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Basics of Multivariate Analysis in Neuroimaging Data
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Big Data and Neuroimaging.

Yenny Webb-Vargas, Shaojie Chen, Aaron Fisher

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    Summary
    This summary is machine-generated.

    Statisticians are crucial for advancing Big Data in biosciences. This work highlights the need for statistical methods in Big Data analysis, using statistical neuroimaging as a key example.

    Keywords:
    Big datadata fusiondynamic networkshigh dimensional causal inferencehigh dimensional computationhigh dimensional inferenceneuroimagingshrinkage

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    Area of Science:

    • Biosciences
    • Neuroimaging
    • Data Science

    Background:

    • Big Data is increasingly vital in biosciences, necessitating advanced tools and methods.
    • Statistical thinking and expertise are essential for meaningful progress in Big Data applications.
    • There is a growing demand for statisticians to address complex Big Data challenges.

    Purpose of the Study:

    • To emphasize the critical role of statisticians in Big Data analysis.
    • To showcase the application of Big Data tools in neuroimaging.
    • To present novel methodological developments for Big Data in biosciences.

    Main Methods:

    • Review of Big Data applications in statistical neuroimaging.
    • Discussion of novel methodological developments for analyzing large-scale neuroimaging datasets.
    • Highlighting the integration of statistical principles with Big Data techniques.

    Main Results:

    • Demonstrated the significant impact of statistical input on Big Data analysis in neuroimaging.
    • Showcased diverse applications of Big Data tools within the neuroimaging field.
    • Identified key areas for methodological innovation in statistical neuroimaging.

    Conclusions:

    • Statistical expertise is indispensable for unlocking the potential of Big Data in biosciences.
    • Neuroimaging serves as a prime example of the need for robust statistical approaches to Big Data.
    • Further development and application of statistical methods are required for Big Data challenges.