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Relating Global and Local Connectome Changes to Dementia and Targeted Gene Expression in Alzheimer’S Disease Samar S

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Relating Global and Local Connectome Changes to Dementia and Targeted Gene Expression in Alzheimer’S Disease Samar S bioRxiv preprint doi: https://doi.org/10.1101/730416; this version posted November 4, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license. Relating Global and Local Connectome Changes to Dementia and Targeted Gene Expression in Alzheimer’s Disease Samar S. M. Elsheikh1, Emile R. Chimusa2, Alzheimer’s Disease Neuroimaging 1,† 3,4 Initiative∗, Nicola J. Mulder , and Alessandro Crimi 1Computational Biology Division, Department of Integrative Biomedical Sciences, Institute of Infectious Disease and Molecular Medicine, Faculty of Health Sciences, University of Cape Town, Cape Town, 7925, South Africa 2Division of Human Genetics, Department of Pathology, Institute of Infectious Disease and Molecular Medicine, Faculty of Health Sciences, University of Cape Town, Cape Town, 7925, South Africa 3University Hospital of Zurich,¨ Zurich,¨ 8091, Switzerland 4African Institute for Mathematical Sciences, Ghana †[email protected] ∗Data used in preparation of this article were obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database (adni.loni.usc.edu). As such, the investigators within the ADNI contributed to the design and implementation of ADNI and/or provided data but did not participate in analysis or writing of this report. A complete listing of ADNI investigators can be found at: http: //adni.loni.usc.edu/wp-content/uploads/how_to_apply/ADNI_Acknowledgement_List.pdf ABSTRACT Networks are present in many aspects of our lives, and networks in neuroscience have recently gained much attention leading to novel representations of brain connectivity. The integration of neuroimaging and genetics allows a better understanding of the effects of the genetic variations on brain structural and functional connections. The current work uses whole-brain tractography in a longitudinal setting, and by measuring the brain structural connectivity changes studies the neurodegeneration of Alzheimer’s disease. This is accomplished by examining the effect of targeted genetic risk factors on the most common local and global brain connectivity measures. Furthermore, we examined the extent to which Clinical Dementia Rating relates to brain connections longitudinally, as well as to gene expression. Here, we show that the expression of PLAU and HFE genes increases the change over time respectively in betweenness centrality related to the fusiform gyrus and clustering coefficient of the cingulum bundle. We also show that the betweenness centrality metric highlights impact dementia-related changes in distinct brain regions. Ourfindings provide insights into the complex longitudinal interplay between genetics and brain characteristics and highlight the role of Alzheimer’s genetic risk factors in the estimation of regional brain connection alterations. Introduction integration)5. Given such measures of the brain, it is also pos- sible to represent each individual brain as single scalar metrics The advancement in technologies and the integration of ge- which summarize peculiar properties of the network’s segre- netic and neuroimaging datasets have taken Alzheimer’s re- gation and integration6, and calculate what is known as global search steps further, and produced detailed descriptions of connectivity metrics. Moreover, these global metrics can also 1 molecular and brain aspects of the disease . Previous studies be used to quantify local properties at specific nodes/areas. 2 have utilised the connectome to study different brain diseases This is relevant for neurodegeneration, as neuronal apoptosis 3 through associating genetic variants to brain connectivity .A in the end also reflects reduction of structural connectivity7,8. structural connectome is a representation of the brain as a network of distinct brain regions (nodes) and their structural There are many factors which may affect susceptibility to connections (edges), calculated as the number of anatomi- Alzheimer’s disease (AD) and various ways to measure the cal tracts. Those anatomical tracts are generally obtained by disease status. However, there is no single factor which can diffusion-weighted imaging (DWI)4. DWI is the most com- be used to predict the disease risk sufficiently9. Genetics is monly used in-vivo method for mapping and characterizing believed to be the most common risk factor in AD develop- the diffusion of water molecules in three-dimensions, as a ment10. Genetic variants located in about 20 genes have been function of the location, and ultimately to construct the struc- reported to affect the disease through many cell-type specific tural connectome. The connectome representation of the brain biological functions11. Genome-Wide Associations Studies allows measuring of important properties, such as the ability (GWAS), also highlighted dozens of multi-scale genetic vari- of the brain to form separated sub-networks (network segrega- ations associated with AD risk8,12,13. From the early stages tion), or the gathering of edges around specific nodes (network of studying the disease, the well known genetic risk factors bioRxiv preprint doi: https://doi.org/10.1101/730416; this version posted November 4, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license. of AD were found to lie within the coding genes of proteins of characteristic path length. For the local connectivity fea- involved in amyloid-β(Aβ) processing. These include the tures, we used one measure to represent node’s segregation, Apolipoprotein E gene (ApoE) which increases the risk of integration and centrality. The local connectivity metrics of a developing AD14, the Amyloid precursor protein (APP)15, network represent large-scale organization which in turn may presenilin-1 (PSEN1) and presenilin-2 (PSEN2)16, 17. be used to represent well functioning cognitive functions26. Early work demonstrated that ApoE-4 carriers have an ac- Measures of centrality (e.g. degree and betweeness centrality) celerated age-related loss of global brain inter-connectivity usually measure similar features in a network, and hence, can in AD subjects18, and topological alterations of both struc- be highly related to each other. Indeed, it has been shown tural and functional brain networks are present even in healthy that many of them can lead back to the degree centrality at a 27 subjects carrying the ApoE gene19. A meta-analysis study node . Therefore, along with using local connectivity mea- also showed the impact of APOE, phosphatidylinositol bind- sures, we computed their correlation with the nodal degree ing clathrin assembly protein (PICALM), clusterin (CLU), metric - the simplest feature we can extract from a graph node. and bridging integrator 1 (BIN1) gene expression on resting Our work aimed to answer questions such as, are there brain state functional connectivity in AD patients20. Going beyond connectivity metrics that discriminate changes longitudinally the ApoE gene, Jahanshad et al.21 used a dataset from the in AD patients compared to healthy control subjects? Is there Alzheimer’s Disease Neuroimaging Initiative (ADNI) to carry a most representative metric, or a redundancy in the chosen out a GWAS of brain connectivity measures and found an metrics? Is there a correlation between the metrics used and associated variant in F-spondin (SPON1), previously known expression of known AD-related genes? Is there a correlation for its association with dementia severity. A more recent study between connectivity metrics and clinical ratings? has also used the ADNI dataset to conduct a GWAS on the We address these questions considering the global brain by longitudinal brain connectivity, and provided an evidence of using global connectivity, and also considering specific brain association between some single nucleotide polymorphisms regions using the local connectivity metrics. We regressed the (SNPs) in the CDH18 gene and Louvain modularity (a mea- absolute changes in connectivity metrics on gene expression, sure of network segregation)8. studied the association between the longitudinal changes of connectivity and CDR scores, and performed a ridge regres- AD is a common dementia-related illness; in the elderly, sion between gene expression, the changes in CDR scores and AD represents the most progressive and common form of de- brain connectivity. mentia. Accordingly, incorporating and assessing dementia Using a dataset from ADNI (http://adni.loni. severity when studying AD provides more insights into the usc.edu/) this paper presents an integrated association disease progression from a clinical point of view. A reliable study of specific AD risk genes, dementia scores and structural global rating of dementia severity is the Clinical Dementia connectome characteristics. Specifically, we used the longitu- Rating (CDR)22, which represents a series of specific evalua- dinal case-control dataset to examine the association of known tions for memory, orientation, judgment and problem solving, AD risk-gene expression with local and global connectivity community affairs, home and hobbies (intellectual interests metrics. We tested the longitudinal effect of brain connectivity maintained at home), and personal care. The ultimate CDR on different CDR scores, and carried
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