How do you design data science competitions that yield real breakthroughs in mental health research? New recommendations in peer-reviewed journal Nature Mental Health from researchers at the Child Mind Institute, plus a free checklist for organizers.

@gkiar.bsky.social
@ariannazuanazzi.bsky.social

Reproducible Brain Charts: An open data resource for mapping brain development and its associations with mental health Major mental disorders are increasingly understood as disorders of brain development. Large and heterogeneous samples are required to define generalizable links between brain development and psychopat...

(1/18) Now out on BioRxiv‼️ Reproducible Brain Charts: An open data resource for mapping brain development and its associations with mental health | doi.org/10.1101/2025...

Funded by National Institute of Mental Health (NIMH)

Why experimental variation in neuroimaging should be embraced - Nature Communications Brain imaging analysis lacks accessible ground-truth approaches, leading to varied results across the field. Embracing analytical variability may allow researchers to enhance the generalizability of f...

📚 Read the full perspective here: www.nature.com/articles/s41...

... For a deeper dive into how embracing variation can advance neuroimaging research.

#SciComm #NeuroscienceResearch

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🧠 Overall, we argue for a paradigm shift in neuroimaging research:

Moving beyond mere reproducibility towards replicability, generalizability, and robustness.

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Variability Capture Checklist NMIND (this Neuroimaging Method Is Not Duplicated) is a collaborative dedicated to accelerating scientific discovery in neuroimaging research that was formed in 2020 as a grassroots initiative- resp...

✅ We’ve made a checklist that we share through the #NMIND website to help researchers incorporate and report variability analysis in their studies.

This simple tool aims to standardize and encourage these practices.
nmind.org/variability-...

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While there are challenges like increased costs and complexity, these can be mitigated through open science practices and shared resources 🤝.

The long-term benefits outweigh the initial hurdles. #CollaborativeScience

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💡 Embracing variation offers several benefits:

It improves transparency, enhances generalizability, reduces the risk of p-hacking, and allows for quantification of result stability.

This leads to more robust scientific findings 🦾. #ReproducibleScience

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🔍 Each of these can significantly impact results, so our perspective is this

As much as possible, we should avoid making these decisions, and instead explore the multiverse-of-methods.

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🛠️ When you’re constructing a study, there are many decisions you make:

What data to use, experts to include, analytic options to explore, tools to use, systems to run on, and perturbations/contrasts to introduce.

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🚨New paper alert!🚨
Does the sheer number of available analysis workflows (and their potential for conflicting results!!) keep you up at night?

Our new 📄🔽 in Nature Comms explores how embracing this variability may actually improve generalizability of results.

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Why experimental variation in neuroimaging should be embraced - Nature Communications Brain imaging analysis lacks accessible ground-truth approaches, leading to varied results across the field. Embracing analytical variability may allow researchers to enhance the generalizability of f...

I figure a fitting start to my life on this 'new' app is to share a recent piece I put out in Nature Communications. @natureportfolio.bsky.social

🔗 www.nature.com/articles/s41...

Checkout my ported 🧵 from the-other-site, below 👇

Seems like a good time to make the move over to #bsky?!

Hi, all! 👋

You'll mostly find me hanging out and seeing what the rest of you are talking about, with some occasional chiming in to talk about transparency and rigor in computational medicine & neuro research...

(... or sports)