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

Uniform Depth Channel Flow: Problem Solving01:18

Uniform Depth Channel Flow: Problem Solving

To calculate the flow rate for a trapezoidal channel, first, identify the bottom width, side slope, and flow depth of the channel. The cross-sectional area (A) corresponding to the depth of flow (y), channel bottom width (B), and side slope (θ) is determined by:Next, calculate the wetted perimeter, which includes the bottom width and the sloped side lengths in contact with the water. Using the values of the cross-sectional area and the wetted perimeter, determine the hydraulic radius by...
Curvilinear Motion: Normal and Tangential Components01:27

Curvilinear Motion: Normal and Tangential Components

When a car traverses a curved road, its motion can be elucidated by breaking it down into tangential and normal components. The car-centric coordinates attached to the vehicle move with it.
The positive direction of the t-axis aligns with the increasing position of the car along the curved path, denoted by the unit vector ut. Simultaneously, the n-axis, perpendicular to the t-axis, dissects the curved path into differential arc segments, each forming the arc of a circle with a radius of...
Uniform Depth Channel Flow01:27

Uniform Depth Channel Flow

Uniform depth channel flow keeps fluid depth consistent along channels such as irrigation canals. In natural channels, such as rivers, approximate uniform flow is often assumed. This condition occurs when the channel’s bottom slope matches the energy slope, balancing potential energy lost from gravity with head loss due to shear stress. This balance prevents depth changes along the channel length, resulting in a steady, uniform flow.Uniform flow in open channels with a constant cross-section...
Cylinders in Three-Dimensional Space01:28

Cylinders in Three-Dimensional Space

A cylindrical surface is generated when a two-dimensional profile curve is translated along a straight line in three-dimensional space. The translated copies of the curve form a surface composed of parallel rulings, each oriented in the same fixed direction. This construction allows many three-dimensional forms to be described using relatively simple planar equations.In Cartesian coordinates, a cylindrical surface is often recognized by an equation that omits one of the three variables. For...
Transformation of Plane Strain01:12

Transformation of Plane Strain

When analyzing elongated structures like bars subjected to uniformly distributed loads, it is essential to understand the transformation of plane strain when coordinate axes are rotated. This transformation helps to assess how material deformation characteristics vary with orientation, which is crucial in materials science and structural engineering.
Under plane strain conditions, typical for members where one dimension significantly exceeds the others, deformations and resultant strains are...
Oriented Surfaces01:30

Oriented Surfaces

A surface is called orientable if a consistent choice of unit normal vector can be made at every point on the surface. A thin soap film stretched across a wire loop provides a familiar example. The film separates the air on one side from the air on the other, so one side can be selected as positive and the opposite side as negative. Once this choice is made, a unit normal vector can be assigned smoothly across the entire surface.At each point on the soap film, a unit normal vector points...

You might also read

Related Articles

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

Sort by
Same author

Genetic architecture of the limbic white matter microstructure in aging and Alzheimer's Disease.

Alzheimer's & dementia : the journal of the Alzheimer's Association·2026
Same author

MCH-Guard: Multimodal Machine Learning Framework for Risk Stratification of Cerebral Microhemorrhage Risk in the Alzheimer's Disease Neuroimaging Initiative.

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

Gray Matter Volume Predicts Improvement in Depression Symptoms Following Psychotherapy in Late Life Depression.

The American journal of geriatric psychiatry : official journal of the American Association for Geriatric Psychiatry·2026
Same author

The time interval from amyloid to tau PET positivity varies by age, sex and APOE-ε4 status.

The journal of prevention of Alzheimer's disease·2026
Same author

Contributions of the Alzheimer's Disease Neuroimaging Initiative to advancing AD research: a targeted review of recent publications.

Alzheimer's & dementia : the journal of the Alzheimer's Association·2026
Same author

Predicting Autopsy-Confirmed Neuropathology across Clinical, Neuroimaging, and CSF Biomarkers using Machine Learning.

bioRxiv : the preprint server for biology·2026

Related Experiment Video

Updated: Jun 27, 2026

Profiling Maternal Behavior Responses During Whole-Brain Imaging
07:12

Profiling Maternal Behavior Responses During Whole-Brain Imaging

Published on: January 24, 2025

A geometry-driven optical flow warping for spatial normalization of cortical surfaces.

Duygu Tosun1, Jerry L Prince

  • 1Department of Electrical and Computer Engineering, Whiting School of Engineering, Johns Hopkins University, Baltimore, MD 21218, USA.

IEEE Transactions on Medical Imaging
|November 27, 2008
PubMed
Summary

This study introduces an automated surface normalization method for brain imaging. It improves the alignment of cortical surfaces, outperforming volumetric methods for anatomical matching.

More Related Videos

Medical-grade Sterilizable Target for Fluid-immersed Fetoscope Optical Distortion Calibration
07:03

Medical-grade Sterilizable Target for Fluid-immersed Fetoscope Optical Distortion Calibration

Published on: February 23, 2017

Related Experiment Videos

Last Updated: Jun 27, 2026

Profiling Maternal Behavior Responses During Whole-Brain Imaging
07:12

Profiling Maternal Behavior Responses During Whole-Brain Imaging

Published on: January 24, 2025

Medical-grade Sterilizable Target for Fluid-immersed Fetoscope Optical Distortion Calibration
07:03

Medical-grade Sterilizable Target for Fluid-immersed Fetoscope Optical Distortion Calibration

Published on: February 23, 2017

Area of Science:

  • Neuroimaging
  • Computational Anatomy
  • Medical Image Analysis

Background:

  • Spatial normalization is crucial for group analysis in neuroimaging.
  • Current methods often struggle with accurately aligning complex cortical structures.
  • Automated techniques are needed to identify homologous landmarks on brain surfaces.

Purpose of the Study:

  • To develop an automated cortical surface normalization technique.
  • To accurately identify and map homologous cortical landmarks to a standard manifold.
  • To improve intersubject alignment for neuroimaging data.

Main Methods:

  • Analysis of cortical surface geometry using multiscale shape measures (sulcal and gyral regions).
  • Application of a multichannel optical flow warping procedure for surface alignment.
  • Implementation of a partial differential equation in a Euclidean framework for multiresolution analysis.

Main Results:

  • Demonstrated successful alignment of 33 normal cortical surfaces.
  • Showed improved structural alignment in manually labeled sulci.
  • Achieved better functional alignment with positron emission tomography data.
  • Outperformed a leading volumetric spatial normalization method in matching homologous cortical anatomies.

Conclusions:

  • The proposed surface-based spatial normalization method offers superior accuracy in aligning cortical surfaces.
  • Automated landmark identification and multiscale shape analysis enhance normalization precision.
  • This technique facilitates more robust group analyses in neuroimaging studies.