iiinova — where your digital self keeps evolving

Reposted by Micaela Chan

Kendra Seaman @kendraseaman.bsky.social · 18/09/2026

I'm hiring! Come join us in the Aging Well Lab - you'll get to work on a newly funded NIA study and collaborate with the amazing @colleencfrank.bsky.social and @klempert.bsky.social. Don't wait - apply now! jobs.utdallas.edu/postings/32753

static.klipy.com

Moira Rose: Why Wait?

ALT: Moira Rose: Why Wait?

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Micaela Chan @micaelachan.bsky.social · 01/09/2026

Take-home: Combining data increases power, but a larger N won’t always improve inferences if data features differ across cohorts. When combining cohorts/studies (e.g., multiple lifespan segments of the HCP), data harmonization and reporting of how this was achieved is critical.

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Micaela Chan @micaelachan.bsky.social · 01/09/2026

Harmonization can mitigate these HCP cohort differences, but implementation matters! Example here shows harmonization correcting for ‘protocol’ was more optimal than correcting for ‘site.’

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Micaela Chan @micaelachan.bsky.social · 01/09/2026

These young-adult dips and blips are not typical lifespan patterns, and we show this using two independent reference lifespan datasets: NKI-Rockland Sample (NKI) and Dallas Lifespan Brain Study (DLBS). Neither showed the marked young-adult deviation seen in the HCP data.

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Micaela Chan @micaelachan.bsky.social · 01/09/2026

The cohort-level signal differences in HCP-YA distort measurements of brain network organization, resulting in surprising decreases and increases in multiple measure of brain organization during young-adulthood.

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Micaela Chan @micaelachan.bsky.social · 01/09/2026

The human connectome project (HCP) is an amazing open resource that spans from development to old adulthood. But the scanners/protocol weren’t identical across cohorts, resulting in cohort-differences in signal-to-noise ratio in the functional scans (e.g., resting-state).

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Micaela Chan @micaelachan.bsky.social · 01/09/2026

Combining data to study lifespan brain aging is tempting: more data and wider age range! But mixing data across scanners/protocols without proper harmonization can bias age effects, including studies using high-quality HCP data. Imaging Neuroscience🔔doi.org/10.1162/IMAG.a.1338

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Micaela Chan @micaelachan.bsky.social · 01/09/2026

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Micaela Chan @micaelachan.bsky.social · 01/09/2026

Harmonization can mitigate these HCP cohort differences, but implementation matters! Example here shows harmonization correcting for ‘protocol’ was more optimal than correcting for ‘site.’

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Micaela Chan @micaelachan.bsky.social · 01/09/2026

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Micaela Chan @micaelachan.bsky.social · 01/09/2026

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Micaela Chan @micaelachan.bsky.social · 01/09/2026

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Micaela Chan @micaelachan.bsky.social · 01/09/2026

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Micaela Chan @micaelachan.bsky.social · 01/09/2026

Harmonization can mitigate these HCP cohort differences, but implementation matters! Example here shows harmonization correcting for ‘protocol’ was more optimal than correcting for ‘site.’

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Micaela Chan @micaelachan.bsky.social · 01/09/2026

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Micaela Chan @micaelachan.bsky.social · 01/09/2026

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Micaela Chan @micaelachan.bsky.social · 01/09/2026

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Reposted by Micaela Chan

Gagan Wig @gaganwig.bsky.social · 06/05/2026

We're hiring! FT research assistant @ UT Dallas Join our team to work on a longitudinal neuroimaging study of midlife brain aging. Strong opportunity for those interested in brain networks, aging, and Alzheimer’s disease risk. App. deadline now May 15. Details: jobs.utdallas.edu/postings/31680

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Reposted by Micaela Chan

Gagan Wig @gaganwig.bsky.social · 21/04/2026

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