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 characteristics and genetics data allows a better understanding of the effects of the gene expression 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. For instance, here we show that the expression of PLAU gene increases the change over time in betweenness centrality related to the fusiform gyrus. We also show that the betweenness centrality metric impact dementia-related changes in distinct brain regions. Our findings 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 connectivity alterations.
Highlights
The advancement in technologies and the integration of genetic and neuroimaging datasets have taken Alzheimer’s research steps further, and produced detailed descriptions of molecular and brain aspects of the disease (Shaw et al, 2007)
Our work aimed to answer questions such as are there brain connectivity metrics that discriminate changes longitudinally in Alzheimer’s disease (AD) patients compared to healthy control subjects? Is there a most representative metric, or a redundancy in the chosen metrics? Is there a correlation between the metrics used and expression of known AD-related genes? Is there a correlation between connectivity metrics and clinical ratings?. We address these questions considering the global brain by using global connectivity, and considering specific brain regions using the local connectivity metrics
In other words, comparing the two groups (AD and healthy elderly) in terms of the change in global connectivity metrics overtime, the only significant differences found between baseline and follow-up were within the AD group
Summary
The advancement in technologies and the integration of genetic and neuroimaging datasets have taken Alzheimer’s research steps further, and produced detailed descriptions of molecular and brain aspects of the disease (Shaw et al, 2007). A structural connectome is a representation of the brain as a network of distinct brain regions (nodes) and their structural connections (edges), calculated as the number of anatomical fibers Those anatomical fibers are generally obtained by diffusion-weighted imaging (DWI) (Alexander et al, 2007). The connectome representation of the brain allows measuring of important properties, such as the ability of the brain to form separated sub-networks (network segregation), or the measure of a network dispersion (i.e., how segregated subnetworks are connected network integration) (Deco et al, 2015) Given such measures of the brain, it is possible to represent each individual brain as single scalar metrics which summarize peculiar properties of the network’s segregation and integration (Rubinov and Sporns, 2010), and calculate what is known as global connectivity metrics. These measures (i.e., global and local connectivity metrics) can reflect neurodegeneration in the sense that neuronal apoptosis (i.e., programmed cell death) can be represented as a reduction in structural connectivity (Douaud et al, 2007; Elsheikh et al, 2020b)
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