09 Connectome Analysis and NeuroAI

Technical Training: Nanoscale Connectomics

Session outcomes (60 minutes)

• Formulate a motif/graph hypothesis with explicit estimand.
• Choose and justify a null model.
• Report bounded claims with uncertainty and reproducibility metadata.

Pedagogical arc

• Hook: why graph/motif results are often overclaimed.
• Model: hypothesis -> query -> null -> interpretation.
• Practice: design and critique analysis plans.
• Check: one bounded claim + non-claim pair.

Motivation and framing

• Structure can constrain models; it does not automatically explain intelligence.

Representation framing

• Define representation before inference.

Limits of reverse engineering claims

• Teach boundary statements as required output.

Analysis workflow overview

Hypothesis -> Query -> Search -> Null comparison -> Interpretation -> Reproducibility package

Motif search context

• Distinguish candidate motifs from validated mechanisms.

Query language and reproducibility

• Human-readable queries reduce hidden assumptions.

Complexity constraints and feasibility

• Computational limits are part of methodological validity.

Historical benchmark caution

• Use old benchmark values as context, not current truth.

Comparative analysis caveats

• Cross-dataset claims require aligned preprocessing and null assumptions.

Misconceptions to correct

• “Significant motif enrichment implies mechanism.”
• “One null model is enough for any claim.”
• “Query scripts without provenance are acceptable.”

Activity

Design one analysis card with:

• hypothesis,
• estimand,
• null model,
• success criterion,
• non-claim,
• provenance fields.

Rubric checkpoint

• Pass: coherent hypothesis-null-estimand chain.
• Strong: includes sensitivity analysis and boundary statement.
• Flag: result-first narrative without methodological controls.

External paper figure integration

• Bassett, Zurn, Gold 2018 (model taxonomy and claim type framing).
• Large-scale connectome motif-analysis papers with null-model details.
• Graph-method papers showing sensitivity to preprocessing choices.

External inserted figure (open license)

• Source URL: https://commons.wikimedia.org/wiki/Special:FilePath/Feed-forward_motif.GIF
• License: CC BY-SA 3.0 (Wikimedia Commons feed-forward motif asset).

References and attribution

• Internal visuals: outreach/neuroAI historical deck assets.
• Journal-club tie-ins:
  • https://doi.org/10.1038/s41592-018-0049-4
  • https://doi.org/10.1038/s41583-018-0038-8