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Related Experiment Video

Updated: Jun 19, 2026

Dynamic Inter-subject Functional Connectivity Reveals Moment-to-Moment Brain Network Configurations Driven by Continuous or Communication Paradigms

Dynamic Inter-subject Functional Connectivity Reveals Moment-to-Moment Brain Network Configurations Driven by Continuous or Communication Paradigms

Published on: March 21, 2019

Parallel workflows for data-driven structural equation modeling in functional neuroimaging.

Sarah Kenny¹, Michael Andric, Steven M Boker

¹Computation Institute, The University of Chicago Chicago, IL, USA.

Frontiers in Neuroinformatics

|October 31, 2009

Summary

This summary is machine-generated.

We developed a computational framework for data-driven structural equation modeling (SEM) in neuroimaging. This system efficiently handles large functional magnetic resonance imaging (fMRI) datasets using high-performance computing and workflow management.

Keywords:

OpenMx SEM exhaustive search swift workflows

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Published on: October 13, 2023

Area of Science:

Computational neuroscience
Statistical modeling
Neuroimaging analysis

Background:

Structural Equation Modeling (SEM) is a powerful statistical technique for analyzing complex relationships between variables.
Functional Magnetic Resonance Imaging (fMRI) generates large, complex datasets requiring robust analytical methods.
Existing computational frameworks may not be optimized for the scale and complexity of neuroimaging data analysis.

Purpose of the Study:

To present a novel computational framework for data-driven SEM applied to neuroimaging.
To describe workflows for modeling fMRI data within this framework.
To enable exhaustive searches of the model space for advanced neuroimaging analysis.

Main Methods:

Development of the Computational Neuroscience Applications Research Infrastructure (CNARI) using the Swift scripting language.
Leveraging high-performance computing (HPC) to run numerous simultaneous R processes.
Utilizing OpenMx, an R plug-in, to generate self-contained SEM model objects for parameter estimation.

Main Results:

The framework supports a data-driven approach to SEM for neuroimaging.
Workflows are described for modeling fMRI data, enabling efficient analysis.
The system facilitates exhaustive model space exploration.

Conclusions:

The CNARI framework provides a scalable solution for complex neuroimaging SEM.
Workflow management techniques are crucial for addressing large computational challenges in neuroimaging.
This approach enhances the capability for data-driven discovery in neuroscience.