Module 09: Neuron Morphology and Skeletonization

Teaching Deck

Learning Objectives

• Generate skeleton representations from reconstructed neurites
• Compute core morphology descriptors
• Relate morphology metrics to biological interpretation
• Report morphology uncertainty and classification limits

Session Outcomes

• Learners can complete the module capability target.
• Learners can produce one evidence-backed artifact.
• Learners can state one limitation or uncertainty.

Agenda (60 min)

• 0-10 min: Frame and model
• 10-35 min: Guided practice
• 35-50 min: Debrief and misconception correction
• 50-60 min: Competency check + exit ticket

Capability Target

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

Concept Focus

1) What is skeletonization and why do we need it?

A segmented neuron occupies millions of voxels in the EM volume. To analyze its morphology efficiently, we reduce it to a skeleton: a tree graph where nodes represent points along the neurite centerline and edges represent the path between them. Skeletons compress a neuron's 3D structure from gigabytes to kilobytes while preserving topology (branching pattern, path lengths, connectivity) — at the cost of surface geometry and spine shape, which are discarded.

Core Workflow

• 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?

60-Minute Run-of-Show

• Review the data formats content library entry (skeletons section)
• Install/check NeuroM or equivalent morphology analysis package
• 00:00-10:00 | Morphology overview
• "Why do we care about neuron shape?" — Shape constrains function: a neuron's dendritic arbor determines what inputs it can receive; its axonal arbor determines where it can send output.
• Show 3 neuron types (pyramidal, basket, Martinotti) and their characteristic morphologies.
• "Today you'll learn to quantify these shapes from EM data."
• 10:00-24:00 | Skeleton extraction demo
• Live demo: take a segmented neuron, run skeletonization, visualize result in Neuroglancer.
• Walk through SWC format: "Each line is a node. Parent ID tells you the tree structure."
• Common pitfall: show a skeleton with spurious branches from noisy segmentation. Demonstrate pruning.
• 24:00-38:00 | Descriptor calculation
• Hands-on: learners compute 5 descriptors for one neuron using NeuroM or provided scripts.
• Compare results across the group: did everyone get the same numbers? Discuss sources of variation.
• Introduce Sholl analysis with live visualization.
• 38:00-50:00 | Interpretation and caveats
• "Your neuron has total cable length of 2,100 μm and 47 branch points. Is that a lot?" — Compare to published values for the putative cell type.
• Discussion: which descriptors are robust to reconstruction errors? (Cable length is sensitive to splits; branch count is sensitive to both splits and spurious branches; spine density is robust if the segmentation boundary is accurate.)
• "What if 30% of the arbor is outside the volume? How does that change your interpretation?"
• 50:00-60:00 | Competency check
• Each learner submits their morphology descriptor table with:
• At least 3 descriptors with values
• Putative cell-type classification based on morphology
• One explicit limitation of the measurement
• Exit ticket: "Name one morphology feature that could be confounded by reconstruction quality."

Misconceptions to Watch

• Misconception guardrail: a skeleton is a lossless summary of a neuron.
• Misconception guardrail: morphological measurements are comparable across cells that were proofread to different levels.
• Misconception guardrail: total dendritic length is a property of the neuron itself, independent of how it was reconstructed.
• Misconception guardrail: a cell type assigned from morphology alone needs no corroboration from connectivity or molecular identity.

Studio Activity

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).

Activity Output Checklist

• Evidence-linked artifact submitted.
• At least one limitation or uncertainty stated.
• Revision point captured from feedback.

Assessment Rubric

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.

Assessment Rubric

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.

Assessment Rubric

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.

Exit Ticket

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

References (Instructor)

• Use module references listed on the module page.

Teaching Materials

• Module page: /modules/module09/
• Slide page: /modules/slides/module09/
• Worksheet: /assets/worksheets/module09/module09-activity.md