{
  "id": 134039,
  "title": "Disruption of interareal control during propofol anesthesia",
  "url": "https://urgent.news/2026/08/04/disruption-of-interareal-control-during-propofol-anesthesia",
  "topic": "ai",
  "section": "AI",
  "published": "2026-08-04T00:00:00.000Z",
  "source": {
    "name": "bioRxiv",
    "slug": "biorxiv",
    "url": "https://www.biorxiv.org/content/10.64898/2026.07.28.741350v1?rss=1"
  },
  "original_language": "en",
  "account": "The study explores how anesthesia may disrupt the ability of different brain regions to control each other's activity. Researchers used advanced mathematical tools to analyze how easily systems within complex networks can influence one another. They adapted existing data-driven methods to work with neural recordings, enabling them to uncover directional, nonlinear control from partial brain activity observations.\n\nThese techniques were tested on simulated systems and real neural data from primates, comparing wakeful states with those under propofol anesthesia. The findings revealed that anesthesia markedly decreased the ability of brain areas to manipulate each other, both in terms of paving new paths and maintaining existing ones. This reduction was linked to a drop in interregional coupling strength.\n\nInterestingly, the research also noted that certain directional controls became easier under anesthesia. For instance, the PPC could exert more influence over the vlPFC, while FEF could more readily control the vlPFC. This seemingly paradoxical increase in control from some areas to others may offer an explanation for the sometimes unexpected effects observed during propofol-induced unconsciousness. The study essentially presents anesthetic-induced unconsciousness as a result of a breakdown in the way cortical regions direct their control over one another.",
  "summary": "Anesthetic-induced unconsciousness may arise partly from a change in how brain areas can manipulate each other's activity. To quantify this change, we use control-theoretic tools that can precisely characterize how easily subsystems in complex networks can control each other. These tools rely on the Jacobian of the dynamics, an object that fully specifies how inputs to a function affect the…",
  "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."
}