.ExcitationSource at 0x...	CMOSCamera pynwb.device.Device at 0x5312316432...	DichroicMirror1 abc.DichroicMirror at 0x531227...	[460, 674, 180]	False	[0.65, 1.04, 1.35]	OpticalFilter525 abc.BandOpticalFilter at 0x53...	OpticalFilter470 abc.BandOpticalFilter at 0x53...
3	Caudoputamen	dLight1.3b abc.Indicator at 0x5312211472\nFiel...	FiberArray abc.OpticalFiber at 0x5312205648\nF...	ExcitationSource470 abc.ExcitationSource at 0x...	CMOSCamera pynwb.device.Device at 0x5312316432...	DichroicMirror1 abc.DichroicMirror at 0x531227...	[495, 685, 260]	False	[0.3, 1.15, 2.05]	OpticalFilter525 abc.BandOpticalFilter at 0x53...	OpticalFilter470 abc.BandOpticalFilter at 0x53...
4	Primary motor area Layer 6a	dLight1.3b abc.Indicator at 0x5312211472\nFiel...	FiberArray abc.OpticalFiber at 0x5312205648\nF...	ExcitationSource470 abc.ExcitationSource at 0x...	CMOSCamera pynwb.device.Device at 0x5312316432...	DichroicMirror1 abc.DichroicMirror at 0x531227...	[400, 686, 160]	False	[1.25, 1.16, 1.17]	OpticalFilter525 abc.BandOpticalFilter at 0x53...	OpticalFilter470 abc.BandOpticalFilter at 0x53...

The metadata on the optical fiber used to record the GCaMP fluorescence is added to nwbfile.devices and can be accessed as nwbfile.devices["FiberArray"] or can be accessed from the referenced optical fiber in the fiber_photometry_table_region of the FiberPhotometryResponseSeries.

In [15]:

fiber_photometry_table_region["optical_fiber"][0]

fiber_photometry_table_region["optical_fiber"][0]

Out[15]:

FiberArray (OpticalFiber)

description: The optical fiber used in a multi-fiber arrays configuration, fabricated in-house to enable large scale measurements across deep brain volumes. Bare fibers were cut into pieces (ca. 3cm) then mounted under a microscope into 55-60μm diameter holes in a custom 3D printed grid (3mm W x 5mm L, Boston Micro Fabrication), measured under a dissection microscope to target a particular depth beneath the grid, and secured in place with UV glue (Norland Optical Adhesive 61). Each array contained between 30 and 103 fibers separated by a minimum of 220μm radially and 250μm axially. Separation was calculated to achieve maximal coverage of the striatum volume with no overlap in the collection fields of individual fibers. Fibers were cut with a fiber scribe, and the distal ends were inspected to ensure a uniform cut, and re-cut as necessary. Distal ends were then glued inside an 1cm ca. section of polyimide tube (0.8-1.3mm diameter, MicroLumen) then cut with a fresh razorblade. The bundled fibers inside the tube were then polished on fine grained polishing paper (ThorLabs,polished first with 6 μm, followed by 3 μm) to create a smooth, uniform fiber bundle surface for imaging. A larger diameter post was mounted on one side of the plastic grid to facilitate holding during implantation.

manufacturer: Fiber Optics Tech

model: not specified

numerical_aperture: 0.66

core_diameter_in_um: 34.0

In [16]:

fiber_photometry_table_region["indicator"][0]

fiber_photometry_table_region["indicator"][0]

Out[16]:

dLight1.3b (Indicator)

description: green dopamine sensor

label: pAAV-CAG-dLight1.3b(AAV5)

In [17]:

fiber_photometry_table_region["excitation_source"][0]

fiber_photometry_table_region["excitation_source"][0]

Out[17]:

ExcitationSource470 (ExcitationSource)

description: Blue excitation light (470 nm LED, Thorlabs, No. SOLIS-470C) and violet excitation light (for the isosbestic control) were coupled into the optic fiber such that a power of 0.75 mW was delivered to the fiber tip. Then, 470 nm and 405 nm excitation were alternated at 100 Hz using a waveform generator, each filtered with a corresponding filter.

manufacturer: Thorlabs

model: SOLIS-470C

illumination_type: LED

excitation_wavelength_in_nm: 470.0

In [18]:

fiber_photometry_table_region["photodetector"][0]

fiber_photometry_table_region["photodetector"][0]

Out[18]:

CMOSCamera (Device)

description: A tube lens in each path (Thor labs, No TTL165-A) focused emission light onto the CMOS sensors of the cameras to form an image of the fiber bundle.

manufacturer: Hamamatsu Photonics, Orca Fusion BT Gen III

In [19]:

fiber_photometry_table_region["dichroic_mirror"][0]

fiber_photometry_table_region["dichroic_mirror"][0]

Out[19]:

DichroicMirror1 (DichroicMirror)

description: The dichroic mirror used to reflect green and pass red fluorescence

manufacturer: Chroma Tech Corp

cut_on_wavelength_in_nm: 532.0

reflection_band_in_nm

[405. 532.]

transmission_band_in_nm

[545. 750.]

model: ZT532rdc

In [20]:

fiber_photometry_table_region["emission_filter"][0]

fiber_photometry_table_region["emission_filter"][0]

Out[20]:

OpticalFilter525 (BandOpticalFilter)

description: The band-pass filter used to isolate the green emitted light after passing through a dichroic (Chroma, No. 532rdc) that reflected green and passed red fluorescence.

manufacturer: Chroma

center_wavelength_in_nm: 525.0

bandwidth_in_nm: 50.0

filter_type: Bandpass

model: 525/50m

In [21]:

fiber_photometry_table_region["excitation_filter"][0]

fiber_photometry_table_region["excitation_filter"][0]

Out[21]:

OpticalFilter470 (BandOpticalFilter)

description: The band-pass filter used to isolate the 470 nm excitation light.

manufacturer: Chroma

center_wavelength_in_nm: 473.0

bandwidth_in_nm: 24.0

filter_type: Bandpass

model: ET473/24

Access processed fiber photometry data¶

This section demonstrates how to access the processed fiber photometry data in the NWBFile.

The processed fiber photometry data is stored in "processing/ophys" which can be accessed as nwbfile.processing["ophys"]. Within this processing module we can access the ∆F/F traces as nwbfile.processing["ophys"]["DfOverFFiberPhotometryResponseSeriesGreen"].

In [24]:

nwbfile.processing["ophys"]

nwbfile.processing["ophys"]

In [22]:

df_over_f_traces = nwbfile.processing["ophys"]["DfOverFFiberPhotometryResponseSeriesGreen"]

df_over_f_traces = nwbfile.processing["ophys"]["DfOverFFiberPhotometryResponseSeriesGreen"]

In [25]:

# Visualize the DF/F traces.
from matplotlib import pyplot as plt

# Prepare data for plotting
data = df_over_f_traces.data[100:500, :5]
timestamps = df_over_f_traces.get_timestamps()[100:500]

fig, axes = plt.subplots(nrows=data.shape[1], ncols=1, figsize=(6, 4), sharey=True, sharex=True, dpi=300)

for i, ax in enumerate(axes):
    ax.plot(timestamps, data[:, i], linewidth=0.5, color="green")

    ax.tick_params(axis='y', labelsize=6)
    ax.tick_params(axis='x', labelsize=6)

    ax.legend([f"∆F/F Fiber {i+1}"], frameon=False, bbox_to_anchor=(.95, 1), loc='upper left', prop={'size': 6})

    ax.spines['top'].set_visible(False)
    ax.spines['right'].set_visible(False)
    if i != data.shape[1] - 1:
        ax.spines['bottom'].set_visible(False)

axes[0].set_title("DF/F Fluorescence traces", fontsize=6)
plt.xlabel('Time (s)', fontsize=6)
plt.tick_params(axis='x', labelsize=6)

plt.tight_layout()
plt.show()

# Visualize the DF/F traces. from matplotlib import pyplot as plt # Prepare data for plotting data = df_over_f_traces.data[100:500, :5] timestamps = df_over_f_traces.get_timestamps()[100:500] fig, axes = plt.subplots(nrows=data.shape[1], ncols=1, figsize=(6, 4), sharey=True, sharex=True, dpi=300) for i, ax in enumerate(axes): ax.plot(timestamps, data[:, i], linewidth=0.5, color="green") ax.tick_params(axis='y', labelsize=6) ax.tick_params(axis='x', labelsize=6) ax.legend([f"∆F/F Fiber {i+1}"], frameon=False, bbox_to_anchor=(.95, 1), loc='upper left', prop={'size': 6}) ax.spines['top'].set_visible(False) ax.spines['right'].set_visible(False) if i != data.shape[1] - 1: ax.spines['bottom'].set_visible(False) axes[0].set_title("DF/F Fluorescence traces", fontsize=6) plt.xlabel('Time (s)', fontsize=6) plt.tick_params(axis='x', labelsize=6) plt.tight_layout() plt.show()

Access motion corrected imaging data¶

This section demonstrates how to access the motion corrected imaging data in the NWBFile.

Similarly to the raw imaging data, the processed imaging data is stored in a pynwb.ophys.OnePhotonSeries object and is added to nwbfile.processing["ophys"].

In [26]:

motion_corrected = nwbfile.processing["ophys"]["OnePhotonSeriesMotionCorrectedGreen"]
motion_corrected

motion_corrected = nwbfile.processing["ophys"]["OnePhotonSeriesMotionCorrectedGreen"] motion_corrected

Out[26]:

OnePhotonSeriesMotionCorrectedGreen (OnePhotonSeries)

starting_time: 0.7785000000000001

rate: 29.99490086685263

resolution: -1.0

comments: no comments

description: The motion corrected fiber bundle imaging data.

conversion: 1.0

offset: 0.0

unit: n.a.

data

starting_time_unit: seconds

dimension

imaging_plane

optical_channel

0

description: An optical channel of the microscope.

emission_lambda: 511.0

description: Imaging plane for the one-photon microscope.

device

description: The microscope (Hamamatsu Orca Fusion BT Gen III) used to acquire the image of the fiber bundle.

manufacturer: Hamamatsu Photonics

excitation_lambda: 470.0

indicator: dLight1.3b

location: STR

conversion: 1.0

unit: meters

In [27]:

# Visualize the motion corrected imaging data.

from matplotlib import pyplot as plt

fig, axes = plt.subplots(nrows=1, ncols=2, sharex=True, sharey=True, dpi=300)

axes[0].imshow(one_photon_series.data[50])
axes[0].set_title("Raw Fiber Array Imaging", fontsize=6)
axes[0].tick_params(axis='x', labelsize=6)
axes[0].tick_params(axis='y', labelsize=6)

axes[1].imshow(motion_corrected.data[50])
axes[1].set_title("Motion Corrected Fiber Array Imaging", fontsize=6)
axes[1].tick_params(axis='x', labelsize=6)
axes[1].tick_params(axis='y', labelsize=6)

plt.tight_layout()
plt.show()

# Visualize the motion corrected imaging data. from matplotlib import pyplot as plt fig, axes = plt.subplots(nrows=1, ncols=2, sharex=True, sharey=True, dpi=300) axes[0].imshow(one_photon_series.data[50]) axes[0].set_title("Raw Fiber Array Imaging", fontsize=6) axes[0].tick_params(axis='x', labelsize=6) axes[0].tick_params(axis='y', labelsize=6) axes[1].imshow(motion_corrected.data[50]) axes[1].set_title("Motion Corrected Fiber Array Imaging", fontsize=6) axes[1].tick_params(axis='x', labelsize=6) axes[1].tick_params(axis='y', labelsize=6) plt.tight_layout() plt.show()

Access ROIs¶

This section demonstrates how to access the ROIs corresponding to fiber tops in the NWBFile.

The centroids and image masks of the ROIs are stored in a PlaneSegmentation added to an ImageSegmentation object in nwbfile.processing["ophys"].

In [28]:

rois_table = nwbfile.processing["ophys"]["ImageSegmentation"]["PlaneSegmentation"][:]
rois_table.head()

rois_table = nwbfile.processing["ophys"]["ImageSegmentation"]["PlaneSegmentation"][:] rois_table.head()

Out[28]:

	image_mask	ROICentroids	Accepted	Rejected
id
0	[[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...	[166.47541258012367, 59.00562445763765]	0	1
1	[[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...	[163.1369152701555, 80.29428559091824]	0	1
2	[[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...	[144.14977970147507, 83.10331721340896]	0	1
3	[[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...	[123.4401911607665, 85.54001655461578]	0	1
4	[[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...	[111.00957492596739, 100.35934701854168]	0	1

In [29]:

# Visualize the ROIs and the motion corrected imaging data.
from matplotlib import pyplot as plt
import numpy as np

plt.imshow(motion_corrected.data[50])

roi_masks = rois_table["image_mask"].values
roi_masks_combined = np.zeros(roi_masks[0].shape)
for i in range(roi_masks.shape[0]):
    inds = np.where(roi_masks[i] != 0)
    roi_masks_combined[inds] = 1

masked_roi_masks_combined = np.ma.masked_where(roi_masks_combined == 0, roi_masks_combined)
plt.imshow(masked_roi_masks_combined, cmap='gray', interpolation='none', alpha=0.5)
plt.title("ROIs on motion corrected image")
plt.show()

# Visualize the ROIs and the motion corrected imaging data. from matplotlib import pyplot as plt import numpy as np plt.imshow(motion_corrected.data[50]) roi_masks = rois_table["image_mask"].values roi_masks_combined = np.zeros(roi_masks[0].shape) for i in range(roi_masks.shape[0]): inds = np.where(roi_masks[i] != 0) roi_masks_combined[inds] = 1 masked_roi_masks_combined = np.ma.masked_where(roi_masks_combined == 0, roi_masks_combined) plt.imshow(masked_roi_masks_combined, cmap='gray', interpolation='none', alpha=0.5) plt.title("ROIs on motion corrected image") plt.show()

Access behavior data¶

This section demonstrates how to access the behavior data in the NWBFile.

The velocity for the roll and pitch (x, y) measured in m/s is added to nwbfile.processing["behavior"] stored in a pynwb.base.TimeSeries object. It can be accessed as nwbfile.processing["behavior"]["Velocity"].

The angular velocity from yaw (rotational) velocity is converted to radians/s and is also stored in a pynwb.base.TimeSeries object. It can be accessed as nwbfile.processing["behavior"]["AngularVelocity"].

In [ ]:

nwbfile.processing["behavior"]

nwbfile.processing["behavior"]

In [31]:

velocity = nwbfile.processing["behavior"]["Velocity"]

velocity = nwbfile.processing["behavior"]["Velocity"]

In [32]:

# Visualize the DF/F traces and velocity for the roll in m/s.

import pandas as pd
from matplotlib import pyplot as plt

# Prepare data for plotting
data = df_over_f_traces.data[200:600, :2]
timestamps = df_over_f_traces.get_timestamps()[200:600]

velocity_x = velocity.data[200:600, 0]

fig, axes = plt.subplots(nrows=3, ncols=1, figsize=(6, 4), sharey=False, sharex=True, dpi=300)

for i in range(len(axes)-1):
    axes[i].plot(timestamps, data[:, i], linewidth=0.5, color="green")
    axes[i].spines['top'].set_visible(False)
    axes[i].spines['right'].set_visible(False)
    
    axes[i].tick_params(axis='y', labelsize=6)
    axes[i].tick_params(axis='x', labelsize=6)
    axes[i].spines['bottom'].set_visible(False)
    axes[i].set_ylim([-0.01, 0.09])

    axes[i].legend([f"∆F/F Fiber {i+1}"], frameon=False, bbox_to_anchor=(.95, 1), loc='upper left', prop={'size': 6})

    axes[i].spines['top'].set_visible(False)
    axes[i].spines['right'].set_visible(False)
            
axes[-1].plot(timestamps, velocity_x, color="black", alpha=0.8, linewidth=0.5)
axes[-1].spines['top'].set_visible(False)
axes[-1].spines['right'].set_visible(False)
axes[-1].legend(["Roll velocity"], frameon=False, bbox_to_anchor=(.95, 1), loc='upper left', prop={'size': 6})
axes[-1].tick_params(axis='y', labelsize=6)
axes[-1].tick_params(axis='x', labelsize=6)

plt.xlabel('Time (s)', fontsize=6)
plt.tight_layout()
plt.show()

# Visualize the DF/F traces and velocity for the roll in m/s. import pandas as pd from matplotlib import pyplot as plt # Prepare data for plotting data = df_over_f_traces.data[200:600, :2] timestamps = df_over_f_traces.get_timestamps()[200:600] velocity_x = velocity.data[200:600, 0] fig, axes = plt.subplots(nrows=3, ncols=1, figsize=(6, 4), sharey=False, sharex=True, dpi=300) for i in range(len(axes)-1): axes[i].plot(timestamps, data[:, i], linewidth=0.5, color="green") axes[i].spines['top'].set_visible(False) axes[i].spines['right'].set_visible(False) axes[i].tick_params(axis='y', labelsize=6) axes[i].tick_params(axis='x', labelsize=6) axes[i].spines['bottom'].set_visible(False) axes[i].set_ylim([-0.01, 0.09]) axes[i].legend([f"∆F/F Fiber {i+1}"], frameon=False, bbox_to_anchor=(.95, 1), loc='upper left', prop={'size': 6}) axes[i].spines['top'].set_visible(False) axes[i].spines['right'].set_visible(False) axes[-1].plot(timestamps, velocity_x, color="black", alpha=0.8, linewidth=0.5) axes[-1].spines['top'].set_visible(False) axes[-1].spines['right'].set_visible(False) axes[-1].legend(["Roll velocity"], frameon=False, bbox_to_anchor=(.95, 1), loc='upper left', prop={'size': 6}) axes[-1].tick_params(axis='y', labelsize=6) axes[-1].tick_params(axis='x', labelsize=6) plt.xlabel('Time (s)', fontsize=6) plt.tight_layout() plt.show()

The onset times of the events (licking, tone, light or reward delivery) are added to nwbfile.processing["behavior"]["TimeIntervals"] table.

In [33]:

events = nwbfile.processing["behavior"]["TimeIntervals"][:]
events

events = nwbfile.processing["behavior"]["TimeIntervals"][:] events

Out[33]:

	start_time	stop_time	event_type
id
0	10.21300	11.08000	Light
1	25.31550	26.18225	Light
2	36.25050	37.58400	Tone
3	69.78875	70.65550	Light
4	87.65825	88.99175	Tone
...	...	...	...
158	1312.37725	1312.54400	Lick
159	1313.64425	1313.74425	Lick
160	1318.24500	1319.07825	Light
161	1325.11250	1326.41275	Tone
162	1330.61350	1331.48025	Light

163 rows × 3 columns

In [34]:

from matplotlib import pyplot as plt
import matplotlib.lines as mlines

# Prepare data for plotting
data = df_over_f_traces.data[700:1200, 0]
timestamps = df_over_f_traces.get_timestamps()[700:1200]

fig, ax = plt.subplots(nrows=1, ncols=1, figsize=(6, 2), dpi=300, sharex=True)

light_events = events[events["event_type"] == "Light"]
light_events = light_events[(light_events["start_time"] >= timestamps[0]) & (light_events["stop_time"] < timestamps[-1])]

tone_events = events[events["event_type"] == "Tone"]
tone_events = tone_events[(tone_events["start_time"] >= timestamps[0]) & (tone_events["stop_time"] < timestamps[-1])]

ax.plot(timestamps, data, color="green", linewidth=0.5)

for ind, row in light_events.iterrows():
    ax.fill_between(timestamps, min(data), max(data), where=(timestamps >= row["start_time"]) & (timestamps <= row["stop_time"]), color='blue', edgecolor='none', alpha=0.1)

for ind, row in tone_events.iterrows():
    ax.fill_between(timestamps, min(data), max(data), where=(timestamps >= row["start_time"]) & (timestamps <= row["stop_time"]), color='red', edgecolor='none', alpha=0.1)

# Create proxy lines for legend entries with corresponding colors and transparency
green_line = mlines.Line2D([], [], color='green', label='∆F/F from striatum', alpha=0.5)
blue_line = mlines.Line2D([], [], color='blue', label='Light', alpha=0.1)
red_line = mlines.Line2D([], [], color='red', label='Tone', alpha=0.1)

ax.legend(handles=[green_line, blue_line, red_line], frameon=False, bbox_to_anchor=(.95, 1), loc='upper left', prop={'size': 6})

ax.spines['top'].set_visible(False)
ax.spines['right'].set_visible(False)
ax.tick_params(axis='y', labelsize=6)
ax.tick_params(axis='x', labelsize=6)
plt.xlabel('Time (s)', fontsize=6)

plt.show()

from matplotlib import pyplot as plt import matplotlib.lines as mlines # Prepare data for plotting data = df_over_f_traces.data[700:1200, 0] timestamps = df_over_f_traces.get_timestamps()[700:1200] fig, ax = plt.subplots(nrows=1, ncols=1, figsize=(6, 2), dpi=300, sharex=True) light_events = events[events["event_type"] == "Light"] light_events = light_events[(light_events["start_time"] >= timestamps[0]) & (light_events["stop_time"] < timestamps[-1])] tone_events = events[events["event_type"] == "Tone"] tone_events = tone_events[(tone_events["start_time"] >= timestamps[0]) & (tone_events["stop_time"] < timestamps[-1])] ax.plot(timestamps, data, color="green", linewidth=0.5) for ind, row in light_events.iterrows(): ax.fill_between(timestamps, min(data), max(data), where=(timestamps >= row["start_time"]) & (timestamps <= row["stop_time"]), color='blue', edgecolor='none', alpha=0.1) for ind, row in tone_events.iterrows(): ax.fill_between(timestamps, min(data), max(data), where=(timestamps >= row["start_time"]) & (timestamps <= row["stop_time"]), color='red', edgecolor='none', alpha=0.1) # Create proxy lines for legend entries with corresponding colors and transparency green_line = mlines.Line2D([], [], color='green', label='∆F/F from striatum', alpha=0.5) blue_line = mlines.Line2D([], [], color='blue', label='Light', alpha=0.1) red_line = mlines.Line2D([], [], color='red', label='Tone', alpha=0.1) ax.legend(handles=[green_line, blue_line, red_line], frameon=False, bbox_to_anchor=(.95, 1), loc='upper left', prop={'size': 6}) ax.spines['top'].set_visible(False) ax.spines['right'].set_visible(False) ax.tick_params(axis='y', labelsize=6) ax.tick_params(axis='x', labelsize=6) plt.xlabel('Time (s)', fontsize=6) plt.show()

In [ ]: