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Brain. 2015 Aug;138(Pt 8):2332-46. doi: 10.1093/brain/awv145. Epub 2015 Jun 9.

Network topology and functional connectivity disturbances precede the onset of Huntington's disease.

Author information

  • 11 Department of Radiology, University of California, San Diego, La Jolla, CA, 92093, USA 2 Research Service, VA San Diego Healthcare System, San Diego, CA, 92161, USA.
  • 23 Department of Psychiatry, University of Cambridge, Cambridge, CB3 2QQ, UK 4 Churchill College, University of Cambridge, Cambridge, CB3 0DS, UK.
  • 35 Department of Neurology, Medical College of Wisconsin, Milwaukee, WI, 53226, USA.
  • 46 Schey Centre for Cognitive Neuroimaging, Neurological Institute, Cleveland Clinic, Cleveland, OH, 44195, USA.
  • 57 Imaging Sciences, Imaging Institute, Cleveland Clinic, Cleveland, OH, 44195, USA.
  • 63 Department of Psychiatry, University of Cambridge, Cambridge, CB3 2QQ, UK.
  • 78 Carver College of Medicine, The University of Iowa, Iowa City, IA, 52242, USA.
  • 86 Schey Centre for Cognitive Neuroimaging, Neurological Institute, Cleveland Clinic, Cleveland, OH, 44195, USA raos2@ccf.org.

Abstract

Cognitive, motor and psychiatric changes in prodromal Huntington's disease have nurtured the emergent need for early interventions. Preventive clinical trials for Huntington's disease, however, are limited by a shortage of suitable measures that could serve as surrogate outcomes. Measures of intrinsic functional connectivity from resting-state functional magnetic resonance imaging are of keen interest. Yet recent studies suggest circumscribed abnormalities in resting-state functional magnetic resonance imaging connectivity in prodromal Huntington's disease, despite the spectrum of behavioural changes preceding a manifest diagnosis. The present study used two complementary analytical approaches to examine whole-brain resting-state functional magnetic resonance imaging connectivity in prodromal Huntington's disease. Network topology was studied using graph theory and simple functional connectivity amongst brain regions was explored using the network-based statistic. Participants consisted of gene-negative controls (n = 16) and prodromal Huntington's disease individuals (n = 48) with various stages of disease progression to examine the influence of disease burden on intrinsic connectivity. Graph theory analyses showed that global network interconnectivity approximated a random network topology as proximity to diagnosis neared and this was associated with decreased connectivity amongst highly-connected rich-club network hubs, which integrate processing from diverse brain regions. However, functional segregation within the global network (average clustering) was preserved. Functional segregation was also largely maintained at the local level, except for the notable decrease in the diversity of anterior insula intermodular-interconnections (participation coefficient), irrespective of disease burden. In contrast, network-based statistic analyses revealed patterns of weakened frontostriatal connections and strengthened frontal-posterior connections that evolved as disease burden increased. These disturbances were often related to long-range connections involving peripheral nodes and interhemispheric connections. A strong association was found between weaker connectivity and decreased rich-club organization, indicating that whole-brain simple connectivity partially expressed disturbances in the communication of highly-connected hubs. However, network topology and network-based statistic connectivity metrics did not correlate with key markers of executive dysfunction (Stroop Test, Trail Making Test) in prodromal Huntington's disease, which instead were related to whole-brain connectivity disturbances in nodes (right inferior parietal, right thalamus, left anterior cingulate) that exhibited multiple aberrant connections and that mediate executive control. Altogether, our results show for the first time a largely disease burden-dependent functional reorganization of whole-brain networks in prodromal Huntington's disease. Both analytic approaches provided a unique window into brain reorganization that was not related to brain atrophy or motor symptoms. Longitudinal studies currently in progress will chart the course of functional changes to determine the most sensitive markers of disease progression.

KEYWORDS:

Huntington disease; graph theory; network based statistic; network topology; resting-state connectivity

PMID:
26059655
PMCID:
PMC5022662
DOI:
10.1093/brain/awv145
[PubMed - indexed for MEDLINE]
Free PMC Article
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