Paper Info
Title
A Data-Driven Method To Study Brain Structural Connectivities Via Joint Analysis Of Microarray Data And Dmri Data
Abstract
Connective structure is an outstanding feature of the brain. Exploring the mechanism of its development and its variation across individual and species might hold the key to understanding brain functions and brain diseases. Genes are widely recognized as the fundamental regulators of the connective architecture, but it is still far from fully understanding genetics impacts on brain connectivities. Quantitative analysis of advanced imaging data, such as diffusion MRI (dMRI), provides phenotypic features and plausible clues for exploring the genetic reasons. Therefore, in this paper, we jointly analyzed dMRI data and microarray gene expression data. By developing a novel method to compare the dMRI derived structural connectivity matrix and gene expression distance matrix, we identified gene groups which might contribute to structural wiring diagram. Effectiveness and reproducibility of this method has been demonstrated.
Year
DOI
Venue
2016
10.1109/ISBI.2016.7493394
2016 IEEE 13TH INTERNATIONAL SYMPOSIUM ON BIOMEDICAL IMAGING (ISBI)
Keywords
Field
DocType
brain structural connectivity, dMRI, microarray, joint analysis
Diffusion MRI,Data-driven,Pattern recognition,Computer science,Microarray analysis techniques,Artificial intelligence,Distance matrix,Computational biology,Microarray gene expression,Bioinformatics
Conference
ISSN
Citations 
PageRank 
1945-7928
1
0.37
References 
Authors
1
7
Name
Order
Citations
PageRank
Xiao Li110.37
Tuo Zhang223332.92
Tao Liu310.37
Jinglei Lv420526.70
Xintao Hu511813.53
Lei Guo618111.67
Tianming Liu71033112.95