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

Synteny and Evolution02:31

Synteny and Evolution

John H. Renwick first coined the term “synteny” in 1971, which refers to the genes present on the same chromosomes, even if they are not genetically linked. The species with common ancestry tend to show conserved syntenic regions. Therefore, the concept of synteny is nowadays used to describe the evolutionary relationship between species.
Around 80 million years ago, the human and mice lineages diverged from the common ancestor. During the course of evolution, the ancestral chromosome underwent...
The Evidence for Evolution02:55

The Evidence for Evolution

Genetic variations accumulating within populations over generations give rise to biological evolution. Evolutionary changes can result in the formation of novel varieties and entire new species. These changes are responsible for the diverse forms of life inhabiting the planet. The evidence for evolution suggests that all living organisms descended from common ancestors.The collection of fossils within sedimentary rocks give a record of common ancestry and often depicts the history of evolution.
Modeling with Differential Equations01:25

Modeling with Differential Equations

Population dynamics can be described mathematically by considering the population size P(t) as a function of time. The rate of change of the population is then represented by the derivative of P(t). A simple assumption is that the rate of growth is proportional to the size of the population itself. This leads to an exponential growth model, where the population increases rapidly without bound. While this is a useful first approximation, it does not reflect realistic long-term...
Convergent Evolution01:54

Convergent Evolution

Evolution shapes the features of organisms over time, ensuring that they are suited for the environments in which they live. Sometimes, selection pressure leads to the rise of similar but unrelated adaptations in organisms with no recent common ancestors, a process known as convergent evolution.The structures that arise from convergent evolution are called analogous structures. They are similar in function even if they are dissimilar in structure. Further, structures can be analogous while also...
Euler's Equations of Motion01:28

Euler's Equations of Motion

In fluid mechanics, shear stresses arise from viscosity, which represents a fluid's internal resistance to deformation. For low-viscosity fluids, like water, these stresses are minimal, simplifying flow analysis by allowing the fluid to be treated as inviscid, or frictionless. In an inviscid fluid, shear stresses are absent, leaving only normal stresses, which act perpendicularly to fluid elements. Notably, pressure — defined as the negative of the normal stress — remains uniform across...
Gene Evolution - Fast or Slow?02:05

Gene Evolution - Fast or Slow?

The genomes of eukaryotes are punctuated by long stretches of sequence which do not code for proteins or RNAs. Although some of these regions do contain crucial regulatory sequences, the vast majority of this DNA serves no known function. Typically, these regions of the genome are the ones in which the fastest change, in evolutionary terms, is observed, because there is typically little to no selection pressure acting on these regions to preserve their sequences.
In contrast, regions which code...

You might also read

Related Articles

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

Sort by
Same author

Associations of cerebrospinal fluid measures of synaptic function with white matter microstructure and cognition in older adults.

Frontiers in aging neuroscience·2026
Same author

Predicting future cognitive impairment in preclinical Alzheimer's disease using amyloid PET and MRI: A multisite machine learning study.

Neurobiology of aging·2026
Same author

Rostral Associations of MRI Atrophy of the Amygdala and Entorhinal Cortex Across the AD Spectrum.

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

Automated deep learning pipeline for callosal angle quantification.

Fluids and barriers of the CNS·2025
Same author

Biomarkers.

Alzheimer's & dementia : the journal of the Alzheimer's Association·2025
Same author

Biomarkers.

Alzheimer's & dementia : the journal of the Alzheimer's Association·2025
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

Related Experiment Video

Updated: Jun 27, 2026

Computational Modeling of Retinal Neurons for Visual Prosthesis Research - Fundamental Approaches
10:50

Computational Modeling of Retinal Neurons for Visual Prosthesis Research - Fundamental Approaches

Published on: June 21, 2022

Evolutions equations in computational anatomy.

Laurent Younes1, Felipe Arrate, Michael I Miller

  • 1Center for Imaging Science, The Johns Hopkins University, 3400N Charles St., Baltimore, MD 21218, USA. laurent.younes@jhu.edu

Neuroimage
|December 9, 2008
PubMed
Summary
This summary is machine-generated.

This study reviews computational anatomy equations for measuring anatomical variations. These shape evolution equations, based on Riemannian geometry, are crucial for understanding organ differences in the brain and heart.

More Related Videos

Building Finite Element Models to Investigate Zebrafish Jaw Biomechanics
14:11

Building Finite Element Models to Investigate Zebrafish Jaw Biomechanics

Published on: December 3, 2016

Tracking Morphogenetic Tissue Deformations in the Early Chick Embryo
08:19

Tracking Morphogenetic Tissue Deformations in the Early Chick Embryo

Published on: October 17, 2011

Related Experiment Videos

Last Updated: Jun 27, 2026

Computational Modeling of Retinal Neurons for Visual Prosthesis Research - Fundamental Approaches
10:50

Computational Modeling of Retinal Neurons for Visual Prosthesis Research - Fundamental Approaches

Published on: June 21, 2022

Building Finite Element Models to Investigate Zebrafish Jaw Biomechanics
14:11

Building Finite Element Models to Investigate Zebrafish Jaw Biomechanics

Published on: December 3, 2016

Tracking Morphogenetic Tissue Deformations in the Early Chick Embryo
08:19

Tracking Morphogenetic Tissue Deformations in the Early Chick Embryo

Published on: October 17, 2011

Area of Science:

  • Computational anatomy
  • Medical image analysis
  • Differential geometry

Background:

  • Measuring anatomical variations is key in computational anatomy.
  • Previous work focused on Riemannian geometry of diffeomorphisms and shape spaces.

Purpose of the Study:

  • To provide an overview of shape evolution equations in computational anatomy.
  • To place these equations in their theoretical context.
  • To illustrate applications of these equations.

Main Methods:

  • Review of Riemannian geometry concepts.
  • Discussion of shape spaces and groups of diffeomorphisms.
  • Presentation of various shape evolution equations, including energy minimizing evolutions, geodesics, parallel transport, and Jacobi fields.

Main Results:

  • Several important shape evolution equations have been developed and are used routinely.
  • These equations offer a framework for analyzing anatomical variations.
  • The paper categorizes equations by increasing complexity.

Conclusions:

  • The reviewed equations provide a robust theoretical and practical framework for computational anatomy.
  • These methods are applicable to statistical studies of organ variations, particularly in the brain and heart.
  • Further development and application of these geometric approaches are encouraged.