Debbie Yee

@debyee.bsky.social

computational affective motivational (neuro)psychiatry. interested in serotonin, stress, & decision-making. Assistant Prof at UIowa. debyeeneuro.com | PI of @yeelab.bsky.social

What predicts mental health disorders' onset and treatment success? Join our free #CCN2026 satellite meeting on the latest computational cognitive neuroscience parsing the multi-scale, dynamical systems of disorders that emerge across development, symptom expression, presentation & treatment.

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Headed to #cpconf2026? Come check out some of our lab's work, presented by superstar student Tony El Nemer! B51. Investigating the role of serotonin in stressor controllability and mental effort allocation C27. Learning and generalization of stressor controllability for mental effort allocation

Hope you will join us at the Center for Computational Psychiatry at Mount Sinai for a #CCN2026 satellite on computational cognitive science approaches to modeling mental health dynamics. Big shoutout to @meghangallo.bsky.social for making it happen! @sinaiccp.bsky.social @sinaibrain.bsky.social

Meghan Gallo@meghangallo.bsky.social · 4w ago

What predicts mental health disorders' onset and treatment success? Join our free #CCN2026 satellite meeting on the latest computational cognitive neuroscience parsing the multi-scale, dynamical systems of disorders that emerge across development, symptom expression, presentation & treatment.

Now in press at Cortex! "False discovery rate correction promotes confounded neuroimaging designs" Paper 🔓: www.sciencedirect.com/science/arti...

Effect of confound mass on true positive rates under test-wise FDR correction in completely arbitrary data. Confound mass represents how large a confound is in terms of the product of the number of tests it is present in, and its mean effect size across these tests. Results are shown at differing combinations of true effect size, number of tests with true (i.e., non-confound) effects, and sample size. Inflated surface maps of meta-analytic z-statistics from Neurosynth for low-level confounds (top) and high-level cognitive tasks (bottom). Red reflects positive activations, blue reflects negative (de)activations, and darker colors indicate larger z-statistics. Maps are thresholded at |z| = 1 for visualization purposes.The difference in integrated true positive rate between parcel-wise FDR and parcel-wise FWER (y-axis) is plotted as a function of confound effect size. Each point represents 1 out of 5000 studies simulated for each combination of task and confound. Smoothed loess lines are used to better show the overall trends.Effect of FDR-based publication bias on observed confound effects sizes. Simulated meta-analytic confound effect sizes are visualized through violin plots for each combination of task effect and confound effect examined in the neural data simulations. Meta-analyses featuring publication bias (orange) substantially inflate these effect size estimates in all cases, relative to meta-analyses featuring no publication bias (blue). Moreover, this bias was present – and in most cases larger – in the subset of studies that were included in the meta-analysis specifically when the publication bias was based on FDR instead of FWER integrated true positive rates (green).
Mark Thornton@markthornton.bsky.social · 11mo ago

After 5 years, I finally carved out time to turn this blog post on FDR (markallenthornton.com/blog/fdr-pro...) into a manuscript. The preprint features a much broader range of simulations showing how FDR promotes confounds, and how this effect compounds with publication bias: osf.io/preprints/ps...

Effect of confound mass on true positive rates under FDR correction. Confound mass represents how large a confound is in terms of the product of its voxel extent and effect size. Results are shown at differing combinations of true effect size, true effect voxel extent, and sample size.

New paper 🧵! There's a lot of interest in how brain regions represent information in fMRI. One popular approach uses searchlights that roam across the brain to decode conditions. We built a method to uncover the sub-networks within searchlight maps.

Thrilled to be featured in the @thetransmitter.bsky.social Liftoff Series! Grateful for the opportunity to share more about our launching lab's ethos and the big questions surrounding mechanisms of affect and mental health we aim to tackle in the coming years! 🧠

The Transmitter @thetransmitter.bsky.social · last mo.

In this month’s “Liftoff,” @marisosa.bsky.social discusses the challenges and excitement of task-switching as a new PI, and @debyee.bsky.social shares how she plans to foster a culture of reproducibility in her lab. #neuroskyence By @franciscorr25.bsky.social www.thetransmitter.org/liftoff-new-...

Please RT - We are looking for someone who wants to establish and lead a cutting-edge neuroimaging center at Northwestern, working closely with multiple labs, including my own, in neuroscience research broadly construed. If you are curious, dm me and I'm happy to answer questions

Rodrigo Braga@rodbraga.bsky.social · 2mo ago

We are hiring an Associate or Full Professor to lead the neuroimaging research program at Northwestern's Center for Translational Imaging in the Department of Radiology careers.northwestern.edu/psc/hrnu_er/...

How do human minds make sense of big, messy problems? 😵‍💫🌀 How do we distill complexity into something simple enough to solve? 🤔💡 We’ll be tackling these questions (and more!) at two workshops on task representations, abstractions, and construals #CogSci2026 #CCN2026 🧵 framing-the-problem.github.io

Framing the Problem — Workshop Series

A workshop series on representation construction in cognitive science and AI. CogSci 2026 (Rio) and CCN 2026 (NYU).

framing-the-problem.github.io