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Related Concept Videos

Lateralization01:28

Lateralization

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Brain lateralization refers to the division of mental processes and functions between the two hemispheres of the brain, a phenomenon that optimizes neural efficiency and underpins complex abilities in humans. This specialization allows each hemisphere to perform tasks where it has a comparative advantage, facilitating more refined cognitive capabilities across different domains.
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Modeling the Functional Network for Spatial Navigation in the Human Brain
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Modeling the association between functional connectivity and lateralization with the activity flow framework.

Xue Zhan1, Jinwei Lang1, Li-Zhuang Yang2

  • 1Anhui Province Key Laboratory of Medical Physics and Technology, Institute of Health and Medical Technology, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei 230031, PR China; University of Science and Technology of China, Hefei 230026, PR China.

Brain Research
|February 27, 2024
PubMed
Summary
This summary is machine-generated.

Causally valid brain connectivity metrics better predict brain lateralization than simple correlations. This finding holds across different cognitive tasks and clinical populations, suggesting causal interactions shape brain organization.

Keywords:
Activity flowFunctional connectivityFunctional lateralizationNetwork mechanismTask fMRI

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Area of Science:

  • Neuroscience
  • Cognitive Neuroscience
  • Network Science

Background:

  • Brain function involves both localized processing (lateralization) and distributed network interactions (functional connectivity).
  • The relationship between brain lateralization and functional connectivity (FC) is an area of active research.
  • Existing FC metrics vary in their ability to capture causal associations, impacting their utility in understanding brain organization.

Purpose of the Study:

  • To investigate whether causally valid functional connectivity metrics predict the lateralization index (LI) better than correlation-based FC.
  • To compare four brain connectivity metrics (correlation-based FC, multiple-regression FC, partial-correlation FC, combinedFC) within the activity flow framework.
  • To evaluate two modeling approaches (one-step and two-step) for assessing the association between lateralization and FC.

Main Methods:

  • Employed the activity flow framework to analyze brain connectivity.
  • Compared four FC metrics: correlation-based, multiple-regression, partial-correlation, and combinedFC.
  • Utilized one-step and two-step modeling approaches to predict LI from FC metrics.

Main Results:

  • Multiple-regression FC, partial-correlation FC, and combinedFC significantly improved model prediction compared to correlation-based FC.
  • These findings were consistent across a spatial working memory task and a language task.
  • The results were replicated in clinical samples including schizophrenia, bipolar disorder, and ADHD.

Conclusions:

  • Causally valid functional connectivity metrics provide a better understanding of brain lateralization than simple correlation-based methods.
  • Brain network interactions, particularly causal ones, play a crucial role in shaping lateralization patterns.
  • The findings have implications for understanding brain organization in both healthy and clinical populations.