Ziwei Zhang

Ongoing thoughts at rest reflect functional brain organization and behavior Resting-state functional connectivity (rsFC)-brain connectivity observed when people rest with no external tasks-predicts individual differences in behavior. Yet, rest is not idle; it involves streams...

New preprint! 🧠

Our mind wanders at rest. By periodically probing ongoing thoughts during resting-state fMRI, we show these thoughts are reflected in brain network dynamics and contribute to pervasive links between functional brain architecture and everyday behavior (1/10).
doi.org/10.1101/2025...

Brain network dynamics predict moments of surprise across contexts Nature Human Behaviour - Zhang and Rosenberg built a model that predicts surprise from brain network dynamics measured with fMRI revealing similarities across distinct contexts (task learning,...

Out now in @naturehumbehav.bsky.social: We developed a generalizable brain network model predicting moment-to-moment surprise. This edge-fluctuation-based predictive model (EFPM) of surprise works across tasks, from adaptive learning to watching basketball games or cartoons! rdcu.be/d4y3g

We thank James Antony, Joseph McGuire, Chang-Hao Kao for sharing the data, Joshua Faskowitz & the brain networks & behavior lab ( www.brainnetworkslab.com ) for sharing the edge time series code. Thanks to @monicarosenb.bsky.social for the help and support on this project. 9/9

Nor did models built from related behavioral measures (e.g., participants’ prediction, reward). 8/9

Moreover, models built from BOLD activation alone failed to generalize across datasets to predict surprise. 7/9

The data-driven surprise EFPM outperformed models built from interactions between and/or within predefined functional brain networks. 6/9

The same model generalized to predict surprise when people watched NCAA basketball games (www.sciencedirect.com/science/arti...), even when controlling for other features in the games (e.g., video motion). 5/9

The model predicted surprise in the adaptive learning task (www.sciencedirect.com/science/arti...) in held-out individuals from their functional network dynamics. 4/9

Using insights from edge-centric neuroscience (www.nature.com/articles/s41...), we built an edge-fluctuation-based predictive model (EFPM) to identify functional interactions predicting moment-to-moment changes in surprise in an adaptive learning task. 3/9

This is difficult to assess with behavioral measures alone because in some paradigms surprise is measured explicitly whereas in others it is hidden. Characterizing brain dynamics allows us to discover commonalities between surprise in different contexts. 2/9

Brain network dynamics predict moments of surprise across contexts bioRxiv - the preprint server for biology, operated by Cold Spring Harbor Laboratory, a research and educational institution

New preprint! www.biorxiv.org/content/10.1...
We’re surprised in many situations, like surprise parties, lab tasks, & suspenseful basketball games. Despite being in completely different situations, does our brain process unexpectedness similarly? 1/9