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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: Jul 31, 2025

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

Statistical Learning Methods for Neuroimaging Data Analysis with Applications.

Hongtu Zhu^1,2, Tengfei Li^2,3, Bingxin Zhao⁴

¹Department of Biostatistics, Department of Statistics, Department of Genetics, and Department of Computer Science, University of North Carolina, Chapel Hill, North Carolina, USA;

Annual Review of Biomedical Data Science

|May 1, 2023

Summary

This summary is machine-generated.

This review surveys statistical challenges in neuroimaging data analysis, covering techniques, large-scale studies, and statistical learning. It highlights methods for individual and population-level analysis, addressing key challenges in the field.

Keywords:

causal pathway heterogeneity image processing analysis neuroimaging techniques population-based statistical analysis study design

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Last Updated: Jul 31, 2025

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Published on: June 30, 2020

Area of Science:

Neuroscience
Biostatistics
Medical Imaging

Background:

Neuroimaging techniques are crucial for neuroscience research and clinical applications.
Analyzing complex neuroimaging data presents significant statistical challenges.
Large-scale studies and advanced statistical learning are increasingly important.

Purpose of the Study:

To provide a comprehensive survey of statistical challenges in neuroimaging data analysis.
To review neuroimaging techniques, data processing methods, and statistical analysis approaches.
To discuss recent progress in statistical methodology for neuroimaging.

Main Methods:

Review of eight popular neuroimaging techniques.
Delineation of four themes of neuroimaging data and associated processing methods.
Review of four large-scale neuroimaging studies and nine population-based statistical analysis methods.

Main Results:

Identified statistical challenges across individual and population-level neuroimaging data analysis.
Summarized major image processing and statistical analysis methods.
Highlighted progress in statistical methodology to address identified challenges.

Conclusions:

Statistical rigor is essential for advancing neuroimaging data analysis.
Addressing statistical challenges is key for reliable neuroscience research and clinical translation.
Continued development of statistical methods is needed for complex neuroimaging studies.