Module 06: Hypothesis Testing in Connectomics

🧠 From Curiosity to Question

Every scientific journey begins with a question. In connectomics, questions might concern the structure, connectivity, or variability of specific neural circuits. This module guides you through crafting meaningful and testable hypotheses.

• Observations from EM volumes
• Generating hypotheses from structure
• Choosing appropriate controls and comparisons

🔬 Designing Connectomic Experiments

Using large datasets like MICrONS or FlyWire, researchers can simulate experiments by analyzing connectivity motifs, synapse distributions, or circuit asymmetries. Experimental design involves framing a hypothesis, defining metrics, and selecting analysis techniques.

• Using existing data to ask new questions
• Metrics: synapse counts, partner diversity, path length
• Tools for analysis: Python, Neuroglancer, Jupyter

⚖️ Pitfalls and Ethics

Interpretation of structural data comes with challenges. Structure alone doesn’t reveal function. Hypothesis-driven work in connectomics must acknowledge these limits—and be grounded in ethical research practices.

• Limitations of inference from anatomy
• Responsible data use and attribution
• Working with animal and human brain data

🎯 COMPASS Integration

• Knowledge: Framing scientific questions in a connectomic context
• Skills: Designing structured inquiry and controlled comparisons
• Character: Scientific honesty and rigor
• Meta-Learning: Learning from failed or ambiguous results

📚 References & Resources

• Helmstaedter et al., 2013. Connectomic reconstruction of the inner plexiform layer in the mouse retina. Nature.
• FlyWire Tutorials: flywire.ai
• Open Source Analysis: microns-explorer.org

✅ Assessment

• Write a testable hypothesis based on a sample EM volume
• Describe a potential comparison or control
• Explain a challenge in interpreting structural findings