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

Anatomy of the Brain: Major Regions01:20

Anatomy of the Brain: Major Regions

10.6K
The brain is the most complex organ in the human body. It consists of four main parts: the cerebrum, diencephalon, cerebellum, and brainstem.
The cerebrum is the largest section of the brain and divides into left and right hemispheres, separated by a deep fissure. The cerebral outer layer of grey matter — the cerebral cortex — comprises elevations called gyri and shallow groves called sulci. The inner portion of white matter includes long nerve fibers known as axons, which connect...
10.6K
Distance Problem01:29

Distance Problem

74
When an object's velocity changes over time, the total distance traveled can be determined by summing small displacement intervals over short increments. This approach approximates the true distance through numerical summation and the use of integral calculus. An estimate of the total displacement can be obtained by measuring velocity at regular intervals and multiplying each value by the corresponding time step.If a runner accelerates over the first three seconds of a race, speed measurements...
74
Brain Waves01:23

Brain Waves

4.2K
Brain waves are electrical signals generated by the neurons in the brain, which are regularly monitored to measure mental activities. Brain waves and their frequency ranges can be measured using an electroencephalogram or EEG. There are four main types of brain waves, each with distinct characteristics:
4.2K
The Distance Formula01:20

The Distance Formula

654
In geometry, measuring the direct distance between two points on a plane is essential in various practical and theoretical applications. Whether in navigation, engineering, or computer graphics, determining the shortest path between two locations involves using the distance formula. This formula is derived from the Pythagorean Theorem, which relates the lengths of the sides of a right triangle. On a coordinate plane, the horizontal and vertical distances between two points serve as the legs of...
654
Distance Corrections01:15

Distance Corrections

297
To achieve precise distance measurements, especially in surveying and construction, certain corrections must be applied to account for potential sources of error like the standardization errors, temperature variations, and slope adjustments.Standardization error emerges when measurement equipment undergoes changes, such as wear, repairs, or weather impacts. To address this, surveyors compare the equipment’s readings to a standard. This process identifies any deviation that might lead to...
297
Network Covalent Solids02:18

Network Covalent Solids

16.2K
Network covalent solids contain a three-dimensional network of covalently bonded atoms as found in the crystal structures of nonmetals like diamond, graphite, silicon, and some covalent compounds, such as silicon dioxide (sand) and silicon carbide (carborundum, the abrasive on sandpaper). Many minerals have networks of covalent bonds.
To break or to melt a covalent network solid, covalent bonds must be broken. Because covalent bonds are relatively strong, covalent network solids are typically...
16.2K

You might also read

Related Articles

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

Sort by
Same author

Machine learning-assisted Mn-N-C nanozyme colorimetric sensor array for trace-level detection of biogenic amines in meat.

Food chemistry·2026
Same author

2D In-Plane Molecular Superlattice Heterojunctions for High-Performance Ambipolar Electronics and Low-Dose X-Ray Sensing.

Advanced materials (Deerfield Beach, Fla.)·2026
Same author

The Protective Effects of Small-Molecule Compound 0242 Against LPS-Induced Neuroinflammation and in P301S Tau Transgenic Mice.

Neurochemical research·2026
Same author

Antibody-drug conjugates in breast cancer: from mechanism to revolutionizing clinical practice.

Molecular cancer·2026
Same author

A Genetically Encoded Calcium Ion Biosensor with an Exceptionally Large Ratiometric Response.

ACS sensors·2026
Same author

CAMP: A Context-Aware, Multimodal, and Privacy-Preserving Pedestrian Trajectory Prediction Framework.

Journal of imaging·2026

Related Experiment Video

Updated: Feb 9, 2026

Cross-Modal Multivariate Pattern Analysis
13:51

Cross-Modal Multivariate Pattern Analysis

Published on: November 9, 2011

20.5K

Individual Morphological Brain Network Construction Based on Multivariate Euclidean Distances Between Brain Regions.

Kaixin Yu1,2, Xuetong Wang1,2, Qiongling Li1,2

  • 1School of Biological Science & Medical Engineering, Beihang University, Beijing, China.

Frontiers in Human Neuroscience
|June 12, 2018
PubMed
Summary

This study introduces a new method for building brain networks using multiple features, improving diagnosis of mild cognitive impairment (MCI) and Alzheimer's disease (AD). The approach shows high accuracy in distinguishing MCI from healthy controls and predicting disease progression.

Keywords:
classificationindividual morphological brain networkmild cognitive impairmentmultiple morphological featuresmultivariate Euclidean distance

More Related Videos

Collection of Frozen Rodent Brain Regions for Downstream Analyses
07:06

Collection of Frozen Rodent Brain Regions for Downstream Analyses

Published on: April 23, 2020

22.2K
Modeling the Functional Network for Spatial Navigation in the Human Brain
05:55

Modeling the Functional Network for Spatial Navigation in the Human Brain

Published on: October 13, 2023

1.6K

Related Experiment Videos

Last Updated: Feb 9, 2026

Cross-Modal Multivariate Pattern Analysis
13:51

Cross-Modal Multivariate Pattern Analysis

Published on: November 9, 2011

20.5K
Collection of Frozen Rodent Brain Regions for Downstream Analyses
07:06

Collection of Frozen Rodent Brain Regions for Downstream Analyses

Published on: April 23, 2020

22.2K
Modeling the Functional Network for Spatial Navigation in the Human Brain
05:55

Modeling the Functional Network for Spatial Navigation in the Human Brain

Published on: October 13, 2023

1.6K

Area of Science:

  • Neuroscience
  • Medical Imaging
  • Computational Biology

Background:

  • Morphological brain networks are crucial for understanding neurological diseases like mild cognitive impairment (MCI) and Alzheimer's disease (AD).
  • Existing methods often rely on single morphological features, limiting diagnostic potential.
  • Combining multiple features enhances diagnostic performance, highlighting the need for multi-feature individual brain networks.

Purpose of the Study:

  • To develop a novel method for constructing individual morphological brain networks using multiple features.
  • To evaluate the robustness and reproducibility of the proposed method.
  • To assess the diagnostic accuracy of the multi-feature network in distinguishing MCI from normal controls (NC) and classifying MCI subtypes.

Main Methods:

  • A new method was proposed to construct individual morphological brain networks by calculating the exponential function of multivariate Euclidean distance to assess region similarity.
  • The method was validated on two datasets: one for reproducibility assessment in healthy subjects and another for disease classification (MCI vs. NC, stable MCI vs. progressive MCI).
  • Support vector machines (SVM) were used to classify subjects based on extracted edge features from the constructed networks.

Main Results:

  • The proposed method demonstrated high reproducibility and small-world properties in healthy subjects.
  • Classification accuracy for MCI vs. NC was 79.65% (1D) and 80.53% (multi-dimensional).
  • Classification accuracy for stable MCI vs. progressive MCI was 70.59% (1D) and 77.06% (multi-dimensional), outperforming single-feature methods.

Conclusions:

  • The novel method effectively constructs individual morphological brain networks using multiple features.
  • The approach accurately discriminates between MCI and NC, and between stable and progressive MCI.
  • This method offers a valuable tool for investigating individual brain networks in neurological diseases.