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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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Causality-Based Feature Fusion for Brain Neuro-Developmental Analysis.

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IEEE Transactions on Medical Imaging

|April 29, 2020

Summary

This summary is machine-generated.

This study introduces effective connectivity (EC) using Granger causality (GC) to analyze human brain development, revealing significant differences between children and young adults. Combining EC with functional connectivity (FC) enhances brain age prediction accuracy.

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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

Area of Science:

Neuroscience
Developmental Neuroscience
Computational Neuroscience

Background:

Human brain development is influenced by genetics, hormones, and environment.
Functional connectivity (FC) analysis, based on temporal correlations, is commonly used to study brain development.
Existing methods often overlook the directional flow of information during brain maturation.

Purpose of the Study:

To incorporate effective connectivity (EC) analysis into the study of human brain development.
To investigate age-related differences in brain connectivity between children and young adults.
To develop novel methods for extracting EC features and assess their utility in discriminating age groups and improving brain age prediction.

Main Methods:

Extraction of effective connectivity (EC) using Granger causality (GC) in children and young adults.
Development of a novel kernel-based GC (KGC) method employing a reduced Sine hyperbolic polynomial (RSP) neural network to capture nonlinearities.
Comparison of EC-based features against traditional FC-based analysis using data from the Philadelphia Neurocohort (PNC) study.

Main Results:

Effective connectivity (EC) analysis revealed significantly stronger brain connections in young adults compared to children.
The proposed EC-based features demonstrated superior performance in discriminating between age groups compared to FC-based analysis.
Fusion of functional connectivity (FC) and effective connectivity (EC) features improved brain age prediction accuracy by over 4%.

Conclusions:

Effective connectivity (EC) provides valuable insights into human brain development, complementing functional connectivity (FC) analysis.
The novel kernel-based Granger causality (KGC) method effectively captures complex nonlinear dynamics in brain networks.
Integrating both FC and EC measures is recommended for comprehensive studies of brain maturation and age prediction.