Back

Large Scale Functional and Effective Connectivity Alterations cross the Huntington's Disease Integrated Staging System

Demirtas, M.; Pustina, D.; Wood, A.; Sampaio, C.; Vohryzek, J.; Deco, G.

2025-12-02 neuroscience
10.64898/2025.11.30.691364 bioRxiv
Show abstract

Huntingtons disease (HD) is a progressive neurodegenerative disease with severe motor, cognitive and behavioral symptoms. There is a recent impetus to develop treatments that slow progression before clinical signs emerge. Such early interventions require biomarkers sensitive to the very earliest HD progression. Here we applied cutting-edge fMRI analysis on data collected in the Track-On HD study to evaluate whether functional and effective connectivity obtained from model-free and model-based approaches can produce useful biomarkers of HD progression. We analyzed data from 231 participants with up to three annual visits each and created five groups comprising normative controls and four HD groups according to the Huntingtons Disease Integrated Staging System (HD-ISS). We found significant differences across the HD-ISS stages but not in their longitudinal change. Specifically, we found attenuated functional and effective connectivity in the caudate nucleus in HD-ISS-2, and this effect extended to other corticostriatal connections in HD-ISS-3. Overall, most of the alterations were only evident in advanced HD stages, and we did not observe widespread alterations in cortical connectivity and graph topography. Although HD-ISS groups did not differ in the amount of in-scanner motion, we did find measures of functional and effective connectivity to be sensitive to motion. We conclude that fMRI can indeed capture attenuation of cortico-striatal functional and effective connectivity across HD progression. This is the first study to investigate fMRI alterations across the recently created HD-ISS stages within a novel framework of signal flow across the whole brain.

Published in NeuroImage: Clinical (predicted rank #3) · training set

Matching journals

The top 5 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.