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

You might also read

Related Articles

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

Sort by

Same author

Feasibility and acceptability of a guided internet-based acceptance and commitment therapy intervention (MobACT) for adults with chronic pain in Italy: A pilot mixed-methods randomized controlled trial.

Internet interventions·2026

Same author

Incidental DWI-Positive Lesions in 2 Cohorts of CAA and CADASIL: Prevalence, Distribution, and Associations With Clinical Variables.

Neurology·2026

Same author

Youth Soccer Participation and Brain Health Outcomes in Adolescent Athletes.

JAMA network open·2026

Same author

Brain regional susceptibility to tauopathy in individuals at risk for chronic traumatic encephalopathy.

Alzheimer's & dementia : the journal of the Alzheimer's Association·2026

Same author

Differential associations of plasma biomarkers with Alzheimer's disease and small vessel disease: A multimodal imaging study.

Alzheimer's & dementia : the journal of the Alzheimer's Association·2026

Same author

Longitudinal cortical thickness changes in children with posterior fossa tumors.

Neuro-oncology practice·2026

Same journal

Decoding functional changes in the brain following ischemic stroke: a multimodal feature approach integrating fNIRS with machine learning and deep learning.

NeuroImage. Clinical·2026

Same journal

Structural brain alterations in chronic primary pain: a multimodal MRI study.

NeuroImage. Clinical·2026

Same journal

Using functional MRI neurofeedback to modulate self-blame in major depressive disorder: A pilot study.

NeuroImage. Clinical·2026

Same journal

Language network reorganization in temporal lobe epilepsy: insights from task-based functional MRI.

NeuroImage. Clinical·2026

Same journal

Functional brain biomarkers of self-referential bias in remitted depressed outpatients: a randomized controlled trial.

NeuroImage. Clinical·2026

Same journal

Stress-related hypofrontality in depression and its relation to altered activation prior to the stress response.

NeuroImage. Clinical·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Aug 25, 2025

Diffusion Tensor Magnetic Resonance Imaging in the Analysis of Neurodegenerative Diseases

Diffusion Tensor Magnetic Resonance Imaging in the Analysis of Neurodegenerative Diseases

Published on: July 28, 2013

Improved sensitivity and precision in multicentre diffusion MRI network analysis using thresholding and

Bruno M de Brito Robalo¹, Alberto de Luca¹, Christopher Chen²

¹Department of Neurology and Neurosurgery, UMC Utrecht Brain Center, University Medical Center Utrecht, Utrecht, the Netherlands; Image Sciences Institute, University Medical Center Utrecht, Utrecht University, Utrecht, the Netherlands.

Neuroimage. Clinical

|October 14, 2022

Summary

This summary is machine-generated.

Network thresholding and data harmonization enhance diffusion MRI (dMRI) brain network analysis. These methods improve consistency, precision, and sensitivity for detecting disease effects in multicentre studies.

Keywords:

Connectivity Diffusion MRI Harmonization: cerebral small vessel disease Thresholding White matter

More Related Videos

Neuroimaging-Guided TMS–EEG for Real-Time Cortical Network Mapping

Neuroimaging-Guided TMS–EEG for Real-Time Cortical Network Mapping

Published on: June 13, 2025

Tracking the Mammary Architectural Features and Detecting Breast Cancer with Magnetic Resonance Diffusion Tensor Imaging

Tracking the Mammary Architectural Features and Detecting Breast Cancer with Magnetic Resonance Diffusion Tensor Imaging

Published on: December 15, 2014

Related Experiment Videos

Last Updated: Aug 25, 2025

Diffusion Tensor Magnetic Resonance Imaging in the Analysis of Neurodegenerative Diseases

Diffusion Tensor Magnetic Resonance Imaging in the Analysis of Neurodegenerative Diseases

Published on: July 28, 2013

Neuroimaging-Guided TMS–EEG for Real-Time Cortical Network Mapping

Neuroimaging-Guided TMS–EEG for Real-Time Cortical Network Mapping

Published on: June 13, 2025

Tracking the Mammary Architectural Features and Detecting Breast Cancer with Magnetic Resonance Diffusion Tensor Imaging

Tracking the Mammary Architectural Features and Detecting Breast Cancer with Magnetic Resonance Diffusion Tensor Imaging

Published on: December 15, 2014

Area of Science:

Neuroimaging
Computational Neuroscience
Medical Image Analysis

Background:

Diffusion MRI (dMRI) enables the reconstruction of brain networks.
Multicentre dMRI studies face challenges in data consistency and sensitivity to disease effects.
Cerebral small vessel disease (SVD) serves as a model for studying network integrity.

Purpose of the Study:

To assess if network thresholding and data harmonization improve consistency of dMRI-based brain networks.
To determine if these techniques enhance precision and sensitivity in detecting disease effects across multicentre datasets.
To validate these methods using SVD as an exemplar condition.

Main Methods:

Brain networks were reconstructed from dMRI data of 629 SVD patients and 166 controls across five sites.
Network consistency was evaluated in controls by measuring cross-site differences in connection probability and fractional anisotropy (FA).
Precision and sensitivity to SVD effects were assessed by identifying FA disruptions in patients compared to controls.

Main Results:

Thresholding and harmonization improved network consistency, reducing cross-site variability in connection probability and FA in controls.
Thresholding significantly increased precision in detecting disrupted connections in SVD patients (0.38-0.70 vs. 0.09-0.19).
Harmonization and data pooling enhanced sensitivity (38 connections) and, with thresholding, achieved high precision (0.97).

Conclusions:

Network thresholding and harmonization effectively improve consistency, precision, and sensitivity in dMRI-based brain network analysis.
These techniques are recommended for multicentre studies to better leverage existing data and understand disease impacts on brain networks.
The findings support the integration of these methods for robust analysis of neuroimaging data in conditions like SVD.