Large-scale brain network abnormalities in Huntington's disease revealed by structural covariance

Area of Science:

• Neuroscience
• Neuroimaging
• Genetics

Background:

• Huntington's disease (HD) is a progressive neurodegenerative disorder detectable years before symptoms.
• Previous research using MRI identified grey matter (GM) loss in specific brain regions but couldn't assess inter-regional relationships.
• Voxel-based morphometry studies often analyze brain regions in isolation, limiting understanding of network dynamics.

Purpose of the Study:

• To investigate structural covariance in grey matter (GM) volumes across key brain networks in Huntington's disease (HD).
• To compare GM volume interrelations in manifest HD (mHD), presymptomatic gene carriers (pre-HD), and healthy controls (HC).
• To identify how disease progression and burden influence structural brain network changes.

Main Methods:

• Structural covariance analysis of GM volumes in motor, working memory, cognitive flexibility, and social-affective networks.
• Comparison of 99 mHD patients, 106 pre-HD carriers, and 108 HC, with correction for global brain volume.
• Analysis of structural connections modified by disease burden score (DBS) in frontoparietal motor regions.

Main Results:

• Increased positive correlations in GM volumes were observed in mHD patients compared to pre-HD and HC groups.
• No significant differences in structural covariance were found between HC and pre-HD groups.
• Structural connections between frontoparietal motor regions were linearly modified by DBS, with effects becoming significant at the onset of motor symptoms.

Conclusions:

• Structural covariance patterns differ significantly between manifest HD patients and controls/presymptomatic carriers.
• HD-related neuronal loss may heterogeneously affect brain networks, with changes becoming globally apparent in manifest disease.
• Frontoparietal motor network alterations are critical for the onset of motor signs in Huntington's disease.