Annotation Tables

The minnie65_public data release includes a number of annotation tables that help label the dataset. This section describes the content of each of these tables — see here for instructions for how to query and filter tables. Unless otherwise specificied (i.e. via desired_resolution), all positions are in units of 4,4,40 nm/voxel resolution.

Common Fields

Several fields (or column names) are common to many tables. These fall into two main classes: the spatial point columns that are how we assign annotations to cells via points in the 3d space and book-keeping columns, that are used internally to track the state of the data.

Spatial Point Columns

Most tables have one or more Bound Spatial Points, which is a location in the 3d space that tells the annotation to remain associated with the root id at that location. Bound spatial points have will have one prefix, usually pt (i.e. “point”) and three associated columns with different suffixes: _position, _supervoxel_id, and _root_id.

For a given prefix {pt}, the three columns are as follows:

• The {pt}_position indicates the location of the point in 3d space.

• The {pt}_supervoxel_id indicates a unique identifier in the segmentation, and is mostly internal bookkeeping.

• The {pt}_root_id indicates the root id of the annotation at that location.

Book-keeping Columns

Several columns are common to many or all tables, and mostly used as internal book-keeping. Rather than describe these for every table, they will just be mentioned briefly here:

Column	Description
id	A unique ID specific to the annotation within that table.
created	The date that the annotation was created.
valid	Internal bookkeeping column, should always be t for data you can download.
target_id (optional)	Some tables reference other tables, particularly the nucleus table. If present, this column will be the same as id.
created_ref / valid_ref / id_ref (optional)	For reference tables, the data shows both the created/valid/id of the reference annotation and the target annotation. The values with the _ref suffix are those of the reference table (usually something like proofreading state or cell type) and the values without a suffix ar ethose of the target table (usually a nucleus).

Synapse Table

Table name: synapses_pni_v2

The only synapse table is synapses_pni_v2. This is by far the largest table in the dataset with 337 million entries, one for each synapse. It contains the following columns (in addition to the bookkeeping columns):

Column Definitions

Column	Description
pre_pt_position / pre_pt_supervoxel_id / pre_pt_root_id	The bound spatial point data for the presynaptic side of the synapse.
post_pt_position / post_pt_supervoxel_id / post_pt_root_id	The bound spatial point data for the postsynaptic side of the synapse.
size	The size of the synapse in voxels. This correlates well, but not perfectly, with the surface area of synapse.
ctr_pt_position	A position in the center of the detected synaptic junction. Of all points in the synapse table, this is usually the closest point to the surface (and thus mesh) of both neurons. Because it is at the edge of cells, it is not associated with a root id.

Nucleus Table

Table name: nucleus_ref_neuron_svm

Nucleus detection has been used to define unique cells in the dataset. Distinct from the neuronal segmentation, a convolutional neural network was trained to segment nuclei. Each nucleus detection was given a unique ID, and the centroid of the nucleus was recorded as well as its volume. 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:

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.

Note that the id column is the same as the nucleus id.

Cell Type Tables

There are several tables that contain information about the cell type of neurons in the dataset, with each table representing a different method of doing the classificaiton. Because each method requires a different kind of information, not all cells are present in all tables. Each of the cell types tables has the same format and in all cases the id column references the nucleus id of the cell in question.

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Predictions from soma/nucleus features

Table name: aibs_soma_nuc_metamodel_preds_v117

This table contains the results of a hierarchical classifier trained on features of the cell body and nucleus of cells. This was applied to most cells in the dataset that had complete cell bodies (e.g. not cut off by the edge of the data). For more details, see Elabbady et al. 2022. In general, this does a good job, but sometimes confuses layer 5 inhibitory neurons as being excitatory: The key columns are:

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 cell nucleus.
classification-system	Either aibs_neuronal for neurons (Excitatory or inhibitory) or aibs_non-neuronal for non-neurons (glia/pericytes).
cell_type	One of several cell types: 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 cells. In practice, this label was used for all inhibitory neurons that appeared to be Somatostatin cell, not only those with a Martinotti cell morphology. NGC Inhibitory Neurogliaform cells. In practice, this label also is used for all inhibitory neurons in layer 1, many of which may not be neurogliaform cells although they might be in the same molecular family. OPC Non-neuronal Oligodendrocyte precursor cells astrocyte Non-neuronal Astrocytes microglia Non-neuronal Microglia pericyte Non-neuronal Pericytes oligo Non-neuronal Oligodendrocytes

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Coarse prediction from spine detection

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.

The key columns are:

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 cell nucleus.
classification-system	baylor_log_reg_cell_type_coarse for all entries.
cell_type	excitatory or inhibitory

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Fine prediction from dendritic features

Table name: allen_column_mtypes_v1

This table contains all neurons within a well-proofread 100 micron square column in VISp spanning all layers. Excitatory neurons and inhibitory neurons were distinguished manually, and subclasses were assigned based on a data-driven clustering of the neuronal features. Inhibitory neurons were classified based on how they distributed they synaptic outputs onto target cells, while exictatory neurons were classified based on a collection of dendritic features. For more details, see the section on the minnie column or read the preprint Schneider-Mizell et al. [2023]. Note that all cell type labels in this column come from a clustering specific to this paper, and while they are intended to align with the broader literature they are not a direct mapping or a well-established convention. For a more conventional set of labels on the same set of cells, look at the table allen_v1_column_types_slanted_ref. Cell types in that table align with those in aibs_soma_nuc_metamodel_preds_v117 above.

The key columns are:

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 cell nucleus.
classification-system	excitatory or inhibitory.
cell_type	One of several cell types. Cell Type Subclass Description L2a Excitatory A cluster of layer 2 (upper layer 2/3) excitatory neurons. L2b Excitatory A cluster of layer 2 (upper layer 2/3) excitatory neurons. L3a Excitatory A cluster of excitatory neurons transitioning between upper and lower layer 2/3. L3b Excitatory A cluster of layer 3 (upper layer 2/3) excitatory neurons. L3c Excitatory A cluster of layer 3 (upper layer 2/3) excitatory neurons. L4a Excitatory The largest cluster of layer 4 excitatory neurons. L4b Excitatory Another cluster of layer 4 excitatory neurons. L4c Excitatory A cluster of layer 4 excitatory neurons along the border with layer 5. L5a Excitatory A cluster of layer 5 IT neurons at the top of layer 5. L5b Excitatory A cluster of layer 5 IT neurons throughout layer 5. L5ET Excitatory The cluster of layer 5 ET neurons. L5NP Excitatory The cluster of layer 5 NP neurons. L6a Excitatory A cluster of layer 6 IT neurons at the top of layer 6. L6b Excitatory A cluster of layer 6 IT neurons throughout layer 6. Note that this is different than the label “Layer 6b” which refers to a narrow band at the border between layer 6 and white matter. L6c Excitatory A cluster of tall layer 6 cells (unsure if IT or CT). L6CT Excitatory A cluster of tall layer 6 cells matching manual CT labels. L6wm Excitatory A cluster of layer 6 cells along the border with white matter. PTC Inhibitory Perisomatic targeting cells, a cluster of inhibitory neurons that target the soma and proximal dendrites of excitatory neurons. Approximately corresponds to basket cells. DTC Inhibitory Dendrite targeting cells, a cluster of inhibitory neurons that target the distal dendrites of excitatory neurons. Most SST cells would be DTCS. STC Inhibitory Sparsely targeting cells, a cluster of inhibitory neurons that don’t concentrate multiple synapses onto the same target neurons. Many neurogliaform cells and layer 1 interneurons fall into this category. ITC Inhibitory Inhibitory targeting cells, a cluster of inhibitory neurons that preferntially target other inhibitory neurons. Most VIP cells would be ITCs.

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Proofreading Tables

Table name: proofreading_status_public_release

The table proofreading_status_public_release describes the status of cells selected for manual proofreading. Because of the inherent difference in the challenge and time required for different kinds of proofreading, we describe the status of axons and dendrites separately. Further, we distinguish three different categories of proofreading:

• non: No proofreading has been comprehensively performed.

• clean: Proofreading has comprehensively removed false merges, but not necessarily added missing parts.

• extended: Proofreading has comprehensively removed false merges and attempted to add all or most missing parts.

Note that many cells not in this table have been edited in some places, but not comprehensively worked on. For more information, please see Proofreading and Data Quality.

The key columns are:

Column Definitions

Column	Description
id	ID within the proofreading table (not cell id).
pt_position \ pt_supervoxel_id \ pt_root_id	Bound spatial point columns associated with the centroid of the cell nucleus being proofread.
valid_id	The root id of the neuron when it the proofreading assessment was made.
status_dendrite	The status of the dendrite proofreading. One of the three categories described above.
status_axon	The status of the axon proofreading. One of the three categories described above.

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Functional Coregistration Tables

To relate the structural data to functional data, cell bodies must be coregistered between the functional imaging and EM volumes. The results of this coregistration are stored in two tables with the same columns:

• coregistration_manual_v3 : The results of manually verified coregistration. This table is well-verified, but contains fewer ROIs (N=12,052 root ids, 13,925 ROIs).

• apl_functional_coreg_forward_v5 : The results of automated functional matching between the EM and 2-p functional data. This table is not manually verified, but contains more ROIs (N=36,078 root ids, 68,873 ROIs).

Please see the Functional Data section for more information about using this data.

The column descriptions are:

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 cell nucleus being proofread.
session	The session index from functional imaging.
scan_idx	The scan index from functional imaging.
unit_id	The ROI index from functional imaging. Only unique within scan and session.
field	The field index from functional imaging.
residual	The residual distance between the functional and the assigned structural points after transformation, in microns. Smaller values indicate a closer match.
score	A separation score, measuring the difference between the residual distance to the assigned neuron and the distance to the nearest non-assigned neuron, in microns. This can be negative if the non-assigned neuron is closer than the assigned neuron. Larger values indicate fewer nearby neurons that could be confused with the assigned neuron.

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All Tables

Table Name	Number of Annotations	Description
synapses_pni_v2	337,312,429	The locations of synapses and the segment ids of the pre and post-synaptic automated synapse detection.
nucleus_detection_v0	144,120	The locations of nuclei detected via a fully automated method.
nucleus_alternative_points	8,388	A reference annotation table marking alternative segment_id lookup locations for a subset of nuclei in nucleus_detection_v0 that is more accurate than the centroid location listed there.
nucleus_ref_neuron_svm	144,120	A reference annotation indicating the output of a model detecting which nucleus detections are neurons versus which are not.1
coregistration_manual_v4	13,658	A table indicating the association between individual units in the functional imaging data and nuclei in the structural data, derived from human powered matching. Includes residual and separation scores to help assess confidence.
apl_functional_coreg_forward_v5	68,436	A table indicating the association between individual units in the functional imaging data and nuclei in the structural data, derived from the automated procedure. Includes residuals and separation scores to help assess confidence.
proofreading_status_public_release	1272	A table indicating which neurons have been proofread on their axons or dendrites.
proofreading_strategy	1039	A reference table on “proofreading_status_public_release” indicating what axon proofreading strategy was executed on each neuron.
proofreading_edits	121,271	A table containing the number of edits on every segment_id associated with a nucleus in the volume.
aibs_column_nonneuronal_ref	542	Cell type reference annotations from a human expert of non-neuronal cells located amongst the Minnie Column.
allen_v1_column_types_slanted_ref	1,357	Neuron cell type reference annotations from human experts of neuronal cells located amongst the Minnie Column.
allen_column_mtypes_v1	1,357	Neuron cell type reference annotations from data driven unsupervised clustering of neuronal cells
aibs_soma_nuc_exc_mtype_preds_v117	58,624	Reference annotations indicating the output of a model predicting cell types across the dataset based on the labels from allen_column_mtypes_v1.1
aibs_soma_nuc_metamodel_preds_v117	86,916	Reference annotations indicating the output of a model predicting cell classes based on the labels from allen_v1_column_types_slanted_ref and aibs_column_nonneuronal_ref.
baylor_log_reg_cell_type_coarse_v1	55,063	Reference annotations indicated the output of a logistic regression model predicting whether the nucleus is part of an excitatory or inhibitory cell.50
baylor_gnn_cell_type_fine_model_v2	49,051	Reference annotations indicated the output of a graph neural network model predicting the cell type based on the human labels in allen_v1_column_types_slanted_ref.