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

• Published in: Human brain mapping Vol. 40; no. 8; pp. 2546 - 2555
• Main Authors: Jacob, Yael, Rosenberg‐Katz, Keren, Gurevich, Tanya, Helmich, Rick C., Bloem, Bastiaan R., Orr‐Urtreger, Avi, Giladi, Nir, Mirelman, Anat, Hendler, Talma, Thaler, Avner
• Format: Journal Article
• Language: English
• Published: Hoboken, USA John Wiley & Sons, Inc 01.06.2019
• Subjects: Abnormalities; Adult; Artificial intelligence; Cerebrum - diagnostic imaging; Cerebrum - physiopathology; Classification; Connectivity analysis; Connectome; Correlation analysis; Dependence; Female; Functional magnetic resonance imaging; graph theory network analysis; Heterozygote; Humans; Integrity; Learning algorithms; Leucine-Rich Repeat Serine-Threonine Protein Kinase-2 - genetics; LRRK2; LRRK2 protein; Machine Learning; machine learning classification; Magnetic Resonance Imaging; Male; Middle Aged; Movement disorders; Mutation; Nerve Net - diagnostic imaging; Nerve Net - physiopathology; Network analysis; Networks; Neurodegenerative diseases; Parkinson Disease - diagnostic imaging; Parkinson Disease - genetics; Parkinson Disease - physiopathology; Parkinson's disease; resting state fMRI; Parkinson's disease; graph theory network analysis; machine learning classification; resting state fMRI; LRRK2
• ISSN: 1065-9471; 1097-0193
• DOI: 10.1002/hbm.24543

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 (DEPNA). 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.