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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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Updated: May 6, 2026

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Published on: June 26, 2013

Bayesian spatial transformation models with applications in neuroimaging data.

Michelle F Miranda¹, Hongtu Zhu, Joseph G Ibrahim

¹Department of Statistics and Operations Research, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, U.S.A.

|October 17, 2013

Summary

This summary is machine-generated.

This study introduces spatial transformation models (STM) to analyze brain imaging data, outperforming traditional methods. The new models reveal brain changes in children with attention deficit hyperactivity disorder.

Keywords:

Bayesian analysis Big data Box-Cox transformation Gaussian Markov random field MCMC Neuroimaging data

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Area of Science:

Neuroimaging
Statistical Modeling
Biostatistics

Background:

Analyzing complex 3D imaging data requires advanced statistical methods.
Non-Gaussian data distributions and spatial relationships pose challenges for traditional models.

Purpose of the Study:

To develop novel spatial transformation models (STM) for analyzing varying associations between imaging measures and covariates.
To address non-Gaussian data and incorporate spatial smoothness in imaging analysis.

Main Methods:

Developed a class of spatial transformation models (STM).
Incorporated a varying Box-Cox transformation for non-Gaussian data.
Utilized a Gaussian Markov random field for spatial smoothness.
Employed Markov chain Monte Carlo for posterior computation.

Main Results:

Simulations and real data analysis showed STM significantly outperforms voxel-wise linear models.
STM effectively recovers meaningful geometric patterns in imaging data.
The models successfully identified brain regions with morphological changes.

Conclusions:

The proposed spatial transformation models offer a superior approach for neuroimaging analysis.
STM can effectively detect morphological alterations in pediatric attention deficit hyperactivity disorder.