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Related Experiment Video

Updated: May 28, 2026

Basics of Multivariate Analysis in Neuroimaging Data

Basics of Multivariate Analysis in Neuroimaging Data

Published on: July 24, 2010

Sparse Multivariate Analysis Reveals Dissociable White Matter Networks for Cognitive and Motor Processing Speed.

Shahwar Yasir^1,2, Nzamukiza Fidele³, Eduardo Martinez-Montes^2,4

¹Clinical Hospital of Chengdu Brain Science Institute, University of Electronic Science and Technology of China, Chengdu 610054, China.

|May 27, 2026

Summary

This summary is machine-generated.

Distinct white matter networks map to cognitive and motor processing speed components. Brain structure, not alpha oscillations, explains reaction time variability in healthy adults.

Keywords:

EEG alpha peak canonical correlation analysis fractional anisotropy intra-individual variability multimodal neuroimaging reaction time white matter

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Last Updated: May 28, 2026

Basics of Multivariate Analysis in Neuroimaging Data

Basics of Multivariate Analysis in Neuroimaging Data

Published on: July 24, 2010

Co-analysis of Brain Structure and Function using fMRI and Diffusion-weighted Imaging

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Published on: November 8, 2012

A Method for Investigating Age-related Differences in the Functional Connectivity of Cognitive Control Networks Associated with Dimensional Change Card Sort Performance

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Published on: May 7, 2014

Area of Science:

Cognitive Neuroscience
Neuroimaging
Brain Mapping

Background:

Reaction time (RT) measures information processing speed, influenced by brain structure and function.
Previous research linked white matter and EEG alpha oscillations to cognition separately.
The combined influence on distinct RT aspects remains unclear.

Purpose of the Study:

Investigate multimodal data's role in dissociating neural systems for cognitive and motor processing speed.
Examine associations between white matter tracts, EEG alpha frequency, and RT metrics.
Utilize sparse canonical correlations to find multivariate links across modalities.

Main Methods:

Diffusion tensor imaging (DTI) and resting-state EEG data from 24 healthy adults.
GO/NO-GO paradigm for behavioral RT (mean, standard deviation, skewness).
Sparse multiple canonical correlation analysis (SMCCA) for cross-modal associations.

Main Results:

Two dimensions revealed: fronto-temporal tracts linked to complex RT (cognitive processing).
Motor/interhemispheric tracts associated with RT skewness (motor consistency).
Individual alpha peak frequency (IAF) showed minimal contribution; sex was strongly associated.

Conclusions:

Separate white matter networks underpin distinct cognitive and motor RT aspects.
Resting-state alpha frequency did not consistently link to behavioral variability in this sample.
Multimodal, multivariate approaches are crucial for complex brain-behavior relationships.