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 Stimulation (TMS).

You might also read

Related Articles

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

Sort by

Same author

Latent Space Projections and Atlases, a Cautionary Tale in Deep Neuroimaging using Autoencoders.

International journal of neural systems·2026

Same author

An explainable framework for the relationship between dementia and metabolism patterns.

NeuroImage·2026

Same author

Management of extensively drug-resistant <i>Acinetobacter baumannii</i> bacteremia with cefiderocol and sulbactam-durlobactam: a case report.

ASM case reports·2025

Same author

Phenotypic differences between equol-producing and non-equol-producing intestinal microbiota.

Beneficial microbes·2025

Same author

Statistical agnostic regression: A machine learning method to validate regression models.

Journal of advanced research·2025

Same author

CDK4/6 inhibitors for metastatic breast cancer in routine clinical practice in Spain: survey of patterns of use and oncologists' perceptions.

Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico·2025

Search research articles

Related Experiment Video

Updated: Jun 15, 2026

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Published on: June 26, 2013

Classification of functional brain images using a GMM-based multi-variate approach.

F Segovia¹, J M Górriz, J Ramírez

¹Department of Signal Theory, Networking and Communications, University of Granada, fuentenueva s/n, Granada, Spain.

Neuroscience Letters

|March 16, 2010

Summary

This study introduces a new method using Gaussian mixture models (GMM) to automatically identify brain regions for Alzheimer's disease (AD) diagnosis. This approach improves classification accuracy for functional brain images, achieving up to 96.67%.

More Related Videos

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

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

Published on: January 5, 2024

Basics of Multivariate Analysis in Neuroimaging Data

Basics of Multivariate Analysis in Neuroimaging Data

Published on: July 24, 2010

Related Experiment Videos

Last Updated: Jun 15, 2026

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Published on: June 26, 2013

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

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

Published on: January 5, 2024

Basics of Multivariate Analysis in Neuroimaging Data

Basics of Multivariate Analysis in Neuroimaging Data

Published on: July 24, 2010

Area of Science:

Neuroimaging
Medical Image Analysis
Artificial Intelligence in Medicine

Background:

Alzheimer's disease (AD) diagnosis often faces challenges with small sample sizes in functional brain imaging.
Accurate identification of relevant brain regions (ROIs) is crucial for effective AD classification.
Existing methods may struggle with the variability inherent in functional brain scans.

Purpose of the Study:

To present a novel, automated method for selecting regions of interest (ROIs) in functional brain images.
To address the small sample size problem in Alzheimer's disease (AD) classification.
To enhance the accuracy of computer-aided diagnosis (CAD) systems for AD.

Main Methods:

Preprocessing functional brain images to create an average image highlighting differences between controls and AD patients.
Utilizing Gaussian mixture models (GMM) optimized with the expectation-maximization (EM) algorithm for ROI extraction.
Employing support vector machines (SVM) for statistical classification based on extracted features (activation maps).

Main Results:

An automated ROI selection method based on GMM was successfully developed.
The method effectively reduces feature dimensionality, creating activation maps for classification.
Leave-one-out cross-validation on SPECT and PET image databases achieved a high accuracy rate of up to 96.67% for AD diagnosis.

Conclusions:

The proposed GMM-based ROI selection method offers a robust solution for AD diagnosis using functional brain imaging.
This approach mitigates the small sample size issue, leading to improved classification performance.
The developed CAD system demonstrates significant potential for accurate and automated Alzheimer's disease detection.