These are fantastic, thanks for putting in the time to make this easy to follow!!

ortical Thickness Accounts for the Majority of Substance Use Associations. A) The p values of associations between all available drug variables and brain measures before controlling for global brain thickness are pictured. Most of the significant drug variable associations are also strongly associated with global brain thickness, including mAUDIT-C and lifetime marijuana use. B) Analyses pictured in Panel A were repeated with global cortical thickness as an additional covariate. Colors indicate drug type. Shades of each color indicate a dimension of use (e.g., dependency, lifetime use, drug test). Individual points represent the p value of the association between a drug variable and a brain measure. Points outlined in black indicate survival of multiple test correction.

And if you're worried that we possibly missed an important regional association, we did some post-hoc analyses to show that more than 90% of all brain-wide associations with substance use (thickness, surface area, and volume) are explained by global thickness!

I'm particularly excited that we're seeing evidence for both exposure and risk effects in the same people at the same time! I hope this will help push the field toward stage‑based studies that incorporate risk effects and explore the timing of when risk effects emerge.

These results highlight the complexity of substance use associations. There are both shared and unique components across dimensions of use, which reflect a combination of genetic and environmental influences.

Drug Use Associations With Brain Structure Reflect a Combination of Predispositional Risk and Exposure Effects. A) Regression estimates of within- and between-family drug use variables fit to predict whole brain thickness are pictured. Y-axis variables are split by mAUDIT-C and Marijuana Use. X-axis data are split by within- (left) and between- (right) family estimates. Color denotes the sample included in the analysis. B) Regression estimates of within- and between-family whole brain thickness fit to predict substance use variables are pictured. Y-axis variables are split by mAUDIT-C and Marijuana Use, the two drugs evidencing unique-effects on whole brain thickness. X-axis data are split by within- (left) and between- (right) family whole brain estimates. Color denotes the sample included in the analysis. Bold indicates significance. C) Standardized regression estimates of environmental variance (green) and heritability (pink) fit to predict mAUDIT-C, Marijuana Use, and Brain Thickness are pictured. D) Variance component correlations of additive genetics (blue) and non-shared environment (red) between mAUDIT-C and Marijuana Use are pictured. Points reflect estimates, lines indicate 95% confidence intervals. MJ Use = Marijuana Use. Bold indicates significance.

Comparing within- and between-families, we see evidence for predispositional risk with marijuana use (significant between family effect and genetic correlation), as well as evidence for exposure effects (possibly bi-directional) for both alcohol and marijuana (significant within-family effects).

Shared- and Unique-Drug Associations with Attenuated Brain Thickness. Standardized regression estimates of alcohol, marijuana, tobacco, and illicit drug use variables predicting whole brain cortical thickness. Lines reflect the 95% confidence interval of the estimate. Note that these dimensions were not standardized across drug type, as collection of these data varied. Bold indicates survival of multiple test correction and evidence for shared-effects on global brain thickness. Starred indicates drug-specific, unique effects on global brain thickness.

Looking across a several dimensions of substance use, age of onset of alcohol use, as well as lifetime marijuana and tobacco use, are also associated with global thickness. But only marijuana shows a unique effect over and above alcohol use (and vice versa)!

A) Composite mAUDIT-C scores ranged from zero to 12 and were categorized into four levels of past-year hazardous risk: low (green; 0 to 3), moderate (yellow; 4 to 5), high (blue; 6 to 7), and severe (pink; 8 to 12). B) Color indicates the number of drugs used across the lifetime. Participant endorsement of having ever used any of the four major substance types are pictured. 65% (n = 726) of the sample endorsed a pattern of polysubstance use throughout the lifetime (i.e., 2+ substances).

Global Brain Thickness Explains Regional Alcohol Effects on Brain Structure. Standardized regression estimate associations between brain ROI and mAUDIT-C are pictured. Brain structures on the y-axis are split by modality, i.e., volume, thickness, and global measures. X-axis panels are split before (left, blue) and after (right, red) controlling for global brain thickness (grey). Bold indicates significance following multiple test correction.

Hazardous Alcohol Use Predicts Attenuated Global Brain Thickness. Association of hazardous alcohol use (mAUDIT-C) with global cortical thickness, residualized for covariates and standardized (i.e., SD=1). Covariates included age, age2, sex, socioeconomic status, level of educational attainment, intracranial volume, and sibling status. Shading reflects the density of overlapping points. mAUDIT-C was uniquely associated with attenuated global brain thickness (β = -0.12, p < 0.001).

In the HCP, which includes many people with moderate-severe alcohol use and polysubstance use, we replicate associations of hazardous alcohol use with many brain structures (onlinelibrary.wiley.com/doi/full/10....). But, these effects are largely attributable the association with global thickness!

This is a paper of many firsts. My first senior-author paper, my first two-author empirical research paper 😁, and the first first-author paper from my fantastic Research Tech Daniella Fernandez, who did all of the heavy lifting, including all of the stats, figures, and writing the first full draft!

Brain Structure and Substance Use: Disentangling Risk, Exposure, and Drug-Specific Effects Importance: Polysubstance use is common, but substance use associations with neuroimaging measures have largely been investigated within individual drug types. Whether effects are substance-specific o...

The first preprint from the lab is up! Are structural MRI correlates of substance use shared across substances, or are there unique associations? And do these reflect predispositional risk and/or possibly the effects of substance exposure? www.medrxiv.org/content/10.6... #neuroskyence

See david-baranger.shinyapps.io/InteractionP... for continuous variables! NB intxpower assumes your main effects are null, so will tend to be a bit conservative.

Genetic Data From Over 20,000 U.S. Children Misused for ‘Race Science’

So infuriating.
www.nytimes.com/2026/01/24/u...

Please say hi at #ACNP if you'd like to chat with either of us about this opportunity!!

darth vader is standing in front of a wall with the words join me below him ALT: darth vader is standing in front of a wall with the words join me below him

Toddler requested a "Master Yoda" bedtime story

palmerpenguins R data package Data for three penguin species observed in the Palmer Archipelago, Antarctica, collected by Dr. Kristen Gorman with Palmer Station LTER. A great intro dataset for data science teaching and learning, a...

See also palmerpenguins - allisonhorst.github.io/palmerpengui...

𝐘𝐨𝐮𝐭𝐡 𝐂𝐨𝐫𝐫𝐞𝐥𝐚𝐭𝐞𝐬 𝐨𝐟 𝐆𝐞𝐧𝐞𝐭𝐢𝐜 𝐋𝐢𝐚𝐛𝐢𝐥𝐢𝐭𝐲 𝐭𝐨 𝐒𝐮𝐛𝐬𝐭𝐚𝐧𝐜𝐞 𝐔𝐬𝐞 𝐃𝐢𝐬𝐨𝐫𝐝𝐞𝐫𝐬. New from us, led by @sarahepaul.bsky.social. PheWAS in ABCD identifies many potentially modifiable substance use risk factors!

www.medrxiv.org/content/10.1...

Certainly

Most people use MID contrasts (eg Big Win > Neut), which would be less reliable than any of these estimates.

I'm also surprised by how low the PET reliability is, but I'm less familiar with that literature.

Thanks Nicola! Given that they're looking at activation relative to an implicit baseline, and not a contrast, the ICC here is around what I would expect. Certainly longer time between measurements lowers reliability in many of the adolescent samples. Harder to say if there are age effects.

Great work led by Andrew Castillo extending sample size stability analyses to interactions! We've also added a function implementing these analyses to the InteractionPoweR R package: dbaranger.github.io/InteractionP... #rstats

When do interaction/moderation effects stabilize in linear regression?: https://osf.io/35t84

Reliability and sample size have a non-linear relationship. Linear increases in reliability yield diminishing reductions in the required sample size.

If you are ever working on a project and see r=0.98 between variables that are supposedly different, your first thought should be "oh $&*! what went wrong?", NOT "I wonder what mediates this???"

Shout out to BEAR Lab research tech Daniella Fernandez (who only joined 4 months ago!) whose poster abstract was selected for a nanosymposium at the annual meeting of the Upper Midwest Chapter of SFN this weekend! #neuroskyence #MRI 🧠🍺

Applications for the Career Development Institute (CDI) in Psychiatry are open! Strongly recommend for senior grad students and postdocs.
cdipsychiatry.org

TransBrain: A computational framework for translating brain-wide phenotypes between humans and mice Despite remarkable advances in whole-brain imaging technologies, the lack of quantitative approaches to bridge rodent preclinical and human studies remains a critical challenge. Here we present TransB...

Interesting approach that aligns mouse and human brain-wide data using transcriptomics and structural connectivity. I'm curious if anyone here has tried using it yet? #neuroskyence www.biorxiv.org/content/10.1... transbrain.readthedocs.io/en/latest/

Official job ad is up! careers.peopleclick.com/careerscp/cl...

A new favorite citation appeared!

TIL in Windows you can use PowerShell to search not only file names but also text file contents - including .R and .Rhistory files. In a moment of pure insanity, 1.5 years ago I did not save the code for a figure, but it was recorded in an old .Rhistory file!

Lol thanks!!!

Also, I will be at #SRP this week if anyone wants to chat!