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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 Stimulation (TMS).

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Related Experiment Video

Updated: Jun 21, 2026

Probing the Brain in Autism Using fMRI and Diffusion Tensor Imaging

Probing the Brain in Autism Using fMRI and Diffusion Tensor Imaging

Published on: September 12, 2011

Probabilistic approaches for atlasing normal and disease-specific brain variability.

A W Toga¹, P M Thompson, M S Mega

¹Reed Neurological Research Center, Department of Neurology, UCLA School of Medicine, Los Angeles, CA 90095-1769, USA. toga@loni.ucla.edu

Anatomy and Embryology

|November 27, 2001

Summary

This summary is machine-generated.

Creating probabilistic brain atlases helps understand population variability and disease differences. These atlases map structural MRI data, aiding comparisons across diverse brain populations.

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Last Updated: Jun 21, 2026

Probing the Brain in Autism Using fMRI and Diffusion Tensor Imaging

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Published on: September 12, 2011

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

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Published on: June 26, 2013

Whole-brain Segmentation and Change-point Analysis of Anatomical Brain MRI—Application in Premanifest Huntington's Disease

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Published on: June 9, 2018

Area of Science:

Neuroimaging
Medical Image Analysis
Computational Anatomy

Background:

Human brain structural variability presents challenges for creating population-based atlases.
Statistical and visual comparison of brain images is crucial for understanding normal variation and disease differentiation.

Purpose of the Study:

Introduce probabilistic atlases for specific subpopulations.
Measure variability and characterize structural differences between subpopulations.
Provide a framework for mapping multimodal imaging data.

Main Methods:

Utilized structural Magnetic Resonance Imaging (MRI) data.
Developed atlases with defined coordinate systems.
Employed mathematical constructs for probabilistic atlas calculation.

Main Results:

Successfully built probabilistic atlases for several populations.
Demonstrated the characterization of structural differences.
Provided examples from normal and diseased populations.

Conclusions:

Probabilistic atlases offer a robust framework for analyzing brain structure variability.
This approach facilitates the differentiation between normal and diseased populations.
The methodology supports the integration of diverse neuroimaging data types.