Connectomics of (part of) the MICrONS mm3 dataset

This dataset provides the connectome of a large part of the IARPA MICrONS mm^3 dataset (https://www.microns-explorer.org/cortical-mm3). Specifically, it contains internal connectivity between most neurons of "portion 65" of the EM volume (see above link for details), i.e. synapses between neurons inside the volume, but no synapses from neurons extrinsic to the volume. The volume contains parts of the regions VISp, VISrl, VISal and VISlm.

---NEW VERSION 2.0.0---

This new version of the dataset is based on a newer version of the data on the side of MICrONS. While 1.0.0 was based on v117 of the MICrONS data, 2.0.0 is based on v1181. Main differences are to my understanding: 

 - More proofreading: A larger number of neurons have been manually proofread.

 - Cell type classification. A better cell type classification has been added. See below for details.

Details

The file combines data from various tables of the "minnie65_public" release (see link above).

v2.0.0 of the dataset ueses version 1181 of the following tables:

- aibs_metamodel_mtypes_v661_v2 for neuron identifiers, tentative classes and soma locations- proofreading_status_and_strategy for information about axon / dendrite completeness- synapses_pni_2 for synaptic connection locations, sizes, source and target neurons

The full description of those tables, as originally provided, can be found below. We converted location indicated in voxel indices in the original data to locations in nm, additionally we provide very tentative region annotations for neurons (but see below!).The main utility of this release lies in its formatting for the Connectome-utilities python package (https://github.com/BlueBrain/ConnectomeUtilities). As such, you can easily load it and use Connectome-utilities functionality for various analyses. Exemplary notebooks are provided.

The file contains two representations of the connectome:- "full" represent multiple synapses between neurons as multiple directed edges.- "condensed" only has (at most) a single edge between neurons, but it is associated with a property "count" that specifies the number of synapses. Other synapse properties (such as their locations) are mostly lost in the condensed representation, only the mean and sum of the "size" property is provided. You can also get the condensed version from the full version by using the .condense() function of Connectome-utilities (see documentation).

Some notes:- The raw data contained some neuron identifiers associated with multiple types. Manual inspection of their meshes indicated that they really are merges of several neurons. Since this only seemed to affect a few hundred of neurons, we simply filtered them out for this dataset.

Getting started:

To start, check the documentation of Connectome-utilities or just dive into the included exemplary jupyter notebooks.

Contact:

If you have questions or notes: [e-mail]

CREDITAll of this is based on MICrONS, with very little work by me. To give full credit, I will include below the original description of the datasets used. Many thanks to everyone mentioned below and everyone else that worked hard to provide that highly valuable data!

aibs_metamodel_mtypes_v661_v2:This table contains Mtype predictions (Schneider-Mizell 2023) for cells throughout the entire dataset at materialization version 661. The predictions come from a soma and nucleus feature trained metamodel (Elabbady 2022). This is a reference table where id refers to the unique nucleus id in the "nucleus_detection_v0" table. Classification_system refers to the coarse class predictions (excitatory or inhibitory) and cell_type denotes neuronal mtype predictions. Errors, nonneurons, and soma-soma mergers have been filtered out. For questions please contact Leila Elabbady or Forrest Collman.

proofreading_status_and_strategy:The proofreading status of neurons that have been manually cleaned, extended, or both. Axon and dendrite compartment status are marked separately under status_axon and status_dendrite, as proofreading effort was applied differently to the different compartments in some cells. status_axon and status_dendrite are TRUE if the compartment is at least clean, meaning the synapses are accurate but possibly incomplete. strategy_axon and strategy_dendrite represent the specific strategy used for each compartment, the full details of which are available at www.microns-explorer.org/manifests/mm3-proofreading. Uploaded and maintained by Bethanny Danskin, with the help of Forrest Collman and and Casey Schneider-Mizell. Proofreading collected in this table represents the work by many proofreaders. 

synapses_pni_2:Automated synapse detection performed by Nick Turner from the Seung Lab.  size represents the number of (4x4x40 nm) voxels painted by the automated cleft segmentation, and the IDs reference the IDs of the cleft segmentation.  Ctr_pt reflects the centroid of the cleft segmentation. The cleft segmentation volume is located in the flat_segmentation_source field.