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jamielarsh.bsky.social
Jamie Hanson
@jamielarsh.bsky.social
3.5K followers 2.4K following 290 posts
Psychology & Neuroscience Researcher at Pitt & LRDC | Studying the Impact of Early Life #Adversity on 🧠 Development | #Stress | Reposts ≠Endorsements
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jamielarsh.bsky.social
Jamie Hanson @jamielarsh.bsky.social · 27d
🚨 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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jamielarsh.bsky.social
Jamie Hanson @jamielarsh.bsky.social · 6h
Linking reinforcement learning, working memory, and choice dynamics to age and symptoms of anxiety and depression in adolescence www.sciencedirect.com/science/arti...
Linking reinforcement learning, working memory, and choice dynamics to age and symptoms of anxiety and depression in adolescence
Adolescence is a sensitive period characterized by significant neurocognitive development, with important implications for learning and decision-makin…
www.sciencedirect.com
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jamielarsh.bsky.social
Jamie Hanson @jamielarsh.bsky.social · 1d
Identification of five sleep-biopsychosocial profiles with specific neural signatures linking sleep variability with health, cognition, and lifestyle factors journals.plos.org/plosbiology/...
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jamielarsh.bsky.social
Jamie Hanson @jamielarsh.bsky.social · 1d
Reduced Threat-Related Neural Efficiency: A Possible Biomarker for Pediatric Anxiety Disorders psychiatryonline.org/doi/abs/10.1...
Reduced Threat-Related Neural Efficiency: A Possible Biomarker for Pediatric Anxiety Disorders | American Journal of Psychiatry
Objective: Pediatric anxiety disorders are common and predict adult psychopathology, yet current treatments, such as cognitive-behavioral therapy (CBT), produce lasting remission in less than 50% of a...
psychiatryonline.org
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jamielarsh.bsky.social
Jamie Hanson @jamielarsh.bsky.social · 2d
EEG-based brain connectivity and sentiment analysis from smartphone social communication: insights into remitted major depressive disorder among adolescents www.nature.com/articles/s44...
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jamielarsh.bsky.social
Jamie Hanson @jamielarsh.bsky.social · 12d
Understanding adolescent anxiety through a neurodevelopmental lens: A comparative review of rodents and humans www.sciencedirect.com/science/arti...
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jamielarsh.bsky.social
Jamie Hanson @jamielarsh.bsky.social · 12d
Longitudinal associations of structural and functional brain connectivity with dimensions of psychopathology in adolescence www.sciencedirect.com/science/arti...
Longitudinal associations of structural and functional brain connectivity with dimensions of psychopathology in adolescence
Adolescence is a critical period of neurodevelopment marked by ongoing maturation of structural and functional brain connectivity. Simultaneously, thi…
www.sciencedirect.com
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jamielarsh.bsky.social
Jamie Hanson @jamielarsh.bsky.social · 12d
Oxytocin and vasopressin enhance social pain empathy via common and distinct of neural expressions, genetic pathways, and networks www.pnas.org/doi/10.1073/...
PNAS
Proceedings of the National Academy of Sciences (PNAS), a peer reviewed journal of the National Academy of Sciences (NAS) - an authoritative source of high-impact, original research that broadly spans...
www.pnas.org
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jamielarsh.bsky.social
Jamie Hanson @jamielarsh.bsky.social · 12d
The Development and Validation of a Dimensional Childhood Adversity Measure journals.sagepub.com/doi/10.1177/...
The Development and Validation of a Dimensional Childhood Adversity Measure - Angelina Pei-Tzu Tsai, Peter F. Halpin, Lucy Lurie, Meredith Gruhn, Maya Rosen, Donald H. Baucom, Michael B. Sarabosing, S...
Research on the developmental consequences of early adversity has grown rapidly, yet measures of childhood adversity have not kept pace with evolving theoretica...
journals.sagepub.com
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jamielarsh.bsky.social
Jamie Hanson @jamielarsh.bsky.social · 19d
www.medrxiv.org/content/10.1...
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jamielarsh.bsky.social
Jamie Hanson @jamielarsh.bsky.social · 20d
📝✅ 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
jamielarsh.bsky.social
Jamie Hanson @jamielarsh.bsky.social · 20d
🏥📋 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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jamielarsh.bsky.social
Jamie Hanson @jamielarsh.bsky.social · 20d
🔍💡 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
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jamielarsh.bsky.social
Jamie Hanson @jamielarsh.bsky.social · 20d
📋 Differences remained significant after controlling for age, sex, and scan quality
#MedicalAI #HealthEquity #Neuroscience #AlgorithmicBias #BrainAge

www.medrxiv.org/content/10.1... /2
Racial and Ethnic Disparities in Brain Age Algorithm Performance: Investigating Bias Across Six Popular Methods
Brain age algorithms, which estimate biological aging from neuroimaging data, are increasingly used as biomarkers for health and disease. However, most algorithms are trained on datasets with limited ...
www.medrxiv.org
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jamielarsh.bsky.social
Jamie Hanson @jamielarsh.bsky.social · 20d
🧠📊 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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jamielarsh.bsky.social
Jamie Hanson @jamielarsh.bsky.social · 27d
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
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jamielarsh.bsky.social
Jamie Hanson @jamielarsh.bsky.social · 27d
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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jamielarsh.bsky.social
Jamie Hanson @jamielarsh.bsky.social · 27d
🎯 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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jamielarsh.bsky.social
Jamie Hanson @jamielarsh.bsky.social · 27d
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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jamielarsh.bsky.social
Jamie Hanson @jamielarsh.bsky.social · 27d
🚨 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
a woman in a black coat stands in front of a sign that says hey
ALT: a woman in a black coat stands in front of a sign that says hey
media.tenor.com
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jamielarsh.bsky.social
Jamie Hanson @jamielarsh.bsky.social · 29d
Age-related patterns of resting EEG power in infancy: Associations with prenatal socioeconomic disadvantage www.sciencedirect.com/science/arti...
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jamielarsh.bsky.social
Jamie Hanson @jamielarsh.bsky.social · Aug 25
A common neural signature between genetic and environmental risk for mental illness www.nature.com/articles/s41...
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Reposted by Jamie Hanson
dingdingpeng.the100.ci
Julia M. Rohrer @dingdingpeng.the100.ci · Aug 25
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...
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.
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jamielarsh.bsky.social
Jamie Hanson @jamielarsh.bsky.social · Aug 20
Cortical travelling waves relate to variation in personality traits direct.mit.edu/imag/article...
Cortical travelling waves relate to variation in personality traits
Abstract. Personality traits must relate to stable neural processes, yet few robust neural correlates of personality have been discovered. Recent methodological advances enable measurement of cortical...
direct.mit.edu
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jamielarsh.bsky.social
Jamie Hanson @jamielarsh.bsky.social · Aug 20
How we stumbled upon the rat play vocalizations: A recollection www.sciencedirect.com/science/arti...
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jamielarsh.bsky.social
Jamie Hanson @jamielarsh.bsky.social · Aug 20
Developmental differences in social information use under uncertainty: A neurocomputational approach www.sciencedirect.com/science/arti...
Developmental differences in social information use under uncertainty: A neurocomputational approach
Adolescence is a period of social re-orientation, with studies suggesting that adolescents may be more sensitive to peer influence than other age grou…
www.sciencedirect.com
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