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Predicting Repetitive Negative Thinking in Daily Life: Insights From Brain-Based Graph-Theoretical Predictive

Martino Schettino1, Rotem Dan2, Chiara Parrillo3

  • 1Center for Depression, Anxiety, and Stress Research, McLean Hospital, Belmont, Massachusetts; Department of Psychology, Sapienza University of Rome, Rome, Italy; IRCCS Institute of Neurological Science of Bologna, Bologna, Italy.

Biological Psychiatry. Cognitive Neuroscience and Neuroimaging
|October 2, 2025
PubMed
Summary
This summary is machine-generated.

Brain network abnormalities predict daily repetitive negative thinking (RNT). Graph-theoretical modeling identified specific network features linked to RNT severity and intrusiveness, offering insights into RNT's emergence.

Keywords:
Ecological momentary assessmentFunctional connectomeGraph theoryPredictive modelingRuminationfMRI

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Area of Science:

  • Neuroscience
  • Cognitive Science
  • Psychiatry

Background:

  • Repetitive negative thinking (RNT) is linked to altered functional connectivity in large-scale brain networks.
  • The predictive potential of these network abnormalities for daily RNT remains unclear.

Purpose of the Study:

  • To predict daily-life RNT using brain-based graph-theoretical predictive modeling (GPM).
  • To identify specific functional brain network properties associated with RNT severity and its clinical features.

Main Methods:

  • Utilized GPM with functional magnetic resonance imaging (fMRI) data from 54 individuals during resting and RNT-induced states.
  • Assessed RNT severity and fluctuations using ecological momentary assessments (EMAs).

Main Results:

  • GPM identified key functional properties of the default mode network (DMN), frontoparietal network (FPN), and limbic network predicting RNT.
  • Medial prefrontal cortex centrality predicted RNT intrusiveness; orbitofrontal cortex strength/centrality predicted repetitiveness; temporal pole segregation predicted RNT severity.
  • Daily-life EMA assessments provided better RNT prediction than lab-based questionnaires.

Conclusions:

  • GPM effectively predicts the emergence of daily-life RNT.
  • Specific network attributes like centrality and segregation underlie RNT and its clinical manifestations.