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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).

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Related Experiment Video

Updated: Jun 13, 2026

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

dcSBM: A federated constrained source-based morphometry approach for multivariate brain structure mapping.

Debbrata K Saha¹, Rogers F Silva¹, Bradley T Baker¹

¹Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, and Emory University, Atlanta, Georgia, USA.

Human Brain Mapping

|October 14, 2023

Summary

This summary is machine-generated.

Constrained source-based morphometry (cSBM) enhances brain anatomy analysis by extracting independent patterns. Decentralized cSBM (dcSBM) enables secure, multi-site studies without sharing private brain imaging data.

Keywords:

SBM federated learning neuroimaging sMRI

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Last Updated: Jun 13, 2026

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

Three-Dimensional Shape Modeling and Analysis of Brain Structures

Three-Dimensional Shape Modeling and Analysis of Brain Structures

Published on: November 14, 2019

A Standardized Pipeline for Examining Human Cerebellar Grey Matter Morphometry using Structural Magnetic Resonance Imaging

A Standardized Pipeline for Examining Human Cerebellar Grey Matter Morphometry using Structural Magnetic Resonance Imaging

Published on: February 4, 2022

Area of Science:

Neuroimaging and Computational Neuroscience
Brain Morphometry Analysis

Background:

Multivariate brain morphometry patterns are crucial for understanding patient-control differences.
Source-based morphometry (SBM) is a data-driven tool for exploratory analysis of brain anatomy from structural MRI.
Existing SBM methods require improvements in pattern extraction and multi-site data integration.

Purpose of the Study:

To introduce constrained source-based morphometry (cSBM) as a semi-blind extension of SBM.
To develop a fully automated SBM framework using reference components from a large dataset (UKBiobank).
To propose a decentralized constrained SBM (dcSBM) for federated analysis of non-locally accessible neuroimaging data.

Main Methods:

Implemented constrained SBM by combining SBM with reference components derived from UKBiobank data.
Developed dcSBM, a federated approach where local sites perform constrained independent component analysis (ICA) on private data.
An aggregator node combines local results for statistical analysis to estimate source significance.
Validated cSBM and dcSBM using two multisite patient datasets, comparing group difference estimates.

Main Results:

Constrained SBM successfully extracts maximally independent, reference-alike sources from brain morphometry data.
The proposed dcSBM framework enables multi-site analysis without centralizing sensitive neuroimaging data.
Validation demonstrated comparable group difference estimates between centralized cSBM and decentralized dcSBM approaches.

Conclusions:

Constrained SBM provides a powerful, automated framework for exploring brain morphometry patterns.
Decentralized constrained SBM (dcSBM) offers a privacy-preserving solution for large-scale, multi-site neuroimaging studies.
These methods advance the analysis of brain anatomy in clinical and research settings, facilitating discoveries in neurological disorders.