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Using Informational Connectivity to Measure the Synchronous Emergence of fMRI Multi-voxel Information Across Time

Using Informational Connectivity to Measure the Synchronous Emergence of fMRI Multi-voxel Information Across Time

Published on: July 1, 2014

Tracking cognitive fluctuations with multivoxel pattern time course (MVPTC) analysis.

Yu-Chin Chiu¹, Michael S Esterman, Leon Gmeindl

¹Department of Psychological and Brain Sciences, Johns Hopkins University, Baltimore, MD, USA. chiu.yuchin@gmail.com

Neuropsychologia

|July 27, 2011

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A new method using multivoxel pattern analysis (MVPA) reveals brain connectivity during cognitive control tasks. This technique, called multivoxel pattern time course (MVPTC), offers greater sensitivity and specificity than traditional analyses for understanding brain function.

Area of Science:

Cognitive Neuroscience
Neuroimaging Analysis

Background:

The posterior parietal cortex, particularly the medial superior parietal lobule (mSPL), shows increased activity during cognitive control.
Multivoxel pattern analysis (MVPA) can distinguish specific cognitive control states within the mSPL based on spatial activity patterns.

Purpose of the Study:

To introduce a novel multivoxel pattern-based analysis to reveal inter-regional functional connectivity by examining fluctuations in cognitive state over time.
To compare the sensitivity and specificity of this new method against conventional functional connectivity analyses.

Main Methods:

Modeled mSPL activity patterns associated with shifting or maintaining spatial attention using MVPA.
Computed a multivoxel pattern time course (MVPTC) reflecting real-time shifts between attention states.

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Used the MVPTC as a regressor in a general linear model (GLM) to identify covarying brain regions.

Main Results:

The MVPTC analysis identified significant correlations between the mSPL and regions including the striatum and bilateral middle frontal gyrus.
Conventional functional connectivity analysis using mean BOLD signals revealed widespread frontal correlations but missed subcortical connections.
The MVPTC approach demonstrated enhanced sensitivity, detecting striatal connectivity, and greater specificity, identifying more focal clusters.

Conclusions:

The novel MVPTC analysis provides a more sensitive and specific method for investigating functional connectivity related to cognitive control.
This technique has broad applicability for uncovering brain network dynamics in various neuroimaging research contexts.