LRRK2 and GBA Variants Exert Distinct Influences on Parkinson's Disease-Specific Metabolic Networks

Katharina A. Schindlbeck, An Vo, Nha Nguyen, Chris C. Tang, Martin Niethammer, Vijay Dhawan, Vicky Brandt, Rachel Saunders-Pullman, Susan B. Bressman, David Eidelberg

Research output: Contribution to journalArticlepeer-review

31 Scopus citations


The natural history of idiopathic Parkinson's disease (PD) varies considerably across patients. While PD is generally sporadic, there are known genetic influences: the two most common, mutations in the LRRK2 or GBA1 gene, are associated with slower and more aggressive progression, respectively. Here, we applied graph theory to metabolic brain imaging to understand the effects of genotype on the organization of previously established PD-specific networks. We found that closely matched PD patient groups with the LRRK2-G2019S mutation (PD-LRRK2) or GBA1 variants (PD-GBA) expressed the same disease networks as sporadic disease (sPD), but PD-LRRK2 and PD-GBA patients exhibited abnormal increases in network connectivity that were not present in sPD. Using a community detection strategy, we found that the location and modular distribution of these connections differed strikingly across genotypes. In PD-LRRK2, connections were gained within the network core, with the formation of distinct functional pathways linking the cerebellum and putamen. In PD-GBA, by contrast, the majority of functional connections were formed outside the core, involving corticocortical pathways at the network periphery. Strategically localized connections within the core in PD-LRRK2 may maintain PD network activity at lower levels than in PD-GBA, resulting in a less aggressive clinical course.

Original languageEnglish (US)
Pages (from-to)2867-2878
Number of pages12
JournalCerebral Cortex
Issue number5
StatePublished - May 14 2020


  • GBA
  • LRRK2
  • Parkinson's disease
  • functional connectivity
  • metabolic imaging

ASJC Scopus subject areas

  • Cognitive Neuroscience
  • Cellular and Molecular Neuroscience


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