Module 20: Statistical Models and Inference for Connectomics

Teaching Deck

Learning Objectives

• Choose statistical models aligned to connectomics question types
• Construct and justify appropriate null models for graph analyses
• Control multiplicity and uncertainty in high-dimensional motif tests
• Report inferential claims with explicit assumptions and 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

Design and execute a connectomics inference plan that includes null-model choice, multiplicity control, uncertainty reporting, and explicit claim boundaries.

Concept Focus

1) Null models encode scientific assumptions

• Technical: null models should preserve relevant graph constraints (degree sequence, spatial limits, cell-class composition) while randomizing the tested structure.
• Plain language: your “chance baseline” must reflect biology and data collection realities.
• Misconception guardrail: a generic random graph is rarely an adequate connectomics null.

Core Workflow

• See module page for details.

60-Minute Run-of-Show

• See module page for details.

Misconceptions to Watch

• Misconception guardrail: a generic random graph is rarely an adequate connectomics null.
• Misconception guardrail: reporting only p-values without multiplicity context is incomplete.
• Misconception guardrail: post-hoc storytelling is not confirmatory inference.

Studio Activity

Activity Output Checklist

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

Assessment Rubric

• Minimum pass
• Null model is justified and constraints are explicit.
• Multiplicity handling is documented and applied.
• Claims are partitioned by confidence level.
• Strong performance
• Demonstrates sensitivity analysis against preprocessing and sampling choices.
• Reports effect sizes and uncertainty, not significance alone.
• Provides clear boundaries on generalization.
• Common failure modes
• Null model choice disconnected from biological question.
• Selective reporting of significant outcomes.
• Conflation of exploratory signal with validated inference.

Exit Ticket

Write a 6-8 sentence inference note that includes:

1. hypothesis and estimand,
2. null-model assumptions,
3. multiplicity strategy,
4. one robust conclusion and one unresolved uncertainty.

References (Instructor)

• Bassett, Zurn, and Gold (2018) - model use in network neuroscience.
• Januszewski et al. (2018) - segmentation performance and uncertainty context.
• MICrONS/FlyWire/H01 analyses for cross-dataset inference constraints.

Teaching Materials

• Module page: /modules/module20/
• Slide page: /modules/slides/module20/
• Worksheet: /assets/worksheets/module20/module20-activity.md