NeuroTrailblazers banner

Lecture 2 of the connectomics lecture series. 56 slides in three parts, about 150 minutes. Openly licensed — CC BY-SA 4.0.

What this lecture covers

How tissue becomes a queryable petascale dataset: preparation and its artifact signatures, storage and infrastructure, and the versioning that makes a result reproducible.

Open the deck (HTML) Slide source (Markdown) Series overview

The HTML deck presents directly in a browser. The Markdown source carries the speaker notes as HTML comments and is what to edit if you want to change wording — see Formats for PowerPoint and Google Slides.

Learning objectives

By the end of this lecture, participants will be able to:

  1. Identify the tools and formats used for nanoscale acquisition, storage, and serving.
  2. Trace an artifact in a reconstruction back to the pipeline stage that produced it.
  3. Apply reproducible-pipeline principles to a query against a public connectomics volume.
  4. Estimate the capacity, compute, and labor cost of a proposed acquisition.

Structure

Part A — From tissue to voxels

The preparation chain step by step with the failure each step produces, sectioning and imaging families, the dose budget, and the QA gates that stop acquisition.

Part B — Storage, infrastructure, and what it costs

The eight-stage reference pipeline, chunked multi-resolution arrays, chunk shape as an access-pattern decision, and a worked capacity and cost model.

Part C — Reproducible pipelines

Why object IDs are not stable, the ChunkedGraph and materialization versions, the reproducibility header, and the platform landscape.

What students produce

A reproducible query against a public volume: a notebook carrying a six-line reproducibility header, a pinned materialization version, stated inclusion criteria, and one stated limitation — that runs end to end from a clean environment.

The centrepiece

The version problem in Part C. A figure reports 1,412 input synapses; re-running the notebook returns 1,530 for “the same cell”. Both numbers are correct and the paper is wrong, because it reported a version-dependent quantity without its version. This is the field’s most common silent failure and students have usually never met it.

Notes for whoever teaches it

The diagnostic question runs through the whole lecture. For any defect: which coordinate system does it live in — block position, anatomy, acquisition time, or the processing grid? That single question identifies the stage that produced it, and it is the transferable skill here.

The staining-gradient case is worth dwelling on. A depth-dependent staining gradient runs in the same direction as cortical layer depth. A team that does not check the coordinate system can publish a “laminar difference in synapse density” that is entirely a penetration artifact. Ask the room how they would distinguish the two.

Make them physically add the reproducibility header. It takes ninety seconds, it is the highest-value habit in the lecture, and essentially nobody adopts it until they have been burned once. The assignment rubric awards points for it explicitly.

Licence and credit

CC BY-SA 4.0. Teach from this lecture anywhere, including commercially; copy and redistribute it in any medium; and re-cut, shorten, translate, or merge it into your own material. No permission needed. Two conditions: credit the original and say if you changed anything, and distribute your adapted version under the same licence.

Gray Roncal, W. (2026). Nanoscale Connectomics: Tools and Methods (EN.585.781 Frontiers in Neuroengineering, Module 8). NeuroTrailblazers. CC BY-SA 4.0. https://neurotrailblazers.org/teaching/lectures/

For an adaptation, prefix with “Adapted from” and note what you changed.

Found something wrong or out of date — or built a version worth sharing back? Open an issue.