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Related Experiment Videos

Combining region- and network-level brain-behavior relationships in a structural equation model.

Taylor Bolt1, Emily B Prince1, Jason S Nomi1

  • 1Department of Psychology, University of Miami, Coral Gables, FL, USA.

Neuroimage
|October 15, 2017
PubMed
Summary
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Analyzing brain activity with functional magnetic resonance imaging (fMRI) reveals that brain networks, not just individual regions, are key to task performance. This new approach combines region-of-interest and network analyses for better brain-behavior insights.

Area of Science:

  • Neuroimaging
  • Cognitive Neuroscience
  • Brain-Behavior Relationships

Background:

  • fMRI studies often analyze brain-behavior associations at either the region-of-interest (ROI) or network level.
  • This common practice may overlook interdependencies within networks or unique ROI information.
  • A combined approach is needed to fully capture complex brain-behavior relationships.

Purpose of the Study:

  • To develop and validate a novel framework combining ROI and network-level analyses in fMRI studies.
  • To investigate how individual ROIs and their associated whole-brain networks relate to task performance.
  • To explore the interplay between ROI-specific and network-level brain activity in cognitive tasks.

Main Methods:

  • Employed structural equation modeling (SEM) integrating measurement and structural components.

Related Experiment Videos

  • Empirically derived brain networks from ROI activity using three large task-fMRI datasets.
  • Assessed associations of individual ROIs and derived networks with task performance across two parcellation schemes.

    • Main Results:

    • For working memory and relational tasks, ROI-performance associations became non-significant or reversed when network effects were considered; the network itself showed robust performance associations.
    • For arithmetic tasks, some ROIs maintained robust performance associations even after accounting for network effects.
    • Demonstrated that the combined SEM framework offers a more nuanced understanding of brain-behavior links.

    Conclusions:

    • The proposed SEM framework provides a flexible and principled method for integrating ROI and network-level analyses in fMRI research.
    • Findings highlight the importance of considering network-level dynamics alongside ROI activity for a comprehensive understanding of brain function.
    • This approach enhances the ability to test complex brain-behavior relationships in cognitive neuroscience.