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

Testing and tracking in the UK: A dynamic causal modelling study.

Wellcome open research·2026

Same author

PsiConnect: Multimodal Neuroimaging of Context-Dependent Brain and Behaviour Dynamics under Psilocybin.

Scientific data·2026

Same author

Hierarchical Bayesian inference for community detection and connectivity of functional brain networks.

IEEE transactions on medical imaging·2026

Same author

A Two-Track Model of Huntington's Disease Pathology: Striatal Atrophy Mediates Maladaptive Immune Dysregulation.

International journal of molecular sciences·2026

Same author

Identification of Biological Subtypes of Friedreich Ataxia with Structural MRI-based Machine Learning.

Radiology·2026

Same author

Early detection of dementia with default-mode network effective connectivity.

Nature. Mental health·2026

Same journal

Putamen Structural-Functional Decoupling as an Early-Stage Candidate Imaging Marker for Motor Severity in Spinocerebellar Ataxia Type 3.

Movement disorders : official journal of the Movement Disorder Society·2026

Same journal

Melanopsin-Mediated Post-Illumination Pupillary Response in Idiopathic Rapid Eye Movement (REM) Sleep Behavior Disorder and Parkinson's Disease.

Movement disorders : official journal of the Movement Disorder Society·2026

Same journal

High-Fat Diet Exacerbates Neuropathology in a Transgenic Mouse Model of Multiple System Atrophy.

Movement disorders : official journal of the Movement Disorder Society·2026

Same journal

Addressing the Challenges of Translating LRRK2 Biology into Disease-Modifying Therapies: The LRRK2 Investigative Therapeutics Exchange Initiative.

Movement disorders : official journal of the Movement Disorder Society·2026

Same journal

The LRRK2 G2385R Exonic Variant and Multiple System Atrophy: Findings from the Japan MSA Registry.

Movement disorders : official journal of the Movement Disorder Society·2026

Same journal

Alkaline Phosphatases: A Potential Cause of Variability in Therapeutic Response to Continuous Subcutaneous Foslevodopa/Foscarbidopa Infusion?

Movement disorders : official journal of the Movement Disorder Society·2026

See all related articles

Search research articles

Related Experiment Video

Updated: May 1, 2026

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

Mapping Regional Brain Aging in Huntington's Disease Using Structural Magnetic Resonance Imaging and Machine

Mohsen Ghofrani-Jahromi¹, Yalda Amirmoezzi², Pubu M Abeyasinghe¹

¹Turner Institute for Brain and Mental Health, Monash University, Clayton, Australia.

Movement Disorders : Official Journal of the Movement Disorder Society

|December 24, 2025

Summary

This summary is machine-generated.

Region-specific brain age models reveal accelerated aging in subcortical, temporal, and parietal areas in Huntington's disease (HD). This brain aging pattern correlates with disease progression and clinical decline, offering potential biomarkers for HD clinical trials.

Keywords:

Huntington's disease biomarkers clinical trials machine learning neuroimaging regional brain aging

More Related Videos

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

Whole-brain Segmentation and Change-point Analysis of Anatomical Brain MRI—Application in Premanifest Huntington's Disease

Whole-brain Segmentation and Change-point Analysis of Anatomical Brain MRI—Application in Premanifest Huntington's Disease

Published on: June 9, 2018

Related Experiment Videos

Last Updated: May 1, 2026

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

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

Whole-brain Segmentation and Change-point Analysis of Anatomical Brain MRI—Application in Premanifest Huntington's Disease

Whole-brain Segmentation and Change-point Analysis of Anatomical Brain MRI—Application in Premanifest Huntington's Disease

Published on: June 9, 2018

Area of Science:

Neuroscience
Radiology
Biomarkers

Background:

Huntington's disease (HD) is a progressive neurodegenerative disorder characterized by accelerated brain aging.
Existing brain age models often use a single whole-brain measure, limiting insights into region-specific aging patterns.
Region-specific brain age models are underexplored in HD but could enhance biomarker sensitivity for clinical trials.

Purpose of the Study:

To characterize region-specific brain aging patterns across different stages of the Huntington's Disease Integrated Staging System (HD-ISS).
To evaluate the association between regional brain age gap and cognitive, motor, and functional scores in individuals with HD.

Main Methods:

Machine learning models were trained on structural MRI data from 1936 controls to predict brain age.
These models were applied to 531 individuals with HD to calculate regional brain age gaps.
Statistical analyses assessed the relationship between regional brain age gap, HD-ISS stages, and clinical assessments.

Main Results:

Whole-brain aging increased with HD-ISS stages 2 and 3.
Subcortical, temporal, and parietal regions showed significant increases in brain age gap across HD stages.
Elevated brain age gap in these regions correlated with poorer cognitive, motor, and functional performance.
Insular and frontal regions displayed flatter aging patterns with no significant clinical associations.

Conclusions:

Distinct region-specific aging components characterize Huntington's disease progression.
Regional brain age gap analysis offers deeper insights into HD and potential sensitive biomarkers for therapeutic monitoring.
Future research should investigate these findings in younger cohorts and explore network-specific aging using multimodal imaging.