Offering to share: how to put heads together in autism neuroimaging.

Belmonte et al. (2008) wrote a how-to guide for autism brain-imaging labs. They list the exact files, codes, and checks needed so any team can pool their scans.

The paper gives a sample folder tree and naming rules. It shows where to put raw scans, cleaned scans, and participant logs.

The authors did not run an experiment. Instead they offer a ready-to-use protocol that balances common rules with room for each lab’s own tweaks.

They warn that without shared formats, future mega-studies will drown in messy data.

Schultz (2008), printed the same year, extends the call. T adds genetics and urges even bigger samples to find biomarkers that predict autism before behavior shifts.

Klin (2025) picks up the same sharing spirit but moves from brain scans to eye-tracking. The later paper says FDA-cleared eye trackers can now diagnose autism before age three, as long as clinics adopt new delivery models.

Elcoro et al. (2023) echoes the teamwork theme inside behavior analysis. They push citation counts and co-authorship maps to prove — and improve — cross-lab work.

If you ever share data — or hope to — lift the folder tree straight from Belmonte et al. (2008). Rename your files today so the next team can drop them into a mega-analysis without a cleanup nightmare. Clean data today means faster biomarkers tomorrow.