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Related Concept Videos

Brain Imaging01:14

Brain Imaging

334
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...
334

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Updated: Sep 28, 2025

Whole-brain Segmentation and Change-point Analysis of Anatomical Brain MRI—Application in Premanifest Huntington's Disease
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Decentralized Brain Age Estimation Using MRI Data.

Sunitha Basodi1, Rajikha Raja2,3, Bhaskar Ray2,4

  • 1Tri-institutional Center for Translational Research in Neuroimaging and Data Science, Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA, USA. sbasodi1@gsu.edu.

Neuroinformatics
|April 5, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces a decentralized approach for estimating biological brain age using neuroimaging data. Decentralized models achieve comparable performance to centralized methods, overcoming data access limitations.

Keywords:
Brain ageCOINSTACDecentralizedFederated

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Area of Science:

  • Neuroscience
  • Medical Imaging
  • Machine Learning

Background:

  • Neuroimaging data reveals brain changes during development and aging.
  • Limited access to neuroimaging data hinders predictive model development.
  • Decentralized models offer a solution for building robust prediction models without centralizing data.

Purpose of the Study:

  • To propose and evaluate a decentralized method for biological brain age estimation.
  • To assess the performance of decentralized models using diverse neuroimaging features.
  • To demonstrate the efficacy of decentralized approaches in overcoming data access challenges.

Main Methods:

  • Developed a decentralized method for biological brain age estimation.
  • Utilized support vector regression models.
  • Evaluated the method on volumetric and voxelwise structural MRI and resting-state functional MRI data.

Main Results:

  • Decentralized brain age regression models achieved performance comparable to models trained on centrally pooled data.
  • The approach proved effective across multiple feature sets (structural and functional MRI).

Conclusions:

  • Decentralized modeling is a viable strategy for biological brain age estimation.
  • This method enhances model generalizability and accessibility by circumventing traditional data sharing limitations.
  • Future research can leverage decentralized approaches for large-scale neuroimaging studies.