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Identifying dynamic reproducible brain states using a predictive modelling approach.

David O'Connor1,2, Corey Horien2,3, Francesca Mandino2

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Summary
This summary is machine-generated.

No single brain state optimizes trait prediction; combining diverse brain states improves modeling. However, specific, isolated brain states are better for predicting immediate behaviors during fMRI scans.

Keywords:
brain behavior modelingdynamic functional connectivityfMRIgeneralizability

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Area of Science:

  • Neuroimaging
  • Cognitive Neuroscience
  • Computational Psychiatry

Background:

  • Brain states, influenced by internal and external factors, can affect neuroimaging-based trait modeling.
  • Dynamic functional connectivity (dFC) analysis captures rapid, moment-to-moment brain state fluctuations.
  • Previous research suggests brain states can be experimentally manipulated.

Purpose of the Study:

  • To test if specific brain states maximize brain-trait modeling performance.
  • To investigate the utility of dynamic functional connectivity for identifying behavior and trait-related brain states.
  • To compare optimal brain state selection for trait versus in-scanner behavioral prediction.

Main Methods:

  • Utilized a regression-based framework, Connectome-based Predictive Modelling (CPM).
  • Employed a resample aggregating approach to analyze dynamic functional connectivity maps.
  • Identified brain states associated with specific traits and behaviors.

Main Results:

  • No single optimal brain state was found for trait prediction; combining data across diverse states improved performance.
  • Conversely, isolated and temporally specific scan segments were superior for predicting in-scanner behavior.
  • The developed dynamic functional connectivity models for behavior demonstrated replication and predictive success in separate datasets.

Conclusions:

  • Integrating data from multiple brain states enhances predictive modeling of stable traits.
  • Short-term behavioral prediction benefits from focusing on temporally distinct brain state segments.
  • The proposed methodology offers a valuable tool for studying brain states and short-time predictive modeling.