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Related Concept Videos

Proteomics01:33

Proteomics

A proteome is the entire set of proteins that a cell type produces. We can study proteomes using the knowledge of genomes because genes code for mRNAs, and the mRNAs encode proteins. Although mRNA analysis is a step in the right direction, not all mRNAs are translated into proteins.
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Magnetic resonance imaging (MRI) is a noninvasive medical imaging technique based on a phenomenon of nuclear physics discovered in the 1930s, in which matter exposed to magnetic fields and radio waves was found to emit radio signals. In 1970, a physician and researcher named Raymond Damadian noticed that malignant (cancerous) tissue gave off different signals than normal body tissue. He applied for a patent for the first MRI scanning device in clinical use by the early 1980s. The early MRI...

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Related Experiment Video

Updated: Jun 16, 2026

Recording and Analyzing Multimodal Large-Scale Neuronal Ensemble Dynamics on CMOS-Integrated High-Density Microelectrode Array
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MR connectomics: Principles and challenges.

Patric Hagmann1, Leila Cammoun, Xavier Gigandet

  • 1Department of Radiology, University Hospital Center and University of Lausanne (CHUV-UNIL), Switzerland; Signal Processing Laboratory (LTS5), Ecole Polytechnique Fédérale de Lausanne (EPFL), Switzerland. Patric.hagmann@epfl.ch

Journal of Neuroscience Methods
|January 26, 2010
PubMed
Summary
This summary is machine-generated.

Magnetic Resonance (MR) connectomics uses MRI and network science to map brain connections. This framework reveals insights into brain structure and function, facing future challenges in data analysis and visualization.

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Area of Science:

  • Neuroscience
  • Network Science
  • Medical Imaging

Background:

  • MR connectomics integrates diffusion MRI and tractography with network science.
  • It offers a powerful framework for studying brain complexity.

Purpose of the Study:

  • To review current MRI-based structural connectivity mapping methods.
  • To discuss the potential of MR connectomics for understanding brain structure and function.
  • To identify future technical and analytical challenges.

Main Methods:

  • Review of diffusion MRI and whole-brain tractography techniques.
  • Application of network science analytical tools.
  • Discussion of data organization, distribution, and advanced analysis.

Main Results:

  • Current methods enable structural connectivity mapping with MRI.
  • MR connectomics data can infer novel information about brain structure and function.
  • Future work requires addressing challenges in high-resolution mapping and data handling.

Conclusions:

  • MR connectomics is a rapidly developing field with significant potential.
  • Overcoming technical and analytical challenges is crucial for advancing the field.
  • This framework is poised to benefit various fields investigating brain complexity.