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Spontaneous brain-wide rotational dynamics constrain cue-evoked behavior

Sihan Chen, Yunlong Xu, Doruk Efe Gökmen, Michel Fruchart, Brent Doiron, Vincenzo Vitelli · 2026 · bioRxiv (Cold Spring Harbor Laboratory)

DOI: 10.64898/2026.10.17.759884 · cited by 0 works (OpenAlex) · open-access PDF

Japanese

ここでは、我々は、自発的な脳全体の回転タスクダイナミクスがクエス誘導の行動と学習を制限することを示しています。これらの発見を体系的に確立するために、我々は、マウスと人間からモーターコルチカルトスピックトレーニングデータを制限するために、自発的な脳全体の回転タスクダイナミクスが、個々の試験内でさえ低変数回転モードの検出を最適化する回転コンポーネント解析(RCA)と呼ばれる次元回転ダイナミクス削減技術を開発しました。我々は、RCAをモーターコルチカルトスピック誘導のトレーニングデータに適用しました。 ランダムコルチカルト

Machine translation (Meta M2M100); the English original follows.

Abstract

Uncovering low-dimensional representations of brain-wide neural dynamics and their relationship to behavior remains a central challenge. Here, we show that spontaneous brain-wide rotational dynamics constrain cue-evoked behavior and learning. To systematically establish these findings, we developed a dimensionality-reduction technique called Rotational Component Analysis (RCA) that optimizes the detection of low-variance rotational modes even within individual trials. We applied RCA to motor-cortical spike-train data from monkeys and humans, mouse cortex-wide calcium imaging, and human functional magnetic resonance imaging (fMRI) and magnetoencephalography (MEG). In mice, a rotational mode identified from activity recorded at rest predicts cue-evoked behavior: the phase of rotation when a randomly timed cue arrives predicts whether the animal subsequently pulls the lever. We explain this surprisingly strong correlation using both a recurrent neural network (RNN) and a minimal two-dimensional model of response learning, in which the intrinsic rotational mode serves as a dynamical constraint on response and learning. We further identify double-rotation modes containing two centers of rotation that separate preparatory from task dynamics in both mouse and human data. Remarkably, corresponding modes also organize neural dynamics during a non-motor human task, and rotational irreversibility captures early psychosis even when principal component analysis shows no group difference. Our work establishes rotational dynamics as low-dimensional coordinates that constrain how ongoing brain states are converted into behavior. More broadly, RCA provides a versatile tool for detecting hidden nonreciprocal dynamics in biological and physical datasets.

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Topics: Magnetoencephalography, Dynamics (music), Rotation (mathematics), Physics, Rotational dynamics, Rotation around a fixed axis

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