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Bootstrapped Permutation Test for Multiresponse Inference on Brain Behavior Associations.

Bernard Ng, Jean Baptiste Poline, Bertrand Thirion

    Information Processing in Medical Imaging : Proceedings of the ... Conference
    |July 30, 2015
    PubMed
    Summary

    We developed a new method, the bootstrapped permutation test (BPT), to find significant brain-behavior links. BPT improves upon existing methods for analyzing complex neurological data, showing higher accuracy in identifying relevant brain connections.

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    Area of Science:

    • Neuroimaging
    • Computational Neuroscience
    • Biostatistics

    Background:

    • Neurological disorder diagnosis relies on behavioral assessments, but neuroimaging studies often analyze these in isolation.
    • Sparse Multiresponse Regression (SMR) models multiple behavioral scores but requires robust false positive control.
    • Existing methods like permutation tests and stability selection have limitations for models with unknown parameter distributions.

    Purpose of the Study:

    • To introduce a novel statistical technique, the bootstrapped permutation test (BPT), for identifying statistically significant features in models with unknown parameter distributions.
    • To enhance the joint modeling of multiple behavioral scores and neuroimaging data by improving false positive control and sensitivity.
    • To investigate the associations between brain connectivity and ADHD-related behaviors using fMRI data.

    Main Methods:

    • Development and application of the bootstrapped permutation test (BPT), utilizing Studentized statistics to assess parameter estimate variability.
    • Comparison of BPT against traditional permutation tests and stability selection using synthetic data to evaluate sensitivity and false positive rates.
    • Application of BPT to analyze pseudo-rest fMRI data from 1139 adolescents, correlating brain connectivity with ADHD-related behavioral measures.

    Main Results:

    • BPT demonstrated superior sensitivity in identifying relevant features from SMR models compared to permutation tests and stability selection on synthetic data, while maintaining strong false positive control.
    • Significant associations were identified between specific brain network connections and ADHD-related behavioral measures in adolescents using pseudo-rest fMRI data.
    • A regression model built on BPT-identified connections accurately predicted behavioral scores in an independent cohort of 337 subjects using resting-state fMRI data, validating the findings.

    Conclusions:

    • The bootstrapped permutation test (BPT) is a powerful and sensitive method for controlling false positives in neuroimaging studies analyzing complex brain-behavior relationships.
    • BPT successfully identified behaviorally relevant brain connectivity patterns associated with ADHD in adolescents.
    • The findings highlight the utility of BPT in advancing our understanding of the neural underpinnings of neurological disorders.