Network model of pathology spread recapitulates neurodegeneration and selective vulnerability in Huntington's Disease. Academic Article uri icon

Overview

abstract

  • Huntington's Disease (HD), an autosomal dominant genetic disorder caused by a mutation in the Huntingtin gene (HTT), displays a stereotyped topography in the human brain and a stereotyped progression, initially appearing in the striatum. Like other degenerative diseases, spatial topography of HD is divorced from where implicated genes are expressed, a dissociation whose mechanistic underpinning is not currently understood. Cell autonomous molecular factors characterized by gene expression signatures, including proteolytic and post translational modifications, play a role in vulnerability to disease. Non-autonomous mechanisms, likely involving the brain's anatomic or functional connectivity patterns, might also be responsible for selective vulnerability in HD. Leveraging a large dataset of 635 subjects from a multinational study, this paper tests various cell-autonomous and non-autonomous models that can explain HD topography. We test whether the expression patterns of implicated genes is sufficient to explain regional HD atrophy, or whether the network transmission of protein products is required to explain them. We find that network models are capable of predicting, to a high degree, observed atrophy in human subjects. Lastly, we propose a model of anterograde network transmission, and show that it is the most parsimonious yet most likely to explain observed atrophy patterns in HD. Collectively, these data indicate that pathology spread in HD may be mediated by the brain's intrinsic structural network organization. This is the first study to systematically and quantitatively test multiple hypotheses of pathology spread in living human subjects with HD.

publication date

  • March 28, 2021

Research

keywords

  • Brain
  • Huntington Disease
  • Image Interpretation, Computer-Assisted
  • Nerve Degeneration
  • Neural Pathways

Identity

Scopus Document Identifier

  • 85103721771

Digital Object Identifier (DOI)

  • 10.1016/j.neuroimage.2021.118008

PubMed ID

  • 33789134

Additional Document Info

volume

  • 235