Measuring and reducing the carbon footprint of fMRI preprocessing in fMRIPrep Computing needed for data processing has a carbon footprint. We estimated the carbon emissions arising from eight variants of functional magnetic resonance imaging (fMRI) preprocessing pipeline fMRIP...

Specifically cited:

Souter, Nicholas E., Nikhil Bhagwat, Chris Racey, et al. 2024. ‘Measuring and Reducing the Carbon Footprint of fMRI Preprocessing in fMRIPrep’. Human Brain Mapping 45 (12): e70003. doi.org/10.1002/hbm.....

Great to hear from @loiclnlg.bsky.social speaking for the Oxford Healthcare Data Science CDT on Tools for Environmentally Sustainable Research using AI and High Performance -- included a shoutout to great work of @ohbmenvironment.bsky.social on footprint of neuroimaging processing tools

Some Simple Economics of AGI For millennia, human cognition was the primary engine of progress on Earth. As AI decouples cognition from biology, the marginal cost of measurable execution falls to zero, absorbing any labor captura...

Some Simple Economics of AGI: "because intelligence is now an abundant commodity, the nature of human work must adapt—toward roles as intent directors, verifiers and underwriters, or creators in the non-measurable economy where value is anchored in human connection" arxiv.org/abs/2602.20946

Register now for the in-person FSL Course 2026 in Bordeaux, France!

🗓️ June 22-26, 2026
🧠 Covering: lectures & hands-on practicals on structural, functional, diffusion and resting state brain image analysis
🔗 open.oxcin.ox.ac.uk/pages/fslcou...

Calling out to other #DataLad users: At @oxcin.bsky.social we're thinking of replacing an XNAT instance that transfers data from our scanner and archives data with a service built on top of DataLad. Any thoughts or experiences people can share?

Come to the Oxford Big Data Institute and join the @smartbiomed.bsky.social project to work on complex developmental longitudinal data including brain imaging! 3 year postdoc supervised by myself, Naomi Wray and Wesley Thompson

my.corehr.com/pls/uoxrecru...

Organised w/ Moo Chung, Baptiste Couvy-Duchesne @baptistecd.bsky.social, Jian Kang, Hernando Ombao, JB Poline, and Anqi Qiu

NeuroStats2026

Save the date 👆🧵 Neuroimaging Statistics Workshop, 12-13 June, @ohbmofficial.bsky.social 2026 satellite meeting, w/ @ohbmossig.bsky.social's BrainHack. Keith Worsley lecture by Sir. John Aston, + great work at Neuro/Stats/ML/AI interface. Registration opens soon! sites.google.com/view/nsw2026

Organised w/ Moo Chung, Baptiste Couvy-Duchesne @baptistecd.bsky.social, Jian Kang, Hernando Ombao, JB Poline, and Anqi Qiu

Next Stops for OHBM: Toronto, Canada in 2027 and Yokohama, Japan in 2028 — OHBM Communications We are thrilled to announce the locations for our next two upcoming Annual Meetings! The 2027 OHBM Annual Meeting will be held in Toronto, Canada, followed by the 2028 OHBM Annual Meeting in Yokohama,...

Future @ohbmofficial.bsky.social meeting dates just annouced:

* 2027 - Toronto, Canada
* 2028 - Yokohama, Japan

www.ohbm-com.com/blog/futurem...

a man is standing in a kitchen with his hands on his chest and asking for help . Alt: Tom Cruise is standing in a kitchen with his hands on his chest and asking for help, saying "Help me, Help you"

A systematic meta-analysis is hard work—and curation is often the hardest part. Y'all know this better than anyone, and we want to make it easier.

What’s one specific piece of information you consistently had to find or extract from every paper?
Tell us about it: tally.so/r/QKVbQG

#neuroscience

PROVIDENT: Best practice for PROspectiVe Imaging Research DEsign and coNducT. From @nihr.bsky.social Statistics's Imaging Studies section; harnesses many years of experience (& horror stories) integrating imaging into clinical trials.

Biscombe et al. doi.org/10.1016/j.ra...
doi.org/10.1016/j.ra...

Statistical Machine Learning post doc positions, w/ Oxford BDI-Novartis Collaboration for AI in Medicine, working on building causal predictive models for MS using a massive clinical trials MRI dataset. Join us on this unique collaboration!
my.corehr.com/pls/uoxrecru...
my.corehr.com/pls/uoxrecru...

Delighted to see that @ohbmossig.bsky.social has confirmed dates for the 2026 @ohbmofficial.bsky.social brain hack!
ohbm.github.io/hackathon2026/ 11-13 June Thur-Sat, in the usual 3 days before the meeting, at the Campus Victoire, University of Bordeaux

Looking forward to it!

Wrong Tom Nichols

AI in Medicine 2026 The most comprehensive review conference in internal medicine. 73 lectures, 20.5 CME credits. Organized by McMaster University, the home of EBM.

AiMed2026 - 7-9 May in Kraków - a dual-track program to bring together clinical and technical experts at the intersection of AI and medicine. Co-chaired by my brilliant @oxpop.bsky.social colleague @bwpapiez.bsky.social

piebm.org/campaigns/ai...

Excited to share our manuscript about BrainEffeX, a tool for exploring fMRI effect sizes. Includes why we made it, how to use it + contribute, and how we made it.

@sneuroble.bsky.social @psychonetrics.bsky.social
@alexkfischbach.bsky.social
@nichols.bsky.social
@dscheinost.bsky.social & MINDS Lab

Ten Simple Rules for AI-Assisted Coding in Science While AI coding tools have demonstrated potential to accelerate software development, their use in scientific computing raises critical questions about code quality and scientific validity. In this pa...

Ten Simple Rules for AI-Assisted Coding in Science arxiv.org/abs/2510.22254 - our latest, led by @ericwbridgeford.bsky.social

Alcohol use and risk of dementia in diverse populations: evidence from cohort, case–control and Mendelian randomisation approaches Objectives To investigate the relationship between alcohol consumption and dementia. Design Prospective cohort and case–control analyses combined with linear and non-linear Mendelian randomisation. ...

Happy to be a part of Anya Topiwala's new work, using Mendelian Randomisation with @ukbiobank.bsky.social & Million Veteran Program, finding that even modest alcohol consumption increases risk of dementia. ebm.bmj.com/content/earl...

𝐂𝐚𝐥𝐥 𝐟𝐨𝐫 𝐏𝐚𝐩𝐞𝐫𝐬 – 𝐒𝐩𝐚𝐭𝐢𝐚𝐥 𝐒𝐭𝐚𝐭𝐢𝐬𝐭𝐢𝐜𝐬 𝐢𝐧 𝐈𝐦𝐚𝐠𝐢𝐧𝐠

Special issue of 𝘚𝘵𝘢𝘵𝘪𝘴𝘵𝘪𝘤𝘴 𝘢𝘯𝘥 𝘋𝘢𝘵𝘢 𝘚𝘤𝘪𝘦𝘯𝘤𝘦 𝘪𝘯 𝘐𝘮𝘢𝘨𝘪𝘯𝘨, supported by @amstatnews.bsky.social ASA Sections on Statistics and the Environment (ENVR) and Statistics in Epidemiology (SIE)

Submission details:
tinyurl.com/47enwhwz

AI-driven reclassification of multiple sclerosis progression - Nature Medicine Using large cohorts from published clinical trials involving more than 8,000 patients with multiple sclerosis, a probabilistic machine learning model reconstructs the transition probabilities from data-derived diseases statuses, showing patterns that suggest how progression to severe stages occur and potential inversion of the process.

All made possible by an innovative Factor Analysis + Hidden Markov Model. See paper & supp for details

Ganjgahi et al. ‘AI-Driven Reclassification of Multiple Sclerosis Progression’. Nature Medicine, Nature Publishing Group, 20 August 2025, 1–11. doi.org/10.1038/s415....

Well known diagnostic labels (RRMS, SPMS, PPMS) do not explain these states, showing their limitations. This is an important contribution to developing more fully data-driven perspectives on MS disease heterogeneity.

This is a key output of the Oxford Big Data Institute-Novartis Collaboration for AI in Medicine. The paper represents a huge, collaborative effort, with replication done on data from another pharma company (Roche) and another large dataset (MS PATHS).

www.bdi.ox.ac.uk/research/oxf...

Epic work led by Habib Ganjgahi is out in Nature Medicine... data-driven clustering of MS finds 3 distinct states, early/mild, active, advanced, w/ transition to the advanced state only occuring through the active state... replicated twice!

doi.org/10.1038/s415...

This is a gem… really enjoyed reading about stats theory legend Gauss trudging across the countryside surveying.

Really nice study, and extends some of the ideas developed in this paper pubmed.ncbi.nlm.nih.gov/32673043/

For me, this work is a classic @ohbmofficial.bsky.social story: In 2023 I wasn't working with @bttyeo.bsky.social but I overheard him at his poster pointing to some accuracy curves saying "I don't why they have this particular shape". That kicked off the collab that led to these results.

1/11 Excited to share our @Naturestudy led by @leonooi.bsky.social @csabaorban.bsky.social @shaoshiz.bsky.social

AI performance is known to scale with logarithm of sample size (Kaplan 2020), but in many domains, sample size can be # participants or # measurements...

doi.org/10.1038/s415...

Wasn't aware of tinyplot... an overlay package to make base R graphics easier to use. While I use ggplot2 for some tasks there are many situations where I prefer base R plotting, and this tool will be super helpful in those seetings.

Artificial intelligence for electronic health records For our next talk, in Machine Learning, we will be hearing from Dr Shishir Rao, Senior Research Scientist, Deep Medicine research group, NDWRH, University of Oxford. We’re delighted to host Shishir in...

Looking forward to presenting our team's research on AI for EHR at The Big Data Institute at Oxford on July 10th at 2 pm! Thanks to Sumeeta and Prof @nichols.bsky.social for organising.

Link: talks.ox.ac.uk/talks/id/590...