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Related Concept Videos

Brain Imaging01:14

Brain Imaging

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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...
968

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Diffusion Tensor Magnetic Resonance Imaging in the Analysis of Neurodegenerative Diseases
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Edge-Centered DTI Connectivity Analysis: Application to Schizophrenia.

Edward H Herskovits1, L Elliot Hong2, Peter Kochunov2

  • 1Department of Diagnostic Radiology and Nuclear Medicine, University of Maryland, Baltimore, MD, USA. ehh@ieee.org.

Neuroinformatics
|June 17, 2015
PubMed
Summary
This summary is machine-generated.

This study introduces an edge-based algorithm for analyzing brain connectivity using diffusion tensor imaging (DTI). This novel approach models connections between brain regions, improving the understanding of disorders like schizophrenia.

Keywords:
Data miningDiffusion tensor imagingNetwork analysisSchizophreniaTractography

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Area of Science:

  • Neuroimaging
  • Computational Neuroscience
  • Graph Theory

Background:

  • Diffusion Tensor Imaging (DTI) is crucial for understanding brain development and disorders.
  • Graph representations are commonly used for DTI connectivity, but often focus on nodes (brain regions) rather than edges (connections).

Purpose of the Study:

  • To present a novel edge-based algorithm for assessing anatomic connectivity from DTI data.
  • To develop multivariate graph-based models that identify altered connectivity patterns distinguishing experimental groups.

Main Methods:

  • Developed an edge-based algorithm for analyzing brain connectivity graphs derived from DTI.
  • Applied multivariate graph-based modeling to identify group differences in connectivity.

Main Results:

  • The edge-based approach provides insights into connections between brain structures, complementing node-centric measures.
  • Demonstrated the algorithm's utility in analyzing DTI data from a schizophrenia study, highlighting its potential for group discrimination.

Conclusions:

  • An edge-based perspective offers a powerful new method for analyzing brain connectivity with DTI.
  • This approach can reveal subtle alterations in brain networks relevant to psychiatric disorders like schizophrenia.