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

[Research of interaction of fluorescent anti-CD133 DNA aptamers with glioblastoma cell cultures using flow cytometry].

Zhurnal voprosy neirokhirurgii imeni N. N. Burdenko·2026
Same author

[Surgical treatment of hemifacial spasm: a systematic review].

Zhurnal voprosy neirokhirurgii imeni N. N. Burdenko·2026
Same author

New Player in the Field of Glioblastoma Therapy: EGFRvIII-Specific Gol1 Aptamer Shows a Great Therapeutic Potential.

Pharmaceutics·2026
Same author

[Sensations and satisfaction of patients undergoing awake craniotomy].

Zhurnal voprosy neirokhirurgii imeni N. N. Burdenko·2026
Same author

[Lost anatomical structure - pharyngeal pituitary gland (Hypophysis pharyngealis or Erdheim pituitary gland): a review and case report].

Zhurnal voprosy neirokhirurgii imeni N. N. Burdenko·2026
Same author

[Surgical treatment of frontal lobe glial tumors. Current state of the problem. Part III: clinical, neurophysiological and surgical nuances].

Zhurnal voprosy neirokhirurgii imeni N. N. Burdenko·2026

Related Experiment Video

Updated: Sep 2, 2025

Study Design for Navigated Repetitive Transcranial Magnetic Stimulation for Speech Cortical Mapping
09:16

Study Design for Navigated Repetitive Transcranial Magnetic Stimulation for Speech Cortical Mapping

Published on: March 24, 2023

1.6K

[Machine learning for resting state fMRI-based preoperative mapping: comparison with task-based fMRI and direct

I N Pronin1, M G Sharaev2, T V Melnikova-Pitskhelauri1

  • 1Burdenko Neurosurgical Center, Moscow, Russia.

Zhurnal Voprosy Neirokhirurgii Imeni N. N. Burdenko
|August 9, 2022
PubMed
Summary

Resting-state fMRI (rs-fMRI) with machine learning accurately predicts brain activity for glioma patients, outperforming task-based fMRI. This aids in precise surgical planning for motor and speech areas.

Keywords:
brain MRIbrain mappingdirect cortical stimulationfunctional MRImachine learningpreoperative planningresting state fMRI

More Related Videos

Translational Brain Mapping at the University of Rochester Medical Center: Preserving the Mind Through Personalized Brain Mapping
13:12

Translational Brain Mapping at the University of Rochester Medical Center: Preserving the Mind Through Personalized Brain Mapping

Published on: August 12, 2019

45.8K
Functional Mapping with Simultaneous MEG and EEG
06:04

Functional Mapping with Simultaneous MEG and EEG

Published on: June 14, 2010

18.0K

Related Experiment Videos

Last Updated: Sep 2, 2025

Study Design for Navigated Repetitive Transcranial Magnetic Stimulation for Speech Cortical Mapping
09:16

Study Design for Navigated Repetitive Transcranial Magnetic Stimulation for Speech Cortical Mapping

Published on: March 24, 2023

1.6K
Translational Brain Mapping at the University of Rochester Medical Center: Preserving the Mind Through Personalized Brain Mapping
13:12

Translational Brain Mapping at the University of Rochester Medical Center: Preserving the Mind Through Personalized Brain Mapping

Published on: August 12, 2019

45.8K
Functional Mapping with Simultaneous MEG and EEG
06:04

Functional Mapping with Simultaneous MEG and EEG

Published on: June 14, 2010

18.0K

Area of Science:

  • Neurosurgery
  • Neuroimaging
  • Machine Learning

Background:

  • Brain gliomas pose challenges for preserving critical motor and speech functions during surgery.
  • Accurate preoperative mapping of eloquent brain areas is crucial for minimizing neurological deficits.

Purpose of the Study:

  • To develop and validate a machine learning system for predicting individual brain activations in motor and speech areas.
  • To compare the efficacy of resting-state fMRI (rs-fMRI) and task-based fMRI (tb-fMRI) for preoperative mapping in glioma patients.

Main Methods:

  • A machine learning system was pre-trained on 200 healthy subjects' data from The Human Connectome Project.
  • The system was further trained and validated using preoperative MRI (T1, tb-fMRI, rs-fMRI) data from 33 glioma patients.
  • Direct cortical stimulation (DCS) was used intraoperatively to verify functional area predictions.

Main Results:

  • rs-fMRI predicted more positive direct cortical stimulation responses (132) compared to tb-fMRI (112).
  • tb-fMRI showed a higher proportion of negative stimulation sites within activated areas (69 vs. 44).

Conclusions:

  • The developed machine learning method using rs-fMRI demonstrated superior sensitivity in identifying speech zones (0.72 vs. 0.66) and motor areas (0.79 vs. 0.62) compared to tb-fMRI.
  • rs-fMRI-based prediction, verified by DCS, offers a more sensitive approach for preoperative functional mapping in glioma surgery.