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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.
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.
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.
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