Understanding adolescent anxiety through a neurodevelopmental lens: A comparative review of rodents and humans www.sciencedirect.com/science/arti...
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www.medrxiv.org/content/10.1...
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📝✅ We suggest several improvements:
✓ Report demographic composition of training datasets
✓ Validate algorithms across diverse populations
✓ Include performance metrics by demographic groups
✓ Develop more inclusive validation processes /5
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🏥📋 These findings have important clinical implications:
🔸 Brain age algorithms are increasingly used as health biomarkers
🔸 Systematic prediction errors could impact diagnosis accuracy
🔸 May contribute to existing healthcare disparities /4
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🔍💡 What contributes to these algorithmic differences?
Training data composition appears to be a key factor:
🏥 UK Biobank: 94% White participants
📊 Many datasets lack demographic diversity
🧬 Algorithms may not generalize well across populations
#DataDiversity #AIResearch /3
22.09.2025 15:08 — 👍 0 🔁 0 💬 1 📌 0
🧠📊 New research examines potential bias in brain age algorithms across racial groups
📈 Study of 6 popular algorithms found lower accuracy for African American participants (r=0.51-0.85) compared to White/Hispanic participants (r=0.57-0.89)/1
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a cartoon of a coyote wearing binoculars with the words looney tunes below him
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But don't let that stop you! Apply if your background aligns—we'll discuss fit during interviews 💪
More info here: www.psychology.pitt.edu/graduate /5
15.09.2025 15:35 — 👍 2 🔁 0 💬 0 📌 0
a clock that has the hands on the numbers 10 and 12
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We're launching new research on temporal dynamics of positive affect in relation to stress and psychopathology in adolescence.
⏰ Application timeline note: My schedule is packed with this new project launch, so I won't be available for pre-application meetings. /4
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a woman is sitting in a chair with the words one more day then it 's back to the grind again above her
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🎯 What we're looking for:
✅ Clinical psychology and/or developmental neuroscience experience
✅ 2+ years post-bac experience
✅ Neuroimaging exposure
✅ Statistical training (ANOVA, linear regression, etc.)
(Some of these are great... you don't need them all!) /3
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a penguin wearing glasses has a stack of books on his head and the words always be learning behind him
ALT: a penguin wearing glasses has a stack of books on his head and the words always be learning behind him
Pitt offers two incredible pathways:
-Developmental PhD program
-Joint Clinical/Developmental PhD (warning: VERY competitive!)
Both will push you to learn and integrate, and work across the multiple worlds of developmental psychology, neuroscience, and psychiatry 🧠✨ /2
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🚨 GRADUATE PHD APPLICATIONS OPEN 🚨
I'm accepting students for the next psychology PhD admissions cycle! If you're passionate about developmental neuroscience and clinical psychology, this might be for you 👇
#GradSchool #Psychology #Research /1
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Age-related patterns of resting EEG power in infancy: Associations with prenatal socioeconomic disadvantage www.sciencedirect.com/science/arti...
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A common neural signature between genetic and environmental risk for mental illness www.nature.com/articles/s41...
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Models as Prediction Machines: How to Convert Confusing Coefficients into Clear Quantities
Abstract
Psychological researchers usually make sense of regression models by interpreting coefficient estimates directly. This works well enough for simple linear models, but is more challenging for more complex models with, for example, categorical variables, interactions, non-linearities, and hierarchical structures. Here, we introduce an alternative approach to making sense of statistical models. The central idea is to abstract away from the mechanics of estimation, and to treat models as “counterfactual prediction machines,” which are subsequently queried to estimate quantities and conduct tests that matter substantively. This workflow is model-agnostic; it can be applied in a consistent fashion to draw causal or descriptive inference from a wide range of models. We illustrate how to implement this workflow with the marginaleffects package, which supports over 100 different classes of models in R and Python, and present two worked examples. These examples show how the workflow can be applied across designs (e.g., observational study, randomized experiment) to answer different research questions (e.g., associations, causal effects, effect heterogeneity) while facing various challenges (e.g., controlling for confounders in a flexible manner, modelling ordinal outcomes, and interpreting non-linear models).
Figure illustrating model predictions. On the X-axis the predictor, annual gross income in Euro. On the Y-axis the outcome, predicted life satisfaction. A solid line marks the curve of predictions on which individual data points are marked as model-implied outcomes at incomes of interest. Comparing two such predictions gives us a comparison. We can also fit a tangent to the line of predictions, which illustrates the slope at any given point of the curve.
A figure illustrating various ways to include age as a predictor in a model. On the x-axis age (predictor), on the y-axis the outcome (model-implied importance of friends, including confidence intervals).
Illustrated are
1. age as a categorical predictor, resultings in the predictions bouncing around a lot with wide confidence intervals
2. age as a linear predictor, which forces a straight line through the data points that has a very tight confidence band and
3. age splines, which lies somewhere in between as it smoothly follows the data but has more uncertainty than the straight line.
Ever stared at a table of regression coefficients & wondered what you're doing with your life?
Very excited to share this gentle introduction to another way of making sense of statistical models (w @vincentab.bsky.social)
Preprint: doi.org/10.31234/osf...
Website: j-rohrer.github.io/marginal-psy...
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How we stumbled upon the rat play vocalizations: A recollection www.sciencedirect.com/science/arti...
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