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

Clinical Implementation and Oncological Relevance of Molecular Profiling in Brain Metastases Patients-A Multicenter Retrospective Cohort Study.

International journal of cancer·2026

Same author

Craniometric Anatomy of the Middle Meningeal Artery Bifurcation With Relevance for Simultaneous Surgical Occlusion in Chronic Subdural Hematoma.

Operative neurosurgery (Hagerstown, Md.)·2026

Same author

Mixed reality applied to surgical planning and tailoring of carotid endarterectomies.

Acta neurochirurgica·2026

Same author

Effect of Care Bundle Approaches on External Ventricular Drainage-Related Infection: Systematic Literature Review and Meta-Analysis.

Neurosurgery practice·2026

Same author

Deciphering Crossed Cerebellar Diaschisis in Patients with Large-Vessel Occlusion Acute Ischemic Stroke using BOLD Cerebrovascular Reactivity.

Cerebellum (London, England)·2026

Same author

Incidence and patient-related risk factors for external ventricular drain-related cerebrospinal fluid infections.

Brain & spine·2026

Search research articles

Related Experiment Video

Updated: Oct 11, 2025

Basics of Multivariate Analysis in Neuroimaging Data

Basics of Multivariate Analysis in Neuroimaging Data

Published on: July 24, 2010

Machine Learning Algorithms in Neuroimaging: An Overview.

Vittorio Stumpo¹, Julius M Kernbach^2,3, Christiaan H B van Niftrik¹

¹Machine Intelligence in Clinical Neuroscience (MICN) Lab, Department of Neurosurgery, Clinical Neuroscience Center, University Hospital Zurich, University of Zurich, Zurich, Switzerland.

Acta Neurochirurgica. Supplement

|December 4, 2021

Summary

This summary is machine-generated.

Machine learning (ML) and artificial intelligence (AI) are increasingly used in neuroimaging. This chapter explains deep learning (DL) algorithms like Convolutional Neural Networks (CNNs) and Generative Adversarial Networks (GANs) for analyzing brain scans.

Keywords:

Classification Convolutional neural network Deep learning Generative adversarial network Machine learning Segmentation

More Related Videos

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

Measuring Statistical Learning Across Modalities and Domains in School-Aged Children Via an Online Platform and Neuroimaging Techniques

Measuring Statistical Learning Across Modalities and Domains in School-Aged Children Via an Online Platform and Neuroimaging Techniques

Published on: June 30, 2020

Related Experiment Videos

Last Updated: Oct 11, 2025

Basics of Multivariate Analysis in Neuroimaging Data

Basics of Multivariate Analysis in Neuroimaging Data

Published on: July 24, 2010

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

Measuring Statistical Learning Across Modalities and Domains in School-Aged Children Via an Online Platform and Neuroimaging Techniques

Measuring Statistical Learning Across Modalities and Domains in School-Aged Children Via an Online Platform and Neuroimaging Techniques

Published on: June 30, 2020

Area of Science:

Neuroimaging
Artificial Intelligence
Machine Learning
Radiomics

Background:

Machine learning (ML) and artificial intelligence (AI) applications are rapidly expanding in neuroimaging, with growing clinical adoption.
Deep learning (DL), a subset of ML, utilizes multi-layered algorithms to identify complex patterns in large datasets.
The synergy of ML with radiomics enhances medical image characterization and aids in prognosis and outcome prediction.

Purpose of the Study:

To summarize fundamental concepts of ML in neuroimaging.
To discuss technical aspects of key DL algorithms, specifically Convolutional Neural Networks (CNNs) and Generative Adversarial Networks (GANs).
To provide a theoretical foundation for understanding ML applications in neuroimaging.

Main Methods:

Focus on deep learning algorithms, including Convolutional Neural Networks (CNNs) and Generative Adversarial Networks (GANs).
Exploration of radiomics for feature extraction from medical images.
Review of ML techniques applied to neuroimaging data analysis.

Main Results:

ML and radiomics offer valuable tools for enhanced image characterization.
These techniques show promise for improved prognosis and outcome prediction in neurological conditions.
The chapter details practical applications such as image reconstruction, synthesis, registration, segmentation, and classification.

Conclusions:

ML, particularly DL, provides powerful methods for advancing neuroimaging analysis.
Understanding CNNs and GANs is crucial for leveraging ML in clinical neuroimaging.
The discussed applications demonstrate the transformative potential of ML in neuroimaging for diagnosis and patient management.