Associations Between Genetics of Parkinson's Disease, Brain Structure, and Behavioral Phenotypes

292 

Abstract Submission 

Houman Azizi¹, Alexandre Pastor-Bernier², Christina Tremblay³, Nooshin Abbasi⁴, Peter Savadjiev⁵, Eric Yu⁶, Jean-Baptiste Poline⁷, Ziv Gan-Or⁸, Yashar Zeighami⁹, Alain Dagher¹⁰

¹Montreal Neurological Institute, Montreal, Quebec, ²Montreal Neurological Institute and Hospital, McGill University, Montreal, Qc, ³Montreal Neurological Institute and Hospital, McGill University, Verdun, Québec, ⁴Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, ⁵Harvard Medical School, Cambridge, MA, ⁶Department of Human Genetics, McGill University, Montreal, Quebec, ⁷McGill University, Montreal, Quebec, ⁸McGill University, Montreal, QC, ⁹Douglas Research Centre, Montreal, Quebec, ¹⁰Montreal Neurological Institute and Hospital, McGill University, Montreal, QC

Houman Azizi  
Montreal Neurological Institute
Montreal, Quebec

Alexandre Pastor-Bernier  
Montreal Neurological Institute and Hospital, McGill University
Montreal, Qc

Christina Tremblay  
Montreal Neurological Institute and Hospital, McGill University
Verdun, Québec

Nooshin Abbasi  
Montreal Neurological Institute and Hospital, McGill University
Montreal, Quebec

Peter Savadjiev  
Harvard Medical School
Cambridge, MA

Eric Yu  
Department of Human Genetics, McGill University
Montreal, Quebec

Jean-Baptiste Poline  
McGill University
Montreal, Quebec

Ziv Gan-Or  
McGill University
Montreal, QC

Yashar Zeighami  
Douglas Research Centre
Montreal, Quebec

Alain Dagher  
Montreal Neurological Institute and Hospital, McGill University
Montreal, QC

An important hallmark of most neurological disorders is the loss of brain tissue detectable by Magnetic Resonance Imaging (MRI). In Parkinson's disease (PD), patients show higher cortical surface area (SA) (Jubault 2011), and lower white matter fractional anisotropy (FA) (Chan 2007) and subcortical volumes (Charroud 2021). However, the local inter-relationships between these brain features as well as their associations with behavioral phenotypes are unknown. Here we test the relationship between genetic risk for PD and MRI-derived FA, cortical SA, and subcortical volume. We then study the relation between these neuroanatomical measures and behavioral phenotypes.

Demographics, behavioral, genomic and brain imaging data were obtained for 40,000 UK Biobank participants. Diffusion-weighted MRI images were analyzed using the Tractoflow pipeline to generate FA maps for each subject. White matter was then parcellated into 73 anatomical tracts using the O'Donnell's ORG atlas (O'Donnell 2007) and mean FA values of each tract were extracted. T1-weighted MRI images were analyzed using the CIVET pipeline (Zijdenbos 2002) to extract region-wise SA values for 200 cortical regions in the Schaefer atlas (Schaefer 2018). Subcortical volume measures for 14 Harvard-Oxford atlas regions (Makris 2006) were provided by the UK Biobank using the FSL's FIRST pipeline (Patenaude 2011). The relationships between PD polygenic risk score and grey and white matter morphometry were assessed by linear regression using the following set of confound variables: age, age^2, sex, age*sex interaction, center number, scanning motion, scanning bed position, genotype batch, 15 principal genetic components. Results were then corrected for multiple comparisons using False Discovery Rate (FDR) correction with p-value threshold of 0.05. Similarly, the association between each white matter tract's FA and its structurally connected cortical SA was assessed (statistical significance based on spin test). Partial least square (PLS) analysis was then used to investigate the behavioral phenotypes linked with brain features after regressing out the effect of age from all variables.

Polygenic risk score of PD was positively associated with cortical SA, subcortical volume, and white matter FA across the brain (Figure 1). FA in all tracts were positively associated with SA of their structurally connected cortical regions; however, these associations remained spatially significant for only 2/73 tracts after correcting for spatial autocorrelation. The PLS analysis revealed alcohol usage, education level, household income, fluid intelligence, and height as positively associated with these brain features, and multiple deprivation index as negatively associated (Figure 2).

These results reveal a link between genetic susceptibility to PD and brain characteristics indicative of greater size of grey and higher integrity in white matter structures. This indicates that genes implicated in PD may also lead to increases in neuronal numbers and connections. These associations were not specific to pairs of spatially interconnected white matter tracts and cortical regions, suggesting a global link exists between white matter tract's FA and cortical SA rather than a spatially local one. These associations were in turn related to certain demographic and behavioral phenotypes including alcohol usage. The findings are consistent with the view that an increase in neural density may make brains vulnerable to neurodegeneration in PD.

Neurodegenerative/ Late Life (eg. Parkinson’s, Alzheimer’s) ¹

Genetic Association Studies ²

Diffusion MRI Modeling and Analysis

Cortical Anatomy and Brain Mapping

White Matter Anatomy, Fiber Pathways and Connectivity

Cortex

Data analysis

STRUCTURAL MRI

White Matter

Other - Cortical Surface Area; Parkinson's Diease