very much looking forward to try this.

And to match their spirit of openness, we’ve released the code, containers, and data. Anyone can rerun the entire analysis.

Co-lead authors: @clarken.bsky.social and @surchs.bsky.social
Paper: doi.org/10.1093/giga...
Github: github.com/SIMEXP/autis...
Zenodo archive: doi.org/10.5281/zeno...

End/🧵

The signature was discovered in a balanced cohort of ~1,000 individuals and replicated in an independent sample (thanks to the ABIDE I & II wonderful participants and the researchers who shared their data 💜💜💜). 5/🧵

A positive result means someone is about seven times more likely to actually have an autism diagnosis. This rivals the best imaging markers, while still being found in about 1 in 200 people in the general population. 4/🧵

We turned the problem on its head. Instead of trying to classify everyone, we built a brain signature that only makes predictions when it’s confident. 3/🧵

Real life isn’t balanced. Autism affects about 1% of the population. In that setting, a biomarker with 80% balanced accuracy would catch one true case for every twenty false alarms. 2/🧵

I find AI coding most useful to comment / suggest on what I do. Your disastrous experience with code generation matches mine. But as a side kick it's incredibly positive IMO.