Know someone interested in fMRI methods, software development, statistics, machine learning, and neuroimaging? Please share and encourage them to apply.

Paul Taylor@afni-pt.bsky.social · 2mo ago

Job ad (2/2): data scientist in the AFNI group (SSCC, NIMH, NIH, USA). FMRI/MRI methods dev, software design, statistics, analysis, data viz, quality control, machine learning and more! Please see: discuss.afni.nimh.nih.gov/t/job-ad-dat... I am happy to chat at OHBM+Brainhack. Please share!

If you know someone who speaks fluent fMRI, statistics, coding, and curiosity, encourage them to apply.

Paul Taylor@afni-pt.bsky.social · 2mo ago

Job ad (1/2): Postdoc fellowship in the AFNI group (SSCC, NIMH, NIH, USA). FMRI/MRI methods dev, software design, statistics, analysis, data viz, quality control, machine learning and more! Please see: discuss.afni.nimh.nih.gov/t/job-ad-pos... I am happy to chat at OHBM+Brainhack. Please share!

PhD students: Do you have experience with structural or fMRI data analysis? Are you interested in data harmonization, open science, and cognitive aging? 🧠📊 💻 If yes, this post-doc position in my lab is perfect for you! Please share with your networks! jobs-ca.silkroad.com/Baycrest/Car...

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

Want to learn about FMRI visualization, processing and group analysis? Join us for the next AFNI Bootcamp (Jan. 27-29, 2026) for a fun few days of theory and interactive practicals. Details+registration for this virtual course: afni.nimh.nih.gov/bootcamp

Is the “standard workflow” holding back fMRI analysis? Mass-univariate analysis is still the bread-and-butter: intuitive, fast… and chronically overfitted. Add harsh multiple-comparison penalties, and we patch the workflow with statistical band-aids. No wonder the stringency debates never die.

New AFNI Academy playlist! This tutorial presents afni_proc.py's quality control HTML for single subject FMRI. The APQC HTML has systematic views of data and useful derived quantities. Users can instantly rate, comment and query the fully processed subject data. www.youtube.com/watch?v=hD9z...

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...

bioRxiv Neuroscience@biorxiv-neursci.bsky.social · 11mo ago

Resting-State fMRI and the Risk of Overinterpretation: Noise, Mechanisms, and a Missing Rosetta Stone https://www.biorxiv.org/content/10.1101/2025.09.16.676611v1

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...

Hyperbolic trade-off: The importance of balancing trial and subject sample sizes in neuroimaging

Here we investigate the crucial role of trials in task-based neuroimaging from the perspectives of statistical efficiency and condition-level generali…

sciencedirect.com

Peter Sokol-Hessner@p1sh.bsky.social · last yr.

@gangchen6.bsky.social making a strong case that statistical power is related to the number of participants, but ALSO to the number of datapoints *per person* (i.e. number of trials). An excellent point we often have to argue for in my lab's own work!! Couldn't agree more. #SANS2025

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.

Paul Taylor@afni-pt.bsky.social · last yr.

The result of a large (42 authors!) collaboration: "Go Figure: Transparency in neuroscience images preserves context and clarifies interpretation" arxiv.org/abs/2504.07824 TL;DR: The FMRI world can (and should) improve results interpretation and reproducibility *today*, via transparent thresholding.

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.

Shachar Hochman@hochmanshachar.bsky.social · last yr.

1/6 Hello Bluesky! 👋 Excited to join this community and share my new blog. First post: Using Bayesian hierarchical models to rescue "unreliable" cognitive tasks, with the dot-probe task as my case study. cogpsychreserve.netlify.app/posts/dotpro...

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...

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...

discuss.afni.nimh.nih.gov

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?

Manlio De Domenico@manlius.bsky.social · 2y ago

Seriously, neuroscientists here: why do you call "brain connectivity" what it's just correlations of some type? Using the word connectivity is misleading, since it relates to structure and no way this is correct w/o a robust assessment. You can observe this just by changing params in dynamics.

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.

Aperture Neuro@apertureohbm.bsky.social · 2y ago

Chen et al. present an exploration of causal inference principles to guide experimental design, model-building, and result interpretation: doi.org/10.52294/001... @gangchen6.bsky.social @afni-pt.bsky.social @fmri-today.bsky.social