I had the privilege of seeking help from Dr. Fox intermittently between 2008 and 2015 via r-help and email regarding multivariate linear modeling and the R package 'car'. His insight, generosity, and willingness to help the community are greatly missed.

Thanks for the great question! Text can only go so far. Hopefully the attached image does a better job than my words. The key is integrating spatial relatedness among neighboring voxels directly into the hierarchy (the green part), which is what sets it apart from the usual mass-univariate approach.

Memory has a strange way of letting us live multiple lives at once. Thank you for sharing this. Wishing you steadiness in the present.

Thanks! A takeaway from our earlier work is that the role of trials isn’t quite what people assume. If the goal is group-level inference, trial count still matters, but it can be traded off with subject number. It’s the combination of both that matters, not either one alone.
doi.org/10.1016/j.ne...

If your ultimate inference target is the group level, then what matters is the joint contribution of trial number per condition and participant sample size, not either one in isolation. We explored this point in detail here:
www.sciencedirect.com/science/arti...

Next up: bringing this to everyday analysis.

AFNI’s new program SIMBA is in development and aims to make full whole-brain voxel-level hierarchical modeling accessible to users, hopefully within the next few months.

And the next step? Full voxel-level modeling.

Recent numerical advances cracked the scalability barrier. Voxel-level hierarchical modeling is now feasible, revealing just how punishing traditional multiple-comparison adjustments really are.
arxiv.org/abs/2511.12825

Whole-brain hierarchical modeling used to feel impossible under the Bayesian framework. It’s become within reach.

@mandymejia.bsky.social’s group demonstrated computational feasibility on the cortical surface and showed major gains in inferential efficiency.
www.sciencedirect.com/science/arti...

What if multiple comparisons weren’t an afterthought?

Hierarchical modeling at the group level bakes the adjustment into the model. Even early demos, despite brutal computational demands, already showed clear gains when applied to a set of regions.
link.springer.com/content/pdf/...

However, splitting the RSA computation into two steps may lead to information loss. A single-step approach using regression or hierarchical modeling appears to improve precision, reliability and interpretability in estimating representational similarity. arxiv.org/abs/2511.00395

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.

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.