{
  "id": 19178696,
  "title": "Ergodic breakdown of brain dynamics under chronnectome complexity across cognitive states and age",
  "url": "https://urgent.news/2026/11/09/ergodic-breakdown-of-brain-dynamics-under-chronnectome-complexity",
  "topic": "science",
  "section": "Science",
  "published": "2026-11-09T00:00:00.000Z",
  "source": {
    "name": "bioRxiv",
    "slug": "biorxiv",
    "url": "https://www.biorxiv.org/content/10.64898/2026.11.06.764964v1?rss=1"
  },
  "original_language": "en",
  "account": "Ergodic breakdown in brain dynamics emerges when chronnectome complexity escalates across cognitive states and age. Chronnectome complexity denotes the sequential progression of distinct functional connectivity configurations. Its influence on information encoding during resting and task states is yet to be fully understood. Ergodic theory posits that time averages represent ensemble behavior in stationary dynamics. However, breakdown occurs when dynamics shift between distinct regimes, as seen in brain activity traversing diverse configurations over time. This phenomenon, termed the information migration principle, separates time-averaged connectivity in ergodic dynamics from trajectory geometry in non-ergodic dynamics.\n\nTo test this principle, a data-driven model, the Brain Dynamics Classifier (BDC), was developed. It integrates dynamic functional connectivity into a latent space via a graph neural network and learns the governing vector field with a neural ordinary differential equation. The model extracts two representations: time-averaged connectivity, effective under ergodic conditions, and trajectory geometry, preserving temporal organization in non-ergodic conditions.\n\nThe BDC achieved high classification accuracy, 97.4% for eight conditions in young adults and 92.2% for four conditions in the aging cohort. Abolishing the time-averaged representation revealed that trajectory contributions fluctuated with chronnectome complexity, peaking for Working Memory and vanishing for Relational Reasoning. This aligns with the breakdown of ergodic dynamics. Incorporating subcortical regions further amplified trajectory contributions for affective conditions. In the aging cohort, the distinction between high and low complexity conditions persisted; resting-state dynamics grew less ergodic with age, and higher fluid intelligence correlated with more ergodic resting-state dynamics.",
  "summary": "Chronnectome complexity, the extent to which time-varying functional connectivity follows a structured sequence of distinct configurations, contributes to shaping large-scale brain dynamics. However, it remains unclear how this complexity influences the encoding of information that distinguishes resting state and task conditions. In ergodic theory, time averages represent ensemble behavior under…",
  "key_points": [],
  "editors_take": null,
  "illustration": null,
  "coverage": {
    "outlets": 1,
    "also_reported_by": []
  },
  "ai_generated": true,
  "disclaimer": "Summaries, key points and the editor’s take are written by software from other outlets’ reporting and may contain errors — always check the linked original."
}