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The human brain, a complex organ, is functionally divided into two cerebral hemispheres—left and right. These hemispheres are interconnected by a structure of paramount importance, the corpus callosum. This substantial bundle of neural fibers is not just a bridge between the hemispheres but a crucial element for the brain's comprehensive functioning. It enables efficient communication between the two hemispheres, allowing each side of the brain to control and receive sensory and motor...

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Related Experiment Video

Updated: Jan 8, 2026

Evaluation of Hemisphere Lateralization with Bilateral Local Field Potential Recording in Secondary Motor Cortex of Mice

Evaluation of Hemisphere Lateralization with Bilateral Local Field Potential Recording in Secondary Motor Cortex of Mice

Published on: July 31, 2019

Identifying left and right hemispheres using functional connectivity.

Trevor K M Day¹, Peter E Turkeltaub^1,2, Elissa L Newport^1,2

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

Biorxiv : the Preprint Server for Biology

|December 19, 2025

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:

connectome handedness hemispheric organization left-handedness right-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.