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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...
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Magnetic Resonance Imaging01:24

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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

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Modeling the Functional Network for Spatial Navigation in the Human Brain
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Adapted K-Core Decomposition and Visualization for Functional Magnetic Resonance Imaging Connectivity Networks.

Michael de Ridder, Karsten Klein, Jinman Kim

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
    |November 17, 2018
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    Summary
    This summary is machine-generated.

    This study introduces an adapted k-core decomposition method and visualization for analyzing functional connectivity networks (FCNs). It enables efficient grouping and aggregation of brain imaging data without losing crucial network details.

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    Area of Science:

    • Neuroscience
    • Computer Science
    • Data Visualization

    Background:

    • Medical imaging, including functional magnetic resonance imaging (fMRI), is crucial for studying the human brain and identifying neurological disorders.
    • Functional connectivity network (FCN) analysis is a key method for understanding brain activity and comparing subject groups.
    • Current visual analytics for FCNs simplify detailed interpretation but struggle with efficient cohort grouping and aggregation.

    Purpose of the Study:

    • To develop an improved visual analytics approach for grouping and aggregating functional connectivity network data.
    • To incorporate network details into the grouping process, overcoming limitations of existing statistical and visualization methods.
    • To present a novel adapted k-core decomposition algorithm and visualization for enhanced FCN analysis.

    Main Methods:

    • An adapted k-core decomposition algorithm was developed to analyze functional connectivity networks.
    • The method calculates connected component information for nodes within FCNs.
    • A novel visualization was created to combine k-core decomposition with connected component data for high-level FCN display.

    Main Results:

    • The proposed method effectively groups and aggregates functional connectivity network data.
    • Vital network details are preserved during the grouping and aggregation process.
    • A prototype demonstrated the capability to display more high-level FCN details than contemporary methods.

    Conclusions:

    • The adapted k-core decomposition and visualization offer a powerful tool for analyzing large cohorts of functional connectivity networks.
    • This approach facilitates efficient data grouping and aggregation while retaining essential network information.
    • The method enhances the ability to perform detailed analysis of brain imaging data for research and clinical applications.