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name: tooluniverse-neurosynth-meta-analysis description: Meta-analytic decoding of human fMRI coordinates using the Neurosynth coordinate database (14,371 studies, ~3,228 cognitive/neuroscience terms) — given an MNI brain coordinate, statistically identify which cognitive terms are over-represented in studies reporting activation there ("reverse inference"); given a term, find the studies and coordinates most associated with it ("forward" lookup). Use when someone asks "what does this brain coordinate/region do", "decode this fMRI activation peak", "what terms are associated with [MNI coordinate]", "find studies about [cognitive term] and their activation coordinates", or "reverse-infer cognitive function from a brain location". This is real local analysis on a downloaded, cached coordinate database — not a live API — following ToolUniverse's honesty-contract pattern (real computed statistics, no fabricated term associations). disable-model-invocation: true

Neurosynth Meta-Analytic Coordinate Decoding

Statistically decode human fMRI brain coordinates against ~14,000 published neuroimaging studies, or look up which studies/coordinates are associated with a cognitive term — using the real, freely-downloadable Neurosynth coordinate database (no API key, no login).

Honesty contract (read first)

This skill downloads a real coordinate database and runs real statistics on it. It must never fabricate a term association, a study result, or a p-value.

1. Preflight before anything. Run scripts/download_data.py first. If any file fails to download, STOP and report the failure — do not proceed with partial data or describe hypothetical decoding results.
2. Never guess a term. search_by_term.py requires an exact (case-sensitive) vocabulary match. If the term isn't found, it returns suggestions (substring matches, or close spellings when there are none, e.g. hipocampus → hippocampus) — use those, don't invent a plausible-sounding term.
3. Report the multiple-testing caveat every time. decode_coordinate.py tests ~3,228 terms simultaneously per query. Uncorrected p-values will produce false positives at this scale — always surface the FDR-corrected fdr_q_value, not just the raw p_value, when characterizing how confident a result is.
4. This is a simplified reverse-inference test, not a full meta-analysis pipeline. It does binary term-presence comparison (near-coordinate studies vs. rest of corpus) via a two-proportion z-test — a real, defensible statistical test, but not the same as running NiMARE's full activation-likelihood-estimation (ALE) or multilevel kernel density analysis (MKDA) methods. Say so if precision matters (e.g., the user is drafting something for publication).
5. Never overwrite the cached data files — download_data.py writes to a dedicated cache directory and is idempotent (skips re-download if already cached, unless --force).

When to Use This Skill

Apply when users:

• Give an MNI coordinate (or a named landmark you can translate to one, e.g. Broca's area ≈ (-50, 20, 10)) and ask what cognitive function it's associated with
• Ask to "decode" an fMRI activation peak from their own study against the published literature
• Want to know which studies report activation for a specific cognitive term, or where those studies' peaks are located
• Ask for a quick meta-analytic sanity check before writing a discussion section ("is this activation consistent with what's typically reported at this location?")

NOT for (route elsewhere):

• Single-neuron morphology, raw neuroimaging dataset discovery, computational models, or statistical brain-map hosting → tooluniverse-neuroscience (NeuroMorpho/OpenNeuro/ModelDB/NeuroVault sections — genuinely different resource types, see that skill's comparison table)
• A full, publication-grade coordinate-based meta-analysis (ALE/MKDA with proper cluster-level FWE correction) → this skill's method is a real but simplified proxy; point the user to NiMARE for the full pipeline
• Anything requiring raw fMRI voxel data, not just reported peak coordinates — this database only has published peak coordinates and term weights, never raw scans

Setup

python3 scripts/download_data.py

Downloads ~9MB total (coordinates, study metadata, term vocabulary, term×study weight matrix) from the public neurosynth/neurosynth-data GitHub repo — no authentication, no rate limiting observed — and caches it under ~/.cache/tooluniverse/neurosynth/ (override with TOOLUNIVERSE_CACHE_DIR). Re-running is a no-op unless --force is passed. Takes a few seconds.

Workflow

Decode a coordinate ("what does this location do?")

python3 scripts/decode_coordinate.py <x> <y> <z> --radius 8 --top 15

Finds every study with a reported peak within --radius mm of the query point, then ranks all ~3,228 terms by how much more often they appear in that near-coordinate study set than in the rest of the corpus (two-proportion z-test). Returns both raw p_value and BH-corrected fdr_q_value per term — always cite the q-value when characterizing confidence. Fails cleanly (not silently) if fewer than --min-studies (default 5) studies are found nearby — widen the radius rather than trusting an underpowered result.

Verified example: Broca's area, (-50, 20, 10), radius 8mm → top terms were language, inferior frontal, semantic, sentence, comprehension, linguistic, verb — all with q≈0, matching well-established neuroanatomy. Use this as a sanity-check case if something looks wrong after a code change.

More landmarks (radius 8mm; verified 2026-09-20, all q < 1e-5):

Region	MNI (x y z)	Top terms (z-score)
Fusiform face area	40 -52 -20	face 25.1, fusiform 23.5, faces 21.3, ffa 20.1
Left primary motor cortex (hand)	-38 -24 58	motor 21.2, finger 20.4, primary motor 19.8, hand 18.4
Left hippocampus	-24 -18 -18	hippocampus 24.1, hippocampal 18.8, episodic 14.5, memory 13.5
Left amygdala	-22 -4 -18	amygdala 47.3, emotional 23.2, emotion 20.5, faces 18.1
Primary visual cortex (V1)	0 -90 0	primary visual 7.9, visual 7.7, v1 7.3 (fewer studies, 454, so a weaker but still correct signal)

Look up a term ("what studies/coordinates are about X?")

python3 scripts/search_by_term.py "working memory" --top 10 --with-coordinates

Returns the top-N studies with the highest tfidf weight for that exact term (title, authors, year, journal, weight), and optionally their reported MNI coordinates. If the term isn't an exact vocabulary match, returns did_you_mean suggestions (substring matches, else close spellings) instead of guessing. This is a simple ranking, not a statistical test — don't present a high tfidf weight as equivalent to the coordinate-decoding z-score/q-value above.

Reference

• references/method.md — the statistical method in more detail, data provenance, and known limitations
• Neurosynth: https://neurosynth.org
• Data source: https://github.com/neurosynth/neurosynth-data
• For a full ALE/MKDA meta-analysis pipeline instead of this simplified proxy: NiMARE (https://nimare.readthedocs.io)