Brain Observatory

The Allen Brain Observatory is a database of the visually-evoked functional responses of neurons in mouse visual cortex based on 2-photon fluorescence imaging. Characterized responses include orientation tuning, spatial and temporal frequency tuning, temporal dynamics, and spatial receptive field structure.

The data is organized into experiments and experiment containers. An experiment container represents a group of experiments with the same targeted imaging area, imaging depth, and Cre line. The individual experiments within an experiment container have different stimulus protocols, but cover the same imaging field of view.

_images/container_session_layout.png

Note: Data collected after September 2016 uses a new session C stimulus designed to better-characterize spatial receptive fields in higher visual areas. The original locally sparse noise stimulus used 4.65 visual degree pixels. Session C2 broke that stimulus into two separate stimulus blocks: one with 4.65 degree pixels and one with 9.3 degree pixels. Note that the stimulus_info module refers to these as locally_sparse_noise_4deg and locally_sparse_noise_8deg, respectively.

For more information on experimental design and a data overview, please visit the Allen Brain Observatory data portal.

Data Processing

For all data in Allen Brain Observatory, we perform the following processing:

  1. Segment cell masks from each experiment’s 2-photon fluorescence video
  2. Associate cells from experiments belonging to the same experiment container and assign unique IDs
  3. Extract each cell’s mean fluorescence trace
  4. Extract mean fluorescence traces from each cell’s surrounding neuropil
  5. Demix traces from overlapping ROIs
  6. Estimate neuropil-corrected fluorescence traces
  7. Compute dF/F
  8. Compute stimulus-specific tuning metrics

All traces and masks for segmented cells in an experiment are stored in a Neurodata Without Borders (NWB) file. Stored traces include the raw fluoresence trace, neuropil trace, demixed trace, and dF/F trace. Code for extracting neuropil-corrected fluorescence traces, computing dF/F, and computing tuning metrics is available in the SDK.

New in June 2017: Trace demixing is a new addition as of June 2017. All past data was reprocessed using the new demixing algorithm. We have also developed a new module to better characterize a cell’s receptive field. Take a look at the receptive field analysis example notebook

For more information about data processing, please read the technical whitepapers.

Precomputed Cell Metrics

A large table of precomputed metrics are available for download to support population analysis and filtering. The table below describes all of the metrics in the table. The get_cell_specimens() method will download this table as a list of dictionaries which can be converted to a pandas DataFrame as shown in this Jupyter notebook.

Stimulus Metric Field Name
drifting gratings orientation selectivity osi_dg
direction selectivity dsi_dg
preferred direction pref_dir_dg
preferred temporal frequency pref_tf_dg
response p value p_dg
global ori. selectivity g_osi_dg
global dir. selectivity g_dsi_dg
response reliability reliability_dg
running modulation run_mod_dg
running modulation p value p_run_mod_dg
pref. condition mean df/f peak_dff_dg
TF discrimination index tfdi_dg
static gratings orientation selectivity osi_sg
preferred orientation pref_ori_sg
preferred spatial frequency pref_sf_sg
preferred phase pref_phase_sg
mean time to peak response time_to_peak_sg
response p value p_sg
global ori. selectivity g_osi_sg
reponse reliability reliability_sg
running modulation run_mod_sg
running modulation p value p_run_mod_sg
pref. condition mean df/f peak_dff_ns
SF discrimiation index sfdi_sg
natural scenes mean time to peak response time_to_peak_ns
preferred scene index pref_scene_ns
response p value p_ns
image selectivity image_sel_ns
running modulation run_mod_ns
running modulation p value p_run_mod_ns
pref. condition mean df/f peak_dff_ns
natural movie 1 response reliability (session A) reliability_nm1_a
response reliability (session B) reliability_nm1_b
response reliability (session C) reliability_nm1_c
natural movie 2 response reliability reliability_nm2
natural movie 3 response reliability reliability_nm3
locally sparse noise RF area (on subunit) rf_area_on_lsn
RF area (off subunit) rf_area_off_lsn
RF center (on subunit) rf_center_on_x, rf_center_on_y
RF center (off subunit) rf_center_off_x, rf_center_off_y
RF chi^2 rf_chi2_lsn
RF on-off subunit distance rf_distance_lsn
RF on-off subunit overlap index rf_overlap_lsn