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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: Jun 6, 2025

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

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

Published on: June 9, 2018

Multimodal predictive modeling: Scalable imaging informed approaches to predict future brain health.

Meenu Ajith¹, Jeffrey S Spence², Sandra B Chapman²

¹Tri-Institutional Center for Translational Research in Neuroimaging and Data Science(TReNDS), Georgia State University, Georgia Institute of Technology, and Emory University, 55 Park Pl NE, Atlanta, 30303, GA, USA.

Journal of Neuroscience Methods

|November 28, 2024

Summary

Predicting future brain health is enhanced by integrating neuroimaging data. An image-assisted approach using a partially conditional variational autoencoder (PCVAE) improves prediction accuracy for future brain health constructs.

Keywords:

Brain health Connectivity Factors Image-assisted Multimodal Predictive modeling Rs-fMRI

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Last Updated: Jun 6, 2025

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

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Basics of Multivariate Analysis in Neuroimaging Data

Basics of Multivariate Analysis in Neuroimaging Data

Published on: July 24, 2010

Area of Science:

Neuroscience
Computational Psychiatry
Medical Imaging Analysis

Background:

Predicting future brain health necessitates integrating diverse data sources.
Neural patterns from neuroimaging are early indicators of future behavioral states.

Purpose of the Study:

To introduce a multimodal predictive modeling approach for future behavioral outcomes.
To evaluate an imaging-informed methodology against traditional methods.

Main Methods:

Compared three approaches: assessment-only (SVR), neuroimaging-only (RF), and image-assisted.
The image-assisted method integrated static functional network connectivity (sFNC) from rs-fMRI with assessments.
Utilized a partially conditional variational autoencoder (PCVAE) for prediction.

Main Results:

The image-assisted method demonstrated superior performance in predicting future brain health constructs and longitudinal changes.
The PCVAE model effectively utilized neuroimaging data during training to enhance prediction accuracy from assessment data alone.

Conclusions:

Neuroimaging-informed predictive modeling holds significant potential for understanding cognitive performance and neural connectivity.
This approach advances the comprehension of complex relationships between brain structure, function, and behavior.