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

Interpretation of Confidence Intervals01:19

Interpretation of Confidence Intervals

8.4K
A confidence interval is a better estimate of the population than a point estimate, as it uses a range of values from a sample instead of a single value.
Confidence intervals have confidence coefficients that are crucial for their interpretation. The most common confidence coefficients are 0.90, 0.95, and 0.99, which can be written as percentages–90%, 95%, and 99%, respectively.
Suppose a person calculates a confidence interval with a confidence coefficient of 0.95. In that case, they can...
8.4K
Confidence Coefficient01:24

Confidence Coefficient

9.7K
The confidence coefficient is also known as the confidence level or degree of confidence. It is the percent expression for the probability, 1-α, that the confidence interval contains the true population parameter assuming that the confidence interval is obtained after sufficient unbiased sampling; for example, if the CL = 90%, then in 90 out of 100 samples the interval estimate will enclose the true population parameter. Here α is the area under the curve, distributed equally under...
9.7K
Confidence Intervals01:21

Confidence Intervals

9.0K
An unbiased point estimate is often insufficient to predict a population estimate, such as population mean or population proportion. In this scenario, a confidence interval is used. A confidence interval is an estimate similar to a  sample proportion. However, unlike the point estimate which is a single value, the confidence interval  contains a range of values. These values have lower and upper limits, known as confidence limits, and can be designated as L1 and L2, respectively.
A...
9.0K
Uncertainty: Confidence Intervals00:54

Uncertainty: Confidence Intervals

8.2K
The confidence interval is the range of values around the mean that contains the true mean. It is expressed as a probability percentage. The interpretation of a 95% confidence interval, for instance, is that the statistician is 95% confident that the true mean falls within the interval. The upper and lower limits of this range are known as confidence limits. The confidence limits for the true mean are estimated from the sample's mean, the standard deviation, and the statistical factor...
8.2K
Propagation of Uncertainty from Random Error00:59

Propagation of Uncertainty from Random Error

1.4K
An experiment often consists of more than a single step. In this case, measurements at each step give rise to uncertainty. Because the measurements occur in successive steps, the uncertainty in one step necessarily contributes to that in the subsequent step. As we perform statistical analysis on these types of experiments, we must learn to account for the propagation of uncertainty from one step to the next. The propagation of uncertainty depends on the type of arithmetic operation performed on...
1.4K
Probability Histograms01:17

Probability Histograms

12.7K
A probability histogram is a visual representation of a probability distribution. Similar a typical histogram, the probability histogram consists of contiguous (adjoining) boxes. It has both a horizontal axis and a vertical axis. The horizontal axis is labeled with what the data represents. The vertical axis is labeled with probability. Each rectangular bar in the histogram is 1 unit wide, which suggests that the area under each bar equals the probability, P(x), where x is 1, 2, 3, and so on.
12.7K

You might also read

Related Articles

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

Sort by
Same author

Innovative Clinical Trial Approach for Evaluating Digital Medical Devices Under European Fast-Track Regulatory Frameworks.

Statistics in medicine·2026
Same author

Causal mediation analysis with one or multiple mediators: A comparative study.

Psychological methods·2026
Same author

NeuroConText: Contrastive learning for neuroscience meta-analysis with rich text representation.

Imaging neuroscience (Cambridge, Mass.)·2026
Same author

Individual Brain Charting: fifth release of high-resolution fMRI data for cognitive mapping.

Scientific data·2026
Same author

Subject fingerprinting and task classification rely on distinct functional connectivity features.

Brain structure & function·2026
Same author

An Interactive Brain Atlas of Knowledge.

bioRxiv : the preprint server for biology·2025

Related Experiment Video

Updated: Nov 12, 2025

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks
11:18

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks

Published on: March 2, 2015

10.5K

Decoding with confidence: Statistical control on decoder maps.

Jérôme-Alexis Chevalier1, Tuan-Binh Nguyen2, Joseph Salmon3

  • 1Parietal project-team, Inria Saclay-Ile de France, Palaiseau, France; CEA/Neurospin bat 145, Gif-Sur-Yvette, France; Université Paris-Saclay, Gif-Sur-Yvette, France.

Neuroimage
|March 16, 2021
PubMed
Summary

Standard brain imaging decoding lacks statistical guarantees. A new framework, the Ensemble of Clustered Desparsified Lasso (EnCluDL), offers reliable statistical inference for decoding maps, ensuring accuracy in high-dimensional data analysis.

Keywords:
DecodingHigh dimensionInferenceMultivariate modelStatistical controlStatistical methodsSupport recoveryfMRI

More Related Videos

Assessment and Communication for People with Disorders of Consciousness
07:37

Assessment and Communication for People with Disorders of Consciousness

Published on: August 1, 2017

9.4K
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

12.5K

Related Experiment Videos

Last Updated: Nov 12, 2025

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks
11:18

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks

Published on: March 2, 2015

10.5K
Assessment and Communication for People with Disorders of Consciousness
07:37

Assessment and Communication for People with Disorders of Consciousness

Published on: August 1, 2017

9.4K
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

12.5K

Area of Science:

  • Neuroimaging
  • Statistical Inference
  • Machine Learning

Background:

  • Brain imaging decoding is crucial for understanding brain-cognition links and identifying pathologies.
  • Current decoding methods lack statistical guarantees and confidence intervals for interpreting results.
  • High-dimensional data in whole-brain decoding prevents classical statistical inference.

Purpose of the Study:

  • To develop a statistically sound framework for brain imaging decoding.
  • To introduce a novel inference procedure with statistical guarantees.
  • To address limitations of standard decoding map thresholding.

Main Methods:

  • Generalization of Family Wise Error Rate (FWER) to a spatial tolerance δ, creating the δ-Family Wise Error Rate (δ-FWER).
  • Development of the Ensemble of Clustered Desparsified Lasso (EnCluDL) for multivariate statistical inference.
  • Empirical evaluation of EnCluDL against alternative procedures, including map thresholding.

Main Results:

  • EnCluDL effectively controls the proposed δ-FWER.
  • The EnCluDL procedure demonstrates superior recovery properties compared to alternatives.
  • Statistical control is maintained while achieving better results in decoding.

Conclusions:

  • EnCluDL provides a statistically rigorous approach to brain imaging decoding.
  • The δ-FWER offers a more appropriate error control for spatial data.
  • This framework enhances the reliability and interpretability of brain imaging biomarkers.