Gang Chen

Thanks to Zhengchen Cai, @kordinglab.bsky.social, Tom Liu, Josh Faskowitz, @fmri-today.bsky.social, Bharat Biswal, and @afni-pt.bsky.social for fueling this ride and helping turn it into a commentary.

Has resting-state fMRI leaned too much on inductive, data-driven modeling? It can reveal patterns, but also spurious results and weak explanations, the classic "tail wagging the dog." The real challenge is restoring theory-driven, deductive modeling to guide the science.

...but leaning solely on correlation carries hazards: omnipresent noise, over-interpretation, and a canyon separating correlation from true neural mechanisms. And when correlations start masquerading as causes? Welcome to the land of chaos, confusion, and boobytraps.

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?

Well, whenever you take a break from being a task guy… don’t you technically become a rest guy?

Quite interesting! Are we veering into an ontological vs epistemological distinction here? Conceptually, brain activity can be decomposed into task-independent and task-induced components, but practically, the boundary between them is often blurred and difficult to disentangle in real data.

So does it boil down to this: trading one flavor of contamination (task engagement) for another (microsleep roulette)?

Has neuroimaging reached the glorious era where a magical residualization spell can summon the latent resting-state signal from the ashes of task-induced disruption? I’d love to see such an incantation, especially if it comes with a modeling wand.

**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!

Thanks for the kind shoutout! It was a pleasure rambling about statistics, science, and their rocky relationship. Grateful the audience didn’t throw tomatoes or shoes. I'm taking that as strong evidence of tolerance for variability and uncertainty. Am I allowed to skip the p-value for that evidence?

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.

AFNI Bootcamp, Part 2: Jan 29-31, 2025 (Virtual) We are pleased to announce a new AFNI Bootcamp, taking place Jan 29-31, 2025. Registration is free and open to both NIH and non-NIH researchers. The course is aimed at people who have some familiarit...

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?

As an occasional coder, I’d bet LLM-assisted programming could save me from many debugging nightmares and improve code modularity. As for the AI programmer Devin? Sounds intriguing, but I’ll need to save more pennies to experience its full wizardry.

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.