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  3. Identifying Left And Right Hemispheres Using Functional Connectivity.
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  3. Identifying Left And Right Hemispheres Using Functional Connectivity.

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Identifying left and right hemispheres using functional connectivity.

Trevor K M Day1, Peter E Turkeltaub1,2, Elissa L Newport1,2

  • 1Center for Brain Plasticity and Recovery, Georgetown University, Washington, D.C., U.S.A.

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|December 19, 2025

View abstract on PubMed

Summary
This summary is machine-generated.

Supervised learning accurately distinguishes right and left brain hemispheres using functional connectivity. This brain hemisphere classification is highly accurate for right-handed individuals and moderately accurate for left-handed individuals.

Keywords:
connectomehandednesshemispheric organizationleft-handednessright-handedness

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Area of Science:

  • Neuroscience
  • Cognitive Science
  • Machine Learning

Background:

  • The human brain's left and right hemispheres exhibit organizational differences, particularly in language processing.
  • Understanding hemispheric specialization is crucial for neuroscience and clinical applications.
  • Functional connectivity patterns offer a novel approach to studying brain organization.

Purpose of the Study:

  • To determine if supervised learning can classify brain hemispheres (left vs. right) based on functional connectivity data.
  • To investigate the influence of handedness on the accuracy of hemispheric classification.
  • To explore the potential of using functional connectivity to identify handedness and hemisphere chirality simultaneously.

Main Methods:

  • Utilized functional connectivity data from the Human Connectome Project.
  • Employed supervised learning algorithms for hemisphere classification.
  • Analyzed classification accuracy across participants with varying degrees of handedness (Edinburgh Handedness Inventory [EHI]).

    • Main Results:

    • Achieved high classification accuracies (> .90) for distinguishing left and right hemispheres in right-handed participants (EHI > 0).
    • Maintained high, though slightly reduced, accuracies when classifying hemispheres in left-handed participants (EHI ≤ 0).
    • Failed to successfully identify handedness alongside hemisphere chirality, but found reduced hemispheric distinctiveness correlated with stronger left-handedness.

    Conclusions:

    • Supervised learning effectively categorizes human brain hemispheres based on functional connectivity, especially in right-handed individuals.
    • Hemispheric organization and distinctiveness are influenced by handedness.
    • Findings provide insights into developmental and post-injury hemispheric organization and offer potential for future research.