Brain imaging and machine learning reveal uncoupled functional network for contextual threat memory in long sepsis

Sci Rep. 2024 Nov 12;14(1):27747. doi: 10.1038/s41598-024-79259-5.

Abstract

Positron emission tomography (PET) utilizes radiotracers like [18F]fluorodeoxyglucose (FDG) to measure brain activity in health and disease. Performing behavioral tasks between the FDG injection and the PET scan allows the FDG signal to reflect task-related brain networks. Building on this principle, we introduce an approach called behavioral task-associated PET (beta-PET) consisting of two scans: the first after a mouse is familiarized with a conditioning chamber, and the second upon recall of contextual threat. Associative threat conditioning occurs between scans. Beta-PET focuses on brain regions encoding threat memory (e.g., amygdala, prefrontal cortex) and contextual aspects (e.g., hippocampus, subiculum, entorhinal cortex). Our results show that beta-PET identifies a biologically defined network encoding contextual threat memory and its uncoupling in a mouse model of long sepsis. Moreover, machine learning algorithms (linear logistic regression) and ordinal trends analysis demonstrate that beta-PET robustly predicts the behavioral defense response and its breakdown during long sepsis.

MeSH terms

  • Animals
  • Brain Mapping / methods
  • Brain* / diagnostic imaging
  • Brain* / physiopathology
  • Disease Models, Animal
  • Fluorodeoxyglucose F18
  • Machine Learning*
  • Male
  • Memory* / physiology
  • Mice
  • Mice, Inbred C57BL
  • Positron-Emission Tomography* / methods
  • Sepsis* / diagnostic imaging
  • Sepsis* / physiopathology

Substances

  • Fluorodeoxyglucose F18