Dan Handwerker

I recently saw a review with a (paraphrased) editorial comment "It looks like a reviewer used AI & the review is very nitpicky. Take or ignore the feedback." I'm glad the editors proactively commented. My guess is they thought the AI gave some semi-relevant feedback, but,as policy, this felt ad hoc.

That said, clinically targeted tasks (or a task battery) may require more time & customization than spontaneous fluctuations. That may be clinically less practical, which would make spontaneous fluctuations an inferior measure that's more practical to collect & thus (currently) more useful. 2/2

I think it depends on what clinical questions you are asking. My understanding is most clinical fMRI language localization work uses tasks with language. My assumption is, if you have a sense of a neuron-behavioral target, a task that drives that target will outperform spontaneous fluctuations. 1/2

I've been reading a great deal about current and potential impacts of AI on labor markets. Just published: a 2-page overview of frameworks for thinking about this: www.congress.gov/crs-product/...

I was sure the illusion would break at some length: can't keep grow forever, right?

Wrong. My brain hurts.

Probably ok unless you're arriving Sunday late afternoon / evening. @capitalweather.bsky.social updates often and usually has the most details and explanations on the range of possible storm outcomes.

Imaging the spontaneous flow of thought: Distinct periods of cognition contribute to dynamic functional connectivity during rest Brain functional connectivity (FC) changes have been measured across seconds using fMRI. This is true for both rest and task scenarios. Moreover, it i…

Not ICA, but this is using connectivity maps that are mapped to neurosynth labels. A very similar approach could be applied to ICA. This isn't hard to do, but I'd say all work like this should be interpreted with caution. www.sciencedirect.com/science/arti...

I also like building my own stuff, but if you're building something that you want others to possibly use, I wouldn't trust any AI to understand image header nuances yet. Chris Rorden knows them as well, if not better, than anyone & leads NiiVue, MRIcron, dcm2nii, +.

[AFNI Academy] APQC HTML: Overview YouTube video by AFNI Bootcamp

An example of niivue integration in AFNI's QC html report. youtu.be/hD9zTGMrAzQ?...

Or just use niivue.com with a really robust built-in API.

For context, this was a $50K+ full page ad in the NY Times.

From the neuro community on Reddit: Someone should really hire a neuroscientist at USC to look over their press releases... Explore this post and more from the neuro community

I just don't get why people keep relying on AI for stuff like this when one can get similar quality neuroscience images without AI. www.reddit.com/r/neuro/comm...

True story: it took me years to get a passport in my late 20s - at the time I did not have a single piece of identification with my name spelled correctly. All different misspellings. I eventually found a sympathetic clerk who was like 'I can see what happened here'.

Postdoc position to work on neuroimaging methods with @fmri-today.bsky.social (and me) fim.nimh.nih.gov/positions-av...

Over the years, I have written a few Jupyter/Rmd/Matlab notebooks that attempt to teach some statistical concepts, particularly in neuroimaging. You can find them here: www.mrc-cbu.cam.ac.uk/people/rik.h..., though I will say a bit more about each one in a number of posts over next few days.

papers.ssrn.com/sol3/papers....

I am recruiting a Postdoc to join my lab at UMN. If you or someone you know is interested in studying individual differences in brain and cognitive aging, check out the listing and my website in my bio and apply!

I appreciate RTs to help get the word out as well :)

With some trepidation, I'm putting this out into the world:
gershmanlab.com/textbook.html
It's a textbook called Computational Foundations of Cognitive Neuroscience, which I wrote for my class.

My hope is that this will be a living document, continuously improved as I get feedback.

I'll also stress that I think this is nice data that prompts good discussion. From past experience, Nat Neuro likes publishing & promoting "Are we doing everything wrong?" manuscripts. Even if the text is more nuanced, I wished NN better supported & prepped the authors for the inevitable hyping. 5/5

Also, did you collect or analyze respiratory & cardiac traces, particularly for pCASL? I've found chest movement can create problematic artifacts and higher noise levels in the tag-control subtraction. I'm not sure there's a great correction, but it's worth understanding if there's an issue. 4/5

When originally skimming your paper, my first thoughts were this is plausible, but the deviations from previous assumptions were much larger than I'd expect given previous work in this area. Calculating and sharing empirical values for noise at each analysis step would help contextualize this. 3/5

Do you have empirical noise estimates from your existing data or phantom data that can be used to define more appropriate noise levels for @alexanderhuth.bsky.social's simulation? 2/5

@vavatin.bsky.social . Measures like CBF can be very noisy because they are the subtraction of two noisy time series, and you are also using models that multiply and divide data sources that can scale noise levels. 1/5

Five years ago, I had a very sick child and referrals to several pediatric medical specialists and tests in a building a few blocks from the US Capitol. The kid got a diagnosis. Treatment worked. But for me, January 6, 2020 means memories of rescheduling medical appointments for a very sick child.

I suspected something similar, but thank you for creating a simulation and demonstration. I love papers that try to collect more quantitative information to push our understanding of MRI measurements, but we always need to grapple with measurement noise. The openly shared data do look interesting.

So I made a little simulation where there is no actual discordance, i.e. the underlying values from which the CBF, CBV, BOLD, and T2* are measured all move in lockstep. With zero noise this gives you zero discordant voxels (nice little diagonal line).

NIH Lays Groundwork for Advances in Brain Imaging Data is now emerging from the Connectome 2 - an ultra-high-resolution brain imaging system installed at Mass General Hospital - revealing the power and potential of this technology. The imaging techni...

nihrecord.nih.gov/2026/01/02/n...

FWIW, this is my limited experience with pCASL. We tried a breath holding + paced breathing task and realized that the chest movement from paced breathing had a big effect on the tag-control calculation even without any tagging pulse. fim.nimh.nih.gov/assets/prese...

"...where the classic BOLD assumptions fail" -> "...where the classic assumptions about the relationship between the BOLD contrast and neural activity fail"

One thing I'm keeping in mind for my closer read is that my limited experience with pCASL had results that were VERY sensitive to chest movement. While skimming, I didn't see any mention of chest movement or cardiac traces. 2/2