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Brain Imaging

Brain Imaging

Brain imaging technologies provide critical insights into both the structure and function of the human brain, enabling medical professionals and researchers to diagnose, study, and treat neurological disorders or psychiatric disorders more effectively.
These technologies include computerized axial tomography (CAT or CT scans), positron-emission tomography (PET scans), magnetic resonance imaging (MRI), functional magnetic resonance imaging (fMRI), and Transcranial Magnetic Stimulation (TMS).

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Related Experiment Video

Updated: Jun 10, 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

Fully exploratory network ICA (FENICA) on resting-state fMRI data.

V Schöpf¹, C H Kasess, R Lanzenberger

¹MR Centre of Excellence, Medical University Vienna, Lazarettgasse 14, 1090 Vienna, Austria. veronika.schoepf@meduniwien.ac.at

Journal of Neuroscience Methods

|August 7, 2010

Summary

This summary is machine-generated.

Functional network exploration using Independent Component Analysis (ICA) can now be automated. FENICA identifies spatially consistent resting-state networks (RSNs) across subjects without manual inspection or templates.

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Last Updated: Jun 10, 2026

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Modeling the Functional Network for Spatial Navigation in the Human Brain

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Area of Science:

Neuroimaging
Data Analysis
Cognitive Neuroscience

Background:

Independent Component Analysis (ICA) is a key method for analyzing resting-state networks (RSNs) in fMRI.
Current group-level ICA methods often require manual inspection or predefined templates for single-subject components.

Purpose of the Study:

To apply FENICA, a novel group ICA method, to resting-state fMRI data.
To assess FENICA's ability to identify spatially consistent RSNs across subjects without manual or template-based selection.

Main Methods:

Applied FENICA to resting-state fMRI data from 28 healthy subjects.
FENICA relies solely on spatial consistency across subjects to identify networks.

Main Results:

Identified eight distinct group-level RSNs.
The identified RSNs corresponded to known networks: visual, default mode, sensorimotor, dorsolateral prefrontal, temporal prefrontal, basal ganglia, auditory, and working memory networks.

Conclusions:

FENICA provides a truly explorative approach for assessing RSNs.
This method automates the identification of spatially consistent networks, eliminating the need for manual or template-based component selection.