Jove
Visualize
Contact Us

Related Concept Videos

What Are Outliers?01:12

What Are Outliers?

4.2K
Outliers are observed data points that are far from the least squares line. They have unusual values and need to be examined carefully. Though an outlier may result from erroneous data, at other times, it may hold valuable information about the population under study and should be included in the data. Hence, it is crucial to examine what causes a data point to be an outlier.
The z score is used to find outliers or unusual values. It should be noted that any values beyond -2 and +2 are...
4.2K
Outliers and Influential Points01:08

Outliers and Influential Points

5.0K
An outlier is an observation of data that does not fit the rest of the data. It is sometimes called an extreme value. When you graph an outlier, it will appear not to fit the pattern of the graph. Some outliers are due to mistakes (for example, writing down 50 instead of 500), while others may indicate that something unusual is happening. Outliers are present far from the least squares line in the vertical direction. They have large "errors," where the "error" or residual is the...
5.0K
Quantifying and Rejecting Outliers: The Grubbs Test01:02

Quantifying and Rejecting Outliers: The Grubbs Test

4.0K
Sometimes, a data set can have a recorded numerical observation that greatly  deviates from the rest of the data. Assuming that the data is normally distributed, a statistical method called the Grubbs test can be used to determine whether the observation is truly an outlier.  To perform a two-tailed Grubbs test, first, calculate the absolute difference between the outlier and the mean. Then, calculate the ratio between this difference and the standard deviation of the sample. This...
4.0K
Modified Boxplots00:57

Modified Boxplots

8.0K
A standard box and whisker plot informs us about the spread of the data in a given sample. One can identify the minimum value, maximum value, first quartile value, second quartile or median value, and third quartile.
However, the box plot does not tell the reader about outliers - values that lie far from the center of the data. We can modify the standard box and whisker plot to identify the outliers and visualize the actual spread of the data in a sample.
Initially, we calculate the adjusted...
8.0K
Noncompartmental Analysis: Statistical Moment Theory00:56

Noncompartmental Analysis: Statistical Moment Theory

522
Noncompartmental analyses leverage statistical moment theory to examine time-related changes in macroscopic events, encapsulating the collective outcomes stemming from the constituent elements in play. Statistical moment theory is a mathematical approach used to describe the time course of drug concentration in the body without assuming a specific compartmental model. SMT provides insights into drug absorption, distribution, metabolism, and elimination by treating drug concentration versus time...
522
¹H NMR: Interpreting Distorted and Overlapping Signals01:02

¹H NMR: Interpreting Distorted and Overlapping Signals

1.3K
Spin systems where the difference in chemical shifts of the coupled nuclei is greater than ten times J are called first-order spin systems. These nuclei are weakly coupled, and their chemical shifts and coupling constant can generally be estimated from the well-separated signals in the spectrum.
As Δν decreases and the signals move closer, the doublets appear increasingly distorted. The intensities of the inner lines increase at the cost of those of the outer lines as the signals are...
1.3K

You might also read

Related Articles

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

Sort by
Same author

Integrating data across oscillatory power bands predicts the seizure onset zone in focal epilepsies.

Brain communications·2026
Same author

A deep neural network model of audiovisual speech recognition reports the McGurk effect.

Psychonomic bulletin & review·2026
Same author

Human orbitofrontal neural activity is linked to obsessive-compulsive behavioral dynamics.

Cell·2026
Same author

Stereoelectroencephalography Reveals Neural Signatures of Multisensory Integration in the Human Superior Temporal Sulcus during Audiovisual Speech Perception.

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

A Deep Neural Network Model of Audiovisual Speech Recognition Reports the McGurk Effect.

bioRxiv : the preprint server for biology·2025
Same author

The McGurk effect is similar in native Mandarin Chinese and American English speakers.

Frontiers in psychology·2025
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 Experiment Video

Updated: Apr 25, 2026

Using Informational Connectivity to Measure the Synchronous Emergence of fMRI Multi-voxel Information Across Time
07:12

Using Informational Connectivity to Measure the Synchronous Emergence of fMRI Multi-voxel Information Across Time

Published on: July 1, 2014

12.3K

Finding multivariate outliers in fMRI time-series data.

John F Magnotti1, Nedret Billor1

  • 1Department of Mathematics & Statistics, Auburn University, AL 36849, USA.

Computers in Biology and Medicine
|August 18, 2014
PubMed
Summary

This study evaluates multivariate outlier detection algorithms for functional magnetic resonance imaging (fMRI) data. Principal Component based Outlier detection (PCOut) demonstrated superior performance over HD BACON in identifying and handling outliers in complex brain activity patterns.

Keywords:
High dimensional dataOutlier detectionfMRI

More Related Videos

Basics of Multivariate Analysis in Neuroimaging Data
06:35

Basics of Multivariate Analysis in Neuroimaging Data

Published on: July 24, 2010

17.6K
Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data
14:27

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Published on: June 26, 2013

15.2K

Related Experiment Videos

Last Updated: Apr 25, 2026

Using Informational Connectivity to Measure the Synchronous Emergence of fMRI Multi-voxel Information Across Time
07:12

Using Informational Connectivity to Measure the Synchronous Emergence of fMRI Multi-voxel Information Across Time

Published on: July 1, 2014

12.3K
Basics of Multivariate Analysis in Neuroimaging Data
06:35

Basics of Multivariate Analysis in Neuroimaging Data

Published on: July 24, 2010

17.6K
Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data
14:27

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Published on: June 26, 2013

15.2K

Area of Science:

  • Neuroimaging
  • Data Analysis
  • Machine Learning

Background:

  • Multivariate techniques are crucial for analyzing functional magnetic resonance imaging (fMRI) data to understand brain activity patterns.
  • These advanced methods are susceptible to multivariate outliers, which can compromise analysis accuracy.
  • Univariate analysis techniques, while common, do not encounter this specific challenge.

Purpose of the Study:

  • To evaluate the efficacy of two multivariate outlier detection algorithms, HD BACON and PCOut, for high-dimensional fMRI data.
  • To assess the ability of these algorithms to identify outlying voxels within specific regions of interest.
  • To compare the performance of HD BACON and PCOut using simulated fMRI data.

Main Methods:

  • Applied High Dimensional Blocked Adaptive Computationally Efficient Outlier Nominators (HD BACON) and Principal Component based Outlier detection (PCOut) to fMRI data.
  • Utilized individual voxel time-series for outlier detection.
  • Employed simulated data to compare algorithm sensitivity and specificity under varying outlier contamination levels.

Main Results:

  • Both HD BACON and PCOut identified voxels located at the edges of activation clusters and near tissue boundaries.
  • Simulation results indicated that PCOut outperformed HD BACON, showing higher sensitivity and specificity across different outlier percentages.
  • PCOut proved more robust in detecting multivariate outliers in high-dimensional fMRI datasets.

Conclusions:

  • Multivariate outlier detection is a valuable addition to routine data quality checks in fMRI analysis.
  • Implementing methods like PCOut can enhance the reliability and accuracy of multivariate fMRI studies.
  • These findings support the integration of robust outlier detection for improved interpretation of brain activity patterns.