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

Functions of Connective Tissues01:17

Functions of Connective Tissues

17.1K
Connective tissues perform a broad range of functions in the body. Their primary function is to connect and link different tissues in the body and act as packaging material between tissues. The areolar tissue, a connective tissue prototype, commonly cements various tissue types in diverse body organs. In contrast, adipose tissue cushions internal organs while insulating the body from heat loss.
Hard connective tissues, such as bones and cartilage, provide structure and support to the body.
17.1K
Random Sampling Method01:09

Random Sampling Method

15.0K
Sampling is a technique to select a portion (or subset) of the larger population and study that portion (the sample) to gain information about the population. Data are the result of sampling from a population. The sampling method ensures that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest. Among the various sampling methods used by...
15.0K
Random Error01:04

Random Error

9.8K
Random or indeterminate errors originate from various uncontrollable variables, such as variations in environmental conditions, instrument imperfections, or the inherent variability of the phenomena being measured. Usually, these errors cannot be predicted, estimated, or characterized because their direction and magnitude often vary in magnitude and direction even during consecutive measurements. As a result, they are difficult to eliminate. However, the aggregate effect of these errors can be...
9.8K
Random Variables01:09

Random Variables

17.9K
A random variable is a single numerical value that indicates the outcome of a procedure. The concept of random variables is fundamental to the probability theory and was introduced by a Russian mathematician, Pafnuty Chebyshev, in the mid-nineteenth century.
Uppercase letters such as X or Y denote a random variable. Lowercase letters like x or y denote the value of a random variable. If X is a random variable, then X is written in words, and x is given as a number.
For example, let X = the...
17.9K
Randomized Experiments01:13

Randomized Experiments

9.1K
The randomization process involves assigning study participants randomly to experimental or control groups based on their probability of being equally assigned. Randomization is meant to eliminate selection bias and balance known and unknown confounding factors so that the control group is similar to the treatment group as much as possible. A computer program and a random number generator can be used to assign participants to groups in a way that minimizes bias.
Simple randomization
Simple...
9.1K
Random and Systematic Errors01:20

Random and Systematic Errors

15.3K
Scientists always try their best to record measurements with the utmost accuracy and precision. However, sometimes errors do occur. These errors can be random or systematic. Random errors are observed due to the inconsistency or fluctuation in the measurement process, or variations in the quantity itself that is being measured. Such errors fluctuate from being greater than or less than the true value in repeated measurements. Consider a scientist measuring the length of an earthworm using a...
15.3K

You might also read

Related Articles

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

Sort by
Same author

A multiverse approach to heat-evoked skin conductance analysis: evaluating the influence of analytic pipeline on associations between skin conductance and pain.

Pain·2026
Same author

Domain-General Neural Effects of Associative Learning and Expectations on Pain and Hedonic Taste Perception.

The Journal of neuroscience : the official journal of the Society for Neuroscience·2025
Same author

Isolating Brain Mechanisms of Expectancy Effects on Pain: Cue-Based Stimulus Expectancies versus Placebo-Based Treatment Expectancies.

The Journal of neuroscience : the official journal of the Society for Neuroscience·2025
Same author

Domain-general neural effects of associative learning and expectations on pain and hedonic taste perception.

bioRxiv : the preprint server for biology·2025
Same author

Associations of alcohol and tobacco use with psychotic, depressive and developmental disorders revealed via multimodal neuroimaging.

Translational psychiatry·2024
Same author

MARKOV SPATIAL FLOWS IN BOLD FMRI: A NOVEL LENS ON THE BOLD SIGNAL REVEALS ATTRACTING PATTERNS OF SIGNAL INTENSITY.

bioRxiv : the preprint server for biology·2024

Related Experiment Video

Updated: Feb 12, 2026

A Method for Investigating Age-related Differences in the Functional Connectivity of Cognitive Control Networks Associated with Dimensional Change Card Sort Performance
09:01

A Method for Investigating Age-related Differences in the Functional Connectivity of Cognitive Control Networks Associated with Dimensional Change Card Sort Performance

Published on: May 7, 2014

10.6K

A method to assess randomness of functional connectivity matrices.

Victor M Vergara1, Qingbao Yu1, Vince D Calhoun2

  • 1The Mind Research Network and Lovelace Biomedical and Environmental Research Institute, 1101 Yale Blvd. NE, Albuquerque, NM 87106, United States.

Journal of Neuroscience Methods
|March 31, 2018
PubMed
Summary

This study introduces a new mathematical framework using random matrix theory to analyze brain functional connectivity directly from fMRI data. The method reveals that while whole-brain connectivity is non-random, certain submatrices exhibit randomness, offering new insights beyond traditional graph theory.

Keywords:
Functional MRIFunctional connectivityRandom matrix theoryResting state network

More Related Videos

Rewiring Neuronal Circuits: A New Method for Fast Neurite Extension and Functional Neuronal Connection
10:26

Rewiring Neuronal Circuits: A New Method for Fast Neurite Extension and Functional Neuronal Connection

Published on: June 13, 2017

9.2K
Myocardial Infarction and Functional Outcome Assessment in Pigs
12:03

Myocardial Infarction and Functional Outcome Assessment in Pigs

Published on: April 25, 2014

28.7K

Related Experiment Videos

Last Updated: Feb 12, 2026

A Method for Investigating Age-related Differences in the Functional Connectivity of Cognitive Control Networks Associated with Dimensional Change Card Sort Performance
09:01

A Method for Investigating Age-related Differences in the Functional Connectivity of Cognitive Control Networks Associated with Dimensional Change Card Sort Performance

Published on: May 7, 2014

10.6K
Rewiring Neuronal Circuits: A New Method for Fast Neurite Extension and Functional Neuronal Connection
10:26

Rewiring Neuronal Circuits: A New Method for Fast Neurite Extension and Functional Neuronal Connection

Published on: June 13, 2017

9.2K
Myocardial Infarction and Functional Outcome Assessment in Pigs
12:03

Myocardial Infarction and Functional Outcome Assessment in Pigs

Published on: April 25, 2014

28.7K

Area of Science:

  • Neuroscience
  • Mathematical Biology
  • Network Science

Background:

  • Functional magnetic resonance imaging (fMRI) measures brain functional connectivity.
  • Graph theory is commonly used to analyze fMRI connectivity, often employing non-linear transformations.
  • Existing methods may overlook certain aspects of connectivity patterns.

Purpose of the Study:

  • To propose a novel mathematical framework for analyzing randomness in functional connectivity matrices (FCMs).
  • To develop a randomness measure with probability density function and statistical testing.
  • To provide a new perspective on brain network organization beyond traditional graph theory.

Main Methods:

  • Application of random matrix theory to functional connectivity matrices (FCMs).
  • Development of a quantitative measure for assessing randomness in FCMs.
  • Statistical testing of FCM submatrices for deviations from randomness.

Main Results:

  • Whole brain FCMs from 603 healthy individuals were confirmed as non-random.
  • Several FCM submatrices did not significantly deviate from randomness.
  • The proposed method complements graph theory by analyzing different aspects of connectivity.

Conclusions:

  • The developed randomness measure offers a distinct analytical approach to brain functional connectivity.
  • Identifying random submatrices suggests potential for lower-order descriptive models.
  • This method provides insights into the structure of randomness within non-random brain networks.