期刊
HUMAN BRAIN MAPPING
卷 40, 期 8, 页码 2546-2555出版社
WILEY
DOI: 10.1002/hbm.24543
关键词
graph theory network analysis; LRRK2; machine learning classification; Parkinson's disease; resting state fMRI
资金
- Michael J. Fox Foundation for Parkinson's Research
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.
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