Check it out 👇👇👇

www.frontiersin.org/journals/psy...

👉 The key takeaway: these topology-based features seem to capture useful structure in directed brain networks and can complement more standard approaches for connectome-based classification

- Feed these fingerprints into a neural network to help distinguish ASD vs typically developing participants tested on a subset of 871 subjects (Autism Brain Imaging Dataset)

In this work, we:
- Build directed brain networks from resting-state fMRI (using a simple time-lagged correlation)
- Summarise each network with a set of topology-based fingerprints (called Betti curves) that capture global patterns like how the network connects and forms loops

Autism spectrum disorder is linked to differences in how brain regions communicate. Many fMRI studies build “brain connectivity maps” using correlations between regions, but those maps are usually treated as undirected, even though real brain interactions often have a direction

Thanks for sharing!

And many more! Check it out 👇

📝 Preprint: lnkd.in/dUP-Vn4p
💾 Dataset: lnkd.in/dzzMcEvt
💻 Code: lnkd.in/d4Vy9Ngw

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Motor activation maps!

Head movements were under good control!

👉 What’s inside:
• Full movie-watching inside the scanner (“Back to the Future” 🏎️💨)
• Eye-tracking during movie-watching 👀
• Tasks to create individual maps of somatomotor, auditory and visual cortices (somatotopy, tonotopy, and retinotopy) 🧠
• Pulse oximetry 📈

🙋