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

Assessment of Diffusion and Perfusion

Assessment of Diffusion and Perfusion

Understanding and evaluating diffusion and perfusion is critical in assessing a patient's respiratory and circulatory health. These processes play key roles in maintaining the body's internal environment, ensuring that tissues receive adequate oxygen while waste products are efficiently removed.
The Role of Diffusion in Respiration
Diffusion is the process by which molecules move from an area of higher concentration to an area of lower concentration. In the respiratory system, this...

You might also read

Related Articles

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

Sort by

Same author

The aryl hydrocarbon receptor (AhR) activity and DNA-damaging effects of chlorinated polycyclic aromatic hydrocarbons (Cl-PAHs).

Chemosphere·2018

Same author

A nuclear-encoded protein, mTERF6, mediates transcription termination of rpoA polycistron for plastid-encoded RNA polymerase-dependent chloroplast gene expression and chloroplast development.

Scientific reports·2018

Same author

Cumulative metabolic effects of low-dose benzo(<i>a</i>)pyrene exposure on human cells.

Toxicology research·2018

Same author

New MS network analysis pattern for the rapid identification of constituents from traditional Chinese medicine prescription Lishukang capsules in vitro and in vivo based on UHPLC/Q-TOF-MS.

Talanta·2018

Same author

Efficient Catalytic Performance for Acylation-Nazarov Cyclization Based on an Unusual Postsynthetic Oxidization Strategy in a Fe(II)-MOF.

Inorganic chemistry·2018

Same author

A prospective, mixed-methods, before and after study to identify the evidence base for the core components of an effective Paediatric Early Warning System and the development of an implementation package containing those core recommendations for use in the UK: Paediatric early warning system - utilisation and mortality avoidance- the PUMA study protocol.

BMC pediatrics·2018

Same journal

Enhancing Alzheimer's Diagnosis: Leveraging Anatomical Landmarks in Graph Convolutional Neural Networks on Tetrahedral Meshes.

Information processing in medical imaging : proceedings of the ... conference·2026

Same journal

Cycle-Consistent Zero-Shot Through-Plane Super-Resolution for Anisotropic Head MRI.

Information processing in medical imaging : proceedings of the ... conference·2026

Same journal

Brightness-Invariant Tracking Estimation in Tagged MRI.

Information processing in medical imaging : proceedings of the ... conference·2025

Same journal

Multi-View and Multi-Scale Alignment for Contrastive Language-Image Pre-training in Mammography.

Information processing in medical imaging : proceedings of the ... conference·2025

Same journal

Using Multiple Instance Learning to Build Multimodal Representations.

Information processing in medical imaging : proceedings of the ... conference·2025

Same journal

mSPD-NN: A Geometrically Aware Neural Framework for Biomarker Discovery from Functional Connectomics Manifolds.

Information processing in medical imaging : proceedings of the ... conference·2024

See all related articles

Search research articles

Related Experiment Video

Updated: Feb 22, 2026

Diffusion Tensor Magnetic Resonance Imaging in the Analysis of Neurodegenerative Diseases

Diffusion Tensor Magnetic Resonance Imaging in the Analysis of Neurodegenerative Diseases

Published on: July 28, 2013

HFPRM: Hierarchical Functional Principal Regression Model for Diffusion Tensor Image Bundle Statistics.

Jingwen Zhang¹, Chao Huang¹, Joseph G Ibrahim¹

¹Department of Biostatistics, University of North Carolina at Chapel Hill, USA.

Information Processing in Medical Imaging : Proceedings of the ... Conference

|September 27, 2017

Summary

This summary is machine-generated.

This study introduces a new statistical model, the hierarchical functional principal regression model (HFPRM), to analyze brain white matter structure using diffusion-weighted MRI. HFPRM effectively links complex brain connectivity data with factors like age and genetics.

Keywords:

Factor Analysis Fiber Bundle Statistics Functional Principal Component Analysis Imaging Genetics Varying Coefficient Model

More Related Videos

Diffusion Imaging in the Rat Cervical Spinal Cord

Diffusion Imaging in the Rat Cervical Spinal Cord

Published on: April 7, 2015

Co-analysis of Brain Structure and Function using fMRI and Diffusion-weighted Imaging

Co-analysis of Brain Structure and Function using fMRI and Diffusion-weighted Imaging

Published on: November 8, 2012

Related Experiment Videos

Last Updated: Feb 22, 2026

Diffusion Tensor Magnetic Resonance Imaging in the Analysis of Neurodegenerative Diseases

Diffusion Tensor Magnetic Resonance Imaging in the Analysis of Neurodegenerative Diseases

Published on: July 28, 2013

Diffusion Imaging in the Rat Cervical Spinal Cord

Diffusion Imaging in the Rat Cervical Spinal Cord

Published on: April 7, 2015

Co-analysis of Brain Structure and Function using fMRI and Diffusion-weighted Imaging

Co-analysis of Brain Structure and Function using fMRI and Diffusion-weighted Imaging

Published on: November 8, 2012

Area of Science:

Neuroimaging
Biostatistics
Genetics

Background:

Diffusion-weighted magnetic resonance imaging (MRI) is crucial for studying brain white matter structure and connectivity.
Abnormalities in structural connectivity are linked to various brain disorders.
Existing methods struggle to analyze high-dimensional fiber bundle statistics efficiently.

Purpose of the Study:

To develop a novel, robust, and efficient framework, the hierarchical functional principal regression model (HFPRM).
To correlate high-dimensional fiber bundle statistics with predictors like age, diagnosis, and genetic markers.
To enable simultaneous analysis of numerous fiber bundles and disentangle latent factors.

Main Methods:

Developed the hierarchical functional principal regression model (HFPRM).
Utilized dimensional reduction and regression techniques.
Performed simulations to assess model performance.
Applied HFPRM to a genome-wide association study.

Main Results:

HFPRM effectively correlates fiber bundle statistics with predictors.
The model successfully disentangles global and individual latent factors.
Simulations demonstrated the finite sample performance of HFPRM.
Identified genetic variants associated with neonatal white matter development.

Conclusions:

HFPRM offers a powerful new tool for analyzing brain white matter structure and connectivity.
The model facilitates the discovery of relationships between neuroimaging data and clinical/genetic factors.
HFPRM has significant implications for diagnosing brain disorders and understanding genetic influences on brain development.