Work

A Parallel Independent Component Analysis Approach to Investigate Genomic Influence on Brain Function

Year: 2008

Type: article

Abstract: Relationships between genomic data and functional brain images are of great interest but require new analysis approaches to integrate the high-dimensional data types. This letter presents an extension of a technique called parallel independent component analysis (paraICA), which enables the joint analysis of multiple modalities including interconnections between them. We extend our earlier work by allowing for multiple interconnections and by providing important overfitting controls. Performance was assessed by simulations under different conditions, and indicated reliable results can be extracted by properly balancing overfitting and underfitting. An application to functional magnetic resonance images and single nucleotide polymorphism array produced interesting findings.

Source: IEEE Signal Processing Letters

DOI: 10.1109/lsp.2008.922513

Authors: Jingyu Liu, O. Demirci, V.D. Calhoun

Institution: University of New Mexico

Language: English

Topic: Blind Source Separation Techniques

Subfield: Signal Processing

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

Funder: National Institute of Biomedical Imaging and Bioengineering

Awards: A Unified Framework for Flexible Brain Image Analysis, Unified multivariate data-driven solutions for static and dynamic brain connectivity, Informed Data-Driven Fusion of Behavior, Brain Function, and Genes, Multivariate methods for identifying multitask/multimodal brain imaging biomarkers