yes definitely!!

Congrats, Armin!!!

thanks ted!!!

Many thanks to my co-first author @maxwellelliott.bsky.social, Annchen Knodt, our awesome team at Duke and Otago, and my advisors Av Caspi, Terrie Moffitt, and Ahmad Hariri!

We want this measure to be available to the scientific community to help uncover how aging is related to disease, exposure, and interventions.

The algorithm to estimate DunedinPACNI is publicly available at github.com/etw11/Dunedi...

DunedinPACNI is a novel, distinct approach to measuring aging from neuroimaging that can be easily estimated from simple measures from T1 MRI data.

DunedinPACNI tended to explain a bit more variance in clinical outcomes. Notably, this variance did not overlap with brain age gap very much and DunedinPACNI and brain age gap were not very correlated.

Lastly, we wanted to test whether DunedinPACNI was distinct from brain age gap, a neuroimaging biomarker trained on chronological age.

DunedinPACNI’s association with cognitive impairment in BrainLat was similar to that in ADNI, an initial indication of good generalization among people from Latin America.

Brain-based predictive models often fail when there are demographic differences between training and test samples – posing a hurdle for clinical translation. We tested if this was true for DunedinPACNI using the BrainLat dataset, a sample of Latin American adults with and without dementia.

In UK Biobank, faster DunedinPACNI scores were related to frailty, number of chronic illnesses, risk of new chronic illness, and risk of death from any cause. Thus, DunedinPACNI appears to index aging across the entire body

So, DunedinPACNI seems like a useful predictor of cognitive and brain decline. But what about aging more broadly?

Furthermore, faster DunedinPACNI scores at baseline predicted more rapid hippocampal atrophy in the future.

In ADNI and UK Biobank, faster DunedinPACNI scores were correlated with worse cognition, cognitive impairment, and risk of cognitive decline

Using Dunedin Study data, we trained an algorithm to estimate the Pace of Aging from brain structure. We call this measure “DunedinPACNI” – Pace of Aging Calculated from NeuroImaging

Then we applied this algorithm to external data to test its association with clinical outcomes.