Module 09 Activity Worksheet

Module: Module 09: Neuron Morphology and Skeletonization
Duration: 4 hours
Generated from the module page. Edit modules/module09.md, not this file.

Capability target

Produce a skeleton-based morphology summary with at least three descriptors and one explicit limitation.

You are done when you can demonstrate this, not when you have filled in every box below.

Before you start

Check that you have:

• Modules 01-08

Bring one question you already have about this topic. Write it here so you can check at the end whether it was answered:

My question:

Questions this module answers

Keep these in view. At the end, answer each in one sentence.

1. Which morphology features are robust across reconstructions?
   • Your answer:
2. How should skeleton uncertainty be communicated?
   • Your answer:

The task

Scenario: You have skeletons for 10 neurons in L2/3 of mouse visual cortex. Your task is to classify them as pyramidal vs interneuron based on morphology alone, then validate against synapse-based classification (excitatory vs inhibitory output synapses).

1. Compute morphological descriptors for all 10 neurons (cable length, branch points, spine density, Strahler number, arbor volume).
2. Create a summary table and scatter plot (e.g., spine density vs cable length).
3. Classify each neuron as pyramidal or interneuron based on morphological criteria.
4. Compare your morphological classification to the synapse-based classification (provided). Do they agree?
5. For any mismatches, investigate: was the morphological measurement affected by reconstruction quality?

What you hand in

• Morphology descriptor table (10 neurons × 5 descriptors)
• Scatter plot with proposed classification boundary
• Classification comparison table (morphology call vs synapse call)
• Brief report on any mismatches and their likely cause

Working checklist

Tick as you go. If you skip a step, write why — a skipped step with a stated reason is a decision; a skipped step without one is a gap.

• Build skeleton from volumetric segmentation using TEASAR or equivalent algorithm.
• Quality-check the skeleton: prune spurious branches, verify branch points, check for disconnected fragments.
• Compute descriptors: cable length, branch points, Strahler number, Sholl profile, spine density.
• Compare against reference patterns: does this neuron match the expected morphology for its putative cell type?
• Report interpretation confidence: which descriptors are robust, which are affected by reconstruction quality?

Evidence and reasoning

Fill one row per claim you make in your artifact. A claim without a limitation is not finished.

Confidence. For your main claim, mark one and say why:

• High — two or more independent lines of evidence agree
• Medium — one strong line, or several that share a weakness
• Uncertain — the deciding evidence is not available to me

Why:

One alternative I considered and rejected, and the reason:

Misconception self-check

These are the errors this module is designed to prevent. Confirm you did not make them, or note where you nearly did:

• I did not assume: A skeleton is a lossless summary of a neuron.
• I did not assume: Morphological measurements are comparable across cells that were proofread to different levels.
• I did not assume: Total dendritic length is a property of the neuron itself, independent of how it was reconstructed.
• I did not assume: A cell type assigned from morphology alone needs no corroboration from connectivity or molecular identity.

Session timing (facilitator reference)

Rubric

Score yourself before anyone else does. Where you fall short, name the specific next action rather than a general intention.

• Minimum pass
  • Valid skeleton and descriptor set for all 10 neurons, with at least 3 descriptors each.
  • Every classification carries a stated evidence chain, not just a label.
  • At least one explicit measurement limitation named, tied to a specific reconstruction issue.
• Strong performance
  • Descriptors partitioned into robust versus reconstruction-sensitive for each borderline cell.
  • Mismatches between morphological and synapse-based calls traced to a root cause (truncation, split, spurious branches) rather than logged as disagreement.
  • Per-cell completeness estimated and reported next to every absolute measurement.
  • Ratios computed on contained compartments wherever truncation is present.
• Common failure to flag
  • Descriptor list without biological context — numbers with no statement of what they mean for identity.
  • Classification from a single descriptor when the others disagree.
  • Absolute cable length or arbor volume reported for truncated cells without a lower-bound qualifier.

My self-assessment:

• Strongest part of my work, and the evidence for that:
• Weakest part, and the specific next action:

Exit prompt

Explain one morphology feature that could be confounded by reconstruction quality.

Your answer:

Peer review (swap worksheets)

Reviewing someone else’s reasoning is the fastest way to see the gaps in your own. Assess the evidence quality, not whether you agree with the conclusion.

• Is every claim paired with specific evidence?
• Is at least one limitation stated, and is it a real one?
• Is the confidence level justified by the number of independent evidence lines?
• One thing this person did better than me:
• One question I would ask them:

Module page: /modules/module09/ · Slides: /modules/slides/module09/ · Facilitator guide