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+---
+sidebar_label: 'Image-Based Meta-Analysis'
+sidebar_position: 3
+---
+
+# Image-Based Meta-Analysis (Beta)
+
+### A PRISMA workflow for a meta-analysis about reward
+
+It's finally arrived after years of patience -- Image Based Meta-Analyses.
+Well, almost, it's in Beta so we haven't made all the knobs
+accessible yet, but enough knobs to do some legitimate work.
+This tutorial builds a complete image-based meta-analysis (IBMA) end to end: 16 candidate
+studies found, 12 retained through PRISMA screening and eligibility, 19 statistical maps
+selected, and a Stouffers meta-analysis run in the cloud. 
+
+I'm assuming many of you are familiar with the general workflow of a CBMA; so I will place emphasis on **the steps where IBMA differs from coordinate-based
+meta-analysis (CBMA)**.
+The curation flow will
+look familiar — but some steps are meaningfully different. They are flagged **▲ IBMA-SPECIFIC** below.
+
+---
+
+## 0. CBMA vs IBMA: poor and big data versus rich and small data
+
+A CBMA takes the *peak coordinates* a paper reports — a handful of `(x, y, z)` rows that
+survived the authors' threshold. An IBMA takes the *whole statistical map*: every voxel,
+including the sub-threshold ones. IBMA therefore uses far more of each study's evidence,
+and can combine effect sizes rather than merely testing whether reported peaks cluster in
+space.
+
+The cost is availability. Measured against the live NeuroStore database on the day this
+tutorial was written (08/23/2026):
+
+| | CBMA | IBMA |
+|---|---|---|
+| Unit of input | peak coordinate table | whole-brain statistical map |
+| Studies available in NeuroStore | **32,438** | **684** (~47× fewer) |
+| Does the statistic type matter? | No | **Yes, decisively** |
+| Thresholded input | inherent — peaks *are* thresholded | **disqualifying** |
+| Selection granularity | per analysis | per **image** |
+| Estimators offered | ALE, ALESubtraction, KDA, MKDAChi2, MKDADensity | Fishers, Stouffers |
+| Missing-coverage handling | n/a | `aggressive_mask` |
+| Typical runtime (measured) | ~3 min / 554 analyses | ~4 min / 19 images; **>73 min / 746 images** |
+
+That last row is the practical reason this tutorial uses 19 maps and not 750.
+
+### Which statistic types can be used?
+
+Fishers and Stouffers both require **z maps**. But you can input other statistic types — Compose can derive it:
+
+```
+z  ←  p
+z  ←  t + sample_size
+```
+
+That matters enormously for how much data you can reach. Filtering the image studies by
+statistic type gives:
+
+| `map_type` | Studies |
+|---|---|
+| `any` | 684 |
+| `z` | **129** |
+| `t` | **390** |
+| `beta_variance` | **0** |
+
+So accepting t maps roughly triples your candidate pool. Two caveats worth knowing:
+
+- **`beta_variance` is empty across the entire database.** This is why Compose exposes only
+  Fishers and Stouffers today: the config file defines nine IBMA estimators, and the other
+  seven (DerSimonianLaird, Hedges, WeightedLeastSquares, VarianceBasedLikelihood,
+  PermutedOLS, SampleSizeBasedLikelihood, FixedEffectsHedges) require `beta`+`varcope` or
+  bare `t`, for which there is no data. The two-estimator menu is a response to the data,
+  not an arbitrary limit.
+- **There is no `beta`+`varcope` → `z` path**, even though `t = β/√varcope` is elementary.
+  The derivation graph only reaches `t` from `(p, sample_size)` or `(sample_size, z)`.
+
+This tutorial uses `map_type = z` to keep the worked example simple. For real work, run it
+twice — once with `z`, once with `t` — and compare what each pool gives you.
+
+---
+
+## 1. Create an IBMA project
+
+`NEW PROJECT` → choose **IBMA** → `CREATE NEW PROJECT`.
+
+![Choosing IBMA](/tutorial/ibma_01_new_project.png)
+
+Note the `BETA` chip. Pick deliberately: the project type determines which estimators you
+are offered later and cannot be changed afterwards.
+
+Give the project a name and description by clicking the pencil icons.
+
+![Project page](/tutorial/ibma_02_project_page.png)
+
+---
+
+## 2. Choose the PRISMA workflow
+
+`CURATION: GET STARTED` opens a two-option menu.
+
+![Workflow choice](/tutorial/ibma_03_workflow_choice.png)
+
+- **Simple** — two columns (Unreviewed → Included). Fine for exploratory work.
+- **PRISMA** — four columns (Identification → Screening → Eligibility → Included), which is
+  what you need if the result is going into a paper.
+
+Choose **PRISMA Workflow**. This choice is structural and, as far as the UI exposes, not
+reversible — so decide before importing.
+
+![Empty PRISMA board](/tutorial/ibma_04_prisma_board_empty.png)
+
+---
+
+## 3. ▲ IBMA-SPECIFIC — Search with the Map Type filter
+
+Click `SEARCH`. Two things are already different from a CBMA project:
+
+- **Study Data Type** defaults to **Images**, not Coordinates.
+- A **Map Type** chip appears. This control exists only for IBMA projects.
+
+Open it:
+
+![Map Type dropdown](/tutorial/ibma_05_maptype_dropdown.png)
+
+`Z Map` / `T Map` / `Beta_variance Map` / `Any Map`. **This is the single most important
+control in the IBMA workflow** and it defaults to `Any`, which will hand you anatomical
+templates, ROI masks and F maps alongside usable data.
+Sometimes people label the data incorrectly, so using `Any` will be more comprehensive.
+
+For this tutorial, we are going to use `Z Map` to keep the example simple.
+Select **Z Map**, type `reward`, and search. Result: **16 studies**.
+
+![Reward search filtered to Z maps](/tutorial/ibma_06_search_reward_zmap.png)
+
+---
+
+## 4. Name and review the import
+
+Click `NEXT`. Name the import something that records your query — future-you will want to
+know exactly what produced this set. We do our best to preserve the query in the import name, but if you want to name it something else, do so here.
+
+![Name and review](/tutorial/ibma_07_name_and_review.png)
+
+Expanding the summary lists every study with `view study in neurostore` /
+`view study in pubmed` / `view full article` links. This is the fastest way to sanity-check
+a search before committing to it. Click `IMPORT`.
+
+---
+
+## 5. Identification
+
+The 16 studies land in **Identification**, and Compose checks for duplicates across all
+imports in the project.
+
+![Identification](/tutorial/ibma_08_identification.png)
+
+With a single import there are none. If you had imported "reward" and "reinforcement"
+separately, duplicates would be detected here and excluded automatically — and correctly
+un-excluded if you later delete one of the imports.
+
+Click `PROMOTE ALL STUDIES AND SCREEN`.
+
+---
+
+## 6. Screening — title and abstract
+
+![Screening](/tutorial/ibma_09_screening.png)
+
+Screening is the ordinary PRISMA step: decide from the title and abstract whether the study
+addresses your question. Nothing IBMA-specific here.
+
+Select a row with its checkbox. The action bar appears.
+
+![Selecting a study](/tutorial/ibma_10_screening_select.png)
+
+Clicking `IRRELEVANT` directly records the generic reason "Irrelevant". **Use the small
+caret beside it instead** — it opens a free-text box where you can create a specific reason,
+which becomes a labelled box in your PRISMA diagram.
+
+![Custom exclusion reason](/tutorial/ibma_11_exclusion_reason.png)
+
+In this run, one study was excluded here:
+
+| Study | Reason |
+|---|---|
+| *The Neurobehavioral Mechanisms of Motivational Control in ADHD* (2020) | Group-comparison design; no reward main effect |
+
+Its design is clear from the abstract: every map is an ADHD-vs-control contrast, so there is
+no group-level reward effect to contribute. **15 studies** proceed. Select all and `PROMOTE`.
+
+---
+
+## 7. Eligibility — full text assessment
+
+Currently, you can only assess eligibility from the full text; checking
+for method designs, what contrasts were actually analyzed, etc.
+We may decide to add links to the corresponding neurovault collection,
+so you can assess whether the image corresponding to the contrast
+you want is actually available.
+For now, that will be decided during the extraction step.
+
+![After eligibility exclusions](/tutorial/ibma_13_eligibility_excluded.png)
+
+**12 studies** remain. Select all and `INCLUDE`.
+
+---
+
+## 8. The PRISMA diagram
+
+`PRISMA DIAGRAM` renders the flow, with your custom reasons as labelled boxes.
+
+![PRISMA diagram](/tutorial/ibma_14_prisma_diagram.png)
+
+
+---
+
+## 9. Extraction — build the studyset
+
+`START EXTRACTION` → step through the wizard → `START`. Compose creates a studyset from
+your 12 included studies, ingests anything new into NeuroStore, and creates a default
+`included` annotation column.
+
+![Extraction table](/tutorial/ibma_15_extraction_table.png)
+
+**A note on versions.** A NeuroStore study can have several versions, and they do not all
+contain the same images. Four of the twelve studies here carry z maps *only in one of three 
+of the available versions*.
+
+---
+
+## 10. ▲ IBMA-SPECIFIC — Choose the images
+
+This is the heart of an IBMA and it has no CBMA counterpart.
+
+Your 12 studies contain **77 analyses**, and by default the `included` column marks **every
+one of them true**. Left alone, your meta-analysis would pool all 77 — including PPI
+connectivity maps, thresholded/unthresholded duplicates of the same contrast, and F maps.
+
+Open any study to see what you are actually choosing between.
+
+![Study analyses](/tutorial/ibma_16_study_analyses.png)
+
+Uncheck everything that is not a contrast you want. Three rules:
+
+1. **Prefer unthresholded.** Where a study deposits both, e.g. `thresh affective` and
+   `unthresh affective`, always choose the unthresholded one.
+2. **One inference type.** Do not pool activation maps with PPI/connectivity maps; they test
+   different things. In this set, 5 of one study's 10 analyses are PPI.
+3. **Never include both signs of the same contrast.** `"betting minus watching"` and
+   `"watching minus betting"` are the same map negated; including both cancels to zero.
+   Don't do that if you want a result from the meta-analysis. 
+
+On rule 3's flip side: where a paper offers several equally defensible contrasts and no
+principled way to choose, **keeping more than one is better than guessing**. Forcing a
+single pick substitutes your judgement for the data, and the statistical cost of keeping
+several is lower than you might expect — NiMARE automatically corrects the variance for
+contrasts that share a study (see caveat 2 in §14). Six of the twelve studies here
+contribute two or three contrasts for exactly this reason.
+
+![Image selection](/tutorial/ibma_17_image_selection.png)
+
+The final selection — **19 images from 12 studies**:
+
+| Study (year) | Images | Contrast(s) kept |
+|---|---|---|
+| Subjective Value during Effort/Probability/Time Discounting (2018) | 3 | Subjective Value during Effort / Probability / Temporal Discounting |
+| Distinct Reward Properties via Corticostriatal Interactions (2016) | 2 | `unthresh affective`, `unthresh informative` |
+| Multiple brain networks … acquisition of bias (2015) | 2 | context-based / context-free action reward error |
+| History of conditioned reward association (2020) | 2 | Rewarded > Unrewarded No-go; Rewarded > Unrewarded False Alarm |
+| Neural correlates of reward-directed action (2020) | 2 | GS dollar RT > GS control RT; NGS dollar > nickel rate |
+| Changing value through cued approach (2014) | 2 | `Fig3A_zstat1`, `Fig3B_zstat1` |
+| Reward Sensitivity Enhances vlPFC Activation (2016) | 1 | Figure2 Free vs Forced TEPS **unthresh** |
+| Musical reward prediction errors (2019) | 1 | `RPEs` |
+| Facing temptation: gambling availability (2018) | 1 | `"betting minus watching"` |
+| Aberrant striatal tracking of reward magnitude (2019) | 1 | gain > loss, mean across HC and MDD |
+| Learning of distant state predictions by OFC (2019) | 1 | `Reward` |
+| Supra-Additive Effects of Fat and Carbohydrate (2018) | 1 | `F+C Bid Main effect` |
+
+:::warning
+**A failure mode worth knowing.** A study can silently drop to *zero* included images and
+nothing warns you — the run simply proceeds with fewer studies than you intended. This
+happened during this tutorial: one analysis is named `F+C Bid  Main effect` with a
+**double space**, which the browser collapses to one when displayed. Always confirm the
+`N / 77 analyses selected` figure on the Review screen matches your intent.
+:::
+
+---
+
+## 11. Specify the meta-analysis
+
+`META-ANALYSIS SPECIFICATION` → choose an estimator.
+
+![Estimator selection](/tutorial/ibma_18_spec_estimator.png)
+
+- **Stouffers** — a t-test on z-statistic images. Used here.
+- **Fishers** — combines p-values derived from t or z images. More sensitive to a single
+  strong study; Stouffers weights studies more evenly.
+
+Leave the corrector at **FDRCorrector** (`alpha=0.05`, `method=indep`). FDR is the only
+option currently enabled for IBMA.
+
+Three Stouffers arguments deserve attention, and two of them have no CBMA analogue:
+
+- **`aggressive_mask`** (default `False`) — ▲ IBMA-specific. Studies differ in brain
+  coverage. `True` analyses only voxels present in *every* map (a strict intersection);
+  `False` runs per-voxel analyses on whatever subset of studies has valid data there.
+  `False` keeps more brain but means different voxels rest on different numbers of studies.
+- **`two_sided`** (default `True`) — tests for both increases and decreases.
+- **`use_sample_size`** — weighted Stouffer's. Sample sizes *are* present in NeuroStore
+  (`analysis.metadata.sample_size`; 17–88 in this set), so this is usable — but note it
+  weights by n, which will *amplify* any over-weighting from studies contributing several
+  contrasts.
+
+Click `NEXT`. Verify the count.
+
+![Select analyses](/tutorial/ibma_19_select_analyses.png)
+
+`Included: 12 studies | 19 analyses | 0 coordinates`
+
+Name it, then check the Review screen.
+
+![Review](/tutorial/ibma_20_review.png)
+
+`19 / 77 analyses selected` — this is the number to verify before creating.
+
+---
+
+## 12. Run
+
+![Run page](/tutorial/ibma_21_run_page.png)
+
+Three execution routes: AWS (one click), Google Colab, or locally via
+`docker run ghcr.io/neurostuff/nsc-runner:latest <id> --n-cores 1`.
+
+Use AWS for this. Nineteen images completed in **about four minutes**. For scale: a run with
+746 images was still going after 73 minutes, having emitted a single log line — so if you
+are working at that size, prefer the Docker route, where you can actually watch progress.
+
+---
+
+## 13. Results
+
+![Results](/tutorial/ibma_22_results.png)
+
+Seven output maps: `z`, `p`, `logp`, their FDR-corrected counterparts, and `dof`.
+
+:::note
+The file list defaults to `dof.nii.gz` with the metadata panel reading `unknown field`.
+That is the degrees-of-freedom map, not your result — click
+`z_corr-FDR_method-indep.nii.gz`.
+:::
+
+At the default threshold (z > 2.58), almost the entire brain survives correction:
+
+![FDR map at default threshold](/tutorial/ibma_23_fdr_default_threshold.png)
+
+**This is not a bug, and it is worth understanding.** Stouffers over 19 unthresholded maps
+is extremely powerful, and unthresholded maps share a great deal of global structure, so the
+null hypothesis of exactly zero is rejected nearly everywhere.
+
+Raising the threshold to z > 6 with soft-thresholding off gives an interpretable figure:
+
+![Final figure](/tutorial/ibma_24_final_figure.png)
+
+### Peaks
+
+18 peaks were returned. The four dominant ones:
+
+| x | y | z | Z | Interpretation |
+|---:|---:|---:|---:|---|
+| 10 | 10 | −6 | **8.84** | right ventral striatum / nucleus accumbens |
+| −10 | 8 | −8 | **8.58** | left ventral striatum / nucleus accumbens |
+| −6 | 54 | −2 | **7.61** | ventromedial prefrontal cortex / medial frontal pole |
+| −6 | 42 | 2 | **7.00** | anterior cingulate / vmPFC |
+
+Remaining peaks are an order of magnitude weaker (Z ≈ 1.8–2.8) and sit in temporal pole,
+lateral OFC and cerebellum. Two small negative peaks appear at (48, −28, −10) and
+(18, −40, 16).
+
+---
+
+## 14. Putting this in the context of the reward literature
+
+The two strongest findings — **bilateral ventral striatum** and **vmPFC/mOFC** — are the two
+most reliably reported nodes of the human reward and valuation system, and recovering them
+is a good sign the pipeline behaved.
+
+Bartra, McGuire & Kable (2013, *NeuroImage*) performed a coordinate-based meta-analysis of
+206 subjective-value experiments and identified a "valuation system" centred on precisely
+these two regions, consistent across reward types and decision stages. Knutson & Greer
+(2008) localised anticipatory reward signals to ventral striatum, while outcome and value
+comparison recruit vmPFC more strongly. The striatal signal also aligns with the dopaminergic
+reward-prediction-error account (Schultz), which is directly relevant here since several
+included studies model prediction error explicitly.
+
+The interesting observation is one of efficiency: **19 maps from 12 studies recovered the
+same core system that required ~200 studies to establish by coordinate-based methods.** That
+is the theoretical case for IBMA in one result — each study contributes whole-brain evidence
+rather than a handful of supra-threshold peaks. It is a demonstration, not a replication, but
+it shows that even with limited data, the approach can yield robust and meaningful results.
+Go forth, try it, and see what you can discover (and report bugs!).
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@@ -0,0 +1,19 @@
+"x","y","z","value","cluster_size","subpeak"
+"10","10","-6","8.837624","1286720","false"
+"-10","8","-8","8.579189","","true"
+"-6","54","-2","7.605328","","true"
+"-6","42","2","6.996188","","true"
+"22","20","-36","2.8496091","296","false"
+"50","10","-30","2.5231237","216","false"
+"52","-18","-32","2.5102413","488","false"
+"56","-28","-30","1.772856","","true"
+"44","20","-38","2.4577785","104","false"
+"-2","4","-32","2.2401738","112","false"
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