Connectome Quality Notebooks

A computational path through the quality-assessment ideas in Unit 08 and the Connectome Quality tool page. Work these in order — each step assumes the previous.

What this page is. Reference code and specifications you implement yourself, not downloadable notebooks. The site previously linked five .ipynb files here; every one of them was an empty stub with no code cells, and they have been removed rather than left to look runnable. Building these yourself is a better exercise than running someone else’s notebook, and it is the only version of this page that is honest.

The snippets below are written against the current CAVE and CloudVolume APIs. Client libraries in this field move quickly — check call signatures against the package docs before assuming a failure is your fault.

Environment

pip install caveclient cloud-volume numpy pandas matplotlib networkx
# For hemibrain instead of MICrONS:
pip install neuprint-python
# For BossDB-hosted volumes:
pip install intern

A free CAVE account is required for MICrONS access, and you must accept the dataset’s terms before the API will return data. Verify access before a teaching session — account provisioning is the most common reason a hands-on session loses its first twenty minutes.

Step 1 — Look at the data before measuring it

Goal: develop a visual sense of what good and bad reconstruction look like, before any metric is computed. A metric you cannot connect to something you have seen is a number you will misinterpret.

Do: pull a cutout of raw EM and the corresponding segmentation, display them side by side, and find one place by eye where the segmentation boundary does not follow the membrane. Save the coordinate.

from cloudvolume import CloudVolume
import matplotlib.pyplot as plt

# MICrONS public release. Check the current source paths in the dataset docs --
# these move between releases.
img = CloudVolume("precomputed://gs://iarpa_microns/minnie/minnie65/em",
                  mip=0, use_https=True, progress=False)
seg = CloudVolume("precomputed://gs://iarpa_microns/minnie/minnie65/seg",
                  mip=0, use_https=True, progress=False)

x, y, z = 240000, 100000, 21000          # any coordinate in bounds
box = (slice(x, x + 512), slice(y, y + 512), slice(z, z + 1))

fig, ax = plt.subplots(1, 2, figsize=(12, 6))
ax[0].imshow(img[box][:, :, 0, 0].T, cmap="gray"); ax[0].set_title("EM")
ax[1].imshow(seg[box][:, :, 0, 0].T);              ax[1].set_title("segmentation")
for a in ax: a.axis("off")

Produce: three annotated screenshots — a clean region, a plausible split, a suspected merge — with a sentence each on what in the image supports your reading.

Step 2 — Morphology as a quality signal

Goal: learn to spot reconstruction errors from morphology alone, without returning to the voxels. This is the skill that lets one person screen hundreds of cells.

Do: load meshes for ten neurons. For each, ask: does this look like one cell? Look for two somata in one object, a process terminating abruptly in mid-neuropil, or a branch leaving the expected arbor territory.

mesh = seg.mesh.get(root_id)             # root_id from the cell table, see step 3
m = mesh[root_id]
print(m.vertices.shape, m.faces.shape)

Produce: a table of ten cells with a plausibility call and the morphological feature that drove it. Then check two of your suspicions in the raw data and report whether you were right — that is your first calibration measurement.

Step 3 — Synapse queries, pinned to a version

Goal: retrieve connectivity reproducibly, and understand why the version matters.

from caveclient import CAVEclient

client = CAVEclient("minnie65_public")

# Always look at what versions exist, then pin one explicitly.
print(client.materialize.get_versions())
VERSION = 1300                            # replace with a version you chose deliberately

root_id = 864691135474648896              # any proofread neuron

syn_in  = client.materialize.synapse_query(post_ids=root_id,
                                           materialization_version=VERSION)
syn_out = client.materialize.synapse_query(pre_ids=root_id,
                                           materialization_version=VERSION)

print(f"inputs {len(syn_in)}  outputs {len(syn_out)}  "
      f"distinct presynaptic partners {syn_in.pre_pt_root_id.nunique()}")

# The heavy tail: most partners contribute a single synapse.
per_partner = syn_in.groupby("pre_pt_root_id").size().sort_values(ascending=False)
per_partner.value_counts().sort_index().plot(loglog=True, marker="o")

Then re-run against a second materialization version and report what changed, and why.

Produce: a notebook with a five-line reproducibility header — dataset, version, client version, date, author — and the two-version comparison.

Why this step matters more than it looks: analysis against an unpinned segmentation is the most common silent correctness bug in connectomics. Your code runs fine; it answers a different question than it did last week. See Unit 04 §2.

Step 4 — Measure the effect of proofreading on your own endpoint

Goal: produce the quality statement that actually persuades.

Build this yourself. Specification:

  1. Choose 10–20 cells and an endpoint metric — mean input count, or the ratio of synapses onto spines versus shafts.
  2. Record each cell’s proofreading status from the dataset’s cell tables.
  3. Compute the endpoint separately for well-proofread and less-proofread cells.
  4. Report the difference — with the caveat that proofreading status is not randomly assigned. Cells get proofread for reasons that correlate with size, position and type, so this is an association, not a controlled comparison. State that explicitly.

Produce: one number and one honest paragraph. “Cells at proofreading level N have a mean input count of X; cells below it, Y” is the kind of statement that belongs in a methods section, and almost no analysis includes it.

Step 5 — Error sensitivity simulation

Goal: quantify how much your conclusion depends on the reconstruction errors you already know are there.

Build this yourself. Specification:

  1. Build a connectivity graph for your population, recording the six construction decisions from Unit 09 §1.
  2. Compute your headline statistic — reciprocity, a motif count, a degree ratio.
  3. Write a perturbation function: with probability m, merge two random nodes (union their edge sets); with probability s, split a random node in two.
  4. Run 100 perturbations at your measured error rates and record the statistic each time.
  5. Report the resulting spread as an error band on your effect size.
import random, networkx as nx

def perturb(G, merge_p, split_p, rng=random.Random(0)):
    H = G.copy()
    for n in list(H.nodes()):
        if n in H and rng.random() < merge_p:
            others = [m for m in H.nodes() if m != n]
            if others:
                nx.contracted_nodes(H, n, rng.choice(others),
                                    self_loops=False, copy=False)
    return H

Produce: a figure showing your observed value against the perturbation distribution. If the band crosses the null expectation, your result is not robust to your own measured error rate — and reporting that is more valuable than not knowing it.

This is roughly forty lines of code and among the strongest things you can put in a supplement.