Representational Similarity Analysis (RSA) is a popular method in cognitive neuroscience for comparing representational patterns across conditions. It follows a "correlation-of-correlations" logic: compute (dis)similarities within each representational space, then correlate them across spaces.

Data only shows associations. Turning those into claims about mechanism or causation? That requires a Rosetta Stone of prior knowledge + theory. Resting-state fMRI is purely observational; correlation is its currency. From this, plenty of "theoretical toys" about brain function can be built...

Blind data cleaning, automated pipelines and dichotomized results may give the illusion of standardization, rigor and reproducibility, but they risk turning science into ritual over inquiry. When mechanisms are obscure, don’t pretend they’re fixed; perhaps embrace variability and think creatively?

**FMRI/neuroimaging folks**

Quick reminder @ the next AFNI Bootcamp: May 28-30, 2025. Learn through interactive data analysis!

Day 1-2: data viz, single subject analysis and QC.
Day 3: statistics, results reporting and group analysis.

Details, registration and schedule:
afni.nimh.nih.gov/bootcamp

We are pleased to announce the next AFNI Bootcamp, May 28-30, 2025.

First 2 days: data visualization, single subject analysis and QC. 3rd day: statistics, results reporting and group analysis.

Please see here for details, registration link and preliminary schedule:
afni.nimh.nih.gov/bootcamp

Science doesn’t grow in a vacuum; it thrives on shared ideas and fresh perspectives. Thanks to #sans2025 for building bridges and connecting the dots, and to @elisabaek.bsky.social & @jfguassimoreira.bsky.social for creating the opportunity!

For those who think more data just means more headcount, here’s a quirky twist: the number of data points per individual actually matters--a lot. If you're into a bit of rigor, this article highlights a factor that’s often overlooked. Thanks for the shoutout!
www.sciencedirect.com/science/arti...

The mind craves binaries: good or bad, true or false, on or off. It’s tidy. It’s comforting. But the world rarely plays along. Reality tends to unfold in gradients, not in absolutes. And so does statistical evidence. Data analysis doesn’t speak in black and white, but in shades of uncertainty.

Do you like genes and estimating heritability? Then @gangchen6.bsky.social and D. Moraczewski have important news for you.

Conventional estimation methods ignore measurement error, leading to a bias. Don't worry: hierarchical modeling to the rescue!

www.frontiersin.org/journals/gen...

The p-value arms race has reached a new milestone -- 10⁻²⁶². At this quantum level of super precision, statistical modeling in quantitative genetics is on the verge of breaking the uncertainty principle.

Another great example of modeling philosophy: Respect the data-generating process as much as the theoretical constructs when building models.

Research is the ultimate adventure--riddled with unexpected hurdles and moments of frustration. Yet, it's the rare light at the end of the tunnel and the thrill of surprises that illuminate the path and propel the journey forward.

Maybe slightly odd timing, but we'd like to announce:

A new AFNI Bootcamp for FMRI/MRI, Jan 29-31, 2025. This part will focus on group analysis, statistics, surface analyses, results reporting and more.

This event will be virtual. Please see here:
discuss.afni.nimh.nih.gov/t/afni-bootc...

Let’s flip the script on calling a spade a spade:

1) Does calling a correlation a correlation hurt its feelings or make it less accurate?

2) Does calling a correlation a correlation mislead the public or cause mass confusion?

Programming: where failure lurks around every corner, and debugging feels like trudging through a minefield. Yet, there's magic in the madness—when the code finally works and offers a generic solution, it's like wielding a Swiss Army knife with a triumphant smile.

Does anyone know of an open or publicly available by request #EEG or #MEG dataset suitable for studying the interaction between circadian rhythms and changes in the signal?

Science is about uncovering how causes create effects. Covariate selection may seem like a small step in model building -- but it can spark big chaos if mishandled. Glad to share the lesson we learned: don’t let your model wag the science; let science lead the way in model building.