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Dissociating frontoparietal brain networks with neuroadaptive Bayesian optimization.

Romy Lorenz1, Ines R Violante2, Ricardo Pio Monti3

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Area of Science:

  • Cognitive Neuroscience
  • Neuroimaging Analysis

Background:

  • Frontoparietal networks (FPN) are crucial for cognition but challenging to study due to spatial overlap and co-activation.
  • Previous characterization relied on meta-analyses across diverse tasks.

Purpose of the Study:

  • To discover cognitive tasks that can segregate ventral and dorsal FPN activity using novel neuroimaging analysis.
  • To establish a method for differentiating FPNs based on their activity profiles across tasks.

Main Methods:

  • Employed neuroadaptive Bayesian optimization, integrating real-time functional neuroimaging data with machine learning.
  • Identified and refined Deductive Reasoning and Tower of London tasks for maximal FPN dissociation.

Main Results:

  • Successfully identified tasks that maximally dissociate dorsal from ventral FPN activity.
  • Demonstrated that analyzing activity profiles across multiple tasks is key to differentiating FPNs, challenging prior meta-analyses.

Conclusions:

  • The identified tasks provide a foundation for a neurobiologically-derived cognitive taxonomy.
  • Findings offer a new approach to understanding FPNs and their unique cognitive contributions, diverging from existing functional labels.