Changes in structural network topology correlate with severity of hallucinatory behavior in Parkinson's disease

Julie M. Hall, Claire O’Callaghan, Alana J. Muller, Kaylena A. Ehgoetz Martens, Joseph R. Phillips, Ahmed A. Moustafa, Simon J. G. Lewis, James M. Shine

Research output: Contribution to journalArticlepeer-review

17 Citations (Scopus)

Abstract

Inefficient integration between bottom-up visual input and higher order visual processing regions is implicated in visual hallucinations in Parkinson’s disease (PD). Here, we investigated white matter contributions to this perceptual imbalance hypothesis. Twenty-nine PD patients were assessed for hallucinatory behavior. Hallucination severity was correlated to connectivity strength of the network using the network-based statistic approach. The results showed that hallucination severity was associated with reduced connectivity within a subnetwork that included the majority of the diverse club. This network showed overall greater between-module scores compared with nodes not associated with hallucination severity. Reduced between-module connectivity in the lateral occipital cortex, insula, and pars orbitalis and decreased within-module connectivity in the prefrontal, somatosensory, and primary visual cortices were associated with hallucination severity. Conversely, hallucination severity was associated with increased between- and within-module connectivity in the orbitofrontal and temporal cortex, as well as regions comprising the dorsal attentional and default mode network. These results suggest that hallucination severity is associated with marked alterations in structural network topology with changes in participation along the perceptual hierarchy. This may result in the inefficient transfer of information that gives rise to hallucinations in PD.
Original languageEnglish
Pages (from-to)521-538
Number of pages18
JournalNetwork Neuroscience
Volume3
Issue number2
DOIs
Publication statusPublished - 2019

Open Access - Access Right Statement

Copyright: © 2019 Massachusetts Institute of Technology Published under a Creative Commons Attribution 4.0 International (CC BY 4.0) license (https://creativecommons.org/licenses/by/4.0/)

Keywords

  • diffusion tensor imaging
  • diseases
  • graph theory
  • hallucinations and illusions
  • nervous system

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