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Brain Imaging

Brain Imaging

Brain imaging technologies provide critical insights into both the structure and function of the human brain, enabling medical professionals and researchers to diagnose, study, and treat neurological disorders or psychiatric disorders more effectively.
These technologies include computerized axial tomography (CAT or CT scans), positron-emission tomography (PET scans), magnetic resonance imaging (MRI), functional magnetic resonance imaging (fMRI), and Transcranial Magnetic...

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Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

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Structured sparse CCA for brain imaging genetics via graph OSCAR.

Lei Du¹, Heng Huang², Jingwen Yan¹

¹School of Medicine, Indiana University, Indianapolis, USA.

BMC Systems Biology

|September 3, 2016

Summary

This summary is machine-generated.

A new structured sparse canonical correlation analysis (SCCA) method using graph OSCAR (GOSCAR) effectively identifies brain imaging genetic associations. It found a key link between Alzheimer's risk SNP rs429358 and frontal amyloid burden.

Keywords:

Brain imaging genetics Canonical correlation analysis Machine learning Structured sparse model

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Published on: August 7, 2017

Area of Science:

Neuroimaging Genetics
Statistical Genetics
Biostatistics

Background:

Structured sparse canonical correlation analysis (SCCA) is increasingly used in brain imaging genetics.
Existing SCCA methods have limitations, such as requiring pre-defined feature structures or relying on sample correlation signs.
These limitations can introduce estimation bias and restrict the identification of complex genetic associations.

Purpose of the Study:

To propose a novel structured SCCA model utilizing the graph OSCAR (GOSCAR) regularizer.
To overcome limitations of existing SCCA methods by not requiring pre-defined correlation signs or structures.
To identify bi-multivariate associations between brain imaging and genetic data with improved accuracy.

Main Methods:

Developed a new structured SCCA model incorporating the GOSCAR regularizer.
GOSCAR encourages highly correlated features to share similar canonical weights, regardless of correlation sign.
Evaluated the method's performance using synthetic datasets and real brain imaging genetics data.

Main Results:

The GOSCAR-based SCCA demonstrated robust performance on synthetic data, yielding accurate estimations of correlations and canonical loadings.
Analysis of amyloid imaging and APOE gene data revealed a significant association between SNP rs429358 and frontal amyloid burden.
The identified canonical weights exhibited a clear, interpretable pattern, facilitating further biological investigation.

Conclusions:

The proposed GOSCAR-based SCCA method offers improved or comparable performance to existing approaches.
Successfully identified a critical association between an Alzheimer's disease risk SNP (rs429358) and frontal amyloid burden.
The method provides a valuable tool for uncovering complex relationships in neuroimaging genetics studies.