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

Towards a general-purpose foundation model for functional MRI analysis.

Nature biomedical engineering·2026
Same author

AD-AutoGPT: An autonomous GPT for Alzheimer's disease infodemiology.

PLOS global public health·2025
Same author

Hallucination Index: An Image Quality Metric for Generative Reconstruction Models.

Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention·2025
Same author

A generalist vision-language foundation model for diverse biomedical tasks.

Nature medicine·2024
Same author

Author Correction: BigNeuron: a resource to benchmark and predict performance of algorithms for automated tracing of neurons in light microscopy datasets.

Nature methods·2024
Same author

How Does ChatGPT Use Source Information Compared With Google? A Text Network Analysis of Online Health Information.

Clinical orthopaedics and related research·2024
Same journal

A Multi-Head Attention Transformer Model for Wearable in Situ Fall Detection.

IEEE access : practical innovations, open solutions·2026
Same journal

Validating Single-Camera Pose Estimation Against Multi-Camera Motion Capture for Accessible Biomechanical Assessment.

IEEE access : practical innovations, open solutions·2026
Same journal

Learning to Diagnose Privately: DP-Powered LLMs for Radiology Report Classification.

IEEE access : practical innovations, open solutions·2026
Same journal

Radio-Frequency Toroid Susceptometry of Magnetic Nanoparticles: What Goes Around Comes Around.

IEEE access : practical innovations, open solutions·2026
Same journal

Cross-Architecture Knowledge Distillation for Histopathological Image Analysis.

IEEE access : practical innovations, open solutions·2026
Same journal

Mislabel Identification Using Transfer Learning-Based Ensemble Method.

IEEE access : practical innovations, open solutions·2026
See all related articles

Related Experiment Video

Updated: Sep 24, 2025

Author Spotlight: Methodologies and Advancements of Chronic Pain Management Research
08:33

Author Spotlight: Methodologies and Advancements of Chronic Pain Management Research

Published on: January 5, 2024

1.3K

Sparse Representation-Based Denoising for High-Resolution Brain Activation and Functional Connectivity Modeling: A

Seongah Jeong1, Xiang Li2, Jiarui Yang3

  • 1School of Electronics Engineering, Kyungpook National University, Daegu 14566, South Korea.

IEEE Access : Practical Innovations, Open Solutions
|May 9, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces a novel denoising method for task functional Magnetic Resonance Imaging (tfMRI) data using dictionary learning and sparse coding (DLSC). The DLSC approach enhances brain activation and connectivity patterns, improving analysis accuracy.

Keywords:
Task fMRIdictionary learning and sparse codingfMRI denoising

More Related Videos

Modeling the Functional Network for Spatial Navigation in the Human Brain
05:55

Modeling the Functional Network for Spatial Navigation in the Human Brain

Published on: October 13, 2023

1.2K
Simultaneous Data Collection of fMRI and fNIRS Measurements Using a Whole-Head Optode Array and Short-Distance Channels
08:19

Simultaneous Data Collection of fMRI and fNIRS Measurements Using a Whole-Head Optode Array and Short-Distance Channels

Published on: October 20, 2023

1.2K

Related Experiment Videos

Last Updated: Sep 24, 2025

Author Spotlight: Methodologies and Advancements of Chronic Pain Management Research
08:33

Author Spotlight: Methodologies and Advancements of Chronic Pain Management Research

Published on: January 5, 2024

1.3K
Modeling the Functional Network for Spatial Navigation in the Human Brain
05:55

Modeling the Functional Network for Spatial Navigation in the Human Brain

Published on: October 13, 2023

1.2K
Simultaneous Data Collection of fMRI and fNIRS Measurements Using a Whole-Head Optode Array and Short-Distance Channels
08:19

Simultaneous Data Collection of fMRI and fNIRS Measurements Using a Whole-Head Optode Array and Short-Distance Channels

Published on: October 20, 2023

1.2K

Area of Science:

  • Neuroimaging
  • Cognitive Neuroscience
  • Biomedical Engineering

Background:

  • Functional Magnetic Resonance Imaging (fMRI) is crucial for studying brain function but suffers from low signal-to-noise ratio (SNR).
  • This noise limitation hinders accurate spatial resolution and interpretation of brain activation and connectivity patterns.

Purpose of the Study:

  • To develop and implement an advanced denoising method for task fMRI (tfMRI) data.
  • To improve the delineation of high-resolution spatial patterns of brain activation and functional connectivity.

Main Methods:

  • Dictionary learning and sparse coding (DLSC) were employed, incorporating both data-driven and model-driven terms.
  • The method was applied to motor tfMRI data from the Human Connectome Project (HCP).
  • Performance was compared against the original data and the temporal non-local means (tNLM) denoising method.

Main Results:

  • The DLSC method effectively reduced noise in tfMRI signals.
  • Denoising recovered and enhanced disruptive brain activation and functional connectivity patterns.
  • DLSC demonstrated superior performance compared to the tNLM method in various settings.

Conclusions:

  • The proposed DLSC-based denoising method significantly enhances the interpretability of fMRI results.
  • This technique serves as a crucial preprocessing step for high-resolution functional brain analysis.