Genetic Association Study of grey matter network dysfunction in Alzheimer's disease Continuum
Alireza Fathian |November, 2021
1. Research Questions
- How are these factors correlated?
- How can we design an effective analysis pipeline for characterizing AD that capture both neuroimaging and genetic data?
- How can these methods help us understanding the disease mechanism?
2. Methodology
2.1. Brain Connectivity Networks
Modeling brain connectivity networks
2.1. Brain Connectivity Networks
Example: Modeling functional connectivity network using fMRI and t1w
2.1. Brain Connectivity Networks
The trend of brain network disruptions
2.2 Genome-Wide Association Study (GWAS)
GWAS analysis can be used to detect associations between genetic variants and AD.
2.3. Combining Neuroimaging and Genetic Data
- First Approach
- Second Approach
3. Fictive Results and Conclusion
3.1. Fictive Results of the First Approach
Network alteration in AD Progression:
Clustering Coefficient, Average Shortest Path Length, and Assortativity are higly involved.
3.1. Fictive Results of the First Approach
GWAS analysis of connectivity metrics:
3.1. Fictive Results of the First Approach
Signifcant associations between SNPs and network metrics.
3.1. Fictive Results of the First Approach
The pathway of significant genes:
3.2 Summery of Pipeline
