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Statistical methods

This is the practitioner summary. The full theory companion is msnpip_methods_theory.md; locked parameters live in msnpip_refactor_spec.md §0.0.

Pipeline of inferences

subjects → per-subject MSN → node strength → GROUP CONTRAST (regional map x)
                                                   │
        demographic correlation (Layer 0)          ▼
                                          imaging transcriptomics (Layers A/B)

1. MSN construction

Per subject, each of the 5 morphometric metrics is z-scored across regions, then inter-regional similarity is the Pearson correlation between regions' standardized feature vectors (diagonal NaN). The MSN is whole-cortex (both hemispheres). Node strength is the signed mean: (mean of positive edges + mean of negative edges) / 2 (positive / absolute selectable). The z-score ddof does not affect the result — a uniform per-column rescale cancels in the row-wise Pearson.

2. Group contrast (subject-level)

Per region, OLS of strength ~ group + covariates; the exported regional statistic is the group coefficient (beta, default), its t, or cohen_d (standardized mean difference on covariate-residualized strength). Categorical covariates and site/scanner are one-hot encoded (reference dropped). This is a subject-level test — no spatial null applies here; spatial autocorrelation only matters when correlating two regional maps (see Layer A).

3. Demographic correlation (Layer 0)

Spearman (default) of node strength vs a continuous variable, globally or per region (per-region gets Benjamini–Hochberg FDR across regions), optionally within a single group. Ordinary correlation p-values; no spatial null.

4. Imaging transcriptomics — two null layers

  • Layer A (spatial) — handled by the engine via the vasa surface spin. Answers: "is this gene/pathway association stronger than under spatially-autocorrelated random brain maps?" msnpip fixes the null to vasa and hard-fails (MsnpipSurfaceNullError) if the engine falls back to a grouped shuffle, so an invalid spin test can never reach a figure.
  • Layer B (sampling) — subject-level resampling of the contrast map's stability. Documented as a future option; not built in v2.

What the engine reports (consume, don't re-derive)

  • Correlation: sign-aware empirical p with +1 smoothing, BH fdr, FWE maxT.
  • PLS: component p is on the cumulative variance through component k; gene columns are weight, zscore, p, fdr, maxT — zscore is a descriptive ranking aid, not significance.
  • Enrichment: ensemble-GCEA (primary, phenotype-side null) and gsea (NES recalibrated against the engine's imaging-permutation null — its fdr is a NES q-value, not BH, and is not numerically comparable to ensemble fdr).

Reporting caveats [PUB]

  • Empirical p-resolution is 1/(B+1); with ~15,677 genes (DK) even 10⁴ permutations may not yield small adjusted single-gene values — report primarily at the component/category level.
  • Cross-run multiplicity (multiple contrasts/components/genesets) is your responsibility; pre-specify the primary analysis and treat the rest as exploratory.
  • Report the exact gene count for your atlas (DK = 15,677) — it is the FDR denominator.
  • Hemisphere/region choices change the science; defaults are recorded in manifest.json and the report. The MSN uses both hemispheres; the engine input hemisphere (default left) is the selectable part.