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Related Concept Videos

Magnetic Resonance Imaging01:24

Magnetic Resonance Imaging

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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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Co-analysis of Brain Structure and Function using fMRI and Diffusion-weighted Imaging
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Tutorial: a guide to diffusion MRI and structural connectomics.

Ittai Shamir1, Yaniv Assaf2,3

  • 1Department of Neurobiology, Faculty of Life Sciences, Tel Aviv University, Tel Aviv, Israel.

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Summary
This summary is machine-generated.

This guide introduces diffusion MRI (dMRI) for neuroscience researchers. It details pipelines for analyzing structural connectomics and neuroplasticity, making dMRI accessible for newcomers.

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Area of Science:

  • Neuroimaging and Computational Neuroscience
  • Biomedical Engineering

Background:

  • Diffusion magnetic resonance imaging (dMRI) measures water molecule displacement in tissues at the micrometer scale.
  • dMRI applications are expanding into structural connectomics and neuroplasticity, requiring specialized image processing and statistical expertise.
  • The diversity of dMRI acquisition and analysis pipelines presents a challenge for researchers new to the field.

Purpose of the Study:

  • To provide an introductory guide for graduate students and researchers in neuroscience on integrating dMRI into their work.
  • To offer a comprehensive, step-by-step pipeline for structural connectomics analysis using dMRI.
  • To demystify dMRI methodologies, parameters, and applications in neuroplasticity and connectomics for non-experts.

Main Methods:

  • Overview of basic dMRI methodologies, including acquisition parameters and clinical applications (ADC, MD, FA, MicroFA).
  • Detailed step-by-step pipeline for structural connectomics, covering experimental design and data processing.
  • Exploration of fiber tracking techniques, evaluation, limitations, and methods for constructing, analyzing, and visualizing structural networks.

Main Results:

  • The guide provides a structured approach to dMRI data analysis for structural connectomics.
  • It covers essential dMRI parameters and their relevance in clinical applications and neuroimaging research.
  • The resource facilitates understanding of fiber tracking and network construction for neuroscience investigations.

Conclusions:

  • This guide equips neuroscience researchers with the foundational knowledge and practical steps to utilize dMRI for studying neuroplasticity and connectomics.
  • It simplifies complex dMRI processing and analysis, enabling broader adoption of this powerful neuroimaging technique.
  • The resource serves as a valuable tool for researchers regardless of their prior neuroimaging or computational background.