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

Implicit brain imaging.

Facundo Mémoli1, Guillermo Sapiro, Paul Thompson

  • 1Electrical and Computer Engineering, University of Minnesota, Minneapolis, MN 55455, USA. memoli@ece.umn.edu

Neuroimage
|October 27, 2004
PubMed
Summary
This summary is machine-generated.

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

Prediction of cognitive performance by demographics, sleep, and brain morphometry: machine learning findings from ENIGMA-Sleep Working Group.

Research square·2026
Same author

PAD4 deletion in synovial macrophages exacerbates pathology in inflammatory arthritis.

Research square·2026
Same author

The interactions between sleep difficulties and clinical features in children with rare genetic syndromes: a structural equation modelling approach.

BMC pediatrics·2026
Same author

Geometric brain signatures of Alzheimer's disease progression and subtypes.

medRxiv : the preprint server for health sciences·2026
Same author

Unravelling the structure of broadband white-emitting silver nanoclusters stabilized in sulfur-doped zeolites.

Nanoscale·2026
Same author

Brain morphology in Anorexia Nervosa and its subtypes: A multi-cohort study of individual participant data.

PLoS medicine·2026
Same journal

Investigating the Neural Origins of Ear-EEG: A Correlation Study Using Scalp EEG Source Reconstruction.

NeuroImage·2026
Same journal

Hysteresis effects in visual and auditory perception and the comparison of underlying neural mechanisms - an EEG study.

NeuroImage·2026
Same journal

Short-term audio-tactile training affects cortical auditory speech-envelope tracking for incongruent but not congruent stimuli.

NeuroImage·2026
Same journal

Dissociable Neurocognitive Mechanisms of State and Trait Anxiety in Working Memory: Threat-Induced Alterations in Decision Dynamics and Attenuation of Large-Scale Network Reconfiguration.

NeuroImage·2026
Same journal

Neuro-Ocular Amyloid Characterization in Alzheimer's Disease via Cross-Site PET-MRI and Hierarchical Cross-Attention Driven Multimodal Representation Learning.

NeuroImage·2026
Same journal

Whole-brain network dynamics underlying intolerance of uncertainty.

NeuroImage·2026
See all related articles

Implicit surface representations offer a computationally efficient solution for brain imaging problems. This method simplifies complex tasks like analyzing cortical thickness and brain warping, improving accuracy in neuroimaging analysis.

Area of Science:

  • Neuroimaging
  • Computational anatomy
  • Medical image analysis

Background:

  • Traditional brain imaging analysis often relies on complex intermediate steps.
  • These steps, involving mapping 3D surfaces to parametric objects, can introduce errors and computational inefficiencies.
  • State-of-the-art segmentation algorithms naturally lead to implicit surface representations.

Purpose of the Study:

  • To demonstrate the utility of implicit surface representations for fundamental brain imaging challenges.
  • To highlight the computational advantages of implicit representations over traditional methods.
  • To showcase applications in cortical analysis and surface comparisons.

Main Methods:

  • Utilizing implicit surface representations for brain tissue segmentation.

Related Experiment Videos

  • Solving partial differential equations (PDEs) and variational problems on implicitly represented surfaces.
  • Applying the framework to extract sulcal beds, regularize cortical thickness, and compute warping fields.

    • Main Results:

    • Implicit surface representations provide a natural and computationally efficient approach for brain imaging.
    • The method successfully addresses challenges in cortical analysis, including sulcal bed identification and thickness regularization.
    • Accurate computation of warping fields between brain surfaces is achieved without intermediate mapping errors.

    Conclusions:

    • Implicit surface representations offer a powerful and efficient framework for advanced brain imaging analysis.
    • This approach overcomes limitations of traditional methods by avoiding complex intermediate mappings.
    • The demonstrated applications highlight the potential of implicit surfaces for improving neuroimaging research and clinical applications.