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).

You might also read

Related Articles

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

Sort by

Same author

New Growth, New Opportunities.

Journal of medical imaging (Bellingham, Wash.)·2025

Same author

Influence of early through late fusion on pancreas segmentation from imperfectly registered multimodal magnetic resonance imaging.

Journal of medical imaging (Bellingham, Wash.)·2025

Same author

White matter hyperintensities and relapse risk in late-life depression.

Journal of affective disorders·2025

Same author

Unsupervised discovery of clinical disease signatures using probabilistic independence.

Journal of biomedical informatics·2025

Same author

Multi-contrast computed tomography atlas of healthy pancreas with dense displacement sampling registration.

Journal of medical imaging (Bellingham, Wash.)·2025

Same author

The effect of Alzheimer's disease genetic factors on limbic white matter microstructure.

Alzheimer's & dementia : the journal of the Alzheimer's Association·2025

Search research articles

Related Experiment Video

Updated: Jun 1, 2026

Basics of Multivariate Analysis in Neuroimaging Data

Basics of Multivariate Analysis in Neuroimaging Data

Published on: July 24, 2010

Robust Biological Parametric Mapping: An Improved Technique for Multimodal Brain Image Analysis.

Xue Yang¹, Lori Beason-Held, Susan M Resnick

¹Electrical Engineering, Vanderbilt University, Nashville, TN, USA 37235.

Proceedings of Spie--The International Society for Optical Engineering

|June 1, 2011

Summary

This summary is machine-generated.

This study introduces a robust regression method to improve the accuracy of biological parametric mapping in neuroimaging. This approach enhances the reliability of analyzing brain structure-function relationships by reducing outlier sensitivity.

More Related Videos

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Published on: June 26, 2013

Functional Near Infrared Spectroscopy of the Sensory and Motor Brain Regions with Simultaneous Kinematic and EMG Monitoring During Motor Tasks

Functional Near Infrared Spectroscopy of the Sensory and Motor Brain Regions with Simultaneous Kinematic and EMG Monitoring During Motor Tasks

Published on: December 5, 2014

Related Experiment Videos

Last Updated: Jun 1, 2026

Basics of Multivariate Analysis in Neuroimaging Data

Basics of Multivariate Analysis in Neuroimaging Data

Published on: July 24, 2010

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Published on: June 26, 2013

Functional Near Infrared Spectroscopy of the Sensory and Motor Brain Regions with Simultaneous Kinematic and EMG Monitoring During Motor Tasks

Functional Near Infrared Spectroscopy of the Sensory and Motor Brain Regions with Simultaneous Kinematic and EMG Monitoring During Motor Tasks

Published on: December 5, 2014

Area of Science:

Neuroimaging
Brain Structure and Function Analysis
Statistical Modeling in Neuroscience

Background:

Mapping quantitative relationships between human brain structure and function is a significant challenge.
Existing methods like volumetric, surface, and voxelwise analyses assess correlations between imaging and non-imaging metrics.
Biological parametric mapping (BPM) extends statistical parametric mapping for multi-modal neuroimaging data using the general linear model (GLM).

Purpose of the Study:

To address the limitations of existing BPM approaches, specifically their lack of robustness to outliers and potential for invalid inferences.
To introduce robust regression and robust inference techniques within the neuroimaging context for applying the GLM.
To enhance the reliability and widespread applicability of voxelwise assessment of structural and functional relationships in the brain.

Main Methods:

Development and implementation of robust regression and robust inference methods for neuroimaging.
Application of the general linear model (GLM) in a robust manner to handle multiple image modalities and scalar observations.
Validation through simulation studies and empirical analyses using neuroimaging data.

Main Results:

The proposed robust approach significantly reduces sensitivity to outliers in neuroimaging analyses.
The method maintains statistical power comparable to traditional approaches without substantial degradation.
Demonstrated reliable quantitative assessment of voxelwise correlations between structural and functional neuroimaging modalities.

Conclusions:

Robust regression and inference provide a more reliable method for analyzing brain structure-function relationships using BPM.
This approach mitigates issues caused by outliers, mis-registration, and anatomical variations, leading to more valid inferences.
The developed software package facilitates the widespread application of robust quantitative voxelwise correlation analyses in neuroimaging research.