Interested in dopamine? Have fMRI data? We’ve found a temporal BOLD feature that carries rich information about dopamine. Measurable in resting-state and task fMRI, this feature opens new ways to indirectly probe dopamine’s role in cognition and disease.

Excited to share our upcoming workshop on neuroscience, reinforcement learning, and decision making at RLDM 2025 in Dublin, Ireland — June 11–14! Check out the terrific speaker lineup: 🔗 https://t.co/LAt5UJSW5V Co-organized with @angelaradulescu

Check out our preprint to learn more about how hemodynamic latencies map regional, individual, and pathological differences linked to dopamine. We'd love to hear your questions, ideas, or feedback! @aberryobie @blaisefrederick

In individuals with cocaine use disorder, we found a spatial gradient of altered hemodynamic latencies in the striatum. This pattern independently correlated with nicotine use, revealing a conserved physiological profile associated with addictive substance use. 5/n

Using PET, genetics and pharmacology, we found that hemodynamic latency is tightly coupled to dopamine physiology. Broadly speaking, higher latency in the NAcc was associated with lower dopamine function and more perseverative behavior—linking BOLD signal timing to cognition. 4/n

Hemodynamic latencies are markedly increased in the nucleus accumbens (NAcc)—a key dopaminergic region linked to addiction. This effect is strikingly precise: the anatomical NAcc boundary (green) aligns closely with the shift in hemodynamic latency. 3/n

fMRI doesn’t directly measure dopamine, but could dopamine influence BOLD signal timing? Dopamine axons wrap around microvessels, and dopamine triggers vasoconstriction. We hypothesized that higher extracellular dopamine increases the latency of the hemodynamic response. 2/n

Will you keep your #NewYear’s #resolutions? https://t.co/BmNrUV88da @UCRiverside @ucrchass @iancballard #psychologist #psychology #neuroscience

Check out our Special Focus on motivation and decision-making in JOCN 🚨. Each paper applies computational process modeling to reveal how motivational factors shape the cognitive processes guiding decision-making. @TarazLee @amitaishenhav @yue_l_zhang @JasonLeng5

ALSO in this month's @jocn@JOCN_Journal @jocn.bsky.social Special Focus: Neural and Computational Mechanisms of Motivation and Decision Making Debbie M. Yee Tyler J. Adkins, Taraz G. Lee Ian C. Ballard, M. Waskom, K. C. Nix, M. D’Esposito Yue Zhang, Xiamin Leng, A.Shenhav

This work would not have been possible without the contributions of my co-authors (who are mostly not on X), and I want to call out @aberryobie for her role and expertise in the PET imaging of dopamine neurophysiology!

Why do we sometimes rely on habits, even when they conflict with our goals? In our new preprint, 'A dopaminergic basis of behavioral control,' we explore how dopamine in the striatum influences the trade-off between automatic and goal-directed action. https://t.co/81spaM74hg

VOTE FOR YOUR FAVORITE SYMPOSIUM! Vote for the Symposium you'd most like to see at the #CNS2025 Annual Meeting. Voting Deadline - October, 28, 2024 For more information visit: https://t.co/1QmmZGYW7C @CogNeuroNews @CNS_TA

You can find a hi-resolution version of the UCR CCN flyer here:

💥New paper from the lab is out today in @NatureNeuro by phenom @laura_pritschet!! See LP's great summary below. This paper launches The Maternal Brain Project, an international effort to longitudinally map the brain & proteome across pregnancy, thanks to @bowers_wbhi @CZIscience

If you are attending @eccvconf, check out @maytusp's poster at the Human-inspired Computer Vision workshop.

Looking forward, I am excited about the promise of using neural networks to design experiments in psychology and neuroscience. I am a fan of this paper using a similar approach: https://t.co/M5MTgQoZIW @ptoncompmemlab

Using our stimulus set, we (@maytusp, @sirawaj, @JohnPappas10) found that both low and high-level visual properties influence human similarity judgments, and the ability of neural network layers to explain behavior corresponded to their Brain-Score.

Conceptually similar objects often share low-level visual properties, but these may separately influence decisions. For example, you may choose a less-preferred dessert at a bakery because it is more visually appealing. Using neural networks, we can distinguish these properties.

New paper alert! How do low-level visual properties influence decision-making? We developed an approach to tackle that question using deep neural networks to design a stimulus set that teases apart low from high-level visual properties. @eccvconf https://t.co/HGzvwEFlUQ