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

Significance Testing: Overview01:04

Significance Testing: Overview

Significance testing is a set of statistical methods used to test whether a claim about a parameter is valid. In analytical chemistry, significance testing is used primarily to determine whether the difference between two values comes from determinate or random errors. The effect of a particular change in the measurement protocol, analyst, or sample itself can cause a deviation from the expected result. In the case of a suspected deviation/outlier, we need to be able to confirm mathematically...
Statistical Significance01:37

Statistical Significance

Once data is collected from both the experimental and the control groups, a statistical analysis is conducted to find out if there are meaningful differences between the two groups. A statistical analysis determines how likely any difference found is due to chance (and thus not meaningful). In psychology, group differences are considered meaningful, or significant, if the odds that these differences occurred by chance alone are 5 percent or less. Stated another way, if we repeated this...
Statistical Methods to Analyze Parametric Data: ANOVA01:12

Statistical Methods to Analyze Parametric Data: ANOVA

Analysis of Variance, or ANOVA, is a powerful statistical technique used to analyze parametric data, primarily in research and experimental studies. It's designed to compare the means of two or more groups, assisting researchers in identifying any significant differences between these group means. There are two main types of ANOVA based on the complexity of the analysis: one-way and two-way.
One-way ANOVA is applied when a single independent variable or factor is scrutinized. It compares the...
Critical Region, Critical Values and Significance Level01:16

Critical Region, Critical Values and Significance Level

The critical region, critical value, and significance level are interdependent concepts crucial in hypothesis testing.
In hypothesis testing, a sample statistic is converted to a test statistic using z, t, or chi-square distribution. A critical region is an area under the curve in  probability distributions demarcated by the critical value. When the test statistic falls in this region, it suggests that the null hypothesis must be rejected. As this region contains all those values of the test...

You might also read

Related Articles

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

Sort by
Same author

Deep Learning-Based Identification of Surgical Candidacy for Cervical Spinal Cord Decompression.

International journal of spine surgery·2026
Same author

Machine learning predicts cerebral vasospasm in patients with subarachnoid haemorrhage.

EBioMedicine·2024
Same author

Machine Learning Predicts Cerebral Vasospasm in Subarachnoid Hemorrhage Patients.

Research square·2024
Same author

Lumbar Spinal Canal Segmentation in Cases with Lumbar Stenosis Using Deep-U-Net Ensembles.

World neurosurgery·2023
Same author

Harmonization of multi-site functional connectivity measures in tangent space improves brain age prediction.

Proceedings of SPIE--the International Society for Optical Engineering·2023
Same author

Tau-Neurodegeneration <i>mismatch</i> reveals vulnerability and resilience to comorbidities in Alzheimer's continuum.

medRxiv : the preprint server for health sciences·2023

Related Experiment Video

Updated: May 12, 2026

Cross-Modal Multivariate Pattern Analysis
13:51

Cross-Modal Multivariate Pattern Analysis

Published on: November 9, 2011

Analytic estimation of statistical significance maps for support vector machine based multi-variate image analysis

Bilwaj Gaonkar1, Christos Davatzikos

  • 1Section for Biomedical image analysis, University of Pennsylvania, 3600 Market St., Suite 380, Philadelphia, PA 19104, USA. bilwaj@gmail.com

Neuroimage
|April 16, 2013
PubMed
Summary

This study introduces an analytical approximation for support vector machine (SVM) permutation testing in neuroimaging. This significantly speeds up the analysis, making complex brain pattern detection more accessible.

Related Experiment Videos

Last Updated: May 12, 2026

Cross-Modal Multivariate Pattern Analysis
13:51

Cross-Modal Multivariate Pattern Analysis

Published on: November 9, 2011

Area of Science:

  • Neuroimaging
  • Machine Learning
  • Brain Imaging Analysis

Background:

  • Multivariate pattern analysis (MVPA) using methods like support vector machines (SVMs) is common in fMRI and sMRI.
  • Identifying significant brain regions for classification via SVMs typically requires computationally intensive permutation testing.

Purpose of the Study:

  • To develop an analytical approximation for SVM permutation testing in neuroimaging.
  • To significantly accelerate the group difference analysis in brain imaging.

Main Methods:

  • Analytical approximation of SVM-permutation testing results.
  • Comparison of approximated testing with standard permutation testing.

Main Results:

  • Achieved over a thousandfold speedup in permutation testing.
  • Demonstrated that the approximated SVM-based group difference analysis is competitive with univariate methods.

Conclusions:

  • Analytical approximation of SVM-permutation testing is feasible and highly efficient.
  • This acceleration makes advanced neuroimaging analysis accessible on standard hardware.