Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

The Absent P3a. Performance Monitoring ERPs Differentiate Trust in Humans and Autonomous Systems.

Psychophysiology·2026
Same author

Postoperative pain after joint replacement surgery : A randomized controlled trial of transcutaneous auricular vagus nerve stimulation and the role of depression and anxiety.

Orthopadie (Heidelberg, Germany)·2026
Same author

A thin line between conflict and reaction time effects on EEG and fMRI brain signals.

Imaging neuroscience (Cambridge, Mass.)·2025
Same author

Decoding deception with the P300: A meta-analysis of the Concealed Information Test.

International journal of psychophysiology : official journal of the International Organization of Psychophysiology·2025
Same author

Methamphetamine-induced adaptation of learning rate dynamics depend on baseline performance.

eLife·2025
Same author

Cognitive impairment and associated neurobehavioral dysfunction in post-COVID syndrome.

Psychiatry research·2025

Related Experiment Video

Updated: Jul 13, 2026

A Randomized, Sham-Controlled Trial of Cranial Electrical Stimulation for Fibromyalgia Pain and Physical Function, Using Brain Imaging Biomarkers
08:33

A Randomized, Sham-Controlled Trial of Cranial Electrical Stimulation for Fibromyalgia Pain and Physical Function, Using Brain Imaging Biomarkers

Published on: January 5, 2024

Using non-negative matrix factorization for single-trial analysis of fMRI data.

Gabriele Lohmann1, Kirsten G Volz, Markus Ullsperger

  • 1Max-Planck-Institute for Human Cognitive and Brain Sciences, Stephanstrasse 1a, D-04103 Leipzig, Germany. lohmann@cbs.mpg.de

Neuroimage
|July 31, 2007
PubMed
Summary

Analyzing single fMRI trials is challenging due to noisy BOLD signals. Non-negative matrix factorization (NMF) offers a novel approach to uncover intrinsic data structures and analyze inter-regional trial dependencies, even with temporal offsets.

More Related Videos

Network Analysis of the Default Mode Network Using Functional Connectivity MRI in Temporal Lobe Epilepsy
12:09

Network Analysis of the Default Mode Network Using Functional Connectivity MRI in Temporal Lobe Epilepsy

Published on: August 5, 2014

Using Informational Connectivity to Measure the Synchronous Emergence of fMRI Multi-voxel Information Across Time
07:12

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

Published on: July 1, 2014

Related Experiment Videos

Last Updated: Jul 13, 2026

A Randomized, Sham-Controlled Trial of Cranial Electrical Stimulation for Fibromyalgia Pain and Physical Function, Using Brain Imaging Biomarkers
08:33

A Randomized, Sham-Controlled Trial of Cranial Electrical Stimulation for Fibromyalgia Pain and Physical Function, Using Brain Imaging Biomarkers

Published on: January 5, 2024

Network Analysis of the Default Mode Network Using Functional Connectivity MRI in Temporal Lobe Epilepsy
12:09

Network Analysis of the Default Mode Network Using Functional Connectivity MRI in Temporal Lobe Epilepsy

Published on: August 5, 2014

Using Informational Connectivity to Measure the Synchronous Emergence of fMRI Multi-voxel Information Across Time
07:12

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

Published on: July 1, 2014

Area of Science:

  • Neuroimaging
  • Data Analysis
  • Machine Learning

Background:

  • Functional magnetic resonance imaging (fMRI) single-trial analysis is hampered by low signal-to-noise ratio and trial-to-trial inconsistency of the Blood-Oxygen-Level-Dependent (BOLD) response.
  • Existing methods often struggle to capture subtle temporal dynamics and inter-regional relationships in fMRI data.

Purpose of the Study:

  • To introduce and evaluate Non-negative Matrix Factorization (NMF) as a novel technique for analyzing single fMRI trials.
  • To demonstrate NMF's capability in uncovering intrinsic structures within single-trial fMRI data.
  • To explore NMF's utility in investigating interdependencies between trials across different brain regions, including those with significant temporal offsets.

Main Methods:

  • Application of Non-negative Matrix Factorization (NMF) for matrix decomposition of single-trial fMRI data.
  • Subsequent processing of NMF results using clustering techniques.
  • Extension of the method to analyze inter-regional trial dependencies and lagged effects.

Main Results:

  • NMF successfully elicits the intrinsic structure of single-trial fMRI data.
  • The proposed method effectively analyzes interdependencies between trials across brain regions.
  • The technique can identify effects of a trial on subsequent trials in different regions with significant temporal delays, outperforming methods requiring near-simultaneous interactions.

Conclusions:

  • Non-negative Matrix Factorization (NMF) is a viable and potentially superior technique for single-trial fMRI analysis compared to other decomposition methods.
  • NMF provides a robust framework for understanding complex temporal dynamics and inter-regional communication in the brain.
  • This method enhances the analysis of fMRI data, offering new insights into neural processing.