Topological gene expression networks recapitulate brain anatomy and function.
Alice PataniaPierluigi SelvaggiMattia VeroneseOttavia DipasqualePaul ExpertGiovanni PetriPublished in: Network neuroscience (Cambridge, Mass.) (2019)
Understanding how gene expression translates to and affects human behavior is one of the ultimate goals of neuroscience. In this paper, we present a pipeline based on Mapper, a topological simplification tool, to analyze gene co-expression data. We first validate the method by reproducing key results from the literature on the Allen Human Brain Atlas and the correlations between resting-state fMRI and gene co-expression maps. We then analyze a dopamine-related gene set and find that co-expression networks produced by Mapper return a structure that matches the well-known anatomy of the dopaminergic pathway. Our results suggest that network based descriptions can be a powerful tool to explore the relationships between genetic pathways and their association with brain function and its perturbation due to illness and/or pharmacological challenges.
Keyphrases
- resting state
- functional connectivity
- gene expression
- poor prognosis
- genome wide
- copy number
- dna methylation
- endothelial cells
- systematic review
- genome wide identification
- binding protein
- long non coding rna
- multiple sclerosis
- transcription factor
- single cell
- uric acid
- public health
- big data
- deep learning
- global health