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Measuring Attention and Visual Processing Speed by Model-based Analysis of Temporal-order Judgments

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Edge-Based General Linear Models Capture Moment-to-Moment Fluctuations in Attention.

Henry M Jones^1,2, Kwangsun Yoo^3,4,5, Marvin M Chun^3,6,7

¹Department of Psychology, The University of Chicago, Chicago, Illinois 60637 henryjones@uchicago.edu.

The Journal of Neuroscience : the Official Journal of the Society for Neuroscience

|February 5, 2024

Summary

This summary is machine-generated.

New fMRI analysis reveals rapid brain network changes during attention tasks. Edge time series capture moment-to-moment fluctuations, offering a more precise view of dynamic functional connectivity and attention.

Keywords:

attention fluctuations edge cofluctuations fMRI replication vigilance

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Mapping Cortical Dynamics Using Simultaneous MEG/EEG and Anatomically-constrained Minimum-norm Estimates: an Auditory Attention Example

Mapping Cortical Dynamics Using Simultaneous MEG/EEG and Anatomically-constrained Minimum-norm Estimates: an Auditory Attention Example

Published on: October 24, 2012

Area of Science:

Neuroscience
Cognitive Neuroscience
Functional Neuroimaging

Background:

Sustaining attention is crucial but fluctuates.
Functional connectivity (FC) networks predict attention, but traditional methods lack temporal precision.
Dynamic FC analysis needs methods to capture rapid, moment-to-moment network changes.

Purpose of the Study:

To apply novel edge time series analysis to fMRI data.
To capture rapid, moment-to-moment fluctuations in brain networks related to attention.
To investigate event-based and parametric changes in functional connectivity.

Main Methods:

Utilized "edge cofluctuation time series" to analyze timepoint-by-timepoint region cofluctuations.
Applied event-based and parametric fMRI analyses to edge time series.
Examined two independent fMRI datasets of young adults performing a sustained attention task.

Main Results:

Identified specific "edges" (connections) that rapidly change with rare task events.
Found other edges that correlate with continuous fluctuations in attention.
Demonstrated that edge-based changes are not fully explained by univariate activity patterns.

Conclusions:

Edge time series analysis provides high temporal precision for dynamic FC.
This approach reveals rapid network reconfigurations critical for attention.
Combines traditional fMRI with edge-based methods for deeper insights into brain dynamics.