🧠Neuroimaging peeps🧠 our field is blessed with beautiful visualizations. Our preprint provides 3 tools to help move toward code-based & replicable visualization: 📗practical guide (why should I?) 📦package selector (which one?) 🏗️code template generator (how do I start?) 👇🧵

Download our updated list of funding opportunities for early-career researchers. It's free and exhaustive: 548 funding opportunities in all fields of research. Download it here: research.jhu.edu/rdt/funding-op…

Excited to share our review on the use of network models for cross-species translational neuroscience! nature.com/articles/s4158… led by Kate Brynildsen (gif below from mathieu ouellet)

Ever wonder how people's brains differ from one another? ➡️ Individual differences in 🧠 organization commonly come in 2 forms: shifts in borders & 'ectopic intrusions' far from expected locations Now out in Nature Neuroscience, led by Ally Dworetsky rdcu.be/dGeIp 🧵soon!

Benchmarking methods for mapping functional connectivity in the brain | doi.org/10.1101/2024.0… What is the best FC metric? Led by Zhen-Qi Liu @[email protected] avec Andrea Luppi Justine Hansen Ye Ella Tian Andrew Zalesky Thomas Yeo Ben Fulcher ⤵️

Really important thread. When it comes right down to it, FC (and fMRI in general) is just really noisy. Interpretations on noisy data will vary depending on the FC metric you use, and would presumably converge if we had super duper tSNR.

(1/13) We are beyond delighted to announce the public release of the data for the Reproducible Brain Charts (#RBC) effort – a 100% open data resource for study of the developing brain + psychiatry, funded by National Institute of Mental Health (NIMH) reprobrainchart.github.io

Paul Hundred, GED Matthias Kirschner Neda Jahanshad ENIGMA Consortium Vince Calhoun @vcalhoun.bsky.social SoCAT Lab I think this is a very important question. Subtypes can be defined clinically or based on neuroimaging, or even based on genetics, or by using joint clustering methods that try to detect clinical-imaging subtypes that lock onto each other (e.g., with canonical covariate analysis

This is the free-energy formulation of the human psyche. 🧵1/11

New in Nature Neuroscience, we present DPAD, a deep learning method for dynamical modeling of neural-behavioral data & dissociating behaviorally relevant dynamics. It enables diverse neurotech & neuroscience use-cases. 👏Omid Sani 💻github.com/ShanechiLab/DP… 📜nature.com/articles/s4159… 🧵⬇️