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Lateralization

Lateralization

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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Related Experiment Video

Updated: Aug 21, 2025

Assessment of Cerebral Lateralization in Children using Functional Transcranial Doppler Ultrasound fTCD

Assessment of Cerebral Lateralization in Children using Functional Transcranial Doppler Ultrasound fTCD

Published on: September 27, 2010

Generalized models for quantifying laterality using functional transcranial Doppler ultrasound.

Paul A Thompson¹, Kate E Watkins¹, Zoe V J Woodhead¹

¹Department of Experimental Psychology, Anna Watts Building, Radcliffe Observatory Quarter, Oxford, UK.

Human Brain Mapping

|November 15, 2022

Summary

This summary is machine-generated.

New statistical models, generalized additive models (GAM), improve brain lateralization analysis using functional transcranial Doppler ultrasound (fTCD). This method offers more accurate results than traditional approaches, especially for complex brain function studies.

Keywords:

fMRI fTCD generalized additive model (GAM)generalized linear model (GLM)laterality

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Last Updated: Aug 21, 2025

Assessment of Cerebral Lateralization in Children using Functional Transcranial Doppler Ultrasound fTCD

Assessment of Cerebral Lateralization in Children using Functional Transcranial Doppler Ultrasound fTCD

Published on: September 27, 2010

Functional Transcranial Doppler Ultrasound for Monitoring Cerebral Blood Flow

Functional Transcranial Doppler Ultrasound for Monitoring Cerebral Blood Flow

Published on: March 15, 2021

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

Area of Science:

Neuroscience
Biomedical Engineering
Statistics

Background:

Functional transcranial Doppler ultrasound (fTCD) is used to assess brain lateralization.
Conventional fTCD analysis relies on averaging signals within a period of interest (POI).
Modern statistical methods like generalized linear models (GLMs) and generalized additive models (GAM) are standard in functional magnetic resonance imaging (fMRI) but less common in fTCD.

Purpose of the Study:

To adapt and evaluate advanced statistical models (GLMs and GAMs) for analyzing fTCD data.
To compare the performance of these models against conventional fTCD averaging methods.
To assess the accuracy of these methods in identifying brain lateralization, including bilateral language representation, and compare with fMRI.

Main Methods:

Applied generalized linear models (GLMs) and generalized additive models (GAMs) to fTCD data from three studies (N=154, 73, 31).
GLM approach incorporated a hemodynamic response function, similar to fMRI analysis.
GAM approaches (simple and complex) estimated the response function directly from the data, with complex GAM including epoch-specific effects.

Main Results:

Individual laterality index estimates were consistent across conventional averaging, GLM, and GAM methods.
Complex GAM demonstrated the lowest error of measurement in fTCD data.
Complex GAM improved the reliable identification of bilateral language representation compared to other methods.
GAM-based approaches effectively analyzed complex experimental designs, including task interactions.

Conclusions:

Generalized additive models (GAMs), particularly complex GAM, offer a more accurate and reliable method for analyzing brain lateralization using fTCD data.
These advanced statistical approaches align fTCD analysis with modern neuroimaging statistical practices.
The GAM framework enhances the ability to detect subtle patterns of brain lateralization and analyze complex experimental designs.