iiinova — where your digital self keeps evolving

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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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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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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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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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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Gagan Wig @gaganwig.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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Using HCP data to examine lifespan differences in functional brain activity or network organization? See below! A reminder that even subtle differences in data acquisition between cohorts can change our conclusions. This issue will only become more relevant as we pool neuroimaging datasets.

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Thomas Yeo @bttyeo.bsky.social · 13/06/2026

Openly shared implementation of TMS targeting is still rare, so we are pleased to make ours freely available for research use: github.com/ThomasYeoLab... Let us know if you have any issue running it!

github.com

GitHub - ThomasYeoLab/Kong2026_TMSTree: Tree-based MS-HBM TMS targeting algorithm

Tree-based MS-HBM TMS targeting algorithm. Contribute to ThomasYeoLab/Kong2026_TMSTree development by creating an account on GitHub.

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Scott Marek @smarek0502.bsky.social · 11/06/2026

What matters most for childhood brain organization? We analyzed 649 variables. The answer: Socioeconomics (SES); with brain patterns pointing at sleep & stress as drivers. Even brain-IQ associations were better explained by SES. In Science today: www.science.org/doi/10.1126/...

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Armin Raznahan @bogglerapture.bsky.social · 28/05/2026

Do humans show sex differences in brain activation? If so, are these task-specific or general — and do they relate to sex differences in brain anatomy and behavior? We dove deep into these questions in a new paper just out in Nature Communications | tinyurl.com/3rdrb524

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Dr Alison Weiss @dralisonweiss.bsky.social · 10/05/2026

Anti-science politicians and activists are spreading misinformation about science in Oregon. These misinformation campaigns have put the Oregon National Primate Research Center (ONPRC) at risk of closure. We’re asking you to use your voice. savescienceoregon.org/butter #standupforscience

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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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WFIU/WTIU News @wfiuwtiunews.bsky.social · 30/04/2026

Indiana University is in great financial health, according to a recent independent analysis. The report says administrative costs have grown significantly while instruction and faculty pay lagged. @weiss-a-woni.bsky.social Read more: www.ipm.org/news/2026-04...

Indiana University's Sample Gates with red and white tulips. The gates are made of limestone.

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Neuroskeptic @neuroskeptic.bsky.social · 28/04/2026

"The differences between modern human and Neanderthal brains, as estimated from endocranial reconstructions, do not meaningfully exceed those among different modern human populations." www.pnas.org/doi/10.1073/...

pnas.org

Neanderthal brain and cognition reconsidered | PNAS

Neanderthal endocrania are different in shape, though slightly larger in size than modern humans on average. These shape differences have long been...

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

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Nico Dosenbach @ndosenbach.bsky.social · 23/04/2026

Function & cytoarchitecture don't overlap ... they're orthogonal. Prefrontal cortex is plastered with chains of functional patches previously mostly known from face processing. Why multi-modal parcellations are wrong ... and other insights hidden by group-averaging of fMRI data. great explainer👇🏻

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Center for Vital Longevity @cvlneuro.bsky.social · 21/04/2026

Join this talented team! 🧠✨ Click on post below for details.

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maplotr.bsky.social @maplotr.bsky.social · 21/04/2026

Come join our awesome team 😎

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

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Mathias V. Schmidt @mathiasvschmidt.bsky.social · 20/04/2026

𝐍𝐞𝐰 𝐉𝐨𝐢𝐧𝐭 𝐂𝐚𝐥𝐥 𝐟𝐨𝐫 𝐏𝐚𝐩𝐞𝐫𝐬 I am thrilled to announce an editorial collaboration between 𝐍𝐞𝐮𝐫𝐨𝐛𝐢𝐨𝐥𝐨𝐠𝐲 𝐨𝐟 𝐒𝐭𝐫𝐞𝐬𝐬 and 𝐍𝐞𝐮𝐫𝐨𝐬𝐜𝐢𝐞𝐧𝐜𝐞 𝐚𝐧𝐝 𝐁𝐢𝐨𝐛𝐞𝐡𝐚𝐯𝐢𝐨𝐫𝐚𝐥 𝐑𝐞𝐯𝐢𝐞𝐰𝐬. With @bartolomuccilab.bsky.social, I am co-editing a virtual special issue focused on: 𝘚𝘵𝘳𝘦𝘴𝘴-𝘪𝘯𝘥𝘶𝘤𝘦𝘥 𝘪𝘮𝘱𝘢𝘪𝘳𝘦𝘥 𝘩𝘦𝘢𝘭𝘵𝘩 𝘢𝘯𝘥 𝘢𝘤𝘤𝘦𝘭𝘦𝘳𝘢𝘵𝘦𝘥 𝘢𝘨𝘪𝘯𝘨

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Imaging Neuroscience @imagingneurosci.bsky.social · 19/04/2026

New paper in Imaging Neuroscience by Peiying Liu, Hanzhang Lu, et al: Non-invasive MRI of choroid plexus vascular function doi.org/10.1162/IMAG...

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Rick Betzel @richardfbetzel.bsky.social · 14/04/2026

White matter pathways mediating dorsolateral prefrontal TMS therapy for depression New @natneuro.nature.com paper led by Caio Seguin, Robin Cash, and Andrew Zalesky. We map (indirect) pathways from DLPFC to SGC and link individual variation with response efficacy. www.nature.com/articles/s41...

nature.com

White matter pathways mediating dorsolateral prefrontal TMS therapy for depression - Nature Neuroscience

Seguin et al. show that the efficacy of transcranial magnetic stimulation for depression depends on how stimulation spreads through the brain’s wiring. Patients with shorter communication pathways bet...

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New papers in Network Science @networkspapers.bsky.social · 09/04/2026

PNAS: Correspondence of large-scale functional brain network decline across aging mice and humans www.pnas.org/doi/abs/10.1073/pnas.2…

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Paul Byrne @theplanetaryguy.com · 03/04/2026

This image of home just came down from the Artemis II crew. Taken after their translunar injection burn, there are aurorae at top right and lower left, and zodiacal light at lower right. Credit: NASA/Reid Wiseman

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Gagan Wig @gaganwig.bsky.social · 02/04/2026

Full thread with more details: bsky.app/profile/gaga...

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Gagan Wig @gaganwig.bsky.social · 02/04/2026

Humans live many more years than mice, but our brain networks are aging much faster. Brain network decline follows a common trajectory across mouse and human adulthood, with features of network organization linked to more rapid decline. www.pnas.org/doi/10.1073/...

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Dan Handwerker @danielhandwerker.bsky.social · 30/03/2026

How do we define "good" fMRI data? Especially with resting state, there are circularity risks if we evaluate data quality as showing the networks we expect to see. Javier Gonzalez-Castillo (& me & others) developed pBOLD, a new metric that uses multi-echo info. www.biorxiv.org/content/10.6... 1/8

biorxiv.org

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Center for Vital Longevity @cvlneuro.bsky.social · 01/04/2026

We’re excited to share a summary thread from the Wig Neuroimaging Lab on their latest PNAS publication. Explore the key findings below ⬇️

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Donna Dierker @donnadierker.bsky.social · 01/04/2026

"These findings point to a general process of functional dedifferentiation over adulthood, occurring at multiple levels of neural organization, from neurons to whole-brain networks."

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Animal Models for Social Dimensions of Health and Aging Network @animalsocaging.bsky.social · 30/03/2026

🎉Huge congratulations to McEwen fellow @ewinternelson.bsky.social and Dr. Gagan Wig of UT Dallas on their recent publication in PNAS on large-scale brain network decline across aging mice and humans and its translational relevance. Read the paper here: shorturl.at/gjau5

shorturl.at

Correspondence of large-scale functional brain network decline across aging mice and humans | PNAS

Human aging is marked by progressive reorganization of large-scale functional brain networks; these brain network changes have been linked to cogni...

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Ezra Winter-Nelson @ewinternelson.bsky.social · 31/03/2026

The first part of my PhD research is out now in PNAS! See the thread below and stay tuned for my dissertation work, which builds on this cross-species model of brain network aging

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Columbia University's Zuckerman Institute @zuckermanbrain.bsky.social · 28/03/2026

By analyzing brain activity throughout the lives of mice, Itamar Kahn, Gagan Wig, Ezra Winter-Nelson & team found that the rodent’s brain ages similarly to that of a human. Studying mice could therefore be a way to learn about how our brains decline as we grow old. @pnas.org tinyurl.com/38xwm5hv

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Education vs. brain network decline & AD prognosis: Chan et al., Nature Aging 2021 AD-specific network alterations: Zhang et al., J Neuroscience 2023 Reliability of the measure: Han et al., Cerebral Cortex 2024

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For those who want to dig into the background on system segregation and aging in humans, here are some key papers from my lab: System segregation across the adult lifespan: Chan et al., PNAS 2014 System segregation review: Wig, TICS 2017 SES stratification: Chan et al., PNAS 2018 cont.

Huge congratulations to lead author @ewinternelson.bsky.social who drove the project and a fantastic team including co-senior author Itamar Kahn, whose group did the mouse imaging @utdallas.bsky.social @cvlneuro.bsky.social @zuckermanbrain.bsky.social

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On methods: mice were imaged at rest while awake, with dense longitudinal sampling. Pipelines matched to human studies & findings robust across analytical choices The data & code are openly available — links in paper We welcome collaborators in mouse aging, longevity & cross-species brain health

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Why does this matter for AD? System segregation decline is linked to AD risk and progression. Many AD drugs that work in mice fail in humans — this could be because brain *function* isn't part of the pipeline. System segregation provides a new cross-species bridge.

Intriguingly, network decline in mice parallels cellular-level changes — reduced neuronal selectivity, declining synaptic differentiation — also beginning early in adulthood. This suggests dedifferentiation across multiple levels of organization simultaneously; we’re now examining the direct links.

Mice also age *more slowly* at this network level — the rate of decline is steeper in humans even after accounting for lifespan differences. This difference in rate of network decline is robust across a wide range of mouse-human age alignments and analytic choices.

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The answer seems to be linked to long-range connectivity. Human brains have stronger connections among distant areas, particularly between different brain systems. Mice have relatively fewer of these — likely reflecting the greater integrative demands of the human brain.

But the story doesn't end there. Despite this shared pattern, the global architecture of mouse and human brain networks, and how they age, are not identical. Mouse networks are actually *more* segregated than human networks — at every age. Why?

We directly compared mouse and human aging trajectories using a common analytical framework — 82 mice alongside 1,179 humans (ages 18-90) from the Human Connectome Project. Both species show declining system segregation. The same aging signature, across two very different brains.

We then scanned mice from 3 to 20 months and mapped their networks. We see the same pattern as in humans: a progressive decline in system segregation.

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We first verified that fMRI in awake mice captures meaningful functional organization — known circuits show expected, dissociable patterns.

Mice are powerful models for brain aging at the cellular and molecular level. But a large-scale network description — the kind we've been building in humans with resting-state fMRI, which measures spontaneous brain activity during rest — has been missing. We set out to establish one.