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

Expected Frequencies in Goodness-of-Fit Tests01:19

Expected Frequencies in Goodness-of-Fit Tests

7.0K
A goodness-of-fit test is conducted to determine whether the observed frequency values are statistically similar to the frequencies expected for the dataset. Suppose the expected frequencies for a dataset are equal such as when predicting the frequency of any number appearing when casting a die. In that case, the expected frequency is the ratio of the total number of observations (n)  to the number of categories (k).
7.0K
Strategies for Assessing and Addressing Confounding01:25

Strategies for Assessing and Addressing Confounding

332
Confounding is a critical issue in epidemiological studies, often leading to misleading conclusions about associations between exposures and outcomes. It occurs when the relationship between the exposure and the outcome is mixed with the effects of other factors that influence the outcome. Given that, addressing confounding is of high importance for drawing accurate inferences in research.
Confounding can be addressed at both the design phase of a study and through analytical methods after data...
332
Confounding in Epidemiological Studies01:27

Confounding in Epidemiological Studies

545
Confounding in statistical epidemiology represents a pivotal challenge, referring to the distortion in the perceived relationship between an exposure and an outcome due to the presence of a third variable, known as a confounder. This variable is associated with both the exposure and the outcome but is not a direct link in their causal chain. Its presence can lead to erroneous interpretations of the exposure's effect, either exaggerating or underestimating the true association. This...
545
Friedman Two-way Analysis of Variance by Ranks01:21

Friedman Two-way Analysis of Variance by Ranks

464
Friedman's Two-Way Analysis of Variance by Ranks is a nonparametric test designed to identify differences across multiple test attempts when traditional assumptions of normality and equal variances do not apply. Unlike conventional ANOVA, which requires normally distributed data with equal variances, Friedman's test is ideal for ordinal or non-normally distributed data, making it particularly useful for analyzing dependent samples, such as matched subjects over time or repeated measures...
464
Cross-Sectional Research01:50

Cross-Sectional Research

12.3K
In cross-sectional research, a researcher compares multiple segments of the population at the same time. If they were interested in people's dietary habits, the researcher might directly compare different groups of people by age. Instead of following a group of people for 20 years to see how their dietary habits changed from decade to decade, the researcher would study a group of 20-year-old individuals and compare them to a group of 30-year-old individuals and a group of 40-year-old...
12.3K
Correlation of Experimental Data01:23

Correlation of Experimental Data

457
Dimensional analysis simplifies complex physical problems and guides experimental investigations, but it does not provide complete solutions. It identifies the dimensionless groups that influence a phenomenon, but experimental data is needed to establish the specific relationships and validate theoretical predictions.
For example, a spherical particle moving through a viscous fluid experiences drag. Dimensional analysis shows that the drag force depends on the particle's diameter, velocity,...
457

You might also read

Related Articles

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

Sort by
Same author

A hierarchical cascade of sleep rhythms supports motor memory and is hijacked by epileptic spikes in human epilepsy.

Proceedings of the National Academy of Sciences of the United States of America·2026
Same author

A modular and flexible pipeline for intraoperative electrode reconstruction and localization in patients with brain lesions.

Frontiers in neural circuits·2026
Same author

Prefrontal cortical pathways mediating cognitive control enhancement from internal capsule stimulation.

bioRxiv : the preprint server for biology·2026
Same author

A mosaic of whole-body representations on the human precentral gyrus.

Nature·2026
Same author

Mapping the neuronal building blocks of human language with language models.

Nature·2026
Same author

Author Correction: Plasticity and language in the anaesthetized human hippocampus.

Nature·2026
Same journal

Arrayed single-gene perturbations identify drivers of human anterior neural tube closure.

eLife·2026
Same journal

Pervasive relaxed selection on spermatogenesis genes coincident with the evolution of polygyny in gorillas.

eLife·2026
Same journal

Impacts of DNA methylation on H2A.Z deposition and nucleosome stability.

eLife·2026
Same journal

Continuous developmental changes in word recognition and language learning across early childhood.

eLife·2026
Same journal

Multiple event segmentation mechanisms in the human brain.

eLife·2026
Same journal

Optimised genome editing for precise DNA insertion and substitution using prime editors in zebrafish.

eLife·2026
See all related articles

Related Experiment Video

Updated: Jan 5, 2026

Author Spotlight: Unlocking New Insights in fNIRS Studies - A Novel Framework for Inter-Brain Synchrony Analysis
05:59

Author Spotlight: Unlocking New Insights in fNIRS Studies - A Novel Framework for Inter-Brain Synchrony Analysis

Published on: October 6, 2023

3.2K

A statistical framework to assess cross-frequency coupling while accounting for confounding analysis effects.

Jessica K Nadalin1, Louis-Emmanuel Martinet2, Ethan B Blackwood3

  • 1Department of Mathematics and Statistics, Boston University, Boston, United States.

Elife
|October 17, 2019
PubMed
Summary
This summary is machine-generated.

Cross frequency coupling (CFC) is a key brain activity feature. This study introduces a new statistical model to accurately measure CFC, improving analysis of brain function and dysfunction.

Keywords:
brain rhythmscomputational neurosciencecross-frequency couplinggeneralized linear modelshumanneural data analysisneurosciencerat

More Related Videos

Combined Invasive Subcortical and Non-invasive Surface Neurophysiological Recordings for the Assessment of Cognitive and Emotional Functions in Humans
08:25

Combined Invasive Subcortical and Non-invasive Surface Neurophysiological Recordings for the Assessment of Cognitive and Emotional Functions in Humans

Published on: May 19, 2016

11.1K
Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms
08:51

Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms

Published on: November 1, 2019

6.0K

Related Experiment Videos

Last Updated: Jan 5, 2026

Author Spotlight: Unlocking New Insights in fNIRS Studies - A Novel Framework for Inter-Brain Synchrony Analysis
05:59

Author Spotlight: Unlocking New Insights in fNIRS Studies - A Novel Framework for Inter-Brain Synchrony Analysis

Published on: October 6, 2023

3.2K
Combined Invasive Subcortical and Non-invasive Surface Neurophysiological Recordings for the Assessment of Cognitive and Emotional Functions in Humans
08:25

Combined Invasive Subcortical and Non-invasive Surface Neurophysiological Recordings for the Assessment of Cognitive and Emotional Functions in Humans

Published on: May 19, 2016

11.1K
Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms
08:51

Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms

Published on: November 1, 2019

6.0K

Area of Science:

  • Neuroscience
  • Computational Neuroscience
  • Signal Processing

Background:

  • Cross-frequency coupling (CFC) is a critical neural mechanism linking brain function and dysfunction.
  • Numerous analysis methods exist for specific CFC types, but inappropriate method selection can reduce statistical power and introduce confounds.
  • Accurate quantification of CFC is essential for understanding neural dynamics.

Purpose of the Study:

  • To develop a robust statistical modeling framework for estimating cross-frequency coupling (CFC).
  • To create a measure of phase-amplitude coupling that accounts for variations in low-frequency amplitude.
  • To provide a more reliable method for analyzing brain activity patterns.

Main Methods:

  • Proposed a statistical modeling framework to estimate high-frequency amplitude as a function of low-frequency amplitude and phase.
  • Developed a measure of phase-amplitude coupling that explicitly controls for low-frequency amplitude.
  • Validated the method using simulations and biologically-motivated examples.

Main Results:

  • The proposed method successfully detects CFC between low-frequency phase/amplitude and high-frequency amplitude.
  • The framework outperforms existing methods in detecting CFC in simulated and real-world neural data.
  • Demonstrated the method's utility in analyzing in vivo data from seizure activity and response to electrical stimuli.

Conclusions:

  • The developed statistical framework offers a more accurate and reliable approach to quantifying cross-frequency coupling.
  • This method enhances the understanding of neural dynamics by accounting for amplitude variations.
  • The findings have implications for studying brain function, dysfunction, and responses to stimuli.