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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).

Magnetic Resonance Imaging

Magnetic Resonance Imaging

Magnetic resonance imaging (MRI) is a noninvasive medical imaging technique based on a phenomenon of nuclear physics discovered in the 1930s, in which matter exposed to magnetic fields and radio waves was found to emit radio signals. In 1970, a physician and researcher named Raymond Damadian noticed that malignant (cancerous) tissue gave off different signals than normal body tissue. He applied for a patent for the first MRI scanning device in clinical use by the early 1980s. The early MRI...

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Related Experiment Video

Updated: May 30, 2026

Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms

Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms

Published on: November 1, 2019

Functional principal component model for high-dimensional brain imaging.

Vadim Zipunnikov¹, Brian Caffo, David M Yousem

¹Department of Biostatistics, Johns Hopkins University, Baltimore, MD 21205, USA. vzipunni@jhsph.edu

|July 30, 2011

Summary

This summary is machine-generated.

This study connects singular value decomposition (SVD) to functional principal component analysis (FPCA) for brain imaging. SVD efficiently estimates FPCA components, revealing key patterns in brain morphometric variation.

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Area of Science:

Neuroimaging
Statistical analysis
Dimensionality reduction

Background:

Functional principal component analysis (FPCA) is crucial for analyzing high-dimensional functional data, such as brain images.
Singular value decomposition (SVD) is a powerful numerical technique for matrix factorization.

Purpose of the Study:

To formally link SVD and FPCA models in the context of high-dimensional neuroimaging.
To leverage the computational efficiency of SVD for estimating FPCA components in brain imaging.

Main Methods:

Establishing a formal connection between right singular vectors (SVD) and principal scores (FPCA).
Utilizing left singular vectors (SVD) as estimators for principal components (FPCA).
Applying the integrated SVD-FPCA approach to high-resolution morphometric (RAVENS) brain images.

Main Results:

Demonstrated that left singular vectors directly estimate FPCA principal components.
Showed that right singular vectors correspond to FPCA principal scores.
Successfully applied the method to identify major directions of morphometric variation in brain volumes.

Conclusions:

SVD provides a computationally efficient method for estimating FPCA components in high-dimensional neuroimaging data.
This approach enhances the analysis of functional objects like brain images.
The study identifies key patterns of anatomical variation within brain structures.