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tooluniverse-neurosynth-meta-analysis

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).

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# 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`).