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Using Informational Connectivity to Measure the Synchronous Emergence of fMRI Multi-voxel Information Across Time
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Single-trial Connectivity Estimation through the Least Absolute Shrinkage and Selection Operator.

Yuri Antonacci, Jlenia Toppi, Donatella Mattia

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
    |January 18, 2020
    PubMed
    Summary

    Least Absolute Shrinkage and Selection Operator (LASSO) regression improves brain connectivity estimation, especially with limited electroencephalographic (EEG) data. This method outperforms traditional Ordinary Least Square (OLS) and Asymptotic Statistics (AS) approaches in low-sample scenarios.

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    Area of Science:

    • Neuroscience
    • Computational Neuroscience
    • Biomedical Engineering

    Background:

    • Multivariate autoregressive (MVAR) models accurately estimate functional brain connectivity.
    • Ordinary Least Square (OLS) with Asymptotic Statistics (AS) is a common parameter estimation method.
    • OLS and AS performance degrades significantly with limited data samples, restricting brain connectivity analysis.

    Purpose of the Study:

    • To introduce and evaluate the Least Absolute Shrinkage and Selection Operator (LASSO) regression for brain connectivity estimation.
    • To expand the applicability of brain connectivity analysis to conditions with limited data points.
    • To compare LASSO regression with OLS and AS in both simulated and real-world scenarios.

    Main Methods:

    • A simulation study was conducted to assess LASSO regression performance under varying data sample sizes.
    • LASSO regression was compared against the classical OLS and AS approach.
    • The methods were applied to real electroencephalographic (EEG) signals recorded during a motor imagery task.

    Main Results:

    • LASSO regression demonstrated superior performance in estimating brain connectivity compared to OLS and AS when data samples were limited.
    • Both simulation results and real EEG data analysis confirmed LASSO's effectiveness in low-data conditions.
    • The study validates LASSO as a robust method for brain connectivity assessment with scarce data.

    Conclusions:

    • LASSO regression offers a significant advancement for estimating brain connectivity, particularly when data is limited.
    • This method broadens the scope of connectivity analysis, enabling studies in previously challenging low-sample scenarios.
    • The findings support the use of LASSO for on-line brain connectivity estimation and assessment.