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Related Experiment Video

Updated: Feb 8, 2026

Basics of Multivariate Analysis in Neuroimaging Data

Basics of Multivariate Analysis in Neuroimaging Data

Published on: July 24, 2010

A Robust Reduced Rank Graph Regression Method for Neuroimaging Genetic Analysis.

Xiaofeng Zhu¹, Weihong Zhang², Yong Fan³

¹Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA.

Neuroinformatics

|June 17, 2018

Summary

This summary is machine-generated.

This study introduces a new method to jointly analyze genetic and neuroimaging data, improving the understanding of brain structure and Single Nucleotide Polymorphisms (SNPs) associations.

Keywords:

Graph representation Image-genetic analysis Sparse learning Variable selection

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Meta-analysis of Voxel-Based Neuroimaging Studies using Seed-based d Mapping with Permutation of Subject Images SDM-PSI

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Published on: November 27, 2019

Area of Science:

Neuroscience
Genetics
Biostatistics

Background:

Neuroimaging genetic studies analyze correlations within neuroimaging or genetic data separately.
Existing methods show promise but do not jointly consider these inherent correlations.

Purpose of the Study:

To develop a novel robust reduced rank graph regression method.
To jointly analyze correlations in both neuroimaging and genetic data for improved association characterization.

Main Methods:

A linear regression framework models genetic data as features and neuroimaging data as responses.
Incorporates a new graph representation for genetic data and robust loss functions.
Employs an iterative optimization method with proven convergence.

Main Results:

The proposed method jointly considers correlations in neuroimaging and genetic data.
Achieved competitive regression performance on the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset.
Demonstrated effectiveness in linking brain structural measures with Single Nucleotide Polymorphisms (SNPs).

Conclusions:

The novel robust reduced rank graph regression method effectively integrates genetic and neuroimaging data.
This approach offers a more comprehensive analysis of brain structure-SNP associations.
The method shows potential for advancing neuroimaging genetic research.