Yep. Don’t check all the time, but I’m there

Thanks. Anytime!

🚨 New preprint: Objective Quality Assessment for Precision fMRI

Precision functional mapping (PFM) enables individual-level brain network studies — but demands more, and better, data.

We introduce an objective framework to determine when a dataset truly supports interpretable, replicable PFM.

Huge thanks to our participants and my incredible co-authors. I'm excited to continue building on this precision neuroimaging work as I set up my new lab at UMN Psychology.

Cluster scanning is highly practical. Collecting 8 rapid scans takes less than 10 minutes, making it an easy addition to existing protocols. This enhanced precision could be a game-changer for development and aging studies, as well as getting faster signals in clinical trials

We observed substantial, unexpected heterogeneity in brain aging over a single year. We captured atypical rapid global decline (in an individual who later developed MCI), asymmetric changes, and even remarkable brain maintenance. Check out the paper to see these case studies.

The findings: We reliably detected expected differences in brain aging rates between younger and older adults, and those with cognitive impairment. That could not have been detected using standard approaches. We also discovered unexpected results...

We put this to the test by scanning individuals six times in just one year. We collected a total of 48 rapid scans per person, which allowed us to reliably capture longitudinal change fully within individuals.

Our solution? "Cluster scanning". By densely repeating rapid, compressed-sensing structural scans (just 1-min each!) at each time point, we reduced measurement error by nearly 3 times compared to a standard scan.

For more details, see our previous work - doi.org/10.1162/imag...

Standard longitudinal MRI struggles to track individual trajectories of brain change over short periods because measurement error is often larger than the expected amount of true change. E.g., for the hippocampus, annual change is ~1-5%, but measurement error is 2-5%.

Are you interested in detecting brain changes in individuals with higher precision over shorter intervals?

Check out our new paper in Nature Communications. With Randy Buckner, @jingnandu.bsky.social, and others.

Link - doi.org/10.1038/s414...

DunedinPACNI: A Next Generation Brain Clock to Assess Brain Aging YouTube video by Integrative Neuroscience & Technology Lab

We had the privilege of hosting @maxwellelliott.bsky.social for the Cognitive and Brain Sciences Seminar yesterday. Max is currently an Assistant Professor at the University of Minnesota, and we worked together as Postdocs at Harvard.

www.youtube.com/watch?v=3_D7...

I am thrilled to share that I’ll be joining the University of Notre Dame (@notredame.bsky.social) as an Assistant Professor of Psychology this July!☘️🧠 Please reach out if you're interested in joining my lab! More details to follow soon.

Is schizophrenia associated with accelerated aging?

In our new paper, we find evidence for consistently faster aging in schizophrenia, though not among unaffected siblings nor clinical high-risk youth

doi.org/10.1017/S003...

Here is the job app link - t.co/3VlRTRdrvw

Learn more about my work here - elliottlab.psych.umn.edu

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 :)

Our new paper is out now in Neuron! 🎉 With @vaibhavtripathi.bsky.social @maxwellelliott.bsky.social Joanna Ladopoulou, Wendy Sun, Mark Eldaief, and Randy Buckner

Paper link: www.sciencedirect.com/science/arti...

This is figure 2, which shows DunedinPACNI model validation and feature importance.

A paper in Nature Aging describes the Dunedin Pace of Aging Calculated from #NeuroImaging measure, an approach that uses a single brain image to measure how fast a person is aging and can help predict mortality or the risk of developing chronic disease. go.nature.com/3GADPij #medsky 🧪

GitHub - etw11/DunedinPACNI: Code to estimate DunedinPACNI scores from FreeSurfer parcellations of brain MRI data. Code to estimate DunedinPACNI scores from FreeSurfer parcellations of brain MRI data. - etw11/DunedinPACNI

Most importantly, this is just the beginning! To estimate DunedinPACNI in your data, all you need is a single T1-weighted structural MRI and our publicly available tool. Please use it and share it widely! github.com/etw11/Dunedi...

Across several datasets, faster DunedinPACNI was associated with poor cognition, physical frailty, poor health, and worse cognitive status. Furthermore, faster DunedinPACNI predicted faster hippocampal atrophy, earlier onset of chronic disease, dementia, and mortality.

Building on the epigenetic clock and brain-age literatures, we built a "next-generation" brain aging clock by predicting an individual's rate of longitudinal biological aging from a single brain scan.

We call our measure "DunedinPACNI"

DunedinPACNI estimates the longitudinal Pace of Aging from a single brain image to track health and disease - Nature Aging Differences in the Pace of Aging are important for many health outcomes but difficult to measure. Here the authors describe the Dunedin Pace of Aging Calculated from NeuroImaging measure, an approach ...

Here's the link - doi.org/10.1038/s435...

This work was an amazing collaboration with my co-first author @ethanwhitman777.bsky.social, as well as a great team at Duke and Otago Annchen Knodt, Av Caspi, Terrie Moffitt, and Ahmad Hariri

Do you want to estimate brain aging from a single MRI scan?

Check out our latest work in Nature Aging

"DunedinPACNI estimates the longitudinal Pace of Aging from a single brain image to track health and disease"

DunedinPACNI estimates the longitudinal Pace of Aging from a single brain image to track health and disease - Nature Aging Differences in the Pace of Aging are important for many health outcomes but difficult to measure. Here the authors describe the Dunedin Pace of Aging Calculated from NeuroImaging measure, an approach ...

Now published in @nataging.nature.com‬ ! We propose DunedinPACNI, a new measure of the rate of aging that can be derived from a single structural brain scan www.nature.com/articles/s43...

@npp-journal.bsky.social @uwpsych.bsky.social @sansmeeting.bsky.social @cnsmtg.bsky.social @avramholmes.bsky.social @jnfrltackett.bsky.social @aidangcw.bsky.social @vijayamittal.bsky.social @kevinmking.bsky.social @torwager.bsky.social @pkragel.bsky.social @maxwellelliott.bsky.social

This was a massive team effort - @jingnandu.bsky.social, @jaredniels.bsky.social, Randy Buckner and many others!

The high precision afforded by cluster scanning promises to accelerate clinical trials, lead to personalized biomarkers, and be a useful tool for the science of individual differences in brain development, aging, and disease. Check out the preprints for more details!

This precision allowed us to see clear deviations from expected aging when they arose. In a striking example, we detected an aggressive atrophy trajectory in an individual who was cognitively unimpaired at baseline but then went on to develop an MCI diagnosis.

Critically, this reduction in error allowed us to see changes in individuals within just one year. Here are hippocampal aging trajectories in 8 individuals across one year where you can see large individual differences and the benefits of pooling multiple measurements

We found a solution - cluster scanning. Utilizing the latest advances in scan acceleration we collected several short, 1-minute long T1s to drive to measurement error within individuals ... and it worked!