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

Abstract:

: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 (DEP 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.

journal_name

Hum Brain Mapp

journal_title

Human brain mapping

authors

Jacob Y,Rosenberg-Katz K,Gurevich T,Helmich RC,Bloem BR,Orr-Urtreger A,Giladi N,Mirelman A,Hendler T,Thaler A

doi

10.1002/hbm.24543

subject

Has Abstract

pub_date

2019-06-01 00:00:00

pages

2546-2555

issue

8

eissn

1065-9471

issn

1097-0193

journal_volume

40

pub_type

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