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

You might also read

Related Articles

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

Sort by

Same author

COUNTERFACTUAL ANALYSIS OF BRAIN NETWORK DYNAMICS.

ArXiv·2026

Same author

SULCAL PATTERN MATCHING WITH THE WASSERSTEIN DISTANCE.

ArXiv·2026

Same author

Thermodynamic rigidity of harmonic brain states relates to general mental ability in juvenile myoclonic epilepsy.

bioRxiv : the preprint server for biology·2026

Same author

Disrupted Higher-Order Topology in OCD Brain Networks Revealed by Hodge Laplacian - an ENIGMA Study.

bioRxiv : the preprint server for biology·2026

Same author

Topological Time Frequency Analysis of Functional Brain Signals.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference·2025

Same author

Broad brain networks support curiosity-motivated incidental learning of naturalistic dynamic stimuli with and without monetary incentives.

Imaging neuroscience (Cambridge, Mass.)·2025

Search research articles

Related Experiment Video

Updated: May 10, 2026

Three-Dimensional Shape Modeling and Analysis of Brain Structures

Three-Dimensional Shape Modeling and Analysis of Brain Structures

Published on: November 14, 2019

Sparse Shape Representation using the Laplace-Beltrami Eigenfunctions and Its Application to Modeling Subcortical

Seung-Goo Kim¹, Moo K Chung, Stacey M Schaefer

¹Department of Brain and Cognitive Sciences, Seoul National University, Korea.

Proceedings. Workshop on Mathematical Methods in Biomedical Image Analysis

|June 21, 2013

Summary

This summary is machine-generated.

This study introduces a novel sparse shape modeling method using Laplace-Beltrami eigenfunctions. The approach effectively identifies significant eigenfunctions for accurate shape representation, aiding brain structure analysis.

More Related Videos

Optical Clearing and Labeling for Light-sheet Fluorescence Microscopy in Large-scale Human Brain Imaging

Optical Clearing and Labeling for Light-sheet Fluorescence Microscopy in Large-scale Human Brain Imaging

Published on: January 26, 2024

Morphology-Based Distinction Between Healthy and Pathological Cells Utilizing Fourier Transforms and Self-Organizing Maps

Morphology-Based Distinction Between Healthy and Pathological Cells Utilizing Fourier Transforms and Self-Organizing Maps

Published on: October 28, 2018

Related Experiment Videos

Last Updated: May 10, 2026

Three-Dimensional Shape Modeling and Analysis of Brain Structures

Three-Dimensional Shape Modeling and Analysis of Brain Structures

Published on: November 14, 2019

Optical Clearing and Labeling for Light-sheet Fluorescence Microscopy in Large-scale Human Brain Imaging

Optical Clearing and Labeling for Light-sheet Fluorescence Microscopy in Large-scale Human Brain Imaging

Published on: January 26, 2024

Morphology-Based Distinction Between Healthy and Pathological Cells Utilizing Fourier Transforms and Self-Organizing Maps

Morphology-Based Distinction Between Healthy and Pathological Cells Utilizing Fourier Transforms and Self-Organizing Maps

Published on: October 28, 2018

Area of Science:

Computational geometry
Neuroimaging analysis
Medical image processing

Background:

Laplace-Beltrami (LB) eigenfunctions are traditionally used for intrinsic surface shape representation via Fourier series.
Current methods often discard higher-frequency terms, potentially losing significant shape information.
Existing techniques may require additional surface-based smoothing to reduce noise.

Purpose of the Study:

To develop a sparse shape modeling framework using LB eigenfunctions.
To filter significant eigenfunctions adaptively by imposing an l1-penalty.
To investigate the influence of age and gender on amygdala and hippocampus shapes and their relation to emotional response.

Main Methods:

A novel sparse shape modeling framework is proposed, utilizing LB eigenfunctions.
An l1-penalty is imposed to selectively filter significant eigenfunctions, avoiding arbitrary truncation.
The method is applied to analyze shape variations in the amygdala and hippocampus in a healthy population.

Main Results:

The sparse framework effectively identifies and utilizes significant LB eigenfunctions for shape reconstruction.
The approach avoids the need for additional surface-based smoothing.
The study reveals age and gender-related shape differences in the amygdala and hippocampus.
A correlation between emotional response and subcortical structure anatomy is demonstrated.

Conclusions:

The proposed sparse LB eigenfunction framework offers a more robust and adaptive approach to shape modeling.
This method enhances the analysis of neuroanatomical variations and their functional correlates.
The findings contribute to understanding the neurobiological basis of emotional processing.