Network abnormalities among non-manifesting Parkinson disease related LRRK2 mutation carriers

Yael Jacob, Keren Rosenberg-Katz, Tanya Gurevich, Rick C. Helmich, Bastiaan R. Bloem, Avi Orr-Urtreger, Nir Giladi, Anat Mirelman, Talma Hendler, Avner Thaler*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

13 Scopus citations


Non-manifesting carriers (NMC) of the G2019S mutation in the LRRK2 gene represent an “at risk” group for future development of Parkinson's disease (PD) and have demonstrated task related fMRI changes. However, resting-state networks have received less research focus, thus this study aimed to assess the integrity of the motor, default mode (DMN), salience (SAL), and dorsal attention (DAN) networks among this unique population by using two different connectivity measures: interregional functional connectivity analysis and Dependency network analysis (D EP NA). Machine learning classification methods were used to distinguish connectivity between the two groups of participants. Forty-four NMC and 41 non-manifesting non-carriers (NMNC) participated in this study; while no behavioral differences on standard questionnaires could be detected, NMC demonstrated lower connectivity measures in the DMN, SAL, and DAN compared to NMNC but not in the motor network. Significant correlations between NMC connectivity measures in the SAL and attention were identified. Machine learning classification separated NMC from NMNC with an accuracy rate above 0.8. Reduced integrity of non-motor networks was detected among NMC of the G2019S mutation in the LRRK2 gene prior to identifiable changes in connectivity of the motor network, indicating significant non-motor cerebral changes among populations “at risk” for future development of PD.

Original languageEnglish
Pages (from-to)2546-2555
Number of pages10
JournalHuman Brain Mapping
Issue number8
StatePublished - 1 Jun 2019


FundersFunder number
Chief Scientist Department of Health, Israel
Dutch Brain Foundation
Israeli Science Foundation Legacy Heritage Fund
Kahn Foundation Israel
Stichting International Parkinson Fonds, Hersenstichting, ZonMw
National Institutes of Health
Michael J. Fox Foundation for Parkinson's Research
ALS Association
National Park Foundation
National Parkinson Foundation
Seventh Framework Programme
Israel Science Foundation
Israeli Centers for Research Excellence


    • LRRK2
    • Parkinson's disease
    • graph theory network analysis
    • machine learning classification
    • resting state fMRI


    Dive into the research topics of 'Network abnormalities among non-manifesting Parkinson disease related LRRK2 mutation carriers'. Together they form a unique fingerprint.

    Cite this