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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: Dec 31, 2025

Basics of Multivariate Analysis in Neuroimaging Data

Basics of Multivariate Analysis in Neuroimaging Data

Published on: July 24, 2010

Brain Imaging Genomics: Integrated Analysis and Machine Learning.

Li Shen¹, Paul M Thompson²

¹Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, PA 19104, USA.

Proceedings of the IEEE. Institute of Electrical and Electronics Engineers

|January 7, 2020

Summary

This summary is machine-generated.

Brain imaging genomics integrates brain scans and genetic data to understand brain function. This review covers statistical and machine learning methods for analyzing this complex data in biomedical research.

Keywords:

Big data brain imaging genomics machine learning statistics

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Area of Science:

Neuroscience
Genomics
Data Science
Biomedical Research

Background:

Brain imaging genomics is an emerging interdisciplinary field.
It integrates brain imaging, genomics, and other data types.
This field aims to uncover brain characteristics and their impact on function and behavior.

Purpose of the Study:

To provide a comprehensive review of statistical and machine learning methods in brain imaging genomics.
To discuss practical method selection for biomedical applications.
To highlight the potential of brain imaging genomics in biomedical discoveries.

Main Methods:

Review of statistical methods.
Review of machine learning methods.
Discussion on method selection criteria.

Main Results:

Identified key statistical and machine learning approaches relevant to brain imaging genomics.
Provided guidance on selecting appropriate methods for diverse biomedical applications.
Emphasized the growing importance of these methods in the field.

Conclusions:

Brain imaging genomics offers significant potential for advancing brain science.
Statistical and machine learning are crucial tools for analyzing integrated brain data.
This review serves as a guide for researchers in the field.