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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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Related Experiment Video

Updated: Dec 12, 2025

Co-analysis of Brain Structure and Function using fMRI and Diffusion-weighted Imaging

Co-analysis of Brain Structure and Function using fMRI and Diffusion-weighted Imaging

Published on: November 8, 2012

Detecting and visualizing differences in brain structures with SPHARM and functional data analysis.

L Ferrando¹, N Ventura-Campos², I Epifanio³

¹Grup Neuropsicologia i Neuroimatge Funcional, Universitat Jaume I, Spain.

|August 11, 2020

Summary

This summary is machine-generated.

A new method classifies brain structures using dimension reduction and linear discriminant analysis. This approach offers superior predictive power and interpretability for neuroeducation research, specifically analyzing mathematical reversal errors.

Keywords:

Functional data analysis Functional discriminant analysis Magnetic resonance imaging Neuroeducation Reversal error Shape analysis

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Last Updated: Dec 12, 2025

Co-analysis of Brain Structure and Function using fMRI and Diffusion-weighted Imaging

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Published on: November 8, 2012

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Published on: October 30, 2018

Area of Science:

Neuroscience
Biostatistics
Machine Learning

Background:

Accurate classification of brain structures is crucial for understanding neurological processes.
Existing methods may lack interpretability or optimal predictive power in complex neuroimaging analyses.

Purpose of the Study:

To introduce a novel procedure for classifying brain structures using SPHARM (Superquadric Primitive-based Representation of Meshes).
To evaluate the performance and interpretability of the proposed method against established techniques in a neuroeducation context.

Main Methods:

Utilized functional principal component analysis (fPCA) or functional independent component analysis (fICA) for dimension reduction.
Employed stepwise variable selection coupled with linear discriminant classification.
Applied the procedure to analyze left and right putamen structures in 33 participants to study mathematical reversal errors.

Main Results:

The proposed method demonstrated outstanding predictive performance compared to well-known classification techniques.
The procedure yielded an interpretable linear discriminant function for analyzing 3D brain structures.

Conclusions:

The new classification procedure offers a powerful and interpretable tool for neuroimaging research.
This method advances the analysis of brain structure variations and their relation to cognitive functions like mathematical problem-solving.