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Brain anticorrelations, crucial for function, are not noise. A new agent-based model shows negative connections significantly improve brain simulation performance, highlighting their importance in functional brain models.

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Area of Science:

  • Neuroscience
  • Computational Neuroscience
  • Functional Neuroimaging

Background:

  • Resting-state fMRI reveals anticorrelations between brain areas, but their functional significance is unclear.
  • Previous simulation models suggest these anticorrelations may have a physiological role.
  • Understanding brain connectivity requires accounting for both positive and negative relationships.

Purpose of the Study:

  • To develop and validate an agent-based brain-inspired model (ABBM) to investigate the role of anticorrelations.
  • To assess how varying fractions of positive and negative connectivity influence model efficiency and fit empirical data.
  • To determine if anticorrelations are essential components of brain functional models.

Main Methods:

  • An agent-based brain-inspired model (ABBM) was implemented using a susceptible-excited-refractory (SER) mechanism.
  • The model was validated against 30 resting-state fMRI images from healthy controls (1000 Functional Connectomes Project).
  • Simulations systematically varied connectivity fractions to evaluate goodness-of-fit with empirical data using correlation coefficients.

Main Results:

  • A small fraction of positive connectivity was found to be necessary for optimal model fit to empirical data.
  • Incorporating negative links significantly improved model goodness-of-fit compared to models with only positive connections.
  • Anticorrelations were identified as essential for improving simulation performance, not mere noise.

Conclusions:

  • Anticorrelations play a crucial role in brain function and should not be disregarded in functional models.
  • Negative connectivity possesses significant intrinsic information vital for accurate brain simulations.
  • The study underscores the necessity of including anticorrelations to refine and enhance the performance of brain simulation models.