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 Experiment Videos

Automated detection of gray matter malformations using optimized voxel-based morphometry: a systematic approach.

M Wilke1, J Kassubek, S Ziyeh

  • 1Imaging Research Center, Cincinnati Children's Hospital Medical Center, Cincinnati, OH 45229, USA.

Neuroimage
|October 7, 2003
PubMed
Summary

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

Diagnostic and therapeutic challenges in PD-associated non-motor symptoms: the roles of neurologists and consultant physicians.

Journal of neural transmission (Vienna, Austria : 1996)·2024
Same author

The spectrum of indications for ultralong-term EEG monitoring.

Seizure·2024
Same author

Response to: Commentary to "Bone conducted vibration is an effective stimulus for otolith testing in cochlear implant patients".

Journal of vestibular research : equilibrium & orientation·2023
Same author

Interpretable functional specialization emerges in deep convolutional networks trained on brain signals.

Journal of neural engineering·2022
Same author

[The new Parkinson's disease pain classification system (PD-PCS)].

Der Nervenarzt·2022
Same author

Gray-White Matter Blurring of the Temporal Pole Associated With Hippocampal Sclerosis: A Microstructural Study Involving 3 T MRI and Ultrastructural Histopathology.

Cerebral cortex (New York, N.Y. : 1991)·2021

Automated voxel-based morphometry can accurately detect malformations of cortical development (MCD) causing epilepsy. Optimizing processing parameters is crucial for reliable diagnosis using this neuroimaging technique.

Area of Science:

  • Neuroimaging
  • Neurology
  • Medical Image Analysis

Background:

  • Malformations of cortical development (MCD) are a significant cause of epilepsy.
  • Accurate diagnosis of MCD via high-resolution MRI is challenging, requiring specialized expertise.
  • Surgical removal of detected MCD can be an effective epilepsy treatment.

Purpose of the Study:

  • To systematically optimize processing parameters for detecting cortical dysplasias using voxel-based morphometry (VBM).
  • To evaluate the accuracy of automated VBM procedures in identifying MCD in patients with epilepsy.
  • To determine the best combination of parameters for VBM analysis in MCD detection.

Main Methods:

  • Utilized an optimized voxel-based morphometry approach with automated procedures (SPM99).

Related Experiment Videos

  • Systematically altered spatial normalization and other processing parameters, evaluating 99 different approaches.
  • Compared 20 patients with known MCD against a database of 53 healthy controls.

    • Main Results:

    • The best parameter combinations correctly identified up to 16 out of 20 malformations.
    • Several tested approaches demonstrated poor performance, highlighting parameter sensitivity.
    • Voxel-based morphometry showed high accuracy in detecting cortical malformations when parameters were optimized.

    Conclusions:

    • Voxel-based morphometry is a powerful tool for accurately detecting cortical malformations associated with epilepsy.
    • An optimized VBM protocol may not be universally optimal, necessitating careful parameter selection for different brain morphology studies.
    • Systematic parameter evaluation and alteration is a valuable methodology for VBM studies.