Beyond the evoked/intrinsic neural process dichotomy

Taylor Bolt, Michael L. Anderson, Lucina Q. Uddin

Research output: Contribution to journalArticle

6 Scopus citations

Abstract

Contemporary functional neuroimaging research has increasingly focused on characterization of intrinsic or “spontaneous” brain activity. Analysis of intrinsic activity is often contrasted with analysis of task-evoked activity that has traditionally been the focus of cognitive neuroscience. But does this evoked/intrinsic dichotomy adequately characterize human brain function? Based on empirical data demonstrating a close functional interdependence between intrinsic and task-evoked activity, we argue that the dichotomy between intrinsic and task-evoked activity as unobserved contributions to brain activity is artificial. We present an alternative picture of brain function in which the brain’s spatiotemporal dynamics do not consist of separable intrinsic and task-evoked components, but reflect the enaction of a system of mutual constraints to move the brain into and out of task-appropriate functional configurations. According to this alternative picture, cognitive neuroscientists are tasked with describing both the temporal trajectory of brain activity patterns across time, and the modulation of this trajectory by task states, without separating this process into intrinsic and task-evoked components. We argue that this alternative picture of brain function is best captured in a novel explanatory framework called enabling constraint. Overall, these insights call for a reconceptualization of functional brain activity, and should drive future methodological and empirical efforts.

Original languageEnglish (US)
Pages (from-to)1-22
Number of pages22
JournalNetwork Neuroscience
Volume2
Issue number1
DOIs
StatePublished - Mar 1 2018

Keywords

  • Enabling constraint
  • Intrinsic activity
  • Neural variability
  • Synergy
  • Task-evoked activity

ASJC Scopus subject areas

  • Neuroscience(all)
  • Computer Science Applications
  • Artificial Intelligence
  • Applied Mathematics

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