Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Brain Imaging

Brain Imaging

Brain imaging technologies provide critical insights into both the structure and function of the human brain, enabling medical professionals and researchers to diagnose, study, and treat neurological disorders or psychiatric disorders more effectively.
These technologies include computerized axial tomography (CAT or CT scans), positron-emission tomography (PET scans), magnetic resonance imaging (MRI), functional magnetic resonance imaging (fMRI), and Transcranial Magnetic Stimulation (TMS).

Imaging Studies IV: Magnetic Resonance Imaging

Imaging Studies IV: Magnetic Resonance Imaging

Introduction:Magnetic Resonance Imaging, or MRI, can include a specialized imaging technique of the urinary system known as Magnetic Resonance Urography (MRU). This radiation-free technique uses strong magnetic fields and radio waves to produce detailed images with the help of a computer. MRU is particularly effective for visualizing fluid-filled structures like the kidneys, ureters, and bladder.Applications of MRI in the Genitourinary SystemKidneys and Ureters: MRI detects tumors, cysts,...

You might also read

Related Articles

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

Sort by

Same author

FEMA-Long: Modeling unstructured covariances for discovery of time-dependent effects in large-scale longitudinal datasets.

PLoS genetics·2026

Same author

Leveraging STRAW +10 criteria to evaluate menopause stage effects on sleep quality.

Climacteric : the journal of the International Menopause Society·2026

Same author

Widespread use of invalid statistical tests in biomedical machine learning.

bioRxiv : the preprint server for biology·2026

Same author

Scalable Bayesian Image-on-Scalar Regression for Population-Scale Neuroimaging Data Analysis.

Journal of the American Statistical Association·2026

Same author

Convergent and divergent brain-cognition development in early adolescence.

Nature communications·2026

Same author

The methodological foundations of lesion network mapping remain sound.

bioRxiv : the preprint server for biology·2026

Same journal

Interactive shape and color representation in visual working memory for colored objects in the human occipitotemporal cortex.

Imaging neuroscience (Cambridge, Mass.)·2026

Same journal

Prediction-based sensory attenuation is related to prediction-based motor attenuation.

Imaging neuroscience (Cambridge, Mass.)·2026

Same journal

Comparing the effect of multi-gradient echo and multi-band fMRI during a semantic task.

Imaging neuroscience (Cambridge, Mass.)·2026

Same journal

The link to steady-state oxidative metabolism and hemodynamics varies across rs-fMRI metrics: A whole-brain assessment using macrovascular correction.

Imaging neuroscience (Cambridge, Mass.)·2026

Same journal

MRI-scale histology validates spatial sensitivity of in-vivo MRI-based axon radius estimation.

Imaging neuroscience (Cambridge, Mass.)·2026

Same journal

Simultaneous confidence regions for image excursion sets: A validation study with applications in fMRI.

Imaging neuroscience (Cambridge, Mass.)·2026

See all related articles

Search research articles

Home
Statistical Inference For Same Data Meta-analysis In Neuroimaging Multiverse Analyzes.

Home
Statistical Inference For Same Data Meta-analysis In Neuroimaging Multiverse Analyzes.

Related Experiment Video

Basics of Multivariate Analysis in Neuroimaging Data

Basics of Multivariate Analysis in Neuroimaging Data

Published on: July 24, 2010

Statistical inference for same data meta-analysis in neuroimaging multiverse analyzes.

Jeremy Lefort-Besnard¹, Thomas E Nichols², Camille Maumet¹

¹Inria, Univ Rennes, CNRS, Inserm, IRISA UMR 6074, Empenn ERL U 1228, Rennes, France.

Imaging Neuroscience (Cambridge, Mass.)

|August 13, 2025

View abstract on PubMed

Summary

This summary is machine-generated.

This study introduces same data meta-analysis (SDMA) to address reproducibility issues in neuroimaging. SDMA methods effectively analyze multiverse outputs, accounting for inter-pipeline dependence in functional magnetic resonance imaging (fMRI) data.

Keywords:

multiverse analysis reproducibility same data meta-analysis statistical inference task-fMRI

More Related Videos

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

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

Published on: November 27, 2019

Brain Morphology of Cannabis Users With or Without Psychosis: A Pilot MRI Study

Brain Morphology of Cannabis Users With or Without Psychosis: A Pilot MRI Study

Published on: August 18, 2020

Related Experiment Videos

Basics of Multivariate Analysis in Neuroimaging Data

Basics of Multivariate Analysis in Neuroimaging Data

Published on: July 24, 2010

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

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

Published on: November 27, 2019

Brain Morphology of Cannabis Users With or Without Psychosis: A Pilot MRI Study

Brain Morphology of Cannabis Users With or Without Psychosis: A Pilot MRI Study

Published on: August 18, 2020

Area of Science:

Neuroimaging analysis
Computational neuroscience
Statistical modeling

Background:

Task-functional magnetic resonance imaging (fMRI) research utilizes numerous analysis tools.
Varied analytical approaches can inflate false positive rates and hinder reproducibility in neuroimaging.
Multiverse analyses explore pipeline variations but create challenges in interpreting multiple outputs from a single dataset.

Purpose of the Study:

To develop and validate methods for meta-analysis in the context of multiverse analyses, specifically addressing inter-pipeline dependence.
To introduce "same data meta-analysis" (SDMA) as a solution for extracting consensus inferences from multiple analytical outputs derived from a single dataset.
To provide reliable tools for interpreting findings from complex neuroimaging studies.

Main Methods:

Developed a suite of same data meta-analysis (SDMA) methods to account for inter-pipeline dependence in multiverse outputs.
Assessed the validity of SDMA methods through simulations.
Tested SDMA models on real-world multiverse analysis outputs from the NARPS and HCP Young Adult studies.

Main Results:

The proposed SDMA models demonstrate validity in the presence of inter-pipeline dependence.
Simulations confirmed the effectiveness of the developed SDMA methods.
Real-world application on NARPS and HCP Young Adult datasets showed the practical utility of SDMA.

Conclusions:

Same data meta-analysis (SDMA) provides a robust framework for analyzing dependent results from multiverse neuroimaging studies.
The validated SDMA methods offer researchers reliable options for drawing consensus inferences from complex analytical pipelines.
This work enhances the interpretability and reproducibility of neuroimaging findings generated through multiverse analyses.