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Reposted by Ziwei Zhang

Jin Ke @jinke.bsky.social · 20/08/2025

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...

biorxiv.org

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...

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Ziwei Zhang @zz112.bsky.social · 23/12/2024

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

rdcu.be

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,...

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Ziwei Zhang @zz112.bsky.social · 04/12/2023

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

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Ziwei Zhang @zz112.bsky.social · 04/12/2023

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

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Ziwei Zhang @zz112.bsky.social · 04/12/2023

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

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Ziwei Zhang @zz112.bsky.social · 04/12/2023

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

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Ziwei Zhang @zz112.bsky.social · 04/12/2023

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

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Ziwei Zhang @zz112.bsky.social · 04/12/2023

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

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Ziwei Zhang @zz112.bsky.social · 04/12/2023

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

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Ziwei Zhang @zz112.bsky.social · 04/12/2023

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

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Ziwei Zhang @zz112.bsky.social · 04/12/2023

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

biorxiv.org

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

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