Spontaneous brain-wide rotational dynamics constrain cue-evoked behavior

Sihan Chen; Yunlong Xu; Doruk Efe Gökmen; Michel Fruchart; Brent Doiron; Vincenzo Vitelli

AI summary · arXiv: 2610.25061v1 · q-bio.NC

Spontaneous brain-wide rotational dynamics constrain cue-evoked behavior

Sihan Chen, Yunlong Xu, Doruk Efe Gökmen, Michel Fruchart, Brent Doiron, Vincenzo Vitelli

Original paper Submitted to arXiv on 18 Oct. 2026 License: CC BY 4.0 Also in: fr

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The essentials

Spontaneous low-variance brain-wide rotations predict mouse cue responses, organize preparatory and task activity, and are reduced in early psychosis, suggesting dynamical constraints on behavior and learning.

Question

Can rotational dynamics be reliably identified in spontaneous brain-wide activity despite large unrelated fluctuations? Do these dynamics relate to subsequent behavior, learning, and psychiatric differences?

Method

The authors developed rotational component analysis (RCA), which selects two-dimensional projections maximizing directed angular current relative to its fluctuations, and a double-rotation extension (DRCA). They reanalyzed reaching-related monkey and human spiking, calcium imaging from 25 mice across 15 training days, fMRI from 590 healthy adults, MEG from 71 participants, and fMRI from 53 controls and 82 participants with non-affective psychosis. They evaluated held-out behavioral predictions and constructed a two-dimensional drift model, a 64-unit recurrent neural network, and a phenomenological reinforcement model.

Findings

Result RCA identified a spontaneous mouse cortical rotation explaining only 0.6% of total variance, compared with 87% for the leading PCA plane.
See the passage in the paper
Notably, the RCA plane accounted for only $0.6\%$ of the total variance, compared with $87\%$ for the PCA plane, highlighting how a rotational mode can be overlooked by conventional dimensionality-reduction methods.
Result Mouse cue-response prediction improved when combining the cue-onset RCA state with the DRCA state measured 2.7 seconds before the cue.
See the passage in the paper
Using separate training and testing data, the cue-onset state in the single-rotation RCA mode predicted response with an area under the receiver operating characteristic curve (AUC) of 0.638, whereas the pre-cue DRCA state achieved an AUC of 0.612. Combining the two states increased the AUC to 0.677 (Fig. 6D), indicating that the preparatory DRCA mode contained information complementary to that provided by the RCA state at cue onset (see Methods for details).
Authors’ interpretation The authors interpret learning as primarily strengthening cue-evoked dynamics within an existing rotational coordinate rather than changing the drift-to-response mapping.
See the passage in the paper
These results suggest that learning primarily alters the cue-evoked dynamics, and hence the distribution of $f^{\mathrm{post}}_{\parallel}$, while preserving the mapping from this dynamical variable to response probability.
Result Resting-state rotational irreversibility was reduced in early psychosis, including when projections were learned from either controls or participants with psychosis.
See the passage in the paper
Thus, reduced APR was detected irrespective of which group supplied the data used to learn the rotational plane.
Result Double-rotation modes separated preparatory or fixation dynamics from task-related dynamics in both mouse calcium imaging and human fMRI.
See the passage in the paper
Thus, double-rotation modes separated behaviorally defined epochs in both mouse cortex-wide calcium activity and human fMRI.

Limitations

Comparisons based on rotation scores are not independent predictive benchmarks because RCA and DRCA optimize those same scores.
See the passage in the paper
Because RCA and DRCA directly optimize the respective scores, these comparisons do not provide an independent test of predictive performance.
The RNN did not reproduce the transverse gradient of the mouse cue-evoked response field.
See the passage in the paper
During learning, the direction of the transverse gradient of $\bm{f}^{\rm rsp}$ remained fixed, which is consistent with the intrinsic rotational dynamics. This gradient was not captured by the RNN (Fig. 3I).
Sample sizes were constrained by existing datasets rather than determined through prospective power calculations.
See the passage in the paper
Sample sizes were determined by the available datasets and were not set by an a priori power calculation.

Why it matters

The authors argue that spontaneous rotational flow provides a low-dimensional scaffold shaping how cues become actions and how rewarded responses are learned. RCA and DRCA offer an alternative to variance-focused reduction for uncovering behaviorally relevant dynamics that can otherwise remain hidden.

Written by AI from the paper. Passages are checked word for word against the text we analysed; the rest may contain errors.

AI-generated keywords: Rotational component analysis; Neural dynamics; Behavioral readiness; Motor learning; Psychosis

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Comprehensive Summary
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Original abstract

This paper investigates whether spontaneous, distributed brain activity contains low-dimensional rotational dynamics that predict responses to external cues and constrain learning. Conventional dimensionality-reduction methods emphasize large fluctuations and may overlook smaller, behaviorally relevant signals. The authors introduce rotational component analysis (RCA) and apply it to existing monkey and human motor-cortical recordings, mouse cortex-wide calcium imaging, and human fMRI and MEG data. RCA identifies a two-dimensional projection that maximizes the strength and consistency of directed angular current. These rotations represent circulation through a latent activity space, not necessarily waves physically rotating across the cortex. Its objective—an estimator of the asymmetry production rate (APR)—quantifies dynamical mirror-symmetry breaking using the squared mean accumulated current relative to its variance. Unlike principal component analysis (PCA), RCA does not maximize explained variance and requires neither behavioral labels nor trial averaging. In reaching benchmarks, RCA recovered more consistent single-trial circulation than jPCA, which fits rotational dynamics within a leading-PCA subspace. For representative conditions, current signal-to-noise ratios for RCA versus jPCA were 0.89 versus 0.05 in macaque recordings and 1.90 versus 0.98 in human recordings. The central behavioral analysis examined recordings from 44 cortical regions in 25 mice learning an auditory-cued lever-pull task over 15 days. A rotational plane learned exclusively from resting activity explained only 0.6% of total variance, compared with 87% for the leading PCA plane, yet clearly distinguished cue-onset states associated with subsequent responses. Its standardized response-gradient magnitude was 0.151, compared with 0.050 for PCA and 0.078 for jPCA, placing it near the upper extreme among 2,000 random planes. In held-out-session classification, the cue-onset RCA state achieved an area under the receiver operating characteristic curve (AUC) of 0.638. In a separate pupil-matched analysis, RCA outperformed pupil-indexed arousal, with AUCs of 0.611 and 0.535, respectively. A two-dimensional stochastic model explained this association through an interaction between ongoing rotational flow and cue-evoked drive: responses were more likely when the combined drift pointed strongly in the response direction. Empirical response probability increased from below 20% to above 70% across the observed drift-score range. A trained 64-unit recurrent neural network reproduced the main geometry without explicitly imposed rotation. Across mouse training, intrinsic drift remained comparatively stable, while cue-evoked drift strengthened. A phenomenological reinforcement model suggested that reward could preferentially strengthen responses compatible with existing dynamics. RCA also detected resting rotations in human fMRI and MEG data, explaining 0.2% and 2.6% of variance, respectively, with estimated periods of 51 and 4.4 seconds. In a comparison of 53 controls and 82 participants with early non-affective psychosis, APR was lower in the psychosis group (P = 5.22 × 10⁻⁷), whereas leading-PC variability did not differ significantly. Split-sample analyses supported the APR finding. An extension that detects two counter-rotating centers distinguished preparatory from post-cue activity in mice and fixation from social-task activity in human fMRI. Combining the mouse preparatory state measured 2.7 seconds before the cue with the cue-onset RCA state increased held-out AUC to 0.677. These findings support rotational flow as a compact coordinate linking ongoing activity, behavioral readiness, and learning. However, the observational reanalyses establish associations rather than causal constraints. The proposed learning mechanism remains hypothetical, predictive accuracy is modest, and the clinical differences do not establish diagnostic utility. APR should not automatically be interpreted as thermodynamic entropy production, and its moment-based estimator is subject to distributional qualifications. Finally, RCA’s superior rotation scores partly reflect the objective it directly optimizes, and code availability was deferred until publication.

The Brain’s Quiet Rotations May Shape Its Next Move

Why does the same signal prompt action one moment and go unanswered the next? Part of the answer may lie in what the brain is doing before the signal arrives. A study suggests that spontaneous brain activity contains subtle rotational patterns that help predict whether an animal will respond—and may help explain how learning builds on existing neural dynamics.

In “Spontaneous brain-wide rotational dynamics constrain cue-evoked behavior,” Sihan Chen, Yunlong Xu, Doruk Efe Gökmen, Michel Fruchart, Brent Doiron, and Vincenzo Vitelli introduce a method for finding these patterns. Their results suggest that some behaviorally informative signals occupy surprisingly little of the brain’s overall activity.

Finding Signals That Bigger Fluctuations Can Hide

Brain recordings contain many signals unfolding together. To make sense of this complexity, researchers often use dimensionality reduction: mathematical tools that compress activity into a few coordinates. Principal component analysis, or PCA, prioritizes the directions with the largest fluctuations. But the biggest fluctuations are not necessarily the most relevant to behavior.

The authors developed rotational component analysis, or RCA, to look for something different: consistent circulation through a two-dimensional activity space. Imagine representing the brain’s changing state as a moving dot. RCA searches for a view in which that dot reliably travels around rather than simply wandering.

These rotations are mathematical patterns in a latent activity space—not necessarily waves physically spinning across the cortex. RCA requires neither behavioral labels nor trial averaging, allowing researchers to identify patterns without first telling the method which moments correspond to particular actions.

A Measure of Directed Circulation

RCA optimizes an estimator called the asymmetry production rate, or APR. It compares the squared mean accumulated current with its variance, rewarding circulation that has a strong, consistent direction. This captures dynamical mirror-symmetry breaking: activity favors one direction of circulation over its mirror opposite.

In reaching datasets, RCA recovered more consistent single-trial circulation than jPCA, a method that fits rotational dynamics within a leading-PCA subspace. For representative macaque conditions, current signal-to-noise ratios were 0.89 for RCA and 0.05 for jPCA; in human recordings, they were 1.90 and 0.98. These comparisons demonstrate RCA’s focus, although stronger rotation scores partly reflect the objective it explicitly optimizes.

A Tiny Signal Linked to Behavioral Readiness

The central behavioral analysis used recordings from 44 cortical regions in 25 mice learning an auditory-cued lever-pull task over 15 days. Crucially, the researchers learned the rotational plane exclusively from resting activity, then examined whether the brain’s position in that plane at cue onset predicted a subsequent response.

The RCA plane captured just 0.6% of total activity variance, compared with 87% for the leading PCA plane. Yet it separated cue-onset states associated with different response probabilities more clearly. Its standardized response-gradient magnitude was 0.151, versus 0.050 for PCA and 0.078 for jPCA, placing it near the upper extreme among 2,000 random planes.

Prediction on held-out sessions was meaningful but modest: the cue-onset RCA state achieved an area under the receiver operating characteristic curve, or AUC, of 0.638, where 0.5 indicates chance performance. In a separate pupil-matched analysis, RCA also outperformed pupil-indexed arousal, with AUCs of 0.611 and 0.535.

When a Cue Pushes With the Flow

A two-dimensional stochastic model offered an intuitive explanation. The cue adds a push to activity already moving through the rotational plane. Responses become more likely when ongoing motion and cue-driven motion combine to point strongly toward the response direction.

Consistent with this account, empirical response probability rose from below 20% to above 70% across the observed drift-score range. A trained 64-unit recurrent neural network reproduced the main geometry without having rotation explicitly imposed.

Across mouse training, intrinsic drift remained comparatively stable while cue-evoked drift strengthened. A phenomenological reinforcement model suggested that reward could preferentially strengthen responses compatible with existing dynamics. This is a proposed learning mechanism, however, rather than an experimentally established causal explanation.

Beyond the Mouse Task

RCA also detected resting rotations in human fMRI and MEG recordings. These accounted for 0.2% and 2.6% of variance, with estimated periods of 51 and 4.4 seconds, respectively.

In a comparison involving 53 controls and 82 participants with early non-affective psychosis, APR was lower in the psychosis group, with split-sample analyses supporting the result. Leading-PC variability did not differ significantly. This suggests a potentially informative dynamical difference, not an established diagnostic test.

An extension detecting two counter-rotating centers distinguished preparatory from post-cue activity in mice and fixation from social-task activity in human fMRI. Combining the mouse preparatory state measured 2.7 seconds before the cue with its cue-onset RCA state improved held-out AUC to 0.677.

A Promising Coordinate, Not a Final Explanation

The study makes a compelling case for looking beyond high-variance signals. Still, these observational reanalyses establish associations, not causal constraints. APR should not automatically be equated with thermodynamic entropy production, and its moment-based estimator has distributional qualifications. Code availability was also deferred until publication.

The central insight remains intriguing: the brain’s readiness to act may depend partly on where it already is—and which way it is moving—within a quiet, low-dimensional flow.

Created on 27 Oct. 2026

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Spontaneous brain-wide rotational dynamics constrain cue-evoked behavior

The essentials

Question

Method

Findings

Limitations

Why it matters

Read further

Definitions

The Brain’s Quiet Rotations May Shape Its Next Move

Finding Signals That Bigger Fluctuations Can Hide

A Measure of Directed Circulation

A Tiny Signal Linked to Behavioral Readiness

When a Cue Pushes With the Flow

Beyond the Mouse Task

A Promising Coordinate, Not a Final Explanation

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