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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).

Organization of the Brain

Organization of the Brain

The brain is an integral component of the nervous system and serves as the center for processing sensory inputs, making decisions, and directing bodily actions. This complex organ is organized into three primary sections: the hindbrain, midbrain, and forebrain, each responsible for a range of vital functions.
Hindbrain
The hindbrain, located at the base of the brain, plays a vital role in regulating automatic processes that sustain life. It includes the medulla oblongata, which is essential for...

Anatomy of the Brain: Major Regions

Anatomy of the Brain: Major Regions

The brain is the most complex organ in the human body. It consists of four main parts: the cerebrum, diencephalon, cerebellum, and brainstem.
The cerebrum is the largest section of the brain and divides into left and right hemispheres, separated by a deep fissure. The cerebral outer layer of grey matter — the cerebral cortex — comprises elevations called gyri and shallow groves called sulci. The inner portion of white matter includes long nerve fibers known as axons, which connect various areas...

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Related Experiment Video

Updated: Jun 27, 2026

Mapping Cortical Dynamics Using Simultaneous MEG/EEG and Anatomically-constrained Minimum-norm Estimates: an Auditory Attention Example

Mapping Cortical Dynamics Using Simultaneous MEG/EEG and Anatomically-constrained Minimum-norm Estimates: an Auditory Attention Example

Published on: October 24, 2012

Encoding probabilistic brain atlases using Bayesian inference.

Koen Van Leemput¹

¹Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA 02129, USA. koen@nmr.mgh.harvard.edu

IEEE Transactions on Medical Imaging

|December 11, 2008

Summary

This summary is machine-generated.

This study introduces a novel method for creating probabilistic brain atlases using Bayesian inference and mesh-based models, improving accuracy with limited data. This approach enhances anatomical structure segmentation in MRI scans.

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Mapping Cortical Dynamics Using Simultaneous MEG/EEG and Anatomically-constrained Minimum-norm Estimates: an Auditory Attention Example

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Area of Science:

Computational neuroimaging
Medical image analysis
Statistical modeling

Background:

Standard probabilistic brain atlases rely on averaging, which performs poorly with limited training data and lacks principled alignment.
Deformable registration is crucial for aligning diverse neuroimaging datasets but is challenging to integrate with traditional atlas construction.
Existing methods struggle to generalize and adapt to variations in unseen cases.

Purpose of the Study:

To develop a generalized generative model for probabilistic brain atlases using mesh-based representations and explicit deformation models.
To enable simultaneous group-wise registration and atlas estimation via Bayesian inference.
To automatically optimize atlas model parameters, including spatial blurring, deformation flexibility, and mesh representation.

Main Methods:

Generalization of the generative image model underlying standard atlases using mesh-based representations.
Application of Bayesian inference for parameter estimation and model comparison.
Simultaneous group-wise registration and atlas estimation scheme.

Main Results:

The proposed Bayesian approach provides a principled method for simultaneous registration and atlas estimation, encompassing standard averaging.
Bayesian model comparison leads to data compression, enabling automatic determination of optimal spatial blurring, deformation flexibility, and mesh resolution.
Experiments in 2D and 3D demonstrate superior performance in capturing training data structure compared to conventional probabilistic atlases.

Conclusions:

The developed technique offers a more robust and accurate method for constructing probabilistic brain atlases, especially with limited training data.
The resulting atlases show significant potential for fully-automated, pulse sequence-adaptive segmentation of neuroanatomical structures in brain MRI.
This framework provides a computationally feasible and interpretable approach to atlas generation and model selection.