661 (Jun 2023)

Data Release v661 (April 6, 2023) is the first quarterly release of 2023.

This version ADDS the following tables

The following tables from previous versions are included:

The following tables have been REMOVED (removed tables still available in version 343):

Information about the version 661 release can be queried with the following:

# report timestamp metadata on the version of interest
client.materialize.get_version_metadata(661)['time_stamp']
datetime.datetime(2023, 4, 6, 20, 17, 9, 199182, tzinfo=datetime.timezone.utc)

Manual Cell Types (V1 Column)

Neurons

Table name: allen_v1_column_types_slanted_ref

A subset of nucleus detections in a 100 um column (n=2204) in VISp were manually classified by anatomists at the Allen Institute into categories of cell subclasses, first distinguishing cells into classes of non-neuronal, excitatory and inhibitory; then into subclasses.

For the non-neuronal subclasses, see aibs_column_nonneuronal_ref

The key columns are:

AIBS Manual Cell Types, V1 Column
Column Description
id Soma ID for the cell
pt_position  pt_supervoxel_id  pt_root_id Bound spatial point columns associated with the centroid of the cell nucleus
classification-system One of aibs_coarse_excitatory or aibs_coarse_inhibitory for detected neurons, or aibs_coarse_nonneuronal for non-neurons (glia/pericytes).
cell_type One of several cell types, detailed below
Tip

This is a reference table on nucleus_detection_v0, and can be indexed by the same nucleus id.

The cell types in the table are:

AIBS Manual Cell Type definitions (neurons)
Cell Type Subclass Description
23P Excitatory Layer 2/3 cells
4P Excitatory Layer 4 cells
5P-IT Excitatory Layer 5 intratelencephalic cells
5P-ET Excitatory Layer 5 extratelencephalic cells
5P-NP Excitatory Layer 5 near-projecting cells
6P-IT Excitatory Layer 6 intratelencephalic cells
6P-CT Excitatory Layer 6 corticothalamic cells
BC Inhibitory Basket cell
BPC Inhibitory Bipolar cell. In practice, this was used for all cells thought to be VIP cell, not only those with a bipolar dendrite
MC Inhibitory Martinotti cell. In practice, this label was used for all inhibitory neurons that appeared to be Somatostatin cell, not only those with a Martinotti cell morphology
Unsure Inhibitory Unsure. In practice, this label also is used for all likely-inhibitory neurons that did not match other types 
# Standard query
client.materialize.query_table('allen_v1_column_types_slanted_ref')

# Content-aware query
client.materialize.tables.allen_v1_column_types_slanted_ref(id=example_nucleus_id).query()

Non-neurons

Table name: aibs_column_nonneuronal_ref

A subset of nucleus detections in a 100 um column (n=2204) in VISp were manually classified by anatomists at the Allen Institute into categories of cell subclasses, first distinguishing cells into classes of non-neuronal, excitatory and inhibitory; then into subclasses.

For the neuronal subclasses, see allen_v1_column_types_slanted_ref

The key columns are:

AIBS Manual Cell Types, V1 Column
Column Description
id Soma ID for the cell
pt_position  pt_supervoxel_id  pt_root_id Bound spatial point columns associated with the centroid of the cell nucleus
classification-system One of aibs_coarse_excitatory or aibs_coarse_inhibitory for detected neurons, or aibs_coarse_nonneuronal for non-neurons (glia/pericytes).
cell_type One of several cell types, detailed below
Tip

This is a reference table on nucleus_detection_v0, and can be indexed by the same nucleus id.

The cell types in the table are:

AIBS Manual Cell Type definitions (non-neurons)
Cell Type Subclass Description
OPC Non-neuronal Oligodendrocyte precursor cell
astrocyte Non-neuronal Astrocyte
microglia Non-neuronal Microglia
pericyte Non-neuronal Pericyte
oligo Non-neuronal Oligodendrocyte 
# Standard query
client.materialize.query_table('aibs_column_nonneuronal_ref')

# Content-aware query
client.materialize.tables.aibs_column_nonneuronal_ref(id=example_nucleus_id).query()

For more on how to interpret the table, see Annotation Tables.

Automated Cell Types

mtypes

Table name: aibs_soma_nuc_exc_mtype_preds_v117

This table contains excitatory M-type predictions for cells throughout the entire dataset (Schneider-Mizell et al. 2025). The model was trained with soma and nucleus features (Elabbady et al. 2025). The cells selected were those that were predicted as excitatory based on the soma and nucleus feature trained metamodel - these prediction are available in the aibs_soma_nuc_metamodel_preds_v117 table. This is a reference table where target_id refers to the unique nucleus id in the nucleus_detection_v0 table. Classification_system refers to the coarse predictions from the soma and nucleus model and cell_type denotes the excitatory M-type prediction. Errors and soma-soma mergers have been filtered out based on the status of cells in materialization version 117. For more details, see (Schneider-Mizell et al. 2025).

The key columns are:

Allen Motif-type (mtype) Table
Column Description
id Soma ID for the cell
pt_position  pt_supervoxel_id  pt_root_id Bound spatial point columns associated with the centroid of the cell nucleus
classification-system excitatory or inhibitory
cell_type One of several cell types, detailed below
Deprecated table

This table remains available from materialization versions: 661, 795

This table is deprecated, use aibs_metamodel_mtypes_v661_v2 instead.

mtypes (V1 column)

Table name: allen_column_mtypes_v1

Clustering of anatomical properties (and connectivity for inhibitory cells) based on morphological features on cells in the V1 column, with segmentation as of v117. For more details, see (Schneider-Mizell et al. 2025).

The key columns are:

Allen Motif-type (mtype) Table
Column Description
id Soma ID for the cell
pt_position  pt_supervoxel_id  pt_root_id Bound spatial point columns associated with the centroid of the cell nucleus
classification-system excitatory or inhibitory
cell_type One of several cell types, detailed below
Deprecated table

This table remains available from materialization versions: 661, 795

This table is deprecated, use aibs_metamodel_mtypes_v661_v2 instead.

mtypes connectivity groups

Table name: connectivity_groups_v507

Inhibitory column connectivity ‘motif’ groups (cells that distribute their outputs onto target cell types in a similar way) for the V1 column.

For details, see (Schneider-Mizell et al. 2025).

The key columns are:

Allen Inhibitory Motif-type (mtype) Table
Column Description
id Soma ID for the cell
pt_position  pt_supervoxel_id  pt_root_id Bound spatial point columns associated with the centroid of the cell nucleus
classification-system connectivity_group
cell_type connectivity motif type
Tip

This is a reference table on nucleus_detection_v0, and can be indexed by the same nucleus id.

# Standard query
client.materialize.query_table('connectivity_groups_v507')

# Content-aware query
client.materialize.tables.connectivity_groups_v507(id=example_nucleus_id).query()

Baylor (coarse)

Table name: baylor_log_reg_cell_type_coarse_v1

This table contains the results of a logistic regression classifier trained on properties of neuronal dendrites. This was applied to many cells in the dataset, but required more data than soma and nucleus features alone and thus more cells did not complete the pipeline. It has very good performance on excitatory vs inhibitory neurons because it focuses on dendritic spines, a characteristic property of excitatory neurons. It is a good table to double check E/I classifications if in doubt.

For details, see (Celii et al. 2025).

The key columns are:

Baylor Coarse Cell Type Table
Column Description
id Soma ID for the cell
pt_position  pt_supervoxel_id  pt_root_id Bound spatial point columns associated with the centroid of the cell nucleus
classification-system baylor_log_reg_cell_type_coarse for all entries
cell_type excitatory or inhibitory
Tip

This is a reference table on nucleus_detection_v0, and can be indexed by the same nucleus id.

# Standard query
client.materialize.query_table('baylor_log_reg_cell_type_coarse_v1')

# Content-aware query
client.materialize.tables.baylor_log_reg_cell_type_coarse_v1(id=example_nucleus_id).query()

Baylor (fine)

Table name: baylor_gnn_cell_type_fine_model_v2

Exciatory/Inhibitory Subclass cell types derived from a supervised GNN classifier trained on the hand labeled cell types(‘23P, 4P, 5P-IT, 5P-NP, 5P-PT, 6P-CT, 6P-IT, BC, BPC, MC, NGC’). Process for generating labels

  1. Cells Automatically Proofread
  2. Graph Objects generated for each neuron with the following node attributes (no z coordinate information included):
  • skeleton information (‘skeletal_length’, ‘skeleton_vector_upstream_theta’, ‘skeleton_vector_upstream_phi’ , ‘skeleton_vector_downstream_theta’, ‘skeleton_vector_downstream_phi’),
  • width information (‘width_upstream’, ‘width_no_spine’, ‘width_downstream’),
  • synapse information(‘n_synapses_post’, ‘n_synapses_pre’, ‘n_synapses_head_postsyn’, ‘n_synapses_neck_postsyn’, ‘n_synapses_shaft_postsyn’, ‘n_synapses_no_head_postsyn’, ‘synapse_volume_shaft_postsyn_sum’, ‘synapse_volume_head_postsyn_sum’, ‘synapse_volume_no_head_postsyn_sum’, ‘synapse_volume_neck_postsyn_sum’, ‘synapse_volume_postsyn_sum’,)
  • spine information (‘n_spines’, ‘spine_volume_sum’,),
  • global neuron information (“soma_start_angle_max”, “max_soma_volume”, “n_syn_soma”)
  1. GCN trained on labeled data with 60:20:20 training/validation/testing split
  2. GCN model generates class probabiliites and highest probability label given

For details, see (Celii et al. 2025).

The key columns are:

Baylor Fine Cell Type Table
Column Description
id Soma ID for the cell
pt_position  pt_supervoxel_id  pt_root_id Bound spatial point columns associated with the centroid of the cell nucleus
classification-system baylor_gnn_cell_type_fine for all entries
cell_type one of: (‘23P, 4P, 5P-IT, 5P-NP, 5P-PT, 6P-CT, 6P-IT, BC, BPC, MC, NGC’)
Tip

This is a reference table on nucleus_detection_v0, and can be indexed by the same nucleus id.

# Standard query
client.materialize.query_table('baylor_gnn_cell_type_fine_model_v2')

# Content-aware query
client.materialize.tables.baylor_gnn_cell_type_fine_model_v2(id=example_nucleus_id).query()

Functional Coregistration

Manual Coregistration

Table name: coregistration_manual_v3

A table of EM nucleus centroids manually matched to Baylor functional units. A unique functional unit is identified by its session, scan_idx and unit_id. An EM nucleus centroid may have matched to more than one functional unit if it was scanned on more than one imaging field.

Deprecated table

This table remains available from materialization versions: 661, 795, 943, 1078

Use coregistration_manual_v4 instead.

Automated Coregistration

Table name: apl_functional_coreg_forward_v5

Table tracking automated functional matches from EM->2P using an automated diffeomorphic-based approach. The “score” column represents the match separation in microns. Functional matching done by Justin Joyce, JHUAPL. Table managed by Daniel Xenes, JHUAPL.

Deprecated table

This table remains available from materialization versions: 661

Use apl_functional_coreg_vess_fwd instead.

Nucleus

Neuron nucleus

Table name: nucleus_ref_neuron_svm

While the table of centroids for all nuclei is nucleus_detection_v0, this includes neuronal nuclei, non-neuronal nuclei, and some erroneous detections. The table nucleus_ref_neuron_svm shows the results of a classifier that was trained to distinguish neuronal nuclei from non-neuronal nuclei and errors. For the purposes of analysis, we recommend using the nucleus_ref_neuron_svm table to get the most broad collection of neurons in the dataset.

The key columns of nucleus_ref_neuron_svm are:

Nucleus table column definitions
Column Description
id Soma ID for the cell
pt_position  pt_supervoxel_id  pt_root_id Bound spatial point columns associated with the centroid of the nucleus
classification-system Describes how the classification was done. All values will be is_neuron for this table
cell_type The output of the classifier. All values will be either neuron or not-neuron (glia or error) for this table
Tip

This is a reference table on nucleus_detection_v0, and can be indexed by the same nucleus id.

# Standard query
client.materialize.query_table('nucleus_ref_neuron_svm')

# Content-aware query
client.materialize.tables.nucleus_ref_neuron_svm(target_id=example_nucleus_id).query()

Nucleus alternative points

Table name: nucleus_alternative_points

A table with alternative segid lookup points for nuclei created using an automated method which looked for nuclei centroids outside nuclie, or centroids which were not in the segmentation. The point at least 320 nm within the nucleus and the segmentation, and closet to the original centroid was choosen in a 3um window.

The key columns of are:

Nucleus alternative points
Column Description
target_id Soma ID for the cell
pt_position Bound spatial point associated with the centroid of the nucleus (manually corrected)
Tip

This is a corrections table, and meant to act upon its reference nucleus_detection_v0.

Use client.materialize.views.nucleus_detection_lookup_v1().query() to access the combined table.

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References

Celii, Brendan, Stelios Papadopoulos, Zhuokun Ding, Paul G. Fahey, Eric Wang, Christos Papadopoulos, Alexander B. Kunin, et al. 2025. “NEURD Offers Automated Proofreading and Feature Extraction for Connectomics.” Nature 640 (8058): 487–96. https://doi.org/10.1038/s41586-025-08660-5.
Elabbady, Leila, Sharmishtaa Seshamani, Shang Mu, Gayathri Mahalingam, Casey M. Schneider-Mizell, Agnes L. Bodor, J. Alexander Bae, et al. 2025. “Perisomatic Ultrastructure Efficiently Classifies Cells in Mouse Cortex.” Nature 640 (8058): 478–86. https://doi.org/10.1038/s41586-024-07765-7.
Schneider-Mizell, Casey M., Agnes L. Bodor, Derrick Brittain, JoAnn Buchanan, Daniel J. Bumbarger, Leila Elabbady, Clare Gamlin, et al. 2025. “Inhibitory Specificity from a Connectomic Census of Mouse Visual Cortex.” Nature 640 (8058): 448–58. https://doi.org/10.1038/s41586-024-07780-8.