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 🧪
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"
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"
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!
A key challenge for brain aging is to measure brain changes in individuals rather than group averages. This has huge implications for clinical trials, basic aging research and individual differences. However, we lack the precision needed to detect changes over short intervals.
New preprint!!!
We can reliably detect brain changes in individuals in just a year by collecting several rapid 1-minute T1s at each time point - i.e. "cluster scanning". We discovered large individual differences, even in healthy adults
t.co/oxD9EBdbvu
[PREPRINT❗️❗️] Very excited to share our latest work on precision mapping of brain networks exclusively using task data!🧠
With @maxwellelliott.bsky.social Joanna Ladopoulou, Mark Eldaief, and Randy Buckner
www.biorxiv.org/content/10.1...
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Really interesting from @maxwellelliott.bsky.social et al: 1-year brain changes reliably detected by cluster-scanning - burst of multiple, very short T1's. Great potential for tracking individual differences in brain change over clinically meaningful intervals. www.medrxiv.org/content/10.1...
