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Foundational perspectives on causality in large-scale brain networks.

Michael Mannino1, Steven L Bressler2

  • 1Center for Complex Systems and Brain Sciences, Florida Atlantic University, 777 Glades Road, Boca Raton, FL 33431, United States.

Physics of Life Reviews
|October 3, 2015
PubMed
Summary

This review clarifies brain causality, arguing probabilistic causality (PC) is better than deterministic causality for understanding complex brain networks. PC accounts for mutual influences and probabilistic outcomes in neural activity.

Keywords:
BrainBrain connectivityCausalityDeterminismLarge-scale neurocognitive networksProbability

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Area of Science:

  • Cognitive Neuroscience
  • Philosophy of Science
  • Computational Neuroscience

Background:

  • Recent cognitive neuroscience research explores causal influences in large-scale brain networks.
  • Existing studies often lack rigorous conceptual analysis of brain causality.
  • This review addresses the theoretical challenges in understanding causality within the brain.

Purpose of the Study:

  • To provide a descriptive and prescriptive account of causality in large-scale brain networks.
  • To clarify the philosophical and scientific concept of causality in the brain.
  • To distinguish between deterministic causality (DC) and probabilistic causality (PC).

Main Methods:

  • Review of philosophical theories of causality (Hume, Kant, Russell, Hitchcock).
  • Discussion of the impact of modern physics on causality.
  • Analysis of the distinction between deterministic causality (DC) and probabilistic causality (PC).

Main Results:

  • The brain, as a complex system, exhibits probabilistic causal influences.
  • Probabilistic causality (PC) is better suited for brain analysis due to mutual causality, connectional convergence, and unobservable effects.
  • Current techniques for characterizing brain causality quantify statistical likelihoods, aligning with PC.

Conclusions:

  • Probabilistic causality (PC) provides a conceptually appropriate framework for describing neural causality.
  • PC addresses the inherent complexities and probabilistic nature of brain function.
  • This approach aligns with philosophical advancements and concepts from modern physics.