This Jupyter notebook illustrates what data is available as part of the Visual Behavior - 2P dataset, and helps you to understand the experimental design and dimensions of the dataset. The notebook will demonstrate how to identify experiments and sessions that you may be interested in analyzing using the data manifests provided by the VisualBehaviorOphysProjectCache, and exploring the metadata columns that describe the experimental conditions including transgenic lines, targeted areas, imaging depths, microscopes that were used, session types, and dataset variants.
We will first install allensdk into your environment by running the appropriate commands below.
You can install AllenSDK locally with:
pip install allensdk
You can install AllenSDK into your notebook environment by executing the cell below.
If using Google Colab, click on the RESTART RUNTIME button that appears at the end of the output when this cell is complete,. Note that running this cell will produce a long list of outputs and some error messages. Clicking RESTART RUNTIME at the end will resolve these issues. You can minimize the cell after you are done to hide the output.
!pip install --upgrade pip
!pip install allensdk
import numpy as np
import pandas as pd
import allensdk
from allensdk.brain_observatory.behavior.behavior_project_cache import VisualBehaviorOphysProjectCache
# Update this to a valid directory in your filesystem
data_storage_directory = r"\Data\visual_behavior_ophys_cache_dir"
cache = VisualBehaviorOphysProjectCache.from_s3_cache(cache_dir=data_storage_directory)
The data manifest is comprised of three types of tables:
behavior_session_table ophys_session_table ophys_experiment_table The behavior_session_table contains metadata for every behavior session in the dataset. Some behavior sessions have 2-photon data associated with them, while others took place during training in the behavior facility. The different training stages that mice are progressed through are described by the session_type.
The ophys_session_table contains metadata for every 2-photon imaging (aka optical physiology, or ophys) session in the dataset, associated with a unique ophys_session_id. An ophys session is one continuous recording session under the microscope, and can contain different numbers of imaging planes (aka experiments) depending on which microscope was used. For Scientifica sessions, there will only be one experiment (aka imaging plane) per session. For Multiscope sessions, there can be up to eight imaging planes per session. Quality Control (QC) is performed on each individual imaging plane within a session, so each can fail QC independent of the others. This means that a Multiscope session may not have exactly eight experiments (imaging planes).
The ophys_experiment_table contains metadata for every ophys experiment in the dataset, which corresponds to a single imaging plane recorded in a single session, and associated with a unique ophys_experiment_id. A key part of our experimental design is targeting a given population of neurons, contained in one imaging plane, across multiple session_types (further described below) to examine the impact of varying sensory and behavioral conditions on single cell responses. The collection of all imaging sessions for a given imaging plane is referred to as an ophys container, associated with a unique ophys_container_id. Each ophys container may contain different numbers of sessions, depending on which experiments passed QC, and how many retakes occured (when a given session_type fails QC on the first try, an attempt is made to re-acquire the session_type on a different recording day - this is called a retake, also described further below).
ophys_experiment, an ophys_session, and an ophys_container, the following schematic can be helpful¶
Note that this represents a multi-plane imaging dataset. For single-plane imaging, there will only be one plane, corresponding to one row of this diagram.
In this dataset, mice are trained on a visual change detection task. This task involves a continuous stream of stimuli, and mice learn to lick in response to a change in the stimulus identity to earn a water reward. There are different stages of training in this task, described below. The metadata for each behavior session in the dataset can be found in the behavior_sessions_table and can be used to identify behavior sessions you may want to analyze.
behavior_sessions_table from the cache¶behavior_sessions = cache.get_behavior_session_table()
print(f"Total number of behavior sessions: {len(behavior_sessions)}")
behavior_sessions.head()
Total number of behavior sessions: 3572
| equipment_name | full_genotype | mouse_id | reporter_line | driver_line | sex | age_in_days | session_type | cre_line | indicator | session_number | prior_exposures_to_session_type | prior_exposures_to_image_set | prior_exposures_to_omissions | ophys_session_id | ophys_experiment_id | ophys_container_id | project_code | date_of_acquisition | file_id | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| behavior_session_id | ||||||||||||||||||||
| 870987812 | BEH.D-Box1 | Sst-IRES-Cre/wt;Ai148(TIT2L-GC6f-ICL-tTA2)/wt | 457841 | Ai148(TIT2L-GC6f-ICL-tTA2) | [Sst-IRES-Cre] | F | 80.0 | TRAINING_0_gratings_autorewards_15min | Sst-IRES-Cre | GCaMP6f | NaN | 0 | NaN | 0 | NaN | NaN | NaN | NaN | 2019-05-17 14:46:33.550 | 1.085368e+09 |
| 872953842 | BEH.D-Box1 | Sst-IRES-Cre/wt;Ai148(TIT2L-GC6f-ICL-tTA2)/wt | 457841 | Ai148(TIT2L-GC6f-ICL-tTA2) | [Sst-IRES-Cre] | F | 84.0 | TRAINING_1_gratings | Sst-IRES-Cre | GCaMP6f | NaN | 0 | NaN | 0 | NaN | NaN | NaN | NaN | 2019-05-21 13:22:20.613 | 1.085368e+09 |
| 873774125 | BEH.D-Box1 | Sst-IRES-Cre/wt;Ai148(TIT2L-GC6f-ICL-tTA2)/wt | 457841 | Ai148(TIT2L-GC6f-ICL-tTA2) | [Sst-IRES-Cre] | F | 85.0 | TRAINING_1_gratings | Sst-IRES-Cre | GCaMP6f | NaN | 1 | NaN | 0 | NaN | NaN | NaN | NaN | 2019-05-22 13:19:52.005 | 1.085368e+09 |
| 874790261 | BEH.D-Box1 | Sst-IRES-Cre/wt;Ai148(TIT2L-GC6f-ICL-tTA2)/wt | 457841 | Ai148(TIT2L-GC6f-ICL-tTA2) | [Sst-IRES-Cre] | F | 86.0 | TRAINING_1_gratings | Sst-IRES-Cre | GCaMP6f | NaN | 2 | NaN | 0 | NaN | NaN | NaN | NaN | 2019-05-23 13:40:31.405 | 1.085368e+09 |
| 875207834 | BEH.D-Box1 | Sst-IRES-Cre/wt;Ai148(TIT2L-GC6f-ICL-tTA2)/wt | 457841 | Ai148(TIT2L-GC6f-ICL-tTA2) | [Sst-IRES-Cre] | F | 87.0 | TRAINING_1_gratings | Sst-IRES-Cre | GCaMP6f | NaN | 3 | NaN | 0 | NaN | NaN | NaN | NaN | 2019-05-24 11:06:39.332 | 1.085368e+09 |
behavior_sessions.columns
Index(['equipment_name', 'full_genotype', 'mouse_id', 'reporter_line',
'driver_line', 'sex', 'age_in_days', 'session_type', 'cre_line',
'indicator', 'session_number', 'prior_exposures_to_session_type',
'prior_exposures_to_image_set', 'prior_exposures_to_omissions',
'ophys_session_id', 'ophys_experiment_id', 'ophys_container_id',
'project_code', 'date_of_acquisition', 'file_id'],
dtype='object')
print('behavior data could be recorded on these experimental systems:\n')
print(np.sort(behavior_sessions.equipment_name.unique()))
behavior data could be recorded on these experimental systems: ['BEH.B' 'BEH.B-Box1' 'BEH.B-Box2' 'BEH.B-Box3' 'BEH.B-Box4' 'BEH.B-Box5' 'BEH.B-Box6' 'BEH.D' 'BEH.D-Box1' 'BEH.D-Box2' 'BEH.D-Box3' 'BEH.D-Box4' 'BEH.D-Box5' 'BEH.D-Box6' 'BEH.F' 'BEH.F-Box1' 'BEH.F-Box2' 'BEH.F-Box3' 'BEH.F-Box4' 'BEH.F-Box5' 'BEH.F-Box6' 'BEH.G' 'BEH.G-Box1' 'BEH.G-Box2' 'BEH.G-Box3' 'BEH.G-Box4' 'BEH.G-Box5' 'BEH.G-Box6' 'CAM2P.3' 'CAM2P.4' 'CAM2P.5' 'MESO.1']
equipment_name values starting with 'BEH' indicate behavioral training in the behavior facility, while values starting with 'CAM2P' or 'MESO' indicate behavior sessions that took place under a 2-photon microscope - either a Scientifica single plane imaging system ('CAMP2P.4', 'CAM2P.4', or 'CAM2P.5') or a modified Mesoscope system, also called Multiscope, for multi-plane imaging ('MESO.1').
The mouse_id is a 6-digit unique identifier for each experimental animal in the dataset
print('there are ', len(behavior_sessions.mouse_id.unique()), 'mice in the dataset')
there are 82 mice in the dataset
print('the different transgenic lines included in this dataset are:\n')
print(np.sort(behavior_sessions.full_genotype.unique()))
the different transgenic lines included in this dataset are: ['Slc17a7-IRES2-Cre/wt;Camk2a-tTA/wt;Ai93(TITL-GCaMP6f)/wt' 'Slc17a7-IRES2-Cre/wt;Camk2a-tTA/wt;Ai94(TITL-GCaMP6s)/wt' 'Sst-IRES-Cre/wt;Ai148(TIT2L-GC6f-ICL-tTA2)/wt' 'Vip-IRES-Cre/wt;Ai148(TIT2L-GC6f-ICL-tTA2)/wt']
full_genotype refers to the full name of the transgenic mouse line, including all driver and reporter lines in the cross. driver_line and reporter_line have their own unique columns in the table. The first element of the full_genotype is the cre_line (which also has its own column in the table, and is a subset of driver_line). The cre_line determines which genetically identified neuron type will be labeled by the reporter_line.
print('the different cre lines used in this dataset are:\n')
print(np.sort(behavior_sessions.cre_line.unique()))
the different cre lines used in this dataset are: ['Slc17a7-IRES2-Cre' 'Sst-IRES-Cre' 'Vip-IRES-Cre']
In this dataset, we have 3 cre_lines, 'Slc17a7-IRES2-Cre', which labels excitatory neurons across all cortical layers, 'Sst-IRES-Cre' which labels somatostatin expressing inhibitory interneurons, and 'Vip-IRES-Cre', which labels vasoactive intestinal peptide expressing inhibitory interneurons. There are also 3 reporter_lines, 'Ai93(TITL-GCaMP6f)' which expresses the genetically encoded calcium indicator GCaMP6f (f is for 'fast', this reporter has fast offset kinetics, but is only moderately sensitive to calcium relative to other sensors) in cre labeled neurons, 'Ai94(TITL-GCaMP6s)' which expresses the indicator GCaMP6s (s is for 'slow', this reporter is very sensitive to calcium but has slow offset kinetics), and 'Ai148(TIT2L-GC6f-ICL-tTA2', which expresses GCaMP6f using a self-enhancing system to achieve higher expression than other reporter lines (which proved necessary to label inhibitory neurons specifically). The specific indicator expressed by each reporter_line also has its own column in the table.
print('the different reporter lines used in this dataset are:\n')
print(np.sort(behavior_sessions.reporter_line.unique()))
the different reporter lines used in this dataset are: ['Ai148(TIT2L-GC6f-ICL-tTA2)' 'Ai93(TITL-GCaMP6f)' 'Ai94(TITL-GCaMP6s)']
print('the different indicators used in this dataset are:\n')
print(np.sort(behavior_sessions.indicator.unique()))
the different indicators used in this dataset are: ['GCaMP6f' 'GCaMP6s']
behavior_sessions.groupby(['full_genotype', 'mouse_id']).count().reset_index().groupby('full_genotype').count()[['mouse_id']]
| mouse_id | |
|---|---|
| full_genotype | |
| Slc17a7-IRES2-Cre/wt;Camk2a-tTA/wt;Ai93(TITL-GCaMP6f)/wt | 37 |
| Slc17a7-IRES2-Cre/wt;Camk2a-tTA/wt;Ai94(TITL-GCaMP6s)/wt | 8 |
| Sst-IRES-Cre/wt;Ai148(TIT2L-GC6f-ICL-tTA2)/wt | 15 |
| Vip-IRES-Cre/wt;Ai148(TIT2L-GC6f-ICL-tTA2)/wt | 22 |
Other mouse specific metadata includes sex and age_in_days
The session_type for each behavior session indicates the behavioral training stage or 2-photon imaging conditions for that particular session. This determines what stimuli were shown and what task parameters were used.
print('the session_types available in this dataset are:\n')
print(np.sort(behavior_sessions.session_type.unique()))
the session_types available in this dataset are: ['OPHYS_0_images_A_habituation' 'OPHYS_0_images_B_habituation' 'OPHYS_1_images_A' 'OPHYS_1_images_B' 'OPHYS_2_images_A_passive' 'OPHYS_2_images_B_passive' 'OPHYS_3_images_A' 'OPHYS_3_images_B' 'OPHYS_4_images_A' 'OPHYS_4_images_B' 'OPHYS_5_images_A_passive' 'OPHYS_5_images_B_passive' 'OPHYS_6_images_A' 'OPHYS_6_images_B' 'OPHYS_7_receptive_field_mapping' 'TRAINING_0_gratings_autorewards_15min' 'TRAINING_1_gratings' 'TRAINING_2_gratings_flashed' 'TRAINING_3_images_A_10uL_reward' 'TRAINING_3_images_B_10uL_reward' 'TRAINING_4_images_A_handoff_lapsed' 'TRAINING_4_images_A_handoff_ready' 'TRAINING_4_images_A_training' 'TRAINING_4_images_B_training' 'TRAINING_5_images_A_epilogue' 'TRAINING_5_images_A_handoff_lapsed' 'TRAINING_5_images_A_handoff_ready' 'TRAINING_5_images_B_epilogue' 'TRAINING_5_images_B_handoff_lapsed' 'TRAINING_5_images_B_handoff_ready']
Mice are progressed through a series of training stages to shape their behavior prior to 2-photon imaging. Mice are automatically advanced between stages depending on their behavioral performance. For a detailed description of the change detection task and advancement criteria, please see the technical whitepaper: LINK
Training with the change detection task begins with simple static grating stimuli, changing between 0 and 90 degrees in orientation. On the very first day, mice are automatically given a water reward when the orientation of the stimulus changes (TRAINING_0_gratings_autorewards_15min). On subsequent days, mice must lick following the change in order to receive a water reward (TRAINING_1_gratings). In the next stage, stimuli are flashed, with a 500ms inter stimulus interal of mean luminance gray screen (TRAINING_2_gratings_flashed).
Once mice perform the task well with gratings, they are transitioned to natural image stimuli. Different groups of mice are trained with different sets of images, image set A or image set B (described further below). In the following description, we use X as a placeholder for image set A or B in the session_type name. Training with images begins with a 10ul water reward volume (TRAINING_3_images_X_10uL_reward), which is then decreased to 7ul once mice perform the task consistently with images (TRAINING_4_images_X_training). When mice have reached criterion to be transferred to the 2-photon imaging portion of the experiment, they are labeled as 'handoff_ready' (TRAINING_4_images_X_handoff_ready.) If behavior performance returns to below criterion level, they are labeled as 'handoff_lapsed'(TRAINING_4_images_X_handoff_lapsed).
# reminder about possible session types
print('the different session_types available in this dataset are:\n')
print(np.sort(behavior_sessions.session_type.unique()))
the different session_types available in this dataset are: ['OPHYS_0_images_A_habituation' 'OPHYS_0_images_B_habituation' 'OPHYS_1_images_A' 'OPHYS_1_images_B' 'OPHYS_2_images_A_passive' 'OPHYS_2_images_B_passive' 'OPHYS_3_images_A' 'OPHYS_3_images_B' 'OPHYS_4_images_A' 'OPHYS_4_images_B' 'OPHYS_5_images_A_passive' 'OPHYS_5_images_B_passive' 'OPHYS_6_images_A' 'OPHYS_6_images_B' 'OPHYS_7_receptive_field_mapping' 'TRAINING_0_gratings_autorewards_15min' 'TRAINING_1_gratings' 'TRAINING_2_gratings_flashed' 'TRAINING_3_images_A_10uL_reward' 'TRAINING_3_images_B_10uL_reward' 'TRAINING_4_images_A_handoff_lapsed' 'TRAINING_4_images_A_handoff_ready' 'TRAINING_4_images_A_training' 'TRAINING_4_images_B_training' 'TRAINING_5_images_A_epilogue' 'TRAINING_5_images_A_handoff_lapsed' 'TRAINING_5_images_A_handoff_ready' 'TRAINING_5_images_B_epilogue' 'TRAINING_5_images_B_handoff_lapsed' 'TRAINING_5_images_B_handoff_ready']
You will notice that some mice only go up to TRAINING_4, while others have the final training stage labeled TRAINING_5. This is due to a minor change made partway through data collection, where an epilogue stimulus was introduced during the final training stage prior to 2-photon imaging in order to habituate the mice to this stimulus, which is used during 2-photon imaging to aid in session to session registration. The epilogue is a 30 minute movie clip repeated 10 times, for a total of 5 minutes, and occurs at the end of the 60 minute behavioral session, followed by 5 minutes of blank gray screen. Training sessions with an epilogue movie include TRAINING_5_images_X_epilogue, TRAINING_5_images_X_handoff_ready , TRAINING_5_images_X_handoff_lapsed.
session_types during 2-photon imaging¶When mice are transferred to the 2-photon rig for the imaging portion of the experiment, they first undergo 1-3 habituation sessions to get accustomed to the new experimental environment (OPHYS_0_images_X_habituation). During these sessions, mice perform the task under the microscope, but no experimental data is recorded.
During the 2-photon imaging portion of the experiment, mice perform the task with the same set of images they saw during training (either image set A or B), as well as an additional novel set of images (whichever of A or B that they did not see during training). This allows evaluation of the impact of different sensory contexts on neural activity - familiarity versus novelty. Sessions with familiar images include those starting with OPHYS_0, OPHYS_1, OPHYS_2, and OPHYS_3. Sessions with novel images include those starting with OPHYS_4, OPHYS_5, and OPHYS_6.
Interleaved between active behavior sessions are passive viewing sessions where mice are given their daily water ahead of the sesssion (and are thus satiated) and view the stimulus with the lick spout retracted so they are unable to earn water rewards. This allows comparison of neural activity in response to stimuli under different behavioral context - active task engagement and passive viewing without reward. Passive sessions include OPHYS_2_images_A_passive (passive session with familiar images), and OPHYS_5_images_A_passive (passive session with novel images).
The final session during the 2-photon imaging phase is OPHYS_7_receptive_field_mapping, however 2-photon data is not available for these sessions in this data release (but will be made available in a subsequent release).
As hinted to above, some mice were trained with image set A, and others with image set B. Including these two groups of mice, with swapped stimulus conditions, was included in the dataset as a control for the effects of novelty, to ensure that any observed changes were truly due to lack of familiarity with the novel image set, rather than a result of specific features of the image set that was used. In addition, some mice were imaged on the Scientifica single plane imaging systems, and other mice were imaged on Multiscope for multi-plane imaging. These distinct groups of mice are referred to as dataset variants and can be identified using the project_code column
Project_code is only defined for ophys sessions, for technical reasons, so let's fill in the gaps so that all mice have a project_code
# get a table of the project code for each mouse
project_code_lookup = behavior_sessions[behavior_sessions.project_code.isnull()==False].reset_index().drop_duplicates('mouse_id')[['mouse_id','project_code']]
project_code_lookup
| mouse_id | project_code | |
|---|---|---|
| 0 | 457841 | VisualBehaviorMultiscope |
| 8 | 476970 | VisualBehaviorTask1B |
| 16 | 412036 | VisualBehavior |
| 22 | 429952 | VisualBehavior |
| 29 | 449441 | VisualBehavior |
| ... | ... | ... |
| 514 | 440631 | VisualBehaviorMultiscope |
| 521 | 445002 | VisualBehavior |
| 527 | 480753 | VisualBehaviorTask1B |
| 534 | 461946 | VisualBehaviorTask1B |
| 540 | 453911 | VisualBehaviorMultiscope |
82 rows × 2 columns
# iterate over mice in behavior sessions and fill in project code
for mouse_id in behavior_sessions.mouse_id.unique():
rows = behavior_sessions[behavior_sessions.mouse_id==mouse_id].index
behavior_sessions.at[rows, 'project_code'] = project_code_lookup[project_code_lookup.mouse_id==mouse_id].project_code.values[0]
project_codes are available? What session_types belong to each?¶behavior_sessions.project_code.unique()
array(['VisualBehaviorMultiscope', 'VisualBehaviorTask1B',
'VisualBehavior'], dtype=object)
for project_code in behavior_sessions.project_code.unique():
project_sessions = behavior_sessions[behavior_sessions.project_code==project_code]
print('\n project_code:', project_code)
print('\n has these session types:\n', np.sort(project_sessions.session_type.unique()))
print('\n')
project_code: VisualBehaviorMultiscope has these session types: ['OPHYS_0_images_A_habituation' 'OPHYS_1_images_A' 'OPHYS_2_images_A_passive' 'OPHYS_2_images_B_passive' 'OPHYS_3_images_A' 'OPHYS_3_images_B' 'OPHYS_4_images_B' 'OPHYS_5_images_A_passive' 'OPHYS_5_images_B_passive' 'OPHYS_6_images_A' 'OPHYS_6_images_B' 'OPHYS_7_receptive_field_mapping' 'TRAINING_0_gratings_autorewards_15min' 'TRAINING_1_gratings' 'TRAINING_2_gratings_flashed' 'TRAINING_3_images_A_10uL_reward' 'TRAINING_4_images_A_training' 'TRAINING_5_images_A_epilogue' 'TRAINING_5_images_A_handoff_lapsed' 'TRAINING_5_images_A_handoff_ready'] project_code: VisualBehaviorTask1B has these session types: ['OPHYS_0_images_A_habituation' 'OPHYS_0_images_B_habituation' 'OPHYS_1_images_B' 'OPHYS_2_images_B_passive' 'OPHYS_3_images_B' 'OPHYS_4_images_A' 'OPHYS_4_images_B' 'OPHYS_5_images_A_passive' 'OPHYS_6_images_A' 'TRAINING_0_gratings_autorewards_15min' 'TRAINING_1_gratings' 'TRAINING_2_gratings_flashed' 'TRAINING_3_images_B_10uL_reward' 'TRAINING_4_images_B_training' 'TRAINING_5_images_B_epilogue' 'TRAINING_5_images_B_handoff_lapsed' 'TRAINING_5_images_B_handoff_ready'] project_code: VisualBehavior has these session types: ['OPHYS_0_images_A_habituation' 'OPHYS_1_images_A' 'OPHYS_2_images_A_passive' 'OPHYS_3_images_A' 'OPHYS_4_images_B' 'OPHYS_5_images_B_passive' 'OPHYS_6_images_B' 'TRAINING_0_gratings_autorewards_15min' 'TRAINING_1_gratings' 'TRAINING_2_gratings_flashed' 'TRAINING_3_images_A_10uL_reward' 'TRAINING_4_images_A_handoff_lapsed' 'TRAINING_4_images_A_handoff_ready' 'TRAINING_4_images_A_training' 'TRAINING_5_images_A_epilogue' 'TRAINING_5_images_A_handoff_lapsed' 'TRAINING_5_images_A_handoff_ready']
Notice that for project_codes VisualBehavior and VisualBehaviorMultiscope, mice are trained on image set A, while for VisualBehaviorTask1B, mice are trained on image set B
The ophys_session_table includes all of the metadata columns available in the behavior_session_table, as well as additional information specific to 2-photon imaging, namely the list of ophys_experiment_ids and ophys_container_ids associated with each ophys_session_id.
ophys_sessions = cache.get_ophys_session_table()
print(f"Total number of ophys sessions: {len(ophys_sessions)}\n")
print(ophys_sessions.columns)
ophys_sessions.head()
Total number of ophys sessions: 551
Index(['equipment_name', 'full_genotype', 'mouse_id', 'reporter_line',
'driver_line', 'sex', 'age_in_days', 'session_type', 'cre_line',
'indicator', 'session_number', 'prior_exposures_to_session_type',
'prior_exposures_to_image_set', 'prior_exposures_to_omissions',
'behavior_session_id', 'ophys_experiment_id', 'ophys_container_id',
'project_code', 'date_of_acquisition'],
dtype='object')
| equipment_name | full_genotype | mouse_id | reporter_line | driver_line | sex | age_in_days | session_type | cre_line | indicator | session_number | prior_exposures_to_session_type | prior_exposures_to_image_set | prior_exposures_to_omissions | behavior_session_id | ophys_experiment_id | ophys_container_id | project_code | date_of_acquisition | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| ophys_session_id | |||||||||||||||||||
| 951410079 | MESO.1 | Sst-IRES-Cre/wt;Ai148(TIT2L-GC6f-ICL-tTA2)/wt | 457841 | Ai148(TIT2L-GC6f-ICL-tTA2) | [Sst-IRES-Cre] | F | 206.0 | OPHYS_1_images_A | Sst-IRES-Cre | GCaMP6f | 1.0 | 0 | 65.0 | 0 | 951520319 | [951980471, 951980473, 951980475, 951980479, 9... | [1018028339, 1018028342, 1018028345, 101802835... | VisualBehaviorMultiscope | 2019-09-20 09:45:29.897856 |
| 952430817 | MESO.1 | Sst-IRES-Cre/wt;Ai148(TIT2L-GC6f-ICL-tTA2)/wt | 457841 | Ai148(TIT2L-GC6f-ICL-tTA2) | [Sst-IRES-Cre] | F | 209.0 | OPHYS_2_images_A_passive | Sst-IRES-Cre | GCaMP6f | 2.0 | 0 | 66.0 | 1 | 952554548 | [953659743, 953659745, 953659749, 953659752] | [1018028339, 1018028345, 1018028354, 1018028357] | VisualBehaviorMultiscope | 2019-09-23 08:13:07.627573 |
| 954954402 | MESO.1 | Sst-IRES-Cre/wt;Ai148(TIT2L-GC6f-ICL-tTA2)/wt | 457841 | Ai148(TIT2L-GC6f-ICL-tTA2) | [Sst-IRES-Cre] | F | 210.0 | OPHYS_3_images_A | Sst-IRES-Cre | GCaMP6f | 3.0 | 0 | 67.0 | 2 | 953982960 | [958527464, 958527471, 958527474, 958527479, 9... | [1018028339, 1018028342, 1018028345, 101802835... | VisualBehaviorMultiscope | 2019-09-24 16:00:00.000000 |
| 955775716 | MESO.1 | Sst-IRES-Cre/wt;Ai148(TIT2L-GC6f-ICL-tTA2)/wt | 457841 | Ai148(TIT2L-GC6f-ICL-tTA2) | [Sst-IRES-Cre] | F | 212.0 | OPHYS_3_images_A | Sst-IRES-Cre | GCaMP6f | 3.0 | 1 | 68.0 | 3 | 956010809 | [956941841, 956941844, 956941846] | [1018028339, 1018028342, 1018028345] | VisualBehaviorMultiscope | 2019-09-26 08:44:37.199778 |
| 957020350 | MESO.1 | Sst-IRES-Cre/wt;Ai148(TIT2L-GC6f-ICL-tTA2)/wt | 457841 | Ai148(TIT2L-GC6f-ICL-tTA2) | [Sst-IRES-Cre] | F | 213.0 | OPHYS_4_images_B | Sst-IRES-Cre | GCaMP6f | 4.0 | 0 | 0.0 | 4 | 957032492 | [957759562, 957759564, 957759566, 957759570, 9... | [1018028339, 1018028342, 1018028345, 101802835... | VisualBehaviorMultiscope | 2019-09-27 08:28:05.415005 |
# what do the ophys_experiment_id and ophys_container_id columns look like?
# are there always the same number of experiments and containers in different sessions?
# does the number of experiments and containers depend on the microscope used?
ophys_sessions[['ophys_experiment_id', 'ophys_container_id', 'equipment_name']][:15]
| ophys_experiment_id | ophys_container_id | equipment_name | |
|---|---|---|---|
| ophys_session_id | |||
| 951410079 | [951980471, 951980473, 951980475, 951980479, 9... | [1018028339, 1018028342, 1018028345, 101802835... | MESO.1 |
| 952430817 | [953659743, 953659745, 953659749, 953659752] | [1018028339, 1018028345, 1018028354, 1018028357] | MESO.1 |
| 954954402 | [958527464, 958527471, 958527474, 958527479, 9... | [1018028339, 1018028342, 1018028345, 101802835... | MESO.1 |
| 955775716 | [956941841, 956941844, 956941846] | [1018028339, 1018028342, 1018028345] | MESO.1 |
| 957020350 | [957759562, 957759564, 957759566, 957759570, 9... | [1018028339, 1018028342, 1018028345, 101802835... | MESO.1 |
| 958105827 | [958741219, 958741222, 958741230, 958741232, 9... | [1018028342, 1018028345, 1018028351, 101802835... | MESO.1 |
| 958772311 | [959388788, 959388790, 959388792, 959388796, 9... | [1018028339, 1018028342, 1018028345, 101802835... | MESO.1 |
| 959458018 | [960410023, 960410026, 960410028, 960410038, 9... | [1018028339, 1018028342, 1018028345, 101802835... | MESO.1 |
| 993727065 | [993862620] | [991913453] | CAM2P.4 |
| 993984066 | [994053903] | [991913453] | CAM2P.4 |
| 994199725 | [994278281] | [991913453] | CAM2P.4 |
| 994731371 | [994790561] | [991913453] | CAM2P.4 |
| 994883056 | [994955425] | [991913453] | CAM2P.4 |
| 995115654 | [995280513] | [991913453] | CAM2P.4 |
| 995359867 | [995439942] | [991913453] | CAM2P.4 |
ophys_session_table only includes sessions that pass ophys QC¶behavior_session_table includes all the sessions)¶The ophys_session_table only includes sessions with 2-photon imaging data that passed our QC criteria. Importantly, sessions that took place during 2-photon imaging, but did NOT pass QC, can be found in the behavior_session_table, as it includes the full training history for every mouse. In the behavior_session_table, only sessions with passing ophys data will have an ophys_session_id. We can use this to identify ophys sessions that didnt pass QC, but still have behavior data.
# pick a mouse
mouse_id = 445002
# get behavior sessions that took place on the microscope
mouse_ophys_sessions = behavior_sessions[(behavior_sessions.mouse_id==mouse_id)&
(behavior_sessions.equipment_name=='CAM2P.3')]
# only look at the relevant columns
mouse_ophys_sessions.sort_values(by='date_of_acquisition')[['session_type', 'date_of_acquisition', 'ophys_session_id']]
| session_type | date_of_acquisition | ophys_session_id | |
|---|---|---|---|
| behavior_session_id | |||
| 846331423 | OPHYS_0_images_A_habituation | 2019-04-04 13:13:13.399 | NaN |
| 847036328 | OPHYS_0_images_A_habituation | 2019-04-05 12:47:32.422 | NaN |
| 847874940 | OPHYS_0_images_A_habituation | 2019-04-08 13:27:21.095 | NaN |
| 848613406 | OPHYS_1_images_A | 2019-04-09 13:26:26.372 | 848264483.0 |
| 849147676 | OPHYS_2_images_A_passive | 2019-04-10 13:34:39.264 | 848894137.0 |
| 849651275 | OPHYS_3_images_A | 2019-04-11 12:31:50.074 | 849597937.0 |
| 850989832 | OPHYS_4_images_B | 2019-04-12 13:30:27.946 | 850959102.0 |
| 851773928 | OPHYS_5_images_B_passive | 2019-04-15 12:20:00.123 | NaN |
| 852451567 | OPHYS_6_images_B | 2019-04-16 12:24:46.804 | 852326785.0 |
| 855200229 | OPHYS_5_images_B_passive | 2019-04-22 09:01:33.022 | 855083235.0 |
Notice that only a subset of all OPHYS sessions have an ophys_session_id - these are the sessions that passed QC. Sessions with NaN as the ophys_session_id either do not have 2P data recorded (as in habituation sessions), or failed QC and were retaken on a subsequent day, such as OPHYS_5_images_B_passive in this case
print('there are', len(mouse_ophys_sessions), 'ophys sessions in the behavior_session_table for this mouse')
print('this includes ophys sessions that failed QC for ophys, but still have behavior data')
there are 10 ophys sessions in the behavior_session_table for this mouse this includes ophys sessions that failed QC for ophys, but still have behavior data
ophys_session_table for this mouse?¶print('there are', len(ophys_sessions[ophys_sessions.mouse_id==mouse_id]), 'sessions in the ophys_session_table for this mouse')
print('these are the sessions with valid ophys data')
there are 6 sessions in the ophys_session_table for this mouse these are the sessions with valid ophys data
ophys_sessions[ophys_sessions.mouse_id==mouse_id][['date_of_acquisition', 'session_type']]
| date_of_acquisition | session_type | |
|---|---|---|
| ophys_session_id | ||
| 848264483 | 2019-04-09 17:15:25.000000 | OPHYS_1_images_A |
| 848894137 | 2019-04-10 16:46:31.000000 | OPHYS_2_images_A_passive |
| 849597937 | 2019-04-11 18:56:20.000000 | OPHYS_3_images_A |
| 850959102 | 2019-04-12 19:52:29.000000 | OPHYS_4_images_B |
| 852326785 | 2019-04-16 18:52:04.000000 | OPHYS_6_images_B |
| 855083235 | 2019-04-22 14:29:07.000000 | OPHYS_5_images_B_passive |
ophys_session_table do not always occur in sequential order¶The schematic above depicts ophys sessions OPHYS1-6 in a specific order, however this order is rarely perfectly maintained due to QC failures. The example above shows OPHYS_1-4 in the correct order, but then OPHYS_5 comes after OPHYS_6 because the first attempt at OPHYS_5 failed (as we can see from the behavior_sessions for this mouse), and had to be retaken after OPHYS_6.
# pick a mouse
mouse_id = 453911
# get behavior sessions that took place on the microscope
mouse_ophys_sessions = behavior_sessions[(behavior_sessions.mouse_id==mouse_id)&
(behavior_sessions.equipment_name=='MESO.1')]
# only look at the relevant columns
mouse_ophys_sessions.sort_values(by='date_of_acquisition')[['date_of_acquisition', 'session_type', 'ophys_session_id']]
| date_of_acquisition | session_type | ophys_session_id | |
|---|---|---|---|
| behavior_session_id | |||
| 895562386 | 2019-06-27 09:06:59.897 | OPHYS_0_images_A_habituation | NaN |
| 896561264 | 2019-06-28 09:10:46.512 | OPHYS_0_images_A_habituation | NaN |
| 898432373 | 2019-07-01 08:58:07.192 | OPHYS_0_images_A_habituation | NaN |
| 898874650 | 2019-07-02 08:40:59.746 | OPHYS_1_images_A | NaN |
| 899390684 | 2019-07-03 08:57:26.046 | OPHYS_1_images_A | NaN |
| 901999401 | 2019-07-09 09:28:22.075 | OPHYS_0_images_A_habituation | NaN |
| 903708760 | 2019-07-11 08:46:33.458 | OPHYS_1_images_A | 903621170.0 |
| 904574580 | 2019-07-12 08:48:49.378 | OPHYS_2_images_A_passive | 904418381.0 |
| 906472625 | 2019-07-15 09:17:21.104 | OPHYS_3_images_A | 906299056.0 |
| 907053876 | 2019-07-16 08:48:21.658 | OPHYS_4_images_B | 906968227.0 |
| 907900077 | 2019-07-17 08:48:49.040 | OPHYS_5_images_B_passive | 907753304.0 |
| 908533370 | 2019-07-18 08:50:09.254 | OPHYS_6_images_B | 908441202.0 |
| 910207192 | 2019-07-22 08:48:04.971 | OPHYS_3_images_A | NaN |
| 910829549 | 2019-07-23 08:56:45.674 | OPHYS_3_images_A | NaN |
| 911904717 | 2019-07-25 08:51:59.902 | OPHYS_3_images_A | 911719666.0 |
| 913673477 | 2019-07-29 08:48:52.253 | OPHYS_4_images_B | 913564409.0 |
| 914211263 | 2019-07-30 08:50:36.963 | OPHYS_5_images_B_passive | 914161594.0 |
| 914705301 | 2019-07-31 09:05:32.494 | OPHYS_3_images_A | 914639324.0 |
| 915418411 | 2019-08-01 09:01:07.251 | OPHYS_5_images_B_passive | 915306390.0 |
| 916403851 | 2019-08-02 08:53:07.844 | OPHYS_6_images_B | NaN |
| 918103715 | 2019-08-06 09:02:21.903 | OPHYS_6_images_B | NaN |
| 923410363 | 2019-08-14 08:58:56.375 | OPHYS_1_images_A | NaN |
| 924472023 | 2019-08-15 08:28:25.944 | OPHYS_1_images_A | NaN |
| 925084998 | 2019-08-16 08:47:19.338 | OPHYS_1_images_A | NaN |
Looks like lots of retakes for this one (where ophys_session_id = NaN). Also note that there are multiple retakes for some session_types. This can happen for mice imaged on Multiscope, because retakes can be triggered by QC failure of any one of the 8 imaging planes in the session.
ophys_sessions table for this mouse¶ophys_sessions[ophys_sessions.mouse_id==mouse_id][['date_of_acquisition', 'session_type']]
| date_of_acquisition | session_type | |
|---|---|---|
| ophys_session_id | ||
| 903621170 | 2019-07-11 08:06:16.364510 | OPHYS_1_images_A |
| 904418381 | 2019-07-12 08:07:32.656976 | OPHYS_2_images_A_passive |
| 906299056 | 2019-07-15 08:12:43.118756 | OPHYS_3_images_A |
| 906968227 | 2019-07-16 08:09:56.532288 | OPHYS_4_images_B |
| 907753304 | 2019-07-17 08:06:51.025085 | OPHYS_5_images_B_passive |
| 908441202 | 2019-07-18 08:07:56.713158 | OPHYS_6_images_B |
| 911719666 | 2019-07-25 08:00:28.100055 | OPHYS_3_images_A |
| 913564409 | 2019-07-29 08:03:11.994563 | OPHYS_4_images_B |
| 914161594 | 2019-07-30 08:09:37.000000 | OPHYS_5_images_B_passive |
| 914639324 | 2019-07-31 08:56:17.109568 | OPHYS_3_images_A |
| 915306390 | 2019-08-01 08:14:00.752306 | OPHYS_5_images_B_passive |
It looks like the first set of sessions are taken in sequential order, but after that there are a few retakes of some of the session_types. This suggests that some of the imaging planes for this Multiscope mouse passed QC on the first time around, but retakes were needed to get passing ophys data for other imaging planes.
# pick a mouse
mouse_id = 438912
# get behavior sessions that took place on the microscope
mouse_ophys_sessions = behavior_sessions[(behavior_sessions.mouse_id==mouse_id)&
(behavior_sessions.equipment_name=='MESO.1')]
# only look at the relevant columns
mouse_ophys_sessions.sort_values(by='date_of_acquisition')[['date_of_acquisition', 'session_type', 'ophys_session_id']]
| date_of_acquisition | session_type | ophys_session_id | |
|---|---|---|---|
| behavior_session_id | |||
| 844234790 | 2019-04-01 13:24:11.225 | OPHYS_0_images_A_habituation | NaN |
| 844875410 | 2019-04-02 12:01:18.550 | OPHYS_0_images_A_habituation | NaN |
| 846248008 | 2019-04-04 11:59:05.414 | OPHYS_0_images_A_habituation | NaN |
| 847048485 | 2019-04-05 13:00:43.947 | OPHYS_1_images_A | 846871218.0 |
| 847867029 | 2019-04-08 13:02:01.415 | OPHYS_2_images_A_passive | 847758278.0 |
| 848575992 | 2019-04-09 12:42:24.987 | OPHYS_3_images_A | 848401585.0 |
| 849098851 | 2019-04-10 12:17:58.186 | OPHYS_4_images_B | 848983781.0 |
| 850976604 | 2019-04-12 12:39:48.864 | OPHYS_6_images_B | 850894918.0 |
| 853266283 | 2019-04-17 14:01:59.667 | OPHYS_5_images_B_passive | 853177377.0 |
Well, nearly perfectly, OPHYS_5 came after OPHYS_6
Because the session types can be out of order due to retakes, and because of some of our other experimental design decisions, it is helpful to know some information about the history of the mouse relative to a given session. To serve this purpose, we have included metadata describing the prior_exposures_to_image_set, prior_exposures_to_session_type, and prior_exposures_to_omissions as columns in all the manifest data tables.
prior_exposures_to_image_set¶A key aspect of our experimental design is the inclusion of novel stimuli during the imaging phase of the experiment. However, after the very first session with these novel images, they actually start to become more and more familiar. So, it is important to know whether a given session is truly the first exposure to that image set. In addition, it is useful to know whether subsequent sessions are the second, third, fourth, etc. exposure to that image set, for analysis of changes in activity with experience following novelty. The prior_exposures_to_image_set column describes the number of sessions that a given mouse has observed the stimulus set that was shown in that session, prior to the start of that session. For the very first exposure to a novel image set, the value of prior_exposures_to_image_set will be 0.
prior_exposures_to_image_set column for one of the mice we looked at above, first in the behavior_session_table, which contains all sessions the mouse experienced, then in the ophys_session_table, which only includes sessions that passed ophys QC¶mouse_id = 445002
# get behavior sessions that took place on the microscope
mouse_ophys_sessions = behavior_sessions[(behavior_sessions.mouse_id==mouse_id)&
(behavior_sessions.equipment_name=='CAM2P.3')]
# only look at the relevant columns
mouse_ophys_sessions.sort_values(by='date_of_acquisition')[['session_type', 'date_of_acquisition', 'ophys_session_id', 'prior_exposures_to_image_set']]
| session_type | date_of_acquisition | ophys_session_id | prior_exposures_to_image_set | |
|---|---|---|---|---|
| behavior_session_id | ||||
| 846331423 | OPHYS_0_images_A_habituation | 2019-04-04 13:13:13.399 | NaN | 8.0 |
| 847036328 | OPHYS_0_images_A_habituation | 2019-04-05 12:47:32.422 | NaN | 9.0 |
| 847874940 | OPHYS_0_images_A_habituation | 2019-04-08 13:27:21.095 | NaN | 10.0 |
| 848613406 | OPHYS_1_images_A | 2019-04-09 13:26:26.372 | 848264483.0 | 11.0 |
| 849147676 | OPHYS_2_images_A_passive | 2019-04-10 13:34:39.264 | 848894137.0 | 12.0 |
| 849651275 | OPHYS_3_images_A | 2019-04-11 12:31:50.074 | 849597937.0 | 13.0 |
| 850989832 | OPHYS_4_images_B | 2019-04-12 13:30:27.946 | 850959102.0 | 0.0 |
| 851773928 | OPHYS_5_images_B_passive | 2019-04-15 12:20:00.123 | NaN | 1.0 |
| 852451567 | OPHYS_6_images_B | 2019-04-16 12:24:46.804 | 852326785.0 | 2.0 |
| 855200229 | OPHYS_5_images_B_passive | 2019-04-22 09:01:33.022 | 855083235.0 | 3.0 |
Note that prior_exposures_to_image_set is a high number for OPHYS_0-3, because that is the image set the mouse was trained on, and that it re-sets to zero for the first exposure to the novel image set in OPHYS_4
mouse_id = 445002
# get behavior sessions that took place on the microscope
mouse_ophys_sessions = behavior_sessions[(behavior_sessions.mouse_id==mouse_id)]
# only look at the relevant columns
mouse_ophys_sessions.sort_values(by='date_of_acquisition')[['session_type', 'date_of_acquisition', 'ophys_session_id', 'prior_exposures_to_image_set']]
| session_type | date_of_acquisition | ophys_session_id | prior_exposures_to_image_set | |
|---|---|---|---|---|
| behavior_session_id | ||||
| 837658854 | TRAINING_0_gratings_autorewards_15min | 2019-03-15 14:49:24.015 | NaN | NaN |
| 838515247 | TRAINING_1_gratings | 2019-03-18 13:44:01.474 | NaN | NaN |
| 839219841 | TRAINING_1_gratings | 2019-03-19 12:50:12.068 | NaN | NaN |
| 839565422 | TRAINING_1_gratings | 2019-03-20 13:16:44.461 | NaN | NaN |
| 839912316 | TRAINING_2_gratings_flashed | 2019-03-21 12:54:25.388 | NaN | NaN |
| 840581827 | TRAINING_2_gratings_flashed | 2019-03-22 12:56:37.162 | NaN | NaN |
| 841426832 | TRAINING_3_images_A_10uL_reward | 2019-03-25 13:33:53.275 | NaN | 0.0 |
| 841827774 | TRAINING_3_images_A_10uL_reward | 2019-03-26 12:43:01.470 | NaN | 1.0 |
| 842385277 | TRAINING_3_images_A_10uL_reward | 2019-03-27 12:13:04.923 | NaN | 2.0 |
| 842819933 | TRAINING_4_images_A_training | 2019-03-28 12:11:36.413 | NaN | 3.0 |
| 843400453 | TRAINING_5_images_A_epilogue | 2019-03-29 13:03:16.029 | NaN | 4.0 |
| 844199758 | TRAINING_5_images_A_handoff_ready | 2019-04-01 11:59:06.026 | NaN | 5.0 |
| 844908261 | TRAINING_5_images_A_handoff_ready | 2019-04-02 12:34:16.170 | NaN | 6.0 |
| 845572044 | TRAINING_5_images_A_handoff_ready | 2019-04-03 12:16:13.522 | NaN | 7.0 |
| 846331423 | OPHYS_0_images_A_habituation | 2019-04-04 13:13:13.399 | NaN | 8.0 |
| 847036328 | OPHYS_0_images_A_habituation | 2019-04-05 12:47:32.422 | NaN | 9.0 |
| 847874940 | OPHYS_0_images_A_habituation | 2019-04-08 13:27:21.095 | NaN | 10.0 |
| 848613406 | OPHYS_1_images_A | 2019-04-09 13:26:26.372 | 848264483.0 | 11.0 |
| 849147676 | OPHYS_2_images_A_passive | 2019-04-10 13:34:39.264 | 848894137.0 | 12.0 |
| 849651275 | OPHYS_3_images_A | 2019-04-11 12:31:50.074 | 849597937.0 | 13.0 |
| 850989832 | OPHYS_4_images_B | 2019-04-12 13:30:27.946 | 850959102.0 | 0.0 |
| 851773928 | OPHYS_5_images_B_passive | 2019-04-15 12:20:00.123 | NaN | 1.0 |
| 852451567 | OPHYS_6_images_B | 2019-04-16 12:24:46.804 | 852326785.0 | 2.0 |
| 855200229 | OPHYS_5_images_B_passive | 2019-04-22 09:01:33.022 | 855083235.0 | 3.0 |
Knowing the prior exposures number is especially important for the ophys_session_table, because the sessions that failed ophys QC are not visible there, so it is difficult to know whether a given session was the first of its type or a retake.
ophys_sessions[ophys_sessions.mouse_id==mouse_id][['date_of_acquisition', 'session_type', 'prior_exposures_to_image_set']]
| date_of_acquisition | session_type | prior_exposures_to_image_set | |
|---|---|---|---|
| ophys_session_id | |||
| 848264483 | 2019-04-09 17:15:25.000000 | OPHYS_1_images_A | 11.0 |
| 848894137 | 2019-04-10 16:46:31.000000 | OPHYS_2_images_A_passive | 12.0 |
| 849597937 | 2019-04-11 18:56:20.000000 | OPHYS_3_images_A | 13.0 |
| 850959102 | 2019-04-12 19:52:29.000000 | OPHYS_4_images_B | 0.0 |
| 852326785 | 2019-04-16 18:52:04.000000 | OPHYS_6_images_B | 2.0 |
| 855083235 | 2019-04-22 14:29:07.000000 | OPHYS_5_images_B_passive | 3.0 |
prior_exposures_to_session_type¶In some cases, you may want to know how many times a given session_type was seen by the mouse. For example, to know whether a passive viewing session was the very first time the mouse experienced a passive session with no lick spout, as there may be a difference in expectation of reward between the first passive session and a later one where the mouse has become accustomed to sometimes having the lick spout removed.
prior_exposures_to_session_type in the behavior_session_table with the ophys_session_table for a given mouse¶# pick a mouse
mouse_id = 456915
# get behavior sessions that took place on the microscope
mouse_ophys_sessions = behavior_sessions[(behavior_sessions.mouse_id==mouse_id)&
(behavior_sessions.equipment_name=='MESO.1')]
# only look at the relevant columns
mouse_ophys_sessions.sort_values(by='date_of_acquisition')[['date_of_acquisition', 'session_type', 'ophys_session_id', 'prior_exposures_to_session_type']]
| date_of_acquisition | session_type | ophys_session_id | prior_exposures_to_session_type | |
|---|---|---|---|---|
| behavior_session_id | ||||
| 895809864 | 2019-06-27 11:10:31.672 | OPHYS_0_images_A_habituation | NaN | 0 |
| 896882463 | 2019-06-28 12:27:37.786 | OPHYS_0_images_A_habituation | NaN | 1 |
| 898665096 | 2019-07-01 11:59:09.080 | OPHYS_0_images_A_habituation | NaN | 2 |
| 898938920 | 2019-07-02 11:38:33.248 | OPHYS_1_images_A | NaN | 0 |
| 900227468 | 2019-07-05 09:41:36.089 | OPHYS_2_images_A_passive | NaN | 0 |
| 901316636 | 2019-07-08 12:30:43.670 | OPHYS_3_images_A | NaN | 0 |
| 902211897 | 2019-07-09 12:13:37.777 | OPHYS_1_images_A | NaN | 1 |
| 902995627 | 2019-07-10 12:51:05.866 | OPHYS_1_images_A | 902884228.0 | 2 |
| 903977177 | 2019-07-11 12:34:27.495 | OPHYS_2_images_A_passive | 903813946.0 | 1 |
| 904922800 | 2019-07-12 13:39:52.674 | OPHYS_3_images_A | 904771513.0 | 1 |
| 906658064 | 2019-07-15 12:18:06.720 | OPHYS_4_images_B | 906521029.0 | 0 |
| 907327499 | 2019-07-16 12:19:34.888 | OPHYS_5_images_B_passive | 907177554.0 | 0 |
| 908122381 | 2019-07-17 13:01:35.482 | OPHYS_6_images_B | 907991198.0 | 0 |
| 912640306 | 2019-07-26 10:27:08.157 | OPHYS_1_images_A | NaN | 3 |
ophys_sessions[ophys_sessions.mouse_id==mouse_id][['date_of_acquisition', 'session_type', 'prior_exposures_to_session_type']]
| date_of_acquisition | session_type | prior_exposures_to_session_type | |
|---|---|---|---|
| ophys_session_id | |||
| 902884228 | 2019-07-10 12:09:00.013603 | OPHYS_1_images_A | 2 |
| 903813946 | 2019-07-11 11:09:31.388229 | OPHYS_2_images_A_passive | 1 |
| 904771513 | 2019-07-12 12:22:04.270010 | OPHYS_3_images_A | 1 |
| 906521029 | 2019-07-15 11:15:20.640855 | OPHYS_4_images_B | 0 |
| 907177554 | 2019-07-16 11:23:05.829504 | OPHYS_5_images_B_passive | 0 |
| 907991198 | 2019-07-17 11:26:37.671800 | OPHYS_6_images_B | 0 |
Without the prior_exposures_to_session_type column in the ophys_session_table, it would be difficult to know that OPHYS_2_images_A_passive was actually the second time (1 prior exposure) that the mouse had experienced a passive vieweing session
prior_exposures_to_omissions¶Another unique aspect of the experimental design of this dataset is the inclusion of stimulus omissions in the 2-photon portion of the experiment. During behavioral training, mice experience a highly regular cadence of himage presentations, with 250ms per stimulus presentation, with a 500ms gray screen in between. During imaging sessions, stimulus presentations (other than the change and pre-change images) are omitted with a 5% probability, resulting in some inter stimlus intervals appearing as an extended gray screen period. This allows exploration of potential effects of temporal expectation on neural activity.
prior_exposures_to_omissions in a few mice¶np.sort(behavior_sessions[behavior_sessions.equipment_name=='CAM2P.4'].mouse_id.unique())
array([412036, 412366, 423606, 425493, 425496, 429952, 431252, 436662,
442709, 447663, 448900, 453913, 456564, 462468, 467953, 468866,
476970, 479458, 480753, 489066, 492395, 523927, 524274, 533161,
533162, 538219, 539518, 544261])
# pick a mouse
mouse_id = 436662
# get behavior sessions that took place on the microscope
mouse_ophys_sessions = behavior_sessions[(behavior_sessions.mouse_id==mouse_id)&
(behavior_sessions.equipment_name=='CAM2P.4')]
# only look at the relevant columns
mouse_ophys_sessions.sort_values(by='date_of_acquisition')[['date_of_acquisition', 'session_type', 'ophys_session_id', 'equipment_name', 'prior_exposures_to_omissions']]
| date_of_acquisition | session_type | ophys_session_id | equipment_name | prior_exposures_to_omissions | |
|---|---|---|---|---|---|
| behavior_session_id | |||||
| 846316916 | 2019-04-04 13:09:27.173 | OPHYS_0_images_A_habituation | NaN | CAM2P.4 | 0 |
| 847036338 | 2019-04-05 12:45:42.496 | OPHYS_0_images_A_habituation | NaN | CAM2P.4 | 0 |
| 847871771 | 2019-04-08 13:19:46.613 | OPHYS_0_images_A_habituation | NaN | CAM2P.4 | 0 |
| 848599542 | 2019-04-09 13:06:04.283 | OPHYS_1_images_A | 848253761.0 | CAM2P.4 | 0 |
| 849136474 | 2019-04-10 13:19:29.465 | OPHYS_2_images_A_passive | 848891498.0 | CAM2P.4 | 1 |
| 849648618 | 2019-04-11 12:20:52.232 | OPHYS_3_images_A | 849600749.0 | CAM2P.4 | 2 |
| 850985993 | 2019-04-12 13:19:42.646 | OPHYS_4_images_B | 850959798.0 | CAM2P.4 | 3 |
| 851757903 | 2019-04-15 12:01:12.752 | OPHYS_5_images_B_passive | 851438454.0 | CAM2P.4 | 4 |
| 852440775 | 2019-04-16 12:13:06.144 | OPHYS_6_images_B | NaN | CAM2P.4 | 5 |
| 853496105 | 2019-04-18 09:13:57.308 | OPHYS_6_images_B | 853416014.0 | CAM2P.4 | 6 |
In this case (and in most cases), omissions do not occur until the first true imaging session on the 2-photon rig, OPHYS_1, i.e. they are not included in habituation sessions. However, in a small number of mice from the beginning of our data collection process, omissions did occur in habituation sessions (but never during training). This is something to be careful of if you are looking at something like the change in omission related activity with experience.
Here is a mouse that saw omissions during habituation sessions. Also note that the first two habituation sessions took place on different microscopes (this is extremely rare, every effort is made to image a given mouse on the same 2-photon rig during its entire lifetime).
# pick a mouse
mouse_id = 423606
# get behavior sessions - include training as well
mouse_ophys_sessions = behavior_sessions[(behavior_sessions.mouse_id==mouse_id)]
# only look at the relevant columns
mouse_ophys_sessions.sort_values(by='date_of_acquisition')[['date_of_acquisition', 'session_type', 'ophys_session_id', 'equipment_name', 'prior_exposures_to_omissions']]
| date_of_acquisition | session_type | ophys_session_id | equipment_name | prior_exposures_to_omissions | |
|---|---|---|---|---|---|
| behavior_session_id | |||||
| 785529911 | 2018-11-26 14:30:19.454 | TRAINING_0_gratings_autorewards_15min | NaN | BEH.D-Box3 | 0 |
| 786305638 | 2018-11-27 15:00:23.669 | TRAINING_1_gratings | NaN | BEH.D-Box3 | 0 |
| 788343528 | 2018-11-29 13:58:55.180 | TRAINING_1_gratings | NaN | BEH.D-Box3 | 0 |
| 789227183 | 2018-11-30 14:32:25.299 | TRAINING_1_gratings | NaN | BEH.D-Box3 | 0 |
| 790026166 | 2018-12-03 14:34:12.462 | TRAINING_2_gratings_flashed | NaN | BEH.G-Box1 | 0 |
| 791865095 | 2018-12-07 13:34:55.964 | TRAINING_2_gratings_flashed | NaN | BEH.G | 0 |
| 792654939 | 2018-12-10 13:16:02.765 | TRAINING_3_images_A_10uL_reward | NaN | BEH.G | 0 |
| 793318274 | 2018-12-11 12:49:25.684 | TRAINING_3_images_A_10uL_reward | NaN | BEH.G | 0 |
| 794275471 | 2018-12-12 12:26:42.418 | TRAINING_3_images_A_10uL_reward | NaN | BEH.G | 0 |
| 794955196 | 2018-12-13 13:12:42.180 | TRAINING_4_images_A_training | NaN | BEH.G-Box5 | 0 |
| 795838931 | 2018-12-14 14:31:59.653 | TRAINING_4_images_A_training | NaN | BEH.G-Box2 | 0 |
| 795844007 | 2018-12-14 14:37:59.756 | TRAINING_4_images_A_training | NaN | BEH.G-Box5 | 0 |
| 797017509 | 2018-12-17 13:32:01.455 | TRAINING_4_images_A_training | NaN | BEH.G-Box3 | 0 |
| 798022584 | 2018-12-18 14:33:50.331 | TRAINING_4_images_A_handoff_ready | NaN | BEH.G-Box3 | 0 |
| 799110611 | 2018-12-19 14:13:56.030 | TRAINING_4_images_A_handoff_ready | NaN | BEH.G-Box3 | 0 |
| 799919873 | 2018-12-20 13:32:19.525 | TRAINING_4_images_A_handoff_ready | NaN | BEH.G-Box3 | 0 |
| 800317148 | 2018-12-21 14:07:23.188 | TRAINING_4_images_A_handoff_ready | NaN | BEH.G-Box3 | 0 |
| 800922507 | 2018-12-24 10:25:14.620 | TRAINING_4_images_A_handoff_ready | NaN | BEH.G | 0 |
| 802458646 | 2019-01-02 11:52:29.221 | TRAINING_4_images_A_handoff_ready | NaN | BEH.G | 0 |
| 804857121 | 2019-01-07 10:28:11.802 | OPHYS_0_images_A_habituation | NaN | CAM2P.3 | 0 |
| 805422841 | 2019-01-08 09:25:42.231 | OPHYS_0_images_A_habituation | NaN | CAM2P.4 | 1 |
| 805968845 | 2019-01-09 08:44:58.700 | OPHYS_1_images_A | NaN | CAM2P.4 | 2 |
| 806625285 | 2019-01-10 08:46:20.812 | OPHYS_1_images_A | NaN | CAM2P.4 | 3 |
| 807216432 | 2019-01-11 08:59:30.008 | OPHYS_2_images_A_passive | NaN | CAM2P.4 | 4 |
| 808126003 | 2019-01-14 08:58:55.629 | OPHYS_3_images_A | NaN | CAM2P.4 | 5 |
| 808761500 | 2019-01-15 08:31:19.856 | OPHYS_4_images_B | NaN | CAM2P.4 | 6 |
| 809329233 | 2019-01-16 09:09:04.155 | OPHYS_5_images_B_passive | NaN | CAM2P.4 | 7 |
| 809606861 | 2019-01-17 08:56:36.971 | OPHYS_6_images_B | NaN | CAM2P.4 | 8 |
| 812843373 | 2019-01-24 08:49:05.499 | OPHYS_6_images_B | NaN | CAM2P.4 | 9 |
| 815011426 | 2019-01-29 13:11:36.717 | OPHYS_6_images_B | NaN | CAM2P.4 | 10 |
| 817925173 | 2019-02-04 12:38:24.012 | OPHYS_1_images_A | NaN | CAM2P.4 | 11 |
| 821521826 | 2019-02-12 09:11:22.027 | OPHYS_1_images_A | 821471625.0 | CAM2P.3 | 12 |
| 822249041 | 2019-02-13 09:24:05.789 | OPHYS_2_images_A_passive | 822081744.0 | CAM2P.3 | 13 |
| 822995693 | 2019-02-14 09:36:08.947 | OPHYS_3_images_A | 822734832.0 | CAM2P.3 | 14 |
| 824625446 | 2019-02-18 08:56:24.090 | OPHYS_4_images_B | 824433631.0 | CAM2P.3 | 15 |
| 825235376 | 2019-02-19 08:32:09.903 | OPHYS_5_images_B_passive | 825182159.0 | CAM2P.3 | 16 |
| 825801398 | 2019-02-20 08:25:43.970 | OPHYS_6_images_B | 825682144.0 | CAM2P.3 | 17 |
# get all behavior sessions that were habituation sessions (image set A or B)
# where the prior exposures to omissions was not zero
habituation_with_omission = behavior_sessions[((behavior_sessions.session_type=='OPHYS_0_images_A_habituation')|
(behavior_sessions.session_type=='OPHYS_0_images_B_habituation'))&
(behavior_sessions.prior_exposures_to_omissions>0)]
mice_with_omission_during_habituation = habituation_with_omission.mouse_id.unique()
print(len(mice_with_omission_during_habituation), ' mice had omissions during habituation')
17 mice had omissions during habituation
The ophys_experiment_table contains all ophys data that passes QC, organized according to individual imaging planes in individual sessions, each associated with an ophys_experiment_id. The ophys_experiment_table contains all the columns in ophys_session_table, plus a few additional ones specific to individual imaging planes, namely imaging_depth and targeted_structure.
ophys_experiments = cache.get_ophys_experiment_table()
print(f"Total number of ophys experiments: {len(ophys_experiments)}\n")
ophys_experiments.head()
Total number of ophys experiments: 1165
| equipment_name | full_genotype | mouse_id | reporter_line | driver_line | sex | age_in_days | session_type | cre_line | indicator | ... | prior_exposures_to_image_set | prior_exposures_to_omissions | ophys_session_id | behavior_session_id | ophys_container_id | project_code | imaging_depth | targeted_structure | date_of_acquisition | file_id | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| ophys_experiment_id | |||||||||||||||||||||
| 951980471 | MESO.1 | Sst-IRES-Cre/wt;Ai148(TIT2L-GC6f-ICL-tTA2)/wt | 457841 | Ai148(TIT2L-GC6f-ICL-tTA2) | [Sst-IRES-Cre] | F | 206.0 | OPHYS_1_images_A | Sst-IRES-Cre | GCaMP6f | ... | 65.0 | 0 | 951410079 | 951520319 | 1018028342 | VisualBehaviorMultiscope | 150 | VISp | 2019-09-20 09:45:29.897856 | 1085394308 |
| 951980484 | MESO.1 | Sst-IRES-Cre/wt;Ai148(TIT2L-GC6f-ICL-tTA2)/wt | 457841 | Ai148(TIT2L-GC6f-ICL-tTA2) | [Sst-IRES-Cre] | F | 206.0 | OPHYS_1_images_A | Sst-IRES-Cre | GCaMP6f | ... | 65.0 | 0 | 951410079 | 951520319 | 1018028351 | VisualBehaviorMultiscope | 75 | VISl | 2019-09-20 09:45:29.897856 | 1085400876 |
| 951980473 | MESO.1 | Sst-IRES-Cre/wt;Ai148(TIT2L-GC6f-ICL-tTA2)/wt | 457841 | Ai148(TIT2L-GC6f-ICL-tTA2) | [Sst-IRES-Cre] | F | 206.0 | OPHYS_1_images_A | Sst-IRES-Cre | GCaMP6f | ... | 65.0 | 0 | 951410079 | 951520319 | 1018028345 | VisualBehaviorMultiscope | 225 | VISp | 2019-09-20 09:45:29.897856 | 1085400919 |
| 951980479 | MESO.1 | Sst-IRES-Cre/wt;Ai148(TIT2L-GC6f-ICL-tTA2)/wt | 457841 | Ai148(TIT2L-GC6f-ICL-tTA2) | [Sst-IRES-Cre] | F | 206.0 | OPHYS_1_images_A | Sst-IRES-Cre | GCaMP6f | ... | 65.0 | 0 | 951410079 | 951520319 | 1018028354 | VisualBehaviorMultiscope | 150 | VISl | 2019-09-20 09:45:29.897856 | 1085400977 |
| 951980475 | MESO.1 | Sst-IRES-Cre/wt;Ai148(TIT2L-GC6f-ICL-tTA2)/wt | 457841 | Ai148(TIT2L-GC6f-ICL-tTA2) | [Sst-IRES-Cre] | F | 206.0 | OPHYS_1_images_A | Sst-IRES-Cre | GCaMP6f | ... | 65.0 | 0 | 951410079 | 951520319 | 1018028339 | VisualBehaviorMultiscope | 75 | VISp | 2019-09-20 09:45:29.897856 | 1085400920 |
5 rows × 22 columns
ophys_sessions_table with ophys_experiments_table¶ophys_sessions.columns
Index(['equipment_name', 'full_genotype', 'mouse_id', 'reporter_line',
'driver_line', 'sex', 'age_in_days', 'session_type', 'cre_line',
'indicator', 'session_number', 'prior_exposures_to_session_type',
'prior_exposures_to_image_set', 'prior_exposures_to_omissions',
'behavior_session_id', 'ophys_experiment_id', 'ophys_container_id',
'project_code', 'date_of_acquisition'],
dtype='object')
ophys_experiments.columns
Index(['equipment_name', 'full_genotype', 'mouse_id', 'reporter_line',
'driver_line', 'sex', 'age_in_days', 'session_type', 'cre_line',
'indicator', 'session_number', 'prior_exposures_to_session_type',
'prior_exposures_to_image_set', 'prior_exposures_to_omissions',
'ophys_session_id', 'behavior_session_id', 'ophys_container_id',
'project_code', 'imaging_depth', 'targeted_structure',
'date_of_acquisition', 'file_id'],
dtype='object')
imaging_depths and targeted_structures are available? Are they different depending on project_code?¶# loop through project codes and print the available imaging_depths and targeted_structures
for project_code in ophys_experiments.project_code.unique():
project_experiments = ophys_experiments[ophys_experiments.project_code==project_code]
print('\nimaging_depths available for', project_code, 'include: ', project_experiments.imaging_depth.unique())
print('\ntargeted_structures available for', project_code, 'include: ', project_experiments.targeted_structure.unique())
print('\n')
imaging_depths available for VisualBehaviorMultiscope include: [150 75 225 300 359 85 271 73 169 263 167 79 361 267 363 275 175 367 269 177 283 365 373 371 165 81 369 77 171 259 377 279 83 274 227 152 146 71 276 144 158 142 87 160 217 69 219 51 140 215 156 211 162 221 148 166 223 231 282 174 93 213 154 229 302 308 298 278 306 270 312 218 383 161 273 381 265 261 310 235 292 296 187 295 197 91 183 355 277 179 181 281 185 291 95 375 173 67 284 320 145 285 321 228 149 287 322 220 222 226 147 294 315 304 286 316 132 350 348 65 379] targeted_structures available for VisualBehaviorMultiscope include: ['VISp' 'VISl'] imaging_depths available for VisualBehaviorTask1B include: [275 175 375] targeted_structures available for VisualBehaviorTask1B include: ['VISp'] imaging_depths available for VisualBehavior include: [175 375 290 275] targeted_structures available for VisualBehavior include: ['VISp']
ophys_experiment_table is useful for identifying ophys_containers to analyze¶Compare the ophys_container_id column of the ophys_experiment_table with the ophys_session_table. In ophys_session_table, each ophys_session_id is associated with one or more imaging planes (ophys_experiment_ids), while in the ophys_experiment_table, you can evaluate each of those imaging planes indepdently. This is particularly helpful for identifying ophys_containers that you want to analyze - the set of all imaging sessions for a given imaging plane.
The ophys_experient_table has all the same columns as ophys_session_table, just reorgnized by ophys_experiment_id
print(ophys_experiments.columns)
Index(['equipment_name', 'full_genotype', 'mouse_id', 'reporter_line',
'driver_line', 'sex', 'age_in_days', 'session_type', 'cre_line',
'indicator', 'session_number', 'prior_exposures_to_session_type',
'prior_exposures_to_image_set', 'prior_exposures_to_omissions',
'ophys_session_id', 'behavior_session_id', 'ophys_container_id',
'project_code', 'imaging_depth', 'targeted_structure',
'date_of_acquisition', 'file_id'],
dtype='object')
This means that each ophys_experiment_id has a single ophys_container_id.
ophys_container_id and see what ophys_experiments it contains?¶ophys_container_id = ophys_experiments.ophys_container_id.unique()[50]
container_experiments = ophys_experiments[ophys_experiments.ophys_container_id==ophys_container_id]
container_experiments
| equipment_name | full_genotype | mouse_id | reporter_line | driver_line | sex | age_in_days | session_type | cre_line | indicator | ... | prior_exposures_to_image_set | prior_exposures_to_omissions | ophys_session_id | behavior_session_id | ophys_container_id | project_code | imaging_depth | targeted_structure | date_of_acquisition | file_id | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| ophys_experiment_id | |||||||||||||||||||||
| 930996073 | MESO.1 | Vip-IRES-Cre/wt;Ai148(TIT2L-GC6f-ICL-tTA2)/wt | 453988 | Ai148(TIT2L-GC6f-ICL-tTA2) | [Vip-IRES-Cre] | M | 196.0 | OPHYS_1_images_A | Vip-IRES-Cre | GCaMP6f | ... | 55.0 | 1 | 929686773 | 929818797 | 1026722854 | VisualBehaviorMultiscope | 71 | VISp | 2019-08-23 08:11:32.653400 | 1085398435 |
| 932372705 | MESO.1 | Vip-IRES-Cre/wt;Ai148(TIT2L-GC6f-ICL-tTA2)/wt | 453988 | Ai148(TIT2L-GC6f-ICL-tTA2) | [Vip-IRES-Cre] | M | 199.0 | OPHYS_2_images_B_passive | Vip-IRES-Cre | GCaMP6f | ... | 0.0 | 2 | 931326814 | 931566300 | 1026722854 | VisualBehaviorMultiscope | 75 | VISp | 2019-08-26 08:26:26.815263 | 1086012427 |
| 935514366 | MESO.1 | Vip-IRES-Cre/wt;Ai148(TIT2L-GC6f-ICL-tTA2)/wt | 453988 | Ai148(TIT2L-GC6f-ICL-tTA2) | [Vip-IRES-Cre] | M | 201.0 | OPHYS_4_images_B | Vip-IRES-Cre | GCaMP6f | ... | 2.0 | 4 | 933439847 | 933504759 | 1026722854 | VisualBehaviorMultiscope | 71 | VISp | 2019-08-28 08:23:18.718157 | 1085398628 |
| 936500611 | MESO.1 | Vip-IRES-Cre/wt;Ai148(TIT2L-GC6f-ICL-tTA2)/wt | 453988 | Ai148(TIT2L-GC6f-ICL-tTA2) | [Vip-IRES-Cre] | M | 203.0 | OPHYS_6_images_B | Vip-IRES-Cre | GCaMP6f | ... | 4.0 | 6 | 935559843 | 935811563 | 1026722854 | VisualBehaviorMultiscope | 73 | VISp | 2019-08-30 09:36:00.434110 | 1085400954 |
| 938002077 | MESO.1 | Vip-IRES-Cre/wt;Ai148(TIT2L-GC6f-ICL-tTA2)/wt | 453988 | Ai148(TIT2L-GC6f-ICL-tTA2) | [Vip-IRES-Cre] | M | 207.0 | OPHYS_3_images_A | Vip-IRES-Cre | GCaMP6f | ... | 56.0 | 7 | 937162622 | 937416292 | 1026722854 | VisualBehaviorMultiscope | 73 | VISp | 2019-09-03 08:13:17.884589 | 1085400960 |
| 939471278 | MESO.1 | Vip-IRES-Cre/wt;Ai148(TIT2L-GC6f-ICL-tTA2)/wt | 453988 | Ai148(TIT2L-GC6f-ICL-tTA2) | [Vip-IRES-Cre] | M | 208.0 | OPHYS_2_images_A_passive | Vip-IRES-Cre | GCaMP6f | ... | 57.0 | 8 | 938140092 | 938377224 | 1026722854 | VisualBehaviorMultiscope | 73 | VISp | 2019-09-04 08:16:32.270992 | 1086012756 |
| 940433470 | MESO.1 | Vip-IRES-Cre/wt;Ai148(TIT2L-GC6f-ICL-tTA2)/wt | 453988 | Ai148(TIT2L-GC6f-ICL-tTA2) | [Vip-IRES-Cre] | M | 209.0 | OPHYS_5_images_B_passive | Vip-IRES-Cre | GCaMP6f | ... | 5.0 | 9 | 939526443 | 939538572 | 1026722854 | VisualBehaviorMultiscope | 75 | VISp | 2019-09-05 09:19:35.394918 | 1085400427 |
7 rows × 22 columns
Thats 7 different recording sessions for this single imaging plane. Remember that one ophys_container_id is linked to one imaging plane, recorded in multiple sessions
container_experiments.session_type.unique()
array(['OPHYS_1_images_A', 'OPHYS_2_images_B_passive', 'OPHYS_4_images_B',
'OPHYS_6_images_B', 'OPHYS_3_images_A', 'OPHYS_2_images_A_passive',
'OPHYS_5_images_B_passive'], dtype=object)
# get all Sst experiments in the relevant project code
sst_experiments = ophys_experiments[(ophys_experiments.cre_line=='Sst-IRES-Cre')&
(ophys_experiments.project_code=='VisualBehaviorTask1B')]
# pick some container from this set
ophys_container_id = sst_experiments.ophys_container_id.unique()[1]
print(ophys_container_id)
941373529
# what experiments are there for this container?
sst_container_experiments = sst_experiments[sst_experiments.ophys_container_id==ophys_container_id]
sst_container_experiments
| equipment_name | full_genotype | mouse_id | reporter_line | driver_line | sex | age_in_days | session_type | cre_line | indicator | ... | prior_exposures_to_image_set | prior_exposures_to_omissions | ophys_session_id | behavior_session_id | ophys_container_id | project_code | imaging_depth | targeted_structure | date_of_acquisition | file_id | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| ophys_experiment_id | |||||||||||||||||||||
| 955276580 | CAM2P.5 | Sst-IRES-Cre/wt;Ai148(TIT2L-GC6f-ICL-tTA2)/wt | 470784 | Ai148(TIT2L-GC6f-ICL-tTA2) | [Sst-IRES-Cre] | F | 149.0 | OPHYS_1_images_B | Sst-IRES-Cre | GCaMP6f | ... | 20.0 | 0 | 954981981 | 955110047 | 941373529 | VisualBehaviorTask1B | 275 | VISp | 2019-09-25 20:06:45.000000 | 957317925 |
| 956903375 | CAM2P.5 | Sst-IRES-Cre/wt;Ai148(TIT2L-GC6f-ICL-tTA2)/wt | 470784 | Ai148(TIT2L-GC6f-ICL-tTA2) | [Sst-IRES-Cre] | F | 150.0 | OPHYS_2_images_B_passive | Sst-IRES-Cre | GCaMP6f | ... | 21.0 | 1 | 955991376 | 956213722 | 941373529 | VisualBehaviorTask1B | 275 | VISp | 2019-09-26 17:34:33.000000 | 957356891 |
| 957652800 | CAM2P.5 | Sst-IRES-Cre/wt;Ai148(TIT2L-GC6f-ICL-tTA2)/wt | 470784 | Ai148(TIT2L-GC6f-ICL-tTA2) | [Sst-IRES-Cre] | F | 151.0 | OPHYS_3_images_B | Sst-IRES-Cre | GCaMP6f | ... | 22.0 | 2 | 957189583 | 957331258 | 941373529 | VisualBehaviorTask1B | 275 | VISp | 2019-09-27 18:37:17.000000 | 957800643 |
| 959337347 | CAM2P.5 | Sst-IRES-Cre/wt;Ai148(TIT2L-GC6f-ICL-tTA2)/wt | 470784 | Ai148(TIT2L-GC6f-ICL-tTA2) | [Sst-IRES-Cre] | F | 155.0 | OPHYS_4_images_A | Sst-IRES-Cre | GCaMP6f | ... | 2.0 | 5 | 958931715 | 958994511 | 941373529 | VisualBehaviorTask1B | 275 | VISp | 2019-10-01 17:57:39.000000 | 959436242 |
| 960351917 | CAM2P.5 | Sst-IRES-Cre/wt;Ai148(TIT2L-GC6f-ICL-tTA2)/wt | 470784 | Ai148(TIT2L-GC6f-ICL-tTA2) | [Sst-IRES-Cre] | F | 156.0 | OPHYS_5_images_A_passive | Sst-IRES-Cre | GCaMP6f | ... | 3.0 | 6 | 959751299 | 959893902 | 941373529 | VisualBehaviorTask1B | 275 | VISp | 2019-10-02 18:23:56.000000 | 960425985 |
| 960960480 | CAM2P.5 | Sst-IRES-Cre/wt;Ai148(TIT2L-GC6f-ICL-tTA2)/wt | 470784 | Ai148(TIT2L-GC6f-ICL-tTA2) | [Sst-IRES-Cre] | F | 157.0 | OPHYS_6_images_A | Sst-IRES-Cre | GCaMP6f | ... | 4.0 | 7 | 960593969 | 960697332 | 941373529 | VisualBehaviorTask1B | 275 | VISp | 2019-10-03 18:01:02.000000 | 961030683 |
6 rows × 22 columns
import matplotlib.pyplot as plt
# ophys_experiment_ids are the index of the ophys_experiment_table
ophys_experiment_ids = sst_container_experiments.index.values
# create figure axis
fig, ax = plt.subplots(1, len(ophys_experiment_ids), figsize=(20,5))
# enumerate over experiments in this container
for i, ophys_experiment_id in enumerate(ophys_experiment_ids):
# get the dataset object
dataset = cache.get_behavior_ophys_experiment(ophys_experiment_id=ophys_experiment_id)
# get the max intensity projection and plot on the appropriate axis
ax[i].imshow(dataset.max_projection.data, cmap='gray')
ax[i].set_title(ophys_experiment_id)
# get all Vip sessions in the Multiscope project code
vip_sessions = ophys_sessions[(ophys_sessions.cre_line=='Vip-IRES-Cre')&
(ophys_sessions.project_code=='VisualBehaviorMultiscope')&
(ophys_sessions.prior_exposures_to_image_set==0)]
# ophys_session_id is the index of the ophys_session_table
ophys_session_id = vip_sessions.index.values[0]
# look at info for this ophys session
vip_sessions.loc[ophys_session_id]
equipment_name MESO.1 full_genotype Vip-IRES-Cre/wt;Ai148(TIT2L-GC6f-ICL-tTA2)/wt mouse_id 523922 reporter_line Ai148(TIT2L-GC6f-ICL-tTA2) driver_line [Vip-IRES-Cre] sex M age_in_days 183 session_type OPHYS_4_images_B cre_line Vip-IRES-Cre indicator GCaMP6f session_number 4 prior_exposures_to_session_type 0 prior_exposures_to_image_set 0 prior_exposures_to_omissions 7 behavior_session_id 1050620316 ophys_experiment_id [1050762966, 1050762969, 1050762972, 105076297... ophys_container_id [1046576772, 1046576775, 1046576784, 104657678... project_code VisualBehaviorMultiscope date_of_acquisition 2020-09-15 08:52:00.527831 Name: 1050597678, dtype: object
# get all the ophys_experiment_ids (corresponding to imaging planes) for this session
ophys_experiment_ids = vip_sessions.loc[ophys_session_id].ophys_experiment_id
print(ophys_experiment_ids)
[1050762966, 1050762969, 1050762972, 1050762974, 1050762975, 1050762977]
# create figure axis
fig, ax = plt.subplots(1,1, figsize=(15,4))
# enumerate over experiments in this session
for i, ophys_experiment_id in enumerate(ophys_experiment_ids):
# get the dataset object
dataset = cache.get_behavior_ophys_experiment(ophys_experiment_id=ophys_experiment_id)
# get ophys timestamps
ophys_timestamps = dataset.ophys_timestamps
# get the population average dF/F trace
dff_traces = dataset.dff_traces
# dff_traces is a dataframe with a column 'dff'
# get the values of this column and turn into a matrix of n_cells x timepoints
dff_traces = np.vstack(dff_traces.dff.values)
# take the mean over the cell axis
average_dFF = np.mean(dff_traces, axis=0)
# get the imaging_depth and targeted_structure for this experiment
imaging_depth = dataset.metadata['imaging_depth']
targeted_structure = dataset.metadata['targeted_structure']
# plot it, including the imaging_depth and targeted_structure in the legend label
ax.plot(ophys_timestamps, average_dFF, label=targeted_structure+'_'+str(imaging_depth))
ax.set_title(dataset.metadata['cre_line']+', ophys_session_id: '+str(ophys_session_id))
ax.set_ylabel('dF/F')
ax.set_xlabel('time (seconds)')
ax.set_xlim(5*60, 10*60)
ax.legend()
<matplotlib.legend.Legend at 0x7fcf892d20d0>
/Users/marinag/anaconda3/envs/visual_behavior_sdk/lib/python3.7/site-packages/IPython/core/events.py:88: UserWarning: Creating legend with loc="best" can be slow with large amounts of data. func(*args, **kwargs) /Users/marinag/anaconda3/envs/visual_behavior_sdk/lib/python3.7/site-packages/IPython/core/pylabtools.py:128: UserWarning: Creating legend with loc="best" can be slow with large amounts of data. fig.canvas.print_figure(bytes_io, **kw)
