fmrigds

Group fMRI with fmrigds

Accept existing subject estimates/maps/tables or a first-level handoff; do not require BIDS or fmrireg. For an API question, read only API. For a new analysis also read the operating contract and inference decisions. Check installed capabilities.

Establish the observation and estimand

Verify subject identity/order, independent units, repeated sessions/conditions, spatial alignment, contrast semantics and units, masks, missingness, covariates, and the meaning of available assays. Follow the handoff bridge. For subject NIfTI beta/SE maps use the NIfTI recipe, including explicit contrast order and coverage masks; import success does not verify every image's affine. An SE is not a variance; a z/p map cannot uniquely supply an effect and its sampling variance. Effects without uncertainty can support a suitable unweighted group model; do not invent unit variance for precision weighting. Repeated runs are not independent subjects. A fixed-effects teaching example is not a default population model.

Ask only material unresolved group decisions: estimand/model/covariates, repeated-measures structure, uncertainty assumptions, inclusion/missingness, and correction family. Freeze the proposed model and contrast before inspecting its interesting outcomes. Fit neither an unsupported general mixed model nor a whole-brain correction independently by chunk.

Build a lazy plan

Use gds() or documented as_gds() inputs, keyed covariate attachment, explicit reduce() or appropriate public model helper, and posthoc() where justified. Inspect explain() / preview() / validate() before approved compute(). assets/group_template.R constructs a lazy plan from explicit inputs; it does not execute. scripts/smoke_group.R checks a known fixed-effects arithmetic identity on tiny synthetic data, not a production model choice.

Choose public registered methods supported by the installed version. The reviewed LMM family is intentionally restricted: do not assume arbitrary lmer syntax, random effects, missing layouts, or covariance structures. Unsupported science requires an explicit alternative adapter/model, not silent simplification.

Examine, correct, export

Use examine_group() where supported to separate validity concerns, model surprise, and influence. A review priority is not an exclusion rule. Record sensitivity analyses without redefining the primary cohort opportunistically. Preserve actual per-sample N, model/variance diagnostics, convergence flags, heterogeneity where relevant, and missingness. Correct over the declared full family, accounting explicitly for mask, contrasts, sidedness, and post-selection. Retain unthresholded effects/statistics, uncertainty, p/q and rejection masks.

Complete the artifact contract with a frozen inference manifest. A reporting stage is optional; do not rerun a model just to make maps look more impressive. A report renderer's cluster threshold is not a statistical correction. Deliver outputs and limitations, including any failed subjects/samples, numerical approximations, or unsupported assumptions.