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

Magnetic Resonance Imaging01:24

Magnetic Resonance Imaging

Magnetic resonance imaging (MRI) is a noninvasive medical imaging technique based on a phenomenon of nuclear physics discovered in the 1930s, in which matter exposed to magnetic fields and radio waves was found to emit radio signals. In 1970, a physician and researcher named Raymond Damadian noticed that malignant (cancerous) tissue gave off different signals than normal body tissue. He applied for a patent for the first MRI scanning device in clinical use by the early 1980s. The early MRI...

You might also read

Related Articles

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

Sort by
Same author

Metabolic dysfunction in multiple sclerosis: Elevated lactate and impaired post-exercise creatine response in the anterior cingulate cortex.

Multiple sclerosis and related disorders·2026
Same author

Microbiome-behavior coupling shapes infant adaptation to early maternal unpredictability.

Frontiers in microbiology·2026
Same author

Four Directions, One Solution: Enabling Rapid Diffusion Tensor MRI for Ultra-Low Field Using Deep Learning.

Magnetic resonance in medicine·2026
Same author

Linking human brain functional connectivity to underlying neurotransmission.

bioRxiv : the preprint server for biology·2026
Same author

The potential of low-field MRI for global dementia care.

Nature reviews. Neurology·2026
Same author

Increased Brain-Age Gap in Young Adults With Psychotic Experiences.

Biological psychiatry global open science·2026

Related Experiment Video

Updated: Jun 8, 2026

Co-analysis of Brain Structure and Function using fMRI and Diffusion-weighted Imaging
17:06

Co-analysis of Brain Structure and Function using fMRI and Diffusion-weighted Imaging

Published on: November 8, 2012

Twenty-five pitfalls in the analysis of diffusion MRI data.

Derek K Jones1, Mara Cercignani

  • 1CUBRIC, Cardiff University Brain Research Imaging Centre, School of Psychology, Cardiff, UK. jonesd27@cf.ac.uk

NMR in Biomedicine
|October 2, 2010
PubMed
Summary

Diffusion MRI analysis requires careful attention to numerous potential pitfalls across data acquisition and processing. This review details 25 common and novel challenges to ensure robust and reliable diffusion MRI study results.

More Related Videos

Diffusion Imaging in the Rat Cervical Spinal Cord
10:46

Diffusion Imaging in the Rat Cervical Spinal Cord

Published on: April 7, 2015

Diffusion Tensor Magnetic Resonance Imaging in the Analysis of Neurodegenerative Diseases
09:33

Diffusion Tensor Magnetic Resonance Imaging in the Analysis of Neurodegenerative Diseases

Published on: July 28, 2013

Related Experiment Videos

Last Updated: Jun 8, 2026

Co-analysis of Brain Structure and Function using fMRI and Diffusion-weighted Imaging
17:06

Co-analysis of Brain Structure and Function using fMRI and Diffusion-weighted Imaging

Published on: November 8, 2012

Diffusion Imaging in the Rat Cervical Spinal Cord
10:46

Diffusion Imaging in the Rat Cervical Spinal Cord

Published on: April 7, 2015

Diffusion Tensor Magnetic Resonance Imaging in the Analysis of Neurodegenerative Diseases
09:33

Diffusion Tensor Magnetic Resonance Imaging in the Analysis of Neurodegenerative Diseases

Published on: July 28, 2013

Area of Science:

  • Neuroimaging
  • Biomedical Engineering
  • Data Analysis

Background:

  • Diffusion MRI (dMRI) is crucial for neuroscience and clinical research.
  • Acquiring reliable dMRI data and performing accurate analysis is complex.
  • Numerous pitfalls exist throughout the dMRI analysis pipeline, potentially compromising results.

Purpose of the Study:

  • To provide a comprehensive review of potential pitfalls in diffusion MRI analysis.
  • To identify and categorize challenges across the entire dMRI data processing pipeline.
  • To serve as a reference for researchers conducting dMRI studies.

Main Methods:

  • Review of the diffusion MRI analysis pipeline, including data acquisition, pre-processing, tensor estimation, parameter derivation, and statistical comparison.
  • Identification and categorization of 25 specific pitfalls, some previously unreported.
  • Discussion of key analysis aspects: motion correction, distortion correction, model fitting, ROI placement, and various analysis strategies.

Main Results:

  • Detailed breakdown of pitfalls across five key stages: pre-processing, tensor estimation, parameter derivation, parameter extraction, and comparison.
  • Highlights common issues like motion and distortion correction, model fitting errors, and ROI placement inaccuracies.
  • Identifies 25 potential sources of bias that can affect accuracy and precision in dMRI studies.

Conclusions:

  • Researchers must be aware of the numerous pitfalls in diffusion MRI analysis to ensure data reliability.
  • This review offers a valuable checklist for both new and experienced researchers to avoid common confounds.
  • Addressing these pitfalls is essential for drawing meaningful and robust inferences from diffusion MRI data.