Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

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...

You might also read

Related Articles

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

Sort by

Same author

Modeling Relationships among Pain and Function in Individuals with Knee Osteoarthritis in the A2CPS Cohort.

The Clinical journal of pain·2026

Same author

From Structure to Function and Back Again: A GAN-Guided Diffusion Framework for Generating Clinically Meaningful Multimodal Neuroimaging Data.

Research square·2026

Same author

Intensity-dependent topographical expansion of sensory representations.

bioRxiv : the preprint server for biology·2026

Same author

Individualised mapping of living human brain mitochondria by MRI reveals signatures of bioenergetic defects.

bioRxiv : the preprint server for biology·2026

Same author

A predictive corticospinal model for pain perception.

Cell reports. Medicine·2026

Same author

Mitoception via the Metabokine GDF15 and Human Health.

Biopsychosocial science and medicine·2026

Search research articles

Related Experiment Video

Updated: Nov 4, 2025

Mapping Cortical Dynamics Using Simultaneous MEG/EEG and Anatomically-constrained Minimum-norm Estimates: an Auditory Attention Example

Mapping Cortical Dynamics Using Simultaneous MEG/EEG and Anatomically-constrained Minimum-norm Estimates: an Auditory Attention Example

Published on: October 24, 2012

An Approach to Automatically Label and Order Brain Activity/Component Maps.

Mustafa S Salman^1,2, Tor D Wager³, Eswar Damaraju¹

¹Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia Institute of Technology, Georgia State University, and Emory University, Atlanta, Georgia, USA.

Brain Connectivity

|May 27, 2021

Summary

A new automated method, Autolabeler, accurately distinguishes brain signals from noise in functional magnetic resonance imaging (fMRI) data. This tool enhances brain network analysis for faster, more reproducible neuroscience research.

Keywords:

anatomical atlas brain imaging fMRI functional network connectivity functional parcellation

More Related Videos

Concurrent EEG and Functional MRI Recording and Integration Analysis for Dynamic Cortical Activity Imaging

Concurrent EEG and Functional MRI Recording and Integration Analysis for Dynamic Cortical Activity Imaging

Published on: June 30, 2018

Neuroimaging-Guided TMS–EEG for Real-Time Cortical Network Mapping

Neuroimaging-Guided TMS–EEG for Real-Time Cortical Network Mapping

Published on: June 13, 2025

Related Experiment Videos

Last Updated: Nov 4, 2025

Mapping Cortical Dynamics Using Simultaneous MEG/EEG and Anatomically-constrained Minimum-norm Estimates: an Auditory Attention Example

Mapping Cortical Dynamics Using Simultaneous MEG/EEG and Anatomically-constrained Minimum-norm Estimates: an Auditory Attention Example

Published on: October 24, 2012

Concurrent EEG and Functional MRI Recording and Integration Analysis for Dynamic Cortical Activity Imaging

Concurrent EEG and Functional MRI Recording and Integration Analysis for Dynamic Cortical Activity Imaging

Published on: June 30, 2018

Neuroimaging-Guided TMS–EEG for Real-Time Cortical Network Mapping

Neuroimaging-Guided TMS–EEG for Real-Time Cortical Network Mapping

Published on: June 13, 2025

Area of Science:

Neuroimaging
Computational Neuroscience
Brain Network Analysis

Background:

Functional magnetic resonance imaging (fMRI) generates complex brain activity maps requiring extensive postprocessing.
Distinguishing true intrinsic connectivity networks (ICNs) from artifacts is a critical initial step.
Linking ICNs to anatomical and functional parcellations aids interpretation, especially for functional network connectivity (FNC) analysis.

Purpose of the Study:

To develop a novel, automated method (Autolabeler) for integrating and streamlining fMRI data postprocessing.
To enable automatic identification and labeling of ICNs and facilitate structured FNC matrix generation.
To improve the speed and reproducibility of fMRI data analysis.

Main Methods:

Developed the Autolabeler method, pretrained on a general linear model to differentiate ICNs from artifacts.
Implemented automated labeling of brain activity maps using established anatomical and functional parcellations.
Integrated a modularity maximization step to optimize FNC matrix structure for downstream analysis.

Main Results:

The pretrained Autolabeler model achieved 86% accuracy in classifying ICNs from artifacts on an independent dataset.
Automatic labels demonstrated high similarity to those assigned by human experts.
The method successfully generated systematically structured FNC matrices.

Conclusions:

Autolabeler offers a fully automated solution for key fMRI postprocessing tasks, reducing reliance on expert intervention.
The open-source toolbox provides standalone functionality and integrates with existing fMRI analysis software (e.g., GIFT).
This automation accelerates fMRI research, enabling faster and more reproducible scientific discovery.