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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...
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Clinical connectomics: Mapping structural brain networks in disease.

Andrew Zalesky1, Maria A Di Biase2

  • 1Systems Group, Department of Psychiatry, University of Melbourne, Melbourne, VIC 3010, Australia; Department of Biomedical Engineering, University of Melbourne, Melbourne, VIC 3010, Australia.

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Summary

Mapping the human connectome in brain disorders presents challenges due to pathology. This study outlines methods to reliably analyze structural connectivity, aiming to improve clinical applications in neurology and psychiatry.

Keywords:
Connectomebrain connectivityclinical connectomicsconnectome mappingpathologytractography

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Area of Science:

  • Neuroscience
  • Medical Imaging
  • Systems Biology

Background:

  • The human connectome offers insights into brain disorders.
  • Current connectome mapping methods are often validated on healthy subjects, limiting their clinical applicability.
  • Brain pathologies introduce complexities in accurate connectome analysis.

Purpose of the Study:

  • To address the challenges of structural connectome mapping in the presence of brain pathology.
  • To establish best practices for reliable and biologically meaningful clinical connectome mapping.
  • To guide applications in neurology, psychiatry, and neurosurgery.

Main Methods:

  • Classification of connectome abnormalities and their relation to white-matter microstructure.
  • Quantification of structural connectivity alterations using measures like streamline counts and microstructure-informed tractography.
  • Evaluation of connectome-mapping pipelines and algorithmic choices for clinical settings.

Main Results:

  • Structural connectivity measures can effectively quantify the impact of white-matter pathology on the macroscale connectome.
  • Specific methods, including tract-averaged metrics and structural similarity, are discussed for pathological conditions.
  • Consideration of algorithmic choices is crucial for reliable clinical connectome mapping.

Conclusions:

  • Reliable structural connectome mapping in the presence of pathology is feasible with appropriate methodologies.
  • Standardized approaches are needed to enhance the clinical utility of connectome analysis in neurological and psychiatric disorders.
  • This work provides a framework for best practices in clinical connectome mapping.