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

Statistical Inference Techniques in Hypothesis Testing: Parametric Versus Nonparametric Data01:16

Statistical Inference Techniques in Hypothesis Testing: Parametric Versus Nonparametric Data

Statistical inference techniques, paramount in hypothesis testing, differentiate into two broad categories: parametric and nonparametric statistics.
Parametric statistics, as the name suggests, assumes that data follow a specific distribution, often a normal distribution. This assumption enables robust hypothesis testing and estimation. Parametric methods, like the Student's t-test or Goodness-of-fit test, are frequently employed in biostatistics due to their robustness. For instance, comparing...
Introduction to Nonparametric Statistics01:28

Introduction to Nonparametric Statistics

Nonparametric statistics offer a powerful alternative to traditional parametric methods, useful when assumptions about the population distribution cannot be made. Unlike parametric tests, which require data to follow a specific distribution with well-defined parameters (such as the mean and standard deviation), nonparametric tests do not require such constraints. This makes them particularly valuable when dealing with small sample sizes, skewed data, or ordinal and categorical variables.
One of...
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...
Statistical Methods to Analyze Parametric Data: Student t-Test and Goodness-of-Fit Test01:09

Statistical Methods to Analyze Parametric Data: Student t-Test and Goodness-of-Fit Test

In parametric statistics, two fundamental tests stand out for their utility and wide application: the Student's t-test and goodness-of-fit tests. These tests provide researchers with a robust method for drawing insights from data, testing hypotheses, and making informed decisions based on their findings.
The Student's t-test is a statistical test that examines if there is a statistically significant difference between the means of two groups. This test is instrumental when dealing with data...
Parametric Survival Analysis: Weibull and Exponential Methods01:14

Parametric Survival Analysis: Weibull and Exponential Methods

Parametric survival analysis models survival data by assuming a specific probability distribution for the time until an event occurs. The Weibull and exponential distributions are two of the most commonly used methods in this context, due to their versatility and relatively straightforward application.
Weibull Distribution
The Weibull distribution is a flexible model used in parametric survival analysis. It can handle both increasing and decreasing hazard rates, depending on its shape parameter...
Biostatistics: Overview01:20

Biostatistics: Overview

Biostatistics plays a crucial role in understanding and analyzing data in healthcare and biology. Biostatisticians conduct experiments, gather evidence, and draw meaningful conclusions using statistical methods and techniques. Different variables form the foundation of biostatistical analysis, allowing researchers to understand and interpret data effectively. These variables are classified into different types, each serving a specific purpose in statistical analysis.
Discrete variables are...

You might also read

Related Articles

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

Sort by
Same author

New Growth, New Opportunities.

Journal of medical imaging (Bellingham, Wash.)·2025
Same author

Influence of early through late fusion on pancreas segmentation from imperfectly registered multimodal magnetic resonance imaging.

Journal of medical imaging (Bellingham, Wash.)·2025
Same author

White matter hyperintensities and relapse risk in late-life depression.

Journal of affective disorders·2025
Same author

Unsupervised discovery of clinical disease signatures using probabilistic independence.

Journal of biomedical informatics·2025
Same author

Multi-contrast computed tomography atlas of healthy pancreas with dense displacement sampling registration.

Journal of medical imaging (Bellingham, Wash.)·2025
Same author

The effect of Alzheimer's disease genetic factors on limbic white matter microstructure.

Alzheimer's & dementia : the journal of the Alzheimer's Association·2025
Same journal

Segmentation of the parasagittal dura mater on multi-center 3D-FLAIR MRI.

NeuroImage·2026
Same journal

Spatial frequency channels implement a mental ruler in spatial vision.

NeuroImage·2026
Same journal

Exploring the Link Between Intravoxel Incoherent Motion Measured Brain Diffusivity During Wakefulness and Sleep Macrostructure in the Elderly.

NeuroImage·2026
Same journal

Closed-loop adaptation of transcranial magnetic stimulation intensity with electroencephalography feedback.

NeuroImage·2026
Same journal

Volumetric postmortem MRI of the medial temporal lobe in Alzheimer's disease and related disorders: methodological advances and implications for in vivo biomarker development.

NeuroImage·2026
Same journal

Neural responses to equity and inequity when receiving vicarious rewards for self and charity during adolescence.

NeuroImage·2026
See all related articles

Related Experiment Video

Updated: Jun 2, 2026

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

Biological parametric mapping with robust and non-parametric statistics.

Xue Yang1, Lori Beason-Held, Susan M Resnick

  • 1Electrical Engineering, Vanderbilt University, Nashville, TN 37235, USA.

Neuroimage
|May 17, 2011
PubMed
Summary
This summary is machine-generated.

This study introduces a robust regression method to improve brain structure-function mapping. The new approach enhances reliability by reducing outlier sensitivity in neuroimaging analyses.

More Related Videos

Meta-analysis of Voxel-Based Neuroimaging Studies using Seed-based d Mapping with Permutation of Subject Images (SDM-PSI)
06:26

Meta-analysis of Voxel-Based Neuroimaging Studies using Seed-based d Mapping with Permutation of Subject Images (SDM-PSI)

Published on: November 27, 2019

Mapping Cortical Dynamics Using Simultaneous MEG/EEG and Anatomically-constrained Minimum-norm Estimates: an Auditory Attention Example
08:45

Mapping Cortical Dynamics Using Simultaneous MEG/EEG and Anatomically-constrained Minimum-norm Estimates: an Auditory Attention Example

Published on: October 24, 2012

Related Experiment Videos

Last Updated: Jun 2, 2026

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

Meta-analysis of Voxel-Based Neuroimaging Studies using Seed-based d Mapping with Permutation of Subject Images (SDM-PSI)
06:26

Meta-analysis of Voxel-Based Neuroimaging Studies using Seed-based d Mapping with Permutation of Subject Images (SDM-PSI)

Published on: November 27, 2019

Mapping Cortical Dynamics Using Simultaneous MEG/EEG and Anatomically-constrained Minimum-norm Estimates: an Auditory Attention Example
08:45

Mapping Cortical Dynamics Using Simultaneous MEG/EEG and Anatomically-constrained Minimum-norm Estimates: an Auditory Attention Example

Published on: October 24, 2012

Area of Science:

  • Neuroimaging
  • Brain Structure-Function Relationships
  • Statistical Modeling

Background:

  • Mapping brain structure-function correlations is crucial but challenging.
  • Existing methods like statistical parametric mapping (SPM) have limitations, particularly sensitivity to outliers.
  • Biological parametric mapping (BPM) extends SPM but also suffers from outlier issues, potentially invalidating inferences.

Purpose of the Study:

  • To develop a more robust method for quantitative, voxelwise assessment of brain structure-function relationships.
  • To address the limitations of biological parametric mapping concerning outlier sensitivity and anatomical variations.
  • To enhance the reliability and applicability of neuroimaging analyses for correlating structural and functional data.

Main Methods:

  • Introduction of robust regression and non-parametric regression techniques within the neuroimaging context.
  • Application of the general linear model to multiple imaging modalities and scalar observations.
  • Validation through simulation studies and empirical data analysis.

Main Results:

  • The proposed robust approach significantly reduces sensitivity to outliers in neuroimaging data.
  • The method maintains statistical power while improving the reliability of inferences.
  • Demonstrated reduction in artifactual low p-values caused by mis-registration or anatomical variability.

Conclusions:

  • Robust regression provides a reliable solution for quantitative voxelwise assessment of structure-function correlations in neuroimaging.
  • The developed approach enhances the accuracy of mapping relationships between brain structure and function.
  • This work facilitates wider and more dependable application of advanced neuroimaging analysis techniques.