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...

You might also read

Related Articles

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

Sort by

Same author

Comparison of brain normalization software and lesion compensation techniques in chronic perinatal stroke imaging.

Imaging neuroscience (Cambridge, Mass.)·2026

Same author

Fronto-insular circuit mechanisms of accelerated intermittent theta burst stimulation.

Cell·2026

Same author

A clinical neuroimaging platform for rapid, automated lesion detection and personalized post-stroke outcome prediction.

NPJ digital medicine·2026

Same author

Corrigendum to "No seizures observed with 100-Hz intermittent theta burst TMS applied to the dorsolateral prefrontal cortex" [Brain stimulat 19(3) 103107].

Brain stimulation·2026

Same author

The methodological foundations of lesion network mapping remain sound.

bioRxiv : the preprint server for biology·2026

Same author

Fronto-insular circuit mechanisms of accelerated intermittent theta burst stimulation.

Cell·2026

Same journal

Benchmarking fMRI Denoising Pipelines.

Human brain mapping·2026

Same journal

Modeled Long-Term Effects of Psilocybin on Dynamic Activity and Effective Connectivity of Fronto-Striatal-Thalamic Circuits.

Human brain mapping·2026

Same journal

Intrinsic Functional Architecture Reflects Individual Differences in Passive Working Memory: An Exploratory Resting-State fMRI Study.

Human brain mapping·2026

Same journal

Symptom Overlap and Neurobiological Similarities Between Posttraumatic Stress Disorder and Tinnitus.

Human brain mapping·2026

Same journal

Test-Retest Reliability of Sensorimotor Activity Measured With Spinal Cord fMRI.

Human brain mapping·2026

Same journal

The Human Visual Claustrum Responses to Physical Stimulus Properties and Subjective Content During Movie Viewing.

Human brain mapping·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Jun 4, 2025

Brain Infarct Segmentation and Registration on MRI or CT for Lesion-symptom Mapping

Brain Infarct Segmentation and Registration on MRI or CT for Lesion-symptom Mapping

Published on: September 25, 2019

Iowa Brain-Behavior Modeling Toolkit: An Open-Source MATLAB Tool for Inferential and Predictive Modeling of

Joseph C Griffis¹, Joel Bruss^1,2, Stein F Acker³

¹Department of Pediatrics, Carver College of Medicine, University of Iowa, Iowa City, Iowa, USA.

Human Brain Mapping

|December 23, 2024

Summary

This summary is machine-generated.

This study introduces a new MATLAB toolkit for neuroimaging and lesion-behavior analysis, enabling predictive modeling for precision medicine. The toolkit accurately predicts language impairments from brain lesion data, explaining significant variance in patient outcomes.

More Related Videos

Measuring Statistical Learning Across Modalities and Domains in School-Aged Children Via an Online Platform and Neuroimaging Techniques

Measuring Statistical Learning Across Modalities and Domains in School-Aged Children Via an Online Platform and Neuroimaging Techniques

Published on: June 30, 2020

Probing the Brain in Autism Using fMRI and Diffusion Tensor Imaging

Probing the Brain in Autism Using fMRI and Diffusion Tensor Imaging

Published on: September 12, 2011

Related Experiment Videos

Last Updated: Jun 4, 2025

Brain Infarct Segmentation and Registration on MRI or CT for Lesion-symptom Mapping

Brain Infarct Segmentation and Registration on MRI or CT for Lesion-symptom Mapping

Published on: September 25, 2019

Measuring Statistical Learning Across Modalities and Domains in School-Aged Children Via an Online Platform and Neuroimaging Techniques

Measuring Statistical Learning Across Modalities and Domains in School-Aged Children Via an Online Platform and Neuroimaging Techniques

Published on: June 30, 2020

Probing the Brain in Autism Using fMRI and Diffusion Tensor Imaging

Probing the Brain in Autism Using fMRI and Diffusion Tensor Imaging

Published on: September 12, 2011

Area of Science:

Neuroimaging
Computational Neuroscience
Neurology

Background:

Traditional neuroimaging and lesion-behavior studies rely on inferential statistics, limiting precision medicine goals.
Existing tools lack support for predictive modeling and struggle with diverse data modalities and outcome types.

Purpose of the Study:

To develop a versatile MATLAB toolkit for both inferential and predictive modeling in neuroimaging and lesion-behavior research.
To overcome limitations of existing software regarding data types, outcome variables, and analytical frameworks.
To facilitate the adoption of predictive modeling for advancing precision medicine in neurology.

Main Methods:

Developed a MATLAB software toolkit with graphical and scripting interfaces.
Integrated mass-univariate, multivariate, and machine learning models.
Included routines for hyper-parameter optimization, cross-validation, model stacking, and significance testing.
Applied the toolkit to analyze lesion location, structural disconnection, and functional network disruption in relation to language impairments.

Main Results:

Expressive and receptive language impairments are linked to specific lesion locations in the left hemisphere (prefrontal and temporal/parietal, respectively).
Both impairment types show overlapping patterns of fronto-temporal disconnection and involve similar functional networks.
Lesion location and network measures effectively predict language impairments, explaining 30%-40% of variance in unseen data.

Conclusions:

The developed toolkit supports both inferential and predictive analyses across various data modalities and problem types (classification/regression).
The toolkit's predictive models demonstrate high accuracy in forecasting language deficits based on brain lesion characteristics.
The toolkit is publicly available with tutorials, promoting reproducibility and advancing predictive modeling in neuroimaging research.