Hemisphere fingerprinting: Identifying left and right hemispheres using functional connectivity.
- 1Center for Brain Plasticity and Recovery, Georgetown University, Washington, DC, United States.
- 2MedStar National Rehabilitation Hospital, Washington, DC, United States.
Abstract
Many studies have analyzed what organizational features distinguish the left and right hemispheres of the human brain, with differences typically being found in language areas and in fine motor control (i.e., handedness). In this analysis, we test whether supervised learning can categorize ("fingerprint") an unseen hemisphere as right or left based on functional connectivity. Using data from the Human Connectome Project, we find success to be extremely high (accuracies > .90) in models trained on right-handed participants (Edinburgh Handedness Inventory [EHI] > 0) and in models trained on left-handed participants (EHI ≤ 0). In a second analysis, we test whether the same can be done to identify handedness alongside hemisphere left/right sidedness. While individuals' hemispheres are less distinct the more left handed they are, hemiconnectomes cannot be reliably classified as belonging to a left- or right-handed person. Our approach can inform developmental and post-injury work on hemispheric organization and reorganization.
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BibTeX
@article{day2026hemisphere,
title = {{Hemisphere fingerprinting: Identifying left and right hemispheres using functional connectivity.}},
author = {Trevor K M Day and Peter E Turkeltaub and Elissa L Newport and Andrew T DeMarco},
journal = {Imaging neuroscience (Cambridge, Mass.)},
year = {2026},
month = oct,
doi = {10.1162/imag.a.1397},
url = {https://europepmc.org/article/MED/42953869},
note = {CC BY},
}
RIS
TY - JOUR
TI - Hemisphere fingerprinting: Identifying left and right hemispheres using functional connectivity.
AU - Trevor K M Day
AU - Peter E Turkeltaub
AU - Elissa L Newport
AU - Andrew T DeMarco
PY - 2026
DA - 2026/10/27
JO - Imaging neuroscience (Cambridge, Mass.)
DO - 10.1162/imag.a.1397
UR - https://europepmc.org/article/MED/42953869
AB - Many studies have analyzed what organizational features distinguish the left and right hemispheres of the human brain, with differences typically being found in language areas and in fine motor control (i.e., handedness). In this analysis, we test whether supervised learning can categorize ("fingerprint") an unseen hemisphere as right or left based on functional connectivity. Using data from the Human Connectome Project, we find success to be extremely high (accuracies > .90) in models trained on right-handed participants (Edinburgh Handedness Inventory [EHI] > 0) and in models trained on left-handed participants (EHI ≤ 0). In a second analysis, we test whether the same can be done to identify handedness alongside hemisphere left/right sidedness. While individuals' hemispheres are less distinct the more left handed they are, hemiconnectomes cannot be reliably classified as belonging to a left- or right-handed person. Our approach can inform developmental and post-injury work on hemispheric organization and reorganization.
SN - 2837-6056
ER -